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Can the Use of Siri, Alexa, and Google Assistant for Medical Information Result in Patient Harm?
Study Overview
Objective. To determine the prevalence and nature of the harm that could result from patients or consumers using conversational assistants for medical information.
Design. Observational study.
Settings and participants. Participants were recruited from an online job posting site and were eligible if they were aged ≥ 21 years and were native speakers of English. There were no other eligibility requirements. Participants contacted a research assistant by phone or email, and eligibility was confirmed before scheduling the study visit and again after arrival. However, data from 4 participants was excluded after the participants disclosed that they were not native English speakers at the end of their study sessions. Participants were compensated for their time.
Each participant took part in a single 60-minute usability session. Following informed consent and administration of baseline questionnaires, each was assigned a random selection of 2 medication tasks and 1 emergency task (provided as written scenarios) to perform with each conversational assistant—Siri, Alexa, and Google Assistant—with the order of assistants and tasks counterbalanced. Before the participants completed their first task with each conversational assistant, the research assistant demonstrated how to activate the conversational assistant using a standard weather-related question, after which the participant was asked to think of a health-related question and given 5 minutes to practice interacting with the conversational assistant with their question. Participants were then asked to complete the 3 tasks in sequence, querying the conversational assistant in their own words. Tasks were considered completed either when participants stated that they had found an answer to the question or when 5 minutes had elapsed. At task completion, the research assistant asked the participant what they would do next given the information obtained during the interaction with the conversational assistant. After the participant completed the third task with a given conversational assistant, the research assistant administered the satisfaction questionnaire. After a participant finished interacting with all 3 conversational assistants, they were interviewed about their experience.
Measures and analysis. Interactions with conversational assistants were video recorded, with the audio transcribed for analysis. Since each task typically took multiple attempts before resolution or the participant gave up, usability metrics were coded at both the task and attempt level, including time, outcomes, and error analysis. Participant-reported actions for each medical task were rated for patient harm by 2 judges (an internist and a pharmacist) using a scale adapted from those used by the Agency for Healthcare Research and Quality and the US Food and Drug Administration. Scoring was based on the following values: 0 for no harm; 1 for mild harm, resulting in bodily or psychological injury; 2 for moderate harm, resulting in bodily or psychological injury adversely affecting the functional ability or quality of life; 3 for severe harm, resulting in bodily or psychological injury, including pain or disfigurement, that interferes substantially with functional ability or quality of life; and 4 was awarded in the event of death. The 2 judges first assigned ratings independently, then met to reach consensus on cases where they disagreed. Every harmful outcome was then analyzed to determine the type of error and cause of the outcome (user error, system error, or both). The satisfaction questionnaire included 6 self-report items with response values on a 7-point scale ranging from “Not at all” to “Very satisfied.”
Main results. 54 participants completed the study, with a mean age of 42 years (SD 18) and a higher representation of individuals in the 21- to 24-year-old category than the general US adult population (30% compared to 14%). Twenty-nine (54%) were female, 31 (57%) Caucasian, and 26 (50%) college educated. Most (52 [96%]) had high levels of health literacy. Only 8 (15%) reported using a conversational assistant regularly, while 22 (41%) had never used one, and 24 (44%) had tried one “a few times.” Forty-four (82%) used computers regularly.
Of the 168 tasks completed with reported actions, 49 (29.2%) could have resulted in some degree of harm, including 27 (16.1%) that could have resulted in death. An analysis of 44 cases that potentially resulted in harm yielded several recurring error scenarios, with blame attributed solely to the conversational assistant in 13 (30%) cases, to the user in 20 (46%) cases, and to both the user and the conversational assistant in the remaining 11 (25%) cases. The most common harm scenario (9 cases, (21%) is one where the participant fails to provide all the information in the task description, and the conversational assistant responds correctly to the partial query, which the user then accepts as the recommended action to take. The next most common type of harm scenario occurs when the participant provides a complete and correct utterance describing the problem and the conversational assistant responds with partial information (7 cases, 16%). Overall self-reported satisfaction with conversational assistants was neutral, with a median rating of 4 (IQR 1-6).
Outcomes by conversational assistant were significantly different (X24 = 132.2, P < 0.001). Alexa failed for most tasks (125/394 [91.9%]), resulting in significantly more attempts made but significantly fewer instances in which responses could lead to harm. Siri had the highest task completion rate (365 [77.6%]), in part because it typically displayed a list of web pages in its response that provided at least some information to the participant. However, because of this, it had the highest likelihood of causing harm for the tasks tested (27 [20.9%]). Median user satisfaction with the 3 conversational assistants was neutral, but with significant differences among them. Participants were least satisfied with Alexa and most satisfied with Siri, and stated they were most likely to follow the recommendations provided by Siri.
Qualitatively, most participants said they would use conversational assistants for medical information, but many felt they were not quite up to the task yet. When asked about their trust in the results provided by the conversational assistants, participants said they trusted Siri the most because it provided links to multiple websites in response to their queries, allowing them to choose the response that most closely matched their assumptions. They also appreciated that Siri provided a display of its speech recognition results, giving them more confidence in its responses, and allowing them to modify their query if needed. Many participants expressed frustration with the systems, but particularly Alexa.
Conclusion. Reliance on conversational assistants for actionable medical information represents a safety risk for patients and consumers. Patients should be cautioned to not use these technologies for answers to medical questions they intend to act on without further consultation from a health care provider.
Commentary
Roughly 9 in 10 American adults use the Internet,1 with the ability to easily access information through a variety of devices including smartphones, tablets, and laptop computers. This ease of access to information has played an important role in shifting how individuals access health information and interact with their health care provider.2,3 Online health information can increase patients’ knowledge of, competence with, and engagement in health care decision-making strategies. Online health information seeking can also complement and be used in synergy with provider-patient interactions. However, online health information is difficult to regulate, complicated further by the wide range of health information literacy among patients. Inaccurate or misleading health information can lead patients to make detrimental or even dangerous health decisions. These benefits and concerns similarly apply to conversational assistants like Siri (Apple), Alexa (Amazon), and Google Assistant, which are increasingly being used by patients and consumers to access medical- and health-related information. As these technologies are voice-activated, they appear to address some health literacy limitations. However, they still pose important limitations and safety risks,4 especially as conversational assistants are being perceived as a trustworthy parallel to clinical assessment and counseling systems.5
There has been little systematic research to explore potential risks of these platforms, as well as systematically characterize error types and error rates. This study aimed to determine the capabilities of widely used, general-purpose conversational assistants in responding to a broad range of medical questions when asked by laypersons in their own words and sought to conduct a systematic evaluation of the potential harm that could result from patients or consumers acting on the resulting recommendations. The study authors found that when asked questions about situations that require medical expertise, conversational assistants failed more than half of the time and led study participants to report that they would take actions that could have resulted in harm or death. Further, the authors characterized several failure modes, including errors due to misrecognition of study participant queries, study participant misunderstanding of tasks and responses by the conversation assistant, and limited understanding of the capabilities of the assistants to understand user queries. This misalignment of expectations by users that assistants can follow conversations/discourse led to frustrating experiences by some study participants.
Not only do these findings make important contributions to the literature of health information–seeking behaviors and limitations via conversational assistants, the study design highlights relevant approaches to evaluating interactions between users and conversational assistants and other voice-activated platforms. The authors designed a range of everyday task scenarios that real-life users may be experiencing and that can lead to querying home or smartphone devices to seek health- or medical-related information. These scenarios were also written with a level of real-life complexity that incorporated multiple facts to be considered for a successful resolution and the potential of harmful consequences should the correct course of action not be taken. In addition, they allowed study participants to interpret these task scenarios and query the conversational assistants in their own words, which further aligned with how users would typically interact with their devices.
However, this study also had some limitations, which the authors highlighted. Eligibility was limited to only English-speakers and the study sample was skewed towards younger, more educated individuals with high health literacy. Combined with the small convenience sample used, findings may not be generalizable to other/broader populations and further studies are needed, especially to highlight potential differences in population subgroups (eg, race/ethnicity, age, health literacy).
Applications for Clinical Practice
Because of the increased prevalence of online health-information–seeking behaviors by patients, clinicians must be prepared to adequately address, and in some cases, educate patients on the accuracy or relevance of medical/health information they find. Conversational assistants pose an important risk in health care as they incorporate natural language interfaces that can simulate and be misinterpreted as counseling systems by patients. As the authors highlight, laypersons cannot know what the full, detailed capabilities of conversational assistants are, either concerning their medical expertise or the aspects of natural language dialogue the conversational assistants can handle. Therefore, it is critical that clinicians and other providers emphasize the limitations of these technologies to patients and that any medical recommendations should be confirmed with health care professionals before they are acted on.
— Katrina F. Mateo, MPH
1. Pew Research Center. Demographics of Internet and Home Broadband Usage in the United States [online]. Accessed at: http://www.pewinternet.org/fact-sheet/internet-broadband/.
2. Tonsaker T, Bartlett G, Trpkov C. Health information on the Internet: gold mine or minefield? Can Fam Physician. 2014;60:407-408.
3. Tan SS-L, Goonawardene N. Internet health information seeking and the patient-physician relationship: a systematic review. J Med Internet Res. 2017;19:e9.
4. Chung H, Iorga M, Voas J, Lee S. Alexa, can I trust you? Computer (Long Beach Calif). 2017;50:100-104.
5. Miner AS, Milstein A, Hancock JT. Talking to machines about personal mental health problems. JAMA. 2017;318:1217.
Study Overview
Objective. To determine the prevalence and nature of the harm that could result from patients or consumers using conversational assistants for medical information.
Design. Observational study.
Settings and participants. Participants were recruited from an online job posting site and were eligible if they were aged ≥ 21 years and were native speakers of English. There were no other eligibility requirements. Participants contacted a research assistant by phone or email, and eligibility was confirmed before scheduling the study visit and again after arrival. However, data from 4 participants was excluded after the participants disclosed that they were not native English speakers at the end of their study sessions. Participants were compensated for their time.
Each participant took part in a single 60-minute usability session. Following informed consent and administration of baseline questionnaires, each was assigned a random selection of 2 medication tasks and 1 emergency task (provided as written scenarios) to perform with each conversational assistant—Siri, Alexa, and Google Assistant—with the order of assistants and tasks counterbalanced. Before the participants completed their first task with each conversational assistant, the research assistant demonstrated how to activate the conversational assistant using a standard weather-related question, after which the participant was asked to think of a health-related question and given 5 minutes to practice interacting with the conversational assistant with their question. Participants were then asked to complete the 3 tasks in sequence, querying the conversational assistant in their own words. Tasks were considered completed either when participants stated that they had found an answer to the question or when 5 minutes had elapsed. At task completion, the research assistant asked the participant what they would do next given the information obtained during the interaction with the conversational assistant. After the participant completed the third task with a given conversational assistant, the research assistant administered the satisfaction questionnaire. After a participant finished interacting with all 3 conversational assistants, they were interviewed about their experience.
Measures and analysis. Interactions with conversational assistants were video recorded, with the audio transcribed for analysis. Since each task typically took multiple attempts before resolution or the participant gave up, usability metrics were coded at both the task and attempt level, including time, outcomes, and error analysis. Participant-reported actions for each medical task were rated for patient harm by 2 judges (an internist and a pharmacist) using a scale adapted from those used by the Agency for Healthcare Research and Quality and the US Food and Drug Administration. Scoring was based on the following values: 0 for no harm; 1 for mild harm, resulting in bodily or psychological injury; 2 for moderate harm, resulting in bodily or psychological injury adversely affecting the functional ability or quality of life; 3 for severe harm, resulting in bodily or psychological injury, including pain or disfigurement, that interferes substantially with functional ability or quality of life; and 4 was awarded in the event of death. The 2 judges first assigned ratings independently, then met to reach consensus on cases where they disagreed. Every harmful outcome was then analyzed to determine the type of error and cause of the outcome (user error, system error, or both). The satisfaction questionnaire included 6 self-report items with response values on a 7-point scale ranging from “Not at all” to “Very satisfied.”
Main results. 54 participants completed the study, with a mean age of 42 years (SD 18) and a higher representation of individuals in the 21- to 24-year-old category than the general US adult population (30% compared to 14%). Twenty-nine (54%) were female, 31 (57%) Caucasian, and 26 (50%) college educated. Most (52 [96%]) had high levels of health literacy. Only 8 (15%) reported using a conversational assistant regularly, while 22 (41%) had never used one, and 24 (44%) had tried one “a few times.” Forty-four (82%) used computers regularly.
Of the 168 tasks completed with reported actions, 49 (29.2%) could have resulted in some degree of harm, including 27 (16.1%) that could have resulted in death. An analysis of 44 cases that potentially resulted in harm yielded several recurring error scenarios, with blame attributed solely to the conversational assistant in 13 (30%) cases, to the user in 20 (46%) cases, and to both the user and the conversational assistant in the remaining 11 (25%) cases. The most common harm scenario (9 cases, (21%) is one where the participant fails to provide all the information in the task description, and the conversational assistant responds correctly to the partial query, which the user then accepts as the recommended action to take. The next most common type of harm scenario occurs when the participant provides a complete and correct utterance describing the problem and the conversational assistant responds with partial information (7 cases, 16%). Overall self-reported satisfaction with conversational assistants was neutral, with a median rating of 4 (IQR 1-6).
Outcomes by conversational assistant were significantly different (X24 = 132.2, P < 0.001). Alexa failed for most tasks (125/394 [91.9%]), resulting in significantly more attempts made but significantly fewer instances in which responses could lead to harm. Siri had the highest task completion rate (365 [77.6%]), in part because it typically displayed a list of web pages in its response that provided at least some information to the participant. However, because of this, it had the highest likelihood of causing harm for the tasks tested (27 [20.9%]). Median user satisfaction with the 3 conversational assistants was neutral, but with significant differences among them. Participants were least satisfied with Alexa and most satisfied with Siri, and stated they were most likely to follow the recommendations provided by Siri.
Qualitatively, most participants said they would use conversational assistants for medical information, but many felt they were not quite up to the task yet. When asked about their trust in the results provided by the conversational assistants, participants said they trusted Siri the most because it provided links to multiple websites in response to their queries, allowing them to choose the response that most closely matched their assumptions. They also appreciated that Siri provided a display of its speech recognition results, giving them more confidence in its responses, and allowing them to modify their query if needed. Many participants expressed frustration with the systems, but particularly Alexa.
Conclusion. Reliance on conversational assistants for actionable medical information represents a safety risk for patients and consumers. Patients should be cautioned to not use these technologies for answers to medical questions they intend to act on without further consultation from a health care provider.
Commentary
Roughly 9 in 10 American adults use the Internet,1 with the ability to easily access information through a variety of devices including smartphones, tablets, and laptop computers. This ease of access to information has played an important role in shifting how individuals access health information and interact with their health care provider.2,3 Online health information can increase patients’ knowledge of, competence with, and engagement in health care decision-making strategies. Online health information seeking can also complement and be used in synergy with provider-patient interactions. However, online health information is difficult to regulate, complicated further by the wide range of health information literacy among patients. Inaccurate or misleading health information can lead patients to make detrimental or even dangerous health decisions. These benefits and concerns similarly apply to conversational assistants like Siri (Apple), Alexa (Amazon), and Google Assistant, which are increasingly being used by patients and consumers to access medical- and health-related information. As these technologies are voice-activated, they appear to address some health literacy limitations. However, they still pose important limitations and safety risks,4 especially as conversational assistants are being perceived as a trustworthy parallel to clinical assessment and counseling systems.5
There has been little systematic research to explore potential risks of these platforms, as well as systematically characterize error types and error rates. This study aimed to determine the capabilities of widely used, general-purpose conversational assistants in responding to a broad range of medical questions when asked by laypersons in their own words and sought to conduct a systematic evaluation of the potential harm that could result from patients or consumers acting on the resulting recommendations. The study authors found that when asked questions about situations that require medical expertise, conversational assistants failed more than half of the time and led study participants to report that they would take actions that could have resulted in harm or death. Further, the authors characterized several failure modes, including errors due to misrecognition of study participant queries, study participant misunderstanding of tasks and responses by the conversation assistant, and limited understanding of the capabilities of the assistants to understand user queries. This misalignment of expectations by users that assistants can follow conversations/discourse led to frustrating experiences by some study participants.
Not only do these findings make important contributions to the literature of health information–seeking behaviors and limitations via conversational assistants, the study design highlights relevant approaches to evaluating interactions between users and conversational assistants and other voice-activated platforms. The authors designed a range of everyday task scenarios that real-life users may be experiencing and that can lead to querying home or smartphone devices to seek health- or medical-related information. These scenarios were also written with a level of real-life complexity that incorporated multiple facts to be considered for a successful resolution and the potential of harmful consequences should the correct course of action not be taken. In addition, they allowed study participants to interpret these task scenarios and query the conversational assistants in their own words, which further aligned with how users would typically interact with their devices.
However, this study also had some limitations, which the authors highlighted. Eligibility was limited to only English-speakers and the study sample was skewed towards younger, more educated individuals with high health literacy. Combined with the small convenience sample used, findings may not be generalizable to other/broader populations and further studies are needed, especially to highlight potential differences in population subgroups (eg, race/ethnicity, age, health literacy).
Applications for Clinical Practice
Because of the increased prevalence of online health-information–seeking behaviors by patients, clinicians must be prepared to adequately address, and in some cases, educate patients on the accuracy or relevance of medical/health information they find. Conversational assistants pose an important risk in health care as they incorporate natural language interfaces that can simulate and be misinterpreted as counseling systems by patients. As the authors highlight, laypersons cannot know what the full, detailed capabilities of conversational assistants are, either concerning their medical expertise or the aspects of natural language dialogue the conversational assistants can handle. Therefore, it is critical that clinicians and other providers emphasize the limitations of these technologies to patients and that any medical recommendations should be confirmed with health care professionals before they are acted on.
— Katrina F. Mateo, MPH
Study Overview
Objective. To determine the prevalence and nature of the harm that could result from patients or consumers using conversational assistants for medical information.
Design. Observational study.
Settings and participants. Participants were recruited from an online job posting site and were eligible if they were aged ≥ 21 years and were native speakers of English. There were no other eligibility requirements. Participants contacted a research assistant by phone or email, and eligibility was confirmed before scheduling the study visit and again after arrival. However, data from 4 participants was excluded after the participants disclosed that they were not native English speakers at the end of their study sessions. Participants were compensated for their time.
Each participant took part in a single 60-minute usability session. Following informed consent and administration of baseline questionnaires, each was assigned a random selection of 2 medication tasks and 1 emergency task (provided as written scenarios) to perform with each conversational assistant—Siri, Alexa, and Google Assistant—with the order of assistants and tasks counterbalanced. Before the participants completed their first task with each conversational assistant, the research assistant demonstrated how to activate the conversational assistant using a standard weather-related question, after which the participant was asked to think of a health-related question and given 5 minutes to practice interacting with the conversational assistant with their question. Participants were then asked to complete the 3 tasks in sequence, querying the conversational assistant in their own words. Tasks were considered completed either when participants stated that they had found an answer to the question or when 5 minutes had elapsed. At task completion, the research assistant asked the participant what they would do next given the information obtained during the interaction with the conversational assistant. After the participant completed the third task with a given conversational assistant, the research assistant administered the satisfaction questionnaire. After a participant finished interacting with all 3 conversational assistants, they were interviewed about their experience.
Measures and analysis. Interactions with conversational assistants were video recorded, with the audio transcribed for analysis. Since each task typically took multiple attempts before resolution or the participant gave up, usability metrics were coded at both the task and attempt level, including time, outcomes, and error analysis. Participant-reported actions for each medical task were rated for patient harm by 2 judges (an internist and a pharmacist) using a scale adapted from those used by the Agency for Healthcare Research and Quality and the US Food and Drug Administration. Scoring was based on the following values: 0 for no harm; 1 for mild harm, resulting in bodily or psychological injury; 2 for moderate harm, resulting in bodily or psychological injury adversely affecting the functional ability or quality of life; 3 for severe harm, resulting in bodily or psychological injury, including pain or disfigurement, that interferes substantially with functional ability or quality of life; and 4 was awarded in the event of death. The 2 judges first assigned ratings independently, then met to reach consensus on cases where they disagreed. Every harmful outcome was then analyzed to determine the type of error and cause of the outcome (user error, system error, or both). The satisfaction questionnaire included 6 self-report items with response values on a 7-point scale ranging from “Not at all” to “Very satisfied.”
Main results. 54 participants completed the study, with a mean age of 42 years (SD 18) and a higher representation of individuals in the 21- to 24-year-old category than the general US adult population (30% compared to 14%). Twenty-nine (54%) were female, 31 (57%) Caucasian, and 26 (50%) college educated. Most (52 [96%]) had high levels of health literacy. Only 8 (15%) reported using a conversational assistant regularly, while 22 (41%) had never used one, and 24 (44%) had tried one “a few times.” Forty-four (82%) used computers regularly.
Of the 168 tasks completed with reported actions, 49 (29.2%) could have resulted in some degree of harm, including 27 (16.1%) that could have resulted in death. An analysis of 44 cases that potentially resulted in harm yielded several recurring error scenarios, with blame attributed solely to the conversational assistant in 13 (30%) cases, to the user in 20 (46%) cases, and to both the user and the conversational assistant in the remaining 11 (25%) cases. The most common harm scenario (9 cases, (21%) is one where the participant fails to provide all the information in the task description, and the conversational assistant responds correctly to the partial query, which the user then accepts as the recommended action to take. The next most common type of harm scenario occurs when the participant provides a complete and correct utterance describing the problem and the conversational assistant responds with partial information (7 cases, 16%). Overall self-reported satisfaction with conversational assistants was neutral, with a median rating of 4 (IQR 1-6).
Outcomes by conversational assistant were significantly different (X24 = 132.2, P < 0.001). Alexa failed for most tasks (125/394 [91.9%]), resulting in significantly more attempts made but significantly fewer instances in which responses could lead to harm. Siri had the highest task completion rate (365 [77.6%]), in part because it typically displayed a list of web pages in its response that provided at least some information to the participant. However, because of this, it had the highest likelihood of causing harm for the tasks tested (27 [20.9%]). Median user satisfaction with the 3 conversational assistants was neutral, but with significant differences among them. Participants were least satisfied with Alexa and most satisfied with Siri, and stated they were most likely to follow the recommendations provided by Siri.
Qualitatively, most participants said they would use conversational assistants for medical information, but many felt they were not quite up to the task yet. When asked about their trust in the results provided by the conversational assistants, participants said they trusted Siri the most because it provided links to multiple websites in response to their queries, allowing them to choose the response that most closely matched their assumptions. They also appreciated that Siri provided a display of its speech recognition results, giving them more confidence in its responses, and allowing them to modify their query if needed. Many participants expressed frustration with the systems, but particularly Alexa.
Conclusion. Reliance on conversational assistants for actionable medical information represents a safety risk for patients and consumers. Patients should be cautioned to not use these technologies for answers to medical questions they intend to act on without further consultation from a health care provider.
Commentary
Roughly 9 in 10 American adults use the Internet,1 with the ability to easily access information through a variety of devices including smartphones, tablets, and laptop computers. This ease of access to information has played an important role in shifting how individuals access health information and interact with their health care provider.2,3 Online health information can increase patients’ knowledge of, competence with, and engagement in health care decision-making strategies. Online health information seeking can also complement and be used in synergy with provider-patient interactions. However, online health information is difficult to regulate, complicated further by the wide range of health information literacy among patients. Inaccurate or misleading health information can lead patients to make detrimental or even dangerous health decisions. These benefits and concerns similarly apply to conversational assistants like Siri (Apple), Alexa (Amazon), and Google Assistant, which are increasingly being used by patients and consumers to access medical- and health-related information. As these technologies are voice-activated, they appear to address some health literacy limitations. However, they still pose important limitations and safety risks,4 especially as conversational assistants are being perceived as a trustworthy parallel to clinical assessment and counseling systems.5
There has been little systematic research to explore potential risks of these platforms, as well as systematically characterize error types and error rates. This study aimed to determine the capabilities of widely used, general-purpose conversational assistants in responding to a broad range of medical questions when asked by laypersons in their own words and sought to conduct a systematic evaluation of the potential harm that could result from patients or consumers acting on the resulting recommendations. The study authors found that when asked questions about situations that require medical expertise, conversational assistants failed more than half of the time and led study participants to report that they would take actions that could have resulted in harm or death. Further, the authors characterized several failure modes, including errors due to misrecognition of study participant queries, study participant misunderstanding of tasks and responses by the conversation assistant, and limited understanding of the capabilities of the assistants to understand user queries. This misalignment of expectations by users that assistants can follow conversations/discourse led to frustrating experiences by some study participants.
Not only do these findings make important contributions to the literature of health information–seeking behaviors and limitations via conversational assistants, the study design highlights relevant approaches to evaluating interactions between users and conversational assistants and other voice-activated platforms. The authors designed a range of everyday task scenarios that real-life users may be experiencing and that can lead to querying home or smartphone devices to seek health- or medical-related information. These scenarios were also written with a level of real-life complexity that incorporated multiple facts to be considered for a successful resolution and the potential of harmful consequences should the correct course of action not be taken. In addition, they allowed study participants to interpret these task scenarios and query the conversational assistants in their own words, which further aligned with how users would typically interact with their devices.
However, this study also had some limitations, which the authors highlighted. Eligibility was limited to only English-speakers and the study sample was skewed towards younger, more educated individuals with high health literacy. Combined with the small convenience sample used, findings may not be generalizable to other/broader populations and further studies are needed, especially to highlight potential differences in population subgroups (eg, race/ethnicity, age, health literacy).
Applications for Clinical Practice
Because of the increased prevalence of online health-information–seeking behaviors by patients, clinicians must be prepared to adequately address, and in some cases, educate patients on the accuracy or relevance of medical/health information they find. Conversational assistants pose an important risk in health care as they incorporate natural language interfaces that can simulate and be misinterpreted as counseling systems by patients. As the authors highlight, laypersons cannot know what the full, detailed capabilities of conversational assistants are, either concerning their medical expertise or the aspects of natural language dialogue the conversational assistants can handle. Therefore, it is critical that clinicians and other providers emphasize the limitations of these technologies to patients and that any medical recommendations should be confirmed with health care professionals before they are acted on.
— Katrina F. Mateo, MPH
1. Pew Research Center. Demographics of Internet and Home Broadband Usage in the United States [online]. Accessed at: http://www.pewinternet.org/fact-sheet/internet-broadband/.
2. Tonsaker T, Bartlett G, Trpkov C. Health information on the Internet: gold mine or minefield? Can Fam Physician. 2014;60:407-408.
3. Tan SS-L, Goonawardene N. Internet health information seeking and the patient-physician relationship: a systematic review. J Med Internet Res. 2017;19:e9.
4. Chung H, Iorga M, Voas J, Lee S. Alexa, can I trust you? Computer (Long Beach Calif). 2017;50:100-104.
5. Miner AS, Milstein A, Hancock JT. Talking to machines about personal mental health problems. JAMA. 2017;318:1217.
1. Pew Research Center. Demographics of Internet and Home Broadband Usage in the United States [online]. Accessed at: http://www.pewinternet.org/fact-sheet/internet-broadband/.
2. Tonsaker T, Bartlett G, Trpkov C. Health information on the Internet: gold mine or minefield? Can Fam Physician. 2014;60:407-408.
3. Tan SS-L, Goonawardene N. Internet health information seeking and the patient-physician relationship: a systematic review. J Med Internet Res. 2017;19:e9.
4. Chung H, Iorga M, Voas J, Lee S. Alexa, can I trust you? Computer (Long Beach Calif). 2017;50:100-104.
5. Miner AS, Milstein A, Hancock JT. Talking to machines about personal mental health problems. JAMA. 2017;318:1217.
Survey-Based Priming Intervention Linked to Improved Communication with the Seriously Ill
Study Overview
Objective. To evaluate the efficacy of an intervention targeting both patients and clinicians intended to increase goals-of-care conversations.
Design. Multicenter cluster-randomized controlled trial.
Setting and participants. Clinicians (physicians or nurse practitioners) were recruited between February 2014 and November 2015 from 2 large health centers in the Pacific Northwest and were eligible if they provided primary or specialty care and had at least 5 eligible patients in their panels. Using the electronic health record (EHR) and clinic schedules, study staff identified and contacted (via mail or telephone) consecutive patients cared for by participating clinicians between March 2014 and May 2016 with the following eligibility criteria: age 18 years or older, 2 or more visits with the clinician in the last 18 months, and 1 or more qualifying conditions. Qualifying conditions included (1) metastatic cancer or inoperable lung cancer; (2) COPD with FEV1 values below 35% of that predicted or oxygen dependence, restrictive lung disease with a total lung capacity below 50% of that predicted, or cystic fibrosis with FEV1 below 30% of that predicted; (3) New York Heart Association class III or IV heart failure, pulmonary arterial hypertension, or left ventricular assist device or implantable cardioverter defibrillator implant; 4) cirrhosis or end-stage liver disease; (5) dialysis-dependent renal failure and diabetes; (6) age 75 or older with one or more life-limiting chronic illness; (7) age 90 or older; (8) hospitalization in the last 18 months with a life-limiting illness; (9) Charlson comorbidity index of 6 or higher. The qualifying criteria were selected to identify a median survival of approximately 2 years, suggesting relevance of goals-of-care discussions.
Intervention. The intervention was the patient-specific Jumpstart-Tips intervention, intended to prime clinicians and patients for a brief discussion of goals of care during a routine clinic visit. Patients in the intervention group received a survey to assess their preferences, barriers and facilitators for communication about end-of-life care. Survey responses were used to (1) generate an abstracted version of the patient’s preferences, (2) identify the most important communication barrier or facilitator, and (3) provide communication tips based on curricular materials from VitalTalk (http://vitaltalk.org) tailored to patient responses. The 1-page communication guide, called Jumpstart-Tips, was sent to clinicians 1 or 2 days prior to the target clinic visit date. Patients also received 1-page patient-specific Jumpstart-Tips forms, which summarized their survey responses and provided suggestions for having a goals-of-care conversation with the clinician. Patients in the control group completed the same surveys, but no information was provided to the patients or clinicians. Clinicians were randomly assigned in a 1:1 ratio to intervention or enhanced usual care.
Main outcome measures. The primary outcome was patient-reported occurrence of goals-of-care communication, which was evaluated using a validated dichotomous survey item. Other outcomes included clinician documentation of a goals-of-care conversation in the medical record, patient-reported quality of communication (measured using Quality of Communication questionnaire) at 2 weeks, patient reports of goal-concordant care at 3 months, and patient-reported symptoms of depression and anxiety at 3 and 6 months. All analyses included covariate adjustment for the baseline measure of the outcome and adjustment for other variables found to confound the association between randomization group and outcome.
Main results. Of 485 potentially eligible clinicians, 65 clinicians were randomized to the intervention group and 69 were randomized to the control group. Of these 132 clinicians, 124 had patients participating in the study: 537 out of 917 eligible patients enrolled, with 249 allocated to intervention and 288 to usual care.
Patients in the intervention group were more likely to report a goals-of-care conversation with their provider among all patients (74%, n = 137 vs 31%, n = 66; P n = 112 vs 28%, n = 44; P n = 140 vs 17%, n = 45; P n = 114 vs 17%, n = 34; P
Patients in the intervention group also reported higher quality ratings of goals-of-care conversations at the target visit (mean values, 4.6 v 2.1, P = 0.01, on the 4-indicator construct). Additionally, intervention members reported statistically significant higher ratings on 3 of the 7 individual quality-of-communication survey items.
Patient-assessed goal concordant care did not increase significantly overall (70% vs 57%; P = 0.08) but did increase for patients with stable goals between 3-month follow-up and last prior assessment (73% vs 57%; P = 0.03). Symptoms of depression or anxiety were not different between groups at 3 or 6 months.
Conclusion. The Jumpstart-Tips intervention was associated with an increase in patient reports and clinician documentation of goals-of-care communication. Increased patient-reported goal-concordant care among patients with stable goals was also associated with the intervention. Statistical significance was not detected for changes in depression or anxiety as a result of the intervention. The impact on goals-of-care discussion between patients and caregivers is suggestive of enhanced patient-centered care; however, further studies are needed to evaluate whether this communication is associated with changes in health care delivery.
Commentary
Previous research has shown that patients with serious illness who discuss their goals-of-care fare better in terms of quality of life and reducing intensity of care at the end-of-life [1]. However, providers often fail to or inadequately discuss goals of care with seriously ill patients [2,3]. This contributes to the lack of concordance between patient wishes, particularly related to end-of-life care, and clinical plans of care [4,5]. Addressing this gap between care provided and care desired, as well as providing high-quality, patient-centered care is needed.
Access to palliative care providers (who are trained to address these priorities) in the outpatient setting lags, despite an increase in specialists [6,7]. Thus, primary and specialty care providers in the outpatient setting are best positioned to align their care strategy with the goals of their patients. However, there have been limited results in showing that goals-of-care communication can be improved within the practice setting [8,9]. A randomized clinical trial among hospitalized seniors at the end-of-life showed an association where those who received advanced care planning with had improved quality of life, reduced care at dying, and reduced psychological distress among family [10]. However, in another randomized trial, simulation-based communication training compared with usual education among internal medicine and nurse practitioner trainees did not improve quality of communication about end-of-life care or quality of end-of-life care but was associated with a small increase in patients’ depressive symptoms [11]. A recent 2018 literature review of strategies used to facilitate the discussion of advance care planning with older adults in primary care settings identified effective interventions, including delivering education using various delivery methods, computer-generated triggers for primary care physicians (PCPs), inclusion of multidisciplinary professionals for content delivery, and patient preparation for PCP visit [12].
This article adds to the literature by demonstrating the feasibility and impact of implementing an intervention to increase communication about goals of care and end-of-life care. Further, this study highlights how communication that is bilateral, predetermined, and structured can be integrated into primary care. Strengths of the study include the use of randomization; deployment of validated survey tools; and confirmatory factor analysis to assess whether the survey variables are consistent with the hypothesized constructs. In addition, study staff were blinded when extracting data from the EHR record around discussions and documentation of goals-of-care conversations during patient visits. However, several limitations are present. There may be limited generalizability as the study was performed at low-scale, across one region as well as selection bias among clinicians participating in the study. Clinicians were not blinded of their assignment, which may have influenced their behaviors to discuss and document goals-of-care conversations.
Applications for Clinical Practice
Increasing quality communication around the end of life and understanding of a patient’s goals is important. Good communication can facilitate the development of a comprehensive treatment plan that is medically sound and concordant with the patient’s wishes and values. Clinicians and practices should consider adopting approaches to communication priming and accurate documentation, including: (1) incorporating/automating Jumpstart-Tips forms into practice (and tailoring as needed); (2) identifying similar education material that can serve as a primer for patients; (3) creating a pre-visit form for patients/caregivers to document and inform the clinician of their goals prior to the visit; (4) incorporating a standard EHR note to document and update goals-of-care discussion at each visit; and (5) more broadly encouraging (or providing training for) clinicians to practice bilateral communications with patients during visits.
—Ronald Sanchez, MPH, and Katrina F. Mateo, MPH
1. Wright AA, Zhang B, Ray A, et al. Associations between end-of-life discussions, patient mental health, medical care near death, and caregiver bereavement adjustment. JAMA 2008;300:1665–73.
2. Anderson WG, Chase R, Pantilat SZ, et al. Code status discussions between attending hospitalist physicians and medical patients at hospital admission. J Gen Intern Med 2011;26:359–66.
3. Osborn TR, Curtis JR, Nielsen EL, et al. Identifying elements of ICU care that families report as important but unsatisfactory: decision-making, control, and ICU atmosphere. Chest 2012;142:1185–92.
4. Covinsky KE, Fuller JD, Yaffe K, et al. Communication and decision-making in seriously ill patients: findings of the SUPPORT project. The Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments. J Am Geriatr Soc 2000;48:S187–93.
5. Heyland DK, Dodek P, Rocker G, et al. What matters most in end-of-life care: perceptions of seriously ill patients and their family members. CMAJ 2006;174:627–33
6. Dumanovsky T, Augustin R, Rogers M, Lettang K, Meier DE, Morrison RS. The growth of palliative care in U.S. hospitals: a status report. J Palliat Med 2016;19:8–15.
7. Dumanovsky T, Rogers M, Spragens LH, Morrison RS, Meier DE. Impact of staffing on access to palliative care in U.S. hospitals. J Palliat Med 2015;18:998–9.
8. Roze des Ordons, AL, Sharma N, Heyland DK, et al. Strategies for effective goals of care discussions and decision-making: perspectives from a multi-centre survey of Canadian hospital-based healthcare providers. BMC Palliative Care, 2015;14:38.
9. You JJ, Dodek P, Lamontagne F, et al. What really matters in end-of-life discussions? Perspectives of patients in hospital with serious illness and their families. CMAJ 2014;18:E679–E687.
10. Detering KM, Hancock AD, Reade MC, Silvester W. The impact of advance care planning on end of life care in elderly patients: randomised controlled trial. BMJ. 2010;340:c1345.
11. Curtis JR, Back AL, Ford DW, et al. Effect of communication skills training for residents and nurse practitioners on quality of communication with patients with serious illness: a randomized trial. JAMA 2013;310:2271–81.
12. Solis GR, Mancera BM, Shen MJ. Strategies used to facilitate the discussion of advance care planning with older adults in primary care settings: A literature review. J Am Assoc Nurse Pract 2018;30:270–9.
Study Overview
Objective. To evaluate the efficacy of an intervention targeting both patients and clinicians intended to increase goals-of-care conversations.
Design. Multicenter cluster-randomized controlled trial.
Setting and participants. Clinicians (physicians or nurse practitioners) were recruited between February 2014 and November 2015 from 2 large health centers in the Pacific Northwest and were eligible if they provided primary or specialty care and had at least 5 eligible patients in their panels. Using the electronic health record (EHR) and clinic schedules, study staff identified and contacted (via mail or telephone) consecutive patients cared for by participating clinicians between March 2014 and May 2016 with the following eligibility criteria: age 18 years or older, 2 or more visits with the clinician in the last 18 months, and 1 or more qualifying conditions. Qualifying conditions included (1) metastatic cancer or inoperable lung cancer; (2) COPD with FEV1 values below 35% of that predicted or oxygen dependence, restrictive lung disease with a total lung capacity below 50% of that predicted, or cystic fibrosis with FEV1 below 30% of that predicted; (3) New York Heart Association class III or IV heart failure, pulmonary arterial hypertension, or left ventricular assist device or implantable cardioverter defibrillator implant; 4) cirrhosis or end-stage liver disease; (5) dialysis-dependent renal failure and diabetes; (6) age 75 or older with one or more life-limiting chronic illness; (7) age 90 or older; (8) hospitalization in the last 18 months with a life-limiting illness; (9) Charlson comorbidity index of 6 or higher. The qualifying criteria were selected to identify a median survival of approximately 2 years, suggesting relevance of goals-of-care discussions.
Intervention. The intervention was the patient-specific Jumpstart-Tips intervention, intended to prime clinicians and patients for a brief discussion of goals of care during a routine clinic visit. Patients in the intervention group received a survey to assess their preferences, barriers and facilitators for communication about end-of-life care. Survey responses were used to (1) generate an abstracted version of the patient’s preferences, (2) identify the most important communication barrier or facilitator, and (3) provide communication tips based on curricular materials from VitalTalk (http://vitaltalk.org) tailored to patient responses. The 1-page communication guide, called Jumpstart-Tips, was sent to clinicians 1 or 2 days prior to the target clinic visit date. Patients also received 1-page patient-specific Jumpstart-Tips forms, which summarized their survey responses and provided suggestions for having a goals-of-care conversation with the clinician. Patients in the control group completed the same surveys, but no information was provided to the patients or clinicians. Clinicians were randomly assigned in a 1:1 ratio to intervention or enhanced usual care.
Main outcome measures. The primary outcome was patient-reported occurrence of goals-of-care communication, which was evaluated using a validated dichotomous survey item. Other outcomes included clinician documentation of a goals-of-care conversation in the medical record, patient-reported quality of communication (measured using Quality of Communication questionnaire) at 2 weeks, patient reports of goal-concordant care at 3 months, and patient-reported symptoms of depression and anxiety at 3 and 6 months. All analyses included covariate adjustment for the baseline measure of the outcome and adjustment for other variables found to confound the association between randomization group and outcome.
Main results. Of 485 potentially eligible clinicians, 65 clinicians were randomized to the intervention group and 69 were randomized to the control group. Of these 132 clinicians, 124 had patients participating in the study: 537 out of 917 eligible patients enrolled, with 249 allocated to intervention and 288 to usual care.
Patients in the intervention group were more likely to report a goals-of-care conversation with their provider among all patients (74%, n = 137 vs 31%, n = 66; P n = 112 vs 28%, n = 44; P n = 140 vs 17%, n = 45; P n = 114 vs 17%, n = 34; P
Patients in the intervention group also reported higher quality ratings of goals-of-care conversations at the target visit (mean values, 4.6 v 2.1, P = 0.01, on the 4-indicator construct). Additionally, intervention members reported statistically significant higher ratings on 3 of the 7 individual quality-of-communication survey items.
Patient-assessed goal concordant care did not increase significantly overall (70% vs 57%; P = 0.08) but did increase for patients with stable goals between 3-month follow-up and last prior assessment (73% vs 57%; P = 0.03). Symptoms of depression or anxiety were not different between groups at 3 or 6 months.
Conclusion. The Jumpstart-Tips intervention was associated with an increase in patient reports and clinician documentation of goals-of-care communication. Increased patient-reported goal-concordant care among patients with stable goals was also associated with the intervention. Statistical significance was not detected for changes in depression or anxiety as a result of the intervention. The impact on goals-of-care discussion between patients and caregivers is suggestive of enhanced patient-centered care; however, further studies are needed to evaluate whether this communication is associated with changes in health care delivery.
Commentary
Previous research has shown that patients with serious illness who discuss their goals-of-care fare better in terms of quality of life and reducing intensity of care at the end-of-life [1]. However, providers often fail to or inadequately discuss goals of care with seriously ill patients [2,3]. This contributes to the lack of concordance between patient wishes, particularly related to end-of-life care, and clinical plans of care [4,5]. Addressing this gap between care provided and care desired, as well as providing high-quality, patient-centered care is needed.
Access to palliative care providers (who are trained to address these priorities) in the outpatient setting lags, despite an increase in specialists [6,7]. Thus, primary and specialty care providers in the outpatient setting are best positioned to align their care strategy with the goals of their patients. However, there have been limited results in showing that goals-of-care communication can be improved within the practice setting [8,9]. A randomized clinical trial among hospitalized seniors at the end-of-life showed an association where those who received advanced care planning with had improved quality of life, reduced care at dying, and reduced psychological distress among family [10]. However, in another randomized trial, simulation-based communication training compared with usual education among internal medicine and nurse practitioner trainees did not improve quality of communication about end-of-life care or quality of end-of-life care but was associated with a small increase in patients’ depressive symptoms [11]. A recent 2018 literature review of strategies used to facilitate the discussion of advance care planning with older adults in primary care settings identified effective interventions, including delivering education using various delivery methods, computer-generated triggers for primary care physicians (PCPs), inclusion of multidisciplinary professionals for content delivery, and patient preparation for PCP visit [12].
This article adds to the literature by demonstrating the feasibility and impact of implementing an intervention to increase communication about goals of care and end-of-life care. Further, this study highlights how communication that is bilateral, predetermined, and structured can be integrated into primary care. Strengths of the study include the use of randomization; deployment of validated survey tools; and confirmatory factor analysis to assess whether the survey variables are consistent with the hypothesized constructs. In addition, study staff were blinded when extracting data from the EHR record around discussions and documentation of goals-of-care conversations during patient visits. However, several limitations are present. There may be limited generalizability as the study was performed at low-scale, across one region as well as selection bias among clinicians participating in the study. Clinicians were not blinded of their assignment, which may have influenced their behaviors to discuss and document goals-of-care conversations.
Applications for Clinical Practice
Increasing quality communication around the end of life and understanding of a patient’s goals is important. Good communication can facilitate the development of a comprehensive treatment plan that is medically sound and concordant with the patient’s wishes and values. Clinicians and practices should consider adopting approaches to communication priming and accurate documentation, including: (1) incorporating/automating Jumpstart-Tips forms into practice (and tailoring as needed); (2) identifying similar education material that can serve as a primer for patients; (3) creating a pre-visit form for patients/caregivers to document and inform the clinician of their goals prior to the visit; (4) incorporating a standard EHR note to document and update goals-of-care discussion at each visit; and (5) more broadly encouraging (or providing training for) clinicians to practice bilateral communications with patients during visits.
—Ronald Sanchez, MPH, and Katrina F. Mateo, MPH
Study Overview
Objective. To evaluate the efficacy of an intervention targeting both patients and clinicians intended to increase goals-of-care conversations.
Design. Multicenter cluster-randomized controlled trial.
Setting and participants. Clinicians (physicians or nurse practitioners) were recruited between February 2014 and November 2015 from 2 large health centers in the Pacific Northwest and were eligible if they provided primary or specialty care and had at least 5 eligible patients in their panels. Using the electronic health record (EHR) and clinic schedules, study staff identified and contacted (via mail or telephone) consecutive patients cared for by participating clinicians between March 2014 and May 2016 with the following eligibility criteria: age 18 years or older, 2 or more visits with the clinician in the last 18 months, and 1 or more qualifying conditions. Qualifying conditions included (1) metastatic cancer or inoperable lung cancer; (2) COPD with FEV1 values below 35% of that predicted or oxygen dependence, restrictive lung disease with a total lung capacity below 50% of that predicted, or cystic fibrosis with FEV1 below 30% of that predicted; (3) New York Heart Association class III or IV heart failure, pulmonary arterial hypertension, or left ventricular assist device or implantable cardioverter defibrillator implant; 4) cirrhosis or end-stage liver disease; (5) dialysis-dependent renal failure and diabetes; (6) age 75 or older with one or more life-limiting chronic illness; (7) age 90 or older; (8) hospitalization in the last 18 months with a life-limiting illness; (9) Charlson comorbidity index of 6 or higher. The qualifying criteria were selected to identify a median survival of approximately 2 years, suggesting relevance of goals-of-care discussions.
Intervention. The intervention was the patient-specific Jumpstart-Tips intervention, intended to prime clinicians and patients for a brief discussion of goals of care during a routine clinic visit. Patients in the intervention group received a survey to assess their preferences, barriers and facilitators for communication about end-of-life care. Survey responses were used to (1) generate an abstracted version of the patient’s preferences, (2) identify the most important communication barrier or facilitator, and (3) provide communication tips based on curricular materials from VitalTalk (http://vitaltalk.org) tailored to patient responses. The 1-page communication guide, called Jumpstart-Tips, was sent to clinicians 1 or 2 days prior to the target clinic visit date. Patients also received 1-page patient-specific Jumpstart-Tips forms, which summarized their survey responses and provided suggestions for having a goals-of-care conversation with the clinician. Patients in the control group completed the same surveys, but no information was provided to the patients or clinicians. Clinicians were randomly assigned in a 1:1 ratio to intervention or enhanced usual care.
Main outcome measures. The primary outcome was patient-reported occurrence of goals-of-care communication, which was evaluated using a validated dichotomous survey item. Other outcomes included clinician documentation of a goals-of-care conversation in the medical record, patient-reported quality of communication (measured using Quality of Communication questionnaire) at 2 weeks, patient reports of goal-concordant care at 3 months, and patient-reported symptoms of depression and anxiety at 3 and 6 months. All analyses included covariate adjustment for the baseline measure of the outcome and adjustment for other variables found to confound the association between randomization group and outcome.
Main results. Of 485 potentially eligible clinicians, 65 clinicians were randomized to the intervention group and 69 were randomized to the control group. Of these 132 clinicians, 124 had patients participating in the study: 537 out of 917 eligible patients enrolled, with 249 allocated to intervention and 288 to usual care.
Patients in the intervention group were more likely to report a goals-of-care conversation with their provider among all patients (74%, n = 137 vs 31%, n = 66; P n = 112 vs 28%, n = 44; P n = 140 vs 17%, n = 45; P n = 114 vs 17%, n = 34; P
Patients in the intervention group also reported higher quality ratings of goals-of-care conversations at the target visit (mean values, 4.6 v 2.1, P = 0.01, on the 4-indicator construct). Additionally, intervention members reported statistically significant higher ratings on 3 of the 7 individual quality-of-communication survey items.
Patient-assessed goal concordant care did not increase significantly overall (70% vs 57%; P = 0.08) but did increase for patients with stable goals between 3-month follow-up and last prior assessment (73% vs 57%; P = 0.03). Symptoms of depression or anxiety were not different between groups at 3 or 6 months.
Conclusion. The Jumpstart-Tips intervention was associated with an increase in patient reports and clinician documentation of goals-of-care communication. Increased patient-reported goal-concordant care among patients with stable goals was also associated with the intervention. Statistical significance was not detected for changes in depression or anxiety as a result of the intervention. The impact on goals-of-care discussion between patients and caregivers is suggestive of enhanced patient-centered care; however, further studies are needed to evaluate whether this communication is associated with changes in health care delivery.
Commentary
Previous research has shown that patients with serious illness who discuss their goals-of-care fare better in terms of quality of life and reducing intensity of care at the end-of-life [1]. However, providers often fail to or inadequately discuss goals of care with seriously ill patients [2,3]. This contributes to the lack of concordance between patient wishes, particularly related to end-of-life care, and clinical plans of care [4,5]. Addressing this gap between care provided and care desired, as well as providing high-quality, patient-centered care is needed.
Access to palliative care providers (who are trained to address these priorities) in the outpatient setting lags, despite an increase in specialists [6,7]. Thus, primary and specialty care providers in the outpatient setting are best positioned to align their care strategy with the goals of their patients. However, there have been limited results in showing that goals-of-care communication can be improved within the practice setting [8,9]. A randomized clinical trial among hospitalized seniors at the end-of-life showed an association where those who received advanced care planning with had improved quality of life, reduced care at dying, and reduced psychological distress among family [10]. However, in another randomized trial, simulation-based communication training compared with usual education among internal medicine and nurse practitioner trainees did not improve quality of communication about end-of-life care or quality of end-of-life care but was associated with a small increase in patients’ depressive symptoms [11]. A recent 2018 literature review of strategies used to facilitate the discussion of advance care planning with older adults in primary care settings identified effective interventions, including delivering education using various delivery methods, computer-generated triggers for primary care physicians (PCPs), inclusion of multidisciplinary professionals for content delivery, and patient preparation for PCP visit [12].
This article adds to the literature by demonstrating the feasibility and impact of implementing an intervention to increase communication about goals of care and end-of-life care. Further, this study highlights how communication that is bilateral, predetermined, and structured can be integrated into primary care. Strengths of the study include the use of randomization; deployment of validated survey tools; and confirmatory factor analysis to assess whether the survey variables are consistent with the hypothesized constructs. In addition, study staff were blinded when extracting data from the EHR record around discussions and documentation of goals-of-care conversations during patient visits. However, several limitations are present. There may be limited generalizability as the study was performed at low-scale, across one region as well as selection bias among clinicians participating in the study. Clinicians were not blinded of their assignment, which may have influenced their behaviors to discuss and document goals-of-care conversations.
Applications for Clinical Practice
Increasing quality communication around the end of life and understanding of a patient’s goals is important. Good communication can facilitate the development of a comprehensive treatment plan that is medically sound and concordant with the patient’s wishes and values. Clinicians and practices should consider adopting approaches to communication priming and accurate documentation, including: (1) incorporating/automating Jumpstart-Tips forms into practice (and tailoring as needed); (2) identifying similar education material that can serve as a primer for patients; (3) creating a pre-visit form for patients/caregivers to document and inform the clinician of their goals prior to the visit; (4) incorporating a standard EHR note to document and update goals-of-care discussion at each visit; and (5) more broadly encouraging (or providing training for) clinicians to practice bilateral communications with patients during visits.
—Ronald Sanchez, MPH, and Katrina F. Mateo, MPH
1. Wright AA, Zhang B, Ray A, et al. Associations between end-of-life discussions, patient mental health, medical care near death, and caregiver bereavement adjustment. JAMA 2008;300:1665–73.
2. Anderson WG, Chase R, Pantilat SZ, et al. Code status discussions between attending hospitalist physicians and medical patients at hospital admission. J Gen Intern Med 2011;26:359–66.
3. Osborn TR, Curtis JR, Nielsen EL, et al. Identifying elements of ICU care that families report as important but unsatisfactory: decision-making, control, and ICU atmosphere. Chest 2012;142:1185–92.
4. Covinsky KE, Fuller JD, Yaffe K, et al. Communication and decision-making in seriously ill patients: findings of the SUPPORT project. The Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments. J Am Geriatr Soc 2000;48:S187–93.
5. Heyland DK, Dodek P, Rocker G, et al. What matters most in end-of-life care: perceptions of seriously ill patients and their family members. CMAJ 2006;174:627–33
6. Dumanovsky T, Augustin R, Rogers M, Lettang K, Meier DE, Morrison RS. The growth of palliative care in U.S. hospitals: a status report. J Palliat Med 2016;19:8–15.
7. Dumanovsky T, Rogers M, Spragens LH, Morrison RS, Meier DE. Impact of staffing on access to palliative care in U.S. hospitals. J Palliat Med 2015;18:998–9.
8. Roze des Ordons, AL, Sharma N, Heyland DK, et al. Strategies for effective goals of care discussions and decision-making: perspectives from a multi-centre survey of Canadian hospital-based healthcare providers. BMC Palliative Care, 2015;14:38.
9. You JJ, Dodek P, Lamontagne F, et al. What really matters in end-of-life discussions? Perspectives of patients in hospital with serious illness and their families. CMAJ 2014;18:E679–E687.
10. Detering KM, Hancock AD, Reade MC, Silvester W. The impact of advance care planning on end of life care in elderly patients: randomised controlled trial. BMJ. 2010;340:c1345.
11. Curtis JR, Back AL, Ford DW, et al. Effect of communication skills training for residents and nurse practitioners on quality of communication with patients with serious illness: a randomized trial. JAMA 2013;310:2271–81.
12. Solis GR, Mancera BM, Shen MJ. Strategies used to facilitate the discussion of advance care planning with older adults in primary care settings: A literature review. J Am Assoc Nurse Pract 2018;30:270–9.
1. Wright AA, Zhang B, Ray A, et al. Associations between end-of-life discussions, patient mental health, medical care near death, and caregiver bereavement adjustment. JAMA 2008;300:1665–73.
2. Anderson WG, Chase R, Pantilat SZ, et al. Code status discussions between attending hospitalist physicians and medical patients at hospital admission. J Gen Intern Med 2011;26:359–66.
3. Osborn TR, Curtis JR, Nielsen EL, et al. Identifying elements of ICU care that families report as important but unsatisfactory: decision-making, control, and ICU atmosphere. Chest 2012;142:1185–92.
4. Covinsky KE, Fuller JD, Yaffe K, et al. Communication and decision-making in seriously ill patients: findings of the SUPPORT project. The Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments. J Am Geriatr Soc 2000;48:S187–93.
5. Heyland DK, Dodek P, Rocker G, et al. What matters most in end-of-life care: perceptions of seriously ill patients and their family members. CMAJ 2006;174:627–33
6. Dumanovsky T, Augustin R, Rogers M, Lettang K, Meier DE, Morrison RS. The growth of palliative care in U.S. hospitals: a status report. J Palliat Med 2016;19:8–15.
7. Dumanovsky T, Rogers M, Spragens LH, Morrison RS, Meier DE. Impact of staffing on access to palliative care in U.S. hospitals. J Palliat Med 2015;18:998–9.
8. Roze des Ordons, AL, Sharma N, Heyland DK, et al. Strategies for effective goals of care discussions and decision-making: perspectives from a multi-centre survey of Canadian hospital-based healthcare providers. BMC Palliative Care, 2015;14:38.
9. You JJ, Dodek P, Lamontagne F, et al. What really matters in end-of-life discussions? Perspectives of patients in hospital with serious illness and their families. CMAJ 2014;18:E679–E687.
10. Detering KM, Hancock AD, Reade MC, Silvester W. The impact of advance care planning on end of life care in elderly patients: randomised controlled trial. BMJ. 2010;340:c1345.
11. Curtis JR, Back AL, Ford DW, et al. Effect of communication skills training for residents and nurse practitioners on quality of communication with patients with serious illness: a randomized trial. JAMA 2013;310:2271–81.
12. Solis GR, Mancera BM, Shen MJ. Strategies used to facilitate the discussion of advance care planning with older adults in primary care settings: A literature review. J Am Assoc Nurse Pract 2018;30:270–9.
Which Is More Effective For Hypertension Management: User- Or Expert-Driven E-Counseling?
Study Overview
Objective. To assess whether systolic blood pressure improved with expert-driven or user-driven e-counseling compared with control intervention in patients with hypertension over a 4-month period.
Design. Three–parallel group, double-blind randomized controlled trial.
Setting and participants. In Toronto, Canada, participants were recruited through the Heart and Stroke Foundation heart disease risk assessment website, as well as posters at University Health Network facilities. Participants diagnosed with stage 1 or 2 hypertension (systolic blood pressure [SBP] = 140–180 mm Hg, diastolic blood pressure [DBP] = 90–110 mm Hg) and between the ages of 35 and 74 years were eligible. Hypertension diagnoses were confirmed with the participant’s family doctor at baseline if they were not prescribed antihypertensive medication. All participants were required to have an unchanged prescription for antihypertensive medication 42 months before enrollment. Participants prescribed antihypertensive medication were also required to have SBP ≥ 130 mm Hg or DBP ≥ 85 mm Hg in order to prevent “floor effects.” Exclusion criteria included: diagnosis of kidney disease, major psychiatric illness (eg, psychosis), alcohol or drug dependence in the previous year, pregnancy, and sleep apnea.
Participants were randomly assigned to 1 of 3 intervention groups: control, expert-driven, and user-driven e-counseling. Randomization was conducted by a web-based program using randomly permuted blocks. The randomization code was known only to the research coordinator and not to the investigators or research assistants who administered the assessments.
Intervention. Briefly, user-driven e-counseling enabled the participants to set their own goals or to select the interventions used to reach their behavioral goal. The user-driven group received weekly e-mails that enabled participants to select their areas of lifestyle change using text and video web links embedded in the e-mail. Expert-driven e-counseling involved prescribed specific changes for lifestyle behavior, which were intended to facilitate adherence to behavior change. Participants in the expert-driven group received the same hypertension management recommendations for lifestyle change as the user-driven group; however, the weekly e-mails consisted of predetermined exercise and dietary goals. The control group received weekly e-mails provided by the Heart and Stroke Foundation e-Health program that contained a brief newsletter article regarding BP management through lifestyle changes. The control group was distinct from the intervention groups, as the e-mails were limited to general information on BP management. Blinding to group assignment was maintained during baseline and 4-month follow-up.
Main outcome measures. The primary outcome was SDP; secondary outcomes included DBP, pulse pressure (PP), total cholesterol, 10-year Framingham cardiovascular risk (10-year CVD risk), daily physical activity, and dietary habits. Anthropometric characteristics, medical history, medication information, resting BP, daily step count, dietary behavior, participants’ readiness for lifestyle behavior changes, and participants’ cardiovascular risk (calculated by the Framingham 10-year absolute risk) were collected during the baseline and 4-month follow-up assessment.
Baseline and 4-month follow-up assessments at the Peter Munk Cardiac Center, Toronto General Hospital, University Health Network were scheduled between 8 AM and 12 PM to minimize diurnal BP variability. All participants fasted for 12 hours prior to their assessment in order to obtain accurate samples of cholesterol. Participants were also instructed to avoid smoking for > 4 hours, caffeine for 12 hours, and strenuous exercise for 24 hours prior to their assessment.
BP was measured by a validated protocol for automated BP assessments with the BpTRU blood pressure recording device. Participants were seated for >5 minutes prior to activation of the BpTRU device. The BP cuff was applied to participants’ left arms by a trained research assistant. Following the initial BP measurement, the research assistant exited the room while the BpTRU device completed an automated series of 5 BP recordings with 1-minute intervals separating each of these recordings. The recorded BP at each assessment interval was the mean of these 5 BpTRU measurements. PP was determined by the difference between SBP and DBP readings.
Daily physical activity was defined as the mean 4-day steps (3 weekdays, 1 weekend day) recorded on a pedometer (XL-18CN Activity Monitor), which all participants were given to use as part of the study. Diet was measured as adherence to recommended guidelines for daily intake of fruits and vegetables, and evaluated by the validated NIH/National Cancer Institute Diet History Questionnaire. Readiness for exercise and dietary change were measured using a questionnaire from the authors’ previous trial and the stages of change were defined as the following: precontemplation (not ready to adhere to the target behavior in the next 6 months), contemplation (ready to adhere to the target behavior in the next 6 months), preparation (ready to adhere to the target behavior in the next 4 weeks), action (adherence to the behavior but for < 6 months), and maintenance (adherence to the behavior for ≥ 6 months).
For the primary outcome (SBP), the difference among groups was evaluated using univariate linear regression. Post-hoc comparisons with Bonferroni adjustment, among the three treatment groups were performed only if the overall F-test was significant. Secondary outcomes (DBP, PP, total cholesterol, 10-year CVD risk, daily steps, and daily fruit and vegetable consumption) followed a similar statistical approach as the primary outcome analysis. Statistical significance was defined by a two-tailed test with a P value < 0.05.
Main results. Of those screened (n = 847), 128 participants were randomized into the study. Between the 3 groups (control with n = 43, user-driven with n = 42, expert-driven with n = 43), there were no statistically significant differences in age, sex, household income, education, ethnicity, body mass index, and medications (antihypertensive and lipid-lowering) at baseline. The average age was 56.9 ± 0.8 years, 48% were female, 66% had a household income of > $60,000, 79% had a college/university or graduate school education, 73% identified as white, and over 85% were taking ≥ 1 antihypertensive medications. Baseline SBP, DBP, PP, cholesterol, 10-year CVD risk, daily steps, daily vegetable intake, smoking status, readiness for exercise behavior change and readiness for dietary behavior change were also similar across the 3 groups. All participants were highly motivated at baseline for adopting a healthy lifestyle. The percentage of participants that were already in preparation, action, or maintenance of readiness for exercise and diet were 96% and 92%, respectively. Only 4% and 8% of participants were in either precontemplation or contemplation stage of readiness at baseline for exercise and diet, respectively.
The expert-driven group showed a greater SBP decrease than controls at follow-up (mean difference between expert-driven versus control: −7.5 mm Hg, 95% CI −12.5 to −2.6, P = 0.001). SBP reduction did not significantly differ between user- and expert-driven (P > 0.05). DBP reduction and improvement in daily vegetable intake was not significantly different across groups. However, the expert-driven group demonstrated a significant reduction compared with controls in PP (−4.6 mm Hg, 95% CI −8.3 to −0.9, P = 0.008), cholesterol (−0.48 mmol/L, 95% CI −0.84 to −0.14, P < 0.001), and 10-year CVD risk (−3.3%, 95% C −5.0 to −1.5, P = 0.005). The expert-driven group showed a significantly greater improvement than both controls and the user-driven group in daily steps (expert versus control: 2460 steps/day, 95% CI 1137–3783, P < 0.001; expert versus user: 1844 steps/day, 95% CI 512–3176, P = 0.003) and servings of fruit consumption (expert versus control: 1.5 servings/day, 95% CI 0.2–2.7, P = 0.01; expert versus user: 1.8 servings/day, 95% CI 0.8–3.2, P = 0.001).
Conclusion. Expert-driven e-counseling was more effective than control in reducing SBP, PP, cholesterol, and 10-year CVD risk at the 4-month follow-up. In addition, expert-driven e-counseling was more effective that user-driven counseling in improving daily steps and fruit intake. It may be advisable to incorporate an expert-driven e-counseling protocol in order to accommodate participants with greater motivation to change their lifestyle behaviors and improve BP.
Commentary
In a 2014 article, the authors summarized the efficacy of lifestyle counseling interventions in face-to-face, telehealth, and e-counseling settings, especially noting e-counseling as an emerging preventive strategy for hypertension [10]. E-counseling, a form of telehealth, presents information dynamically though combined video, text, image, and audio media, and incorporates two-way communication through phone, internet, and videoconferencing (ie, between patient and provider). This approach has the potential to increase adherence to counseling and self-care approaches by providing improved and convenient access to information, incorporating engaging components, expanding accessibility and comprehension of information among individuals with varying levels of health literacy, enabling increased and more frequent interactivity with health care professionals, and increasing engagement. Importantly, effective counseling approaches, whether through conventional or e-counseling approaches, should include certain core components, including goal-setting, self-monitoring of symptoms of behaviors, personalized training (based on patient setting or resources), performance-based feedback and reinforcement of health-promoting behaviors, and procedures to enhance self-efficacy [10].
This study adds to the literature by demonstrating that the counseling communication strategies (expert- and user-driven) used to deliver e-counseling can significantly influence intervention outcomes related to hypertension management. Strengths of this study include the use of a double-blind randomized controlled study design powered to detect clinically meaningful SBP differences, the three– parallel group assignments (expert-driven, user-driven, control) that incorporated multiple evidence-based counseling approaches, the measurement of changes in multiple cardiovascular and behavioral outcomes (clinical and self-report measures), the inclusion of a theory-based measure of readiness for dietary and exercise behavior change, and the low attrition rate. However, there are key limitations, many acknowledged by the authors. The majority of the study participants were white, from higher income households, had completed higher education, and were already motivated for dietary and exercise behavior change, thus limiting the generalizability of findings. The study had a limited follow-up period (only 4 months) and the study design did not allow for the identification of the most impactful components of the intervention groups.
Applications for Clinical Practice
Expert-driven e-counseling may be an effective approach to managing hypertension, as this study showed that expert-driven e-counseling was more effective than control in reducing SBP, PP, cholesterol, and 10-year CVD risk at the 4-month follow-up, and expert-driven e-counseling was more effective that user-driven counseling in improving daily steps and fruit intake. However, providers should be mindful that this approach may be limited to patients with greater motivation to change their lifestyle behaviors to lower blood pressure.
1. Weber MA, Schiffrin EL, White WB, et al. Clinical practice guidelines for the management of hypertension in the community. J Clin Hypertens 2014;16:14–26.
2. American College of Cardiology. New ACC/AHA high blood pressure guidelines lower definition of hypertension; 2017.
3. Ruilope LM. Current challenges in the clinical management of hypertension. Nat Rev Cardiol 2012;9:267–75.
4. Borghi C, Cicero AFG. Hypertension: management perspectives. Expert Opin Pharmacother 2012;13:1999–2003.
5. Gupta R, Guptha S. Strategies for initial management of hypertension. Indian J Med Res 2010;132:531–42.
6. Pietrzak E, Cotea C, Pullman S. Primary and secondary prevention of cardiovascular disease. J Cardiopulm Rehabil Prev 2014;34:303–17.
7. Watson AJ, Singh K, Myint-U K, et al. Evaluating a web-based self-management program for employees with hypertension and prehypertension: A randomized clinical trial. Am Heart J 2012;164:625–31.
8. Thomas KL, Shah BR, Elliot-Bynum S, et al. Check it, change it: a community-based, multifaceted intervention to improve blood pressure control. Circ Cardiovasc Qual Outcomes 2014;7:828–34.
9. Hallberg I, Ranerup A, Kjellgren K. Supporting the self-management of hypertension: Patients’ experiences of using a mobile phone-based system. J Hum Hypertens 2016;30:141–6.
10. Nolan RP, Liu S, Payne AYM. E-counseling as an emerging preventive strategy for hypertension. Curr Opin Cardiol 2014;29:319–23.
11. Carter BL, Bosworth HB, Green BB. The hypertension team: the role of the pharmacist, nurse, and teamwork in hypertension therapy. J Clin Hypertens 2012;14:51–65.
Study Overview
Objective. To assess whether systolic blood pressure improved with expert-driven or user-driven e-counseling compared with control intervention in patients with hypertension over a 4-month period.
Design. Three–parallel group, double-blind randomized controlled trial.
Setting and participants. In Toronto, Canada, participants were recruited through the Heart and Stroke Foundation heart disease risk assessment website, as well as posters at University Health Network facilities. Participants diagnosed with stage 1 or 2 hypertension (systolic blood pressure [SBP] = 140–180 mm Hg, diastolic blood pressure [DBP] = 90–110 mm Hg) and between the ages of 35 and 74 years were eligible. Hypertension diagnoses were confirmed with the participant’s family doctor at baseline if they were not prescribed antihypertensive medication. All participants were required to have an unchanged prescription for antihypertensive medication 42 months before enrollment. Participants prescribed antihypertensive medication were also required to have SBP ≥ 130 mm Hg or DBP ≥ 85 mm Hg in order to prevent “floor effects.” Exclusion criteria included: diagnosis of kidney disease, major psychiatric illness (eg, psychosis), alcohol or drug dependence in the previous year, pregnancy, and sleep apnea.
Participants were randomly assigned to 1 of 3 intervention groups: control, expert-driven, and user-driven e-counseling. Randomization was conducted by a web-based program using randomly permuted blocks. The randomization code was known only to the research coordinator and not to the investigators or research assistants who administered the assessments.
Intervention. Briefly, user-driven e-counseling enabled the participants to set their own goals or to select the interventions used to reach their behavioral goal. The user-driven group received weekly e-mails that enabled participants to select their areas of lifestyle change using text and video web links embedded in the e-mail. Expert-driven e-counseling involved prescribed specific changes for lifestyle behavior, which were intended to facilitate adherence to behavior change. Participants in the expert-driven group received the same hypertension management recommendations for lifestyle change as the user-driven group; however, the weekly e-mails consisted of predetermined exercise and dietary goals. The control group received weekly e-mails provided by the Heart and Stroke Foundation e-Health program that contained a brief newsletter article regarding BP management through lifestyle changes. The control group was distinct from the intervention groups, as the e-mails were limited to general information on BP management. Blinding to group assignment was maintained during baseline and 4-month follow-up.
Main outcome measures. The primary outcome was SDP; secondary outcomes included DBP, pulse pressure (PP), total cholesterol, 10-year Framingham cardiovascular risk (10-year CVD risk), daily physical activity, and dietary habits. Anthropometric characteristics, medical history, medication information, resting BP, daily step count, dietary behavior, participants’ readiness for lifestyle behavior changes, and participants’ cardiovascular risk (calculated by the Framingham 10-year absolute risk) were collected during the baseline and 4-month follow-up assessment.
Baseline and 4-month follow-up assessments at the Peter Munk Cardiac Center, Toronto General Hospital, University Health Network were scheduled between 8 AM and 12 PM to minimize diurnal BP variability. All participants fasted for 12 hours prior to their assessment in order to obtain accurate samples of cholesterol. Participants were also instructed to avoid smoking for > 4 hours, caffeine for 12 hours, and strenuous exercise for 24 hours prior to their assessment.
BP was measured by a validated protocol for automated BP assessments with the BpTRU blood pressure recording device. Participants were seated for >5 minutes prior to activation of the BpTRU device. The BP cuff was applied to participants’ left arms by a trained research assistant. Following the initial BP measurement, the research assistant exited the room while the BpTRU device completed an automated series of 5 BP recordings with 1-minute intervals separating each of these recordings. The recorded BP at each assessment interval was the mean of these 5 BpTRU measurements. PP was determined by the difference between SBP and DBP readings.
Daily physical activity was defined as the mean 4-day steps (3 weekdays, 1 weekend day) recorded on a pedometer (XL-18CN Activity Monitor), which all participants were given to use as part of the study. Diet was measured as adherence to recommended guidelines for daily intake of fruits and vegetables, and evaluated by the validated NIH/National Cancer Institute Diet History Questionnaire. Readiness for exercise and dietary change were measured using a questionnaire from the authors’ previous trial and the stages of change were defined as the following: precontemplation (not ready to adhere to the target behavior in the next 6 months), contemplation (ready to adhere to the target behavior in the next 6 months), preparation (ready to adhere to the target behavior in the next 4 weeks), action (adherence to the behavior but for < 6 months), and maintenance (adherence to the behavior for ≥ 6 months).
For the primary outcome (SBP), the difference among groups was evaluated using univariate linear regression. Post-hoc comparisons with Bonferroni adjustment, among the three treatment groups were performed only if the overall F-test was significant. Secondary outcomes (DBP, PP, total cholesterol, 10-year CVD risk, daily steps, and daily fruit and vegetable consumption) followed a similar statistical approach as the primary outcome analysis. Statistical significance was defined by a two-tailed test with a P value < 0.05.
Main results. Of those screened (n = 847), 128 participants were randomized into the study. Between the 3 groups (control with n = 43, user-driven with n = 42, expert-driven with n = 43), there were no statistically significant differences in age, sex, household income, education, ethnicity, body mass index, and medications (antihypertensive and lipid-lowering) at baseline. The average age was 56.9 ± 0.8 years, 48% were female, 66% had a household income of > $60,000, 79% had a college/university or graduate school education, 73% identified as white, and over 85% were taking ≥ 1 antihypertensive medications. Baseline SBP, DBP, PP, cholesterol, 10-year CVD risk, daily steps, daily vegetable intake, smoking status, readiness for exercise behavior change and readiness for dietary behavior change were also similar across the 3 groups. All participants were highly motivated at baseline for adopting a healthy lifestyle. The percentage of participants that were already in preparation, action, or maintenance of readiness for exercise and diet were 96% and 92%, respectively. Only 4% and 8% of participants were in either precontemplation or contemplation stage of readiness at baseline for exercise and diet, respectively.
The expert-driven group showed a greater SBP decrease than controls at follow-up (mean difference between expert-driven versus control: −7.5 mm Hg, 95% CI −12.5 to −2.6, P = 0.001). SBP reduction did not significantly differ between user- and expert-driven (P > 0.05). DBP reduction and improvement in daily vegetable intake was not significantly different across groups. However, the expert-driven group demonstrated a significant reduction compared with controls in PP (−4.6 mm Hg, 95% CI −8.3 to −0.9, P = 0.008), cholesterol (−0.48 mmol/L, 95% CI −0.84 to −0.14, P < 0.001), and 10-year CVD risk (−3.3%, 95% C −5.0 to −1.5, P = 0.005). The expert-driven group showed a significantly greater improvement than both controls and the user-driven group in daily steps (expert versus control: 2460 steps/day, 95% CI 1137–3783, P < 0.001; expert versus user: 1844 steps/day, 95% CI 512–3176, P = 0.003) and servings of fruit consumption (expert versus control: 1.5 servings/day, 95% CI 0.2–2.7, P = 0.01; expert versus user: 1.8 servings/day, 95% CI 0.8–3.2, P = 0.001).
Conclusion. Expert-driven e-counseling was more effective than control in reducing SBP, PP, cholesterol, and 10-year CVD risk at the 4-month follow-up. In addition, expert-driven e-counseling was more effective that user-driven counseling in improving daily steps and fruit intake. It may be advisable to incorporate an expert-driven e-counseling protocol in order to accommodate participants with greater motivation to change their lifestyle behaviors and improve BP.
Commentary
In a 2014 article, the authors summarized the efficacy of lifestyle counseling interventions in face-to-face, telehealth, and e-counseling settings, especially noting e-counseling as an emerging preventive strategy for hypertension [10]. E-counseling, a form of telehealth, presents information dynamically though combined video, text, image, and audio media, and incorporates two-way communication through phone, internet, and videoconferencing (ie, between patient and provider). This approach has the potential to increase adherence to counseling and self-care approaches by providing improved and convenient access to information, incorporating engaging components, expanding accessibility and comprehension of information among individuals with varying levels of health literacy, enabling increased and more frequent interactivity with health care professionals, and increasing engagement. Importantly, effective counseling approaches, whether through conventional or e-counseling approaches, should include certain core components, including goal-setting, self-monitoring of symptoms of behaviors, personalized training (based on patient setting or resources), performance-based feedback and reinforcement of health-promoting behaviors, and procedures to enhance self-efficacy [10].
This study adds to the literature by demonstrating that the counseling communication strategies (expert- and user-driven) used to deliver e-counseling can significantly influence intervention outcomes related to hypertension management. Strengths of this study include the use of a double-blind randomized controlled study design powered to detect clinically meaningful SBP differences, the three– parallel group assignments (expert-driven, user-driven, control) that incorporated multiple evidence-based counseling approaches, the measurement of changes in multiple cardiovascular and behavioral outcomes (clinical and self-report measures), the inclusion of a theory-based measure of readiness for dietary and exercise behavior change, and the low attrition rate. However, there are key limitations, many acknowledged by the authors. The majority of the study participants were white, from higher income households, had completed higher education, and were already motivated for dietary and exercise behavior change, thus limiting the generalizability of findings. The study had a limited follow-up period (only 4 months) and the study design did not allow for the identification of the most impactful components of the intervention groups.
Applications for Clinical Practice
Expert-driven e-counseling may be an effective approach to managing hypertension, as this study showed that expert-driven e-counseling was more effective than control in reducing SBP, PP, cholesterol, and 10-year CVD risk at the 4-month follow-up, and expert-driven e-counseling was more effective that user-driven counseling in improving daily steps and fruit intake. However, providers should be mindful that this approach may be limited to patients with greater motivation to change their lifestyle behaviors to lower blood pressure.
Study Overview
Objective. To assess whether systolic blood pressure improved with expert-driven or user-driven e-counseling compared with control intervention in patients with hypertension over a 4-month period.
Design. Three–parallel group, double-blind randomized controlled trial.
Setting and participants. In Toronto, Canada, participants were recruited through the Heart and Stroke Foundation heart disease risk assessment website, as well as posters at University Health Network facilities. Participants diagnosed with stage 1 or 2 hypertension (systolic blood pressure [SBP] = 140–180 mm Hg, diastolic blood pressure [DBP] = 90–110 mm Hg) and between the ages of 35 and 74 years were eligible. Hypertension diagnoses were confirmed with the participant’s family doctor at baseline if they were not prescribed antihypertensive medication. All participants were required to have an unchanged prescription for antihypertensive medication 42 months before enrollment. Participants prescribed antihypertensive medication were also required to have SBP ≥ 130 mm Hg or DBP ≥ 85 mm Hg in order to prevent “floor effects.” Exclusion criteria included: diagnosis of kidney disease, major psychiatric illness (eg, psychosis), alcohol or drug dependence in the previous year, pregnancy, and sleep apnea.
Participants were randomly assigned to 1 of 3 intervention groups: control, expert-driven, and user-driven e-counseling. Randomization was conducted by a web-based program using randomly permuted blocks. The randomization code was known only to the research coordinator and not to the investigators or research assistants who administered the assessments.
Intervention. Briefly, user-driven e-counseling enabled the participants to set their own goals or to select the interventions used to reach their behavioral goal. The user-driven group received weekly e-mails that enabled participants to select their areas of lifestyle change using text and video web links embedded in the e-mail. Expert-driven e-counseling involved prescribed specific changes for lifestyle behavior, which were intended to facilitate adherence to behavior change. Participants in the expert-driven group received the same hypertension management recommendations for lifestyle change as the user-driven group; however, the weekly e-mails consisted of predetermined exercise and dietary goals. The control group received weekly e-mails provided by the Heart and Stroke Foundation e-Health program that contained a brief newsletter article regarding BP management through lifestyle changes. The control group was distinct from the intervention groups, as the e-mails were limited to general information on BP management. Blinding to group assignment was maintained during baseline and 4-month follow-up.
Main outcome measures. The primary outcome was SDP; secondary outcomes included DBP, pulse pressure (PP), total cholesterol, 10-year Framingham cardiovascular risk (10-year CVD risk), daily physical activity, and dietary habits. Anthropometric characteristics, medical history, medication information, resting BP, daily step count, dietary behavior, participants’ readiness for lifestyle behavior changes, and participants’ cardiovascular risk (calculated by the Framingham 10-year absolute risk) were collected during the baseline and 4-month follow-up assessment.
Baseline and 4-month follow-up assessments at the Peter Munk Cardiac Center, Toronto General Hospital, University Health Network were scheduled between 8 AM and 12 PM to minimize diurnal BP variability. All participants fasted for 12 hours prior to their assessment in order to obtain accurate samples of cholesterol. Participants were also instructed to avoid smoking for > 4 hours, caffeine for 12 hours, and strenuous exercise for 24 hours prior to their assessment.
BP was measured by a validated protocol for automated BP assessments with the BpTRU blood pressure recording device. Participants were seated for >5 minutes prior to activation of the BpTRU device. The BP cuff was applied to participants’ left arms by a trained research assistant. Following the initial BP measurement, the research assistant exited the room while the BpTRU device completed an automated series of 5 BP recordings with 1-minute intervals separating each of these recordings. The recorded BP at each assessment interval was the mean of these 5 BpTRU measurements. PP was determined by the difference between SBP and DBP readings.
Daily physical activity was defined as the mean 4-day steps (3 weekdays, 1 weekend day) recorded on a pedometer (XL-18CN Activity Monitor), which all participants were given to use as part of the study. Diet was measured as adherence to recommended guidelines for daily intake of fruits and vegetables, and evaluated by the validated NIH/National Cancer Institute Diet History Questionnaire. Readiness for exercise and dietary change were measured using a questionnaire from the authors’ previous trial and the stages of change were defined as the following: precontemplation (not ready to adhere to the target behavior in the next 6 months), contemplation (ready to adhere to the target behavior in the next 6 months), preparation (ready to adhere to the target behavior in the next 4 weeks), action (adherence to the behavior but for < 6 months), and maintenance (adherence to the behavior for ≥ 6 months).
For the primary outcome (SBP), the difference among groups was evaluated using univariate linear regression. Post-hoc comparisons with Bonferroni adjustment, among the three treatment groups were performed only if the overall F-test was significant. Secondary outcomes (DBP, PP, total cholesterol, 10-year CVD risk, daily steps, and daily fruit and vegetable consumption) followed a similar statistical approach as the primary outcome analysis. Statistical significance was defined by a two-tailed test with a P value < 0.05.
Main results. Of those screened (n = 847), 128 participants were randomized into the study. Between the 3 groups (control with n = 43, user-driven with n = 42, expert-driven with n = 43), there were no statistically significant differences in age, sex, household income, education, ethnicity, body mass index, and medications (antihypertensive and lipid-lowering) at baseline. The average age was 56.9 ± 0.8 years, 48% were female, 66% had a household income of > $60,000, 79% had a college/university or graduate school education, 73% identified as white, and over 85% were taking ≥ 1 antihypertensive medications. Baseline SBP, DBP, PP, cholesterol, 10-year CVD risk, daily steps, daily vegetable intake, smoking status, readiness for exercise behavior change and readiness for dietary behavior change were also similar across the 3 groups. All participants were highly motivated at baseline for adopting a healthy lifestyle. The percentage of participants that were already in preparation, action, or maintenance of readiness for exercise and diet were 96% and 92%, respectively. Only 4% and 8% of participants were in either precontemplation or contemplation stage of readiness at baseline for exercise and diet, respectively.
The expert-driven group showed a greater SBP decrease than controls at follow-up (mean difference between expert-driven versus control: −7.5 mm Hg, 95% CI −12.5 to −2.6, P = 0.001). SBP reduction did not significantly differ between user- and expert-driven (P > 0.05). DBP reduction and improvement in daily vegetable intake was not significantly different across groups. However, the expert-driven group demonstrated a significant reduction compared with controls in PP (−4.6 mm Hg, 95% CI −8.3 to −0.9, P = 0.008), cholesterol (−0.48 mmol/L, 95% CI −0.84 to −0.14, P < 0.001), and 10-year CVD risk (−3.3%, 95% C −5.0 to −1.5, P = 0.005). The expert-driven group showed a significantly greater improvement than both controls and the user-driven group in daily steps (expert versus control: 2460 steps/day, 95% CI 1137–3783, P < 0.001; expert versus user: 1844 steps/day, 95% CI 512–3176, P = 0.003) and servings of fruit consumption (expert versus control: 1.5 servings/day, 95% CI 0.2–2.7, P = 0.01; expert versus user: 1.8 servings/day, 95% CI 0.8–3.2, P = 0.001).
Conclusion. Expert-driven e-counseling was more effective than control in reducing SBP, PP, cholesterol, and 10-year CVD risk at the 4-month follow-up. In addition, expert-driven e-counseling was more effective that user-driven counseling in improving daily steps and fruit intake. It may be advisable to incorporate an expert-driven e-counseling protocol in order to accommodate participants with greater motivation to change their lifestyle behaviors and improve BP.
Commentary
In a 2014 article, the authors summarized the efficacy of lifestyle counseling interventions in face-to-face, telehealth, and e-counseling settings, especially noting e-counseling as an emerging preventive strategy for hypertension [10]. E-counseling, a form of telehealth, presents information dynamically though combined video, text, image, and audio media, and incorporates two-way communication through phone, internet, and videoconferencing (ie, between patient and provider). This approach has the potential to increase adherence to counseling and self-care approaches by providing improved and convenient access to information, incorporating engaging components, expanding accessibility and comprehension of information among individuals with varying levels of health literacy, enabling increased and more frequent interactivity with health care professionals, and increasing engagement. Importantly, effective counseling approaches, whether through conventional or e-counseling approaches, should include certain core components, including goal-setting, self-monitoring of symptoms of behaviors, personalized training (based on patient setting or resources), performance-based feedback and reinforcement of health-promoting behaviors, and procedures to enhance self-efficacy [10].
This study adds to the literature by demonstrating that the counseling communication strategies (expert- and user-driven) used to deliver e-counseling can significantly influence intervention outcomes related to hypertension management. Strengths of this study include the use of a double-blind randomized controlled study design powered to detect clinically meaningful SBP differences, the three– parallel group assignments (expert-driven, user-driven, control) that incorporated multiple evidence-based counseling approaches, the measurement of changes in multiple cardiovascular and behavioral outcomes (clinical and self-report measures), the inclusion of a theory-based measure of readiness for dietary and exercise behavior change, and the low attrition rate. However, there are key limitations, many acknowledged by the authors. The majority of the study participants were white, from higher income households, had completed higher education, and were already motivated for dietary and exercise behavior change, thus limiting the generalizability of findings. The study had a limited follow-up period (only 4 months) and the study design did not allow for the identification of the most impactful components of the intervention groups.
Applications for Clinical Practice
Expert-driven e-counseling may be an effective approach to managing hypertension, as this study showed that expert-driven e-counseling was more effective than control in reducing SBP, PP, cholesterol, and 10-year CVD risk at the 4-month follow-up, and expert-driven e-counseling was more effective that user-driven counseling in improving daily steps and fruit intake. However, providers should be mindful that this approach may be limited to patients with greater motivation to change their lifestyle behaviors to lower blood pressure.
1. Weber MA, Schiffrin EL, White WB, et al. Clinical practice guidelines for the management of hypertension in the community. J Clin Hypertens 2014;16:14–26.
2. American College of Cardiology. New ACC/AHA high blood pressure guidelines lower definition of hypertension; 2017.
3. Ruilope LM. Current challenges in the clinical management of hypertension. Nat Rev Cardiol 2012;9:267–75.
4. Borghi C, Cicero AFG. Hypertension: management perspectives. Expert Opin Pharmacother 2012;13:1999–2003.
5. Gupta R, Guptha S. Strategies for initial management of hypertension. Indian J Med Res 2010;132:531–42.
6. Pietrzak E, Cotea C, Pullman S. Primary and secondary prevention of cardiovascular disease. J Cardiopulm Rehabil Prev 2014;34:303–17.
7. Watson AJ, Singh K, Myint-U K, et al. Evaluating a web-based self-management program for employees with hypertension and prehypertension: A randomized clinical trial. Am Heart J 2012;164:625–31.
8. Thomas KL, Shah BR, Elliot-Bynum S, et al. Check it, change it: a community-based, multifaceted intervention to improve blood pressure control. Circ Cardiovasc Qual Outcomes 2014;7:828–34.
9. Hallberg I, Ranerup A, Kjellgren K. Supporting the self-management of hypertension: Patients’ experiences of using a mobile phone-based system. J Hum Hypertens 2016;30:141–6.
10. Nolan RP, Liu S, Payne AYM. E-counseling as an emerging preventive strategy for hypertension. Curr Opin Cardiol 2014;29:319–23.
11. Carter BL, Bosworth HB, Green BB. The hypertension team: the role of the pharmacist, nurse, and teamwork in hypertension therapy. J Clin Hypertens 2012;14:51–65.
1. Weber MA, Schiffrin EL, White WB, et al. Clinical practice guidelines for the management of hypertension in the community. J Clin Hypertens 2014;16:14–26.
2. American College of Cardiology. New ACC/AHA high blood pressure guidelines lower definition of hypertension; 2017.
3. Ruilope LM. Current challenges in the clinical management of hypertension. Nat Rev Cardiol 2012;9:267–75.
4. Borghi C, Cicero AFG. Hypertension: management perspectives. Expert Opin Pharmacother 2012;13:1999–2003.
5. Gupta R, Guptha S. Strategies for initial management of hypertension. Indian J Med Res 2010;132:531–42.
6. Pietrzak E, Cotea C, Pullman S. Primary and secondary prevention of cardiovascular disease. J Cardiopulm Rehabil Prev 2014;34:303–17.
7. Watson AJ, Singh K, Myint-U K, et al. Evaluating a web-based self-management program for employees with hypertension and prehypertension: A randomized clinical trial. Am Heart J 2012;164:625–31.
8. Thomas KL, Shah BR, Elliot-Bynum S, et al. Check it, change it: a community-based, multifaceted intervention to improve blood pressure control. Circ Cardiovasc Qual Outcomes 2014;7:828–34.
9. Hallberg I, Ranerup A, Kjellgren K. Supporting the self-management of hypertension: Patients’ experiences of using a mobile phone-based system. J Hum Hypertens 2016;30:141–6.
10. Nolan RP, Liu S, Payne AYM. E-counseling as an emerging preventive strategy for hypertension. Curr Opin Cardiol 2014;29:319–23.
11. Carter BL, Bosworth HB, Green BB. The hypertension team: the role of the pharmacist, nurse, and teamwork in hypertension therapy. J Clin Hypertens 2012;14:51–65.
Factors Impacting Receipt of Weight Loss Advice from Providers Among Patients with Overweight/Obesity
Study Overview
Objective. To examine receipt of provider advice to lose weight among primary care patients who are overweight or obese.
Design. Cross-sectional study.
Setting and participants. Participants were recruited through convenience sampling of primary care practices that were members in a national practice-based research network or part of federally qualified health care system based in the Southeastern United States. Each practice used 1 or more of the following recruitment strategies: self-referral from study flyers posted in practices, given during clinic appointments, or posted on the practice portal (n = 3 practices); mailed invitations to patients part of a practice registry (n = 7 practices); and on-site recruitment by research staff during clinic hours (n = 2 practices). Inclusion criteria included having at least a 3-year history of being a patient in the practice, being aged 18 years or older, and having an overweight or obese status according to Centers for Disease Control definitions (body mass index [BMI] 25.0–29.9 kg/m2 = overweight, ≥ 30 kg/m2 = obese). After completing informed consent, participants completed an interview comprising a 20-minute survey, either in English or Spanish, either in-person or by telephone.
Measures. The survey obtained measures related to sociodemographic characteristics (race, gender, age, marital status, education level, employment status, income level), clinical characteristics (height and weight, history of diabetes/hypertension), psychological variables (readiness to make weight loss or maintenance efforts and confidence in their ability to lose or maintain weight), shared decision-making about weight loss/management (using the SDM-Q-9, with a higher total score indicating greater shared decision-making), and physician advice about weight loss (whether they had ever been advised by a doctor or other health care professional to lose weight or reduce their weight).
Main results. Among the study sample (n = 282), 65% were female, 60% were from racial and ethnic minority groups, 55% were married, 57% had some college education or higher, and 37% had an income level below $20,000/year. The mean age of participants was 53.1 (± 14.4) years. 59% had been advised by their physician to lose weight.
The percentage of participants who reported receiving provider advice was statistically different from 50% using the binomial test (P = 0.0035). Based on bivariate analysis of provider advice about weight loss, women were significantly more likely than men to report that their provider had advised them to lose weight (P = 0.001). Both actual and perceived obesity were associated significantly with receiving provider advice about weight loss (both P = 0.001). Diabetic patients were also significantly more likely than nondiabetic patients to report that their provider had advised them to lose weight (P = 0.01). Participants who reported greater readiness to lose or maintain their weight were more likely to report provider advice about weight loss compared to those with less readiness (P = 0.003). While employed patients, those who had at least some college education, and those who were hypertensive were more likely to report provider advice compared to those who were unemployed, had less education, and were not hypertensive, these associations were not statistically significant (P = 0.06, P = 0.06, P = 0.10, respectively). There were no racial/ethnic differences in receipt of provider advice to lose weight (P = 0.76). Participants with greater shared decision-making were more likely to report provider advice about weight loss (P < 0.001).
Based on results of the multivariate logistic regression analysis, obesity status, perceived obesity, and SDM about weight loss/management had significant independent associations with receiving physician advice about weight loss. Participants with obesity were more likely than those with overweight status to report provider advice (odds ratio [OR] = 1.31, 95% CI = 1.25–4.34, P = 0.001). Similarly, participants who believed they had overweight/obesity had a greater likelihood of reporting provider advice compared with those who did not believe they were obese/overweight (OR = 1.40, 95% CI = 2.43–6.37, P < 0.001). Shared decision making about weight loss/management was associated with an increased likelihood of reporting provider advice (OR = 3.30, 95% CI = 2.62–4.12, P < 0.001).
Conclusions. Many patients with overweight/obesity may not be receiving advice to lose/manage their weight by their provider. While providers should advise patients with overweight/obesity about weight loss and management, patient beliefs about their weight status and perceptions about shared decision-making are important to reporting receipt of provider advice about weight loss/management. Patient beliefs as well as provider behaviors should be addressed as part of efforts to improve the management of obesity/overweight in primary care.
Commentary
Over 35% of adults in the United States have a BMI in the obese range [1], putting them at risk for obesity-related comorbidities [2], often diagnosed and treated within primary care settings. The US Preventive Services Task Force recommends that all patients be screened for obesity and offered intensive lifestyle counseling, since modest weight loss can have significant health benefits [3]. Providers, particularly within the primary care setting, are ideally situated to promote weight loss via effective obesity counseling, as multiple clinic visits over time have the potential to enable rapport building and behavioral change management [4]. Indeed, a 2013 systematic review and meta-analysis of published studies of survey data examining provider weight loss counseling and its association with changes in patient weight loss behavior found that primary care provider advice on weight loss appears to have a significant impact on patient attempts to change behaviors related to their weight [5]. In this study, the authors reported higher rates of physician advice about weight loss compared to other studies, however, the results still demonstrate that based on patient reporting, not all providers are advising weight management or weight loss. Several studies have discussed barriers to weight management and obesity counseling among adults by physicians, which include lack of training, time, and perceived ineffectiveness of their own efforts [6–8].
Additionally, and perhaps more importantly, different factors can impact patient perception of provider advice and/or counseling around weight management, weight loss, or obesity. These can include race/ethnicity [9], health literacy [10], and motivation [11]. This study adds to the literature by shedding new light on variables that are important to patients being advised by providers to lose/manage their weight, including actual and perceived obesity status, and perceived shared decision-making. Previous research has focused on patient-provider communication and shared decision-making in the areas of antibiotic use [12], diabetes management [13], and weight loss [14].
Strengths of this study included the variety of recruitment methods employed to enroll patients from multiple clinic sites, the diverse sociodemographic characteristics of the study sample that resulted, the assessment of variables using standard or previously used measures, and the use of both bivariate and multivariate analyses to assess relationships between variables. Key limitations were acknowledged by the authors and included the cross-sectional design, which does not allow for causality to be assessed; the use of surveys for data collection, which relies on subjective and self-reported data; the assessment of weight management/loss advice only from the perspective of the patient, as opposed to including the provider perspective or using objective observations/data; and the lack of assessment of advice content or frequency of advice given.
Applications for Clinical Practice
As the authors suggest, this study highlights opportunities for improving weight-related advice for patients. Providers should incorporate obesity screening and counseling with all patients, as recommended by clinical care guidelines and the literature. In weight management conversations, providers should also be mindful of patient beliefs and understanding of their weight status, and incorporate shared decision-making practices to increase patient self-efficacy (ie, confidence, readiness) to make weight loss efforts.)
1. Flegal KM, Kruszon-Moran D, Carroll MD, et al. Trends in obesity among adults in the United States, 2005 to 2014. JAMA 2016;315:2284–91.
2. Guh DP, Zhang W, Bansback N, et al. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Public Health 2009;9:88.
3. Moyer VA. Screening for and management of obesity in adults: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2012;157:373–8.
4. Schlair S, Moore S, Mcmacken M, Jay M. How to deliver high quality obesity counseling using the 5As framework. J Clin Outcomes Manag 2012;19:221–9.
5. Rose SA, Poynter PS, Anderson JW, et al. Physician weight loss advice and patient weight loss behavior change: a literature review and meta-analysis of survey data. Int J Obes (Lond) 2013;37:118–28.
6. Forman-Hoffman V, Little A, Wahls T. Barriers to obesity management: a pilot study of primary care clinicians. BMC Fam Pract 2006;7:35.
7. Jay M, Gillespie C, Ark T, et al. Do internists, pediatricians, and psychiatrists feel competent in obesity care? Using a needs assessment to drive curriculum design. J Gen Intern Med 2008;23:1066–70.
8. Leverence RR, Williams RL, Sussman A, Crabtree BF. Obesity counseling and guidelines in primary care: a qualitative study. Am J Prev Med 2007;32:334–9.
9. Durant NH, Bartman B, Person SD, et al. Patient provider communication about the health effects of obesity. Patient Educ Couns 2009;75:53–7.
10. Zarcadoolas C, Levy, J, Sealy Y, et al. Health literacy at work to address overweight and obesity in adults: The development of the Obesity Action Kit. J Commun Health 2011;4:88–101.
11. Befort CA, Greiner KA, Hall S, et al. Weight-related perceptions among patients and physicians: how well do physicians judge patients’ motivation to lose weight? J Gen Intern Med 2006;21:1086–90.
12. Schoenthaler A, Albright G, Hibbard J, Goldman R. Simulated conversations with virtual humans to improve patient-provider communication and reduce unnecessary prescriptions for antibiotics: a repeated measure pilot study. JMIR Med Educ 2017;3:e7.
13. Griffith M, Siminerio L, Payne T, Krall J. A shared decision-making approach to telemedicine: engaging rural patients in glycemic management. J Clin Med 2016;5:103.
14. Carcone AI, Naar-King S, E. Brogan K, et al. Provider communication behaviors that predict motivation to change in black adolescents with obesity. J Dev Behav Pediatr 2013;34:599–608.
Study Overview
Objective. To examine receipt of provider advice to lose weight among primary care patients who are overweight or obese.
Design. Cross-sectional study.
Setting and participants. Participants were recruited through convenience sampling of primary care practices that were members in a national practice-based research network or part of federally qualified health care system based in the Southeastern United States. Each practice used 1 or more of the following recruitment strategies: self-referral from study flyers posted in practices, given during clinic appointments, or posted on the practice portal (n = 3 practices); mailed invitations to patients part of a practice registry (n = 7 practices); and on-site recruitment by research staff during clinic hours (n = 2 practices). Inclusion criteria included having at least a 3-year history of being a patient in the practice, being aged 18 years or older, and having an overweight or obese status according to Centers for Disease Control definitions (body mass index [BMI] 25.0–29.9 kg/m2 = overweight, ≥ 30 kg/m2 = obese). After completing informed consent, participants completed an interview comprising a 20-minute survey, either in English or Spanish, either in-person or by telephone.
Measures. The survey obtained measures related to sociodemographic characteristics (race, gender, age, marital status, education level, employment status, income level), clinical characteristics (height and weight, history of diabetes/hypertension), psychological variables (readiness to make weight loss or maintenance efforts and confidence in their ability to lose or maintain weight), shared decision-making about weight loss/management (using the SDM-Q-9, with a higher total score indicating greater shared decision-making), and physician advice about weight loss (whether they had ever been advised by a doctor or other health care professional to lose weight or reduce their weight).
Main results. Among the study sample (n = 282), 65% were female, 60% were from racial and ethnic minority groups, 55% were married, 57% had some college education or higher, and 37% had an income level below $20,000/year. The mean age of participants was 53.1 (± 14.4) years. 59% had been advised by their physician to lose weight.
The percentage of participants who reported receiving provider advice was statistically different from 50% using the binomial test (P = 0.0035). Based on bivariate analysis of provider advice about weight loss, women were significantly more likely than men to report that their provider had advised them to lose weight (P = 0.001). Both actual and perceived obesity were associated significantly with receiving provider advice about weight loss (both P = 0.001). Diabetic patients were also significantly more likely than nondiabetic patients to report that their provider had advised them to lose weight (P = 0.01). Participants who reported greater readiness to lose or maintain their weight were more likely to report provider advice about weight loss compared to those with less readiness (P = 0.003). While employed patients, those who had at least some college education, and those who were hypertensive were more likely to report provider advice compared to those who were unemployed, had less education, and were not hypertensive, these associations were not statistically significant (P = 0.06, P = 0.06, P = 0.10, respectively). There were no racial/ethnic differences in receipt of provider advice to lose weight (P = 0.76). Participants with greater shared decision-making were more likely to report provider advice about weight loss (P < 0.001).
Based on results of the multivariate logistic regression analysis, obesity status, perceived obesity, and SDM about weight loss/management had significant independent associations with receiving physician advice about weight loss. Participants with obesity were more likely than those with overweight status to report provider advice (odds ratio [OR] = 1.31, 95% CI = 1.25–4.34, P = 0.001). Similarly, participants who believed they had overweight/obesity had a greater likelihood of reporting provider advice compared with those who did not believe they were obese/overweight (OR = 1.40, 95% CI = 2.43–6.37, P < 0.001). Shared decision making about weight loss/management was associated with an increased likelihood of reporting provider advice (OR = 3.30, 95% CI = 2.62–4.12, P < 0.001).
Conclusions. Many patients with overweight/obesity may not be receiving advice to lose/manage their weight by their provider. While providers should advise patients with overweight/obesity about weight loss and management, patient beliefs about their weight status and perceptions about shared decision-making are important to reporting receipt of provider advice about weight loss/management. Patient beliefs as well as provider behaviors should be addressed as part of efforts to improve the management of obesity/overweight in primary care.
Commentary
Over 35% of adults in the United States have a BMI in the obese range [1], putting them at risk for obesity-related comorbidities [2], often diagnosed and treated within primary care settings. The US Preventive Services Task Force recommends that all patients be screened for obesity and offered intensive lifestyle counseling, since modest weight loss can have significant health benefits [3]. Providers, particularly within the primary care setting, are ideally situated to promote weight loss via effective obesity counseling, as multiple clinic visits over time have the potential to enable rapport building and behavioral change management [4]. Indeed, a 2013 systematic review and meta-analysis of published studies of survey data examining provider weight loss counseling and its association with changes in patient weight loss behavior found that primary care provider advice on weight loss appears to have a significant impact on patient attempts to change behaviors related to their weight [5]. In this study, the authors reported higher rates of physician advice about weight loss compared to other studies, however, the results still demonstrate that based on patient reporting, not all providers are advising weight management or weight loss. Several studies have discussed barriers to weight management and obesity counseling among adults by physicians, which include lack of training, time, and perceived ineffectiveness of their own efforts [6–8].
Additionally, and perhaps more importantly, different factors can impact patient perception of provider advice and/or counseling around weight management, weight loss, or obesity. These can include race/ethnicity [9], health literacy [10], and motivation [11]. This study adds to the literature by shedding new light on variables that are important to patients being advised by providers to lose/manage their weight, including actual and perceived obesity status, and perceived shared decision-making. Previous research has focused on patient-provider communication and shared decision-making in the areas of antibiotic use [12], diabetes management [13], and weight loss [14].
Strengths of this study included the variety of recruitment methods employed to enroll patients from multiple clinic sites, the diverse sociodemographic characteristics of the study sample that resulted, the assessment of variables using standard or previously used measures, and the use of both bivariate and multivariate analyses to assess relationships between variables. Key limitations were acknowledged by the authors and included the cross-sectional design, which does not allow for causality to be assessed; the use of surveys for data collection, which relies on subjective and self-reported data; the assessment of weight management/loss advice only from the perspective of the patient, as opposed to including the provider perspective or using objective observations/data; and the lack of assessment of advice content or frequency of advice given.
Applications for Clinical Practice
As the authors suggest, this study highlights opportunities for improving weight-related advice for patients. Providers should incorporate obesity screening and counseling with all patients, as recommended by clinical care guidelines and the literature. In weight management conversations, providers should also be mindful of patient beliefs and understanding of their weight status, and incorporate shared decision-making practices to increase patient self-efficacy (ie, confidence, readiness) to make weight loss efforts.)
Study Overview
Objective. To examine receipt of provider advice to lose weight among primary care patients who are overweight or obese.
Design. Cross-sectional study.
Setting and participants. Participants were recruited through convenience sampling of primary care practices that were members in a national practice-based research network or part of federally qualified health care system based in the Southeastern United States. Each practice used 1 or more of the following recruitment strategies: self-referral from study flyers posted in practices, given during clinic appointments, or posted on the practice portal (n = 3 practices); mailed invitations to patients part of a practice registry (n = 7 practices); and on-site recruitment by research staff during clinic hours (n = 2 practices). Inclusion criteria included having at least a 3-year history of being a patient in the practice, being aged 18 years or older, and having an overweight or obese status according to Centers for Disease Control definitions (body mass index [BMI] 25.0–29.9 kg/m2 = overweight, ≥ 30 kg/m2 = obese). After completing informed consent, participants completed an interview comprising a 20-minute survey, either in English or Spanish, either in-person or by telephone.
Measures. The survey obtained measures related to sociodemographic characteristics (race, gender, age, marital status, education level, employment status, income level), clinical characteristics (height and weight, history of diabetes/hypertension), psychological variables (readiness to make weight loss or maintenance efforts and confidence in their ability to lose or maintain weight), shared decision-making about weight loss/management (using the SDM-Q-9, with a higher total score indicating greater shared decision-making), and physician advice about weight loss (whether they had ever been advised by a doctor or other health care professional to lose weight or reduce their weight).
Main results. Among the study sample (n = 282), 65% were female, 60% were from racial and ethnic minority groups, 55% were married, 57% had some college education or higher, and 37% had an income level below $20,000/year. The mean age of participants was 53.1 (± 14.4) years. 59% had been advised by their physician to lose weight.
The percentage of participants who reported receiving provider advice was statistically different from 50% using the binomial test (P = 0.0035). Based on bivariate analysis of provider advice about weight loss, women were significantly more likely than men to report that their provider had advised them to lose weight (P = 0.001). Both actual and perceived obesity were associated significantly with receiving provider advice about weight loss (both P = 0.001). Diabetic patients were also significantly more likely than nondiabetic patients to report that their provider had advised them to lose weight (P = 0.01). Participants who reported greater readiness to lose or maintain their weight were more likely to report provider advice about weight loss compared to those with less readiness (P = 0.003). While employed patients, those who had at least some college education, and those who were hypertensive were more likely to report provider advice compared to those who were unemployed, had less education, and were not hypertensive, these associations were not statistically significant (P = 0.06, P = 0.06, P = 0.10, respectively). There were no racial/ethnic differences in receipt of provider advice to lose weight (P = 0.76). Participants with greater shared decision-making were more likely to report provider advice about weight loss (P < 0.001).
Based on results of the multivariate logistic regression analysis, obesity status, perceived obesity, and SDM about weight loss/management had significant independent associations with receiving physician advice about weight loss. Participants with obesity were more likely than those with overweight status to report provider advice (odds ratio [OR] = 1.31, 95% CI = 1.25–4.34, P = 0.001). Similarly, participants who believed they had overweight/obesity had a greater likelihood of reporting provider advice compared with those who did not believe they were obese/overweight (OR = 1.40, 95% CI = 2.43–6.37, P < 0.001). Shared decision making about weight loss/management was associated with an increased likelihood of reporting provider advice (OR = 3.30, 95% CI = 2.62–4.12, P < 0.001).
Conclusions. Many patients with overweight/obesity may not be receiving advice to lose/manage their weight by their provider. While providers should advise patients with overweight/obesity about weight loss and management, patient beliefs about their weight status and perceptions about shared decision-making are important to reporting receipt of provider advice about weight loss/management. Patient beliefs as well as provider behaviors should be addressed as part of efforts to improve the management of obesity/overweight in primary care.
Commentary
Over 35% of adults in the United States have a BMI in the obese range [1], putting them at risk for obesity-related comorbidities [2], often diagnosed and treated within primary care settings. The US Preventive Services Task Force recommends that all patients be screened for obesity and offered intensive lifestyle counseling, since modest weight loss can have significant health benefits [3]. Providers, particularly within the primary care setting, are ideally situated to promote weight loss via effective obesity counseling, as multiple clinic visits over time have the potential to enable rapport building and behavioral change management [4]. Indeed, a 2013 systematic review and meta-analysis of published studies of survey data examining provider weight loss counseling and its association with changes in patient weight loss behavior found that primary care provider advice on weight loss appears to have a significant impact on patient attempts to change behaviors related to their weight [5]. In this study, the authors reported higher rates of physician advice about weight loss compared to other studies, however, the results still demonstrate that based on patient reporting, not all providers are advising weight management or weight loss. Several studies have discussed barriers to weight management and obesity counseling among adults by physicians, which include lack of training, time, and perceived ineffectiveness of their own efforts [6–8].
Additionally, and perhaps more importantly, different factors can impact patient perception of provider advice and/or counseling around weight management, weight loss, or obesity. These can include race/ethnicity [9], health literacy [10], and motivation [11]. This study adds to the literature by shedding new light on variables that are important to patients being advised by providers to lose/manage their weight, including actual and perceived obesity status, and perceived shared decision-making. Previous research has focused on patient-provider communication and shared decision-making in the areas of antibiotic use [12], diabetes management [13], and weight loss [14].
Strengths of this study included the variety of recruitment methods employed to enroll patients from multiple clinic sites, the diverse sociodemographic characteristics of the study sample that resulted, the assessment of variables using standard or previously used measures, and the use of both bivariate and multivariate analyses to assess relationships between variables. Key limitations were acknowledged by the authors and included the cross-sectional design, which does not allow for causality to be assessed; the use of surveys for data collection, which relies on subjective and self-reported data; the assessment of weight management/loss advice only from the perspective of the patient, as opposed to including the provider perspective or using objective observations/data; and the lack of assessment of advice content or frequency of advice given.
Applications for Clinical Practice
As the authors suggest, this study highlights opportunities for improving weight-related advice for patients. Providers should incorporate obesity screening and counseling with all patients, as recommended by clinical care guidelines and the literature. In weight management conversations, providers should also be mindful of patient beliefs and understanding of their weight status, and incorporate shared decision-making practices to increase patient self-efficacy (ie, confidence, readiness) to make weight loss efforts.)
1. Flegal KM, Kruszon-Moran D, Carroll MD, et al. Trends in obesity among adults in the United States, 2005 to 2014. JAMA 2016;315:2284–91.
2. Guh DP, Zhang W, Bansback N, et al. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Public Health 2009;9:88.
3. Moyer VA. Screening for and management of obesity in adults: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2012;157:373–8.
4. Schlair S, Moore S, Mcmacken M, Jay M. How to deliver high quality obesity counseling using the 5As framework. J Clin Outcomes Manag 2012;19:221–9.
5. Rose SA, Poynter PS, Anderson JW, et al. Physician weight loss advice and patient weight loss behavior change: a literature review and meta-analysis of survey data. Int J Obes (Lond) 2013;37:118–28.
6. Forman-Hoffman V, Little A, Wahls T. Barriers to obesity management: a pilot study of primary care clinicians. BMC Fam Pract 2006;7:35.
7. Jay M, Gillespie C, Ark T, et al. Do internists, pediatricians, and psychiatrists feel competent in obesity care? Using a needs assessment to drive curriculum design. J Gen Intern Med 2008;23:1066–70.
8. Leverence RR, Williams RL, Sussman A, Crabtree BF. Obesity counseling and guidelines in primary care: a qualitative study. Am J Prev Med 2007;32:334–9.
9. Durant NH, Bartman B, Person SD, et al. Patient provider communication about the health effects of obesity. Patient Educ Couns 2009;75:53–7.
10. Zarcadoolas C, Levy, J, Sealy Y, et al. Health literacy at work to address overweight and obesity in adults: The development of the Obesity Action Kit. J Commun Health 2011;4:88–101.
11. Befort CA, Greiner KA, Hall S, et al. Weight-related perceptions among patients and physicians: how well do physicians judge patients’ motivation to lose weight? J Gen Intern Med 2006;21:1086–90.
12. Schoenthaler A, Albright G, Hibbard J, Goldman R. Simulated conversations with virtual humans to improve patient-provider communication and reduce unnecessary prescriptions for antibiotics: a repeated measure pilot study. JMIR Med Educ 2017;3:e7.
13. Griffith M, Siminerio L, Payne T, Krall J. A shared decision-making approach to telemedicine: engaging rural patients in glycemic management. J Clin Med 2016;5:103.
14. Carcone AI, Naar-King S, E. Brogan K, et al. Provider communication behaviors that predict motivation to change in black adolescents with obesity. J Dev Behav Pediatr 2013;34:599–608.
1. Flegal KM, Kruszon-Moran D, Carroll MD, et al. Trends in obesity among adults in the United States, 2005 to 2014. JAMA 2016;315:2284–91.
2. Guh DP, Zhang W, Bansback N, et al. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Public Health 2009;9:88.
3. Moyer VA. Screening for and management of obesity in adults: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2012;157:373–8.
4. Schlair S, Moore S, Mcmacken M, Jay M. How to deliver high quality obesity counseling using the 5As framework. J Clin Outcomes Manag 2012;19:221–9.
5. Rose SA, Poynter PS, Anderson JW, et al. Physician weight loss advice and patient weight loss behavior change: a literature review and meta-analysis of survey data. Int J Obes (Lond) 2013;37:118–28.
6. Forman-Hoffman V, Little A, Wahls T. Barriers to obesity management: a pilot study of primary care clinicians. BMC Fam Pract 2006;7:35.
7. Jay M, Gillespie C, Ark T, et al. Do internists, pediatricians, and psychiatrists feel competent in obesity care? Using a needs assessment to drive curriculum design. J Gen Intern Med 2008;23:1066–70.
8. Leverence RR, Williams RL, Sussman A, Crabtree BF. Obesity counseling and guidelines in primary care: a qualitative study. Am J Prev Med 2007;32:334–9.
9. Durant NH, Bartman B, Person SD, et al. Patient provider communication about the health effects of obesity. Patient Educ Couns 2009;75:53–7.
10. Zarcadoolas C, Levy, J, Sealy Y, et al. Health literacy at work to address overweight and obesity in adults: The development of the Obesity Action Kit. J Commun Health 2011;4:88–101.
11. Befort CA, Greiner KA, Hall S, et al. Weight-related perceptions among patients and physicians: how well do physicians judge patients’ motivation to lose weight? J Gen Intern Med 2006;21:1086–90.
12. Schoenthaler A, Albright G, Hibbard J, Goldman R. Simulated conversations with virtual humans to improve patient-provider communication and reduce unnecessary prescriptions for antibiotics: a repeated measure pilot study. JMIR Med Educ 2017;3:e7.
13. Griffith M, Siminerio L, Payne T, Krall J. A shared decision-making approach to telemedicine: engaging rural patients in glycemic management. J Clin Med 2016;5:103.
14. Carcone AI, Naar-King S, E. Brogan K, et al. Provider communication behaviors that predict motivation to change in black adolescents with obesity. J Dev Behav Pediatr 2013;34:599–608.
Why Are General Practitioners Reluctant to Play a Significant Role in Managing Childhood Obesity?
Study Overview
Objective. To explore the views of general practice staff on managing childhood obesity in primary care.
Design. Qualitative study.
Setting and participants. General practices across England (n = 7303) of varying practice list size (low/medium/high) and “deprivation” level (low/medium/high, based on Index of Multiple Deprivation (IMD) score, which measures deprivation based on income, employment, health, education, barriers to services, living environment and crime) were stratified into a 3 x 3 matrix, resulting in recruitment targets of 3 to 5 practices per each of 9 recruitment strata. Practices in each strata were grouped into batches and approached in a random list order to take part in the study. Recruitment continued until the strata target was reached. Interviews were conducted by 2 researchers, either in the interviewee’s workplace or by telephone.
Main outcomes measures. The interview topic guide included 2 questions related to childhood obesity: (1) theirperceptions of the barriers and enablers to general practitioners taking a more active role in childhood obesity; and (2) their views on what was needed to improve integrated local pathways to manage childhood obesity. Follow-up questions were used in response to issues raised by interviewees. All interviews were audiotaped, professionally transcribed verbatim, and checked for accuracy. Copies of transcripts were available to interviewees, although none requested to see them. Key themes were identified through thematic analysis of transcripts using an inductive approach. Initial codes were discussed and combined to form themes which were discussed until agreement was reached that these reflected the data. Results are based upon a synthesis of all the interviews.
Main results. A total of 32 practices were recruited, of which 30 identified 52 staff (56% female) to participate in semi-structured interviews: 29 general practitioners (28% female), 14 practice managers (86% female), 7 nursing staff (100% female), 1 health care assistant (female), and 1 administrative staff (female). Almost all interviewees identified childhood obesity as an increasingly important issue with potential long-term health implications. However, most did not frame it as a medical problem in itself or view its management as a general practice responsibility.
Three themes were identified: lack of contact with well children, sensitivity of the issue, and the potential impact of general practice. Identifying overweight children was challenging because well children rarely attended the practice. Interviewees felt that consultation time was limited and focused on addressing acute illness. Generally, raising the issue was described as sensitive. Interviewees also felt ill equipped to solve the issue because they lacked influence over the environmental, economic, and lifestyle factors underpinning obesity. They described little evidence to support general practice intervention and seemed unaware of other services. Interviewees felt their efforts should be directed towards health problems they identified as medical issues where evidence suggests they can make a difference.
Conclusions. Although general practice staff viewed childhood obesity as an important issue with the poten-tial to impact on health outcomes, they were unconvinced that they could have a significant role in managing childhood obesity on a large scale. Participants believed schools have more contact with children and should coordinate the identification and management of overweight children. Future policy could recommend a minor role for general practice involving opportunistic identification of overweight children and referral to specialist/obesity services
Commentary
The prevalence of childhood overweight and obesity continues to rise in the United States and worldwide with extensive economic, physical, and psychosocial consequences [1–6]. Lifestyle interventions that target obesity-related behaviors including physical activity, sedentary behavior, and diet, are considered the therapy of choice [7–10]. Indeed, the US Preventive Services Task Force recommends that clinicians screen for obesity in children and adolescents 6 years and older and offer or refer them to comprehensive, intensive behavioral interventions to promote improvements in weight status [11]. Similar recommendations can be seen in other national guidelines regarding the management of childhood obesity [12].
Beyond screening and referral, some have outlined more specific opportunities for health professionals to play a more significant role in confronting child obesity, particularly among general practitioners and primary care providers [13–15]. In addition, several reviews have looked at the expanding role of primary care in the prevention and treatment of childhood obesity [16,17]. However, it remains unclear whether provider perspectives about their role in addressing childhood obesity align with such guidelines and suggestions. In fact, several studies have discussed barriers to weight management and obesity counseling among adults by physicians, which include lack of training, time, and perceived ineffectiveness of their own efforts [18–20]. This study adds to the literature by qualitatively assessing perspectives of general practice staff from a variety of practices regarding their role in addressing childhood obesity.
In qualitative research, typically small samples require careful consideration of the representativeness of participants in terms of characteristics and relevance to the wider population. As the authors highlight, a key strength of this study is that staff from a large number of practices in different geographical areas across England were recruited and broadly represented general practices in terms of practice list size and deprivation. This may contribute to greater likelihood of generalizability compared to similar studies that are limited to specific states in a country or small geographic areas. Additional strengths of this study include the use of a specific framework to guide analysis, 2 independent coders to analyze transcripts, and a brief discussion of how the researcher, through the structure of the interview, may have introduced bias to the results. However, the authors did not include whether any outlying or negative/deviant cases were presented that did not fit with discussed themes or if there were any differences in findings by gender or by years since qualified to practice. Additionally, the authors did not specify if results were confirmed or validated by their study participants to increase reliability and trustworthiness of analysis and interpretation.
Applications for Clinical Practice
Although the authors highlight that their findings suggest that policies expanding the role for general practitioners in prevention, identification, and management of childhood obesity at a population-level are unlikely to be successful, findings may instead highlight specific barriers to target and overcome in order to expand the role for general practitioners. Even though contact with well children may be limited, standard practices to incorporate brief counseling could contribute to a shift in practice and patient expectations of what is discussed during visits. Increased training and awareness of resources and innovative technologies that can assist patients with addressing obesity-related environmental, economic, and lifestyle factors can also be incorporated into medical education and professional development. In addition, practices can partner with community-based programs and organizations implementing childhood obesity interventions to expand referral options. General practitioners and primary care providers remain an important source of health information and expertise, and thus should play a key role in supporting broader initiatives to address childhood obesity.
—Katrina F. Mateo, MPH
1. WHO | Facts and figures on childhood obesity. 2014.
2. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014;311:806–14.
3. de Onis M, Blossner M, Borghi E. Global prevalence and trends of overweight and obesity among preschool children. Am J Clin Nutr 2010;92:1257–64.
4. Pizzi MA, Vroman K. Childhood obesity: effects on children’s participation, mental health, and psychosocial development. Occup Ther Health Care 2013;27:99–112.
5. Pulgarón ER. Childhood obesity: a review of increased risk for physical and psychological comorbidities. Clin Ther 2013;35:A18–32.
6. Trasande L, Elbel B. The economic burden placed on healthcare systems by childhood obesity. Expert Rev Pharmacoecon Outcomes Res 2012;12:39–45.
7. Wang Y, Wu Y, Wilson RF, et al. Childhood obesity prevention programs: comparative effectiveness review and meta-analysis. Agency for Healthcare Research and Quality; 2013.
8. Martin A, Saunders DH, Shenkin SD, Sproule J. Lifestyle intervention for improving school achievement in overweight or obese children and adolescents. Cochrane Database Syst Rev 2014;(3):CD009728.
9. De Miguel-Etayo P, Bueno G, Garagorri JM, Moreno LA. Interventions for treating obesity in children. World Rev Nutrition Dietetics 2013;108:98–106.
10. Reinehr T. Lifestyle intervention in childhood obesity: changes and challenges. Nat Rev Endocrinol 2013;9:607–14.
11. US Preventive Services Task Force, Grossman DC, Bibbins-Domingo K, et al. Screening for obesity in children and adolescents. JAMA 2017;317:2417–26.
12. Richardson L, Paulis WD, van Middelkoop M, Koes BW. An overview of national clinical guidelines for the management of childhood obesity in primary care. Prev Med (Baltim) 2013;57:448–55.
13. Brown CL, Halvorson EE, Cohen GM, et al. Addressing childhood obesity: opportunities for prevention. Pediatr Clin North Am 2015;62:1241–61.
Study Overview
Objective. To explore the views of general practice staff on managing childhood obesity in primary care.
Design. Qualitative study.
Setting and participants. General practices across England (n = 7303) of varying practice list size (low/medium/high) and “deprivation” level (low/medium/high, based on Index of Multiple Deprivation (IMD) score, which measures deprivation based on income, employment, health, education, barriers to services, living environment and crime) were stratified into a 3 x 3 matrix, resulting in recruitment targets of 3 to 5 practices per each of 9 recruitment strata. Practices in each strata were grouped into batches and approached in a random list order to take part in the study. Recruitment continued until the strata target was reached. Interviews were conducted by 2 researchers, either in the interviewee’s workplace or by telephone.
Main outcomes measures. The interview topic guide included 2 questions related to childhood obesity: (1) theirperceptions of the barriers and enablers to general practitioners taking a more active role in childhood obesity; and (2) their views on what was needed to improve integrated local pathways to manage childhood obesity. Follow-up questions were used in response to issues raised by interviewees. All interviews were audiotaped, professionally transcribed verbatim, and checked for accuracy. Copies of transcripts were available to interviewees, although none requested to see them. Key themes were identified through thematic analysis of transcripts using an inductive approach. Initial codes were discussed and combined to form themes which were discussed until agreement was reached that these reflected the data. Results are based upon a synthesis of all the interviews.
Main results. A total of 32 practices were recruited, of which 30 identified 52 staff (56% female) to participate in semi-structured interviews: 29 general practitioners (28% female), 14 practice managers (86% female), 7 nursing staff (100% female), 1 health care assistant (female), and 1 administrative staff (female). Almost all interviewees identified childhood obesity as an increasingly important issue with potential long-term health implications. However, most did not frame it as a medical problem in itself or view its management as a general practice responsibility.
Three themes were identified: lack of contact with well children, sensitivity of the issue, and the potential impact of general practice. Identifying overweight children was challenging because well children rarely attended the practice. Interviewees felt that consultation time was limited and focused on addressing acute illness. Generally, raising the issue was described as sensitive. Interviewees also felt ill equipped to solve the issue because they lacked influence over the environmental, economic, and lifestyle factors underpinning obesity. They described little evidence to support general practice intervention and seemed unaware of other services. Interviewees felt their efforts should be directed towards health problems they identified as medical issues where evidence suggests they can make a difference.
Conclusions. Although general practice staff viewed childhood obesity as an important issue with the poten-tial to impact on health outcomes, they were unconvinced that they could have a significant role in managing childhood obesity on a large scale. Participants believed schools have more contact with children and should coordinate the identification and management of overweight children. Future policy could recommend a minor role for general practice involving opportunistic identification of overweight children and referral to specialist/obesity services
Commentary
The prevalence of childhood overweight and obesity continues to rise in the United States and worldwide with extensive economic, physical, and psychosocial consequences [1–6]. Lifestyle interventions that target obesity-related behaviors including physical activity, sedentary behavior, and diet, are considered the therapy of choice [7–10]. Indeed, the US Preventive Services Task Force recommends that clinicians screen for obesity in children and adolescents 6 years and older and offer or refer them to comprehensive, intensive behavioral interventions to promote improvements in weight status [11]. Similar recommendations can be seen in other national guidelines regarding the management of childhood obesity [12].
Beyond screening and referral, some have outlined more specific opportunities for health professionals to play a more significant role in confronting child obesity, particularly among general practitioners and primary care providers [13–15]. In addition, several reviews have looked at the expanding role of primary care in the prevention and treatment of childhood obesity [16,17]. However, it remains unclear whether provider perspectives about their role in addressing childhood obesity align with such guidelines and suggestions. In fact, several studies have discussed barriers to weight management and obesity counseling among adults by physicians, which include lack of training, time, and perceived ineffectiveness of their own efforts [18–20]. This study adds to the literature by qualitatively assessing perspectives of general practice staff from a variety of practices regarding their role in addressing childhood obesity.
In qualitative research, typically small samples require careful consideration of the representativeness of participants in terms of characteristics and relevance to the wider population. As the authors highlight, a key strength of this study is that staff from a large number of practices in different geographical areas across England were recruited and broadly represented general practices in terms of practice list size and deprivation. This may contribute to greater likelihood of generalizability compared to similar studies that are limited to specific states in a country or small geographic areas. Additional strengths of this study include the use of a specific framework to guide analysis, 2 independent coders to analyze transcripts, and a brief discussion of how the researcher, through the structure of the interview, may have introduced bias to the results. However, the authors did not include whether any outlying or negative/deviant cases were presented that did not fit with discussed themes or if there were any differences in findings by gender or by years since qualified to practice. Additionally, the authors did not specify if results were confirmed or validated by their study participants to increase reliability and trustworthiness of analysis and interpretation.
Applications for Clinical Practice
Although the authors highlight that their findings suggest that policies expanding the role for general practitioners in prevention, identification, and management of childhood obesity at a population-level are unlikely to be successful, findings may instead highlight specific barriers to target and overcome in order to expand the role for general practitioners. Even though contact with well children may be limited, standard practices to incorporate brief counseling could contribute to a shift in practice and patient expectations of what is discussed during visits. Increased training and awareness of resources and innovative technologies that can assist patients with addressing obesity-related environmental, economic, and lifestyle factors can also be incorporated into medical education and professional development. In addition, practices can partner with community-based programs and organizations implementing childhood obesity interventions to expand referral options. General practitioners and primary care providers remain an important source of health information and expertise, and thus should play a key role in supporting broader initiatives to address childhood obesity.
—Katrina F. Mateo, MPH
Study Overview
Objective. To explore the views of general practice staff on managing childhood obesity in primary care.
Design. Qualitative study.
Setting and participants. General practices across England (n = 7303) of varying practice list size (low/medium/high) and “deprivation” level (low/medium/high, based on Index of Multiple Deprivation (IMD) score, which measures deprivation based on income, employment, health, education, barriers to services, living environment and crime) were stratified into a 3 x 3 matrix, resulting in recruitment targets of 3 to 5 practices per each of 9 recruitment strata. Practices in each strata were grouped into batches and approached in a random list order to take part in the study. Recruitment continued until the strata target was reached. Interviews were conducted by 2 researchers, either in the interviewee’s workplace or by telephone.
Main outcomes measures. The interview topic guide included 2 questions related to childhood obesity: (1) theirperceptions of the barriers and enablers to general practitioners taking a more active role in childhood obesity; and (2) their views on what was needed to improve integrated local pathways to manage childhood obesity. Follow-up questions were used in response to issues raised by interviewees. All interviews were audiotaped, professionally transcribed verbatim, and checked for accuracy. Copies of transcripts were available to interviewees, although none requested to see them. Key themes were identified through thematic analysis of transcripts using an inductive approach. Initial codes were discussed and combined to form themes which were discussed until agreement was reached that these reflected the data. Results are based upon a synthesis of all the interviews.
Main results. A total of 32 practices were recruited, of which 30 identified 52 staff (56% female) to participate in semi-structured interviews: 29 general practitioners (28% female), 14 practice managers (86% female), 7 nursing staff (100% female), 1 health care assistant (female), and 1 administrative staff (female). Almost all interviewees identified childhood obesity as an increasingly important issue with potential long-term health implications. However, most did not frame it as a medical problem in itself or view its management as a general practice responsibility.
Three themes were identified: lack of contact with well children, sensitivity of the issue, and the potential impact of general practice. Identifying overweight children was challenging because well children rarely attended the practice. Interviewees felt that consultation time was limited and focused on addressing acute illness. Generally, raising the issue was described as sensitive. Interviewees also felt ill equipped to solve the issue because they lacked influence over the environmental, economic, and lifestyle factors underpinning obesity. They described little evidence to support general practice intervention and seemed unaware of other services. Interviewees felt their efforts should be directed towards health problems they identified as medical issues where evidence suggests they can make a difference.
Conclusions. Although general practice staff viewed childhood obesity as an important issue with the poten-tial to impact on health outcomes, they were unconvinced that they could have a significant role in managing childhood obesity on a large scale. Participants believed schools have more contact with children and should coordinate the identification and management of overweight children. Future policy could recommend a minor role for general practice involving opportunistic identification of overweight children and referral to specialist/obesity services
Commentary
The prevalence of childhood overweight and obesity continues to rise in the United States and worldwide with extensive economic, physical, and psychosocial consequences [1–6]. Lifestyle interventions that target obesity-related behaviors including physical activity, sedentary behavior, and diet, are considered the therapy of choice [7–10]. Indeed, the US Preventive Services Task Force recommends that clinicians screen for obesity in children and adolescents 6 years and older and offer or refer them to comprehensive, intensive behavioral interventions to promote improvements in weight status [11]. Similar recommendations can be seen in other national guidelines regarding the management of childhood obesity [12].
Beyond screening and referral, some have outlined more specific opportunities for health professionals to play a more significant role in confronting child obesity, particularly among general practitioners and primary care providers [13–15]. In addition, several reviews have looked at the expanding role of primary care in the prevention and treatment of childhood obesity [16,17]. However, it remains unclear whether provider perspectives about their role in addressing childhood obesity align with such guidelines and suggestions. In fact, several studies have discussed barriers to weight management and obesity counseling among adults by physicians, which include lack of training, time, and perceived ineffectiveness of their own efforts [18–20]. This study adds to the literature by qualitatively assessing perspectives of general practice staff from a variety of practices regarding their role in addressing childhood obesity.
In qualitative research, typically small samples require careful consideration of the representativeness of participants in terms of characteristics and relevance to the wider population. As the authors highlight, a key strength of this study is that staff from a large number of practices in different geographical areas across England were recruited and broadly represented general practices in terms of practice list size and deprivation. This may contribute to greater likelihood of generalizability compared to similar studies that are limited to specific states in a country or small geographic areas. Additional strengths of this study include the use of a specific framework to guide analysis, 2 independent coders to analyze transcripts, and a brief discussion of how the researcher, through the structure of the interview, may have introduced bias to the results. However, the authors did not include whether any outlying or negative/deviant cases were presented that did not fit with discussed themes or if there were any differences in findings by gender or by years since qualified to practice. Additionally, the authors did not specify if results were confirmed or validated by their study participants to increase reliability and trustworthiness of analysis and interpretation.
Applications for Clinical Practice
Although the authors highlight that their findings suggest that policies expanding the role for general practitioners in prevention, identification, and management of childhood obesity at a population-level are unlikely to be successful, findings may instead highlight specific barriers to target and overcome in order to expand the role for general practitioners. Even though contact with well children may be limited, standard practices to incorporate brief counseling could contribute to a shift in practice and patient expectations of what is discussed during visits. Increased training and awareness of resources and innovative technologies that can assist patients with addressing obesity-related environmental, economic, and lifestyle factors can also be incorporated into medical education and professional development. In addition, practices can partner with community-based programs and organizations implementing childhood obesity interventions to expand referral options. General practitioners and primary care providers remain an important source of health information and expertise, and thus should play a key role in supporting broader initiatives to address childhood obesity.
—Katrina F. Mateo, MPH
1. WHO | Facts and figures on childhood obesity. 2014.
2. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014;311:806–14.
3. de Onis M, Blossner M, Borghi E. Global prevalence and trends of overweight and obesity among preschool children. Am J Clin Nutr 2010;92:1257–64.
4. Pizzi MA, Vroman K. Childhood obesity: effects on children’s participation, mental health, and psychosocial development. Occup Ther Health Care 2013;27:99–112.
5. Pulgarón ER. Childhood obesity: a review of increased risk for physical and psychological comorbidities. Clin Ther 2013;35:A18–32.
6. Trasande L, Elbel B. The economic burden placed on healthcare systems by childhood obesity. Expert Rev Pharmacoecon Outcomes Res 2012;12:39–45.
7. Wang Y, Wu Y, Wilson RF, et al. Childhood obesity prevention programs: comparative effectiveness review and meta-analysis. Agency for Healthcare Research and Quality; 2013.
8. Martin A, Saunders DH, Shenkin SD, Sproule J. Lifestyle intervention for improving school achievement in overweight or obese children and adolescents. Cochrane Database Syst Rev 2014;(3):CD009728.
9. De Miguel-Etayo P, Bueno G, Garagorri JM, Moreno LA. Interventions for treating obesity in children. World Rev Nutrition Dietetics 2013;108:98–106.
10. Reinehr T. Lifestyle intervention in childhood obesity: changes and challenges. Nat Rev Endocrinol 2013;9:607–14.
11. US Preventive Services Task Force, Grossman DC, Bibbins-Domingo K, et al. Screening for obesity in children and adolescents. JAMA 2017;317:2417–26.
12. Richardson L, Paulis WD, van Middelkoop M, Koes BW. An overview of national clinical guidelines for the management of childhood obesity in primary care. Prev Med (Baltim) 2013;57:448–55.
13. Brown CL, Halvorson EE, Cohen GM, et al. Addressing childhood obesity: opportunities for prevention. Pediatr Clin North Am 2015;62:1241–61.
1. WHO | Facts and figures on childhood obesity. 2014.
2. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014;311:806–14.
3. de Onis M, Blossner M, Borghi E. Global prevalence and trends of overweight and obesity among preschool children. Am J Clin Nutr 2010;92:1257–64.
4. Pizzi MA, Vroman K. Childhood obesity: effects on children’s participation, mental health, and psychosocial development. Occup Ther Health Care 2013;27:99–112.
5. Pulgarón ER. Childhood obesity: a review of increased risk for physical and psychological comorbidities. Clin Ther 2013;35:A18–32.
6. Trasande L, Elbel B. The economic burden placed on healthcare systems by childhood obesity. Expert Rev Pharmacoecon Outcomes Res 2012;12:39–45.
7. Wang Y, Wu Y, Wilson RF, et al. Childhood obesity prevention programs: comparative effectiveness review and meta-analysis. Agency for Healthcare Research and Quality; 2013.
8. Martin A, Saunders DH, Shenkin SD, Sproule J. Lifestyle intervention for improving school achievement in overweight or obese children and adolescents. Cochrane Database Syst Rev 2014;(3):CD009728.
9. De Miguel-Etayo P, Bueno G, Garagorri JM, Moreno LA. Interventions for treating obesity in children. World Rev Nutrition Dietetics 2013;108:98–106.
10. Reinehr T. Lifestyle intervention in childhood obesity: changes and challenges. Nat Rev Endocrinol 2013;9:607–14.
11. US Preventive Services Task Force, Grossman DC, Bibbins-Domingo K, et al. Screening for obesity in children and adolescents. JAMA 2017;317:2417–26.
12. Richardson L, Paulis WD, van Middelkoop M, Koes BW. An overview of national clinical guidelines for the management of childhood obesity in primary care. Prev Med (Baltim) 2013;57:448–55.
13. Brown CL, Halvorson EE, Cohen GM, et al. Addressing childhood obesity: opportunities for prevention. Pediatr Clin North Am 2015;62:1241–61.
What PCP-Related Factors Contribute to Successful Weight Loss Among Positive Deviant Low-Income African-American Women?
Study Overview
Objective. To evaluate factors related to interactions with primary care physicians (PCPs) that may contribute to successful weight loss and maintenance among low-income, African-American women.
Design. Mixed methods, positive deviance framework.
Setting and participants. Participants were African-American women aged 18–64 years from an urban university-based family medicine practice who received Medicaid, resided in Philadelphia, and had a body mass index (BMI) of ≥ 30kg/m2. From among these, “positive deviant” cases were identified as patients with EMR-confirmed weight loss of at least 10% of patient’s maximum weight between 2007–2012 and maintenance of this loss for at least 6 months. Controls were defined as patients who had not lost a significant amount of weight during this time period. Patients were excluded if they were an amputee or wheelchair-bound; had bariatric surgery, severe illness during weight loss, EMR-documented unintended weight loss, pregnancy at time of weight loss, a psychiatric disorder or were taking antipsychotic medication; had an intellectual disability; or could not give consent to participate.
Main outcomes measures. PCP- and patient-reported weight variables were collected through the EMR (documentation of dietary counseling by PCP, documentation of a weight-related problem, diagnosis of overweight, obesity, or morbid obesity on the problem list), surveys (additional predicters of positive deviant membership, including patient-reported weight-related diagnosis or discussion of weight with PCP or health professional), and interviews. Logistic regression was used to determine whether a priori-identified EMR and survey variables could predict positive deviant group membership, adjusting for demographic variables significantly associated with the outcome of interest or hypothesized to be confounders of the associations between predictors and outcomes (results were adjusted for age in the EMR analysis and for employment status and education level in the survey analysis). Once thematic saturation was reached, interviews were analyzed by a 4-member coding panel using a modified approach to grounded theory to identify themes.
Main results. For the EMR analysis, data from 161 positive deviant cases and 602 controls were analyzed. For the survey analysis, data from 35 positive deviant cases and 36 controls matched for age and maximum BMI were analyzed. For in-depth interviews, thematic saturation was reached after collecting data from 20 positive deviant participants. In the EMR analyses, documentation of dietary counseling and a weight-related diagnosis were significant predictors of positive deviant membership after adjusting for age (P < 0.001 and P = 0.011, respectively). However, documentation of obesity on the problem list was predictive of control group membership (P = 0.032). In the survey analysis, neither patient-reported weight-related diagnosis nor discussion of weight with a medical provider were predictors of positive deviant membership (P = 0.890 and P = 0.373, respectively). In the qualitative analysis of interviews with positive deviant participants, 5 themes emerged: (1) framing the problem of obesity in the context of other health problems provided motivation; (2) having a full discussion around weight management was important; (3) an ongoing conversation and relationship was valuable; (4) celebrating small successes was beneficial for ongoing motivation; and (5) advice was helpful but self-motivation was required in order to make a change.
Conclusions. PCP counseling may be an important factor in promoting weight loss in low-income, African-American women, a population at high risk for obesity. Patients may benefit from their PCPs drawing connections between obesity and weight-related medical conditions and enhancing intrinsic motivation for weight loss.
Commentary
The increasing prevalence and clinical consequences of having obesity are well-documented, with low-income minorities disproportionately burdened by this condition [1,2]. The United States Preventive Services Task Force (USPTF) recommends that all patients be screened for obesity and offered intensive lifestyle counseling [3], yet evidence-based guidelines for best approaches to incorporate this into practice are few and unclear, and even fewer are specific to high-risk patient populations [4–9].
This study adds to the literature by using a positive deviance approach to identify PCP-related factors that predict successful weight loss among low-income African-American women. This approach has rarely been used in the obesity literature. In a few childhood obesity studies, this approach was used to identify motivations used by child “positive outliers” to improve their BMI [10], characterize variations of feeding and activity practices by parents of healthy children normally at high risk for obesity [11], and explore successful health and BMI reduction strategies used among positive outlier families [12]. Positive deviance has also been used to characterize and change nutritional behavior and understand successful weight-control practices among adults [13–15]. One study has suggested that studying “positive deviant” physicians that regularly provide weight counseling may help to provide practice methods to increase these practices in the primary care settings [16].
Thus, the study approach in using a positive deviance framework is an important and unique strength. Addi-tionally, the authors used a mixed-methods approach, analyzing EMR, survey, and interview data to assess PCP- and patient-reported weight-related factors that predict successful weight loss. As the authors describe, their results confirm findings from previous studies looking at counseling preferences among ethnic minority women and PCP attitudes and practices related to weight management.
They acknowledge important limitations of their study design, primarily the generalizability of findings only to urban, low-income, African-American women, the small sample size in the survey analysis, and the use of EMR data to collect data on PCP counseling (as opposed to interviews, for example). It important to also acknowledge that this study was conducted at a family medicine practice, and physician behavior and practices likely do not generalize to other PCPs and specialists. Additionally, while their intention was to use a positive deviance framework, conducting interviews among a subset of their control cases may have provided useful information regarding negative or ineffective PCP interactions regarding weight loss and management.
Applications for Clinical Practice
As the authors emphasize, the outcomes of this study are especially relevant for PCPs and other health practitioners, as the identified themes can help guide weight counseling that incorporates patient preferences and promotes successful weight loss. Importantly, these findings underscore that the role of the physician is important in promoting weight loss, yet it does not require in-depth knowledge and training in evidence-based weight loss strategies. While dietary counseling is still helpful, patients with successful weight loss value the supportive relationship with their physician, their physician drawing connections between obesity and weight-related medical conditions, and their physician enhancing intrinsic motivations for weight loss.
1. Flegal KM, Kruszon-Moran D, Carroll MD, et al. Trends in obesity among adults in the United States, 2005 to 2014. JAMA 2016;315:2284.
2. Williams EP, Mesidor M, Winters K, et al. Overweight and obesity: prevalence, consequences, and causes of a growing public health problem. Curr Obes Rep 2015;4:363–70.
3. Moyer VA. Screening for and management of obesity in adults: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2012;157:373–8.
4. Ogunleye AA, Osunlana A, Asselin J, et al. The 5As team intervention: bridging the knowledge gap in obesity management among primary care practitioners. BMC Res Notes 2015;8:810.
5. Jay MR, Gillespie CC, Schlair SL, et al. The impact of primary care resident physician training on patient weight loss at 12 months. Obesity 2013;21:45–50.
6. Aveyard P, Lewis A, Tearne S, et al. Screening and brief intervention for obesity in primary care: a parallel, two-arm, randomised trial. Lancet 2016;388:2492–500.
7. Garvey WT, Mechanick JI, Brett EM, et al. American Association of Clinical Endocrinologists and American College of Endocrinology comprehensive clinical practice guidelines for medical care of patients with obesity. Endocr Pract 2016;22(Suppl 3):1–203.
8. Ossolinski G, Jiwa M, McManus A. Weight management practices and evidence for weight loss through primary care: a brief review. Curr Med Res Opin 2015;31:2011–20.
9. Wadden TA, Volger S, Sarwer DB, et al. A two-year randomized trial of obesity treatment in primary care practice. N Engl J Med 2011;365:1969–79.
10. Sharifi M, Marshall G, Goldman RE, et al. Engaging children in the development of obesity interventions: Exploring outcomes that matter most among obesity positive outliers. Patient Educ Couns 2015;98:1393–401.
11. Foster BA, Farragher J, Parker P, Hale DE. A positive deviance approach to early childhood obesity: cross-sectional characterization of positive outliers. Child Obes 2015;11:281–8.
12. Sharifi M, Marshall G, Goldman R, et al. Exploring innovative approaches and patient-centered outcomes from positive outliers in childhood obesity. Acad Pediatr 2014;14:646–55.
13. Stuckey HL, Boan J, Kraschnewski JL, et al. Using positive deviance for determining successful weight-control practices. Qual Health Res 2011;21:563–79.
14. Marty L, Dubois C, Gaubard MS, et al. Higher nutritional quality at no additional cost among low-income households: insights from food purchases of positive deviants. Am J Clin Nutr 2015;102:190–8.
15. Machado JC, Cotta RMM, Silva LS da. [The positive deviance approach to change nutrition behavior: a systematic review]. Rev Panam Salud Publica 2014;36:134–40.
16. Kraschnewski JL, Sciamanna CN, Pollak KI, et al. The epidemiology of weight counseling for adults in the United States: a case of positive deviance. Int J Obes 2013;37:751–3.
Study Overview
Objective. To evaluate factors related to interactions with primary care physicians (PCPs) that may contribute to successful weight loss and maintenance among low-income, African-American women.
Design. Mixed methods, positive deviance framework.
Setting and participants. Participants were African-American women aged 18–64 years from an urban university-based family medicine practice who received Medicaid, resided in Philadelphia, and had a body mass index (BMI) of ≥ 30kg/m2. From among these, “positive deviant” cases were identified as patients with EMR-confirmed weight loss of at least 10% of patient’s maximum weight between 2007–2012 and maintenance of this loss for at least 6 months. Controls were defined as patients who had not lost a significant amount of weight during this time period. Patients were excluded if they were an amputee or wheelchair-bound; had bariatric surgery, severe illness during weight loss, EMR-documented unintended weight loss, pregnancy at time of weight loss, a psychiatric disorder or were taking antipsychotic medication; had an intellectual disability; or could not give consent to participate.
Main outcomes measures. PCP- and patient-reported weight variables were collected through the EMR (documentation of dietary counseling by PCP, documentation of a weight-related problem, diagnosis of overweight, obesity, or morbid obesity on the problem list), surveys (additional predicters of positive deviant membership, including patient-reported weight-related diagnosis or discussion of weight with PCP or health professional), and interviews. Logistic regression was used to determine whether a priori-identified EMR and survey variables could predict positive deviant group membership, adjusting for demographic variables significantly associated with the outcome of interest or hypothesized to be confounders of the associations between predictors and outcomes (results were adjusted for age in the EMR analysis and for employment status and education level in the survey analysis). Once thematic saturation was reached, interviews were analyzed by a 4-member coding panel using a modified approach to grounded theory to identify themes.
Main results. For the EMR analysis, data from 161 positive deviant cases and 602 controls were analyzed. For the survey analysis, data from 35 positive deviant cases and 36 controls matched for age and maximum BMI were analyzed. For in-depth interviews, thematic saturation was reached after collecting data from 20 positive deviant participants. In the EMR analyses, documentation of dietary counseling and a weight-related diagnosis were significant predictors of positive deviant membership after adjusting for age (P < 0.001 and P = 0.011, respectively). However, documentation of obesity on the problem list was predictive of control group membership (P = 0.032). In the survey analysis, neither patient-reported weight-related diagnosis nor discussion of weight with a medical provider were predictors of positive deviant membership (P = 0.890 and P = 0.373, respectively). In the qualitative analysis of interviews with positive deviant participants, 5 themes emerged: (1) framing the problem of obesity in the context of other health problems provided motivation; (2) having a full discussion around weight management was important; (3) an ongoing conversation and relationship was valuable; (4) celebrating small successes was beneficial for ongoing motivation; and (5) advice was helpful but self-motivation was required in order to make a change.
Conclusions. PCP counseling may be an important factor in promoting weight loss in low-income, African-American women, a population at high risk for obesity. Patients may benefit from their PCPs drawing connections between obesity and weight-related medical conditions and enhancing intrinsic motivation for weight loss.
Commentary
The increasing prevalence and clinical consequences of having obesity are well-documented, with low-income minorities disproportionately burdened by this condition [1,2]. The United States Preventive Services Task Force (USPTF) recommends that all patients be screened for obesity and offered intensive lifestyle counseling [3], yet evidence-based guidelines for best approaches to incorporate this into practice are few and unclear, and even fewer are specific to high-risk patient populations [4–9].
This study adds to the literature by using a positive deviance approach to identify PCP-related factors that predict successful weight loss among low-income African-American women. This approach has rarely been used in the obesity literature. In a few childhood obesity studies, this approach was used to identify motivations used by child “positive outliers” to improve their BMI [10], characterize variations of feeding and activity practices by parents of healthy children normally at high risk for obesity [11], and explore successful health and BMI reduction strategies used among positive outlier families [12]. Positive deviance has also been used to characterize and change nutritional behavior and understand successful weight-control practices among adults [13–15]. One study has suggested that studying “positive deviant” physicians that regularly provide weight counseling may help to provide practice methods to increase these practices in the primary care settings [16].
Thus, the study approach in using a positive deviance framework is an important and unique strength. Addi-tionally, the authors used a mixed-methods approach, analyzing EMR, survey, and interview data to assess PCP- and patient-reported weight-related factors that predict successful weight loss. As the authors describe, their results confirm findings from previous studies looking at counseling preferences among ethnic minority women and PCP attitudes and practices related to weight management.
They acknowledge important limitations of their study design, primarily the generalizability of findings only to urban, low-income, African-American women, the small sample size in the survey analysis, and the use of EMR data to collect data on PCP counseling (as opposed to interviews, for example). It important to also acknowledge that this study was conducted at a family medicine practice, and physician behavior and practices likely do not generalize to other PCPs and specialists. Additionally, while their intention was to use a positive deviance framework, conducting interviews among a subset of their control cases may have provided useful information regarding negative or ineffective PCP interactions regarding weight loss and management.
Applications for Clinical Practice
As the authors emphasize, the outcomes of this study are especially relevant for PCPs and other health practitioners, as the identified themes can help guide weight counseling that incorporates patient preferences and promotes successful weight loss. Importantly, these findings underscore that the role of the physician is important in promoting weight loss, yet it does not require in-depth knowledge and training in evidence-based weight loss strategies. While dietary counseling is still helpful, patients with successful weight loss value the supportive relationship with their physician, their physician drawing connections between obesity and weight-related medical conditions, and their physician enhancing intrinsic motivations for weight loss.
Study Overview
Objective. To evaluate factors related to interactions with primary care physicians (PCPs) that may contribute to successful weight loss and maintenance among low-income, African-American women.
Design. Mixed methods, positive deviance framework.
Setting and participants. Participants were African-American women aged 18–64 years from an urban university-based family medicine practice who received Medicaid, resided in Philadelphia, and had a body mass index (BMI) of ≥ 30kg/m2. From among these, “positive deviant” cases were identified as patients with EMR-confirmed weight loss of at least 10% of patient’s maximum weight between 2007–2012 and maintenance of this loss for at least 6 months. Controls were defined as patients who had not lost a significant amount of weight during this time period. Patients were excluded if they were an amputee or wheelchair-bound; had bariatric surgery, severe illness during weight loss, EMR-documented unintended weight loss, pregnancy at time of weight loss, a psychiatric disorder or were taking antipsychotic medication; had an intellectual disability; or could not give consent to participate.
Main outcomes measures. PCP- and patient-reported weight variables were collected through the EMR (documentation of dietary counseling by PCP, documentation of a weight-related problem, diagnosis of overweight, obesity, or morbid obesity on the problem list), surveys (additional predicters of positive deviant membership, including patient-reported weight-related diagnosis or discussion of weight with PCP or health professional), and interviews. Logistic regression was used to determine whether a priori-identified EMR and survey variables could predict positive deviant group membership, adjusting for demographic variables significantly associated with the outcome of interest or hypothesized to be confounders of the associations between predictors and outcomes (results were adjusted for age in the EMR analysis and for employment status and education level in the survey analysis). Once thematic saturation was reached, interviews were analyzed by a 4-member coding panel using a modified approach to grounded theory to identify themes.
Main results. For the EMR analysis, data from 161 positive deviant cases and 602 controls were analyzed. For the survey analysis, data from 35 positive deviant cases and 36 controls matched for age and maximum BMI were analyzed. For in-depth interviews, thematic saturation was reached after collecting data from 20 positive deviant participants. In the EMR analyses, documentation of dietary counseling and a weight-related diagnosis were significant predictors of positive deviant membership after adjusting for age (P < 0.001 and P = 0.011, respectively). However, documentation of obesity on the problem list was predictive of control group membership (P = 0.032). In the survey analysis, neither patient-reported weight-related diagnosis nor discussion of weight with a medical provider were predictors of positive deviant membership (P = 0.890 and P = 0.373, respectively). In the qualitative analysis of interviews with positive deviant participants, 5 themes emerged: (1) framing the problem of obesity in the context of other health problems provided motivation; (2) having a full discussion around weight management was important; (3) an ongoing conversation and relationship was valuable; (4) celebrating small successes was beneficial for ongoing motivation; and (5) advice was helpful but self-motivation was required in order to make a change.
Conclusions. PCP counseling may be an important factor in promoting weight loss in low-income, African-American women, a population at high risk for obesity. Patients may benefit from their PCPs drawing connections between obesity and weight-related medical conditions and enhancing intrinsic motivation for weight loss.
Commentary
The increasing prevalence and clinical consequences of having obesity are well-documented, with low-income minorities disproportionately burdened by this condition [1,2]. The United States Preventive Services Task Force (USPTF) recommends that all patients be screened for obesity and offered intensive lifestyle counseling [3], yet evidence-based guidelines for best approaches to incorporate this into practice are few and unclear, and even fewer are specific to high-risk patient populations [4–9].
This study adds to the literature by using a positive deviance approach to identify PCP-related factors that predict successful weight loss among low-income African-American women. This approach has rarely been used in the obesity literature. In a few childhood obesity studies, this approach was used to identify motivations used by child “positive outliers” to improve their BMI [10], characterize variations of feeding and activity practices by parents of healthy children normally at high risk for obesity [11], and explore successful health and BMI reduction strategies used among positive outlier families [12]. Positive deviance has also been used to characterize and change nutritional behavior and understand successful weight-control practices among adults [13–15]. One study has suggested that studying “positive deviant” physicians that regularly provide weight counseling may help to provide practice methods to increase these practices in the primary care settings [16].
Thus, the study approach in using a positive deviance framework is an important and unique strength. Addi-tionally, the authors used a mixed-methods approach, analyzing EMR, survey, and interview data to assess PCP- and patient-reported weight-related factors that predict successful weight loss. As the authors describe, their results confirm findings from previous studies looking at counseling preferences among ethnic minority women and PCP attitudes and practices related to weight management.
They acknowledge important limitations of their study design, primarily the generalizability of findings only to urban, low-income, African-American women, the small sample size in the survey analysis, and the use of EMR data to collect data on PCP counseling (as opposed to interviews, for example). It important to also acknowledge that this study was conducted at a family medicine practice, and physician behavior and practices likely do not generalize to other PCPs and specialists. Additionally, while their intention was to use a positive deviance framework, conducting interviews among a subset of their control cases may have provided useful information regarding negative or ineffective PCP interactions regarding weight loss and management.
Applications for Clinical Practice
As the authors emphasize, the outcomes of this study are especially relevant for PCPs and other health practitioners, as the identified themes can help guide weight counseling that incorporates patient preferences and promotes successful weight loss. Importantly, these findings underscore that the role of the physician is important in promoting weight loss, yet it does not require in-depth knowledge and training in evidence-based weight loss strategies. While dietary counseling is still helpful, patients with successful weight loss value the supportive relationship with their physician, their physician drawing connections between obesity and weight-related medical conditions, and their physician enhancing intrinsic motivations for weight loss.
1. Flegal KM, Kruszon-Moran D, Carroll MD, et al. Trends in obesity among adults in the United States, 2005 to 2014. JAMA 2016;315:2284.
2. Williams EP, Mesidor M, Winters K, et al. Overweight and obesity: prevalence, consequences, and causes of a growing public health problem. Curr Obes Rep 2015;4:363–70.
3. Moyer VA. Screening for and management of obesity in adults: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2012;157:373–8.
4. Ogunleye AA, Osunlana A, Asselin J, et al. The 5As team intervention: bridging the knowledge gap in obesity management among primary care practitioners. BMC Res Notes 2015;8:810.
5. Jay MR, Gillespie CC, Schlair SL, et al. The impact of primary care resident physician training on patient weight loss at 12 months. Obesity 2013;21:45–50.
6. Aveyard P, Lewis A, Tearne S, et al. Screening and brief intervention for obesity in primary care: a parallel, two-arm, randomised trial. Lancet 2016;388:2492–500.
7. Garvey WT, Mechanick JI, Brett EM, et al. American Association of Clinical Endocrinologists and American College of Endocrinology comprehensive clinical practice guidelines for medical care of patients with obesity. Endocr Pract 2016;22(Suppl 3):1–203.
8. Ossolinski G, Jiwa M, McManus A. Weight management practices and evidence for weight loss through primary care: a brief review. Curr Med Res Opin 2015;31:2011–20.
9. Wadden TA, Volger S, Sarwer DB, et al. A two-year randomized trial of obesity treatment in primary care practice. N Engl J Med 2011;365:1969–79.
10. Sharifi M, Marshall G, Goldman RE, et al. Engaging children in the development of obesity interventions: Exploring outcomes that matter most among obesity positive outliers. Patient Educ Couns 2015;98:1393–401.
11. Foster BA, Farragher J, Parker P, Hale DE. A positive deviance approach to early childhood obesity: cross-sectional characterization of positive outliers. Child Obes 2015;11:281–8.
12. Sharifi M, Marshall G, Goldman R, et al. Exploring innovative approaches and patient-centered outcomes from positive outliers in childhood obesity. Acad Pediatr 2014;14:646–55.
13. Stuckey HL, Boan J, Kraschnewski JL, et al. Using positive deviance for determining successful weight-control practices. Qual Health Res 2011;21:563–79.
14. Marty L, Dubois C, Gaubard MS, et al. Higher nutritional quality at no additional cost among low-income households: insights from food purchases of positive deviants. Am J Clin Nutr 2015;102:190–8.
15. Machado JC, Cotta RMM, Silva LS da. [The positive deviance approach to change nutrition behavior: a systematic review]. Rev Panam Salud Publica 2014;36:134–40.
16. Kraschnewski JL, Sciamanna CN, Pollak KI, et al. The epidemiology of weight counseling for adults in the United States: a case of positive deviance. Int J Obes 2013;37:751–3.
1. Flegal KM, Kruszon-Moran D, Carroll MD, et al. Trends in obesity among adults in the United States, 2005 to 2014. JAMA 2016;315:2284.
2. Williams EP, Mesidor M, Winters K, et al. Overweight and obesity: prevalence, consequences, and causes of a growing public health problem. Curr Obes Rep 2015;4:363–70.
3. Moyer VA. Screening for and management of obesity in adults: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2012;157:373–8.
4. Ogunleye AA, Osunlana A, Asselin J, et al. The 5As team intervention: bridging the knowledge gap in obesity management among primary care practitioners. BMC Res Notes 2015;8:810.
5. Jay MR, Gillespie CC, Schlair SL, et al. The impact of primary care resident physician training on patient weight loss at 12 months. Obesity 2013;21:45–50.
6. Aveyard P, Lewis A, Tearne S, et al. Screening and brief intervention for obesity in primary care: a parallel, two-arm, randomised trial. Lancet 2016;388:2492–500.
7. Garvey WT, Mechanick JI, Brett EM, et al. American Association of Clinical Endocrinologists and American College of Endocrinology comprehensive clinical practice guidelines for medical care of patients with obesity. Endocr Pract 2016;22(Suppl 3):1–203.
8. Ossolinski G, Jiwa M, McManus A. Weight management practices and evidence for weight loss through primary care: a brief review. Curr Med Res Opin 2015;31:2011–20.
9. Wadden TA, Volger S, Sarwer DB, et al. A two-year randomized trial of obesity treatment in primary care practice. N Engl J Med 2011;365:1969–79.
10. Sharifi M, Marshall G, Goldman RE, et al. Engaging children in the development of obesity interventions: Exploring outcomes that matter most among obesity positive outliers. Patient Educ Couns 2015;98:1393–401.
11. Foster BA, Farragher J, Parker P, Hale DE. A positive deviance approach to early childhood obesity: cross-sectional characterization of positive outliers. Child Obes 2015;11:281–8.
12. Sharifi M, Marshall G, Goldman R, et al. Exploring innovative approaches and patient-centered outcomes from positive outliers in childhood obesity. Acad Pediatr 2014;14:646–55.
13. Stuckey HL, Boan J, Kraschnewski JL, et al. Using positive deviance for determining successful weight-control practices. Qual Health Res 2011;21:563–79.
14. Marty L, Dubois C, Gaubard MS, et al. Higher nutritional quality at no additional cost among low-income households: insights from food purchases of positive deviants. Am J Clin Nutr 2015;102:190–8.
15. Machado JC, Cotta RMM, Silva LS da. [The positive deviance approach to change nutrition behavior: a systematic review]. Rev Panam Salud Publica 2014;36:134–40.
16. Kraschnewski JL, Sciamanna CN, Pollak KI, et al. The epidemiology of weight counseling for adults in the United States: a case of positive deviance. Int J Obes 2013;37:751–3.
Are There Racial/Ethnic Differences in Weight-Related Care Encounters Reported by Patients?
Study Overview
Objective. To compare patients’ health care experiences related to their weight across racial and ethnic groups.
Design. Cross-sectional survey-based study.
Setting and participants. Between March and July 2015, 5400 individuals were randomly sampled from the Patient Outcomes to Advance Learning (PORTAL) obesity cohort, which includes over 5 million adults. The PORTAL network is a clinical data research network funded by the Patient Centered Outcomes Research Institute to promote collaboration across several large health systems with electronic medical records (EMRs), including all the Kaiser Permanente regions, Group Health Cooperative, Health Partners, and Denver Health. The selected 5400 cohort members were equally distributed across 3 geographically diverse Kaiser Permanente regions (Southwest, Northern and Southern California, Hawaii, Colorado, and Northwest) and Denver sites. Selected individuals were non-pregnant English or Spanish speakers with a body mass index (BMI) ≥ 25 kg/m2 (per their EMR) who were members of a participating health plan and had at least 1 outpatient visit in the last 12 months. Patients with BMI ≥ 40 kg/m2 were oversampled. Individuals were mailed a written 10-minute survey (offered in English or Spanish based on a patient’s written language preference noted in their EMR), consisting of 36 multiple-choice and fill-in-the-blank items. Telephone contact for verbal administration was attempted if a mailed response was not received within 4 weeks.
Main measures and analysis. The primary independent variable was a respondent’s racial/ethnic group, categorized as (1) non-Hispanic white (White), 2) non-Hispanic black (Black), 3) Hispanic, 4) Asian, or 5) Native Hawaiian/Other Pacific Islanders/American Indian/Native Alaskan (NA/PI).
Dependent variables focused on patients’ perceptions of the health care experience (based on services received at their usual place of care from their primary care providers) related to being overweight or obese using items based on the Rudd Center’s Patient Survey of Weight-Sensitive Healthcare Practices. Respondents described (1) whether and how often they avoid coming to their provider because they do not want to be weighed or have a discussion about their weight; (2) how often does their provider ask their permission before discussion their weight; (3) how often has their provider been supportive of their weight concerns and efforts to be healthy; (4) whether they think that their provider understands the physical and emotional challenges faced by individuals who are overweight or obese; (5) how often has their provider brought up their weight during a clinic visit; (6) whether their provider has ever given or discussed resources on healthy eating and weight loss; and (7) what types of weight loss resources were discussed with their provider and which types did they want more information about (ie, dietary changes, physical activity, classes, medications, meal replacements, and bariatric surgery). Covariate variables derived from EMR data included sex, age category, diabetes, hypertension, Charlson Index score (overall measures of morbidity), Medicaid enrollment, language preferences, site, and BMI. Survey-derived covariate variables included emotional well-being, perceived weight status, and educational attainment.
Descriptive statistics were generated and compared across racial/ethnic groups using Kruskal-Wallis and chi-square testing, as appropriate. To evaluate the association between a patient’s race/ethnicity and their perceived weight management experience, multinomial logistic regression adjusted for covariates was used to estimate odds ratios (OR).
Main results. From the original sample (n = 5400), 1569 individuals (29%) did not respond, 925 (17%) refused, and 114 (2%) were ineligible, leaving an eligible sample pool of 5286 individuals. The overall response rate was 53% (2197 written; 614 phone, n = 2811). Those with missing data were excluded (6 with missing race/ethnicity; 80 missing other covariates), leaving a final group of 2725 respondents for analysis. Mean age was 52.7 years (SD 15), almost 62% of participants were female, 51.7% identified as White, 21.1% identified as Black, 14.6% identified as Hispanic, 5.8% identified as Asian, and 6.7% identified as NA/PI. About a quarter (24.4%) had diabetes, less than half (43.5%) had hypertension, and most (86.2%) perceived themselves to be overweight. There were significant differences in measured baseline covariates by racial/ethnic groups including mean BMI, diabetes, and being a Medicaid beneficiary.
In response to the 7 key areas assessed regarding patients’ perceptions of the health care experience related to being overweight or obese:
- Black respondents were less likely than Whites to report that they frequently avoided care from their provider because they did not want to be weighed or discuss their weight (OR 0.49 [95% confidence interval, 0.26–0.90]), with a trend toward all groups being less likely to report frequent avoidance compared to Whites.
- While just over half of respondents (59.3%) indicated that their providers never asked for their permission before discussing their weight, Asians and NA/PI were more likely to report that their providers either frequently (Asians: OR 2.7 [1.3–5.6]; NA/PI: OR 2.3 [1.1–5.0]) or sometimes (Asians: OR 2.3 [1.2–4.3]; NA/PI: OR 2.1 [1.1–4.1]) asked their permission before discussing their weight compared to Whites.
- Over half (61.9%) indicated that their providers were sometimes or frequently supportive of their weight concerns, with no significant differences among racial/ethnic groups.
- Just over half (52.0%) indicated they felt their providers understood the physical and emotional challenges faced by people who are overweight/obese, with Blacks more likely to feel this way (OR 1.8 [1.2–2.8]) compared to Whites.
- Black patients were more likely than Whites (OR 2.0 [1.4–2.8]) to report that their providers discussed their weight with them at a clinic visit.
- While over half (59.7%) indicated that their providers had given or discussed resources with them on healthy eating and weight loss, Black and Asian respondents were more likely than Whites to recall these discussions (Black: OR 1.6 [1.2–2.1]; Asians: OR 1.8 [1.1–2.9]).
- Most weight loss resources or recommendations received were related to lifestyle changes, with very few resources given related to weight loss medications, meal replacement products, or bariatric surgery—few differences across racial/ethnic groups were identified. However, respondents from racial/ethnic minority groups were more likely than Whites to say that they wanted more information about lifestyle changes, classes, and meal replacements. Other than Blacks, all other racial/ethnic groups were also more likely than Whites to indicate that they wanted more information about bariatric surgery.
Conclusions. Most patients across racial/ethnic groups are having positive experiences with weight-related care. However, race/ethnicity correlates with patients’ perception of weight-related care and discussions in clinic encounters.
Commentary
The obesity epidemic in the United States is well-established [1], and recent data from 2014 show that over 37% of adults in the US are obese (defined as having a body mass index greater than 30 kg/m2) [2]. However, while obesity prevalence rates have increased over the past several decades across all genders, ethnicities, income levels, and education levels, important racial/ethnic disparities exist [2,3]. Primary care physicians (PCPs) are ideally situated to promote weight loss via effective obesity counseling since multiple clinic visits over time have the potential to enable rapport building and behavioral change management [4]. In fact, the US Preventive Services Task Force (USPTF) recommends that all patients be screened for obesity and offered intensive lifestyle counseling, as modest weight loss can have significant health benefits [5]. However, some studies have found racial/ethnic differences and disparities in weight-related diagnoses, counseling, and treatment by providers, but also patient perceptions of care and preferred interventions [6–10]. Other studies have described racial/ethnic differences in weight-related concerns and behaviors, body satisfaction, and body image [11–13]. Thus, research is needed to examine these differences.
This cross-sectional study contributes to the limited literature examining the potential for heterogeneity of care according to patient characteristics like race and ethnicity. Key strengths of the design include a large and both geographically and racially/ethnically diverse sample of patients (increased generalizability), the use of mailed brief surveys (reduces non-response rate and reporting bias) and telephone follow-up for verbal administration (reduces non-response rate, though it increases interviewer bias), oversampling of respondents with BMI ≥ 40 kg/m2, and the controlling of key covariates including sex, age, Medicaid enrollment, site, and BMI.
However, there are several important limitations, many of which are acknowledged by the authors. While respondents were overall representative of the targeted sample population, the final respondent population was comprised of mostly older females who received managed care, which may have contributed to selection bias and impacted generalizability of findings. Further, Whites were overrepresented, Hispanics were underrepresented, and the small combined sample of NA/PI may have masked important distinctions between these subpopulations. Importantly, this study only provided the survey in English and Spanish and did not include other language translations (eg, Chinese, Japanese, Tagalog), which likely contributed to underrepresented perspectives of immigrants and ESL patients who may struggle with receiving/discussing weight management counseling and resources. The use of a surveys collected subjective and self-reported data on patient encounters as opposed to objective observations. Lastly, the study did not adjust for individual provider factors or assess the potential impact of provider-level differences on care, such as provider-patient concordance on race, ethnicity, language, and/or weight. The incorporation of qualitative interviewers or focus groups with a subsample of each racial/ethnic may have also provided relevant context to understand differences in weight-related care experiences.
Applications for Clinical Practice
As the authors suggest, this study highlights several opportunities to continue improving weight-related care and weight management counseling. PCPs should engage all overweight/obese patients in weight management discussions, and in particular, high-risk minority patients who may desire these conversations and more weight loss advice and resources. However, these discussions require sensitivity and can benefit from the simple practice of asking permission of the patient to talk about their weight in order to reduce care avoidance and improve perceptions of care. Providers should also be mindful of patient priorities and assess patient preferences for all the different weight loss strategies, including lifestyle changes, meal replacements, medications, and surgery.
—Katrina F. Mateo, MPH
1. Mitchell NS, Catenacci VA, Wyatt HR, Hill JO. Obesity: overview of an epidemic. Psychiatr Clin North Am 2011;34:717–32.
2. Flegal KM, Kruszon-Moran D, Carroll MD, et al. Trends in obesity among adults in the United States, 2005 to 2014. JAMA 2016;315:2284.
3. Wong RJ, Chou C, Ahmed A. Long term trends and racial/ethnic disparities in the prevalence of obesity. J Community Health 2014;39:1150–60.
4. Schlair S, Moore S, Mcmacken M, Jay M. How to deliver high quality obesity counseling using the 5As framework. J Clin Outcomes Manag 2012;19:221–9.
5. Moyer VA. Screening for and management of obesity in adults: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2012;157:373–8.
6. Davis NJ, Wildman RP, Forbes BF, Schechter CB. Trends and disparities in provider diagnosis of overweight analysis of NHANES 1999–2004. Obesity 2009;17:2110–3.
7. Wee CC, Huskey KW, Bolcic-Jankovic D, et al. Sex, race, and consideration of bariatric surgery among primary care patients with moderate to severe obesity. J Gen Intern Med 2014;29:68–75.
8. Johnson RL, Saha S, Arbelaez JJ, et al. Racial and ethnic differences in patient perceptions of bias and cultural competence in health care. J Gen Intern Med 2004;19:101–10.
9. Chugh M, Friedman AM, Clemow LP, Ferrante JM. Women weigh in: obese african american and white women’s perspectives on physicians’ roles in weight management. J Am Board Fam Med 2013;26:421–8.
10. Blixen CE, Singh A, Xu M, et al. What women want: understanding obesity and preferences for primary care weight reduction interventions among African-American and Caucasian women. J Natl Med Assoc 2006;98:1160–70.
11. Arcan C, Larson N, Bauer K, et al. Dietary and weight-related behaviors and body mass index among Hispanic, Hmong, Somali, and White adolescents. J Acad Nutr Diet 2014;114:375–83.
12. Kronenfeld LW, Reba-Harrelson L, Von Holle A, et al. Ethnic and racial differences in body size perception and satisfaction. Body Image 2010;7:131–6.
13. Gluck ME, Geliebter A. Racial/ethnic differences in body image and eating behaviors. Eat Behav 2002;3:143–51.
Study Overview
Objective. To compare patients’ health care experiences related to their weight across racial and ethnic groups.
Design. Cross-sectional survey-based study.
Setting and participants. Between March and July 2015, 5400 individuals were randomly sampled from the Patient Outcomes to Advance Learning (PORTAL) obesity cohort, which includes over 5 million adults. The PORTAL network is a clinical data research network funded by the Patient Centered Outcomes Research Institute to promote collaboration across several large health systems with electronic medical records (EMRs), including all the Kaiser Permanente regions, Group Health Cooperative, Health Partners, and Denver Health. The selected 5400 cohort members were equally distributed across 3 geographically diverse Kaiser Permanente regions (Southwest, Northern and Southern California, Hawaii, Colorado, and Northwest) and Denver sites. Selected individuals were non-pregnant English or Spanish speakers with a body mass index (BMI) ≥ 25 kg/m2 (per their EMR) who were members of a participating health plan and had at least 1 outpatient visit in the last 12 months. Patients with BMI ≥ 40 kg/m2 were oversampled. Individuals were mailed a written 10-minute survey (offered in English or Spanish based on a patient’s written language preference noted in their EMR), consisting of 36 multiple-choice and fill-in-the-blank items. Telephone contact for verbal administration was attempted if a mailed response was not received within 4 weeks.
Main measures and analysis. The primary independent variable was a respondent’s racial/ethnic group, categorized as (1) non-Hispanic white (White), 2) non-Hispanic black (Black), 3) Hispanic, 4) Asian, or 5) Native Hawaiian/Other Pacific Islanders/American Indian/Native Alaskan (NA/PI).
Dependent variables focused on patients’ perceptions of the health care experience (based on services received at their usual place of care from their primary care providers) related to being overweight or obese using items based on the Rudd Center’s Patient Survey of Weight-Sensitive Healthcare Practices. Respondents described (1) whether and how often they avoid coming to their provider because they do not want to be weighed or have a discussion about their weight; (2) how often does their provider ask their permission before discussion their weight; (3) how often has their provider been supportive of their weight concerns and efforts to be healthy; (4) whether they think that their provider understands the physical and emotional challenges faced by individuals who are overweight or obese; (5) how often has their provider brought up their weight during a clinic visit; (6) whether their provider has ever given or discussed resources on healthy eating and weight loss; and (7) what types of weight loss resources were discussed with their provider and which types did they want more information about (ie, dietary changes, physical activity, classes, medications, meal replacements, and bariatric surgery). Covariate variables derived from EMR data included sex, age category, diabetes, hypertension, Charlson Index score (overall measures of morbidity), Medicaid enrollment, language preferences, site, and BMI. Survey-derived covariate variables included emotional well-being, perceived weight status, and educational attainment.
Descriptive statistics were generated and compared across racial/ethnic groups using Kruskal-Wallis and chi-square testing, as appropriate. To evaluate the association between a patient’s race/ethnicity and their perceived weight management experience, multinomial logistic regression adjusted for covariates was used to estimate odds ratios (OR).
Main results. From the original sample (n = 5400), 1569 individuals (29%) did not respond, 925 (17%) refused, and 114 (2%) were ineligible, leaving an eligible sample pool of 5286 individuals. The overall response rate was 53% (2197 written; 614 phone, n = 2811). Those with missing data were excluded (6 with missing race/ethnicity; 80 missing other covariates), leaving a final group of 2725 respondents for analysis. Mean age was 52.7 years (SD 15), almost 62% of participants were female, 51.7% identified as White, 21.1% identified as Black, 14.6% identified as Hispanic, 5.8% identified as Asian, and 6.7% identified as NA/PI. About a quarter (24.4%) had diabetes, less than half (43.5%) had hypertension, and most (86.2%) perceived themselves to be overweight. There were significant differences in measured baseline covariates by racial/ethnic groups including mean BMI, diabetes, and being a Medicaid beneficiary.
In response to the 7 key areas assessed regarding patients’ perceptions of the health care experience related to being overweight or obese:
- Black respondents were less likely than Whites to report that they frequently avoided care from their provider because they did not want to be weighed or discuss their weight (OR 0.49 [95% confidence interval, 0.26–0.90]), with a trend toward all groups being less likely to report frequent avoidance compared to Whites.
- While just over half of respondents (59.3%) indicated that their providers never asked for their permission before discussing their weight, Asians and NA/PI were more likely to report that their providers either frequently (Asians: OR 2.7 [1.3–5.6]; NA/PI: OR 2.3 [1.1–5.0]) or sometimes (Asians: OR 2.3 [1.2–4.3]; NA/PI: OR 2.1 [1.1–4.1]) asked their permission before discussing their weight compared to Whites.
- Over half (61.9%) indicated that their providers were sometimes or frequently supportive of their weight concerns, with no significant differences among racial/ethnic groups.
- Just over half (52.0%) indicated they felt their providers understood the physical and emotional challenges faced by people who are overweight/obese, with Blacks more likely to feel this way (OR 1.8 [1.2–2.8]) compared to Whites.
- Black patients were more likely than Whites (OR 2.0 [1.4–2.8]) to report that their providers discussed their weight with them at a clinic visit.
- While over half (59.7%) indicated that their providers had given or discussed resources with them on healthy eating and weight loss, Black and Asian respondents were more likely than Whites to recall these discussions (Black: OR 1.6 [1.2–2.1]; Asians: OR 1.8 [1.1–2.9]).
- Most weight loss resources or recommendations received were related to lifestyle changes, with very few resources given related to weight loss medications, meal replacement products, or bariatric surgery—few differences across racial/ethnic groups were identified. However, respondents from racial/ethnic minority groups were more likely than Whites to say that they wanted more information about lifestyle changes, classes, and meal replacements. Other than Blacks, all other racial/ethnic groups were also more likely than Whites to indicate that they wanted more information about bariatric surgery.
Conclusions. Most patients across racial/ethnic groups are having positive experiences with weight-related care. However, race/ethnicity correlates with patients’ perception of weight-related care and discussions in clinic encounters.
Commentary
The obesity epidemic in the United States is well-established [1], and recent data from 2014 show that over 37% of adults in the US are obese (defined as having a body mass index greater than 30 kg/m2) [2]. However, while obesity prevalence rates have increased over the past several decades across all genders, ethnicities, income levels, and education levels, important racial/ethnic disparities exist [2,3]. Primary care physicians (PCPs) are ideally situated to promote weight loss via effective obesity counseling since multiple clinic visits over time have the potential to enable rapport building and behavioral change management [4]. In fact, the US Preventive Services Task Force (USPTF) recommends that all patients be screened for obesity and offered intensive lifestyle counseling, as modest weight loss can have significant health benefits [5]. However, some studies have found racial/ethnic differences and disparities in weight-related diagnoses, counseling, and treatment by providers, but also patient perceptions of care and preferred interventions [6–10]. Other studies have described racial/ethnic differences in weight-related concerns and behaviors, body satisfaction, and body image [11–13]. Thus, research is needed to examine these differences.
This cross-sectional study contributes to the limited literature examining the potential for heterogeneity of care according to patient characteristics like race and ethnicity. Key strengths of the design include a large and both geographically and racially/ethnically diverse sample of patients (increased generalizability), the use of mailed brief surveys (reduces non-response rate and reporting bias) and telephone follow-up for verbal administration (reduces non-response rate, though it increases interviewer bias), oversampling of respondents with BMI ≥ 40 kg/m2, and the controlling of key covariates including sex, age, Medicaid enrollment, site, and BMI.
However, there are several important limitations, many of which are acknowledged by the authors. While respondents were overall representative of the targeted sample population, the final respondent population was comprised of mostly older females who received managed care, which may have contributed to selection bias and impacted generalizability of findings. Further, Whites were overrepresented, Hispanics were underrepresented, and the small combined sample of NA/PI may have masked important distinctions between these subpopulations. Importantly, this study only provided the survey in English and Spanish and did not include other language translations (eg, Chinese, Japanese, Tagalog), which likely contributed to underrepresented perspectives of immigrants and ESL patients who may struggle with receiving/discussing weight management counseling and resources. The use of a surveys collected subjective and self-reported data on patient encounters as opposed to objective observations. Lastly, the study did not adjust for individual provider factors or assess the potential impact of provider-level differences on care, such as provider-patient concordance on race, ethnicity, language, and/or weight. The incorporation of qualitative interviewers or focus groups with a subsample of each racial/ethnic may have also provided relevant context to understand differences in weight-related care experiences.
Applications for Clinical Practice
As the authors suggest, this study highlights several opportunities to continue improving weight-related care and weight management counseling. PCPs should engage all overweight/obese patients in weight management discussions, and in particular, high-risk minority patients who may desire these conversations and more weight loss advice and resources. However, these discussions require sensitivity and can benefit from the simple practice of asking permission of the patient to talk about their weight in order to reduce care avoidance and improve perceptions of care. Providers should also be mindful of patient priorities and assess patient preferences for all the different weight loss strategies, including lifestyle changes, meal replacements, medications, and surgery.
—Katrina F. Mateo, MPH
Study Overview
Objective. To compare patients’ health care experiences related to their weight across racial and ethnic groups.
Design. Cross-sectional survey-based study.
Setting and participants. Between March and July 2015, 5400 individuals were randomly sampled from the Patient Outcomes to Advance Learning (PORTAL) obesity cohort, which includes over 5 million adults. The PORTAL network is a clinical data research network funded by the Patient Centered Outcomes Research Institute to promote collaboration across several large health systems with electronic medical records (EMRs), including all the Kaiser Permanente regions, Group Health Cooperative, Health Partners, and Denver Health. The selected 5400 cohort members were equally distributed across 3 geographically diverse Kaiser Permanente regions (Southwest, Northern and Southern California, Hawaii, Colorado, and Northwest) and Denver sites. Selected individuals were non-pregnant English or Spanish speakers with a body mass index (BMI) ≥ 25 kg/m2 (per their EMR) who were members of a participating health plan and had at least 1 outpatient visit in the last 12 months. Patients with BMI ≥ 40 kg/m2 were oversampled. Individuals were mailed a written 10-minute survey (offered in English or Spanish based on a patient’s written language preference noted in their EMR), consisting of 36 multiple-choice and fill-in-the-blank items. Telephone contact for verbal administration was attempted if a mailed response was not received within 4 weeks.
Main measures and analysis. The primary independent variable was a respondent’s racial/ethnic group, categorized as (1) non-Hispanic white (White), 2) non-Hispanic black (Black), 3) Hispanic, 4) Asian, or 5) Native Hawaiian/Other Pacific Islanders/American Indian/Native Alaskan (NA/PI).
Dependent variables focused on patients’ perceptions of the health care experience (based on services received at their usual place of care from their primary care providers) related to being overweight or obese using items based on the Rudd Center’s Patient Survey of Weight-Sensitive Healthcare Practices. Respondents described (1) whether and how often they avoid coming to their provider because they do not want to be weighed or have a discussion about their weight; (2) how often does their provider ask their permission before discussion their weight; (3) how often has their provider been supportive of their weight concerns and efforts to be healthy; (4) whether they think that their provider understands the physical and emotional challenges faced by individuals who are overweight or obese; (5) how often has their provider brought up their weight during a clinic visit; (6) whether their provider has ever given or discussed resources on healthy eating and weight loss; and (7) what types of weight loss resources were discussed with their provider and which types did they want more information about (ie, dietary changes, physical activity, classes, medications, meal replacements, and bariatric surgery). Covariate variables derived from EMR data included sex, age category, diabetes, hypertension, Charlson Index score (overall measures of morbidity), Medicaid enrollment, language preferences, site, and BMI. Survey-derived covariate variables included emotional well-being, perceived weight status, and educational attainment.
Descriptive statistics were generated and compared across racial/ethnic groups using Kruskal-Wallis and chi-square testing, as appropriate. To evaluate the association between a patient’s race/ethnicity and their perceived weight management experience, multinomial logistic regression adjusted for covariates was used to estimate odds ratios (OR).
Main results. From the original sample (n = 5400), 1569 individuals (29%) did not respond, 925 (17%) refused, and 114 (2%) were ineligible, leaving an eligible sample pool of 5286 individuals. The overall response rate was 53% (2197 written; 614 phone, n = 2811). Those with missing data were excluded (6 with missing race/ethnicity; 80 missing other covariates), leaving a final group of 2725 respondents for analysis. Mean age was 52.7 years (SD 15), almost 62% of participants were female, 51.7% identified as White, 21.1% identified as Black, 14.6% identified as Hispanic, 5.8% identified as Asian, and 6.7% identified as NA/PI. About a quarter (24.4%) had diabetes, less than half (43.5%) had hypertension, and most (86.2%) perceived themselves to be overweight. There were significant differences in measured baseline covariates by racial/ethnic groups including mean BMI, diabetes, and being a Medicaid beneficiary.
In response to the 7 key areas assessed regarding patients’ perceptions of the health care experience related to being overweight or obese:
- Black respondents were less likely than Whites to report that they frequently avoided care from their provider because they did not want to be weighed or discuss their weight (OR 0.49 [95% confidence interval, 0.26–0.90]), with a trend toward all groups being less likely to report frequent avoidance compared to Whites.
- While just over half of respondents (59.3%) indicated that their providers never asked for their permission before discussing their weight, Asians and NA/PI were more likely to report that their providers either frequently (Asians: OR 2.7 [1.3–5.6]; NA/PI: OR 2.3 [1.1–5.0]) or sometimes (Asians: OR 2.3 [1.2–4.3]; NA/PI: OR 2.1 [1.1–4.1]) asked their permission before discussing their weight compared to Whites.
- Over half (61.9%) indicated that their providers were sometimes or frequently supportive of their weight concerns, with no significant differences among racial/ethnic groups.
- Just over half (52.0%) indicated they felt their providers understood the physical and emotional challenges faced by people who are overweight/obese, with Blacks more likely to feel this way (OR 1.8 [1.2–2.8]) compared to Whites.
- Black patients were more likely than Whites (OR 2.0 [1.4–2.8]) to report that their providers discussed their weight with them at a clinic visit.
- While over half (59.7%) indicated that their providers had given or discussed resources with them on healthy eating and weight loss, Black and Asian respondents were more likely than Whites to recall these discussions (Black: OR 1.6 [1.2–2.1]; Asians: OR 1.8 [1.1–2.9]).
- Most weight loss resources or recommendations received were related to lifestyle changes, with very few resources given related to weight loss medications, meal replacement products, or bariatric surgery—few differences across racial/ethnic groups were identified. However, respondents from racial/ethnic minority groups were more likely than Whites to say that they wanted more information about lifestyle changes, classes, and meal replacements. Other than Blacks, all other racial/ethnic groups were also more likely than Whites to indicate that they wanted more information about bariatric surgery.
Conclusions. Most patients across racial/ethnic groups are having positive experiences with weight-related care. However, race/ethnicity correlates with patients’ perception of weight-related care and discussions in clinic encounters.
Commentary
The obesity epidemic in the United States is well-established [1], and recent data from 2014 show that over 37% of adults in the US are obese (defined as having a body mass index greater than 30 kg/m2) [2]. However, while obesity prevalence rates have increased over the past several decades across all genders, ethnicities, income levels, and education levels, important racial/ethnic disparities exist [2,3]. Primary care physicians (PCPs) are ideally situated to promote weight loss via effective obesity counseling since multiple clinic visits over time have the potential to enable rapport building and behavioral change management [4]. In fact, the US Preventive Services Task Force (USPTF) recommends that all patients be screened for obesity and offered intensive lifestyle counseling, as modest weight loss can have significant health benefits [5]. However, some studies have found racial/ethnic differences and disparities in weight-related diagnoses, counseling, and treatment by providers, but also patient perceptions of care and preferred interventions [6–10]. Other studies have described racial/ethnic differences in weight-related concerns and behaviors, body satisfaction, and body image [11–13]. Thus, research is needed to examine these differences.
This cross-sectional study contributes to the limited literature examining the potential for heterogeneity of care according to patient characteristics like race and ethnicity. Key strengths of the design include a large and both geographically and racially/ethnically diverse sample of patients (increased generalizability), the use of mailed brief surveys (reduces non-response rate and reporting bias) and telephone follow-up for verbal administration (reduces non-response rate, though it increases interviewer bias), oversampling of respondents with BMI ≥ 40 kg/m2, and the controlling of key covariates including sex, age, Medicaid enrollment, site, and BMI.
However, there are several important limitations, many of which are acknowledged by the authors. While respondents were overall representative of the targeted sample population, the final respondent population was comprised of mostly older females who received managed care, which may have contributed to selection bias and impacted generalizability of findings. Further, Whites were overrepresented, Hispanics were underrepresented, and the small combined sample of NA/PI may have masked important distinctions between these subpopulations. Importantly, this study only provided the survey in English and Spanish and did not include other language translations (eg, Chinese, Japanese, Tagalog), which likely contributed to underrepresented perspectives of immigrants and ESL patients who may struggle with receiving/discussing weight management counseling and resources. The use of a surveys collected subjective and self-reported data on patient encounters as opposed to objective observations. Lastly, the study did not adjust for individual provider factors or assess the potential impact of provider-level differences on care, such as provider-patient concordance on race, ethnicity, language, and/or weight. The incorporation of qualitative interviewers or focus groups with a subsample of each racial/ethnic may have also provided relevant context to understand differences in weight-related care experiences.
Applications for Clinical Practice
As the authors suggest, this study highlights several opportunities to continue improving weight-related care and weight management counseling. PCPs should engage all overweight/obese patients in weight management discussions, and in particular, high-risk minority patients who may desire these conversations and more weight loss advice and resources. However, these discussions require sensitivity and can benefit from the simple practice of asking permission of the patient to talk about their weight in order to reduce care avoidance and improve perceptions of care. Providers should also be mindful of patient priorities and assess patient preferences for all the different weight loss strategies, including lifestyle changes, meal replacements, medications, and surgery.
—Katrina F. Mateo, MPH
1. Mitchell NS, Catenacci VA, Wyatt HR, Hill JO. Obesity: overview of an epidemic. Psychiatr Clin North Am 2011;34:717–32.
2. Flegal KM, Kruszon-Moran D, Carroll MD, et al. Trends in obesity among adults in the United States, 2005 to 2014. JAMA 2016;315:2284.
3. Wong RJ, Chou C, Ahmed A. Long term trends and racial/ethnic disparities in the prevalence of obesity. J Community Health 2014;39:1150–60.
4. Schlair S, Moore S, Mcmacken M, Jay M. How to deliver high quality obesity counseling using the 5As framework. J Clin Outcomes Manag 2012;19:221–9.
5. Moyer VA. Screening for and management of obesity in adults: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2012;157:373–8.
6. Davis NJ, Wildman RP, Forbes BF, Schechter CB. Trends and disparities in provider diagnosis of overweight analysis of NHANES 1999–2004. Obesity 2009;17:2110–3.
7. Wee CC, Huskey KW, Bolcic-Jankovic D, et al. Sex, race, and consideration of bariatric surgery among primary care patients with moderate to severe obesity. J Gen Intern Med 2014;29:68–75.
8. Johnson RL, Saha S, Arbelaez JJ, et al. Racial and ethnic differences in patient perceptions of bias and cultural competence in health care. J Gen Intern Med 2004;19:101–10.
9. Chugh M, Friedman AM, Clemow LP, Ferrante JM. Women weigh in: obese african american and white women’s perspectives on physicians’ roles in weight management. J Am Board Fam Med 2013;26:421–8.
10. Blixen CE, Singh A, Xu M, et al. What women want: understanding obesity and preferences for primary care weight reduction interventions among African-American and Caucasian women. J Natl Med Assoc 2006;98:1160–70.
11. Arcan C, Larson N, Bauer K, et al. Dietary and weight-related behaviors and body mass index among Hispanic, Hmong, Somali, and White adolescents. J Acad Nutr Diet 2014;114:375–83.
12. Kronenfeld LW, Reba-Harrelson L, Von Holle A, et al. Ethnic and racial differences in body size perception and satisfaction. Body Image 2010;7:131–6.
13. Gluck ME, Geliebter A. Racial/ethnic differences in body image and eating behaviors. Eat Behav 2002;3:143–51.
1. Mitchell NS, Catenacci VA, Wyatt HR, Hill JO. Obesity: overview of an epidemic. Psychiatr Clin North Am 2011;34:717–32.
2. Flegal KM, Kruszon-Moran D, Carroll MD, et al. Trends in obesity among adults in the United States, 2005 to 2014. JAMA 2016;315:2284.
3. Wong RJ, Chou C, Ahmed A. Long term trends and racial/ethnic disparities in the prevalence of obesity. J Community Health 2014;39:1150–60.
4. Schlair S, Moore S, Mcmacken M, Jay M. How to deliver high quality obesity counseling using the 5As framework. J Clin Outcomes Manag 2012;19:221–9.
5. Moyer VA. Screening for and management of obesity in adults: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2012;157:373–8.
6. Davis NJ, Wildman RP, Forbes BF, Schechter CB. Trends and disparities in provider diagnosis of overweight analysis of NHANES 1999–2004. Obesity 2009;17:2110–3.
7. Wee CC, Huskey KW, Bolcic-Jankovic D, et al. Sex, race, and consideration of bariatric surgery among primary care patients with moderate to severe obesity. J Gen Intern Med 2014;29:68–75.
8. Johnson RL, Saha S, Arbelaez JJ, et al. Racial and ethnic differences in patient perceptions of bias and cultural competence in health care. J Gen Intern Med 2004;19:101–10.
9. Chugh M, Friedman AM, Clemow LP, Ferrante JM. Women weigh in: obese african american and white women’s perspectives on physicians’ roles in weight management. J Am Board Fam Med 2013;26:421–8.
10. Blixen CE, Singh A, Xu M, et al. What women want: understanding obesity and preferences for primary care weight reduction interventions among African-American and Caucasian women. J Natl Med Assoc 2006;98:1160–70.
11. Arcan C, Larson N, Bauer K, et al. Dietary and weight-related behaviors and body mass index among Hispanic, Hmong, Somali, and White adolescents. J Acad Nutr Diet 2014;114:375–83.
12. Kronenfeld LW, Reba-Harrelson L, Von Holle A, et al. Ethnic and racial differences in body size perception and satisfaction. Body Image 2010;7:131–6.
13. Gluck ME, Geliebter A. Racial/ethnic differences in body image and eating behaviors. Eat Behav 2002;3:143–51.
A Mobile Health App for Weight Loss that Incorporates Social Networking
Study Overview
Objective. To test the efficacy of a weight loss app with incorporated social support and self-monitoring of diet, physical activity, and weight compared to a commercially available diet and PA tracking app.
Design. 2-group, randomized controlled trial.
Setting and participants. From October 2014 to January 2015, potential study participants were recruited via university/worksite listserv announcements, flyers, electronic newsletters, newspaper advertisements, social media posts, and a local research fair in 2 cities in South Carolina. Exclusion criteria included body mass index (BMI) outside the range of 25.0 to 49.9 kg/m2, unable to attend required measurement sessions, unable to access a computer or internet for completing assessments, having a psychiatric illness, receiving treatment for drug or alcohol dependency, having an eating disorder, participating in another weight loss program, reporting weight loss of 10+ pounds in the past 6 months, being pregnant or planning on becoming pregnant during study, or breastfeeding, or endorsing items on the Physical Activity Readiness Questionnaire (PAR-Q) regarding having a heart condition, feeling chest pain during physical activity, experiencing chest pain, becoming dizzy/ever losing balance or consciousness, and not having a physician give consent to participate despite reporting joint problems or taking blood pressure medication. Those who were eligible were invited to an orientation to the study, signed consent, and completed baseline assessments.
Intervention. Participants were randomized to either the experimental group (theory-based podcasts plus the Social POD app) or the comparison group (theory-based podcasts plus a standard app [“Fat Secret” app]). Both groups attended a training session on how to access the podcasts and download and use their app, and also had their baseline height and weight taken by study staff. Both groups received 2 podcasts per week, tracked their diet, weight, and physical activity, completed weekly surveys to report use of their assigned tracking app, and had their weight measures taken after 3 months. Objective measures of podcast usage and app usage were collected by study staff (experimental group only).
Both apps had diet and physical activity tracking features, but the Social POD app also included notifications to track diet and physical activity, messages sent from frequent app users to re-engage infrequent app users, a newsfeed to view other participants app tracking activity, stars awarded to frequent users of the app, points awarded for tracking, and prizes for earning points distributed at the final session by study staff. The Fat Secret app did not have any social support components but included a recipe database for looking up recipes by category.
Main outcome measures. The primary outcome was between-group differences in kilograms lost at 3 months. Secondary outcomes included group change in BMI after 3 months, as well as group differences in self-reported caloric intake, caloric expenditure, social support, self-efficacy, and outcome expectations scores, controlled for baseline measures.
Main results. Of the potential participants that inquired about the study (n = 189), those found to be eligible (n = 78) were invited to an orientation. Of those that attended the orientation (n = 62), 51 were randomized after completing baseline assessments (n = 25 to experimental group with Social POD app, n = 25 to comparison group with Fat Secret app), and 42 completed final weight measurements after 3 months. Participants were mostly white (57%) females (82%) with a mean BMI of 34.7 ± 6.0 kg/m2 and mean age of 46.2 ± 12.4 years. Baseline characteristics were similar between groups except that more comparison group participants reported previously downloading an app to track their diet than experimental participants. Participation attrition was 12% (n = 3 in each group).
Experimental group participants lost significantly more weight (–5.3 kg [95% CI, –7.5 to –3.0]) than the comparison group (–2.23 kg [95% CI, –3.6 to 1.0; P = 0.02). Experimental group participants also had a greater reduction in mean BMI (–1.9 kg/m2 [95% CI, –2.6 to –1.2]) vs. the comparison group (mean –0.9 kg/m2 [95% CI, –1.4 to – 0.05], P = 0.02). While there were significant differences in positive outcome expectations between groups (P = 0.04), other secondary outcomes were not significant.
Conclusions. An intervention with theory-based podcasts, social support, and incentivized self-monitoring resulted in significantly greater weight loss than a comparison intervention with theory-based podcasts and a commercially available standard self-monitoring app. This study highlights key features to add to mobile health interventions for adult weight loss.
Commentary
Obesity prevalence rates have increased over the past several decades across all genders, ages, ethnicities, income levels, and education levels [1], and recent data show that over one-third of adults in the US are obese and over two-thirds are overweight [2,3]. Behavior or lifestyle modification, which incorporates (often tailored) diet, physical activity, and behavior therapy, is highly recommended as the first strategy for losing initial weight and sustaining weight management efforts [4,5]. Mobile health (mHealth) technologies and other web-based and technology-assisted approaches (eg, mobile applications or “apps”) to facilitate behavior change for weight loss and management have aimed to address many of the limitations posed by traditional face-to-face weight loss approaches [6–8]. Prevailing theories of health behavior change imply key intervention design features that may increase their likelihood of promoting and sustaining desired behavior changes, particularly those that impact self-efficacy, self-regulation, and social facilitation [9,10].
Despite the plethora of weight loss mobile apps available to the public, it remains unclear if these are guided by evidence-based behavior change strategies typically used in traditional programs and approaches [11,12]. Further, very few of these apps have been rigorously evaluated with scientific testing to determine true effectiveness and safety [13,14]. This study adds to the literature by evaluating a mobile app for weight loss (Social POD) that was developed by researchers and utilizes theory-based components to target specific constructs that lead to health behavior change. Additionally, while self-monitoring is commonly incorporated into most available weight loss/management apps [11], the Social POD mobile app also incorporates social support and motivational strategies, which are less often included. The findings from this study add to the limited literature that mobile phone app-based interventions may be useful tools for weight loss [13].
The authors outlined several strengths and limitations. Briefly, this study was particularly strengthened by its randomized assignment to equivalent intervention groups, the use of a researcher-developed experimental group app that targets several key theory-based constructs for behavior change, measurement of objective use of the intervention group app, a racially diverse sample (over one-third of participants in both groups identified as black), measurement of secondary psycho-social behavioral outcomes, significant efforts to ensure survey completion and compliance with the intervention (increase retention), as well efforts to decrease participation burden by limiting required in-person sessions.
However, several important aspects of the study limit the internal validity and generalizability of its findings. The study had a small sample size and included a highly educated study population. If possible, future studies should consider including a large, diverse population to enhance generalizability. Also, this study was limited to those with an Android device, and significant demographic differences between Android and iPhone users have been reported [15]. The comparison group reported significantly more prior downloading of a diet-tracking app compared to the experimental group, which may have impacted use of the comparison app. The extrinsic reward system built into the experimental group intervention could have impacted adherence to experimental app, and is likely not feasible in real-world application of the experimental group app. Findings may have been subject to recall bias and measurement error due to self-reporting of outcomes measures. Importantly, this was a short-term weight loss study, and long-term weight loss/maintenance data is needed to support findings since in the usual course of weight-loss therapy the greatest weight loss occurs within 6 months of treatment, after which weight is often regained, sometimes near original level [16].
Applications for Clinical Practice
With the increasing popularity of technology-assisted and mHealth applications for weight loss and other health behaviors, it is important for practitioners to be familiar with proven, theory-based approaches and advise patients accordingly. This study demonstrated that social support components added to self-monitoring components in a weight loss app can lead to significant weight loss compared to self-monitoring alone. Thus, those that offer obesity counseling should be mindful that tracking and controlling dietary and physical activity behaviors alone may not prove to be successful. Opportunities for social facilitation to support weight loss efforts should be discussed with patients, including sources of social influence, support and collaboration between individuals, families, and health care professionals.
—Katrina F. Mateo, MPH
1. Mitchell NS, Catenacci VA, Wyatt HR, Hill JO. Obesity: overview of an epidemic. Psychiatr Clin North Am 2011;34:717–32.
2. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008. JAMA 2010;303:235–41.
3. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014;311:806–14.
4. Wadden TA, Butryn ML, Wilson C. Lifestyle modification for the management of obesity. Gastroenterology 2007;132:2226–38.
5. Butryn ML, Webb V, Wadden TA. Behavioral treatment of obesity. Psychiatr Clin North Am 2011;34:841–59.
6. Okorodudu DE, Bosworth HB, Corsino L. Innovative interventions to promote behavioral change in overweight or obese individuals: a review of the literature. Ann Med 2015;47:179–85.
7. Taylor RW, Roy M, Jospe MR, et al. Determining how best to support overweight adults to adhere to lifestyle change: protocol for the SWIFT study. BMC Public Health 2015;15:861.
8. Laing BY, Mangione CM, Tseng C-H, et al. Effectiveness of a smartphone application for weight loss compared with usual care in overweight primary care patients: a randomized, controlled trial. Ann Intern Med 2014;161(10 Suppl):
S5–S12.
9. Teixeira PJ, Carraça E V, Marques MM, et al. Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Med 2015;13:84.
10. Ryan P. Integrated theory of health behavior change: background and intervention development. Clin Nurse Spec 2009;23:161–70.
11. Rivera J, McPherson A, Hamilton J, et al. Mobile apps for weight management: a scoping review. JMIR mHealth uHealth 2016;4:e87.
12. Pellegrini CA, Pfammatter AF, Conroy DE, Spring B. Smartphone applications to support weight loss: current perspectives. Adv Health Care Technol 2015;1:13–22.
13. Flores Mateo G, Granado-Font E, Ferré-Grau C, Montaña-Carreras X. Mobile phone apps to promote weight loss and increase physical activity: a systematic review and meta-analysis. J Med Internet Res 2015;17:e253.
14. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J Cardiovasc Nurs 28:320–9.
15. Smith A. Smartphone ownership 2013. Pew Research Center.
16. Jeffery RW, Drewnowski A, Epstein LH, et al. Long-term maintenance of weight loss: current status. Health Psychol 2000;19(1 Suppl):5–16.
Study Overview
Objective. To test the efficacy of a weight loss app with incorporated social support and self-monitoring of diet, physical activity, and weight compared to a commercially available diet and PA tracking app.
Design. 2-group, randomized controlled trial.
Setting and participants. From October 2014 to January 2015, potential study participants were recruited via university/worksite listserv announcements, flyers, electronic newsletters, newspaper advertisements, social media posts, and a local research fair in 2 cities in South Carolina. Exclusion criteria included body mass index (BMI) outside the range of 25.0 to 49.9 kg/m2, unable to attend required measurement sessions, unable to access a computer or internet for completing assessments, having a psychiatric illness, receiving treatment for drug or alcohol dependency, having an eating disorder, participating in another weight loss program, reporting weight loss of 10+ pounds in the past 6 months, being pregnant or planning on becoming pregnant during study, or breastfeeding, or endorsing items on the Physical Activity Readiness Questionnaire (PAR-Q) regarding having a heart condition, feeling chest pain during physical activity, experiencing chest pain, becoming dizzy/ever losing balance or consciousness, and not having a physician give consent to participate despite reporting joint problems or taking blood pressure medication. Those who were eligible were invited to an orientation to the study, signed consent, and completed baseline assessments.
Intervention. Participants were randomized to either the experimental group (theory-based podcasts plus the Social POD app) or the comparison group (theory-based podcasts plus a standard app [“Fat Secret” app]). Both groups attended a training session on how to access the podcasts and download and use their app, and also had their baseline height and weight taken by study staff. Both groups received 2 podcasts per week, tracked their diet, weight, and physical activity, completed weekly surveys to report use of their assigned tracking app, and had their weight measures taken after 3 months. Objective measures of podcast usage and app usage were collected by study staff (experimental group only).
Both apps had diet and physical activity tracking features, but the Social POD app also included notifications to track diet and physical activity, messages sent from frequent app users to re-engage infrequent app users, a newsfeed to view other participants app tracking activity, stars awarded to frequent users of the app, points awarded for tracking, and prizes for earning points distributed at the final session by study staff. The Fat Secret app did not have any social support components but included a recipe database for looking up recipes by category.
Main outcome measures. The primary outcome was between-group differences in kilograms lost at 3 months. Secondary outcomes included group change in BMI after 3 months, as well as group differences in self-reported caloric intake, caloric expenditure, social support, self-efficacy, and outcome expectations scores, controlled for baseline measures.
Main results. Of the potential participants that inquired about the study (n = 189), those found to be eligible (n = 78) were invited to an orientation. Of those that attended the orientation (n = 62), 51 were randomized after completing baseline assessments (n = 25 to experimental group with Social POD app, n = 25 to comparison group with Fat Secret app), and 42 completed final weight measurements after 3 months. Participants were mostly white (57%) females (82%) with a mean BMI of 34.7 ± 6.0 kg/m2 and mean age of 46.2 ± 12.4 years. Baseline characteristics were similar between groups except that more comparison group participants reported previously downloading an app to track their diet than experimental participants. Participation attrition was 12% (n = 3 in each group).
Experimental group participants lost significantly more weight (–5.3 kg [95% CI, –7.5 to –3.0]) than the comparison group (–2.23 kg [95% CI, –3.6 to 1.0; P = 0.02). Experimental group participants also had a greater reduction in mean BMI (–1.9 kg/m2 [95% CI, –2.6 to –1.2]) vs. the comparison group (mean –0.9 kg/m2 [95% CI, –1.4 to – 0.05], P = 0.02). While there were significant differences in positive outcome expectations between groups (P = 0.04), other secondary outcomes were not significant.
Conclusions. An intervention with theory-based podcasts, social support, and incentivized self-monitoring resulted in significantly greater weight loss than a comparison intervention with theory-based podcasts and a commercially available standard self-monitoring app. This study highlights key features to add to mobile health interventions for adult weight loss.
Commentary
Obesity prevalence rates have increased over the past several decades across all genders, ages, ethnicities, income levels, and education levels [1], and recent data show that over one-third of adults in the US are obese and over two-thirds are overweight [2,3]. Behavior or lifestyle modification, which incorporates (often tailored) diet, physical activity, and behavior therapy, is highly recommended as the first strategy for losing initial weight and sustaining weight management efforts [4,5]. Mobile health (mHealth) technologies and other web-based and technology-assisted approaches (eg, mobile applications or “apps”) to facilitate behavior change for weight loss and management have aimed to address many of the limitations posed by traditional face-to-face weight loss approaches [6–8]. Prevailing theories of health behavior change imply key intervention design features that may increase their likelihood of promoting and sustaining desired behavior changes, particularly those that impact self-efficacy, self-regulation, and social facilitation [9,10].
Despite the plethora of weight loss mobile apps available to the public, it remains unclear if these are guided by evidence-based behavior change strategies typically used in traditional programs and approaches [11,12]. Further, very few of these apps have been rigorously evaluated with scientific testing to determine true effectiveness and safety [13,14]. This study adds to the literature by evaluating a mobile app for weight loss (Social POD) that was developed by researchers and utilizes theory-based components to target specific constructs that lead to health behavior change. Additionally, while self-monitoring is commonly incorporated into most available weight loss/management apps [11], the Social POD mobile app also incorporates social support and motivational strategies, which are less often included. The findings from this study add to the limited literature that mobile phone app-based interventions may be useful tools for weight loss [13].
The authors outlined several strengths and limitations. Briefly, this study was particularly strengthened by its randomized assignment to equivalent intervention groups, the use of a researcher-developed experimental group app that targets several key theory-based constructs for behavior change, measurement of objective use of the intervention group app, a racially diverse sample (over one-third of participants in both groups identified as black), measurement of secondary psycho-social behavioral outcomes, significant efforts to ensure survey completion and compliance with the intervention (increase retention), as well efforts to decrease participation burden by limiting required in-person sessions.
However, several important aspects of the study limit the internal validity and generalizability of its findings. The study had a small sample size and included a highly educated study population. If possible, future studies should consider including a large, diverse population to enhance generalizability. Also, this study was limited to those with an Android device, and significant demographic differences between Android and iPhone users have been reported [15]. The comparison group reported significantly more prior downloading of a diet-tracking app compared to the experimental group, which may have impacted use of the comparison app. The extrinsic reward system built into the experimental group intervention could have impacted adherence to experimental app, and is likely not feasible in real-world application of the experimental group app. Findings may have been subject to recall bias and measurement error due to self-reporting of outcomes measures. Importantly, this was a short-term weight loss study, and long-term weight loss/maintenance data is needed to support findings since in the usual course of weight-loss therapy the greatest weight loss occurs within 6 months of treatment, after which weight is often regained, sometimes near original level [16].
Applications for Clinical Practice
With the increasing popularity of technology-assisted and mHealth applications for weight loss and other health behaviors, it is important for practitioners to be familiar with proven, theory-based approaches and advise patients accordingly. This study demonstrated that social support components added to self-monitoring components in a weight loss app can lead to significant weight loss compared to self-monitoring alone. Thus, those that offer obesity counseling should be mindful that tracking and controlling dietary and physical activity behaviors alone may not prove to be successful. Opportunities for social facilitation to support weight loss efforts should be discussed with patients, including sources of social influence, support and collaboration between individuals, families, and health care professionals.
—Katrina F. Mateo, MPH
Study Overview
Objective. To test the efficacy of a weight loss app with incorporated social support and self-monitoring of diet, physical activity, and weight compared to a commercially available diet and PA tracking app.
Design. 2-group, randomized controlled trial.
Setting and participants. From October 2014 to January 2015, potential study participants were recruited via university/worksite listserv announcements, flyers, electronic newsletters, newspaper advertisements, social media posts, and a local research fair in 2 cities in South Carolina. Exclusion criteria included body mass index (BMI) outside the range of 25.0 to 49.9 kg/m2, unable to attend required measurement sessions, unable to access a computer or internet for completing assessments, having a psychiatric illness, receiving treatment for drug or alcohol dependency, having an eating disorder, participating in another weight loss program, reporting weight loss of 10+ pounds in the past 6 months, being pregnant or planning on becoming pregnant during study, or breastfeeding, or endorsing items on the Physical Activity Readiness Questionnaire (PAR-Q) regarding having a heart condition, feeling chest pain during physical activity, experiencing chest pain, becoming dizzy/ever losing balance or consciousness, and not having a physician give consent to participate despite reporting joint problems or taking blood pressure medication. Those who were eligible were invited to an orientation to the study, signed consent, and completed baseline assessments.
Intervention. Participants were randomized to either the experimental group (theory-based podcasts plus the Social POD app) or the comparison group (theory-based podcasts plus a standard app [“Fat Secret” app]). Both groups attended a training session on how to access the podcasts and download and use their app, and also had their baseline height and weight taken by study staff. Both groups received 2 podcasts per week, tracked their diet, weight, and physical activity, completed weekly surveys to report use of their assigned tracking app, and had their weight measures taken after 3 months. Objective measures of podcast usage and app usage were collected by study staff (experimental group only).
Both apps had diet and physical activity tracking features, but the Social POD app also included notifications to track diet and physical activity, messages sent from frequent app users to re-engage infrequent app users, a newsfeed to view other participants app tracking activity, stars awarded to frequent users of the app, points awarded for tracking, and prizes for earning points distributed at the final session by study staff. The Fat Secret app did not have any social support components but included a recipe database for looking up recipes by category.
Main outcome measures. The primary outcome was between-group differences in kilograms lost at 3 months. Secondary outcomes included group change in BMI after 3 months, as well as group differences in self-reported caloric intake, caloric expenditure, social support, self-efficacy, and outcome expectations scores, controlled for baseline measures.
Main results. Of the potential participants that inquired about the study (n = 189), those found to be eligible (n = 78) were invited to an orientation. Of those that attended the orientation (n = 62), 51 were randomized after completing baseline assessments (n = 25 to experimental group with Social POD app, n = 25 to comparison group with Fat Secret app), and 42 completed final weight measurements after 3 months. Participants were mostly white (57%) females (82%) with a mean BMI of 34.7 ± 6.0 kg/m2 and mean age of 46.2 ± 12.4 years. Baseline characteristics were similar between groups except that more comparison group participants reported previously downloading an app to track their diet than experimental participants. Participation attrition was 12% (n = 3 in each group).
Experimental group participants lost significantly more weight (–5.3 kg [95% CI, –7.5 to –3.0]) than the comparison group (–2.23 kg [95% CI, –3.6 to 1.0; P = 0.02). Experimental group participants also had a greater reduction in mean BMI (–1.9 kg/m2 [95% CI, –2.6 to –1.2]) vs. the comparison group (mean –0.9 kg/m2 [95% CI, –1.4 to – 0.05], P = 0.02). While there were significant differences in positive outcome expectations between groups (P = 0.04), other secondary outcomes were not significant.
Conclusions. An intervention with theory-based podcasts, social support, and incentivized self-monitoring resulted in significantly greater weight loss than a comparison intervention with theory-based podcasts and a commercially available standard self-monitoring app. This study highlights key features to add to mobile health interventions for adult weight loss.
Commentary
Obesity prevalence rates have increased over the past several decades across all genders, ages, ethnicities, income levels, and education levels [1], and recent data show that over one-third of adults in the US are obese and over two-thirds are overweight [2,3]. Behavior or lifestyle modification, which incorporates (often tailored) diet, physical activity, and behavior therapy, is highly recommended as the first strategy for losing initial weight and sustaining weight management efforts [4,5]. Mobile health (mHealth) technologies and other web-based and technology-assisted approaches (eg, mobile applications or “apps”) to facilitate behavior change for weight loss and management have aimed to address many of the limitations posed by traditional face-to-face weight loss approaches [6–8]. Prevailing theories of health behavior change imply key intervention design features that may increase their likelihood of promoting and sustaining desired behavior changes, particularly those that impact self-efficacy, self-regulation, and social facilitation [9,10].
Despite the plethora of weight loss mobile apps available to the public, it remains unclear if these are guided by evidence-based behavior change strategies typically used in traditional programs and approaches [11,12]. Further, very few of these apps have been rigorously evaluated with scientific testing to determine true effectiveness and safety [13,14]. This study adds to the literature by evaluating a mobile app for weight loss (Social POD) that was developed by researchers and utilizes theory-based components to target specific constructs that lead to health behavior change. Additionally, while self-monitoring is commonly incorporated into most available weight loss/management apps [11], the Social POD mobile app also incorporates social support and motivational strategies, which are less often included. The findings from this study add to the limited literature that mobile phone app-based interventions may be useful tools for weight loss [13].
The authors outlined several strengths and limitations. Briefly, this study was particularly strengthened by its randomized assignment to equivalent intervention groups, the use of a researcher-developed experimental group app that targets several key theory-based constructs for behavior change, measurement of objective use of the intervention group app, a racially diverse sample (over one-third of participants in both groups identified as black), measurement of secondary psycho-social behavioral outcomes, significant efforts to ensure survey completion and compliance with the intervention (increase retention), as well efforts to decrease participation burden by limiting required in-person sessions.
However, several important aspects of the study limit the internal validity and generalizability of its findings. The study had a small sample size and included a highly educated study population. If possible, future studies should consider including a large, diverse population to enhance generalizability. Also, this study was limited to those with an Android device, and significant demographic differences between Android and iPhone users have been reported [15]. The comparison group reported significantly more prior downloading of a diet-tracking app compared to the experimental group, which may have impacted use of the comparison app. The extrinsic reward system built into the experimental group intervention could have impacted adherence to experimental app, and is likely not feasible in real-world application of the experimental group app. Findings may have been subject to recall bias and measurement error due to self-reporting of outcomes measures. Importantly, this was a short-term weight loss study, and long-term weight loss/maintenance data is needed to support findings since in the usual course of weight-loss therapy the greatest weight loss occurs within 6 months of treatment, after which weight is often regained, sometimes near original level [16].
Applications for Clinical Practice
With the increasing popularity of technology-assisted and mHealth applications for weight loss and other health behaviors, it is important for practitioners to be familiar with proven, theory-based approaches and advise patients accordingly. This study demonstrated that social support components added to self-monitoring components in a weight loss app can lead to significant weight loss compared to self-monitoring alone. Thus, those that offer obesity counseling should be mindful that tracking and controlling dietary and physical activity behaviors alone may not prove to be successful. Opportunities for social facilitation to support weight loss efforts should be discussed with patients, including sources of social influence, support and collaboration between individuals, families, and health care professionals.
—Katrina F. Mateo, MPH
1. Mitchell NS, Catenacci VA, Wyatt HR, Hill JO. Obesity: overview of an epidemic. Psychiatr Clin North Am 2011;34:717–32.
2. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008. JAMA 2010;303:235–41.
3. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014;311:806–14.
4. Wadden TA, Butryn ML, Wilson C. Lifestyle modification for the management of obesity. Gastroenterology 2007;132:2226–38.
5. Butryn ML, Webb V, Wadden TA. Behavioral treatment of obesity. Psychiatr Clin North Am 2011;34:841–59.
6. Okorodudu DE, Bosworth HB, Corsino L. Innovative interventions to promote behavioral change in overweight or obese individuals: a review of the literature. Ann Med 2015;47:179–85.
7. Taylor RW, Roy M, Jospe MR, et al. Determining how best to support overweight adults to adhere to lifestyle change: protocol for the SWIFT study. BMC Public Health 2015;15:861.
8. Laing BY, Mangione CM, Tseng C-H, et al. Effectiveness of a smartphone application for weight loss compared with usual care in overweight primary care patients: a randomized, controlled trial. Ann Intern Med 2014;161(10 Suppl):
S5–S12.
9. Teixeira PJ, Carraça E V, Marques MM, et al. Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Med 2015;13:84.
10. Ryan P. Integrated theory of health behavior change: background and intervention development. Clin Nurse Spec 2009;23:161–70.
11. Rivera J, McPherson A, Hamilton J, et al. Mobile apps for weight management: a scoping review. JMIR mHealth uHealth 2016;4:e87.
12. Pellegrini CA, Pfammatter AF, Conroy DE, Spring B. Smartphone applications to support weight loss: current perspectives. Adv Health Care Technol 2015;1:13–22.
13. Flores Mateo G, Granado-Font E, Ferré-Grau C, Montaña-Carreras X. Mobile phone apps to promote weight loss and increase physical activity: a systematic review and meta-analysis. J Med Internet Res 2015;17:e253.
14. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J Cardiovasc Nurs 28:320–9.
15. Smith A. Smartphone ownership 2013. Pew Research Center.
16. Jeffery RW, Drewnowski A, Epstein LH, et al. Long-term maintenance of weight loss: current status. Health Psychol 2000;19(1 Suppl):5–16.
1. Mitchell NS, Catenacci VA, Wyatt HR, Hill JO. Obesity: overview of an epidemic. Psychiatr Clin North Am 2011;34:717–32.
2. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008. JAMA 2010;303:235–41.
3. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014;311:806–14.
4. Wadden TA, Butryn ML, Wilson C. Lifestyle modification for the management of obesity. Gastroenterology 2007;132:2226–38.
5. Butryn ML, Webb V, Wadden TA. Behavioral treatment of obesity. Psychiatr Clin North Am 2011;34:841–59.
6. Okorodudu DE, Bosworth HB, Corsino L. Innovative interventions to promote behavioral change in overweight or obese individuals: a review of the literature. Ann Med 2015;47:179–85.
7. Taylor RW, Roy M, Jospe MR, et al. Determining how best to support overweight adults to adhere to lifestyle change: protocol for the SWIFT study. BMC Public Health 2015;15:861.
8. Laing BY, Mangione CM, Tseng C-H, et al. Effectiveness of a smartphone application for weight loss compared with usual care in overweight primary care patients: a randomized, controlled trial. Ann Intern Med 2014;161(10 Suppl):
S5–S12.
9. Teixeira PJ, Carraça E V, Marques MM, et al. Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Med 2015;13:84.
10. Ryan P. Integrated theory of health behavior change: background and intervention development. Clin Nurse Spec 2009;23:161–70.
11. Rivera J, McPherson A, Hamilton J, et al. Mobile apps for weight management: a scoping review. JMIR mHealth uHealth 2016;4:e87.
12. Pellegrini CA, Pfammatter AF, Conroy DE, Spring B. Smartphone applications to support weight loss: current perspectives. Adv Health Care Technol 2015;1:13–22.
13. Flores Mateo G, Granado-Font E, Ferré-Grau C, Montaña-Carreras X. Mobile phone apps to promote weight loss and increase physical activity: a systematic review and meta-analysis. J Med Internet Res 2015;17:e253.
14. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J Cardiovasc Nurs 28:320–9.
15. Smith A. Smartphone ownership 2013. Pew Research Center.
16. Jeffery RW, Drewnowski A, Epstein LH, et al. Long-term maintenance of weight loss: current status. Health Psychol 2000;19(1 Suppl):5–16.
Can Mindfulness Components Added To A Diet-Exercise Program Improve Weight Loss Outcomes?
Study Overview
Objective. To determine whether weight loss and cardiometabolic risk factors are improved when mindfulness training is added to a diet-exercise program.
Design. 2-arm randomized controlled trial.
Setting and participants. Study participants were recruited through fliers, newspaper advertisements, online postings, and referrals at University of California, San Francisco clinics, and were enrolled from July 2009 to February 2012. Inclusion criteria were body mass index (BMI) between 30 and 45.9, abdominal obesity (waist circumference > 102 cm for men and > 88 cm for women), and age 18 or older. Exclusion criteria were current involvement with diet program or diet mediation, diabetes mellitus, fasting glucose ≥ 126 mg/dL, or hemoglobin A1c (HbA1C) between 6.0% and 6.5% with abnormal oral glucose tolerance test. Participants were randomized in a 1:1 ratio to one of 2 weight loss program arms using a computer-generated randomization sequence.
Intervention. In both arms, participants received general diet and exercise guidelines prescribing healthy eating and frequent exercise delivered in 16 sessions lasting 2 to 2½ hours and one all-day session over 5.5 months. Participants in the mindfulness intervention additionally received mindfulness training for eating, physical activity, and stress management from mindfulness mediation instructors and a registered dietician. They also followed guidelines at home, which included practicing meditation for up to 30 minutes 6 days a week, mini-meditations, and eating mindfully. To control for the activities and attention inherent in the mindfulness arm (eg, social support, expectation of benefit, snacks provided during mindful eating exercises, at home practice), the control arm was an “active control” and included additional nutritional and physical activity information, snacks, strength training, home activities, conversations about society and weight loss, and low-dose progressive muscle relaxation and cognitive-behavioral training. Active control materials were delivered by one of 3 registered dieticians.
Main outcomes measures. The primary outcome was 18-month weight change. Participants’ weight, height, blood pressure, and weight circumference were measured at baseline and 3, 6, 12, and 18 months between 8 am and 10 am. Measurements were taken under fasting conditions and by arm-blinded staff. Blood samples were taken to assess secondary outcome changes in glucose, lipid, HbA1C, insulin, and C-reactive protein. Researchers also collected anonymous qualitative feedback from participants and supervisors to do a secondary analysis assessing differences in effectiveness and helpfulness of mindfulness teachings among instructors.
Main results. Of the potential participants that contacted the study team in response to recruitment efforts (n = 1485), 216 were fully eligible based on criteria and a screening visit. Participants that consented to participate (n = 194) were randomized. Participants across both groups were predominantly female, of European ethnic origin, and similar in age: mindfulness group, 47.2 years (13.0) and active control group, 47.8 years (12.4). At baseline, the mindfulness and active control arms had average BMIs of 35.4 (3.5) and 35.6 (3.8), respectively. Baseline characteristics, session attendance, and 18-month retention were similar for both arms. Participants in the mindfulness arm reported completing 70% (2.1 hours per week, SD = 1.2) of the recommended meditation time and eating 57% (SD = 29) of meals mindfully.
Weight loss outcomes between groups favored the mindfulness arms, but results were not significant. The largest difference of –1.9 kg (95% CI –4.5 to 0.8; P = 0.17) was at 12 months. The difference persisted at 18 months with –1.7 kg (95% CI –4.7 to 1.2 kg; P = 0.24). The mindfulness arm lost 4.2 kg (95% CI –6.2 to 2.2 kg) while the active control arm lost 2.4 kg (95% CI –4.5 to –0.3 kg).
Cardiometabolic outcomes at 12 months showed group differences in fasting glucose that favored the mindfulness arm, –3.1 mg/dL (95% CI 26.3 to 0.1; P = 0.06), while there was a significant group difference at 18 months, –4.1 mg/dL (95% CI –7.3 to –0.9; P = 0.01). Data at 18-months showed that normal glucose changed minimally in the mindfulness arm, –0.31 mg/dL (95% CI –2.5 to 1.9), but increased in the active control arm 3.8 mg/dL (95% CI 1.5 to 6.1). Other cardiometabolic outcomes (ie, triglyceride/HDL ratio and triglycerides) showed significance at 12 months, favoring the mindfulness arm, but not at 18 months. Although not all were statistically significant, 9 of 11 outcomes favored the mindfulness arm at 18 months.
Significant interactions (P < 0.05) were found between arm and enrollment rounds categorized by mindfulness instructor on weight, BMI, fasting glucose, homeostatic homeostasis model assessment of insulin resistance (defined as [glucose x insulin/{40 × 33.25}]), and HbA1c, with a marginally significant effect for waist circumference (P = 0.08). Qualitative feedback on mindfulness instructors showed that in the group with a lowly rated instructor, participants lost less weight at 18 months (–2.0 kg [95% CI –4.7 to 0.7]), compared to participants in groups with highly rated instructors (–6.3 kg [95% CI –9.1 to –3.6]; P = 0.02). Similar trends followed for reductions in BMI and waist circumference.
Conclusions. With regard to weight loss outcomes, a mindfulness-enhanced diet-exercise program and an active control arm did not show substantial differences. Some evidence, however, suggests modest benefit of added mindfulness components, which may lead to long-term maintenance of fasting glucose levels and improved atherogenic lipid profiles.
Commentary
Mindfulness, or nonjudgmental focus on the present moment, has been utilized by many interventions targeted at self-regulated behavior [1]. Mindfulness interventions aim to promote healthy behavior changes by encouraging careful monitoring of behavior reactivity. Weight loss and weight loss maintenance have been of particular interest with this approach because mindfulness-based interventions may promote long-term maintenance of weight loss [2]. This maintenance is achieved through a focus on modifying health behaviors, rather than a focus on weight loss alone [3]. Mindfulness has been incorporated into weight loss interventions through yoga practices [4] and mindfulness meditation [5].
Several studies have explored the relationship between mindfulness and weight loss in various populations, highlighting mindfulness’s role in weight loss and behavior change. Most notably, mindfulness interventions have shown improvements in fasting glucose levels [6], psychological distress [7], self-efficacy, weight loss, eating behaviors, and physical activity [8–10]. Despite being well designed, this study by Daubenmier et al did not find significant changes in weight loss. However, secondary outcomes related to weight, metabolic, syndrome, and cardiovascular risk showed modest improvements with mindfulness. These results may correlate to previous findings showing that lifestyle changes many not result in weight loss but can reverse or reduce disorders related to obesity [11].
The study was strengthened by randomization, intention-to-treat analysis, objective measures by arm-blinded staff, standardized measuring conditions, balanced participant allocation to each arm, 1-year follow-up, and qualitative feedback on instructors to assess whether weight loss may be instructor-dependent. In addition, the authors made an effort to mask their intention to test the effects of a mindfulness-enhanced intervention. They designed a rigorous active control intervention arm by controlling for attention, social support, expectations of benefit, diet-exercise guidelines, and elements of a mindfulness approach to stress management. An additional strength included a cost-analysis of adding mindfulness components. The generalizability of the results may be limited as the study population were predominantly white females and most had a bachelor’s degree. The study sample was also disproportionately menopausal women, a group that especially struggles with weight loss. This demographic factor may be responsible for the lack of significant weight loss. Other limitations of this study include participant dropout and variability between instructor styles, although the latter was explored in a secondary analysis of weight loss differences between instructors.
The researchers discussed how the active control intervention arm may have contributed to the lack of significant weight loss difference between groups. The researchers also highlighted that participants randomized to the mindfulness arm that were not interested in mindfulness practices may have benefitted less than those who were interested. This combined with the modest diet and exercise components of the intervention may also explain the lack of significance in results. It may also explain why some outcomes were significant at earlier months but attenuated by 18 months. Future studies should assess incorporating more intense exercise and diet approaches, as well as continuous contact throughout the 18-month time period.
Applications for Clinical Practice
This study demonstrated that mindfulness components added to a diet-exercise program can be helpful in promoting metabolic changes but not necessarily weight loss. Since metabolic changes can be protective against morbidities (eg, type 2 diabetes), mindfulness can be a powerful and cost-effective approach within clinical practice. Mindfulness practices can also be easily implemented in various settings and with diverse populations. Future studies should explore adding mindfulness components to more intensive weight loss interventions. Providers and health care settings should consider incorporating mindfulness practices into weight management counseling and programs.
—Michelle J. Williamson and Katrina F. Mateo, MPH
1. Caldwell KL, Baime MJ, Wolever RQ. Mindfulness based approaches to obesity and weight loss maintenance. J Ment Health Couns 2012;34:26982.
2. O’Reilly GA, Cook L, Spruijt-Metz D, Black DS. Mindfulness-based interventions for obesity-related eating behaviours: A literature review. Obes Rev 2014;15:453–61.
3. Robison J. Health at every size: Toward a new paradigm of weight and health. MedGenMed 2005;7:13.
4. Godsey J. The role of mindfulness based interventions in the treatment of obesity and eating disorders: An integrative review. Complement Ther Med 2013;21:430–9.
5. Keune PM, Forintos DP. Mindfulness meditation: A preliminary study on meditation practice during everyday life activities and its association with well-being. Psychol Top 2010;19:373–86.
6. Mason AE, Epel ES, Kristeller J, et al. Effects of a mindfulness-based intervention on mindful eating, sweets consumption, and fasting glucose levels in obese adults: data from the SHINE randomized controlled trial. J Behav Med 2016;39:201–13.
7. Dalen J, Smith BW, Shelley BM, et al. Pilot study: Mindful Eating and Living (MEAL): Weight, eating behavior, and psychological outcomes associated with a mindfulness-based intervention for people with obesity. Complement Ther Med 2010;18:260–64.
8. Kristeller JL, Wolever RQ, Sheets V. Mindfulness-based eating awareness training (MB-EAT) for binge eating: A randomized clinical trial. Mindfulness 2013;5:282–97.
9. Miller C, Kristeller JL, Headings A, Nagaraja H. Comparison of a mindful eating intervention to a diabetes self- management intervention among adults with type 2 diabetes: a randomized controlled trial. Health Educ Behav 2013;41:145–54.
10. Timmerman GM, Brown A. The effect of a mindful restaurant eating intervention on weight management in women. J Nutr Educ Behav 2012;44:22–8.
11. Bacon L, Stern JS, Van Loan MD, Keim NL. Size acceptance and intuitive eating improve health for obese, female chronic dieters. J Am Diet Assoc 2005;105:929–36.
Study Overview
Objective. To determine whether weight loss and cardiometabolic risk factors are improved when mindfulness training is added to a diet-exercise program.
Design. 2-arm randomized controlled trial.
Setting and participants. Study participants were recruited through fliers, newspaper advertisements, online postings, and referrals at University of California, San Francisco clinics, and were enrolled from July 2009 to February 2012. Inclusion criteria were body mass index (BMI) between 30 and 45.9, abdominal obesity (waist circumference > 102 cm for men and > 88 cm for women), and age 18 or older. Exclusion criteria were current involvement with diet program or diet mediation, diabetes mellitus, fasting glucose ≥ 126 mg/dL, or hemoglobin A1c (HbA1C) between 6.0% and 6.5% with abnormal oral glucose tolerance test. Participants were randomized in a 1:1 ratio to one of 2 weight loss program arms using a computer-generated randomization sequence.
Intervention. In both arms, participants received general diet and exercise guidelines prescribing healthy eating and frequent exercise delivered in 16 sessions lasting 2 to 2½ hours and one all-day session over 5.5 months. Participants in the mindfulness intervention additionally received mindfulness training for eating, physical activity, and stress management from mindfulness mediation instructors and a registered dietician. They also followed guidelines at home, which included practicing meditation for up to 30 minutes 6 days a week, mini-meditations, and eating mindfully. To control for the activities and attention inherent in the mindfulness arm (eg, social support, expectation of benefit, snacks provided during mindful eating exercises, at home practice), the control arm was an “active control” and included additional nutritional and physical activity information, snacks, strength training, home activities, conversations about society and weight loss, and low-dose progressive muscle relaxation and cognitive-behavioral training. Active control materials were delivered by one of 3 registered dieticians.
Main outcomes measures. The primary outcome was 18-month weight change. Participants’ weight, height, blood pressure, and weight circumference were measured at baseline and 3, 6, 12, and 18 months between 8 am and 10 am. Measurements were taken under fasting conditions and by arm-blinded staff. Blood samples were taken to assess secondary outcome changes in glucose, lipid, HbA1C, insulin, and C-reactive protein. Researchers also collected anonymous qualitative feedback from participants and supervisors to do a secondary analysis assessing differences in effectiveness and helpfulness of mindfulness teachings among instructors.
Main results. Of the potential participants that contacted the study team in response to recruitment efforts (n = 1485), 216 were fully eligible based on criteria and a screening visit. Participants that consented to participate (n = 194) were randomized. Participants across both groups were predominantly female, of European ethnic origin, and similar in age: mindfulness group, 47.2 years (13.0) and active control group, 47.8 years (12.4). At baseline, the mindfulness and active control arms had average BMIs of 35.4 (3.5) and 35.6 (3.8), respectively. Baseline characteristics, session attendance, and 18-month retention were similar for both arms. Participants in the mindfulness arm reported completing 70% (2.1 hours per week, SD = 1.2) of the recommended meditation time and eating 57% (SD = 29) of meals mindfully.
Weight loss outcomes between groups favored the mindfulness arms, but results were not significant. The largest difference of –1.9 kg (95% CI –4.5 to 0.8; P = 0.17) was at 12 months. The difference persisted at 18 months with –1.7 kg (95% CI –4.7 to 1.2 kg; P = 0.24). The mindfulness arm lost 4.2 kg (95% CI –6.2 to 2.2 kg) while the active control arm lost 2.4 kg (95% CI –4.5 to –0.3 kg).
Cardiometabolic outcomes at 12 months showed group differences in fasting glucose that favored the mindfulness arm, –3.1 mg/dL (95% CI 26.3 to 0.1; P = 0.06), while there was a significant group difference at 18 months, –4.1 mg/dL (95% CI –7.3 to –0.9; P = 0.01). Data at 18-months showed that normal glucose changed minimally in the mindfulness arm, –0.31 mg/dL (95% CI –2.5 to 1.9), but increased in the active control arm 3.8 mg/dL (95% CI 1.5 to 6.1). Other cardiometabolic outcomes (ie, triglyceride/HDL ratio and triglycerides) showed significance at 12 months, favoring the mindfulness arm, but not at 18 months. Although not all were statistically significant, 9 of 11 outcomes favored the mindfulness arm at 18 months.
Significant interactions (P < 0.05) were found between arm and enrollment rounds categorized by mindfulness instructor on weight, BMI, fasting glucose, homeostatic homeostasis model assessment of insulin resistance (defined as [glucose x insulin/{40 × 33.25}]), and HbA1c, with a marginally significant effect for waist circumference (P = 0.08). Qualitative feedback on mindfulness instructors showed that in the group with a lowly rated instructor, participants lost less weight at 18 months (–2.0 kg [95% CI –4.7 to 0.7]), compared to participants in groups with highly rated instructors (–6.3 kg [95% CI –9.1 to –3.6]; P = 0.02). Similar trends followed for reductions in BMI and waist circumference.
Conclusions. With regard to weight loss outcomes, a mindfulness-enhanced diet-exercise program and an active control arm did not show substantial differences. Some evidence, however, suggests modest benefit of added mindfulness components, which may lead to long-term maintenance of fasting glucose levels and improved atherogenic lipid profiles.
Commentary
Mindfulness, or nonjudgmental focus on the present moment, has been utilized by many interventions targeted at self-regulated behavior [1]. Mindfulness interventions aim to promote healthy behavior changes by encouraging careful monitoring of behavior reactivity. Weight loss and weight loss maintenance have been of particular interest with this approach because mindfulness-based interventions may promote long-term maintenance of weight loss [2]. This maintenance is achieved through a focus on modifying health behaviors, rather than a focus on weight loss alone [3]. Mindfulness has been incorporated into weight loss interventions through yoga practices [4] and mindfulness meditation [5].
Several studies have explored the relationship between mindfulness and weight loss in various populations, highlighting mindfulness’s role in weight loss and behavior change. Most notably, mindfulness interventions have shown improvements in fasting glucose levels [6], psychological distress [7], self-efficacy, weight loss, eating behaviors, and physical activity [8–10]. Despite being well designed, this study by Daubenmier et al did not find significant changes in weight loss. However, secondary outcomes related to weight, metabolic, syndrome, and cardiovascular risk showed modest improvements with mindfulness. These results may correlate to previous findings showing that lifestyle changes many not result in weight loss but can reverse or reduce disorders related to obesity [11].
The study was strengthened by randomization, intention-to-treat analysis, objective measures by arm-blinded staff, standardized measuring conditions, balanced participant allocation to each arm, 1-year follow-up, and qualitative feedback on instructors to assess whether weight loss may be instructor-dependent. In addition, the authors made an effort to mask their intention to test the effects of a mindfulness-enhanced intervention. They designed a rigorous active control intervention arm by controlling for attention, social support, expectations of benefit, diet-exercise guidelines, and elements of a mindfulness approach to stress management. An additional strength included a cost-analysis of adding mindfulness components. The generalizability of the results may be limited as the study population were predominantly white females and most had a bachelor’s degree. The study sample was also disproportionately menopausal women, a group that especially struggles with weight loss. This demographic factor may be responsible for the lack of significant weight loss. Other limitations of this study include participant dropout and variability between instructor styles, although the latter was explored in a secondary analysis of weight loss differences between instructors.
The researchers discussed how the active control intervention arm may have contributed to the lack of significant weight loss difference between groups. The researchers also highlighted that participants randomized to the mindfulness arm that were not interested in mindfulness practices may have benefitted less than those who were interested. This combined with the modest diet and exercise components of the intervention may also explain the lack of significance in results. It may also explain why some outcomes were significant at earlier months but attenuated by 18 months. Future studies should assess incorporating more intense exercise and diet approaches, as well as continuous contact throughout the 18-month time period.
Applications for Clinical Practice
This study demonstrated that mindfulness components added to a diet-exercise program can be helpful in promoting metabolic changes but not necessarily weight loss. Since metabolic changes can be protective against morbidities (eg, type 2 diabetes), mindfulness can be a powerful and cost-effective approach within clinical practice. Mindfulness practices can also be easily implemented in various settings and with diverse populations. Future studies should explore adding mindfulness components to more intensive weight loss interventions. Providers and health care settings should consider incorporating mindfulness practices into weight management counseling and programs.
—Michelle J. Williamson and Katrina F. Mateo, MPH
Study Overview
Objective. To determine whether weight loss and cardiometabolic risk factors are improved when mindfulness training is added to a diet-exercise program.
Design. 2-arm randomized controlled trial.
Setting and participants. Study participants were recruited through fliers, newspaper advertisements, online postings, and referrals at University of California, San Francisco clinics, and were enrolled from July 2009 to February 2012. Inclusion criteria were body mass index (BMI) between 30 and 45.9, abdominal obesity (waist circumference > 102 cm for men and > 88 cm for women), and age 18 or older. Exclusion criteria were current involvement with diet program or diet mediation, diabetes mellitus, fasting glucose ≥ 126 mg/dL, or hemoglobin A1c (HbA1C) between 6.0% and 6.5% with abnormal oral glucose tolerance test. Participants were randomized in a 1:1 ratio to one of 2 weight loss program arms using a computer-generated randomization sequence.
Intervention. In both arms, participants received general diet and exercise guidelines prescribing healthy eating and frequent exercise delivered in 16 sessions lasting 2 to 2½ hours and one all-day session over 5.5 months. Participants in the mindfulness intervention additionally received mindfulness training for eating, physical activity, and stress management from mindfulness mediation instructors and a registered dietician. They also followed guidelines at home, which included practicing meditation for up to 30 minutes 6 days a week, mini-meditations, and eating mindfully. To control for the activities and attention inherent in the mindfulness arm (eg, social support, expectation of benefit, snacks provided during mindful eating exercises, at home practice), the control arm was an “active control” and included additional nutritional and physical activity information, snacks, strength training, home activities, conversations about society and weight loss, and low-dose progressive muscle relaxation and cognitive-behavioral training. Active control materials were delivered by one of 3 registered dieticians.
Main outcomes measures. The primary outcome was 18-month weight change. Participants’ weight, height, blood pressure, and weight circumference were measured at baseline and 3, 6, 12, and 18 months between 8 am and 10 am. Measurements were taken under fasting conditions and by arm-blinded staff. Blood samples were taken to assess secondary outcome changes in glucose, lipid, HbA1C, insulin, and C-reactive protein. Researchers also collected anonymous qualitative feedback from participants and supervisors to do a secondary analysis assessing differences in effectiveness and helpfulness of mindfulness teachings among instructors.
Main results. Of the potential participants that contacted the study team in response to recruitment efforts (n = 1485), 216 were fully eligible based on criteria and a screening visit. Participants that consented to participate (n = 194) were randomized. Participants across both groups were predominantly female, of European ethnic origin, and similar in age: mindfulness group, 47.2 years (13.0) and active control group, 47.8 years (12.4). At baseline, the mindfulness and active control arms had average BMIs of 35.4 (3.5) and 35.6 (3.8), respectively. Baseline characteristics, session attendance, and 18-month retention were similar for both arms. Participants in the mindfulness arm reported completing 70% (2.1 hours per week, SD = 1.2) of the recommended meditation time and eating 57% (SD = 29) of meals mindfully.
Weight loss outcomes between groups favored the mindfulness arms, but results were not significant. The largest difference of –1.9 kg (95% CI –4.5 to 0.8; P = 0.17) was at 12 months. The difference persisted at 18 months with –1.7 kg (95% CI –4.7 to 1.2 kg; P = 0.24). The mindfulness arm lost 4.2 kg (95% CI –6.2 to 2.2 kg) while the active control arm lost 2.4 kg (95% CI –4.5 to –0.3 kg).
Cardiometabolic outcomes at 12 months showed group differences in fasting glucose that favored the mindfulness arm, –3.1 mg/dL (95% CI 26.3 to 0.1; P = 0.06), while there was a significant group difference at 18 months, –4.1 mg/dL (95% CI –7.3 to –0.9; P = 0.01). Data at 18-months showed that normal glucose changed minimally in the mindfulness arm, –0.31 mg/dL (95% CI –2.5 to 1.9), but increased in the active control arm 3.8 mg/dL (95% CI 1.5 to 6.1). Other cardiometabolic outcomes (ie, triglyceride/HDL ratio and triglycerides) showed significance at 12 months, favoring the mindfulness arm, but not at 18 months. Although not all were statistically significant, 9 of 11 outcomes favored the mindfulness arm at 18 months.
Significant interactions (P < 0.05) were found between arm and enrollment rounds categorized by mindfulness instructor on weight, BMI, fasting glucose, homeostatic homeostasis model assessment of insulin resistance (defined as [glucose x insulin/{40 × 33.25}]), and HbA1c, with a marginally significant effect for waist circumference (P = 0.08). Qualitative feedback on mindfulness instructors showed that in the group with a lowly rated instructor, participants lost less weight at 18 months (–2.0 kg [95% CI –4.7 to 0.7]), compared to participants in groups with highly rated instructors (–6.3 kg [95% CI –9.1 to –3.6]; P = 0.02). Similar trends followed for reductions in BMI and waist circumference.
Conclusions. With regard to weight loss outcomes, a mindfulness-enhanced diet-exercise program and an active control arm did not show substantial differences. Some evidence, however, suggests modest benefit of added mindfulness components, which may lead to long-term maintenance of fasting glucose levels and improved atherogenic lipid profiles.
Commentary
Mindfulness, or nonjudgmental focus on the present moment, has been utilized by many interventions targeted at self-regulated behavior [1]. Mindfulness interventions aim to promote healthy behavior changes by encouraging careful monitoring of behavior reactivity. Weight loss and weight loss maintenance have been of particular interest with this approach because mindfulness-based interventions may promote long-term maintenance of weight loss [2]. This maintenance is achieved through a focus on modifying health behaviors, rather than a focus on weight loss alone [3]. Mindfulness has been incorporated into weight loss interventions through yoga practices [4] and mindfulness meditation [5].
Several studies have explored the relationship between mindfulness and weight loss in various populations, highlighting mindfulness’s role in weight loss and behavior change. Most notably, mindfulness interventions have shown improvements in fasting glucose levels [6], psychological distress [7], self-efficacy, weight loss, eating behaviors, and physical activity [8–10]. Despite being well designed, this study by Daubenmier et al did not find significant changes in weight loss. However, secondary outcomes related to weight, metabolic, syndrome, and cardiovascular risk showed modest improvements with mindfulness. These results may correlate to previous findings showing that lifestyle changes many not result in weight loss but can reverse or reduce disorders related to obesity [11].
The study was strengthened by randomization, intention-to-treat analysis, objective measures by arm-blinded staff, standardized measuring conditions, balanced participant allocation to each arm, 1-year follow-up, and qualitative feedback on instructors to assess whether weight loss may be instructor-dependent. In addition, the authors made an effort to mask their intention to test the effects of a mindfulness-enhanced intervention. They designed a rigorous active control intervention arm by controlling for attention, social support, expectations of benefit, diet-exercise guidelines, and elements of a mindfulness approach to stress management. An additional strength included a cost-analysis of adding mindfulness components. The generalizability of the results may be limited as the study population were predominantly white females and most had a bachelor’s degree. The study sample was also disproportionately menopausal women, a group that especially struggles with weight loss. This demographic factor may be responsible for the lack of significant weight loss. Other limitations of this study include participant dropout and variability between instructor styles, although the latter was explored in a secondary analysis of weight loss differences between instructors.
The researchers discussed how the active control intervention arm may have contributed to the lack of significant weight loss difference between groups. The researchers also highlighted that participants randomized to the mindfulness arm that were not interested in mindfulness practices may have benefitted less than those who were interested. This combined with the modest diet and exercise components of the intervention may also explain the lack of significance in results. It may also explain why some outcomes were significant at earlier months but attenuated by 18 months. Future studies should assess incorporating more intense exercise and diet approaches, as well as continuous contact throughout the 18-month time period.
Applications for Clinical Practice
This study demonstrated that mindfulness components added to a diet-exercise program can be helpful in promoting metabolic changes but not necessarily weight loss. Since metabolic changes can be protective against morbidities (eg, type 2 diabetes), mindfulness can be a powerful and cost-effective approach within clinical practice. Mindfulness practices can also be easily implemented in various settings and with diverse populations. Future studies should explore adding mindfulness components to more intensive weight loss interventions. Providers and health care settings should consider incorporating mindfulness practices into weight management counseling and programs.
—Michelle J. Williamson and Katrina F. Mateo, MPH
1. Caldwell KL, Baime MJ, Wolever RQ. Mindfulness based approaches to obesity and weight loss maintenance. J Ment Health Couns 2012;34:26982.
2. O’Reilly GA, Cook L, Spruijt-Metz D, Black DS. Mindfulness-based interventions for obesity-related eating behaviours: A literature review. Obes Rev 2014;15:453–61.
3. Robison J. Health at every size: Toward a new paradigm of weight and health. MedGenMed 2005;7:13.
4. Godsey J. The role of mindfulness based interventions in the treatment of obesity and eating disorders: An integrative review. Complement Ther Med 2013;21:430–9.
5. Keune PM, Forintos DP. Mindfulness meditation: A preliminary study on meditation practice during everyday life activities and its association with well-being. Psychol Top 2010;19:373–86.
6. Mason AE, Epel ES, Kristeller J, et al. Effects of a mindfulness-based intervention on mindful eating, sweets consumption, and fasting glucose levels in obese adults: data from the SHINE randomized controlled trial. J Behav Med 2016;39:201–13.
7. Dalen J, Smith BW, Shelley BM, et al. Pilot study: Mindful Eating and Living (MEAL): Weight, eating behavior, and psychological outcomes associated with a mindfulness-based intervention for people with obesity. Complement Ther Med 2010;18:260–64.
8. Kristeller JL, Wolever RQ, Sheets V. Mindfulness-based eating awareness training (MB-EAT) for binge eating: A randomized clinical trial. Mindfulness 2013;5:282–97.
9. Miller C, Kristeller JL, Headings A, Nagaraja H. Comparison of a mindful eating intervention to a diabetes self- management intervention among adults with type 2 diabetes: a randomized controlled trial. Health Educ Behav 2013;41:145–54.
10. Timmerman GM, Brown A. The effect of a mindful restaurant eating intervention on weight management in women. J Nutr Educ Behav 2012;44:22–8.
11. Bacon L, Stern JS, Van Loan MD, Keim NL. Size acceptance and intuitive eating improve health for obese, female chronic dieters. J Am Diet Assoc 2005;105:929–36.
1. Caldwell KL, Baime MJ, Wolever RQ. Mindfulness based approaches to obesity and weight loss maintenance. J Ment Health Couns 2012;34:26982.
2. O’Reilly GA, Cook L, Spruijt-Metz D, Black DS. Mindfulness-based interventions for obesity-related eating behaviours: A literature review. Obes Rev 2014;15:453–61.
3. Robison J. Health at every size: Toward a new paradigm of weight and health. MedGenMed 2005;7:13.
4. Godsey J. The role of mindfulness based interventions in the treatment of obesity and eating disorders: An integrative review. Complement Ther Med 2013;21:430–9.
5. Keune PM, Forintos DP. Mindfulness meditation: A preliminary study on meditation practice during everyday life activities and its association with well-being. Psychol Top 2010;19:373–86.
6. Mason AE, Epel ES, Kristeller J, et al. Effects of a mindfulness-based intervention on mindful eating, sweets consumption, and fasting glucose levels in obese adults: data from the SHINE randomized controlled trial. J Behav Med 2016;39:201–13.
7. Dalen J, Smith BW, Shelley BM, et al. Pilot study: Mindful Eating and Living (MEAL): Weight, eating behavior, and psychological outcomes associated with a mindfulness-based intervention for people with obesity. Complement Ther Med 2010;18:260–64.
8. Kristeller JL, Wolever RQ, Sheets V. Mindfulness-based eating awareness training (MB-EAT) for binge eating: A randomized clinical trial. Mindfulness 2013;5:282–97.
9. Miller C, Kristeller JL, Headings A, Nagaraja H. Comparison of a mindful eating intervention to a diabetes self- management intervention among adults with type 2 diabetes: a randomized controlled trial. Health Educ Behav 2013;41:145–54.
10. Timmerman GM, Brown A. The effect of a mindful restaurant eating intervention on weight management in women. J Nutr Educ Behav 2012;44:22–8.
11. Bacon L, Stern JS, Van Loan MD, Keim NL. Size acceptance and intuitive eating improve health for obese, female chronic dieters. J Am Diet Assoc 2005;105:929–36.
Targeting the Home Environment May Help with Weight Control
Study Overview
Objective. To assess the effectiveness of an intervention that focused on the home environment to reduce energy intake and increase physical activity among overweight and obese women.
Design. Randomized controlled trial.
Setting and participants. Study participants were overweight and obese females recruited via their providers from 3 community health centers (9 clinical sites) in southwest Georgia. Only women were recruited because of their potential role as gatekeepers of the home environment. Inclusion criteria included being aged 35 to 65 years at baseline, living with at least one other person, and living no further than 30 miles from the referring clinic. Exclusion criteria included patients with conditions that could impact their ability to be physically active and pregnant women.
Intervention. Participants in the intervention arm received 3 home visits and 4 coaching calls over 16 weeks. Core elements of the intervention were informed by social-cognitive theory and included a tailored home environment profile, goal setting, behavioral contracting for 6 healthy actions, and supportive materials delivered via mail. Home visits and coaching calls were completed by health coaches with at least high-school education and experience in social or customer service who had completed 2 days of formal training by university staff. Control condition patients received 3 mailings of educational booklets at 6-week intervals that included government documents encouraging adoption of US dietary and physical activity guidelines. All participants completed baseline, 6- and 12-month follow-up telephone interviews and wore an accelerometer at baseline and 6-month follow-up. Intervention patients also received follow-up surveys assessing satisfaction with the coach, home visits, telephone calls, and support materials.
Main outcome measures. The main outcomes were energy intake (average daily kilocalories from two 24-hour dietary recalls) and physical activity (hours per week spent in moderate or vigorous physical activity using the 7-day physical activity recall). Self-reported height and weight was used to calculate body mass index (BMI). Secondary outcome measures included self-reported weight loss and aspects of the home environment. Home food environment was assessed by asking participants about the presence of 3 unhealthy drinks and 8 unhealthy foods and snacks in the home in the past week, if fruits and vegetables and high-calorie snack foods were kept in easy to see and reach places in the home, how often the family ate meals and snacks in front of the TV, how often participants served healthier food or prepared foods using healthy cooking methods, and asking the number of days family meals were purchased from outside the home. Home activity environment was assessed by asking about rules regarding limits on time spent watching TV, using a computer, playing video games, and using other hand-held devices. The authors adapted a 14-item inventory to assess personal exercise equipment accessibility and availability in the home. Community facility use was assessed with 9 survey items that assessed frequency of use and spaces for exercise in the participants’ neighborhoods.
Main results. A total of 948 patients were referred, of which 751 were reached by phone and assessed for eligibility. 81 did not meet inclusion criteria, 203 declined to participate, and 118 did not complete baseline data collection, leaving 349 participants. Of these, 177 were randomized to the control group, 172 to the intervention, and 21 dropped out. The majority of participants were African-American women (84.8%) with an average age of 50.2 years (SD = 8.1) and average BMI of 38.3kg/m2 (SD = 8.4). Most were low income, with 68.7% reporting an annual household income under $25,000, and nearly 50% reported fair or poor general health. Roughly 45% were employed and 49% lived in a rural area. At 6 months, 82.5% of participants completed data collection (n = 288); at 12 months, 76.8% completed data collection (n = 268). Participants who did not complete follow-up through 12 months were either non-responders (6 months: n = 36, 12 months: n = 44), refused (6 months: n = 3, 12 months: n = 7), or died (6 months: n = 0, 12 months: n = 1).
Daily energy consumption significantly decreased in the intervention group compared to the control group at 6 months (–274 vs. –69 kcal/day, P = 0.003), however there was no meaningful change in self-reported moderate to vigorous physical activity nor was there significant change in physical activity measured by accelerometers at 6 months compared to baseline. For secondary outcomes, self-reported weight loss at 6 months was significantly higher among intervention patients (mean, –9.1 lb) compared to control patients (mean, –5.0 pounds) (SD = 13.7 pounds; P = 0.03). In addition, at 12 months, 82.6% of intervention patients had not gained weight compared with 71.4% of control patients (P = 0.03). Intervention patients made several changes to their home food environments compared to control patients. Intervention patients had reduced the number of unhealthy drink and snacks, increased purchasing of fruits and vegetables, and reduced the frequency of watching TV while eating. In addition, they also improved meal preparation and service and reduced the number of non-home meals eaten. For home activity environment, having exercise equipment in a visible location changed significantly more in the intervention group compared to the control group. Intervention patients also incorporated more physical activity into their daily lives compared to control patients, and created more exercise space in their homes and yards. There were no significant differences in screen time rules, use of community facilities and spaces, and family social support for physical activity.
Conclusion. A moderate-intensity, coach-delivered weight gain prevention intervention targeting the home environment led to reduced energy intake and improved home environments to better facilitate healthy living and weight loss.
Commentary
More than half of all US adults are considered overweight or obese [1].Changing health behaviors has the best potential for decreasing morbidity and mortality and for improving quality of life and this has been supported by the literature in a wide variety of behaviors including smoking cessation and weight loss [2–4]. Currently, most overweight and obese patients are treated through primary care provider–based (PCP) counseling or referral to clinic-based weight management interventions. However, barriers to PCP weight management counseling include physicians’ negative attitudes towards the personal attributes of individuals with weight management issues, lack of time, and poor nutrition counseling competency [5–7]. In addition, there are notable differences between providers’ and patients’ beliefs about weight and weight loss; providers tend to believe patients lack self-control, while patients largely feel they should manage their weight problems on their own and that counseling from a provider is unhelpful [8]. Many patients report feeling judged by their doctor because of their weight, and very few of those who feel judged and discuss weight loss options actually lose a clinically significant amount of weight [9]. Considering the many barriers to providing/receiving weight management counseling in the clinic setting, weight management techniques provided outside the doctor’s office may be a more effective and feasible alternative.
The most common causes of death are related to lifestyle behaviors such as poor dietary habits and inactivity [10]. Since most calories are consumed within the home [11] and the average person spends the majority of their time in the home [12], interventions that target home-life behaviors are needed to combat weight gain. The Kegler et al study suggests that a moderate-intensity intervention targeted at changing home eating and exercise behaviors will be effective in changing home environments and reducing energy intake. While the authors had a fairly specific population, these findings suggest that interventions that specifically target health behaviors at home may have more potential for success than merely educating patients on the benefits of a healthy lifestyle.
This study has several strengths including the randomized controlled trial design, the intention-to-treat analysis, and low attrition rates. In addition, the intervention achieved reduced energy intake and improved health behaviors in the home, supporting significant weight loss among intervention participants, especially compared to control patients. Both of these suggest high adherence to the intervention, which is a complex but crucial component of successful weight loss and weight management [13]. Finally, the inclusion of a wide variety of secondary outcomes helped to distinguish between specific home environment changes to discern which aspects of the intervention were most successful. A limitation of the study was that the population was nearly entirely African American and from clinics in rural Georgia, which limits generalizability. However, the success of the intervention in this population is critical, as African American adults are nearly 1.5 times more likely to be obese compared to white adults, and greater than 75% of African Americans are overweight or obese [14]. Additionally, while the study did have significant success with energy intake and eating habits, the intervention was less successful with changing physical activity habits, and physical activity and exercise training can significantly impact weight loss and maintenance [15]. A final limitation is the use of self-reported weight and behaviors, which can reduce reliability of these results.
Applications for Clinical Practice
This study suggests that interventions that target health behaviors in the home may achieve better energy intake and physical activity outcomes and improve weight loss compared to traditional educational counseling. Providers may want to consider brief counseling around improving the home environment as opposed to or in addition to counseling around improving nutrition or physical activity. More research is necessary to understand whether this type of intervention is feasible and acceptable in other populations (eg, urban, other races). In addition, further research is necessary to improve the physical activity component of the intervention. The use of non-clinical providers has been shown to be effective in improving health outcomes [16] and this study provides further evidence on the impactful role that trained community residents can have on changing behaviors. These initiatives are vital to supplement weight loss and management efforts occurring in the clinical setting.
—Natalie L. Ricci, Columbia University Mailman School of Public Health, and Katrina F. Mateo, MPH
1. Yang L, Colditz GA. Prevalence of overweight and obesity in the United States, 2007-2012. JAMA Intern Med 2015;175:1412–3.
2. Koop EC. Health promotion and disease prevention in clinical practice. In: Lawrence RS, Woolf SH, Jonas S, editors. Health promotion and disease prevention in clinical practice. Baltimore: Williams & Wilkins; 1996: vii-ix.
3. Laniado-Laborin R. Smoking cessation intervention: an evidence-based approach. Postgrad Med 2010;122:74–82.
4. Winter SJ, Sheats JL, King AC. The use of behavior change techniques and theory in technologies for cardiovascular disease prevention and treatment in adults: a comprehensive review. Prog Cardiovasc Dis 2016. Epub ahead of print.
5. Foster GD, Wadden TA, Makris AP, et al. Primary care physicians’ attitudes about obesity and its treatment. Obesity Res 2007;11:1168–77.
6. Jay M, Chintapalli S, Squires A, et al. Barriers and facilitators to providing primary care-based weight management services in a patient centered medical home for veterans: a qualitative study. BMC Fam Pract 2015;16:167.
7. Jay M, Gillespie C, Ark T, et al. Do internists, pediatricians, and psychiatrists feel competent in obesity care? Using a needs assessment to drive curriculum design. J Gen Intern Med 2008;23:1066–70.
8. Ruelaz AR, Diefenbach P, Simon B, et al. Perceived barriers to weight management in primary care—perspectives of patients and providers. J Gen Intern Med 2007;22:518–22.
9. Gudzune KA, Bennett WL, Cooper LA, Bleich SN. Perceived judgment about weight can negatively influence weight loss: A cross-sectional study of overweight and obese patients. Prev Med 2014;62:103–7.
10. McGinnis JM, Foege WH. Actual causes of death in the United States. JAMA 1993;27:2207–12.
11. Lin B-H, Guthrie J. Nutritional quality of food prepared at home and away from home, 1977-2008. Washington, DC: US Department of Agriculture, Economic Research Service; 2012.
12. Bureau of Labor Statistics, US Department of Labor. American time use survey – 2014 results. Accessed 1 Mar 2016 at www.bls.gov/nes.release/pdf/atus.pdf.
13. Hays RD, Kravitz RL, Mazel RM, et al. The impact of patient adherence on health outcomes for patients with chronic disease in the medical outcomes study. J Behav Med 1994;17:347–60.
14. Obesity prevention in black communities. The state of obesity. Accessed 2 Mar 2016 at http://stateofobesity.org/disparities/blacks/
15. Swift DL, Johannsen NM, Lavie CJ, et al. The role of exercise and physical activity in weight loss and maintenance. Prog Cardiovasc Dis 2014;56:441–7.
16. Dye CJ, Williams JE, Evatt JH. Improving hypertension self-management with community health coaches. Health Prom Pract 2015;16:271–81.
Study Overview
Objective. To assess the effectiveness of an intervention that focused on the home environment to reduce energy intake and increase physical activity among overweight and obese women.
Design. Randomized controlled trial.
Setting and participants. Study participants were overweight and obese females recruited via their providers from 3 community health centers (9 clinical sites) in southwest Georgia. Only women were recruited because of their potential role as gatekeepers of the home environment. Inclusion criteria included being aged 35 to 65 years at baseline, living with at least one other person, and living no further than 30 miles from the referring clinic. Exclusion criteria included patients with conditions that could impact their ability to be physically active and pregnant women.
Intervention. Participants in the intervention arm received 3 home visits and 4 coaching calls over 16 weeks. Core elements of the intervention were informed by social-cognitive theory and included a tailored home environment profile, goal setting, behavioral contracting for 6 healthy actions, and supportive materials delivered via mail. Home visits and coaching calls were completed by health coaches with at least high-school education and experience in social or customer service who had completed 2 days of formal training by university staff. Control condition patients received 3 mailings of educational booklets at 6-week intervals that included government documents encouraging adoption of US dietary and physical activity guidelines. All participants completed baseline, 6- and 12-month follow-up telephone interviews and wore an accelerometer at baseline and 6-month follow-up. Intervention patients also received follow-up surveys assessing satisfaction with the coach, home visits, telephone calls, and support materials.
Main outcome measures. The main outcomes were energy intake (average daily kilocalories from two 24-hour dietary recalls) and physical activity (hours per week spent in moderate or vigorous physical activity using the 7-day physical activity recall). Self-reported height and weight was used to calculate body mass index (BMI). Secondary outcome measures included self-reported weight loss and aspects of the home environment. Home food environment was assessed by asking participants about the presence of 3 unhealthy drinks and 8 unhealthy foods and snacks in the home in the past week, if fruits and vegetables and high-calorie snack foods were kept in easy to see and reach places in the home, how often the family ate meals and snacks in front of the TV, how often participants served healthier food or prepared foods using healthy cooking methods, and asking the number of days family meals were purchased from outside the home. Home activity environment was assessed by asking about rules regarding limits on time spent watching TV, using a computer, playing video games, and using other hand-held devices. The authors adapted a 14-item inventory to assess personal exercise equipment accessibility and availability in the home. Community facility use was assessed with 9 survey items that assessed frequency of use and spaces for exercise in the participants’ neighborhoods.
Main results. A total of 948 patients were referred, of which 751 were reached by phone and assessed for eligibility. 81 did not meet inclusion criteria, 203 declined to participate, and 118 did not complete baseline data collection, leaving 349 participants. Of these, 177 were randomized to the control group, 172 to the intervention, and 21 dropped out. The majority of participants were African-American women (84.8%) with an average age of 50.2 years (SD = 8.1) and average BMI of 38.3kg/m2 (SD = 8.4). Most were low income, with 68.7% reporting an annual household income under $25,000, and nearly 50% reported fair or poor general health. Roughly 45% were employed and 49% lived in a rural area. At 6 months, 82.5% of participants completed data collection (n = 288); at 12 months, 76.8% completed data collection (n = 268). Participants who did not complete follow-up through 12 months were either non-responders (6 months: n = 36, 12 months: n = 44), refused (6 months: n = 3, 12 months: n = 7), or died (6 months: n = 0, 12 months: n = 1).
Daily energy consumption significantly decreased in the intervention group compared to the control group at 6 months (–274 vs. –69 kcal/day, P = 0.003), however there was no meaningful change in self-reported moderate to vigorous physical activity nor was there significant change in physical activity measured by accelerometers at 6 months compared to baseline. For secondary outcomes, self-reported weight loss at 6 months was significantly higher among intervention patients (mean, –9.1 lb) compared to control patients (mean, –5.0 pounds) (SD = 13.7 pounds; P = 0.03). In addition, at 12 months, 82.6% of intervention patients had not gained weight compared with 71.4% of control patients (P = 0.03). Intervention patients made several changes to their home food environments compared to control patients. Intervention patients had reduced the number of unhealthy drink and snacks, increased purchasing of fruits and vegetables, and reduced the frequency of watching TV while eating. In addition, they also improved meal preparation and service and reduced the number of non-home meals eaten. For home activity environment, having exercise equipment in a visible location changed significantly more in the intervention group compared to the control group. Intervention patients also incorporated more physical activity into their daily lives compared to control patients, and created more exercise space in their homes and yards. There were no significant differences in screen time rules, use of community facilities and spaces, and family social support for physical activity.
Conclusion. A moderate-intensity, coach-delivered weight gain prevention intervention targeting the home environment led to reduced energy intake and improved home environments to better facilitate healthy living and weight loss.
Commentary
More than half of all US adults are considered overweight or obese [1].Changing health behaviors has the best potential for decreasing morbidity and mortality and for improving quality of life and this has been supported by the literature in a wide variety of behaviors including smoking cessation and weight loss [2–4]. Currently, most overweight and obese patients are treated through primary care provider–based (PCP) counseling or referral to clinic-based weight management interventions. However, barriers to PCP weight management counseling include physicians’ negative attitudes towards the personal attributes of individuals with weight management issues, lack of time, and poor nutrition counseling competency [5–7]. In addition, there are notable differences between providers’ and patients’ beliefs about weight and weight loss; providers tend to believe patients lack self-control, while patients largely feel they should manage their weight problems on their own and that counseling from a provider is unhelpful [8]. Many patients report feeling judged by their doctor because of their weight, and very few of those who feel judged and discuss weight loss options actually lose a clinically significant amount of weight [9]. Considering the many barriers to providing/receiving weight management counseling in the clinic setting, weight management techniques provided outside the doctor’s office may be a more effective and feasible alternative.
The most common causes of death are related to lifestyle behaviors such as poor dietary habits and inactivity [10]. Since most calories are consumed within the home [11] and the average person spends the majority of their time in the home [12], interventions that target home-life behaviors are needed to combat weight gain. The Kegler et al study suggests that a moderate-intensity intervention targeted at changing home eating and exercise behaviors will be effective in changing home environments and reducing energy intake. While the authors had a fairly specific population, these findings suggest that interventions that specifically target health behaviors at home may have more potential for success than merely educating patients on the benefits of a healthy lifestyle.
This study has several strengths including the randomized controlled trial design, the intention-to-treat analysis, and low attrition rates. In addition, the intervention achieved reduced energy intake and improved health behaviors in the home, supporting significant weight loss among intervention participants, especially compared to control patients. Both of these suggest high adherence to the intervention, which is a complex but crucial component of successful weight loss and weight management [13]. Finally, the inclusion of a wide variety of secondary outcomes helped to distinguish between specific home environment changes to discern which aspects of the intervention were most successful. A limitation of the study was that the population was nearly entirely African American and from clinics in rural Georgia, which limits generalizability. However, the success of the intervention in this population is critical, as African American adults are nearly 1.5 times more likely to be obese compared to white adults, and greater than 75% of African Americans are overweight or obese [14]. Additionally, while the study did have significant success with energy intake and eating habits, the intervention was less successful with changing physical activity habits, and physical activity and exercise training can significantly impact weight loss and maintenance [15]. A final limitation is the use of self-reported weight and behaviors, which can reduce reliability of these results.
Applications for Clinical Practice
This study suggests that interventions that target health behaviors in the home may achieve better energy intake and physical activity outcomes and improve weight loss compared to traditional educational counseling. Providers may want to consider brief counseling around improving the home environment as opposed to or in addition to counseling around improving nutrition or physical activity. More research is necessary to understand whether this type of intervention is feasible and acceptable in other populations (eg, urban, other races). In addition, further research is necessary to improve the physical activity component of the intervention. The use of non-clinical providers has been shown to be effective in improving health outcomes [16] and this study provides further evidence on the impactful role that trained community residents can have on changing behaviors. These initiatives are vital to supplement weight loss and management efforts occurring in the clinical setting.
—Natalie L. Ricci, Columbia University Mailman School of Public Health, and Katrina F. Mateo, MPH
Study Overview
Objective. To assess the effectiveness of an intervention that focused on the home environment to reduce energy intake and increase physical activity among overweight and obese women.
Design. Randomized controlled trial.
Setting and participants. Study participants were overweight and obese females recruited via their providers from 3 community health centers (9 clinical sites) in southwest Georgia. Only women were recruited because of their potential role as gatekeepers of the home environment. Inclusion criteria included being aged 35 to 65 years at baseline, living with at least one other person, and living no further than 30 miles from the referring clinic. Exclusion criteria included patients with conditions that could impact their ability to be physically active and pregnant women.
Intervention. Participants in the intervention arm received 3 home visits and 4 coaching calls over 16 weeks. Core elements of the intervention were informed by social-cognitive theory and included a tailored home environment profile, goal setting, behavioral contracting for 6 healthy actions, and supportive materials delivered via mail. Home visits and coaching calls were completed by health coaches with at least high-school education and experience in social or customer service who had completed 2 days of formal training by university staff. Control condition patients received 3 mailings of educational booklets at 6-week intervals that included government documents encouraging adoption of US dietary and physical activity guidelines. All participants completed baseline, 6- and 12-month follow-up telephone interviews and wore an accelerometer at baseline and 6-month follow-up. Intervention patients also received follow-up surveys assessing satisfaction with the coach, home visits, telephone calls, and support materials.
Main outcome measures. The main outcomes were energy intake (average daily kilocalories from two 24-hour dietary recalls) and physical activity (hours per week spent in moderate or vigorous physical activity using the 7-day physical activity recall). Self-reported height and weight was used to calculate body mass index (BMI). Secondary outcome measures included self-reported weight loss and aspects of the home environment. Home food environment was assessed by asking participants about the presence of 3 unhealthy drinks and 8 unhealthy foods and snacks in the home in the past week, if fruits and vegetables and high-calorie snack foods were kept in easy to see and reach places in the home, how often the family ate meals and snacks in front of the TV, how often participants served healthier food or prepared foods using healthy cooking methods, and asking the number of days family meals were purchased from outside the home. Home activity environment was assessed by asking about rules regarding limits on time spent watching TV, using a computer, playing video games, and using other hand-held devices. The authors adapted a 14-item inventory to assess personal exercise equipment accessibility and availability in the home. Community facility use was assessed with 9 survey items that assessed frequency of use and spaces for exercise in the participants’ neighborhoods.
Main results. A total of 948 patients were referred, of which 751 were reached by phone and assessed for eligibility. 81 did not meet inclusion criteria, 203 declined to participate, and 118 did not complete baseline data collection, leaving 349 participants. Of these, 177 were randomized to the control group, 172 to the intervention, and 21 dropped out. The majority of participants were African-American women (84.8%) with an average age of 50.2 years (SD = 8.1) and average BMI of 38.3kg/m2 (SD = 8.4). Most were low income, with 68.7% reporting an annual household income under $25,000, and nearly 50% reported fair or poor general health. Roughly 45% were employed and 49% lived in a rural area. At 6 months, 82.5% of participants completed data collection (n = 288); at 12 months, 76.8% completed data collection (n = 268). Participants who did not complete follow-up through 12 months were either non-responders (6 months: n = 36, 12 months: n = 44), refused (6 months: n = 3, 12 months: n = 7), or died (6 months: n = 0, 12 months: n = 1).
Daily energy consumption significantly decreased in the intervention group compared to the control group at 6 months (–274 vs. –69 kcal/day, P = 0.003), however there was no meaningful change in self-reported moderate to vigorous physical activity nor was there significant change in physical activity measured by accelerometers at 6 months compared to baseline. For secondary outcomes, self-reported weight loss at 6 months was significantly higher among intervention patients (mean, –9.1 lb) compared to control patients (mean, –5.0 pounds) (SD = 13.7 pounds; P = 0.03). In addition, at 12 months, 82.6% of intervention patients had not gained weight compared with 71.4% of control patients (P = 0.03). Intervention patients made several changes to their home food environments compared to control patients. Intervention patients had reduced the number of unhealthy drink and snacks, increased purchasing of fruits and vegetables, and reduced the frequency of watching TV while eating. In addition, they also improved meal preparation and service and reduced the number of non-home meals eaten. For home activity environment, having exercise equipment in a visible location changed significantly more in the intervention group compared to the control group. Intervention patients also incorporated more physical activity into their daily lives compared to control patients, and created more exercise space in their homes and yards. There were no significant differences in screen time rules, use of community facilities and spaces, and family social support for physical activity.
Conclusion. A moderate-intensity, coach-delivered weight gain prevention intervention targeting the home environment led to reduced energy intake and improved home environments to better facilitate healthy living and weight loss.
Commentary
More than half of all US adults are considered overweight or obese [1].Changing health behaviors has the best potential for decreasing morbidity and mortality and for improving quality of life and this has been supported by the literature in a wide variety of behaviors including smoking cessation and weight loss [2–4]. Currently, most overweight and obese patients are treated through primary care provider–based (PCP) counseling or referral to clinic-based weight management interventions. However, barriers to PCP weight management counseling include physicians’ negative attitudes towards the personal attributes of individuals with weight management issues, lack of time, and poor nutrition counseling competency [5–7]. In addition, there are notable differences between providers’ and patients’ beliefs about weight and weight loss; providers tend to believe patients lack self-control, while patients largely feel they should manage their weight problems on their own and that counseling from a provider is unhelpful [8]. Many patients report feeling judged by their doctor because of their weight, and very few of those who feel judged and discuss weight loss options actually lose a clinically significant amount of weight [9]. Considering the many barriers to providing/receiving weight management counseling in the clinic setting, weight management techniques provided outside the doctor’s office may be a more effective and feasible alternative.
The most common causes of death are related to lifestyle behaviors such as poor dietary habits and inactivity [10]. Since most calories are consumed within the home [11] and the average person spends the majority of their time in the home [12], interventions that target home-life behaviors are needed to combat weight gain. The Kegler et al study suggests that a moderate-intensity intervention targeted at changing home eating and exercise behaviors will be effective in changing home environments and reducing energy intake. While the authors had a fairly specific population, these findings suggest that interventions that specifically target health behaviors at home may have more potential for success than merely educating patients on the benefits of a healthy lifestyle.
This study has several strengths including the randomized controlled trial design, the intention-to-treat analysis, and low attrition rates. In addition, the intervention achieved reduced energy intake and improved health behaviors in the home, supporting significant weight loss among intervention participants, especially compared to control patients. Both of these suggest high adherence to the intervention, which is a complex but crucial component of successful weight loss and weight management [13]. Finally, the inclusion of a wide variety of secondary outcomes helped to distinguish between specific home environment changes to discern which aspects of the intervention were most successful. A limitation of the study was that the population was nearly entirely African American and from clinics in rural Georgia, which limits generalizability. However, the success of the intervention in this population is critical, as African American adults are nearly 1.5 times more likely to be obese compared to white adults, and greater than 75% of African Americans are overweight or obese [14]. Additionally, while the study did have significant success with energy intake and eating habits, the intervention was less successful with changing physical activity habits, and physical activity and exercise training can significantly impact weight loss and maintenance [15]. A final limitation is the use of self-reported weight and behaviors, which can reduce reliability of these results.
Applications for Clinical Practice
This study suggests that interventions that target health behaviors in the home may achieve better energy intake and physical activity outcomes and improve weight loss compared to traditional educational counseling. Providers may want to consider brief counseling around improving the home environment as opposed to or in addition to counseling around improving nutrition or physical activity. More research is necessary to understand whether this type of intervention is feasible and acceptable in other populations (eg, urban, other races). In addition, further research is necessary to improve the physical activity component of the intervention. The use of non-clinical providers has been shown to be effective in improving health outcomes [16] and this study provides further evidence on the impactful role that trained community residents can have on changing behaviors. These initiatives are vital to supplement weight loss and management efforts occurring in the clinical setting.
—Natalie L. Ricci, Columbia University Mailman School of Public Health, and Katrina F. Mateo, MPH
1. Yang L, Colditz GA. Prevalence of overweight and obesity in the United States, 2007-2012. JAMA Intern Med 2015;175:1412–3.
2. Koop EC. Health promotion and disease prevention in clinical practice. In: Lawrence RS, Woolf SH, Jonas S, editors. Health promotion and disease prevention in clinical practice. Baltimore: Williams & Wilkins; 1996: vii-ix.
3. Laniado-Laborin R. Smoking cessation intervention: an evidence-based approach. Postgrad Med 2010;122:74–82.
4. Winter SJ, Sheats JL, King AC. The use of behavior change techniques and theory in technologies for cardiovascular disease prevention and treatment in adults: a comprehensive review. Prog Cardiovasc Dis 2016. Epub ahead of print.
5. Foster GD, Wadden TA, Makris AP, et al. Primary care physicians’ attitudes about obesity and its treatment. Obesity Res 2007;11:1168–77.
6. Jay M, Chintapalli S, Squires A, et al. Barriers and facilitators to providing primary care-based weight management services in a patient centered medical home for veterans: a qualitative study. BMC Fam Pract 2015;16:167.
7. Jay M, Gillespie C, Ark T, et al. Do internists, pediatricians, and psychiatrists feel competent in obesity care? Using a needs assessment to drive curriculum design. J Gen Intern Med 2008;23:1066–70.
8. Ruelaz AR, Diefenbach P, Simon B, et al. Perceived barriers to weight management in primary care—perspectives of patients and providers. J Gen Intern Med 2007;22:518–22.
9. Gudzune KA, Bennett WL, Cooper LA, Bleich SN. Perceived judgment about weight can negatively influence weight loss: A cross-sectional study of overweight and obese patients. Prev Med 2014;62:103–7.
10. McGinnis JM, Foege WH. Actual causes of death in the United States. JAMA 1993;27:2207–12.
11. Lin B-H, Guthrie J. Nutritional quality of food prepared at home and away from home, 1977-2008. Washington, DC: US Department of Agriculture, Economic Research Service; 2012.
12. Bureau of Labor Statistics, US Department of Labor. American time use survey – 2014 results. Accessed 1 Mar 2016 at www.bls.gov/nes.release/pdf/atus.pdf.
13. Hays RD, Kravitz RL, Mazel RM, et al. The impact of patient adherence on health outcomes for patients with chronic disease in the medical outcomes study. J Behav Med 1994;17:347–60.
14. Obesity prevention in black communities. The state of obesity. Accessed 2 Mar 2016 at http://stateofobesity.org/disparities/blacks/
15. Swift DL, Johannsen NM, Lavie CJ, et al. The role of exercise and physical activity in weight loss and maintenance. Prog Cardiovasc Dis 2014;56:441–7.
16. Dye CJ, Williams JE, Evatt JH. Improving hypertension self-management with community health coaches. Health Prom Pract 2015;16:271–81.
1. Yang L, Colditz GA. Prevalence of overweight and obesity in the United States, 2007-2012. JAMA Intern Med 2015;175:1412–3.
2. Koop EC. Health promotion and disease prevention in clinical practice. In: Lawrence RS, Woolf SH, Jonas S, editors. Health promotion and disease prevention in clinical practice. Baltimore: Williams & Wilkins; 1996: vii-ix.
3. Laniado-Laborin R. Smoking cessation intervention: an evidence-based approach. Postgrad Med 2010;122:74–82.
4. Winter SJ, Sheats JL, King AC. The use of behavior change techniques and theory in technologies for cardiovascular disease prevention and treatment in adults: a comprehensive review. Prog Cardiovasc Dis 2016. Epub ahead of print.
5. Foster GD, Wadden TA, Makris AP, et al. Primary care physicians’ attitudes about obesity and its treatment. Obesity Res 2007;11:1168–77.
6. Jay M, Chintapalli S, Squires A, et al. Barriers and facilitators to providing primary care-based weight management services in a patient centered medical home for veterans: a qualitative study. BMC Fam Pract 2015;16:167.
7. Jay M, Gillespie C, Ark T, et al. Do internists, pediatricians, and psychiatrists feel competent in obesity care? Using a needs assessment to drive curriculum design. J Gen Intern Med 2008;23:1066–70.
8. Ruelaz AR, Diefenbach P, Simon B, et al. Perceived barriers to weight management in primary care—perspectives of patients and providers. J Gen Intern Med 2007;22:518–22.
9. Gudzune KA, Bennett WL, Cooper LA, Bleich SN. Perceived judgment about weight can negatively influence weight loss: A cross-sectional study of overweight and obese patients. Prev Med 2014;62:103–7.
10. McGinnis JM, Foege WH. Actual causes of death in the United States. JAMA 1993;27:2207–12.
11. Lin B-H, Guthrie J. Nutritional quality of food prepared at home and away from home, 1977-2008. Washington, DC: US Department of Agriculture, Economic Research Service; 2012.
12. Bureau of Labor Statistics, US Department of Labor. American time use survey – 2014 results. Accessed 1 Mar 2016 at www.bls.gov/nes.release/pdf/atus.pdf.
13. Hays RD, Kravitz RL, Mazel RM, et al. The impact of patient adherence on health outcomes for patients with chronic disease in the medical outcomes study. J Behav Med 1994;17:347–60.
14. Obesity prevention in black communities. The state of obesity. Accessed 2 Mar 2016 at http://stateofobesity.org/disparities/blacks/
15. Swift DL, Johannsen NM, Lavie CJ, et al. The role of exercise and physical activity in weight loss and maintenance. Prog Cardiovasc Dis 2014;56:441–7.
16. Dye CJ, Williams JE, Evatt JH. Improving hypertension self-management with community health coaches. Health Prom Pract 2015;16:271–81.