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Study Overview
Objective. To evaluate the effectiveness and impact of using MyFitnessPal, a free, popular smartphone application (“app”), for weight loss.
Study design. 2-arm randomized controlled trial.
Setting and participants. Participants were recruited from 2 primary care clinics in the University of California, Los Angeles heath system. The inclusion criteria for the study were ≥ 18 years of age, body mass index (BMI) ≥ 25 kg/m2, an interest in losing weight, and ownership of a smartphone. The exclusion criteria included pregnancy, hemodialysis, life expectancy less than 6 months, lack of interest in weight loss, and current use of a smartphone app for weight loss. Out of 633 individuals assessed, 212 were eligible for the study. Participants were block randomized by BMI 25–30 kg/m2 and BMI > 30 kg/m2 to either usual primary care (n = 107) or usual primary care plus the app (n = 105).
Intervention. MyFitnessPal (MFP) was selected for this study based on previous focus groups with overweight primary care patients. MFP is a calorie-counting app that incorporates evidence-based and theory-based approaches to weight loss. Users can enter their current weight, goal weight, and goal rate of weight loss, which allows the app to generate the user’s daily, individualized calorie goal. MFP users also input daily weight, food intake, and physical activity, which produce certain outputs, including calorie counts, weight trends, and nutritional summaries based on food consumed.
Participants in the intervention arm received help from research assistants in downloading MFP onto their smartphones and received a phone call 1 week after enrollment to assist with any technical issues with the app. Those in the control group were told to choose any preferred activity for weight loss. Both groups received usual care from their primary care provider, with an additional two follow-up visits at 3 and 6 months. At the 3-month follow-up visit, all participants received a nutrition educational handout from www.myplate.gov.
Main outcomes measures. The main outcome measure was weight change at 6 months. Blood pressure, weight, systolic blood pressure (SPB), and 3 self-reported behavioral mediators of weight loss (exercise, diet, and self-efficacy in weight loss) were measured and collected for all participants at baseline and at 3 and 6 months. This study also gathered data from the MyFitnessPal company to measure frequency of app usage. At the 6-month follow-up visit, research assistants asked participants in the intervention arm about their experience using MFP, while those in the control group were asked if they had used MFP in the past 6 months to assess contamination. The authors used a linear mixed effects model (PROC MIXED) in SAS data processing software to investigate the differences in weight change, SBP change, and change in behavioral survey items between the 2 groups while controlling for clinic site. In addition, they performed 2 sensitivity analyses to evaluate the impact of possible informative dropout (income, education, diet experience, treatment group, and baseline value), and the effect of excluding one control group outlier.
Results. The majority of participants were female (73%) with a mean age of 43.4 years (SD = 14.3). The mean BMI was 33.4 kg/m2 (SD = 7.09), and 48% of the participants identified themselves as non-Hispanic white. At the 3-month visit, 26% and 21% of the participants from the intervention and control arms were lost to follow-up. Additionally, at 6 months, 32% and 19% of intervention and control group participants were lost to follow-up.
There was no significant difference in weight change between the two groups at 3 months (control, + 0.54 lb; intervention, –0.06 lb; P = 0.53) or at 6 months (control, +0.6 lb; intervention, –0.07 lb; P = 0.63); between group difference at 3 months was –0.6 lb (95% confidence interval [CI], –2.5 to 1.3 lb; P = 0.53) and at 6 months was –0.67 lb (CI, –3.3 to 2.1 lb; P = 0.63). The sensitivity analysis based on possible missing data also suggested the same outcome with between group difference at 6 months at 0.08 lb (CI, –3.04 to 3.20 lb; P = 0.96). The difference in systolic blood pressure was not significant between the groups.
Participants in the intervention arm used a personal calorie goal more often than those in the control group, with a mean between group difference at 3 months of 1.9 days per week (CI, 1.0 to 2.8; P < 0.001) and a mean between group difference at 6 months of 2.0 days per week (CI, 1.1 to 2.9; P < 0.001). The results also showed that the use of calorie goal feature was significant at 3 months (P < 0.001) and at 6 months (P < 0.001). At the 3-month visit, the authors found that individuals in the intervention group reported decreased self-efficacy in achieving their weight loss goals when compared to their counterparts (–0.85 on a 10-point scale; CI, –1.6 to –0.1; P = 0.026), but self-efficacy was insignificant at the 6-month follow-up. Additionally, the results suggested no difference in self-reported behaviors regarding diet, exercise, and self-efficacy in weight loss between the groups.
The mean number of logins was 61 during the course of the study, and the median total logins was 19. Interestingly, the data showed that there was a rapid decline of logins after enrollment for most participants in the intervention arm. There were 94 users who logged in to the app during the first month and 34 who logged in during the last month of the study. Out of 107 participants from the control group, 14 used MFP during the trial.
Despite a sharp decline in usage, MFP users in the intervention group were satisfied with the app: 79% were somewhat to completely satisfied, 92% would recommend it to a friend, and 80% planned to continue using MFP after the study. The study indicated that there were several aspects that the users liked about MFP including ease of use (100%), feedback on progress (88%), and 48% indicated that it was fun to use. Fewer participants appreciated features such as the reminder feature (42%) and social networking feature (13%). A common theme the authors found in MFP users was increased awareness of food choices, and more caution about food choices. Of those that stopped using MFP, some comments regarding MFP included that it was tedious (84%) and not easy to use (24%).
Conclusion. While most participants were satisfied with MFP, encouraging use of the app did not lead to more weight loss in primary care patients compared to usual care. There was decreased engagement with MFP over time.
Commentary
Despite efforts by the federal government to address the obesity epidemic in the United States, there is still a high prevalence of obesity among children and adults. Approximately 17% of youth and 35% of adults in the United States have a BMI in the obese range (> 30 kg/m2) [1]. In addition to a high association between obesity and chronic diseases such as type 2 diabetes mellitus, hypertension, and hypercholesterolemia[2], the obesity epidemic carries a staggering financial burden. The annual cost of obesity in the U.S. was estimated to be $147 billion in 2008, and the medical costs for each person with obesity was $1429 higher than those with normal weight [3,4]. Therefore, finding cost-effective and easily accessible methods to manage obesity is imperative. The Pew Research Center found that 64% of adults in the U.S. own a smartphone, and 62% of those have used their phone to look up health information[5]. Thus, smartphone applications that deliver weight management information and strategies may be a cost-effective and feasible means to reach a large population.
This study assessed the impact of MyFitnessPal, a free and widely popular mobile application, as an approach to reduce weight among patients in a primary care setting. The authors compared weight change between patients who received usual care from primary care providers (PCPs) and those who used MFP in addition to their usual care. They found no significant difference in weight loss between the two groups at 3 and 6 months during the trial. Despite this negative finding, this study makes important contributions to the e-health literature and highlights important considerations for similar studies.
While the exact reasons for lack of effect are unclear, the lack of effectiveness of such a brief intervention is not surprising. It is important to note that the PCPs did not assist or reinforce the patients to use the MFP app in this study. A systematic review of technology-assisted interventions in primary care suggests that technologies such as smartphone apps could be used as a successful tools for weight loss in the primary care setting, but that there needs to be guidance and feedback from a health care team[6]. Further, the intervention may not have been intense enough to achieve clinically significant weight loss. The U.S. Preventive Service Task Force recommends intensive interventions with at least 12 to 26 visits over 12 months to address obesity[7]. Thus, future studies of this app should include more intensive counseling from the healthcare team to determine if this improves adherence to lifestyle changes.
Strengths of this study include the use of a randomized controlled trial design, double-blinding of both participants and PCPs, and analyses for outlier and missing data. These design considerations increase the validity of the trial outcome. In addition, the authors rigorously assessed the relationship between the exposure and the outcome. The group set a high goal of enrollment to account for potential high rate of attrition (up to 50%). Since there was considerable loss to follow-up at both 3 and 6 months, the authors performed sensitivity analyses to determine if this biased the outcomes. The team assessed crossover contamination at the end of the study by asking participants in the control group if they used MFP during the trial. The authors also conducted a linear regression to examine if baseline self-efficacy was a significant predictor of weight change, controlling for the interaction between self-efficacy and group assignment. Additionally, sensitivity analyses were performed to see if the result from one outlier in the control group who achieved significant weight loss biased the findings.
As one of the first studies to investigate the use of a mobile app for weight loss in the primary care setting, the pragmatic design was impressive. The study showed that introduction to MFP can be done in 5 minutes and that the intervention was highly acceptable to patients. Since research assistants provided minimal guidance to enrolled participants, this study design provided an opportunity to observe the app’s efficacy in the real-world setting for the general public.
Although the authors efficiently designed a valid and pragmatic study, there were several aspects of the study that could be improved. As previously stated by the authors, this study did not explicitly measure the readiness for change, hence it was challenging to accurately and systematically determine the motivation of those in the intervention arm. Additionally, despite the diverse income and race of participants, the majority of the subjects were college-educated. In 2014, only 34% of the U.S. population completed a bachelor’s degree or high, and only 8% completed a master’s or higher degree [8]. This may limit the generalizability of the study, as education level could influence app acceptance and usage behavior.
Applications for Clinical Practice
With a large number of people using smartphones, there are increasing opportunities to use this delivery system to provide weight management information and support strategies for weight loss and lifestyle behavior changes. Previous studies showed that smartphone technology is a promising tool to facilitate weight management[9, 10], but the best practices for implementation of smart phones in the primary setting for weight management are still unknown. Based on this study and previous research done on technology intervention on health, providing the MFP app alone without additional counseling or intervention will not lead to clinically significant weight loss in the majority of patients. Future studies should determine if adding the MFP app is more efficacious when part of a more intensive behavioral intervention. Further studies should also determine whether PCP support in assessing readiness to use health apps and other behavior change technologies increases adherence and weight loss.
—Pich Seekaew, BS, and Melanie Jay MD, MS
1. 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.
2. Clark JM, Brancati FL. The challenge of obesity-related chronic diseases. J Gen Intern Med 2000;15:828–9.
3. Wang CY, Mcpherson K, Marsh T, et al. Health and economic burden of the projected obesity trends in the USA and the UK. Lancet 2011;378:815–25.
4. Finkelstein EA, Trogdon JG, Cohen JW, et al. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Affairs 2009;28:822–31.
5. Smith A. U.S. smartphone use in 2015. Pew Research Center Internet Science Tech RSS. 2015.
6. Levine DM, Savarimuthu S, Squires A, et al. Technology-assisted weight loss interventions in primary care: a systematic review. J Gen Intern Med 2014;30:107–17.
7. 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.
8. U.S. Department of Education, National Center for Education Statistics. The condition of education 2015 (NCES 2015–144). Educational attainment 2015.
9. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight. J Cardiovasc Nurs 2013; 28:320–9.
10. Hebden L, Cook A, Van Der Ploeg HP, Allman-Farinelli M. Development of smartphone applications for nutrition and physical activity behavior change. JMIR Res Protoc 2012;1:e9.
Study Overview
Objective. To evaluate the effectiveness and impact of using MyFitnessPal, a free, popular smartphone application (“app”), for weight loss.
Study design. 2-arm randomized controlled trial.
Setting and participants. Participants were recruited from 2 primary care clinics in the University of California, Los Angeles heath system. The inclusion criteria for the study were ≥ 18 years of age, body mass index (BMI) ≥ 25 kg/m2, an interest in losing weight, and ownership of a smartphone. The exclusion criteria included pregnancy, hemodialysis, life expectancy less than 6 months, lack of interest in weight loss, and current use of a smartphone app for weight loss. Out of 633 individuals assessed, 212 were eligible for the study. Participants were block randomized by BMI 25–30 kg/m2 and BMI > 30 kg/m2 to either usual primary care (n = 107) or usual primary care plus the app (n = 105).
Intervention. MyFitnessPal (MFP) was selected for this study based on previous focus groups with overweight primary care patients. MFP is a calorie-counting app that incorporates evidence-based and theory-based approaches to weight loss. Users can enter their current weight, goal weight, and goal rate of weight loss, which allows the app to generate the user’s daily, individualized calorie goal. MFP users also input daily weight, food intake, and physical activity, which produce certain outputs, including calorie counts, weight trends, and nutritional summaries based on food consumed.
Participants in the intervention arm received help from research assistants in downloading MFP onto their smartphones and received a phone call 1 week after enrollment to assist with any technical issues with the app. Those in the control group were told to choose any preferred activity for weight loss. Both groups received usual care from their primary care provider, with an additional two follow-up visits at 3 and 6 months. At the 3-month follow-up visit, all participants received a nutrition educational handout from www.myplate.gov.
Main outcomes measures. The main outcome measure was weight change at 6 months. Blood pressure, weight, systolic blood pressure (SPB), and 3 self-reported behavioral mediators of weight loss (exercise, diet, and self-efficacy in weight loss) were measured and collected for all participants at baseline and at 3 and 6 months. This study also gathered data from the MyFitnessPal company to measure frequency of app usage. At the 6-month follow-up visit, research assistants asked participants in the intervention arm about their experience using MFP, while those in the control group were asked if they had used MFP in the past 6 months to assess contamination. The authors used a linear mixed effects model (PROC MIXED) in SAS data processing software to investigate the differences in weight change, SBP change, and change in behavioral survey items between the 2 groups while controlling for clinic site. In addition, they performed 2 sensitivity analyses to evaluate the impact of possible informative dropout (income, education, diet experience, treatment group, and baseline value), and the effect of excluding one control group outlier.
Results. The majority of participants were female (73%) with a mean age of 43.4 years (SD = 14.3). The mean BMI was 33.4 kg/m2 (SD = 7.09), and 48% of the participants identified themselves as non-Hispanic white. At the 3-month visit, 26% and 21% of the participants from the intervention and control arms were lost to follow-up. Additionally, at 6 months, 32% and 19% of intervention and control group participants were lost to follow-up.
There was no significant difference in weight change between the two groups at 3 months (control, + 0.54 lb; intervention, –0.06 lb; P = 0.53) or at 6 months (control, +0.6 lb; intervention, –0.07 lb; P = 0.63); between group difference at 3 months was –0.6 lb (95% confidence interval [CI], –2.5 to 1.3 lb; P = 0.53) and at 6 months was –0.67 lb (CI, –3.3 to 2.1 lb; P = 0.63). The sensitivity analysis based on possible missing data also suggested the same outcome with between group difference at 6 months at 0.08 lb (CI, –3.04 to 3.20 lb; P = 0.96). The difference in systolic blood pressure was not significant between the groups.
Participants in the intervention arm used a personal calorie goal more often than those in the control group, with a mean between group difference at 3 months of 1.9 days per week (CI, 1.0 to 2.8; P < 0.001) and a mean between group difference at 6 months of 2.0 days per week (CI, 1.1 to 2.9; P < 0.001). The results also showed that the use of calorie goal feature was significant at 3 months (P < 0.001) and at 6 months (P < 0.001). At the 3-month visit, the authors found that individuals in the intervention group reported decreased self-efficacy in achieving their weight loss goals when compared to their counterparts (–0.85 on a 10-point scale; CI, –1.6 to –0.1; P = 0.026), but self-efficacy was insignificant at the 6-month follow-up. Additionally, the results suggested no difference in self-reported behaviors regarding diet, exercise, and self-efficacy in weight loss between the groups.
The mean number of logins was 61 during the course of the study, and the median total logins was 19. Interestingly, the data showed that there was a rapid decline of logins after enrollment for most participants in the intervention arm. There were 94 users who logged in to the app during the first month and 34 who logged in during the last month of the study. Out of 107 participants from the control group, 14 used MFP during the trial.
Despite a sharp decline in usage, MFP users in the intervention group were satisfied with the app: 79% were somewhat to completely satisfied, 92% would recommend it to a friend, and 80% planned to continue using MFP after the study. The study indicated that there were several aspects that the users liked about MFP including ease of use (100%), feedback on progress (88%), and 48% indicated that it was fun to use. Fewer participants appreciated features such as the reminder feature (42%) and social networking feature (13%). A common theme the authors found in MFP users was increased awareness of food choices, and more caution about food choices. Of those that stopped using MFP, some comments regarding MFP included that it was tedious (84%) and not easy to use (24%).
Conclusion. While most participants were satisfied with MFP, encouraging use of the app did not lead to more weight loss in primary care patients compared to usual care. There was decreased engagement with MFP over time.
Commentary
Despite efforts by the federal government to address the obesity epidemic in the United States, there is still a high prevalence of obesity among children and adults. Approximately 17% of youth and 35% of adults in the United States have a BMI in the obese range (> 30 kg/m2) [1]. In addition to a high association between obesity and chronic diseases such as type 2 diabetes mellitus, hypertension, and hypercholesterolemia[2], the obesity epidemic carries a staggering financial burden. The annual cost of obesity in the U.S. was estimated to be $147 billion in 2008, and the medical costs for each person with obesity was $1429 higher than those with normal weight [3,4]. Therefore, finding cost-effective and easily accessible methods to manage obesity is imperative. The Pew Research Center found that 64% of adults in the U.S. own a smartphone, and 62% of those have used their phone to look up health information[5]. Thus, smartphone applications that deliver weight management information and strategies may be a cost-effective and feasible means to reach a large population.
This study assessed the impact of MyFitnessPal, a free and widely popular mobile application, as an approach to reduce weight among patients in a primary care setting. The authors compared weight change between patients who received usual care from primary care providers (PCPs) and those who used MFP in addition to their usual care. They found no significant difference in weight loss between the two groups at 3 and 6 months during the trial. Despite this negative finding, this study makes important contributions to the e-health literature and highlights important considerations for similar studies.
While the exact reasons for lack of effect are unclear, the lack of effectiveness of such a brief intervention is not surprising. It is important to note that the PCPs did not assist or reinforce the patients to use the MFP app in this study. A systematic review of technology-assisted interventions in primary care suggests that technologies such as smartphone apps could be used as a successful tools for weight loss in the primary care setting, but that there needs to be guidance and feedback from a health care team[6]. Further, the intervention may not have been intense enough to achieve clinically significant weight loss. The U.S. Preventive Service Task Force recommends intensive interventions with at least 12 to 26 visits over 12 months to address obesity[7]. Thus, future studies of this app should include more intensive counseling from the healthcare team to determine if this improves adherence to lifestyle changes.
Strengths of this study include the use of a randomized controlled trial design, double-blinding of both participants and PCPs, and analyses for outlier and missing data. These design considerations increase the validity of the trial outcome. In addition, the authors rigorously assessed the relationship between the exposure and the outcome. The group set a high goal of enrollment to account for potential high rate of attrition (up to 50%). Since there was considerable loss to follow-up at both 3 and 6 months, the authors performed sensitivity analyses to determine if this biased the outcomes. The team assessed crossover contamination at the end of the study by asking participants in the control group if they used MFP during the trial. The authors also conducted a linear regression to examine if baseline self-efficacy was a significant predictor of weight change, controlling for the interaction between self-efficacy and group assignment. Additionally, sensitivity analyses were performed to see if the result from one outlier in the control group who achieved significant weight loss biased the findings.
As one of the first studies to investigate the use of a mobile app for weight loss in the primary care setting, the pragmatic design was impressive. The study showed that introduction to MFP can be done in 5 minutes and that the intervention was highly acceptable to patients. Since research assistants provided minimal guidance to enrolled participants, this study design provided an opportunity to observe the app’s efficacy in the real-world setting for the general public.
Although the authors efficiently designed a valid and pragmatic study, there were several aspects of the study that could be improved. As previously stated by the authors, this study did not explicitly measure the readiness for change, hence it was challenging to accurately and systematically determine the motivation of those in the intervention arm. Additionally, despite the diverse income and race of participants, the majority of the subjects were college-educated. In 2014, only 34% of the U.S. population completed a bachelor’s degree or high, and only 8% completed a master’s or higher degree [8]. This may limit the generalizability of the study, as education level could influence app acceptance and usage behavior.
Applications for Clinical Practice
With a large number of people using smartphones, there are increasing opportunities to use this delivery system to provide weight management information and support strategies for weight loss and lifestyle behavior changes. Previous studies showed that smartphone technology is a promising tool to facilitate weight management[9, 10], but the best practices for implementation of smart phones in the primary setting for weight management are still unknown. Based on this study and previous research done on technology intervention on health, providing the MFP app alone without additional counseling or intervention will not lead to clinically significant weight loss in the majority of patients. Future studies should determine if adding the MFP app is more efficacious when part of a more intensive behavioral intervention. Further studies should also determine whether PCP support in assessing readiness to use health apps and other behavior change technologies increases adherence and weight loss.
—Pich Seekaew, BS, and Melanie Jay MD, MS
Study Overview
Objective. To evaluate the effectiveness and impact of using MyFitnessPal, a free, popular smartphone application (“app”), for weight loss.
Study design. 2-arm randomized controlled trial.
Setting and participants. Participants were recruited from 2 primary care clinics in the University of California, Los Angeles heath system. The inclusion criteria for the study were ≥ 18 years of age, body mass index (BMI) ≥ 25 kg/m2, an interest in losing weight, and ownership of a smartphone. The exclusion criteria included pregnancy, hemodialysis, life expectancy less than 6 months, lack of interest in weight loss, and current use of a smartphone app for weight loss. Out of 633 individuals assessed, 212 were eligible for the study. Participants were block randomized by BMI 25–30 kg/m2 and BMI > 30 kg/m2 to either usual primary care (n = 107) or usual primary care plus the app (n = 105).
Intervention. MyFitnessPal (MFP) was selected for this study based on previous focus groups with overweight primary care patients. MFP is a calorie-counting app that incorporates evidence-based and theory-based approaches to weight loss. Users can enter their current weight, goal weight, and goal rate of weight loss, which allows the app to generate the user’s daily, individualized calorie goal. MFP users also input daily weight, food intake, and physical activity, which produce certain outputs, including calorie counts, weight trends, and nutritional summaries based on food consumed.
Participants in the intervention arm received help from research assistants in downloading MFP onto their smartphones and received a phone call 1 week after enrollment to assist with any technical issues with the app. Those in the control group were told to choose any preferred activity for weight loss. Both groups received usual care from their primary care provider, with an additional two follow-up visits at 3 and 6 months. At the 3-month follow-up visit, all participants received a nutrition educational handout from www.myplate.gov.
Main outcomes measures. The main outcome measure was weight change at 6 months. Blood pressure, weight, systolic blood pressure (SPB), and 3 self-reported behavioral mediators of weight loss (exercise, diet, and self-efficacy in weight loss) were measured and collected for all participants at baseline and at 3 and 6 months. This study also gathered data from the MyFitnessPal company to measure frequency of app usage. At the 6-month follow-up visit, research assistants asked participants in the intervention arm about their experience using MFP, while those in the control group were asked if they had used MFP in the past 6 months to assess contamination. The authors used a linear mixed effects model (PROC MIXED) in SAS data processing software to investigate the differences in weight change, SBP change, and change in behavioral survey items between the 2 groups while controlling for clinic site. In addition, they performed 2 sensitivity analyses to evaluate the impact of possible informative dropout (income, education, diet experience, treatment group, and baseline value), and the effect of excluding one control group outlier.
Results. The majority of participants were female (73%) with a mean age of 43.4 years (SD = 14.3). The mean BMI was 33.4 kg/m2 (SD = 7.09), and 48% of the participants identified themselves as non-Hispanic white. At the 3-month visit, 26% and 21% of the participants from the intervention and control arms were lost to follow-up. Additionally, at 6 months, 32% and 19% of intervention and control group participants were lost to follow-up.
There was no significant difference in weight change between the two groups at 3 months (control, + 0.54 lb; intervention, –0.06 lb; P = 0.53) or at 6 months (control, +0.6 lb; intervention, –0.07 lb; P = 0.63); between group difference at 3 months was –0.6 lb (95% confidence interval [CI], –2.5 to 1.3 lb; P = 0.53) and at 6 months was –0.67 lb (CI, –3.3 to 2.1 lb; P = 0.63). The sensitivity analysis based on possible missing data also suggested the same outcome with between group difference at 6 months at 0.08 lb (CI, –3.04 to 3.20 lb; P = 0.96). The difference in systolic blood pressure was not significant between the groups.
Participants in the intervention arm used a personal calorie goal more often than those in the control group, with a mean between group difference at 3 months of 1.9 days per week (CI, 1.0 to 2.8; P < 0.001) and a mean between group difference at 6 months of 2.0 days per week (CI, 1.1 to 2.9; P < 0.001). The results also showed that the use of calorie goal feature was significant at 3 months (P < 0.001) and at 6 months (P < 0.001). At the 3-month visit, the authors found that individuals in the intervention group reported decreased self-efficacy in achieving their weight loss goals when compared to their counterparts (–0.85 on a 10-point scale; CI, –1.6 to –0.1; P = 0.026), but self-efficacy was insignificant at the 6-month follow-up. Additionally, the results suggested no difference in self-reported behaviors regarding diet, exercise, and self-efficacy in weight loss between the groups.
The mean number of logins was 61 during the course of the study, and the median total logins was 19. Interestingly, the data showed that there was a rapid decline of logins after enrollment for most participants in the intervention arm. There were 94 users who logged in to the app during the first month and 34 who logged in during the last month of the study. Out of 107 participants from the control group, 14 used MFP during the trial.
Despite a sharp decline in usage, MFP users in the intervention group were satisfied with the app: 79% were somewhat to completely satisfied, 92% would recommend it to a friend, and 80% planned to continue using MFP after the study. The study indicated that there were several aspects that the users liked about MFP including ease of use (100%), feedback on progress (88%), and 48% indicated that it was fun to use. Fewer participants appreciated features such as the reminder feature (42%) and social networking feature (13%). A common theme the authors found in MFP users was increased awareness of food choices, and more caution about food choices. Of those that stopped using MFP, some comments regarding MFP included that it was tedious (84%) and not easy to use (24%).
Conclusion. While most participants were satisfied with MFP, encouraging use of the app did not lead to more weight loss in primary care patients compared to usual care. There was decreased engagement with MFP over time.
Commentary
Despite efforts by the federal government to address the obesity epidemic in the United States, there is still a high prevalence of obesity among children and adults. Approximately 17% of youth and 35% of adults in the United States have a BMI in the obese range (> 30 kg/m2) [1]. In addition to a high association between obesity and chronic diseases such as type 2 diabetes mellitus, hypertension, and hypercholesterolemia[2], the obesity epidemic carries a staggering financial burden. The annual cost of obesity in the U.S. was estimated to be $147 billion in 2008, and the medical costs for each person with obesity was $1429 higher than those with normal weight [3,4]. Therefore, finding cost-effective and easily accessible methods to manage obesity is imperative. The Pew Research Center found that 64% of adults in the U.S. own a smartphone, and 62% of those have used their phone to look up health information[5]. Thus, smartphone applications that deliver weight management information and strategies may be a cost-effective and feasible means to reach a large population.
This study assessed the impact of MyFitnessPal, a free and widely popular mobile application, as an approach to reduce weight among patients in a primary care setting. The authors compared weight change between patients who received usual care from primary care providers (PCPs) and those who used MFP in addition to their usual care. They found no significant difference in weight loss between the two groups at 3 and 6 months during the trial. Despite this negative finding, this study makes important contributions to the e-health literature and highlights important considerations for similar studies.
While the exact reasons for lack of effect are unclear, the lack of effectiveness of such a brief intervention is not surprising. It is important to note that the PCPs did not assist or reinforce the patients to use the MFP app in this study. A systematic review of technology-assisted interventions in primary care suggests that technologies such as smartphone apps could be used as a successful tools for weight loss in the primary care setting, but that there needs to be guidance and feedback from a health care team[6]. Further, the intervention may not have been intense enough to achieve clinically significant weight loss. The U.S. Preventive Service Task Force recommends intensive interventions with at least 12 to 26 visits over 12 months to address obesity[7]. Thus, future studies of this app should include more intensive counseling from the healthcare team to determine if this improves adherence to lifestyle changes.
Strengths of this study include the use of a randomized controlled trial design, double-blinding of both participants and PCPs, and analyses for outlier and missing data. These design considerations increase the validity of the trial outcome. In addition, the authors rigorously assessed the relationship between the exposure and the outcome. The group set a high goal of enrollment to account for potential high rate of attrition (up to 50%). Since there was considerable loss to follow-up at both 3 and 6 months, the authors performed sensitivity analyses to determine if this biased the outcomes. The team assessed crossover contamination at the end of the study by asking participants in the control group if they used MFP during the trial. The authors also conducted a linear regression to examine if baseline self-efficacy was a significant predictor of weight change, controlling for the interaction between self-efficacy and group assignment. Additionally, sensitivity analyses were performed to see if the result from one outlier in the control group who achieved significant weight loss biased the findings.
As one of the first studies to investigate the use of a mobile app for weight loss in the primary care setting, the pragmatic design was impressive. The study showed that introduction to MFP can be done in 5 minutes and that the intervention was highly acceptable to patients. Since research assistants provided minimal guidance to enrolled participants, this study design provided an opportunity to observe the app’s efficacy in the real-world setting for the general public.
Although the authors efficiently designed a valid and pragmatic study, there were several aspects of the study that could be improved. As previously stated by the authors, this study did not explicitly measure the readiness for change, hence it was challenging to accurately and systematically determine the motivation of those in the intervention arm. Additionally, despite the diverse income and race of participants, the majority of the subjects were college-educated. In 2014, only 34% of the U.S. population completed a bachelor’s degree or high, and only 8% completed a master’s or higher degree [8]. This may limit the generalizability of the study, as education level could influence app acceptance and usage behavior.
Applications for Clinical Practice
With a large number of people using smartphones, there are increasing opportunities to use this delivery system to provide weight management information and support strategies for weight loss and lifestyle behavior changes. Previous studies showed that smartphone technology is a promising tool to facilitate weight management[9, 10], but the best practices for implementation of smart phones in the primary setting for weight management are still unknown. Based on this study and previous research done on technology intervention on health, providing the MFP app alone without additional counseling or intervention will not lead to clinically significant weight loss in the majority of patients. Future studies should determine if adding the MFP app is more efficacious when part of a more intensive behavioral intervention. Further studies should also determine whether PCP support in assessing readiness to use health apps and other behavior change technologies increases adherence and weight loss.
—Pich Seekaew, BS, and Melanie Jay MD, MS
1. 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.
2. Clark JM, Brancati FL. The challenge of obesity-related chronic diseases. J Gen Intern Med 2000;15:828–9.
3. Wang CY, Mcpherson K, Marsh T, et al. Health and economic burden of the projected obesity trends in the USA and the UK. Lancet 2011;378:815–25.
4. Finkelstein EA, Trogdon JG, Cohen JW, et al. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Affairs 2009;28:822–31.
5. Smith A. U.S. smartphone use in 2015. Pew Research Center Internet Science Tech RSS. 2015.
6. Levine DM, Savarimuthu S, Squires A, et al. Technology-assisted weight loss interventions in primary care: a systematic review. J Gen Intern Med 2014;30:107–17.
7. 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.
8. U.S. Department of Education, National Center for Education Statistics. The condition of education 2015 (NCES 2015–144). Educational attainment 2015.
9. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight. J Cardiovasc Nurs 2013; 28:320–9.
10. Hebden L, Cook A, Van Der Ploeg HP, Allman-Farinelli M. Development of smartphone applications for nutrition and physical activity behavior change. JMIR Res Protoc 2012;1:e9.
1. 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.
2. Clark JM, Brancati FL. The challenge of obesity-related chronic diseases. J Gen Intern Med 2000;15:828–9.
3. Wang CY, Mcpherson K, Marsh T, et al. Health and economic burden of the projected obesity trends in the USA and the UK. Lancet 2011;378:815–25.
4. Finkelstein EA, Trogdon JG, Cohen JW, et al. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Affairs 2009;28:822–31.
5. Smith A. U.S. smartphone use in 2015. Pew Research Center Internet Science Tech RSS. 2015.
6. Levine DM, Savarimuthu S, Squires A, et al. Technology-assisted weight loss interventions in primary care: a systematic review. J Gen Intern Med 2014;30:107–17.
7. 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.
8. U.S. Department of Education, National Center for Education Statistics. The condition of education 2015 (NCES 2015–144). Educational attainment 2015.
9. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight. J Cardiovasc Nurs 2013; 28:320–9.
10. Hebden L, Cook A, Van Der Ploeg HP, Allman-Farinelli M. Development of smartphone applications for nutrition and physical activity behavior change. JMIR Res Protoc 2012;1:e9.