Melanie Jay, MD, MS

Study Overview

Objective. To determine if the rate at which a person loses weight impacts long-term weight management.

Design. Two-phase, non-masked, randomized controlled trial.

Setting and participants. Study participants were recruited through radio and newspaper advertisements and word of mouth in Melbourne, Australia. Eligible participants were randomized into 2 different weight loss programs—a 12-week rapid program or a 36-week gradual program—using a computer-generated randomization sequence with a block design to account for the potential confounding factors of age, sex, and body mass index (BMI). Investigators and laboratory staff were blind to the group assignments. Inclusion criteria were healthy men and women aged between 18–70 years who were weight stable for 3 months and had a BMI between 30.0–45.0kg/m². Exclusion criteria included use of a very low energy diet or weight loss drugs in the previous 3 months, contraceptive use, pregnancy or lactation, smoking, current use of drugs known to affect body weight, previous weight loss surgery, and the presence of clinically significant disease (including diabetes).

Intervention. Participants were randomized to the rapid or gradual weight loss program, both with the stated goal of 15% weight loss. For phase 1, participants in the rapid weight loss group replaced 3 meals a day with a commercially available meal replacement (Optifast, Nestlé Nutrition) over a period of 12 weeks (450–800 kcal/day). Participants in the gradual group replaced 1 to 2 meals daily with the same supplements and followed a diet program based on recommendations from the Australian Guide to Healthy Eating for the other meals over a period of 36 weeks (400–500 kcal deficit per day). Both groups were given comparable dietary education materials and had appointments every 2 weeks with the same dietician. Participants who achieved 12.5% or greater weight loss were eligible for phase 2. In phase 2, participants met with their same dietician at weeks 4 and 12, and then every 12 weeks until week 144. During appointments, the dietician assessed adherence based on participants’ self-reported food intake, and participants were encouraged to partake in 30 minutes of physical activity of mild to moderate intensity. Participants who gained weight were given a 400–500 kcal deficit diet.

Main outcome measures. The main outcome was mean weight loss maintained at week 144 of phase 2. Secondary outcomes were mean difference in fasting ghrelin and leptin concentrations measured at baseline, end of phase 1 (week 12 for rapid and week 36 for gradual), and at weeks 48 and 144 of phase 2. The authors examined the following changes from baseline: weight, BMI, waist and hip circumferences, fat mass, fat free mass, ghrelin, leptin, and physical activity (steps per day). A standardized protocol was followed for all measurements.

Results. Researchers evaluated 525 participants, of which 321 were excluded for ineligibility, being unwilling to participate, or having type 2 diabetes. Of the 204, 4 dropped out after randomization leaving 97 in the rapid weight loss group and 103 in the gradual group during phase 1. The mean age of participants was 49.8 (SD = 10.9) years with 25.5% men. There were no significant demographic or weight differences between the 2 groups. The completion rate for phase 1 was 94% in the rapid program and 82% of the gradual program. The mean phase 1 weight changes in the rapid and gradual program groups were –13 kg and –8.9 kg, respectively. A higher proportion of participants in the rapid weight loss group lost 12.5% or more of their weight than in the gradual group (76/97 vs. 53/103). 127 participants entered phase 2 of the study (2 in the gradual group who lost 12.5% body weight before 12 weeks were excluded). 1 participant in the rapid group developed cholecystitis requiring cholecystectomy.

In Phase 2, seven participants in the rapid group withdrew due to logistical issues, psychological stress, and other health-related issues; 4 participants in the gradual group withdrew for the same reasons, as well as pregnancy. 2 participants from the rapid group developed cancer. All but 6 participants regained weight (5 in rapid group, 1 in gradual group) and were put on a 400-500 kcal deficit diet. There was no significant difference in mean weight regain of the rapid and gradual participants. By week 144 of phase 2, average weight regain in the gradual group was 10.4 kg (95% confidence interval [CI] 8.4–12.4; 71.2% of lost weight regained, CI 58.1–84.3) and 10.3 kg in rapid weight loss participants (95% CI 8.5–12.1; 70.5% of lost weight regained, CI 57.8–83.2). This result did not change significantly in the intention to treat analysis where dropouts were assumed to return to baseline.

During phase 2, leptin concentrations increased in both groups, and there was no difference in leptin concentrations between the 2 groups at weeks 48 and 144, nor were they significantly different from baseline at week 48. Ghrelin concentrations increased in both groups from baseline, but there was no significant difference between the groups at the end of 144 weeks.

Conclusion. In highly selected Australian participants, rapid weight loss (12 weeks) using a very low calorie meal replacement program led to greater weight loss than a gradual weight loss program (36 weeks) using a combination of meal replacements and diet recommendations. In participants who lost 12.5% or greater body weight, the speed at which participants regained weight was similar in both groups.

Commentary

Obesity rates have increased globally over the past 20 years. In the United States, Yang and Colditz found that approximately 35% of men and 37% of women are obese and approximately 40% of men and 30% of women are overweight, marking the first time that obese Americans outnumber overweight Americans [1]. Approximately 45 million Americans diet each year, and Americans spend $33 billion on weight-loss products annually. Thus, we need to determine the most effective and cost-effective weight management practices. The Purcell et al study suggests that a 12-week intervention may lead to greater weight loss and better adherence than a 36-week program, and that weight regain in participants achieving 12.5% or greater weight loss may be the same in both interventions. While they did not formally evaluate cost effectiveness, these findings suggest that a rapid weight loss program through a very low calorie diet (VLCD) may be more cost-effective since they achieved better results in a shorter period of time. However, caution must be taken before universally recommending VLCDs to promote rapid weight loss.

Many organizations advise patients to lose weight slowly to increase their chances of reaching weight loss goals and long-term success. The American Heart Association, American College of Cardiology, and The Obesity Society (AHA/ACC/TOS) guidelines for the management of overweight and obesity in adults recommend 3 types of diets for weight loss: a 1200–1800 calorie diet, depending on weight and gender; a 500 kcal/day or 750kcal/day energy deficit, or an evidence-based diet that restricts specific food types (such as high-carbohydrate foods) [2]. These guidelines also state that individuals likely need to follow lifestyle changes for more than 6 months to increase their chances of achieving weight loss goals [2]. They acknowledge maximum weight loss is typically achieved at 6 months, and is commonly followed by plateau and gradual regain [2]. The US Preventive Services Task Force (USPSTF) also advises gradual weight loss [3].

The results of the Purcell et al study and others provide evidence that contradicts these recommendations. For example, Nackers et al found that people who lost weight quickly achieved and maintained greater weight loss than participants who lost weight gradually [4]. Further, those who lost weight rapidly were no more susceptible to regaining weight than people who lost weight gradually [4]. Toburo and Astrup also found the rate of initial weight loss had no impact on the long-term outcomes of weight maintenance [5]. Astrup and Rössner found initial weight loss was positively associated with long-term weight maintenance, and rapid weight loss resulted in improved sustained weight maintenance [6]. Finally, Wing and Phelan found the best predictor of weight regain was the length of time weight loss was maintained, not how the weight was lost [7].

VCLDs replace regular meals with prepared formulas to promote rapid weight loss, and are not recommended for the mildly obese or overweight. VLCDs have been shown to greatly reduce cardiovascular risk factors and relieve obesity-related symptoms; however, they result in more side effects compared to a low calorie diet [8]. Individuals who follow VLCDs must be monitored regularly to ensure they do not experience serious side effects, such as gallstones, electrolyte imbalance that can cause muscle and nerve malfunction, and an irregular heartbeat [9]. Indeed, 1 patient in the rapid group required a cholecystectomy. The providers in this study were obesity specialists, which may account for the strong outcomes and relatively few adverse events.

This study has many strengths. First, researchers achieved low rates of attrition (22% compared to about 40% in other studies) [9,10]. This study also followed participants for 2 years post-intervention and achieved high rates of weight loss in both groups. In addition to low dropout rates and long-term follow-up, the population was highly adherent to each intervention. Limitations of the study include that the authors were highly selective in choosing participants—none of the participants had obesity-related comorbidities such as diabetes or significant medical conditions. Individuals with these conditions may not be able to follow the dietary recommendations used in this study, restricting generalizability from a population that is largely overweight and obese. Further, all participants were from Melbourne, Australia. Since the authors did not provide data on race/ethnicity, we can assume a relatively homogeneous population, further limiting generalizability.

Applications for Clinical Practice

This study suggests that rapid weight loss through VLCDs may achieve better weight loss outcomes and adherence when compared to more gradual programs without resulting in higher weight regain over time in highly selected patients treated by obesity specialists. Caution must be advised since primary care practitioners may not have sufficient training to deliver these diets. VLCDs have higher risk of gallstones and other adverse outcomes such as gout or cardiac events [11,12]. A more gradual weight loss program, similar to the 36-week program in the Purcell et al study, used meal replacements and achieved outcomes that were relatively high, with 72% achieving at least 5% weight loss, and 19% achieving 15% weight loss or greater (P < 0.001) [13]. Indeed, meal replacements of 1 to 2 meals per day have been shown to be safe and effective in primary care [14]. Current AHA/ACC/TOS guidelines on VLCDs are inconclusive, stating there is insufficient evidence to comment on the value of VLCDs, or on strategies to provide more supervision of adherence to these diets [2]. Thus, practitioners without training in the use of VLCDs should still follow USPSTF and other recommendations to promote gradual weight loss [2]. However, if patients want to lose weight faster with a VLCD, then providers can refer them to an obesity specialist since this may promote greater adherence and long-term weight maintenance in select patients.

—Natalie L. Ricci, Mailman School of Public Health, New York, NY, and Melanie Jay, MD, MS

Study Overview

Objective. To determine if the rate at which a person loses weight impacts long-term weight management.

Design. Two-phase, non-masked, randomized controlled trial.

Setting and participants. Study participants were recruited through radio and newspaper advertisements and word of mouth in Melbourne, Australia. Eligible participants were randomized into 2 different weight loss programs—a 12-week rapid program or a 36-week gradual program—using a computer-generated randomization sequence with a block design to account for the potential confounding factors of age, sex, and body mass index (BMI). Investigators and laboratory staff were blind to the group assignments. Inclusion criteria were healthy men and women aged between 18–70 years who were weight stable for 3 months and had a BMI between 30.0–45.0kg/m². Exclusion criteria included use of a very low energy diet or weight loss drugs in the previous 3 months, contraceptive use, pregnancy or lactation, smoking, current use of drugs known to affect body weight, previous weight loss surgery, and the presence of clinically significant disease (including diabetes).

Intervention. Participants were randomized to the rapid or gradual weight loss program, both with the stated goal of 15% weight loss. For phase 1, participants in the rapid weight loss group replaced 3 meals a day with a commercially available meal replacement (Optifast, Nestlé Nutrition) over a period of 12 weeks (450–800 kcal/day). Participants in the gradual group replaced 1 to 2 meals daily with the same supplements and followed a diet program based on recommendations from the Australian Guide to Healthy Eating for the other meals over a period of 36 weeks (400–500 kcal deficit per day). Both groups were given comparable dietary education materials and had appointments every 2 weeks with the same dietician. Participants who achieved 12.5% or greater weight loss were eligible for phase 2. In phase 2, participants met with their same dietician at weeks 4 and 12, and then every 12 weeks until week 144. During appointments, the dietician assessed adherence based on participants’ self-reported food intake, and participants were encouraged to partake in 30 minutes of physical activity of mild to moderate intensity. Participants who gained weight were given a 400–500 kcal deficit diet.

Main outcome measures. The main outcome was mean weight loss maintained at week 144 of phase 2. Secondary outcomes were mean difference in fasting ghrelin and leptin concentrations measured at baseline, end of phase 1 (week 12 for rapid and week 36 for gradual), and at weeks 48 and 144 of phase 2. The authors examined the following changes from baseline: weight, BMI, waist and hip circumferences, fat mass, fat free mass, ghrelin, leptin, and physical activity (steps per day). A standardized protocol was followed for all measurements.

Results. Researchers evaluated 525 participants, of which 321 were excluded for ineligibility, being unwilling to participate, or having type 2 diabetes. Of the 204, 4 dropped out after randomization leaving 97 in the rapid weight loss group and 103 in the gradual group during phase 1. The mean age of participants was 49.8 (SD = 10.9) years with 25.5% men. There were no significant demographic or weight differences between the 2 groups. The completion rate for phase 1 was 94% in the rapid program and 82% of the gradual program. The mean phase 1 weight changes in the rapid and gradual program groups were –13 kg and –8.9 kg, respectively. A higher proportion of participants in the rapid weight loss group lost 12.5% or more of their weight than in the gradual group (76/97 vs. 53/103). 127 participants entered phase 2 of the study (2 in the gradual group who lost 12.5% body weight before 12 weeks were excluded). 1 participant in the rapid group developed cholecystitis requiring cholecystectomy.

In Phase 2, seven participants in the rapid group withdrew due to logistical issues, psychological stress, and other health-related issues; 4 participants in the gradual group withdrew for the same reasons, as well as pregnancy. 2 participants from the rapid group developed cancer. All but 6 participants regained weight (5 in rapid group, 1 in gradual group) and were put on a 400-500 kcal deficit diet. There was no significant difference in mean weight regain of the rapid and gradual participants. By week 144 of phase 2, average weight regain in the gradual group was 10.4 kg (95% confidence interval [CI] 8.4–12.4; 71.2% of lost weight regained, CI 58.1–84.3) and 10.3 kg in rapid weight loss participants (95% CI 8.5–12.1; 70.5% of lost weight regained, CI 57.8–83.2). This result did not change significantly in the intention to treat analysis where dropouts were assumed to return to baseline.

During phase 2, leptin concentrations increased in both groups, and there was no difference in leptin concentrations between the 2 groups at weeks 48 and 144, nor were they significantly different from baseline at week 48. Ghrelin concentrations increased in both groups from baseline, but there was no significant difference between the groups at the end of 144 weeks.

Conclusion. In highly selected Australian participants, rapid weight loss (12 weeks) using a very low calorie meal replacement program led to greater weight loss than a gradual weight loss program (36 weeks) using a combination of meal replacements and diet recommendations. In participants who lost 12.5% or greater body weight, the speed at which participants regained weight was similar in both groups.

Commentary

Obesity rates have increased globally over the past 20 years. In the United States, Yang and Colditz found that approximately 35% of men and 37% of women are obese and approximately 40% of men and 30% of women are overweight, marking the first time that obese Americans outnumber overweight Americans [1]. Approximately 45 million Americans diet each year, and Americans spend $33 billion on weight-loss products annually. Thus, we need to determine the most effective and cost-effective weight management practices. The Purcell et al study suggests that a 12-week intervention may lead to greater weight loss and better adherence than a 36-week program, and that weight regain in participants achieving 12.5% or greater weight loss may be the same in both interventions. While they did not formally evaluate cost effectiveness, these findings suggest that a rapid weight loss program through a very low calorie diet (VLCD) may be more cost-effective since they achieved better results in a shorter period of time. However, caution must be taken before universally recommending VLCDs to promote rapid weight loss.

Many organizations advise patients to lose weight slowly to increase their chances of reaching weight loss goals and long-term success. The American Heart Association, American College of Cardiology, and The Obesity Society (AHA/ACC/TOS) guidelines for the management of overweight and obesity in adults recommend 3 types of diets for weight loss: a 1200–1800 calorie diet, depending on weight and gender; a 500 kcal/day or 750kcal/day energy deficit, or an evidence-based diet that restricts specific food types (such as high-carbohydrate foods) [2]. These guidelines also state that individuals likely need to follow lifestyle changes for more than 6 months to increase their chances of achieving weight loss goals [2]. They acknowledge maximum weight loss is typically achieved at 6 months, and is commonly followed by plateau and gradual regain [2]. The US Preventive Services Task Force (USPSTF) also advises gradual weight loss [3].

The results of the Purcell et al study and others provide evidence that contradicts these recommendations. For example, Nackers et al found that people who lost weight quickly achieved and maintained greater weight loss than participants who lost weight gradually [4]. Further, those who lost weight rapidly were no more susceptible to regaining weight than people who lost weight gradually [4]. Toburo and Astrup also found the rate of initial weight loss had no impact on the long-term outcomes of weight maintenance [5]. Astrup and Rössner found initial weight loss was positively associated with long-term weight maintenance, and rapid weight loss resulted in improved sustained weight maintenance [6]. Finally, Wing and Phelan found the best predictor of weight regain was the length of time weight loss was maintained, not how the weight was lost [7].

VCLDs replace regular meals with prepared formulas to promote rapid weight loss, and are not recommended for the mildly obese or overweight. VLCDs have been shown to greatly reduce cardiovascular risk factors and relieve obesity-related symptoms; however, they result in more side effects compared to a low calorie diet [8]. Individuals who follow VLCDs must be monitored regularly to ensure they do not experience serious side effects, such as gallstones, electrolyte imbalance that can cause muscle and nerve malfunction, and an irregular heartbeat [9]. Indeed, 1 patient in the rapid group required a cholecystectomy. The providers in this study were obesity specialists, which may account for the strong outcomes and relatively few adverse events.

This study has many strengths. First, researchers achieved low rates of attrition (22% compared to about 40% in other studies) [9,10]. This study also followed participants for 2 years post-intervention and achieved high rates of weight loss in both groups. In addition to low dropout rates and long-term follow-up, the population was highly adherent to each intervention. Limitations of the study include that the authors were highly selective in choosing participants—none of the participants had obesity-related comorbidities such as diabetes or significant medical conditions. Individuals with these conditions may not be able to follow the dietary recommendations used in this study, restricting generalizability from a population that is largely overweight and obese. Further, all participants were from Melbourne, Australia. Since the authors did not provide data on race/ethnicity, we can assume a relatively homogeneous population, further limiting generalizability.

Applications for Clinical Practice

This study suggests that rapid weight loss through VLCDs may achieve better weight loss outcomes and adherence when compared to more gradual programs without resulting in higher weight regain over time in highly selected patients treated by obesity specialists. Caution must be advised since primary care practitioners may not have sufficient training to deliver these diets. VLCDs have higher risk of gallstones and other adverse outcomes such as gout or cardiac events [11,12]. A more gradual weight loss program, similar to the 36-week program in the Purcell et al study, used meal replacements and achieved outcomes that were relatively high, with 72% achieving at least 5% weight loss, and 19% achieving 15% weight loss or greater (P < 0.001) [13]. Indeed, meal replacements of 1 to 2 meals per day have been shown to be safe and effective in primary care [14]. Current AHA/ACC/TOS guidelines on VLCDs are inconclusive, stating there is insufficient evidence to comment on the value of VLCDs, or on strategies to provide more supervision of adherence to these diets [2]. Thus, practitioners without training in the use of VLCDs should still follow USPSTF and other recommendations to promote gradual weight loss [2]. However, if patients want to lose weight faster with a VLCD, then providers can refer them to an obesity specialist since this may promote greater adherence and long-term weight maintenance in select patients.

—Natalie L. Ricci, Mailman School of Public Health, New York, NY, and Melanie Jay, MD, MS

Study Overview

Objective. To determine if the rate at which a person loses weight impacts long-term weight management.

Design. Two-phase, non-masked, randomized controlled trial.

Setting and participants. Study participants were recruited through radio and newspaper advertisements and word of mouth in Melbourne, Australia. Eligible participants were randomized into 2 different weight loss programs—a 12-week rapid program or a 36-week gradual program—using a computer-generated randomization sequence with a block design to account for the potential confounding factors of age, sex, and body mass index (BMI). Investigators and laboratory staff were blind to the group assignments. Inclusion criteria were healthy men and women aged between 18–70 years who were weight stable for 3 months and had a BMI between 30.0–45.0kg/m². Exclusion criteria included use of a very low energy diet or weight loss drugs in the previous 3 months, contraceptive use, pregnancy or lactation, smoking, current use of drugs known to affect body weight, previous weight loss surgery, and the presence of clinically significant disease (including diabetes).

Intervention. Participants were randomized to the rapid or gradual weight loss program, both with the stated goal of 15% weight loss. For phase 1, participants in the rapid weight loss group replaced 3 meals a day with a commercially available meal replacement (Optifast, Nestlé Nutrition) over a period of 12 weeks (450–800 kcal/day). Participants in the gradual group replaced 1 to 2 meals daily with the same supplements and followed a diet program based on recommendations from the Australian Guide to Healthy Eating for the other meals over a period of 36 weeks (400–500 kcal deficit per day). Both groups were given comparable dietary education materials and had appointments every 2 weeks with the same dietician. Participants who achieved 12.5% or greater weight loss were eligible for phase 2. In phase 2, participants met with their same dietician at weeks 4 and 12, and then every 12 weeks until week 144. During appointments, the dietician assessed adherence based on participants’ self-reported food intake, and participants were encouraged to partake in 30 minutes of physical activity of mild to moderate intensity. Participants who gained weight were given a 400–500 kcal deficit diet.

Main outcome measures. The main outcome was mean weight loss maintained at week 144 of phase 2. Secondary outcomes were mean difference in fasting ghrelin and leptin concentrations measured at baseline, end of phase 1 (week 12 for rapid and week 36 for gradual), and at weeks 48 and 144 of phase 2. The authors examined the following changes from baseline: weight, BMI, waist and hip circumferences, fat mass, fat free mass, ghrelin, leptin, and physical activity (steps per day). A standardized protocol was followed for all measurements.

Results. Researchers evaluated 525 participants, of which 321 were excluded for ineligibility, being unwilling to participate, or having type 2 diabetes. Of the 204, 4 dropped out after randomization leaving 97 in the rapid weight loss group and 103 in the gradual group during phase 1. The mean age of participants was 49.8 (SD = 10.9) years with 25.5% men. There were no significant demographic or weight differences between the 2 groups. The completion rate for phase 1 was 94% in the rapid program and 82% of the gradual program. The mean phase 1 weight changes in the rapid and gradual program groups were –13 kg and –8.9 kg, respectively. A higher proportion of participants in the rapid weight loss group lost 12.5% or more of their weight than in the gradual group (76/97 vs. 53/103). 127 participants entered phase 2 of the study (2 in the gradual group who lost 12.5% body weight before 12 weeks were excluded). 1 participant in the rapid group developed cholecystitis requiring cholecystectomy.

In Phase 2, seven participants in the rapid group withdrew due to logistical issues, psychological stress, and other health-related issues; 4 participants in the gradual group withdrew for the same reasons, as well as pregnancy. 2 participants from the rapid group developed cancer. All but 6 participants regained weight (5 in rapid group, 1 in gradual group) and were put on a 400-500 kcal deficit diet. There was no significant difference in mean weight regain of the rapid and gradual participants. By week 144 of phase 2, average weight regain in the gradual group was 10.4 kg (95% confidence interval [CI] 8.4–12.4; 71.2% of lost weight regained, CI 58.1–84.3) and 10.3 kg in rapid weight loss participants (95% CI 8.5–12.1; 70.5% of lost weight regained, CI 57.8–83.2). This result did not change significantly in the intention to treat analysis where dropouts were assumed to return to baseline.

During phase 2, leptin concentrations increased in both groups, and there was no difference in leptin concentrations between the 2 groups at weeks 48 and 144, nor were they significantly different from baseline at week 48. Ghrelin concentrations increased in both groups from baseline, but there was no significant difference between the groups at the end of 144 weeks.

Conclusion. In highly selected Australian participants, rapid weight loss (12 weeks) using a very low calorie meal replacement program led to greater weight loss than a gradual weight loss program (36 weeks) using a combination of meal replacements and diet recommendations. In participants who lost 12.5% or greater body weight, the speed at which participants regained weight was similar in both groups.

Commentary

Obesity rates have increased globally over the past 20 years. In the United States, Yang and Colditz found that approximately 35% of men and 37% of women are obese and approximately 40% of men and 30% of women are overweight, marking the first time that obese Americans outnumber overweight Americans [1]. Approximately 45 million Americans diet each year, and Americans spend $33 billion on weight-loss products annually. Thus, we need to determine the most effective and cost-effective weight management practices. The Purcell et al study suggests that a 12-week intervention may lead to greater weight loss and better adherence than a 36-week program, and that weight regain in participants achieving 12.5% or greater weight loss may be the same in both interventions. While they did not formally evaluate cost effectiveness, these findings suggest that a rapid weight loss program through a very low calorie diet (VLCD) may be more cost-effective since they achieved better results in a shorter period of time. However, caution must be taken before universally recommending VLCDs to promote rapid weight loss.

Many organizations advise patients to lose weight slowly to increase their chances of reaching weight loss goals and long-term success. The American Heart Association, American College of Cardiology, and The Obesity Society (AHA/ACC/TOS) guidelines for the management of overweight and obesity in adults recommend 3 types of diets for weight loss: a 1200–1800 calorie diet, depending on weight and gender; a 500 kcal/day or 750kcal/day energy deficit, or an evidence-based diet that restricts specific food types (such as high-carbohydrate foods) [2]. These guidelines also state that individuals likely need to follow lifestyle changes for more than 6 months to increase their chances of achieving weight loss goals [2]. They acknowledge maximum weight loss is typically achieved at 6 months, and is commonly followed by plateau and gradual regain [2]. The US Preventive Services Task Force (USPSTF) also advises gradual weight loss [3].

The results of the Purcell et al study and others provide evidence that contradicts these recommendations. For example, Nackers et al found that people who lost weight quickly achieved and maintained greater weight loss than participants who lost weight gradually [4]. Further, those who lost weight rapidly were no more susceptible to regaining weight than people who lost weight gradually [4]. Toburo and Astrup also found the rate of initial weight loss had no impact on the long-term outcomes of weight maintenance [5]. Astrup and Rössner found initial weight loss was positively associated with long-term weight maintenance, and rapid weight loss resulted in improved sustained weight maintenance [6]. Finally, Wing and Phelan found the best predictor of weight regain was the length of time weight loss was maintained, not how the weight was lost [7].

VCLDs replace regular meals with prepared formulas to promote rapid weight loss, and are not recommended for the mildly obese or overweight. VLCDs have been shown to greatly reduce cardiovascular risk factors and relieve obesity-related symptoms; however, they result in more side effects compared to a low calorie diet [8]. Individuals who follow VLCDs must be monitored regularly to ensure they do not experience serious side effects, such as gallstones, electrolyte imbalance that can cause muscle and nerve malfunction, and an irregular heartbeat [9]. Indeed, 1 patient in the rapid group required a cholecystectomy. The providers in this study were obesity specialists, which may account for the strong outcomes and relatively few adverse events.

This study has many strengths. First, researchers achieved low rates of attrition (22% compared to about 40% in other studies) [9,10]. This study also followed participants for 2 years post-intervention and achieved high rates of weight loss in both groups. In addition to low dropout rates and long-term follow-up, the population was highly adherent to each intervention. Limitations of the study include that the authors were highly selective in choosing participants—none of the participants had obesity-related comorbidities such as diabetes or significant medical conditions. Individuals with these conditions may not be able to follow the dietary recommendations used in this study, restricting generalizability from a population that is largely overweight and obese. Further, all participants were from Melbourne, Australia. Since the authors did not provide data on race/ethnicity, we can assume a relatively homogeneous population, further limiting generalizability.

Applications for Clinical Practice

This study suggests that rapid weight loss through VLCDs may achieve better weight loss outcomes and adherence when compared to more gradual programs without resulting in higher weight regain over time in highly selected patients treated by obesity specialists. Caution must be advised since primary care practitioners may not have sufficient training to deliver these diets. VLCDs have higher risk of gallstones and other adverse outcomes such as gout or cardiac events [11,12]. A more gradual weight loss program, similar to the 36-week program in the Purcell et al study, used meal replacements and achieved outcomes that were relatively high, with 72% achieving at least 5% weight loss, and 19% achieving 15% weight loss or greater (P < 0.001) [13]. Indeed, meal replacements of 1 to 2 meals per day have been shown to be safe and effective in primary care [14]. Current AHA/ACC/TOS guidelines on VLCDs are inconclusive, stating there is insufficient evidence to comment on the value of VLCDs, or on strategies to provide more supervision of adherence to these diets [2]. Thus, practitioners without training in the use of VLCDs should still follow USPSTF and other recommendations to promote gradual weight loss [2]. However, if patients want to lose weight faster with a VLCD, then providers can refer them to an obesity specialist since this may promote greater adherence and long-term weight maintenance in select patients.

—Natalie L. Ricci, Mailman School of Public Health, New York, NY, and Melanie Jay, MD, MS

Study Overview

Objective. To determine the effectiveness of a nurse-led, telephone-delivered behavioral intervention for diabetes (DM) and hypertension (HTN) versus an attention control within primary care community practices.

Study design. A 9-site, 2-arm randomized controlled trial.

Setting and participants. Study participants were recruited from 9 community practices within the Duke Primary Care Research Consortium. The practices were chosen because they traditionally operate outside of the academic context. Subjects were required to have both type 2 DM and HTN, as indicated by their medications and confirmed by administrative data as well as patient self-reporting. Participants had to have been seen at participating practices for at least 1 year and have poorly controlled DM (indicated by most recent A1c ≥ 7.5%), but they were not required to have poorly controlled HTN. Exclusion criteria included fewer than 1 primary care clinic visit during the previous year, serious comorbid illness, type 1 diabetes, inability to receive a telephone intervention in English, residence in a nursing home, and participation in another hypertension or diabetes study [1]. Participants were randomly assigned using a computer-generated randomization sequence [1] to either the intervention or control groups at a 1:1 ratio, stratified by clinic and baseline blood pressure (BP) control.

Intervention. A single nurse with extensive experience in case management delivered both the behavioral intervention and attention control by telephone. In both arms, calls were conducted once every 2 months over a 24-month period.

The calls in the intervention arm consisted of tailored behavior-modifying techniques according to patient barriers. This content was divided into a series of modules relevant to behaviors associated with improving control of BP or blood sugar, including physical activity, weight reduction, sodium intake, smoking cessation, medication adherence, and others. These modules were scheduled according to patient needs (based on certain parameters such as high body mass index or use of insulin) and preferences [1].

The calls in the attention control were not tailored but rather consisted of didactic health-related information unrelated to HTN or DM (eg, flu shots, skin cancer prevention). This content was also highly scripted and designed to limit the potential for interaction between the nurse and patient.

Main outcome measures. A1c and systolic blood pressure (SBP) were primary outcomes. Key secondary outcomes were diastolic blood pressure (DBP), overall BP control, weight, physical activity, self-efficacy, and medication adherence. Study staff obtained measurements at baseline and 6, 12, and 24 months [1].

Results. The researchers assessed 2601 patients for eligibility and excluded 2224. Most patients were excluded for not meeting inclusion criteria (n = 1156), in particular because of improved HbA1c control (n = 983), and 1064 declined to participate. They randomized 377 patients—193 to the intervention arm and 184 to the attention control arm. Participants had an average age of 58.7, 49.1% had an education level of high school or less, 50.1% were non-white, and 54.9% were unemployed/retired. Patient characteristics in the intervention and control arms were similar at baseline. Seventy-eight percent of patients completed the 12-month follow-up and 70% (263) reached the 24-month endpoint. Patients in the intervention arm completed 78% of scheduled calls while patients in the control group completed 81%.

After adjusting for stratification variables, the estimated mean A1c and SBP were similar between arms at 24 months (intervention 0.1% higher than control, 95% CI −0.3 % to 0.5 %, P = 0.50 for A1c; intervention 0.9 mm Hg lower than control, 95% CI −5.4 to 3.5, P = 0.69 for SBP). There were also no significant differences between arms in mean A1c or SBP at 6 or 12 months. However, A1c levels did improve within each arm at the end of the study, with the intervention group improving by approx-imately 0.5% and the control group improving by approximately 0.6%. In terms of secondary outcomes, there were no significant differences between arms in DBP, weight, physical activity, or BP control rates throughout the 2-year study period.

Conclusion. Overall, the intervention and control groups did not differ significantly in terms of A1c, SBP, or any of the secondary outcomes at any point during the 2-year study.

Commentary

The prevalence of type 2 diabetes and its comorbidities (such as hypertension and obesity) have increased due to a variety of factors including an aging population and an increasingly sedentary lifestyle. Several nurse management programs for DM and HTN have been shown to be efficacious in reducing blood sugar levels [2–4] and promoting BP control [5,6]. However, these interventions were conducted in tightly controlled academic settings, and it is unclear how well these programs may translate into community settings. The aim of this study was to test the effectiveness of a nurse-led behavioral telephone intervention for the comanagement of DM and HTN within non–academically affiliated community practices. Results indicated no significant differences between the intervention and control groups for A1c levels or SBP at any point during the 2-year study, but A1c levels did improve for both arms.

Despite this being a negative study, it is a unique and important contribution to the literature. It is the only trial as of yet that has tested the effectiveness of a nurse management intervention targeting both DM and HTN in a real-world, community setting. This novel approach is supported by data that suggests BP control is actually more cost-effective than intensive glycemic control in treating patients with type 2 diabetes [7]. There were several strengths to the study design, including the use of intention-to-treat analysis, stratified randomization, a diverse patient population, and blinding of the study staff who took BP and A1c measurements. Furthermore, a single nurse conducted all telephone calls, ensuring that differences in counseling skill levels would not affect the results of the study. The few weaknesses of the study included the fact that the nurse who delivered the intervention (as well as the patients) could not be blinded to treatment allocation, and the income of study participants was not reported.

The reasons for the negative outcomes of this study are unclear. The authors claim that similar interventions within academic settings have been shown to be effective and speculate that time and financial pressures of community practices may be reasons that the intervention was not successful. However, the “successful” interventions that they cite were quite different from and more intensive than this intervention. For instance, many of these studies used at least 1 call per month [3,4,8], and one even conducted several calls each week [3]. Furthermore, a DM study conducted by Blackberry et al in a community setting with less than 1 call per month (8 calls over 18 months) similarly failed to produce significant results [9], and therefore more frequent calls may be necessary in DM and HTN interventions. In a systematic review, Eakin et al demonstrated that 12 or more calls in a 6- to 12-month period were associated with better outcomes in physical activity and diet interventions [10], and this may also translate to closely related DM and HTN interventions.

In addition to the infrequent calls, this intervention also lacked communication and integration with patients’ primary care teams. Several studies have demonstrated that integration with primary care teams can improve outcomes in DM and HTN interventions [11,12], and nearly all of the successful studies cited by the authors also included at least some form of communication with patients’ primary care providers (PCPs) [2–4,5,8]. In many of these studies the nurse also had prescribing rights to alter medications [2,3,5]. The nurse in this study met monthly with an expert team of clinicians to discuss patient issues but did not communicate directly with any of the patients’ PCPs [1]. The authors acknowledge that this lack of integration may have contributed to their negative results and point to the fact that it is harder to integrate interventions within community practices that often lack internal integration. However, Walsh, Harris, and Roberts demonstrated that integration between primary and secondary care teams was both feasible and effective for a diabetes initiative within community practices [13].

An additional important feature not present in this intervention was self-monitoring of BP levels. Home self-monitoring of BP has been demonstrated to significantly improve BP levels [14], and 2 of the successful studies in academic settings cited by the authors also included a BP self-monitoring component [5,6]. In one of these studies [6], Bosworth et al conducted a 2 × 2 randomized trial to improve HTN control in which the arms consisted of (a) usual care, (b) bimonthly nurse administered telephone intervention only (this arm was highly similar to the intervention arm in this study), (c) BP monitoring 3 times a week only, and (d) a combination of the telephone intervention with the BP monitoring. Interestingly, the only arm that was successful relative to usual care was the combination of the telephone intervention and BP self-monitoring; the arm consisting only of bi-monthly telephone calls (very similar to this intervention) failed despite the study taking place in an academic setting (it was also less effective than BP monitoring only). Thus, the addition of self-monitoring to a nurse case management telephone intervention can achieve better results.

Applications for Clinical Practice

A telephone-based intervention delivered by a trained nurse for co-management of DM and HTN was not more effective than an attention control delivered by the same nurse in a community setting. This may have been due to several factors, including low intensity marked by less than 1 call per month, a lack of integration with other members of the primary care team, and lack of a BP self-monitoring component. Future studies are needed to determine the optimal type and duration of nurse case management interventions targeting glucose and BP control for diabetic patients in community settings.

—Sandeep Sikerwar, BA, and Melanie Jay, MD, MS

Study Overview

Objective. To determine the effectiveness of a nurse-led, telephone-delivered behavioral intervention for diabetes (DM) and hypertension (HTN) versus an attention control within primary care community practices.

Study design. A 9-site, 2-arm randomized controlled trial.

Setting and participants. Study participants were recruited from 9 community practices within the Duke Primary Care Research Consortium. The practices were chosen because they traditionally operate outside of the academic context. Subjects were required to have both type 2 DM and HTN, as indicated by their medications and confirmed by administrative data as well as patient self-reporting. Participants had to have been seen at participating practices for at least 1 year and have poorly controlled DM (indicated by most recent A1c ≥ 7.5%), but they were not required to have poorly controlled HTN. Exclusion criteria included fewer than 1 primary care clinic visit during the previous year, serious comorbid illness, type 1 diabetes, inability to receive a telephone intervention in English, residence in a nursing home, and participation in another hypertension or diabetes study [1]. Participants were randomly assigned using a computer-generated randomization sequence [1] to either the intervention or control groups at a 1:1 ratio, stratified by clinic and baseline blood pressure (BP) control.

Intervention. A single nurse with extensive experience in case management delivered both the behavioral intervention and attention control by telephone. In both arms, calls were conducted once every 2 months over a 24-month period.

The calls in the intervention arm consisted of tailored behavior-modifying techniques according to patient barriers. This content was divided into a series of modules relevant to behaviors associated with improving control of BP or blood sugar, including physical activity, weight reduction, sodium intake, smoking cessation, medication adherence, and others. These modules were scheduled according to patient needs (based on certain parameters such as high body mass index or use of insulin) and preferences [1].

The calls in the attention control were not tailored but rather consisted of didactic health-related information unrelated to HTN or DM (eg, flu shots, skin cancer prevention). This content was also highly scripted and designed to limit the potential for interaction between the nurse and patient.

Main outcome measures. A1c and systolic blood pressure (SBP) were primary outcomes. Key secondary outcomes were diastolic blood pressure (DBP), overall BP control, weight, physical activity, self-efficacy, and medication adherence. Study staff obtained measurements at baseline and 6, 12, and 24 months [1].

Results. The researchers assessed 2601 patients for eligibility and excluded 2224. Most patients were excluded for not meeting inclusion criteria (n = 1156), in particular because of improved HbA1c control (n = 983), and 1064 declined to participate. They randomized 377 patients—193 to the intervention arm and 184 to the attention control arm. Participants had an average age of 58.7, 49.1% had an education level of high school or less, 50.1% were non-white, and 54.9% were unemployed/retired. Patient characteristics in the intervention and control arms were similar at baseline. Seventy-eight percent of patients completed the 12-month follow-up and 70% (263) reached the 24-month endpoint. Patients in the intervention arm completed 78% of scheduled calls while patients in the control group completed 81%.

After adjusting for stratification variables, the estimated mean A1c and SBP were similar between arms at 24 months (intervention 0.1% higher than control, 95% CI −0.3 % to 0.5 %, P = 0.50 for A1c; intervention 0.9 mm Hg lower than control, 95% CI −5.4 to 3.5, P = 0.69 for SBP). There were also no significant differences between arms in mean A1c or SBP at 6 or 12 months. However, A1c levels did improve within each arm at the end of the study, with the intervention group improving by approx-imately 0.5% and the control group improving by approximately 0.6%. In terms of secondary outcomes, there were no significant differences between arms in DBP, weight, physical activity, or BP control rates throughout the 2-year study period.

Conclusion. Overall, the intervention and control groups did not differ significantly in terms of A1c, SBP, or any of the secondary outcomes at any point during the 2-year study.

Commentary

The prevalence of type 2 diabetes and its comorbidities (such as hypertension and obesity) have increased due to a variety of factors including an aging population and an increasingly sedentary lifestyle. Several nurse management programs for DM and HTN have been shown to be efficacious in reducing blood sugar levels [2–4] and promoting BP control [5,6]. However, these interventions were conducted in tightly controlled academic settings, and it is unclear how well these programs may translate into community settings. The aim of this study was to test the effectiveness of a nurse-led behavioral telephone intervention for the comanagement of DM and HTN within non–academically affiliated community practices. Results indicated no significant differences between the intervention and control groups for A1c levels or SBP at any point during the 2-year study, but A1c levels did improve for both arms.

Despite this being a negative study, it is a unique and important contribution to the literature. It is the only trial as of yet that has tested the effectiveness of a nurse management intervention targeting both DM and HTN in a real-world, community setting. This novel approach is supported by data that suggests BP control is actually more cost-effective than intensive glycemic control in treating patients with type 2 diabetes [7]. There were several strengths to the study design, including the use of intention-to-treat analysis, stratified randomization, a diverse patient population, and blinding of the study staff who took BP and A1c measurements. Furthermore, a single nurse conducted all telephone calls, ensuring that differences in counseling skill levels would not affect the results of the study. The few weaknesses of the study included the fact that the nurse who delivered the intervention (as well as the patients) could not be blinded to treatment allocation, and the income of study participants was not reported.

The reasons for the negative outcomes of this study are unclear. The authors claim that similar interventions within academic settings have been shown to be effective and speculate that time and financial pressures of community practices may be reasons that the intervention was not successful. However, the “successful” interventions that they cite were quite different from and more intensive than this intervention. For instance, many of these studies used at least 1 call per month [3,4,8], and one even conducted several calls each week [3]. Furthermore, a DM study conducted by Blackberry et al in a community setting with less than 1 call per month (8 calls over 18 months) similarly failed to produce significant results [9], and therefore more frequent calls may be necessary in DM and HTN interventions. In a systematic review, Eakin et al demonstrated that 12 or more calls in a 6- to 12-month period were associated with better outcomes in physical activity and diet interventions [10], and this may also translate to closely related DM and HTN interventions.

In addition to the infrequent calls, this intervention also lacked communication and integration with patients’ primary care teams. Several studies have demonstrated that integration with primary care teams can improve outcomes in DM and HTN interventions [11,12], and nearly all of the successful studies cited by the authors also included at least some form of communication with patients’ primary care providers (PCPs) [2–4,5,8]. In many of these studies the nurse also had prescribing rights to alter medications [2,3,5]. The nurse in this study met monthly with an expert team of clinicians to discuss patient issues but did not communicate directly with any of the patients’ PCPs [1]. The authors acknowledge that this lack of integration may have contributed to their negative results and point to the fact that it is harder to integrate interventions within community practices that often lack internal integration. However, Walsh, Harris, and Roberts demonstrated that integration between primary and secondary care teams was both feasible and effective for a diabetes initiative within community practices [13].

An additional important feature not present in this intervention was self-monitoring of BP levels. Home self-monitoring of BP has been demonstrated to significantly improve BP levels [14], and 2 of the successful studies in academic settings cited by the authors also included a BP self-monitoring component [5,6]. In one of these studies [6], Bosworth et al conducted a 2 × 2 randomized trial to improve HTN control in which the arms consisted of (a) usual care, (b) bimonthly nurse administered telephone intervention only (this arm was highly similar to the intervention arm in this study), (c) BP monitoring 3 times a week only, and (d) a combination of the telephone intervention with the BP monitoring. Interestingly, the only arm that was successful relative to usual care was the combination of the telephone intervention and BP self-monitoring; the arm consisting only of bi-monthly telephone calls (very similar to this intervention) failed despite the study taking place in an academic setting (it was also less effective than BP monitoring only). Thus, the addition of self-monitoring to a nurse case management telephone intervention can achieve better results.

Applications for Clinical Practice

A telephone-based intervention delivered by a trained nurse for co-management of DM and HTN was not more effective than an attention control delivered by the same nurse in a community setting. This may have been due to several factors, including low intensity marked by less than 1 call per month, a lack of integration with other members of the primary care team, and lack of a BP self-monitoring component. Future studies are needed to determine the optimal type and duration of nurse case management interventions targeting glucose and BP control for diabetic patients in community settings.

—Sandeep Sikerwar, BA, and Melanie Jay, MD, MS

Study Overview

Objective. To determine the effectiveness of a nurse-led, telephone-delivered behavioral intervention for diabetes (DM) and hypertension (HTN) versus an attention control within primary care community practices.

Study design. A 9-site, 2-arm randomized controlled trial.

Setting and participants. Study participants were recruited from 9 community practices within the Duke Primary Care Research Consortium. The practices were chosen because they traditionally operate outside of the academic context. Subjects were required to have both type 2 DM and HTN, as indicated by their medications and confirmed by administrative data as well as patient self-reporting. Participants had to have been seen at participating practices for at least 1 year and have poorly controlled DM (indicated by most recent A1c ≥ 7.5%), but they were not required to have poorly controlled HTN. Exclusion criteria included fewer than 1 primary care clinic visit during the previous year, serious comorbid illness, type 1 diabetes, inability to receive a telephone intervention in English, residence in a nursing home, and participation in another hypertension or diabetes study [1]. Participants were randomly assigned using a computer-generated randomization sequence [1] to either the intervention or control groups at a 1:1 ratio, stratified by clinic and baseline blood pressure (BP) control.

Intervention. A single nurse with extensive experience in case management delivered both the behavioral intervention and attention control by telephone. In both arms, calls were conducted once every 2 months over a 24-month period.

The calls in the intervention arm consisted of tailored behavior-modifying techniques according to patient barriers. This content was divided into a series of modules relevant to behaviors associated with improving control of BP or blood sugar, including physical activity, weight reduction, sodium intake, smoking cessation, medication adherence, and others. These modules were scheduled according to patient needs (based on certain parameters such as high body mass index or use of insulin) and preferences [1].

The calls in the attention control were not tailored but rather consisted of didactic health-related information unrelated to HTN or DM (eg, flu shots, skin cancer prevention). This content was also highly scripted and designed to limit the potential for interaction between the nurse and patient.

Main outcome measures. A1c and systolic blood pressure (SBP) were primary outcomes. Key secondary outcomes were diastolic blood pressure (DBP), overall BP control, weight, physical activity, self-efficacy, and medication adherence. Study staff obtained measurements at baseline and 6, 12, and 24 months [1].

Results. The researchers assessed 2601 patients for eligibility and excluded 2224. Most patients were excluded for not meeting inclusion criteria (n = 1156), in particular because of improved HbA1c control (n = 983), and 1064 declined to participate. They randomized 377 patients—193 to the intervention arm and 184 to the attention control arm. Participants had an average age of 58.7, 49.1% had an education level of high school or less, 50.1% were non-white, and 54.9% were unemployed/retired. Patient characteristics in the intervention and control arms were similar at baseline. Seventy-eight percent of patients completed the 12-month follow-up and 70% (263) reached the 24-month endpoint. Patients in the intervention arm completed 78% of scheduled calls while patients in the control group completed 81%.

After adjusting for stratification variables, the estimated mean A1c and SBP were similar between arms at 24 months (intervention 0.1% higher than control, 95% CI −0.3 % to 0.5 %, P = 0.50 for A1c; intervention 0.9 mm Hg lower than control, 95% CI −5.4 to 3.5, P = 0.69 for SBP). There were also no significant differences between arms in mean A1c or SBP at 6 or 12 months. However, A1c levels did improve within each arm at the end of the study, with the intervention group improving by approx-imately 0.5% and the control group improving by approximately 0.6%. In terms of secondary outcomes, there were no significant differences between arms in DBP, weight, physical activity, or BP control rates throughout the 2-year study period.

Conclusion. Overall, the intervention and control groups did not differ significantly in terms of A1c, SBP, or any of the secondary outcomes at any point during the 2-year study.

Commentary

The prevalence of type 2 diabetes and its comorbidities (such as hypertension and obesity) have increased due to a variety of factors including an aging population and an increasingly sedentary lifestyle. Several nurse management programs for DM and HTN have been shown to be efficacious in reducing blood sugar levels [2–4] and promoting BP control [5,6]. However, these interventions were conducted in tightly controlled academic settings, and it is unclear how well these programs may translate into community settings. The aim of this study was to test the effectiveness of a nurse-led behavioral telephone intervention for the comanagement of DM and HTN within non–academically affiliated community practices. Results indicated no significant differences between the intervention and control groups for A1c levels or SBP at any point during the 2-year study, but A1c levels did improve for both arms.

Despite this being a negative study, it is a unique and important contribution to the literature. It is the only trial as of yet that has tested the effectiveness of a nurse management intervention targeting both DM and HTN in a real-world, community setting. This novel approach is supported by data that suggests BP control is actually more cost-effective than intensive glycemic control in treating patients with type 2 diabetes [7]. There were several strengths to the study design, including the use of intention-to-treat analysis, stratified randomization, a diverse patient population, and blinding of the study staff who took BP and A1c measurements. Furthermore, a single nurse conducted all telephone calls, ensuring that differences in counseling skill levels would not affect the results of the study. The few weaknesses of the study included the fact that the nurse who delivered the intervention (as well as the patients) could not be blinded to treatment allocation, and the income of study participants was not reported.

The reasons for the negative outcomes of this study are unclear. The authors claim that similar interventions within academic settings have been shown to be effective and speculate that time and financial pressures of community practices may be reasons that the intervention was not successful. However, the “successful” interventions that they cite were quite different from and more intensive than this intervention. For instance, many of these studies used at least 1 call per month [3,4,8], and one even conducted several calls each week [3]. Furthermore, a DM study conducted by Blackberry et al in a community setting with less than 1 call per month (8 calls over 18 months) similarly failed to produce significant results [9], and therefore more frequent calls may be necessary in DM and HTN interventions. In a systematic review, Eakin et al demonstrated that 12 or more calls in a 6- to 12-month period were associated with better outcomes in physical activity and diet interventions [10], and this may also translate to closely related DM and HTN interventions.

In addition to the infrequent calls, this intervention also lacked communication and integration with patients’ primary care teams. Several studies have demonstrated that integration with primary care teams can improve outcomes in DM and HTN interventions [11,12], and nearly all of the successful studies cited by the authors also included at least some form of communication with patients’ primary care providers (PCPs) [2–4,5,8]. In many of these studies the nurse also had prescribing rights to alter medications [2,3,5]. The nurse in this study met monthly with an expert team of clinicians to discuss patient issues but did not communicate directly with any of the patients’ PCPs [1]. The authors acknowledge that this lack of integration may have contributed to their negative results and point to the fact that it is harder to integrate interventions within community practices that often lack internal integration. However, Walsh, Harris, and Roberts demonstrated that integration between primary and secondary care teams was both feasible and effective for a diabetes initiative within community practices [13].

An additional important feature not present in this intervention was self-monitoring of BP levels. Home self-monitoring of BP has been demonstrated to significantly improve BP levels [14], and 2 of the successful studies in academic settings cited by the authors also included a BP self-monitoring component [5,6]. In one of these studies [6], Bosworth et al conducted a 2 × 2 randomized trial to improve HTN control in which the arms consisted of (a) usual care, (b) bimonthly nurse administered telephone intervention only (this arm was highly similar to the intervention arm in this study), (c) BP monitoring 3 times a week only, and (d) a combination of the telephone intervention with the BP monitoring. Interestingly, the only arm that was successful relative to usual care was the combination of the telephone intervention and BP self-monitoring; the arm consisting only of bi-monthly telephone calls (very similar to this intervention) failed despite the study taking place in an academic setting (it was also less effective than BP monitoring only). Thus, the addition of self-monitoring to a nurse case management telephone intervention can achieve better results.

Applications for Clinical Practice

A telephone-based intervention delivered by a trained nurse for co-management of DM and HTN was not more effective than an attention control delivered by the same nurse in a community setting. This may have been due to several factors, including low intensity marked by less than 1 call per month, a lack of integration with other members of the primary care team, and lack of a BP self-monitoring component. Future studies are needed to determine the optimal type and duration of nurse case management interventions targeting glucose and BP control for diabetic patients in community settings.

—Sandeep Sikerwar, BA, and Melanie Jay, MD, MS

Study Overview

Objective. To compare the efficacy of non-nutritive sweetened beverages (NNS) or water for weight loss during a 12-week behavioral weight loss treatment program.

Study design. 2-arm equivalence randomized clinical trial.

Setting and participants. Participants were recruited at the University of Colorado and Temple University. A total of 506 participants were screened and 308 were enrolled in the study. Inclusion criteria included being weight stable within 10 pounds in the 6 months prior to the trial, engaging in fewer than 300 min of physical activity per week and consuming at least 3 NNS beverages per week. Exclusion criteria included pregnancy, diabetes, cardiovascular disease, uncontrolled hypertension, and the use of medications affecting metabolism or weight. Participants also had physician approval stating they were in good health and could handle the nutrition and exercise requirements of the trial. Participants were randomly assigned to a NNS or water treatment arm using a computer-generated randomization that equally distributed men and women between the 2 groups. Participants had to be willing to discontinue consumption of NNS beverages for the duration of the 1-year study if they were randomized to the water-only group.

Intervention. The study was designed to include a 12-week weight loss phase followed by a 9-month maintenance phase. All participants received a cognitive-behavioral weight loss intervention called The Colorado Weigh. The program involved weekly hour-long group meetings led by registered dieticians or clinical psychologists. Groups were split by research arm and participants were taught about different weight loss strategies including self-monitoring, portion sizes, and physical activity. Participants were weighed at each meeting. The group curriculum was the same for both arms of the study except in the type of beverage they were encouraged to consume.

Participants were given individual energy targets based on their estimated resting metabolic rate (RMR), determined by using a Tanita Model TBF-300A bioelectrical impedance device that assesses body composition. Group leaders adjusted these targets as needed for participants in order to achieve a goal weight loss of 1 to 2 pounds per week. Physical activity targets were set to increase each participant’s typical physical activity by 10 minutes a week with a final target of 60 minutes a day, 6 days a week. Participants filled out daily exercise logs. Additionally, physical activity was assessed by the use of a Body Media armband that participants wore weeks 1 and 12.

Participants in the NNS group were asked to consume at least 24 fluid ounces of NNS beverage per day. Their water consumption was not limited. A beverage was considered NNS if it had less than 5 kcal per 8-ounce serving, was pre-mixed, and contained non-nutritive sweeteners. Participants in the water-only group were asked to drink at least 24 ounces of water a day and not drink any NNS beverages. They were allowed to eat foods that contained NNS but could not intentionally add NNS to beverages such as coffee. Participants in both groups were asked to record their beverage intake daily. Participants were given manufacturers’ coupons for bottled water or NNS beverages.

Main outcome measures. The primary outcomes were weight loss at 12 weeks (weight loss period) and at 1 year (weight loss maintenance). All assessments were conducted at baseline and after 12 weeks. This was designed as an equivalence trial, and the authors’ hypothesis was that there would be no clinically meaningful difference in weight change between the 2 groups. The authors pre-specified that the bounds of equivalence would be 1.7 kg. Waist circumference was recorded in addition to height and weight. Participant’s blood pressure was also recorded and blood samples were collected to measure lipids and glucose. Urine samples were collected to measure urine osmolality. Participants completed questionnaires at baseline and 12 weeks to assess changes in perceived hunger.

Results. A total of 308 patients were randomized following baseline assessment but 5 did not begin treatment. 279 of the remaining 303 participants completed the full 12-week weight loss phase of the study. The dropout rate in the water group was 10% compared to 5.8% in the NNS group, but this was not statistically significant. 80% of participants were female, 68% were white, and 27% African American. There were no significant differences at baseline in age, gender, race/ethnicity or other measures between the water-only and NNS groups. There was no significant difference in adherence to the beverage requirements between the 2 groups (96.6% in the NNS group and 95.7% in the water-only group), and similarly group attendance did not differ between the 2 groups (90.8% for NNS and 89.7% for water-only).

The mean weight loss difference between the water and NNS groups was –1.85 kg (90% confidence interval [CI], –1.12 to –2.58 kg). Because the lower confidence limit of –2.58 kg was outside the equivalence limit set in the hypothesis, the 2 treatments were not considered equivalent and paired comparisons were carried out. Analysis done using an intention-to-treat scheme indicated that the weight loss in the NNS group (5.95 kg ± 3.94 kg) was significantly higher than the weight loss in the water-only group (4.09 ± 3.74 kg, P < 0.001). 43.0% of participants in the water-only group lost > 5% of their body weight and 64.3% of participants in the NNS group lost > 5% of their body weight (P < 0.001).

After 12 weeks of treatment there was no significant difference between the 2 groups in changes in waist circumference, blood pressure, HDL, triglycerides, or urine osmolality. Reductions in total cholesterol and LDL were significantly greater in the NNS group than the water group. There were no significant changes in physical activity between the 2 groups as measured by the exercise logs or the Body Media armbands. There was a statistically significant difference in hunger between the 2 groups (P = 0.013): participants in the water group reported increased hunger, while participants in the NNS group reported a slight decrease in hunger.

Conclusion. Participants who drank at least 3 servings of NNS beverages a day at baseline lost more weight during a behavioral weight loss program when they continued to drink NNS beverages than participants who were asked to cut NNS beverages and drink only water. The study was designed as an equivalence trial but paired comparisons showed a significant difference in weight loss between the 2 groups.

Commentary

Obesity is a major public health concern in the United States and drinking sugar-sweetened beverages has been indicated as a significant contributing factor. Consumption of sugar-sweetened beverages increased considerably from 1994 to 2004 [1]. Fortunately, there is strong evidence that decreasing the consumption of sugar sweetened beverages can lead to weight loss [2]. Most studies look at the effect of replacing sugar-sweetened beverages with water [3] and, in fact, increased consumption of water has been shown to aid weight loss [4]. The relationship between diet drinks and obesity, however, has been a source of controversy. Since NNS beverages contain little to no calories they are a logical replacement for sugar-sweetened beverages, but observational studies have shown a positive correlation between diet drinks and obesity [5,6] as well as type 2 diabetes [7]. Additionally, a recent study by Suez et al [8] found that consumption of artificial sweeteners affects the gut microbiota and increases glucose intolerance. However this correlation may not be causal; NNS beverage consumption may be higher in overweight individuals. A study by Tate et al [9,10] looked at replacing sugar-sweetened beverages with water or artificially sweetened beverages and found no significant difference in weight loss between the 2 groups. However, the Tate et al study used beverage replacement as the primary intervention. This experiment by Peters et al is unique because it tested the hypothesis that NNS is equivalent to water alone when combined with a structured weight loss program. Their results reject the equivalence hypothesis and suggest that NNS beverages facilitate weight loss for patients already consuming them.

Strengths of this study included the use of a randomized, equivalence design. The study also examined secondary outcomes (eg, waist circumference, lipids, and urine osmolality) that helped reinforce that participants consuming NNS were able to lose weight without compromising their health. Further, they measured hunger and found that participants in the NNS beverage group had decreased hunger while those in the water group had increased hunger, which points to a potential mechanism for their findings.

However, the potential for bias in this study is concerning. One major weakness is that all the participants were initially regular drinkers of NNS beverages. The authors never explain why consuming 3 NNS beverages per week was an inclusion criteria. Participants in the water group had to change their behavior to abstain from NNS beverages and this may have impacted results. More concerning, this study was fully funded by the American Beverage Association, who has an obvious interest in promoting NNS beverage consumption. Finally, the authors mention that 5 participants dropped out after randomization but before the start of treatment and were excluded from the study after baseline assessment. The authors do not provide information about group allocation or if the participants knew which group they were assigned to, calling into question the integrity of the intention-to-treat design.

Applications for Clinical Practice

For patients who already drink NNS beverages and are motivated to lose weight, these results support continued use. However, it is unclear how NNS beverages impact weight loss efforts for patients who do not currently drink them. Further, since other studies have shown potential harm of NNS beverages [6–8], more studies are needed to better elucidate their health effects.

—Susan Creighton and Melanie Jay, MD, MS

Study Overview

Objective. To compare the efficacy of non-nutritive sweetened beverages (NNS) or water for weight loss during a 12-week behavioral weight loss treatment program.

Study design. 2-arm equivalence randomized clinical trial.

Setting and participants. Participants were recruited at the University of Colorado and Temple University. A total of 506 participants were screened and 308 were enrolled in the study. Inclusion criteria included being weight stable within 10 pounds in the 6 months prior to the trial, engaging in fewer than 300 min of physical activity per week and consuming at least 3 NNS beverages per week. Exclusion criteria included pregnancy, diabetes, cardiovascular disease, uncontrolled hypertension, and the use of medications affecting metabolism or weight. Participants also had physician approval stating they were in good health and could handle the nutrition and exercise requirements of the trial. Participants were randomly assigned to a NNS or water treatment arm using a computer-generated randomization that equally distributed men and women between the 2 groups. Participants had to be willing to discontinue consumption of NNS beverages for the duration of the 1-year study if they were randomized to the water-only group.

Intervention. The study was designed to include a 12-week weight loss phase followed by a 9-month maintenance phase. All participants received a cognitive-behavioral weight loss intervention called The Colorado Weigh. The program involved weekly hour-long group meetings led by registered dieticians or clinical psychologists. Groups were split by research arm and participants were taught about different weight loss strategies including self-monitoring, portion sizes, and physical activity. Participants were weighed at each meeting. The group curriculum was the same for both arms of the study except in the type of beverage they were encouraged to consume.

Participants were given individual energy targets based on their estimated resting metabolic rate (RMR), determined by using a Tanita Model TBF-300A bioelectrical impedance device that assesses body composition. Group leaders adjusted these targets as needed for participants in order to achieve a goal weight loss of 1 to 2 pounds per week. Physical activity targets were set to increase each participant’s typical physical activity by 10 minutes a week with a final target of 60 minutes a day, 6 days a week. Participants filled out daily exercise logs. Additionally, physical activity was assessed by the use of a Body Media armband that participants wore weeks 1 and 12.

Participants in the NNS group were asked to consume at least 24 fluid ounces of NNS beverage per day. Their water consumption was not limited. A beverage was considered NNS if it had less than 5 kcal per 8-ounce serving, was pre-mixed, and contained non-nutritive sweeteners. Participants in the water-only group were asked to drink at least 24 ounces of water a day and not drink any NNS beverages. They were allowed to eat foods that contained NNS but could not intentionally add NNS to beverages such as coffee. Participants in both groups were asked to record their beverage intake daily. Participants were given manufacturers’ coupons for bottled water or NNS beverages.

Main outcome measures. The primary outcomes were weight loss at 12 weeks (weight loss period) and at 1 year (weight loss maintenance). All assessments were conducted at baseline and after 12 weeks. This was designed as an equivalence trial, and the authors’ hypothesis was that there would be no clinically meaningful difference in weight change between the 2 groups. The authors pre-specified that the bounds of equivalence would be 1.7 kg. Waist circumference was recorded in addition to height and weight. Participant’s blood pressure was also recorded and blood samples were collected to measure lipids and glucose. Urine samples were collected to measure urine osmolality. Participants completed questionnaires at baseline and 12 weeks to assess changes in perceived hunger.

Results. A total of 308 patients were randomized following baseline assessment but 5 did not begin treatment. 279 of the remaining 303 participants completed the full 12-week weight loss phase of the study. The dropout rate in the water group was 10% compared to 5.8% in the NNS group, but this was not statistically significant. 80% of participants were female, 68% were white, and 27% African American. There were no significant differences at baseline in age, gender, race/ethnicity or other measures between the water-only and NNS groups. There was no significant difference in adherence to the beverage requirements between the 2 groups (96.6% in the NNS group and 95.7% in the water-only group), and similarly group attendance did not differ between the 2 groups (90.8% for NNS and 89.7% for water-only).

The mean weight loss difference between the water and NNS groups was –1.85 kg (90% confidence interval [CI], –1.12 to –2.58 kg). Because the lower confidence limit of –2.58 kg was outside the equivalence limit set in the hypothesis, the 2 treatments were not considered equivalent and paired comparisons were carried out. Analysis done using an intention-to-treat scheme indicated that the weight loss in the NNS group (5.95 kg ± 3.94 kg) was significantly higher than the weight loss in the water-only group (4.09 ± 3.74 kg, P < 0.001). 43.0% of participants in the water-only group lost > 5% of their body weight and 64.3% of participants in the NNS group lost > 5% of their body weight (P < 0.001).

After 12 weeks of treatment there was no significant difference between the 2 groups in changes in waist circumference, blood pressure, HDL, triglycerides, or urine osmolality. Reductions in total cholesterol and LDL were significantly greater in the NNS group than the water group. There were no significant changes in physical activity between the 2 groups as measured by the exercise logs or the Body Media armbands. There was a statistically significant difference in hunger between the 2 groups (P = 0.013): participants in the water group reported increased hunger, while participants in the NNS group reported a slight decrease in hunger.

Conclusion. Participants who drank at least 3 servings of NNS beverages a day at baseline lost more weight during a behavioral weight loss program when they continued to drink NNS beverages than participants who were asked to cut NNS beverages and drink only water. The study was designed as an equivalence trial but paired comparisons showed a significant difference in weight loss between the 2 groups.

Commentary

Obesity is a major public health concern in the United States and drinking sugar-sweetened beverages has been indicated as a significant contributing factor. Consumption of sugar-sweetened beverages increased considerably from 1994 to 2004 [1]. Fortunately, there is strong evidence that decreasing the consumption of sugar sweetened beverages can lead to weight loss [2]. Most studies look at the effect of replacing sugar-sweetened beverages with water [3] and, in fact, increased consumption of water has been shown to aid weight loss [4]. The relationship between diet drinks and obesity, however, has been a source of controversy. Since NNS beverages contain little to no calories they are a logical replacement for sugar-sweetened beverages, but observational studies have shown a positive correlation between diet drinks and obesity [5,6] as well as type 2 diabetes [7]. Additionally, a recent study by Suez et al [8] found that consumption of artificial sweeteners affects the gut microbiota and increases glucose intolerance. However this correlation may not be causal; NNS beverage consumption may be higher in overweight individuals. A study by Tate et al [9,10] looked at replacing sugar-sweetened beverages with water or artificially sweetened beverages and found no significant difference in weight loss between the 2 groups. However, the Tate et al study used beverage replacement as the primary intervention. This experiment by Peters et al is unique because it tested the hypothesis that NNS is equivalent to water alone when combined with a structured weight loss program. Their results reject the equivalence hypothesis and suggest that NNS beverages facilitate weight loss for patients already consuming them.

Strengths of this study included the use of a randomized, equivalence design. The study also examined secondary outcomes (eg, waist circumference, lipids, and urine osmolality) that helped reinforce that participants consuming NNS were able to lose weight without compromising their health. Further, they measured hunger and found that participants in the NNS beverage group had decreased hunger while those in the water group had increased hunger, which points to a potential mechanism for their findings.

However, the potential for bias in this study is concerning. One major weakness is that all the participants were initially regular drinkers of NNS beverages. The authors never explain why consuming 3 NNS beverages per week was an inclusion criteria. Participants in the water group had to change their behavior to abstain from NNS beverages and this may have impacted results. More concerning, this study was fully funded by the American Beverage Association, who has an obvious interest in promoting NNS beverage consumption. Finally, the authors mention that 5 participants dropped out after randomization but before the start of treatment and were excluded from the study after baseline assessment. The authors do not provide information about group allocation or if the participants knew which group they were assigned to, calling into question the integrity of the intention-to-treat design.

Applications for Clinical Practice

For patients who already drink NNS beverages and are motivated to lose weight, these results support continued use. However, it is unclear how NNS beverages impact weight loss efforts for patients who do not currently drink them. Further, since other studies have shown potential harm of NNS beverages [6–8], more studies are needed to better elucidate their health effects.

—Susan Creighton and Melanie Jay, MD, MS

Study Overview

Objective. To compare the efficacy of non-nutritive sweetened beverages (NNS) or water for weight loss during a 12-week behavioral weight loss treatment program.

Study design. 2-arm equivalence randomized clinical trial.

Setting and participants. Participants were recruited at the University of Colorado and Temple University. A total of 506 participants were screened and 308 were enrolled in the study. Inclusion criteria included being weight stable within 10 pounds in the 6 months prior to the trial, engaging in fewer than 300 min of physical activity per week and consuming at least 3 NNS beverages per week. Exclusion criteria included pregnancy, diabetes, cardiovascular disease, uncontrolled hypertension, and the use of medications affecting metabolism or weight. Participants also had physician approval stating they were in good health and could handle the nutrition and exercise requirements of the trial. Participants were randomly assigned to a NNS or water treatment arm using a computer-generated randomization that equally distributed men and women between the 2 groups. Participants had to be willing to discontinue consumption of NNS beverages for the duration of the 1-year study if they were randomized to the water-only group.

Intervention. The study was designed to include a 12-week weight loss phase followed by a 9-month maintenance phase. All participants received a cognitive-behavioral weight loss intervention called The Colorado Weigh. The program involved weekly hour-long group meetings led by registered dieticians or clinical psychologists. Groups were split by research arm and participants were taught about different weight loss strategies including self-monitoring, portion sizes, and physical activity. Participants were weighed at each meeting. The group curriculum was the same for both arms of the study except in the type of beverage they were encouraged to consume.

Participants were given individual energy targets based on their estimated resting metabolic rate (RMR), determined by using a Tanita Model TBF-300A bioelectrical impedance device that assesses body composition. Group leaders adjusted these targets as needed for participants in order to achieve a goal weight loss of 1 to 2 pounds per week. Physical activity targets were set to increase each participant’s typical physical activity by 10 minutes a week with a final target of 60 minutes a day, 6 days a week. Participants filled out daily exercise logs. Additionally, physical activity was assessed by the use of a Body Media armband that participants wore weeks 1 and 12.

Participants in the NNS group were asked to consume at least 24 fluid ounces of NNS beverage per day. Their water consumption was not limited. A beverage was considered NNS if it had less than 5 kcal per 8-ounce serving, was pre-mixed, and contained non-nutritive sweeteners. Participants in the water-only group were asked to drink at least 24 ounces of water a day and not drink any NNS beverages. They were allowed to eat foods that contained NNS but could not intentionally add NNS to beverages such as coffee. Participants in both groups were asked to record their beverage intake daily. Participants were given manufacturers’ coupons for bottled water or NNS beverages.

Main outcome measures. The primary outcomes were weight loss at 12 weeks (weight loss period) and at 1 year (weight loss maintenance). All assessments were conducted at baseline and after 12 weeks. This was designed as an equivalence trial, and the authors’ hypothesis was that there would be no clinically meaningful difference in weight change between the 2 groups. The authors pre-specified that the bounds of equivalence would be 1.7 kg. Waist circumference was recorded in addition to height and weight. Participant’s blood pressure was also recorded and blood samples were collected to measure lipids and glucose. Urine samples were collected to measure urine osmolality. Participants completed questionnaires at baseline and 12 weeks to assess changes in perceived hunger.

Results. A total of 308 patients were randomized following baseline assessment but 5 did not begin treatment. 279 of the remaining 303 participants completed the full 12-week weight loss phase of the study. The dropout rate in the water group was 10% compared to 5.8% in the NNS group, but this was not statistically significant. 80% of participants were female, 68% were white, and 27% African American. There were no significant differences at baseline in age, gender, race/ethnicity or other measures between the water-only and NNS groups. There was no significant difference in adherence to the beverage requirements between the 2 groups (96.6% in the NNS group and 95.7% in the water-only group), and similarly group attendance did not differ between the 2 groups (90.8% for NNS and 89.7% for water-only).

The mean weight loss difference between the water and NNS groups was –1.85 kg (90% confidence interval [CI], –1.12 to –2.58 kg). Because the lower confidence limit of –2.58 kg was outside the equivalence limit set in the hypothesis, the 2 treatments were not considered equivalent and paired comparisons were carried out. Analysis done using an intention-to-treat scheme indicated that the weight loss in the NNS group (5.95 kg ± 3.94 kg) was significantly higher than the weight loss in the water-only group (4.09 ± 3.74 kg, P < 0.001). 43.0% of participants in the water-only group lost > 5% of their body weight and 64.3% of participants in the NNS group lost > 5% of their body weight (P < 0.001).

After 12 weeks of treatment there was no significant difference between the 2 groups in changes in waist circumference, blood pressure, HDL, triglycerides, or urine osmolality. Reductions in total cholesterol and LDL were significantly greater in the NNS group than the water group. There were no significant changes in physical activity between the 2 groups as measured by the exercise logs or the Body Media armbands. There was a statistically significant difference in hunger between the 2 groups (P = 0.013): participants in the water group reported increased hunger, while participants in the NNS group reported a slight decrease in hunger.

Conclusion. Participants who drank at least 3 servings of NNS beverages a day at baseline lost more weight during a behavioral weight loss program when they continued to drink NNS beverages than participants who were asked to cut NNS beverages and drink only water. The study was designed as an equivalence trial but paired comparisons showed a significant difference in weight loss between the 2 groups.

Commentary

Obesity is a major public health concern in the United States and drinking sugar-sweetened beverages has been indicated as a significant contributing factor. Consumption of sugar-sweetened beverages increased considerably from 1994 to 2004 [1]. Fortunately, there is strong evidence that decreasing the consumption of sugar sweetened beverages can lead to weight loss [2]. Most studies look at the effect of replacing sugar-sweetened beverages with water [3] and, in fact, increased consumption of water has been shown to aid weight loss [4]. The relationship between diet drinks and obesity, however, has been a source of controversy. Since NNS beverages contain little to no calories they are a logical replacement for sugar-sweetened beverages, but observational studies have shown a positive correlation between diet drinks and obesity [5,6] as well as type 2 diabetes [7]. Additionally, a recent study by Suez et al [8] found that consumption of artificial sweeteners affects the gut microbiota and increases glucose intolerance. However this correlation may not be causal; NNS beverage consumption may be higher in overweight individuals. A study by Tate et al [9,10] looked at replacing sugar-sweetened beverages with water or artificially sweetened beverages and found no significant difference in weight loss between the 2 groups. However, the Tate et al study used beverage replacement as the primary intervention. This experiment by Peters et al is unique because it tested the hypothesis that NNS is equivalent to water alone when combined with a structured weight loss program. Their results reject the equivalence hypothesis and suggest that NNS beverages facilitate weight loss for patients already consuming them.

Strengths of this study included the use of a randomized, equivalence design. The study also examined secondary outcomes (eg, waist circumference, lipids, and urine osmolality) that helped reinforce that participants consuming NNS were able to lose weight without compromising their health. Further, they measured hunger and found that participants in the NNS beverage group had decreased hunger while those in the water group had increased hunger, which points to a potential mechanism for their findings.

However, the potential for bias in this study is concerning. One major weakness is that all the participants were initially regular drinkers of NNS beverages. The authors never explain why consuming 3 NNS beverages per week was an inclusion criteria. Participants in the water group had to change their behavior to abstain from NNS beverages and this may have impacted results. More concerning, this study was fully funded by the American Beverage Association, who has an obvious interest in promoting NNS beverage consumption. Finally, the authors mention that 5 participants dropped out after randomization but before the start of treatment and were excluded from the study after baseline assessment. The authors do not provide information about group allocation or if the participants knew which group they were assigned to, calling into question the integrity of the intention-to-treat design.

Applications for Clinical Practice

For patients who already drink NNS beverages and are motivated to lose weight, these results support continued use. However, it is unclear how NNS beverages impact weight loss efforts for patients who do not currently drink them. Further, since other studies have shown potential harm of NNS beverages [6–8], more studies are needed to better elucidate their health effects.

—Susan Creighton and Melanie Jay, MD, MS

Study Overview

Objective. To determine the efficacy and cost-effectiveness of adding an evidence-based internet behavioral weight loss intervention alone or combined with optional group sessions to ShapeUp Rhode Island 2011 (SURI), a 3-month statewide wellness campaign.

Design. 3-arm randomized clinical trial.

Setting and participants. Study participants were recruited from the Rhode Island community via employers, media, and mass mailings at the time of SURI 2011 registration. Of the 3806 participants that joined the weight loss division, 1139 were willing to be contacted for research, and the first 431 were screened for study eligibility. Exclusion criteria were minimal: age < 18 years or > 70 years, body mass index (BMI) < 25 kg/m², pregnant, nursing, or plans to become pregnant, a serious medical condition (eg, cancer), unreliable internet access, non-English speaking, current or previous participation in our weight loss studies, and planned relocation. Those who reported a medical condition that could interfere with safe participation (eg, diabetes) obtained doctor’s consent to participate. Of those screened, 230 met inclusion criteria, completed orientation procedures, and were randomized using a 1:2:2 randomization scheme to the standard SURI program (S; n = 46); SURI plus internet behavioral weight loss intervention (SI; n = 90); or SURI plus internet behavioral weight loss intervention plus optional group sessions (SIG; n = 94). To avoid contamination, individuals on the same SURI team (see below) were randomized to the same intervention.

Intervention. Participants in the standard SURI program did not receive any behavioral weight loss treatment. SURI is a self-sustaining, annual community campaign designed to help Rhode Islanders lose weight and increase their physical activity through an online, team-based competition. Participants join in teams, enter the weight loss or physical activity division or both, and compete with other teams. Throughout the 3-month program, participants have access to a reporting SURI website where they submit their weekly weight and activity data and view their personal and team progress. They also receive paper logs to record weight and activity, a pedometer, access to newsletters and community workshops, and recognition for meeting goals.

Participants in the SI arm received the 3-month SURI program plus a 3-month internet behavioral weight loss intervention. Before SURI began, SI participants attended a 1-hour group meeting during which they received their weight loss goal (lose 1 to 2 pounds per week), calorie and fat gram goal (starting weight < 250 lbs: 1200–1500 kcal/day, 40–50 g of fat; starting weight ≥ 250 lbs: 1500–1800 kcal/day, 50–60 g of fat), and activity goal (gradually increase to 200 minutes of aerobic activity per week). During this session, participants were also taught self-monitoring skills and oriented to an internet behavioral weight loss intervention website developed by the authors. The intervention website included 12 weekly, 10- to 15-minute multimedia lessons based on the Diabetes Prevention Program and a self-monitoring platform where participants tracked their daily weight, calorie, and activity information. Participants received weekly automated feedback on their progress. The intervention website also included information on meal plans, prepackaged meals, and meal replacements.

Participants in the SIG arm received everything in SI and were additionally given the option to attend weekly group meetings at Miriam Hospital’s Weight Control and Diabetes Research Center during the 3 months. The 12 weekly, optional group sessions were led by masters-level staff with extensive training in behavioral weight loss. Sessions involved private weigh-ins and covered topics that supplemented the internet intervention (eg, recipe modification, portion control).

Main outcomes measures. The main outcome was weight loss at the end of the 3-month program. Participants completed measures (ie, weight, BMI) in person at baseline and 3 months (post-treatment), and at 6- and 12-month follow-up visits. Adherence measures included reported weight and physical activity on the SURI website (S, SI, and SIG), log ins, viewed lessons, and self-monitoring entries on the intervention website (SI, SIG), and number of groups meetings attended (SIG). To measure weight loss behaviors, the authors used the Weight Control Practices questionnaire to assess engagement in core weight loss strategies targeted in treatment, and the Paffenbarger questionnaire to assess weekly kcal expended in moderate to vigorous activity. The authors also assessed costs from the payer (labor, rent, intervention materials), participant (SURI registration fee, transportation, time spent on intervention), and societal perspective (sum of payer and participant costs) in order to calculate the cost per kg of weight lost in each study arm.

Results. Participants were predominantly female, non-Hispanic white, and had a mean BMI of 34.4 kg/m² (SE = 0.05). Groups differed only on education (P = 0.02), and attendance at post-treatment and 6- and 12-month follow-up were high (93%, 91%, and 86% respectively). The authors found that weight loss did not differ by educational attainment (P s > 0.57).

Overall, there was a significant group-by-time interaction for weight loss (P < 0.001). Percentage weight loss at 3 months differed among the 3 groups—S: 1.1% ± 0.9%; SI: 4.2% ± 0.6%; SIG: 6.1% ± 0.6% (P s ≤ 0.04). There was also an overall group effect for percentage of individuals achieving 5% weight loss (P < 0.001). SI and SIG had higher percentages of participants who achieved a 5% weight loss than the control (SI: 42%; SIG: 54%; S: 7%; P s < 0.001) but did not differ from one another (P = 0.01). Initial weight losses and percentage of participants who achieved a 5% weight loss were largely maintained through the no-treatment follow-up phase at 6-months, but the 3 groups no longer differed from one another at 12 months (S: 1.2% [SE =0.9]; SI: 2.2% [SE = 0.6]; SIG: 3.3% [SE = 0.6]; P s > 0.05).

All groups reported significant increases in physical activity over time (p < 0.001). More reporting of weight and physical activity data on the SURI website was associated with greater percentage weight loss (r = 0.25; P < 0.001). Number of log ins and lessons viewed on the intervention website were positively associated with percentage weight loss (r = 0.45; P ≤ 0.001; and r = 0.34; P ≤ 0.001 respectively). Greater attendance to group sessions was associated with better weight outcomes (r = 0.61; P ≤ 0.001). Younger age was associated with poorer adherence, including less reporting on the SURI website, viewing of lessons, and logging in to the weight loss website.

There was a significant group-by-time effect interaction for the use of behavioral weight loss strategies (P < 0.001), and increased use of these strategies was associated with greater percentage weight loss in all 3 groups post-treatment. At 12 months, however, there were no differences between groups in the use of these strategies (P s ≤ 0.07).

Cost per kg of weight loss was similar for S ($39) and SI ($35), but both were lower than SIG ($114).

Conclusion. Both intervention arms (SI and SIG) achieved more weight loss at 6 months than SURI alone. Although mean weight loss was greatest with optional group sessions (SIG), the addition of the behavioral intervention website alone (SI) was the most cost-effective method to enhance weight loss. Thus, adding a novel internet behavioral weight loss intervention to a statewide community health initiative may be a cost-effective approach to improving obesity treatment outcomes.

Commentary

Weight loss treatment is recommended for adults with a BMI of > 30 kg/m², as well as those with BMI < 25 kg/m² with weight-related comorbidities [1]. Intensive behavioral treatment should be the first line of intervention for overweight and obese individuals and can lead to 8% to 10% weight loss [2], particularly in initial months of treatment [3]. However, behavioral treatment is inherently challenging and time-consuming, and readily available to only a fraction of the intended population. Although weight losses achieved from intensive lifestyle interventions such as the Diabetes Prevention Program (DPP) [4] may be higher, innovative community weight loss programs that use a variety of weight loss strategies can provide opportunities to a wider population of overweight and obese individuals and at a lower cost [3].

This study built upon the authors’ previous work [5], which showed that SURI participants with behavioral weight loss strategies via email significantly improved 3-month weight losses. In this current study, they compared SURI alone to SURI with additional access to an internet behavioral weight loss website with or without optional group sessions. Since significant weight loss was not maintained at 12 months, this suggests that perhaps access to the behavioral weight loss website should have continued for longer and/or included a maintenance phase after the 3-month intervention. Weight loss often reaches its peak around 6 months, and weight regain occurs without effective maintenance therapy [6].

General strengths of the study included the use of a randomized, intention-to-treat design, dissemination of evidence-based weight loss strategies, objective outcomes measurement, adherence metrics, and strong retention of participants with clear accounting of all enrolled patients from recruitment through analysis. This study demonstrated significant weight loss in an intervention with minimal/optional health professional interaction. This intervention also placed responsibility on participants to self-monitor their diet and physical activity, participate in online lessons, and attend optional group sessions. The success of this community-based intervention suggests feasibility and scalability within a real-world setting. The authors also conducted cost-effectiveness analyses demonstrating that the SI program was more cost-effective than SIG.

However, there are weaknesses as well. In setting the sample size for each arm of this study, no justification was described for choosing a 1:2:2 randomization scheme. In randomized control trials, the allocation of participants into the different study arms is often balanced to equal numbers which maximizes statistical power [7]. However, the use of unequal randomization ratios among study arms can be beneficial and even necessary for various reasons including cost, availability of the intervention, overcoming intervention/treatment learning curves, and if a higher drop-out rate is anticipated. Providing a justification for unbalanced sample sizes would be helpful to future researchers looking to replicate the study. Additionally, participants were mostly non-Hispanic white and female, thus limiting generalizability. While representative of the broader Rhode Island population, findings based on this population this may not be applicable to vulnerable (ie, low literacy, resource-poor) or underrepresented populations (ie, minorities) [8].

Applications for Clinical Practice

An internet-based behavioral weight loss intervention, when added to a community weight management initiative, is cost-effective and can lead to short-term weight loss. Given that clinicians often lack time, training, and resources to adequately address obesity in the office [9,10], encouraging patients to enroll in similar programs may be an effective strategy to address such barriers. The study also highlights the need for maintenance interventions to help keep weight off. Findings should be replicated in more diverse communities.

—Katrina F. Mateo, MPH, and Melanie Jay, MD, MS

Study Overview

Objective. To determine the efficacy and cost-effectiveness of adding an evidence-based internet behavioral weight loss intervention alone or combined with optional group sessions to ShapeUp Rhode Island 2011 (SURI), a 3-month statewide wellness campaign.

Design. 3-arm randomized clinical trial.

Setting and participants. Study participants were recruited from the Rhode Island community via employers, media, and mass mailings at the time of SURI 2011 registration. Of the 3806 participants that joined the weight loss division, 1139 were willing to be contacted for research, and the first 431 were screened for study eligibility. Exclusion criteria were minimal: age < 18 years or > 70 years, body mass index (BMI) < 25 kg/m², pregnant, nursing, or plans to become pregnant, a serious medical condition (eg, cancer), unreliable internet access, non-English speaking, current or previous participation in our weight loss studies, and planned relocation. Those who reported a medical condition that could interfere with safe participation (eg, diabetes) obtained doctor’s consent to participate. Of those screened, 230 met inclusion criteria, completed orientation procedures, and were randomized using a 1:2:2 randomization scheme to the standard SURI program (S; n = 46); SURI plus internet behavioral weight loss intervention (SI; n = 90); or SURI plus internet behavioral weight loss intervention plus optional group sessions (SIG; n = 94). To avoid contamination, individuals on the same SURI team (see below) were randomized to the same intervention.

Intervention. Participants in the standard SURI program did not receive any behavioral weight loss treatment. SURI is a self-sustaining, annual community campaign designed to help Rhode Islanders lose weight and increase their physical activity through an online, team-based competition. Participants join in teams, enter the weight loss or physical activity division or both, and compete with other teams. Throughout the 3-month program, participants have access to a reporting SURI website where they submit their weekly weight and activity data and view their personal and team progress. They also receive paper logs to record weight and activity, a pedometer, access to newsletters and community workshops, and recognition for meeting goals.

Participants in the SI arm received the 3-month SURI program plus a 3-month internet behavioral weight loss intervention. Before SURI began, SI participants attended a 1-hour group meeting during which they received their weight loss goal (lose 1 to 2 pounds per week), calorie and fat gram goal (starting weight < 250 lbs: 1200–1500 kcal/day, 40–50 g of fat; starting weight ≥ 250 lbs: 1500–1800 kcal/day, 50–60 g of fat), and activity goal (gradually increase to 200 minutes of aerobic activity per week). During this session, participants were also taught self-monitoring skills and oriented to an internet behavioral weight loss intervention website developed by the authors. The intervention website included 12 weekly, 10- to 15-minute multimedia lessons based on the Diabetes Prevention Program and a self-monitoring platform where participants tracked their daily weight, calorie, and activity information. Participants received weekly automated feedback on their progress. The intervention website also included information on meal plans, prepackaged meals, and meal replacements.

Participants in the SIG arm received everything in SI and were additionally given the option to attend weekly group meetings at Miriam Hospital’s Weight Control and Diabetes Research Center during the 3 months. The 12 weekly, optional group sessions were led by masters-level staff with extensive training in behavioral weight loss. Sessions involved private weigh-ins and covered topics that supplemented the internet intervention (eg, recipe modification, portion control).

Main outcomes measures. The main outcome was weight loss at the end of the 3-month program. Participants completed measures (ie, weight, BMI) in person at baseline and 3 months (post-treatment), and at 6- and 12-month follow-up visits. Adherence measures included reported weight and physical activity on the SURI website (S, SI, and SIG), log ins, viewed lessons, and self-monitoring entries on the intervention website (SI, SIG), and number of groups meetings attended (SIG). To measure weight loss behaviors, the authors used the Weight Control Practices questionnaire to assess engagement in core weight loss strategies targeted in treatment, and the Paffenbarger questionnaire to assess weekly kcal expended in moderate to vigorous activity. The authors also assessed costs from the payer (labor, rent, intervention materials), participant (SURI registration fee, transportation, time spent on intervention), and societal perspective (sum of payer and participant costs) in order to calculate the cost per kg of weight lost in each study arm.

Results. Participants were predominantly female, non-Hispanic white, and had a mean BMI of 34.4 kg/m² (SE = 0.05). Groups differed only on education (P = 0.02), and attendance at post-treatment and 6- and 12-month follow-up were high (93%, 91%, and 86% respectively). The authors found that weight loss did not differ by educational attainment (P s > 0.57).

Overall, there was a significant group-by-time interaction for weight loss (P < 0.001). Percentage weight loss at 3 months differed among the 3 groups—S: 1.1% ± 0.9%; SI: 4.2% ± 0.6%; SIG: 6.1% ± 0.6% (P s ≤ 0.04). There was also an overall group effect for percentage of individuals achieving 5% weight loss (P < 0.001). SI and SIG had higher percentages of participants who achieved a 5% weight loss than the control (SI: 42%; SIG: 54%; S: 7%; P s < 0.001) but did not differ from one another (P = 0.01). Initial weight losses and percentage of participants who achieved a 5% weight loss were largely maintained through the no-treatment follow-up phase at 6-months, but the 3 groups no longer differed from one another at 12 months (S: 1.2% [SE =0.9]; SI: 2.2% [SE = 0.6]; SIG: 3.3% [SE = 0.6]; P s > 0.05).

All groups reported significant increases in physical activity over time (p < 0.001). More reporting of weight and physical activity data on the SURI website was associated with greater percentage weight loss (r = 0.25; P < 0.001). Number of log ins and lessons viewed on the intervention website were positively associated with percentage weight loss (r = 0.45; P ≤ 0.001; and r = 0.34; P ≤ 0.001 respectively). Greater attendance to group sessions was associated with better weight outcomes (r = 0.61; P ≤ 0.001). Younger age was associated with poorer adherence, including less reporting on the SURI website, viewing of lessons, and logging in to the weight loss website.

There was a significant group-by-time effect interaction for the use of behavioral weight loss strategies (P < 0.001), and increased use of these strategies was associated with greater percentage weight loss in all 3 groups post-treatment. At 12 months, however, there were no differences between groups in the use of these strategies (P s ≤ 0.07).

Cost per kg of weight loss was similar for S ($39) and SI ($35), but both were lower than SIG ($114).

Conclusion. Both intervention arms (SI and SIG) achieved more weight loss at 6 months than SURI alone. Although mean weight loss was greatest with optional group sessions (SIG), the addition of the behavioral intervention website alone (SI) was the most cost-effective method to enhance weight loss. Thus, adding a novel internet behavioral weight loss intervention to a statewide community health initiative may be a cost-effective approach to improving obesity treatment outcomes.

Commentary

Weight loss treatment is recommended for adults with a BMI of > 30 kg/m², as well as those with BMI < 25 kg/m² with weight-related comorbidities [1]. Intensive behavioral treatment should be the first line of intervention for overweight and obese individuals and can lead to 8% to 10% weight loss [2], particularly in initial months of treatment [3]. However, behavioral treatment is inherently challenging and time-consuming, and readily available to only a fraction of the intended population. Although weight losses achieved from intensive lifestyle interventions such as the Diabetes Prevention Program (DPP) [4] may be higher, innovative community weight loss programs that use a variety of weight loss strategies can provide opportunities to a wider population of overweight and obese individuals and at a lower cost [3].

This study built upon the authors’ previous work [5], which showed that SURI participants with behavioral weight loss strategies via email significantly improved 3-month weight losses. In this current study, they compared SURI alone to SURI with additional access to an internet behavioral weight loss website with or without optional group sessions. Since significant weight loss was not maintained at 12 months, this suggests that perhaps access to the behavioral weight loss website should have continued for longer and/or included a maintenance phase after the 3-month intervention. Weight loss often reaches its peak around 6 months, and weight regain occurs without effective maintenance therapy [6].

General strengths of the study included the use of a randomized, intention-to-treat design, dissemination of evidence-based weight loss strategies, objective outcomes measurement, adherence metrics, and strong retention of participants with clear accounting of all enrolled patients from recruitment through analysis. This study demonstrated significant weight loss in an intervention with minimal/optional health professional interaction. This intervention also placed responsibility on participants to self-monitor their diet and physical activity, participate in online lessons, and attend optional group sessions. The success of this community-based intervention suggests feasibility and scalability within a real-world setting. The authors also conducted cost-effectiveness analyses demonstrating that the SI program was more cost-effective than SIG.

However, there are weaknesses as well. In setting the sample size for each arm of this study, no justification was described for choosing a 1:2:2 randomization scheme. In randomized control trials, the allocation of participants into the different study arms is often balanced to equal numbers which maximizes statistical power [7]. However, the use of unequal randomization ratios among study arms can be beneficial and even necessary for various reasons including cost, availability of the intervention, overcoming intervention/treatment learning curves, and if a higher drop-out rate is anticipated. Providing a justification for unbalanced sample sizes would be helpful to future researchers looking to replicate the study. Additionally, participants were mostly non-Hispanic white and female, thus limiting generalizability. While representative of the broader Rhode Island population, findings based on this population this may not be applicable to vulnerable (ie, low literacy, resource-poor) or underrepresented populations (ie, minorities) [8].

Applications for Clinical Practice

An internet-based behavioral weight loss intervention, when added to a community weight management initiative, is cost-effective and can lead to short-term weight loss. Given that clinicians often lack time, training, and resources to adequately address obesity in the office [9,10], encouraging patients to enroll in similar programs may be an effective strategy to address such barriers. The study also highlights the need for maintenance interventions to help keep weight off. Findings should be replicated in more diverse communities.

—Katrina F. Mateo, MPH, and Melanie Jay, MD, MS

Study Overview

Objective. To determine the efficacy and cost-effectiveness of adding an evidence-based internet behavioral weight loss intervention alone or combined with optional group sessions to ShapeUp Rhode Island 2011 (SURI), a 3-month statewide wellness campaign.

Design. 3-arm randomized clinical trial.

Setting and participants. Study participants were recruited from the Rhode Island community via employers, media, and mass mailings at the time of SURI 2011 registration. Of the 3806 participants that joined the weight loss division, 1139 were willing to be contacted for research, and the first 431 were screened for study eligibility. Exclusion criteria were minimal: age < 18 years or > 70 years, body mass index (BMI) < 25 kg/m², pregnant, nursing, or plans to become pregnant, a serious medical condition (eg, cancer), unreliable internet access, non-English speaking, current or previous participation in our weight loss studies, and planned relocation. Those who reported a medical condition that could interfere with safe participation (eg, diabetes) obtained doctor’s consent to participate. Of those screened, 230 met inclusion criteria, completed orientation procedures, and were randomized using a 1:2:2 randomization scheme to the standard SURI program (S; n = 46); SURI plus internet behavioral weight loss intervention (SI; n = 90); or SURI plus internet behavioral weight loss intervention plus optional group sessions (SIG; n = 94). To avoid contamination, individuals on the same SURI team (see below) were randomized to the same intervention.

Intervention. Participants in the standard SURI program did not receive any behavioral weight loss treatment. SURI is a self-sustaining, annual community campaign designed to help Rhode Islanders lose weight and increase their physical activity through an online, team-based competition. Participants join in teams, enter the weight loss or physical activity division or both, and compete with other teams. Throughout the 3-month program, participants have access to a reporting SURI website where they submit their weekly weight and activity data and view their personal and team progress. They also receive paper logs to record weight and activity, a pedometer, access to newsletters and community workshops, and recognition for meeting goals.

Participants in the SI arm received the 3-month SURI program plus a 3-month internet behavioral weight loss intervention. Before SURI began, SI participants attended a 1-hour group meeting during which they received their weight loss goal (lose 1 to 2 pounds per week), calorie and fat gram goal (starting weight < 250 lbs: 1200–1500 kcal/day, 40–50 g of fat; starting weight ≥ 250 lbs: 1500–1800 kcal/day, 50–60 g of fat), and activity goal (gradually increase to 200 minutes of aerobic activity per week). During this session, participants were also taught self-monitoring skills and oriented to an internet behavioral weight loss intervention website developed by the authors. The intervention website included 12 weekly, 10- to 15-minute multimedia lessons based on the Diabetes Prevention Program and a self-monitoring platform where participants tracked their daily weight, calorie, and activity information. Participants received weekly automated feedback on their progress. The intervention website also included information on meal plans, prepackaged meals, and meal replacements.

Participants in the SIG arm received everything in SI and were additionally given the option to attend weekly group meetings at Miriam Hospital’s Weight Control and Diabetes Research Center during the 3 months. The 12 weekly, optional group sessions were led by masters-level staff with extensive training in behavioral weight loss. Sessions involved private weigh-ins and covered topics that supplemented the internet intervention (eg, recipe modification, portion control).

Main outcomes measures. The main outcome was weight loss at the end of the 3-month program. Participants completed measures (ie, weight, BMI) in person at baseline and 3 months (post-treatment), and at 6- and 12-month follow-up visits. Adherence measures included reported weight and physical activity on the SURI website (S, SI, and SIG), log ins, viewed lessons, and self-monitoring entries on the intervention website (SI, SIG), and number of groups meetings attended (SIG). To measure weight loss behaviors, the authors used the Weight Control Practices questionnaire to assess engagement in core weight loss strategies targeted in treatment, and the Paffenbarger questionnaire to assess weekly kcal expended in moderate to vigorous activity. The authors also assessed costs from the payer (labor, rent, intervention materials), participant (SURI registration fee, transportation, time spent on intervention), and societal perspective (sum of payer and participant costs) in order to calculate the cost per kg of weight lost in each study arm.

Results. Participants were predominantly female, non-Hispanic white, and had a mean BMI of 34.4 kg/m² (SE = 0.05). Groups differed only on education (P = 0.02), and attendance at post-treatment and 6- and 12-month follow-up were high (93%, 91%, and 86% respectively). The authors found that weight loss did not differ by educational attainment (P s > 0.57).

Overall, there was a significant group-by-time interaction for weight loss (P < 0.001). Percentage weight loss at 3 months differed among the 3 groups—S: 1.1% ± 0.9%; SI: 4.2% ± 0.6%; SIG: 6.1% ± 0.6% (P s ≤ 0.04). There was also an overall group effect for percentage of individuals achieving 5% weight loss (P < 0.001). SI and SIG had higher percentages of participants who achieved a 5% weight loss than the control (SI: 42%; SIG: 54%; S: 7%; P s < 0.001) but did not differ from one another (P = 0.01). Initial weight losses and percentage of participants who achieved a 5% weight loss were largely maintained through the no-treatment follow-up phase at 6-months, but the 3 groups no longer differed from one another at 12 months (S: 1.2% [SE =0.9]; SI: 2.2% [SE = 0.6]; SIG: 3.3% [SE = 0.6]; P s > 0.05).

All groups reported significant increases in physical activity over time (p < 0.001). More reporting of weight and physical activity data on the SURI website was associated with greater percentage weight loss (r = 0.25; P < 0.001). Number of log ins and lessons viewed on the intervention website were positively associated with percentage weight loss (r = 0.45; P ≤ 0.001; and r = 0.34; P ≤ 0.001 respectively). Greater attendance to group sessions was associated with better weight outcomes (r = 0.61; P ≤ 0.001). Younger age was associated with poorer adherence, including less reporting on the SURI website, viewing of lessons, and logging in to the weight loss website.

There was a significant group-by-time effect interaction for the use of behavioral weight loss strategies (P < 0.001), and increased use of these strategies was associated with greater percentage weight loss in all 3 groups post-treatment. At 12 months, however, there were no differences between groups in the use of these strategies (P s ≤ 0.07).

Cost per kg of weight loss was similar for S ($39) and SI ($35), but both were lower than SIG ($114).

Conclusion. Both intervention arms (SI and SIG) achieved more weight loss at 6 months than SURI alone. Although mean weight loss was greatest with optional group sessions (SIG), the addition of the behavioral intervention website alone (SI) was the most cost-effective method to enhance weight loss. Thus, adding a novel internet behavioral weight loss intervention to a statewide community health initiative may be a cost-effective approach to improving obesity treatment outcomes.

Commentary

Weight loss treatment is recommended for adults with a BMI of > 30 kg/m², as well as those with BMI < 25 kg/m² with weight-related comorbidities [1]. Intensive behavioral treatment should be the first line of intervention for overweight and obese individuals and can lead to 8% to 10% weight loss [2], particularly in initial months of treatment [3]. However, behavioral treatment is inherently challenging and time-consuming, and readily available to only a fraction of the intended population. Although weight losses achieved from intensive lifestyle interventions such as the Diabetes Prevention Program (DPP) [4] may be higher, innovative community weight loss programs that use a variety of weight loss strategies can provide opportunities to a wider population of overweight and obese individuals and at a lower cost [3].

This study built upon the authors’ previous work [5], which showed that SURI participants with behavioral weight loss strategies via email significantly improved 3-month weight losses. In this current study, they compared SURI alone to SURI with additional access to an internet behavioral weight loss website with or without optional group sessions. Since significant weight loss was not maintained at 12 months, this suggests that perhaps access to the behavioral weight loss website should have continued for longer and/or included a maintenance phase after the 3-month intervention. Weight loss often reaches its peak around 6 months, and weight regain occurs without effective maintenance therapy [6].

General strengths of the study included the use of a randomized, intention-to-treat design, dissemination of evidence-based weight loss strategies, objective outcomes measurement, adherence metrics, and strong retention of participants with clear accounting of all enrolled patients from recruitment through analysis. This study demonstrated significant weight loss in an intervention with minimal/optional health professional interaction. This intervention also placed responsibility on participants to self-monitor their diet and physical activity, participate in online lessons, and attend optional group sessions. The success of this community-based intervention suggests feasibility and scalability within a real-world setting. The authors also conducted cost-effectiveness analyses demonstrating that the SI program was more cost-effective than SIG.

However, there are weaknesses as well. In setting the sample size for each arm of this study, no justification was described for choosing a 1:2:2 randomization scheme. In randomized control trials, the allocation of participants into the different study arms is often balanced to equal numbers which maximizes statistical power [7]. However, the use of unequal randomization ratios among study arms can be beneficial and even necessary for various reasons including cost, availability of the intervention, overcoming intervention/treatment learning curves, and if a higher drop-out rate is anticipated. Providing a justification for unbalanced sample sizes would be helpful to future researchers looking to replicate the study. Additionally, participants were mostly non-Hispanic white and female, thus limiting generalizability. While representative of the broader Rhode Island population, findings based on this population this may not be applicable to vulnerable (ie, low literacy, resource-poor) or underrepresented populations (ie, minorities) [8].

Applications for Clinical Practice

An internet-based behavioral weight loss intervention, when added to a community weight management initiative, is cost-effective and can lead to short-term weight loss. Given that clinicians often lack time, training, and resources to adequately address obesity in the office [9,10], encouraging patients to enroll in similar programs may be an effective strategy to address such barriers. The study also highlights the need for maintenance interventions to help keep weight off. Findings should be replicated in more diverse communities.

—Katrina F. Mateo, MPH, and Melanie Jay, MD, MS

Study Overview

Objective. To evaluate the contribution of dietary habits in midlife on healthy aging.

Study design. Observational investigation of an ongoing cohort study.

Setting and participants. Participants were gathered from the Nurses’ Health Study, a cohort of 121,700 married female nurses who have completed health-related questionnaires every 2 years since 1976. Data on race was not originally collected, but a subsample analysis revealed that the cohort of nurses was > 98% white [1]. A subset of this cohort (n = 19,415) older than age 70 years from 1995 and 2002 and who received additional cognitive testing was chosen as the population of interest for this study. The investigators excluded participants with missing data (n = 5878) on important covariates and participants who had any of 11 chronic diseases in midlife (n = 2585), obtained from questionnaires in the 1980s. 10,670 participants were included in the final analysis.

Main outcome measures. Participants were dichotomized as “healthy agers” or “usual agers” on the basis of 4 health domains measured in 2000. Persons free of 11 chronic diseases, without cognitive impairment, without physical limitations, and with intact mental health were designated “healthy agers,” with the remainder designated “usual agers.” For each domain, specific criteria were employed to indicate impairment. Cognitive impairment was defined as a score of 31 or greater on the Telephone Interview for Cognitive Status. Investigators used the  Medical Outcomes Short-Form 36 health survey (SF-36) to measure physical impairment and mental health. Participants who reported being limited at least “a little” on moderate activities or limited “a lot” on strenuous activities were classified as physically impaired. Intact mental health was defined as a score above the cohort’s median on the mental health subscale of the SF-36.

Dietary habits were ascertained at midlife by an average of the 1984 and 1986 food frequency questionnaire (FFQ) data. Using these data, the authors calculated the Alternative Healthy Eating-2010 (AHEI-2010) and the Alternate Mediterranean Diet (A-MeDi) scores. AHEH-2010 incorporates the latest knowledge on the benefits and harms of foods and nutrients on the risk of chronic disease. It has 11 domains (including whole grain intake, vegetable intake, and lower intake of trans fats, among others) which are each scored 0 (worst) to 10 (best). The A-MeDi score assesses adherence to the traditional Mediterranean diet, which includes intake of vegetables, fruits, nuts, legumes, and moderate alcohol intake, among others. Each of 9 categories is rated 0 or 1, with 1 representing healthy intake.

Covariates included sociodemographic, lifestyle, and health-related measures obtained either in 1984 or 1986. These included age; educational level; household income and home value estimated from census tract data; marital status; family history of diabetes, cancer, and myocardial infarction; physical activity; smoking; multivitamin and aspirin use; BMI; history of high blood pressure; and hypercholesterolemia. BMI was obtained via self-report and averaged from among values obtained in 1984 and 1986; these have previously been shown to have excellent correlation (r = 0.97) to standardized examinations [2].

The authors standardized baseline characteristics for each study participant based upon the age at which they entered the study. They used logistic regression to estimate the odds of being a “healthy ager” in the year 2000 by quintile of AHEI-2010 and A-MeDi scores.

Main results. Of the 10,670 participants, 1171 (11%) were labeled “healthy agers” and 9499 (89%) were labeled “usual agers.” Prevalence in each of the 4 health domains varied widely: 9599 (90%) of the 10670 participants had no cognitive impairment, 7234 (67.8%) had no chronic diseases, 4606 (43.2%) had no mental health limitations, and 2905 (27.2%) had no impairment of physical functioning.

Investigators presented data comparing healthy agers and usual agers at baseline without tests for significance. The mean age of healthy agers and usual agers was comparable (58.6 [SD = 2.5] vs. 59.1 [SD = 2.5]). Healthy agers had lower prevalence of obesity (3% vs. 13%), ever smoking (54% vs. 47%), higher mean physical activity (19.4 MET h/wk [SD = 21.7] vs. 14.1 MET h/wk [SD = 19.8]), lower energy intake (1692 kcal/d [SD = 472] vs. 1743 kcal/d [SD = 477]) and lower prevalence of hypertension (20% vs. 32%) and hypercholesterolemia (12% vs. 17%). Healthy agers also had higher baseline AHEI-2010 (53.2 [SD = 10.3] vs. 50.6 [SD = 10.1]) and A-MeDi scores in midlife (4.5 [SD = 1.6] vs. 4.3 [SD = 1.7]).

Greater scores on the AHEI-2010 and A-MeDi measures in midlife were associated with greater odds of healthy aging in multivariate analysis. After adjusting for all covariates, women in the highest quintile of AHEI-2010 scores at baseline had 34% greater odds (95% CI, 9% to 66%) of being healthy agers compared to women in the lowest quintile. Likewise, adjusted analyses reported women in the highest quintile of A-MeDi scores had 46% greater odds (95% CI, 17% to 83%) of being healthy agers.

Secondary analyses tested each component of healthy aging for associations with AHEI-2010 and A-MeDi scores in midlife. Associations were overall weaker, but no impairment of physical function and no limitation of mental health were both found to be significant after adjustment for covariates. Women in the highest quintile of AHEI-2010 scores at baseline had 23% (95% CI, 11% to 36%) and 13% (95% CI, 5% to 22%) greater odds, respectively, of not having any physical limitations or mental health impairments in late life compared to women in the lowest quintile. Likewise, women in the highest quintile of A-MeDi scores at baseline had 14% (95% CI, 3% to 26%) and 12% (95% CI, 4% to 20%) greater odds, respectively, of not having any physical limitations or mental health impairments in late life compared to women in the lowest quintile.

The authors also tested the effect of individual components of dietary patterns on healthy aging, comparing those in the highest quintile versus those in the lowest quintile for each measure. Persons with the greatest intake of fruits had 46% (95% CI, 15% to 85%) greater odds of being healthy agers compared to those with the lowest intake of fruits. Persons with the highest intake of alcohol had 28% greater odds (95% CI, 4% to 56%) of being healthy agers compared to those with the lowest intake of alcohol. Conversely, those with lower intake of sugar-sweetened beverages (OR, 1.28 [95% CI, 1.03 to 1.58]) and non-omega 3 polyunsaturated fatty acids (OR, 1.38 [CI, 1.10 to 1.73]) had better odds of being healthy agers compared to those with higher intakes.

Conclusion. Women with healthy dietary patterns at midlife had significantly greater odds of being healthy agers in later life after adjusting for potential con-founders. Results were consistent in direction and effect size when using either the AHEI-2010 score or the A-MeDi score. The effects of healthy diet at midlife seemed to have the strongest association with physical impairment scores and mental health scores. Higher intake of fruits and alcohol along with lower intake of sugar-sweetened beverages and polyunsaturated fatty acids seemed to have the most power for predicting healthy aging.

Commentary

These results are consistent with current knowledge, which indicates the health benefits of a balanced, healthy diet high in fruits, vegetables, whole grains, nuts, and legumes and low in red or other processed meats. There is high quality evidence linking each dietary measure to health outcomes. Adherence to the Alternative Healthy Eating Index has been related to lower mortality rates [3], decreased risk of cardiovascular disease [4], and decreased risk of type 2 diabetes and the metabolic syndrome [5]. Likewise, adherence to the Mediterranean diet is  associated with reductions in overall mortality, cardiovascular incidence and mortality, cancer incidence and mortality, and neurodegenerative diseases [6]. Both diets endorse moderate alcohol intake, which was  associated with lower rates of all-cause and cardiovascular mortality in a meta-analysis [7]. Alcohol is theorized to produce decreased platelet aggregation, increase HDL cholesterol, and increase endothelial vasorelaxation [8]. Polyphenols, most prominent in red wines, may have additional effects which include vaso-relaxation of aortic rings, reduced thrombosis and inflammation, and increased fibrinolysis [9]. Nevertheless, heavy alcohol use may increase cardiovascular mortality, hypertension, and hyperlipidemia [8]. This study concluded that higher alcohol intake was related to being a healthy ager; this may be because there are few heavy alcohol users in this cohort, though this hypothesis was not tested in the study.

Any observational study is subject to debate about the confounders chosen for analysis and potential biases. The authors report that the most powerful confounders in this analysis were BMI, physical activity, and smoking, all of which have been well established as predictors for health in later life [10]. Nonetheless, important potential confounders not used in analysis included the baseline prevalence of mental health problems, cognitive limitations, and physical limitations, all of which were not available.

The greatest concern about of this study is a potential lack of generalizability given the population surveyed. The Nurses’ Health Study consists of a cohort of female, married, predominantly white registered nurses [1]. For instance, African Americans have a greater burden of hypertension than non-Hispanic whites after accounting for dietary differences [11], a higher degree of late-life cognitive dysfunction [12], and greater risk of developing late-life physical disability [13].  Also, race and ethnicity may impact eating patterns, food preferences, and food availability in ways that are difficult to predict.  In addition, nurses in the cohort were probably of similar socioeconomic status given their shared occupation, though the authors did not report the variation in median household incomes obtained from census tract analysis in this study [14]. Results might change if the sample was less homogeneous. Nonetheless, the results are consistent with current knowledge, biologically plausible, and clinically meaningful.

Applications for Clinical Practice

Integrating dietary changes in middle-aged women may be an important means of decreasing morbidity in older age and improving physical and mental health functioning later in life. Health care providers should discuss the future benefits of healthy eating on quality of life in order to encourage patients in midlife to alter their diet in meaningful ways. While it may be difficult to generalize these findings to patients of different genders, races, or ethnicities, the biological underpinnings of the data make it hard dispute the conclusions presented in the study.

 —Hector Perez, MD, and Melanie Jay, MD, MS

Study Overview

Objective. To evaluate the contribution of dietary habits in midlife on healthy aging.

Study design. Observational investigation of an ongoing cohort study.

Setting and participants. Participants were gathered from the Nurses’ Health Study, a cohort of 121,700 married female nurses who have completed health-related questionnaires every 2 years since 1976. Data on race was not originally collected, but a subsample analysis revealed that the cohort of nurses was > 98% white [1]. A subset of this cohort (n = 19,415) older than age 70 years from 1995 and 2002 and who received additional cognitive testing was chosen as the population of interest for this study. The investigators excluded participants with missing data (n = 5878) on important covariates and participants who had any of 11 chronic diseases in midlife (n = 2585), obtained from questionnaires in the 1980s. 10,670 participants were included in the final analysis.

Main outcome measures. Participants were dichotomized as “healthy agers” or “usual agers” on the basis of 4 health domains measured in 2000. Persons free of 11 chronic diseases, without cognitive impairment, without physical limitations, and with intact mental health were designated “healthy agers,” with the remainder designated “usual agers.” For each domain, specific criteria were employed to indicate impairment. Cognitive impairment was defined as a score of 31 or greater on the Telephone Interview for Cognitive Status. Investigators used the  Medical Outcomes Short-Form 36 health survey (SF-36) to measure physical impairment and mental health. Participants who reported being limited at least “a little” on moderate activities or limited “a lot” on strenuous activities were classified as physically impaired. Intact mental health was defined as a score above the cohort’s median on the mental health subscale of the SF-36.

Dietary habits were ascertained at midlife by an average of the 1984 and 1986 food frequency questionnaire (FFQ) data. Using these data, the authors calculated the Alternative Healthy Eating-2010 (AHEI-2010) and the Alternate Mediterranean Diet (A-MeDi) scores. AHEH-2010 incorporates the latest knowledge on the benefits and harms of foods and nutrients on the risk of chronic disease. It has 11 domains (including whole grain intake, vegetable intake, and lower intake of trans fats, among others) which are each scored 0 (worst) to 10 (best). The A-MeDi score assesses adherence to the traditional Mediterranean diet, which includes intake of vegetables, fruits, nuts, legumes, and moderate alcohol intake, among others. Each of 9 categories is rated 0 or 1, with 1 representing healthy intake.

Covariates included sociodemographic, lifestyle, and health-related measures obtained either in 1984 or 1986. These included age; educational level; household income and home value estimated from census tract data; marital status; family history of diabetes, cancer, and myocardial infarction; physical activity; smoking; multivitamin and aspirin use; BMI; history of high blood pressure; and hypercholesterolemia. BMI was obtained via self-report and averaged from among values obtained in 1984 and 1986; these have previously been shown to have excellent correlation (r = 0.97) to standardized examinations [2].

The authors standardized baseline characteristics for each study participant based upon the age at which they entered the study. They used logistic regression to estimate the odds of being a “healthy ager” in the year 2000 by quintile of AHEI-2010 and A-MeDi scores.

Main results. Of the 10,670 participants, 1171 (11%) were labeled “healthy agers” and 9499 (89%) were labeled “usual agers.” Prevalence in each of the 4 health domains varied widely: 9599 (90%) of the 10670 participants had no cognitive impairment, 7234 (67.8%) had no chronic diseases, 4606 (43.2%) had no mental health limitations, and 2905 (27.2%) had no impairment of physical functioning.

Investigators presented data comparing healthy agers and usual agers at baseline without tests for significance. The mean age of healthy agers and usual agers was comparable (58.6 [SD = 2.5] vs. 59.1 [SD = 2.5]). Healthy agers had lower prevalence of obesity (3% vs. 13%), ever smoking (54% vs. 47%), higher mean physical activity (19.4 MET h/wk [SD = 21.7] vs. 14.1 MET h/wk [SD = 19.8]), lower energy intake (1692 kcal/d [SD = 472] vs. 1743 kcal/d [SD = 477]) and lower prevalence of hypertension (20% vs. 32%) and hypercholesterolemia (12% vs. 17%). Healthy agers also had higher baseline AHEI-2010 (53.2 [SD = 10.3] vs. 50.6 [SD = 10.1]) and A-MeDi scores in midlife (4.5 [SD = 1.6] vs. 4.3 [SD = 1.7]).

Greater scores on the AHEI-2010 and A-MeDi measures in midlife were associated with greater odds of healthy aging in multivariate analysis. After adjusting for all covariates, women in the highest quintile of AHEI-2010 scores at baseline had 34% greater odds (95% CI, 9% to 66%) of being healthy agers compared to women in the lowest quintile. Likewise, adjusted analyses reported women in the highest quintile of A-MeDi scores had 46% greater odds (95% CI, 17% to 83%) of being healthy agers.

Secondary analyses tested each component of healthy aging for associations with AHEI-2010 and A-MeDi scores in midlife. Associations were overall weaker, but no impairment of physical function and no limitation of mental health were both found to be significant after adjustment for covariates. Women in the highest quintile of AHEI-2010 scores at baseline had 23% (95% CI, 11% to 36%) and 13% (95% CI, 5% to 22%) greater odds, respectively, of not having any physical limitations or mental health impairments in late life compared to women in the lowest quintile. Likewise, women in the highest quintile of A-MeDi scores at baseline had 14% (95% CI, 3% to 26%) and 12% (95% CI, 4% to 20%) greater odds, respectively, of not having any physical limitations or mental health impairments in late life compared to women in the lowest quintile.

The authors also tested the effect of individual components of dietary patterns on healthy aging, comparing those in the highest quintile versus those in the lowest quintile for each measure. Persons with the greatest intake of fruits had 46% (95% CI, 15% to 85%) greater odds of being healthy agers compared to those with the lowest intake of fruits. Persons with the highest intake of alcohol had 28% greater odds (95% CI, 4% to 56%) of being healthy agers compared to those with the lowest intake of alcohol. Conversely, those with lower intake of sugar-sweetened beverages (OR, 1.28 [95% CI, 1.03 to 1.58]) and non-omega 3 polyunsaturated fatty acids (OR, 1.38 [CI, 1.10 to 1.73]) had better odds of being healthy agers compared to those with higher intakes.

Conclusion. Women with healthy dietary patterns at midlife had significantly greater odds of being healthy agers in later life after adjusting for potential con-founders. Results were consistent in direction and effect size when using either the AHEI-2010 score or the A-MeDi score. The effects of healthy diet at midlife seemed to have the strongest association with physical impairment scores and mental health scores. Higher intake of fruits and alcohol along with lower intake of sugar-sweetened beverages and polyunsaturated fatty acids seemed to have the most power for predicting healthy aging.

Commentary

These results are consistent with current knowledge, which indicates the health benefits of a balanced, healthy diet high in fruits, vegetables, whole grains, nuts, and legumes and low in red or other processed meats. There is high quality evidence linking each dietary measure to health outcomes. Adherence to the Alternative Healthy Eating Index has been related to lower mortality rates [3], decreased risk of cardiovascular disease [4], and decreased risk of type 2 diabetes and the metabolic syndrome [5]. Likewise, adherence to the Mediterranean diet is  associated with reductions in overall mortality, cardiovascular incidence and mortality, cancer incidence and mortality, and neurodegenerative diseases [6]. Both diets endorse moderate alcohol intake, which was  associated with lower rates of all-cause and cardiovascular mortality in a meta-analysis [7]. Alcohol is theorized to produce decreased platelet aggregation, increase HDL cholesterol, and increase endothelial vasorelaxation [8]. Polyphenols, most prominent in red wines, may have additional effects which include vaso-relaxation of aortic rings, reduced thrombosis and inflammation, and increased fibrinolysis [9]. Nevertheless, heavy alcohol use may increase cardiovascular mortality, hypertension, and hyperlipidemia [8]. This study concluded that higher alcohol intake was related to being a healthy ager; this may be because there are few heavy alcohol users in this cohort, though this hypothesis was not tested in the study.

Any observational study is subject to debate about the confounders chosen for analysis and potential biases. The authors report that the most powerful confounders in this analysis were BMI, physical activity, and smoking, all of which have been well established as predictors for health in later life [10]. Nonetheless, important potential confounders not used in analysis included the baseline prevalence of mental health problems, cognitive limitations, and physical limitations, all of which were not available.

The greatest concern about of this study is a potential lack of generalizability given the population surveyed. The Nurses’ Health Study consists of a cohort of female, married, predominantly white registered nurses [1]. For instance, African Americans have a greater burden of hypertension than non-Hispanic whites after accounting for dietary differences [11], a higher degree of late-life cognitive dysfunction [12], and greater risk of developing late-life physical disability [13].  Also, race and ethnicity may impact eating patterns, food preferences, and food availability in ways that are difficult to predict.  In addition, nurses in the cohort were probably of similar socioeconomic status given their shared occupation, though the authors did not report the variation in median household incomes obtained from census tract analysis in this study [14]. Results might change if the sample was less homogeneous. Nonetheless, the results are consistent with current knowledge, biologically plausible, and clinically meaningful.

Applications for Clinical Practice

Integrating dietary changes in middle-aged women may be an important means of decreasing morbidity in older age and improving physical and mental health functioning later in life. Health care providers should discuss the future benefits of healthy eating on quality of life in order to encourage patients in midlife to alter their diet in meaningful ways. While it may be difficult to generalize these findings to patients of different genders, races, or ethnicities, the biological underpinnings of the data make it hard dispute the conclusions presented in the study.

 —Hector Perez, MD, and Melanie Jay, MD, MS

Study Overview

Objective. To evaluate the contribution of dietary habits in midlife on healthy aging.

Study design. Observational investigation of an ongoing cohort study.

Setting and participants. Participants were gathered from the Nurses’ Health Study, a cohort of 121,700 married female nurses who have completed health-related questionnaires every 2 years since 1976. Data on race was not originally collected, but a subsample analysis revealed that the cohort of nurses was > 98% white [1]. A subset of this cohort (n = 19,415) older than age 70 years from 1995 and 2002 and who received additional cognitive testing was chosen as the population of interest for this study. The investigators excluded participants with missing data (n = 5878) on important covariates and participants who had any of 11 chronic diseases in midlife (n = 2585), obtained from questionnaires in the 1980s. 10,670 participants were included in the final analysis.

Main outcome measures. Participants were dichotomized as “healthy agers” or “usual agers” on the basis of 4 health domains measured in 2000. Persons free of 11 chronic diseases, without cognitive impairment, without physical limitations, and with intact mental health were designated “healthy agers,” with the remainder designated “usual agers.” For each domain, specific criteria were employed to indicate impairment. Cognitive impairment was defined as a score of 31 or greater on the Telephone Interview for Cognitive Status. Investigators used the  Medical Outcomes Short-Form 36 health survey (SF-36) to measure physical impairment and mental health. Participants who reported being limited at least “a little” on moderate activities or limited “a lot” on strenuous activities were classified as physically impaired. Intact mental health was defined as a score above the cohort’s median on the mental health subscale of the SF-36.

Dietary habits were ascertained at midlife by an average of the 1984 and 1986 food frequency questionnaire (FFQ) data. Using these data, the authors calculated the Alternative Healthy Eating-2010 (AHEI-2010) and the Alternate Mediterranean Diet (A-MeDi) scores. AHEH-2010 incorporates the latest knowledge on the benefits and harms of foods and nutrients on the risk of chronic disease. It has 11 domains (including whole grain intake, vegetable intake, and lower intake of trans fats, among others) which are each scored 0 (worst) to 10 (best). The A-MeDi score assesses adherence to the traditional Mediterranean diet, which includes intake of vegetables, fruits, nuts, legumes, and moderate alcohol intake, among others. Each of 9 categories is rated 0 or 1, with 1 representing healthy intake.

Covariates included sociodemographic, lifestyle, and health-related measures obtained either in 1984 or 1986. These included age; educational level; household income and home value estimated from census tract data; marital status; family history of diabetes, cancer, and myocardial infarction; physical activity; smoking; multivitamin and aspirin use; BMI; history of high blood pressure; and hypercholesterolemia. BMI was obtained via self-report and averaged from among values obtained in 1984 and 1986; these have previously been shown to have excellent correlation (r = 0.97) to standardized examinations [2].

The authors standardized baseline characteristics for each study participant based upon the age at which they entered the study. They used logistic regression to estimate the odds of being a “healthy ager” in the year 2000 by quintile of AHEI-2010 and A-MeDi scores.

Main results. Of the 10,670 participants, 1171 (11%) were labeled “healthy agers” and 9499 (89%) were labeled “usual agers.” Prevalence in each of the 4 health domains varied widely: 9599 (90%) of the 10670 participants had no cognitive impairment, 7234 (67.8%) had no chronic diseases, 4606 (43.2%) had no mental health limitations, and 2905 (27.2%) had no impairment of physical functioning.

Investigators presented data comparing healthy agers and usual agers at baseline without tests for significance. The mean age of healthy agers and usual agers was comparable (58.6 [SD = 2.5] vs. 59.1 [SD = 2.5]). Healthy agers had lower prevalence of obesity (3% vs. 13%), ever smoking (54% vs. 47%), higher mean physical activity (19.4 MET h/wk [SD = 21.7] vs. 14.1 MET h/wk [SD = 19.8]), lower energy intake (1692 kcal/d [SD = 472] vs. 1743 kcal/d [SD = 477]) and lower prevalence of hypertension (20% vs. 32%) and hypercholesterolemia (12% vs. 17%). Healthy agers also had higher baseline AHEI-2010 (53.2 [SD = 10.3] vs. 50.6 [SD = 10.1]) and A-MeDi scores in midlife (4.5 [SD = 1.6] vs. 4.3 [SD = 1.7]).

Greater scores on the AHEI-2010 and A-MeDi measures in midlife were associated with greater odds of healthy aging in multivariate analysis. After adjusting for all covariates, women in the highest quintile of AHEI-2010 scores at baseline had 34% greater odds (95% CI, 9% to 66%) of being healthy agers compared to women in the lowest quintile. Likewise, adjusted analyses reported women in the highest quintile of A-MeDi scores had 46% greater odds (95% CI, 17% to 83%) of being healthy agers.

Secondary analyses tested each component of healthy aging for associations with AHEI-2010 and A-MeDi scores in midlife. Associations were overall weaker, but no impairment of physical function and no limitation of mental health were both found to be significant after adjustment for covariates. Women in the highest quintile of AHEI-2010 scores at baseline had 23% (95% CI, 11% to 36%) and 13% (95% CI, 5% to 22%) greater odds, respectively, of not having any physical limitations or mental health impairments in late life compared to women in the lowest quintile. Likewise, women in the highest quintile of A-MeDi scores at baseline had 14% (95% CI, 3% to 26%) and 12% (95% CI, 4% to 20%) greater odds, respectively, of not having any physical limitations or mental health impairments in late life compared to women in the lowest quintile.

The authors also tested the effect of individual components of dietary patterns on healthy aging, comparing those in the highest quintile versus those in the lowest quintile for each measure. Persons with the greatest intake of fruits had 46% (95% CI, 15% to 85%) greater odds of being healthy agers compared to those with the lowest intake of fruits. Persons with the highest intake of alcohol had 28% greater odds (95% CI, 4% to 56%) of being healthy agers compared to those with the lowest intake of alcohol. Conversely, those with lower intake of sugar-sweetened beverages (OR, 1.28 [95% CI, 1.03 to 1.58]) and non-omega 3 polyunsaturated fatty acids (OR, 1.38 [CI, 1.10 to 1.73]) had better odds of being healthy agers compared to those with higher intakes.

Conclusion. Women with healthy dietary patterns at midlife had significantly greater odds of being healthy agers in later life after adjusting for potential con-founders. Results were consistent in direction and effect size when using either the AHEI-2010 score or the A-MeDi score. The effects of healthy diet at midlife seemed to have the strongest association with physical impairment scores and mental health scores. Higher intake of fruits and alcohol along with lower intake of sugar-sweetened beverages and polyunsaturated fatty acids seemed to have the most power for predicting healthy aging.

Commentary

These results are consistent with current knowledge, which indicates the health benefits of a balanced, healthy diet high in fruits, vegetables, whole grains, nuts, and legumes and low in red or other processed meats. There is high quality evidence linking each dietary measure to health outcomes. Adherence to the Alternative Healthy Eating Index has been related to lower mortality rates [3], decreased risk of cardiovascular disease [4], and decreased risk of type 2 diabetes and the metabolic syndrome [5]. Likewise, adherence to the Mediterranean diet is  associated with reductions in overall mortality, cardiovascular incidence and mortality, cancer incidence and mortality, and neurodegenerative diseases [6]. Both diets endorse moderate alcohol intake, which was  associated with lower rates of all-cause and cardiovascular mortality in a meta-analysis [7]. Alcohol is theorized to produce decreased platelet aggregation, increase HDL cholesterol, and increase endothelial vasorelaxation [8]. Polyphenols, most prominent in red wines, may have additional effects which include vaso-relaxation of aortic rings, reduced thrombosis and inflammation, and increased fibrinolysis [9]. Nevertheless, heavy alcohol use may increase cardiovascular mortality, hypertension, and hyperlipidemia [8]. This study concluded that higher alcohol intake was related to being a healthy ager; this may be because there are few heavy alcohol users in this cohort, though this hypothesis was not tested in the study.

Any observational study is subject to debate about the confounders chosen for analysis and potential biases. The authors report that the most powerful confounders in this analysis were BMI, physical activity, and smoking, all of which have been well established as predictors for health in later life [10]. Nonetheless, important potential confounders not used in analysis included the baseline prevalence of mental health problems, cognitive limitations, and physical limitations, all of which were not available.

The greatest concern about of this study is a potential lack of generalizability given the population surveyed. The Nurses’ Health Study consists of a cohort of female, married, predominantly white registered nurses [1]. For instance, African Americans have a greater burden of hypertension than non-Hispanic whites after accounting for dietary differences [11], a higher degree of late-life cognitive dysfunction [12], and greater risk of developing late-life physical disability [13].  Also, race and ethnicity may impact eating patterns, food preferences, and food availability in ways that are difficult to predict.  In addition, nurses in the cohort were probably of similar socioeconomic status given their shared occupation, though the authors did not report the variation in median household incomes obtained from census tract analysis in this study [14]. Results might change if the sample was less homogeneous. Nonetheless, the results are consistent with current knowledge, biologically plausible, and clinically meaningful.

Applications for Clinical Practice

Integrating dietary changes in middle-aged women may be an important means of decreasing morbidity in older age and improving physical and mental health functioning later in life. Health care providers should discuss the future benefits of healthy eating on quality of life in order to encourage patients in midlife to alter their diet in meaningful ways. While it may be difficult to generalize these findings to patients of different genders, races, or ethnicities, the biological underpinnings of the data make it hard dispute the conclusions presented in the study.

 —Hector Perez, MD, and Melanie Jay, MD, MS

From the New York University School of Medicine, New York, NY (Drs. Gutnick and Jay), University of Colorado Health Sciences Center, Denver, CO (Dr. Reims), University of British Columbia, BC, Canada (Dr. Davis), University College London, London, UK (Dr. Gainforth), and Stonybrook University School of Medicine, Stonybrook, NY (Dr. Cole [Emeritus]).

Abstract

• Objective: To describe Brief Action Planning (BAP), a structured, stepped-care self-management support technique for chronic illness care and disease prevention.
• Methods: A review of the theory and research supporting BAP and the questions and skills that comprise the technique with provision of a clinical example.
• Results: BAP facilitates goal setting and action planning to build self-efficacy for behavior change. It is grounded in the principles and practice of Motivational Interviewing and evidence-based constructs from the behavior change literature. Comprised of a series of 3 questions and 5 skills, BAP can be implemented by medical teams to help meet the self-management support objectives of the Patient-Centered Medical Home.
• Conclusion: BAP is a useful self-management support technique for busy medical practices to promote health behavior change and build patient self-efficacy for improved long-term clinical outcomes in chronic illness care and disease prevention.

Chronic disease is prevalent and time consuming, challenging, and expensive to manage [1]. Half of all adult primary care patients have more than 2 chronic diseases, and 75% of US health care dollars are spent on chronic illness care [2]. Given the health and financial impact of chronic disease, and recognizing that patients make daily decisions that affect disease control, efforts are needed to assist and empower patients to actively self-manage health behaviors that influence chronic illness outcomes. Patients who are supported to actively self-manage their own chronic illnesses have fewer symptoms, improved quality of life, and lower use of health care resources [3]. Historically, providers have tried to influence chronic illness self-management by advising behavior change (eg, smoking cessation, exercise) or telling patients to take medications; yet clinicians often become frustrated when patients do not “adhere” to their professional advice [4,5]. Many times, patients want to make changes that will improve their health but need support—commonly known as self-management support—to be successful.

Involving patients in decision making, emphasizing problem solving, setting goals, creating action plans (ie, when, where and how to enact a goal-directed behavior), and following up on goals are key features of successful self-management support methods [3,6–8]. Multiple approaches from the behavioral change literature, such as the 5 A’s (Assess, Advise, Agree, Assist, Arrange) [9], Motivational Interviewing (MI), and chronic disease self-management programs [10] have been used to provide more effective guidance for patients and their caregivers. However, the practicalities of these approaches in clinical settings have been questioned. The 5A’s, a counseling framework that is used to guide providers in health behavior change counseling, can feel overwhelming because it encompasses several different aspects of counseling [11,12]. Likewise, MI and adaptations of MI, which have been shown to outperform traditional “advice giving” in treatment of a broad range of behaviors and chronic conditions [13–16], have been critiqued since fidelity to this approach often involves multiple sessions of training, practice, and feedback to achieve proficiency [15,17,18]. Finally, while chronic disease self-management programs have been shown to be effective when used by peers in the community [10], similar results in primary care are not well established.

Given the challenges of providers practicing, learning, and using each of these approaches, efforts to develop an approach that supports patients to make behavioral changes that can be implemented in typical practice settings are needed. In addition, health delivery systems are transforming to team-based models with emphasis on leveraging each team member’s expertise and licensure [19]. In acknowledgement of these evolving practice realities, the National Committee for Quality Assurance (NCQA) included development and documentation of patient self-management plans and goals as a critical factor for achieving NCQA Patient-Centered Medical Home (PCMH) recognition [20]. Successful PCMH transformation therefore entails clinical practices developing effective and time efficient ways to incorporate self-management support strategies, a new service for many, into their care delivery systems often without additional staffing.

In this paper, we describe an evidence-informed, efficient self-management support technique called Brief Action Planning (BAP) [21–24]. BAP evolved into its current form through ongoing collaborative efforts of 4 of the authors (SC, DG, CD, KR) and is based on a foundation of original work by Steven Cole with contributions from Mary Cole in 2002 [25]. This technique addresses many of the barriers providers have cited to providing self-management support, as it can be used routinely by both individual providers and health care teams to facilitate patient-centered goal setting and action planning. BAP integrates principles and practice of MI with goal setting and action planning concepts from the self-management support, self-efficacy, and behavior change literature. In addition to reviewing the principles and theory that inform BAP, we introduce the steps of BAP and discuss practical considerations for incorporating BAP into clinical practice. In particular, we include suggestions about how BAP can be used in team-based clinical practice settings within the PCMH. Finally, we present a common clinical scenario to demonstrate BAP and provide resource links to online videos of BAP encounters. Throughout the paper, we use the word “clinician” to refer to professionals or other trained personnel using BAP, and “patient” to refer to those experiencing BAP, recognizing that other terms may be preferred in different settings.

What is BAP?

BAP is a highly structured, stepped-care, self-management support technique. Composed of a series of 3 questions and 5 skills (reviewed in detail below), BAP can be used to facilitate goal setting and action planning to build self-efficacy in chronic illness management and disease prevention [21–24]. The overall goal of BAP is to assist an individual to create an action plan for a self-management behavior that they feel confident that they can achieve. BAP is currently being used in diverse care settings including primary care, home health care, rehabilitation, mental health and public health to assist and empower patients to self-manage chronic illnesses and disabilities including diabetes, depression, spinal cord injury, arthritis, and hypertension. BAP is also being used to assist patients to develop action plans for disease prevention. For example, the Bellevue Hospital Personalized Prevention clinic, a pilot clinic that uses a mathematical model [26] to help patients and providers collaboratively prioritize prevention focus and strategies, systematically utilizes BAP as its self-management support technique for patient-centered action planning. At this time, BAP has been incorporated into teaching curriculums at multiple medical schools, presented at major national health care/academic conferences and is being increasingly integrated into health delivery systems across the United States and Canada to support patient self-management for NCQA-PCMH transformation. We have also developed a series of standardized programing to support fidelity in BAP skills development including a multidisciplinary introductory training curriculum, telephonic coaching, interactive web-based training tools, and a structured “Train the Trainer” curriculum [27]. In addition, a set of guidelines designed to ensure fidelity in BAP research has been developed [27].

Underlying Principles of BAP

BAP is grounded in the principles and practice of MI and the psychology of behavior change. Within behavior change, we draw primarily on self-efficacy and action planning theory and research. We discuss the key concepts in detail below.

The Spirit of MI

MI Spirit (Compassion, Acceptance, Partnership and Evocation) is an important overarching tenet for BAP. Compassionately supporting self-management with MI spirit involves a partnership with the patient rather than a prescription for change and the assurance that the clinician has the patients best interest always in mind (Compassion) [17]. Exemplifying “spirit” accepts that the ultimate choice to change is the patient’s alone (Acceptance) and acknowledges that individuals bring expertise about themselves and their lives to the conversation (Evocation). Adherence to “MI spirit” itself has been associated with positive behavior change outcomes in patients [5,28–32]. Demonstrating MI spirit throughout the change conversation is an essential foundational principle of BAP.

Action Planning and Self-Efficacy

In addition to the spirit of MI, BAP integrates 2 evidence-based constructs from the behavior change literature: action planning and self-efficacy [4,6,33–36]. Action planning requires that individuals specify when, where and how to enact a goal-directed behavior (eg, self-management behaviors). Action planning has been shown to mediate the intention-behavior relationship thereby increasing the likelihood that an individual’s intentions will lead to behavior change [37,38]. Given the demonstrated potential of action planning for ensuring individuals achieve their health goals, the BAP framework aspires to assist patients to create an action plan.

BAP also aims to build patients’ self-efficacy to enact the goals outlined in their action plans. Self-efficacy refers to a patient’s confidence in their ability to enact a behavior [33]. Several reviews of the literature have suggested a strong relationship between self-efficacy and adoption of healthy behaviors such as smoking cessation, weight control, contraception, alcohol abuse and physical activity [39–42]. Furthermore, Lorig et al demonstrated that the process of action planning itself contributes to enhanced self-efficacy [8]. BAP aims to build self-efficacy and ultimately change patients’ behaviors by helping patients to set an action plan that they feel confident in their ability to achieve.

Description of the BAP Steps

Three questions and 3 of the BAP skills (ie, SMART plan, eliciting a commitment statement, and follow-up) are applied during every BAP interaction, while 2 skills (ie, behavioral menu and problem solving for low confidence) are used as needed. The distinct functions and the evidence supporting the 3 questions and 5 BAP skills are described below.

Question 1: Eliciting a Behavioral Focus or Goal

Once engagement has been established and the clinician determines the patient is ready for self-management planning to occur, the first question of BAP can be asked: “Is there anything you would like to do for your health in the next week or two?” 

Although technically Question 1 is a closed-ended question (in that it can be answered “yes” or “no”), in actual practice it generates productive discussions about change. 

1) Have an Idea. A group of patients immediately present an idea that they are ready to do or are ready to consider doing. For these patients, clinicians can proceed directly to Skill 2—SMART Behavioral Planning; that is, asking patients directly if they are ready to turn their idea into a concrete plan. Some evidence suggests that further discussion, assessment, or even additional "motivational" exploration in patients who are ready to make a plan and already have an idea may actually decrease motivation for change [17, 32].

2) Not Sure. Another group of patients may want or need suggestions before committing to something specific they want to work on. For these patients, clinicians should use the opportunity to offer a Behavioral Menu (Skill 1).

3) No or Not at This Time. A third group of patients may not be interested or ready to make a change at this time or at all. Some in this group may be healthy or already self-managing effectively and have no need to make a plan, in which case the clinician acknowledges their active self-management and moves to the next part of the visit. Others in this group may have considerable ambivalence about change or face complex situations where other priorities take precedence. Clinicians frequently label these individuals as "resistant." The Spirit of MI can be very useful when working with these patients to accept and respect their autonomy while encouraging ongoing partnership at a future time. For example, a clinician may say “It sounds like you are not interested in making a plan for your health right now. Would it be OK if I ask you about this again at our next visit?” Pushing forward to make a "plan for change" when a patient is not ready decreases both motivation for change as well as the likelihood for a successful outcome [32].

Other patients may benefit from additional motivational approaches to further explore change and ambivalence. If the clinician does not have these skills, patients may be seamlessly transitioned to another resource within or external to the care team.

Skill 1: Offering a Behavioral Menu

If in response to Question 1 an individual is unable to come up with an idea of their own or needs more information, then offering a Behavioral Menu may be helpful [44,45]. Consistent with the “Spirit of MI,” BAP attempts to elicit ideas from the individual themselves; however, it is important to recognize that some people require assistance to identify possible actions. A behavioral menu is comprised of 2 or 3 suggestions or ideas that will ideally trigger individuals to discover an idea of their own. There are 3 distinct evidence-based steps to follow when presenting a Behavioral Menu.

1) Ask permission to offer a behavioral menu. Asking permission to share ideas respects patient autonomy and prevents the provider from inadvertently assuming an expert role. For example: “Would it be OK if I shared with you some examples of what some other patients I work with have done?”

2) Offer 2 to 3 general yet varied ideas all at once (Figure 2, entry 5). It helps to mention things that other patients have decided to do with some success. Using this approach avoids the clinician assuming too much about the patient or allowing the patient to reject the ideas. It is important to remember that the list is to prompt ideas, not to find a perfect solution [17]. For example: “One patient I work with decided to join a gym and start exercising, another decided to pick up an old hobby he used to enjoy doing and another patient decided to schedule some time with a friend she hadn’t seen in a while.”

3) Ask if any of the ideas appeal to the individual as something that might work for them or if the patient has an idea of his/her own (Figure 2, entry 5). Evocation from the Spirit of MI is built in with this prompt [17]. For example: “These are some ideas that have worked for other patients I work with, do they trigger any ideas that might work for you?”

Skill 2: SMART Planning

Once an individual decides on an area of focus, the clinician partners with the patient to clarify the details and create an action plan to achieve their goal. Given that individuals are more likely to successfully achieve goals that are specific, proximal, and achievable as opposed to vague and distal [46,47], the clinician works with patient to ensure that the patient’s goal is SMART (specific, measurable, achievable, relevant and time-bound). The term SMART has its roots in the business management literature [48] as an adaptation of Locke’s pioneering research (1968) on goal setting and motivation [49]. In particular, Locke and Latham’s theory of Goal Setting and Task performance, states that “specific and achievable” goals are more likely to be successfully reached [47,50].

We suggest helping the patient to make smart goals by eliciting answers to questions applicable to the plan, such as “what?” “where?” “when?” “how long?” “how often?” “how much?” and “when will you start?” [51]. A resulting plan might be “I will walk for 20 minutes, in my neighborhood, every Monday, Wednesday and Friday before dinner.”

Skill 3: Elicit a Commitment Statement

Once the individual has developed a specific plan, the next step of BAP is for the clinician to ask him or her to “tell back” the specifics of the plan. The provider might say something like, “Just to make sure we understand each other, would you repeat back what you’ve decided to do?” The act of “repeating back” organizes the details of the plan in the persons mind and may lead to an unconscious self-reflection about the feasibility of the plan [43,52], which then sets the stage for Question 2 of BAP (Scaling for Confidence). Commitment predicts subsequent behavior change, and the strength of the commitment language is the strongest predictor of success on an action plan [43,52,53]. For example saying “I will” is stronger than saying “I will try.”

Question 2: Scaling for Confidence

After a commitment statement has been elicited, the second question of BAP is asked. “How confident or sure do you feel about carrying out your plan on a scale from 0 to 10, where 0 is not confident at all and 10 is totally confident or sure?” Confidence scaling is a common tool used in behavioral interventions, MI, and chronic disease self-management programs [17,51]. Question 2 assesses an individual’s self-efficacy to complete the plan and facilitates discussion about potential barriers to implementation in order to increase the likelihood of success of a personal action plan.

For patients who have difficulty grasping the concept of a numerical scale, the word “sure” can be substituted for “confident” and a Likert scale including the terms “not at all sure,” “somewhat sure,” and “very sure” substituted for the numerical confidence ruler, ie, “How sure are you that you will be able to carry out your plan? Not at all sure, somewhat sure, or very sure?” Alternatively, people of different cultural backgrounds may find it easier to grasp the concept using familiar images or experiences. For example, Native Americans from the Southwest have adapted the scale to depict a series of images ranging from planting a corn seed to harvesting a crop or climbing a ladder, while in some Latino cultures the image of climbing a mountain (“How far up the mountain are you?”) is useful to demonstrate “level of confidence” concept [54].

Skill 4: Problem Solving for Low Confidence

When confidence is relatively low (ie, below 7), we suggest collaborative problem solving as the next step [8,51]. Low confidence or self-efficacy for plan completion is a concern since low self-efficacy predicts non-completion [8]. Successfully implementing the action plan, no matter how small, increases confidence and self-efficacy for engaging in the behavior [8].

There are several steps that a clinician follows when collaboratively problem-solving with a patient with low confidence (Figure 1).

• Recognize that a low confidence level is greater than no confidence at all. By affirming the strength of a patient’s confidence rather than negatively focusing on a low level of confidence, the provider emphasizes the patient’s strengths.

• Collaboratively explore ways that the plan could be modified in order to improve confidence. A Behavioral Menu can be offered if needed. For example, a clinician might say something like: “That’s great that your confidence level is a 5. A 5 is a lot higher than a 1. People are more likely to have success with their action plans when confidence levels are 7 or more. Do you have any ideas of how you might be able to increase your level confidence to a 7 or more?”

• If the patient has no ideas, ask permission to offer a Behavioral Menu: “Would it be ok to share some ideas about how other patients I’ve worked with have increased their confidence level?” If the patient agrees, then say... “Some people modify their plans to make them easier, some choose a less ambitious goal or adjust the frequency of their plan, and some people involve a friend or family member. Perhaps one of these ideas seems like a good one for you or maybe you have another idea?”

Question 3: Arranging Accountability

Once the details of the plan have been determined and confidence level for success is high, the next step is to ask Question 3: “Would you like to set a specific time to check in about your plan to see how things are going?” This question encourages a patient to be accountable for their plan, and reinforces the concept that the physician and care team consider the plan to be important. Research supports that people are more likely to follow through with a plan if they choose to report back their progress [43] and suggests that checking-in frequently earlier in the process is helpful [55]. Ideally the clinician and patient should agree on a time to check in on the plan within a week or two (Figure 2, entry 29).

Accountability in the form of a check-in may be arranged with the clinical provider, another member of the healthcare team or a support person of the patient’s choice (eg, spouse, friend). The patient may also choose to be accountable to themselves by using a calendar or a goal setting application on their smart phone device or computer.

Skill 5: Follow-up

The purpose of the check-in is for learning and adjustment of the plan as well as to provide support regardless of outcome. Checking-in encourages reflection on challenges and barriers as well as successes. Patients should be given guidance to think through what worked for them and what did not. Focusing just on “success” of the plan will be less helpful. If follow-up is not done with the care team in the near term, checking-in can be accomplished at the next scheduled visit. Patient portals provide another opportunity for patients to dialogue with the care team about their plan.

Experiential Insights from Clinical Experience Using BAP

The authors collective experience to date indicates that between 50% to 75% of individuals who are asked Question 1 go on to develop an action plan for change with relatively little need for additional skills. In other studies of action planning in primary care, 83% of patients made action plans during a visit, and at 3-week follow-up 53% had completed their action plan [56]. A recent study of action planning using an online self-management support program reported that action plans were successfully completed (49%), partially completed (40%) or incomplete (11% of the time) [35].

Another caveat to consider is that the process of planning is more important that the actual plan itself. It is imperative to allow the patient, not the clinician, to determine the plan. For example, a patient with multiple poorly controlled chronic illnesses including depression may decide to focus his action plan around cleaning out his car rather than disease control such as dietary modification, medication adherence or exercise. The clinician may initially fail to view this as a good use of clinician time or healthcare resources since it seems unrelated to health. However, successful completion of an action plan is not the only objective of action planning. Building self-efficacy, which may lead to additional action planning around health, is more important [4,46]. The challenge is therefore for the clinician to take a step back, relinquish the “expert role,” and support the goal setting process regardless of the plan. In this example, successfully cleaning out his car may increase the patient’s self-efficacy to control other aspects of his life including diet and the focus of future plans may shift [4].

When to Use BAP

Opportunities for patient engagement in action planning occur when addressing chronic illness concerns as well as during discussions about health maintenance and preventive care. BAP can be considered as part of any routine clinical agenda unless patient preferences or clinical acuity preclude it. As with most clinical encounters, the flow is often negotiated at the beginning of the visit. BAP can be accomplished at any time that works best for the flow and substance of the visit, but a few patterns have emerged based on our experience.

BAP fits naturally into the part of the visit when the care plan is being discussed. The term “care plan” is commonly used to describe all of the care that will be provided until the next visit. Care plans can include additional recommendations for testing or screening, therapeutic adjustments and or referrals for additional expertise. Ideally the patients “agreed upon” contribution to their care should also be captured and documented in their care plan. This is often described as the patients “self-management goal.” For patients who are ready to make a specific plan to change behavior, BAP is an efficient way to support patients to craft an action plan that can then be incorporated into the overall care plan.

Another variation of when to use BAP is the situation when the patient has had a prior action plan and is being seen for a recheck visit. Discussing the action plan early in the visit agenda focuses attention on the work patients have put into following their plan. Descriptions of success lead readily to action plans for the future. Time spent discussing failures or partial success is valuable to problem solve as well as to affirm continued efforts to self-manage.

BAP can also be used between scheduled visits. The check-in portion of BAP is particularly amenable to follow-up by phone or by another supporter. A pre-arranged follow-up 1 to 2 weeks after creation of a new action plan [8] provides encouragement to patients working on their plan and also helps identify those who need more support.

Finally, BAP can be completed over multiple visits. For patients who are thinking about change but are not yet committed to planning, a brief suggestion about the value of action planning with a behavioral menu may encourage additional self-reflection. Many times patients return to the next visit with clear ideas about changes that would be important for them to make.

Fitting BAP into a 20-Minute Visit

Using BAP is a time-efficient way to provide self-management support within the context of a 20-minute visit with engaged patients who are ready to set goals for health. With practice, clinicians can often conduct all the steps within 3 to 5 minutes. However, patients and clinicians often have competing demands and agendas and may not feel that they have time to conduct all the steps. Thus, utilizing other members of the health care team to deliver some or all of BAP can facilitate implementation.

Teams have been creative in their approach to BAP implementation but 2 common models involve a multidisciplinary approach to BAP. In one model, the clinician assesses the patient readiness to make a specific action plan by asking Question 1, usually after the current status of key problems have been addressed and discussions begin about the interim plan of care. If the patient indicates interest, another staff member trained in BAP, such as an medical assistant, health coach or nurse, guides the development of the specific plan, completes the remaining steps and inputs the patient’s BAP into the care plan.

In another commonly deployed model, the front desk clerk or medical assistant helps to get the patient thinking by asking Question 1 and perhaps by providing a behavioral menu. When the clinician sees the patient, he follows up on the behavior change the patient has chosen and affirms the choice. Clinicians often flex seamlessly with other team members to complete the action plan depending on the schedule and current patient flow.

Regardless of how the workflows are designed, BAP implementation requires staff that can provide BAP with fidelity, effective communication among team members involved in the process and a standardized approach to documentation of the specific action plan, plan for check-in and notes about follow-up. Care teams commonly test different variations of personnel and workflows to find what works best for their particular practice.

Implementing BAP to Support PCMH Transformation

To support PCMH transformation substantial changes are needed to make care more proactive, more patient-centered and more accountable. One of the common elements for PCMH recognition regardless of sponsor is to enhance self-management support [20,57,58]. Practices pursuing PCMH designation are searching for effective evidence-based approaches to provide self-management support and guide action planning for patients. The authors suggest implementation of BAP as a potential strategy to enhance self-management support. In addition to facilitating meeting the actual PCMH criteria, BAP is aligned with the transitions in care delivery that are an important part of the transformation including reliance on team-based care and meaningful engagement of patients in their care [59,60].

In our experience, BAP is introduced incrementally into a practice initially focusing on one or two patient segments and then including more as resources allow. Successful BAP implementation begins with an organizational commitment to self-management support, decisions about which populations would benefit most from self-management support and BAP, training of key staff and clearly defined workflows that ensure reliable BAP provision.

BAP’s stepped-care design makes it easy to teach to all team members and as described above, team-based delivery of BAP functions well in those situations where clinicians and trained ancillary staff can “hand off” the process at any time to optimize the value to the patient while respecting inherent time constraints.

Documentation of the actual goal and follow-up is an important component to fully leverage BAP. Goals captured in a template generate actionable lists for action plan follow-up. Since EHRs vary considerably in their capacity to capture goals, teams adding BAP to their workflow will benefit from discussion of standardized documentation practices and forms.

Summary

Brief Action Planning is a self-management support technique that can be used in busy clinical settings to support patient self-management through patient-centered goal setting. Each step of BAP is based on principles grounded in evidence. Health care teams can learn BAP and integrate it into clinical delivery systems to support self-management for PCMH transformation.

Corresponding author: Damara Gutnick, MD, New York University School of Medicine, New York, NY, [email protected].

Financial disclosures: None.

From the New York University School of Medicine, New York, NY (Drs. Gutnick and Jay), University of Colorado Health Sciences Center, Denver, CO (Dr. Reims), University of British Columbia, BC, Canada (Dr. Davis), University College London, London, UK (Dr. Gainforth), and Stonybrook University School of Medicine, Stonybrook, NY (Dr. Cole [Emeritus]).

Abstract

• Objective: To describe Brief Action Planning (BAP), a structured, stepped-care self-management support technique for chronic illness care and disease prevention.
• Methods: A review of the theory and research supporting BAP and the questions and skills that comprise the technique with provision of a clinical example.
• Results: BAP facilitates goal setting and action planning to build self-efficacy for behavior change. It is grounded in the principles and practice of Motivational Interviewing and evidence-based constructs from the behavior change literature. Comprised of a series of 3 questions and 5 skills, BAP can be implemented by medical teams to help meet the self-management support objectives of the Patient-Centered Medical Home.
• Conclusion: BAP is a useful self-management support technique for busy medical practices to promote health behavior change and build patient self-efficacy for improved long-term clinical outcomes in chronic illness care and disease prevention.

Chronic disease is prevalent and time consuming, challenging, and expensive to manage [1]. Half of all adult primary care patients have more than 2 chronic diseases, and 75% of US health care dollars are spent on chronic illness care [2]. Given the health and financial impact of chronic disease, and recognizing that patients make daily decisions that affect disease control, efforts are needed to assist and empower patients to actively self-manage health behaviors that influence chronic illness outcomes. Patients who are supported to actively self-manage their own chronic illnesses have fewer symptoms, improved quality of life, and lower use of health care resources [3]. Historically, providers have tried to influence chronic illness self-management by advising behavior change (eg, smoking cessation, exercise) or telling patients to take medications; yet clinicians often become frustrated when patients do not “adhere” to their professional advice [4,5]. Many times, patients want to make changes that will improve their health but need support—commonly known as self-management support—to be successful.

Involving patients in decision making, emphasizing problem solving, setting goals, creating action plans (ie, when, where and how to enact a goal-directed behavior), and following up on goals are key features of successful self-management support methods [3,6–8]. Multiple approaches from the behavioral change literature, such as the 5 A’s (Assess, Advise, Agree, Assist, Arrange) [9], Motivational Interviewing (MI), and chronic disease self-management programs [10] have been used to provide more effective guidance for patients and their caregivers. However, the practicalities of these approaches in clinical settings have been questioned. The 5A’s, a counseling framework that is used to guide providers in health behavior change counseling, can feel overwhelming because it encompasses several different aspects of counseling [11,12]. Likewise, MI and adaptations of MI, which have been shown to outperform traditional “advice giving” in treatment of a broad range of behaviors and chronic conditions [13–16], have been critiqued since fidelity to this approach often involves multiple sessions of training, practice, and feedback to achieve proficiency [15,17,18]. Finally, while chronic disease self-management programs have been shown to be effective when used by peers in the community [10], similar results in primary care are not well established.

Given the challenges of providers practicing, learning, and using each of these approaches, efforts to develop an approach that supports patients to make behavioral changes that can be implemented in typical practice settings are needed. In addition, health delivery systems are transforming to team-based models with emphasis on leveraging each team member’s expertise and licensure [19]. In acknowledgement of these evolving practice realities, the National Committee for Quality Assurance (NCQA) included development and documentation of patient self-management plans and goals as a critical factor for achieving NCQA Patient-Centered Medical Home (PCMH) recognition [20]. Successful PCMH transformation therefore entails clinical practices developing effective and time efficient ways to incorporate self-management support strategies, a new service for many, into their care delivery systems often without additional staffing.

In this paper, we describe an evidence-informed, efficient self-management support technique called Brief Action Planning (BAP) [21–24]. BAP evolved into its current form through ongoing collaborative efforts of 4 of the authors (SC, DG, CD, KR) and is based on a foundation of original work by Steven Cole with contributions from Mary Cole in 2002 [25]. This technique addresses many of the barriers providers have cited to providing self-management support, as it can be used routinely by both individual providers and health care teams to facilitate patient-centered goal setting and action planning. BAP integrates principles and practice of MI with goal setting and action planning concepts from the self-management support, self-efficacy, and behavior change literature. In addition to reviewing the principles and theory that inform BAP, we introduce the steps of BAP and discuss practical considerations for incorporating BAP into clinical practice. In particular, we include suggestions about how BAP can be used in team-based clinical practice settings within the PCMH. Finally, we present a common clinical scenario to demonstrate BAP and provide resource links to online videos of BAP encounters. Throughout the paper, we use the word “clinician” to refer to professionals or other trained personnel using BAP, and “patient” to refer to those experiencing BAP, recognizing that other terms may be preferred in different settings.

What is BAP?

BAP is a highly structured, stepped-care, self-management support technique. Composed of a series of 3 questions and 5 skills (reviewed in detail below), BAP can be used to facilitate goal setting and action planning to build self-efficacy in chronic illness management and disease prevention [21–24]. The overall goal of BAP is to assist an individual to create an action plan for a self-management behavior that they feel confident that they can achieve. BAP is currently being used in diverse care settings including primary care, home health care, rehabilitation, mental health and public health to assist and empower patients to self-manage chronic illnesses and disabilities including diabetes, depression, spinal cord injury, arthritis, and hypertension. BAP is also being used to assist patients to develop action plans for disease prevention. For example, the Bellevue Hospital Personalized Prevention clinic, a pilot clinic that uses a mathematical model [26] to help patients and providers collaboratively prioritize prevention focus and strategies, systematically utilizes BAP as its self-management support technique for patient-centered action planning. At this time, BAP has been incorporated into teaching curriculums at multiple medical schools, presented at major national health care/academic conferences and is being increasingly integrated into health delivery systems across the United States and Canada to support patient self-management for NCQA-PCMH transformation. We have also developed a series of standardized programing to support fidelity in BAP skills development including a multidisciplinary introductory training curriculum, telephonic coaching, interactive web-based training tools, and a structured “Train the Trainer” curriculum [27]. In addition, a set of guidelines designed to ensure fidelity in BAP research has been developed [27].

Underlying Principles of BAP

BAP is grounded in the principles and practice of MI and the psychology of behavior change. Within behavior change, we draw primarily on self-efficacy and action planning theory and research. We discuss the key concepts in detail below.

The Spirit of MI

MI Spirit (Compassion, Acceptance, Partnership and Evocation) is an important overarching tenet for BAP. Compassionately supporting self-management with MI spirit involves a partnership with the patient rather than a prescription for change and the assurance that the clinician has the patients best interest always in mind (Compassion) [17]. Exemplifying “spirit” accepts that the ultimate choice to change is the patient’s alone (Acceptance) and acknowledges that individuals bring expertise about themselves and their lives to the conversation (Evocation). Adherence to “MI spirit” itself has been associated with positive behavior change outcomes in patients [5,28–32]. Demonstrating MI spirit throughout the change conversation is an essential foundational principle of BAP.

Action Planning and Self-Efficacy

In addition to the spirit of MI, BAP integrates 2 evidence-based constructs from the behavior change literature: action planning and self-efficacy [4,6,33–36]. Action planning requires that individuals specify when, where and how to enact a goal-directed behavior (eg, self-management behaviors). Action planning has been shown to mediate the intention-behavior relationship thereby increasing the likelihood that an individual’s intentions will lead to behavior change [37,38]. Given the demonstrated potential of action planning for ensuring individuals achieve their health goals, the BAP framework aspires to assist patients to create an action plan.

BAP also aims to build patients’ self-efficacy to enact the goals outlined in their action plans. Self-efficacy refers to a patient’s confidence in their ability to enact a behavior [33]. Several reviews of the literature have suggested a strong relationship between self-efficacy and adoption of healthy behaviors such as smoking cessation, weight control, contraception, alcohol abuse and physical activity [39–42]. Furthermore, Lorig et al demonstrated that the process of action planning itself contributes to enhanced self-efficacy [8]. BAP aims to build self-efficacy and ultimately change patients’ behaviors by helping patients to set an action plan that they feel confident in their ability to achieve.

Description of the BAP Steps

Three questions and 3 of the BAP skills (ie, SMART plan, eliciting a commitment statement, and follow-up) are applied during every BAP interaction, while 2 skills (ie, behavioral menu and problem solving for low confidence) are used as needed. The distinct functions and the evidence supporting the 3 questions and 5 BAP skills are described below.

Question 1: Eliciting a Behavioral Focus or Goal

Once engagement has been established and the clinician determines the patient is ready for self-management planning to occur, the first question of BAP can be asked: “Is there anything you would like to do for your health in the next week or two?” 

Although technically Question 1 is a closed-ended question (in that it can be answered “yes” or “no”), in actual practice it generates productive discussions about change. 

1) Have an Idea. A group of patients immediately present an idea that they are ready to do or are ready to consider doing. For these patients, clinicians can proceed directly to Skill 2—SMART Behavioral Planning; that is, asking patients directly if they are ready to turn their idea into a concrete plan. Some evidence suggests that further discussion, assessment, or even additional "motivational" exploration in patients who are ready to make a plan and already have an idea may actually decrease motivation for change [17, 32].

2) Not Sure. Another group of patients may want or need suggestions before committing to something specific they want to work on. For these patients, clinicians should use the opportunity to offer a Behavioral Menu (Skill 1).

3) No or Not at This Time. A third group of patients may not be interested or ready to make a change at this time or at all. Some in this group may be healthy or already self-managing effectively and have no need to make a plan, in which case the clinician acknowledges their active self-management and moves to the next part of the visit. Others in this group may have considerable ambivalence about change or face complex situations where other priorities take precedence. Clinicians frequently label these individuals as "resistant." The Spirit of MI can be very useful when working with these patients to accept and respect their autonomy while encouraging ongoing partnership at a future time. For example, a clinician may say “It sounds like you are not interested in making a plan for your health right now. Would it be OK if I ask you about this again at our next visit?” Pushing forward to make a "plan for change" when a patient is not ready decreases both motivation for change as well as the likelihood for a successful outcome [32].

Other patients may benefit from additional motivational approaches to further explore change and ambivalence. If the clinician does not have these skills, patients may be seamlessly transitioned to another resource within or external to the care team.

Skill 1: Offering a Behavioral Menu

If in response to Question 1 an individual is unable to come up with an idea of their own or needs more information, then offering a Behavioral Menu may be helpful [44,45]. Consistent with the “Spirit of MI,” BAP attempts to elicit ideas from the individual themselves; however, it is important to recognize that some people require assistance to identify possible actions. A behavioral menu is comprised of 2 or 3 suggestions or ideas that will ideally trigger individuals to discover an idea of their own. There are 3 distinct evidence-based steps to follow when presenting a Behavioral Menu.

1) Ask permission to offer a behavioral menu. Asking permission to share ideas respects patient autonomy and prevents the provider from inadvertently assuming an expert role. For example: “Would it be OK if I shared with you some examples of what some other patients I work with have done?”

2) Offer 2 to 3 general yet varied ideas all at once (Figure 2, entry 5). It helps to mention things that other patients have decided to do with some success. Using this approach avoids the clinician assuming too much about the patient or allowing the patient to reject the ideas. It is important to remember that the list is to prompt ideas, not to find a perfect solution [17]. For example: “One patient I work with decided to join a gym and start exercising, another decided to pick up an old hobby he used to enjoy doing and another patient decided to schedule some time with a friend she hadn’t seen in a while.”

3) Ask if any of the ideas appeal to the individual as something that might work for them or if the patient has an idea of his/her own (Figure 2, entry 5). Evocation from the Spirit of MI is built in with this prompt [17]. For example: “These are some ideas that have worked for other patients I work with, do they trigger any ideas that might work for you?”

Skill 2: SMART Planning

Once an individual decides on an area of focus, the clinician partners with the patient to clarify the details and create an action plan to achieve their goal. Given that individuals are more likely to successfully achieve goals that are specific, proximal, and achievable as opposed to vague and distal [46,47], the clinician works with patient to ensure that the patient’s goal is SMART (specific, measurable, achievable, relevant and time-bound). The term SMART has its roots in the business management literature [48] as an adaptation of Locke’s pioneering research (1968) on goal setting and motivation [49]. In particular, Locke and Latham’s theory of Goal Setting and Task performance, states that “specific and achievable” goals are more likely to be successfully reached [47,50].

We suggest helping the patient to make smart goals by eliciting answers to questions applicable to the plan, such as “what?” “where?” “when?” “how long?” “how often?” “how much?” and “when will you start?” [51]. A resulting plan might be “I will walk for 20 minutes, in my neighborhood, every Monday, Wednesday and Friday before dinner.”

Skill 3: Elicit a Commitment Statement

Once the individual has developed a specific plan, the next step of BAP is for the clinician to ask him or her to “tell back” the specifics of the plan. The provider might say something like, “Just to make sure we understand each other, would you repeat back what you’ve decided to do?” The act of “repeating back” organizes the details of the plan in the persons mind and may lead to an unconscious self-reflection about the feasibility of the plan [43,52], which then sets the stage for Question 2 of BAP (Scaling for Confidence). Commitment predicts subsequent behavior change, and the strength of the commitment language is the strongest predictor of success on an action plan [43,52,53]. For example saying “I will” is stronger than saying “I will try.”

Question 2: Scaling for Confidence

After a commitment statement has been elicited, the second question of BAP is asked. “How confident or sure do you feel about carrying out your plan on a scale from 0 to 10, where 0 is not confident at all and 10 is totally confident or sure?” Confidence scaling is a common tool used in behavioral interventions, MI, and chronic disease self-management programs [17,51]. Question 2 assesses an individual’s self-efficacy to complete the plan and facilitates discussion about potential barriers to implementation in order to increase the likelihood of success of a personal action plan.

For patients who have difficulty grasping the concept of a numerical scale, the word “sure” can be substituted for “confident” and a Likert scale including the terms “not at all sure,” “somewhat sure,” and “very sure” substituted for the numerical confidence ruler, ie, “How sure are you that you will be able to carry out your plan? Not at all sure, somewhat sure, or very sure?” Alternatively, people of different cultural backgrounds may find it easier to grasp the concept using familiar images or experiences. For example, Native Americans from the Southwest have adapted the scale to depict a series of images ranging from planting a corn seed to harvesting a crop or climbing a ladder, while in some Latino cultures the image of climbing a mountain (“How far up the mountain are you?”) is useful to demonstrate “level of confidence” concept [54].

Skill 4: Problem Solving for Low Confidence

When confidence is relatively low (ie, below 7), we suggest collaborative problem solving as the next step [8,51]. Low confidence or self-efficacy for plan completion is a concern since low self-efficacy predicts non-completion [8]. Successfully implementing the action plan, no matter how small, increases confidence and self-efficacy for engaging in the behavior [8].

There are several steps that a clinician follows when collaboratively problem-solving with a patient with low confidence (Figure 1).

• Recognize that a low confidence level is greater than no confidence at all. By affirming the strength of a patient’s confidence rather than negatively focusing on a low level of confidence, the provider emphasizes the patient’s strengths.

• Collaboratively explore ways that the plan could be modified in order to improve confidence. A Behavioral Menu can be offered if needed. For example, a clinician might say something like: “That’s great that your confidence level is a 5. A 5 is a lot higher than a 1. People are more likely to have success with their action plans when confidence levels are 7 or more. Do you have any ideas of how you might be able to increase your level confidence to a 7 or more?”

• If the patient has no ideas, ask permission to offer a Behavioral Menu: “Would it be ok to share some ideas about how other patients I’ve worked with have increased their confidence level?” If the patient agrees, then say... “Some people modify their plans to make them easier, some choose a less ambitious goal or adjust the frequency of their plan, and some people involve a friend or family member. Perhaps one of these ideas seems like a good one for you or maybe you have another idea?”

Question 3: Arranging Accountability

Once the details of the plan have been determined and confidence level for success is high, the next step is to ask Question 3: “Would you like to set a specific time to check in about your plan to see how things are going?” This question encourages a patient to be accountable for their plan, and reinforces the concept that the physician and care team consider the plan to be important. Research supports that people are more likely to follow through with a plan if they choose to report back their progress [43] and suggests that checking-in frequently earlier in the process is helpful [55]. Ideally the clinician and patient should agree on a time to check in on the plan within a week or two (Figure 2, entry 29).

Accountability in the form of a check-in may be arranged with the clinical provider, another member of the healthcare team or a support person of the patient’s choice (eg, spouse, friend). The patient may also choose to be accountable to themselves by using a calendar or a goal setting application on their smart phone device or computer.

Skill 5: Follow-up

The purpose of the check-in is for learning and adjustment of the plan as well as to provide support regardless of outcome. Checking-in encourages reflection on challenges and barriers as well as successes. Patients should be given guidance to think through what worked for them and what did not. Focusing just on “success” of the plan will be less helpful. If follow-up is not done with the care team in the near term, checking-in can be accomplished at the next scheduled visit. Patient portals provide another opportunity for patients to dialogue with the care team about their plan.

Experiential Insights from Clinical Experience Using BAP

The authors collective experience to date indicates that between 50% to 75% of individuals who are asked Question 1 go on to develop an action plan for change with relatively little need for additional skills. In other studies of action planning in primary care, 83% of patients made action plans during a visit, and at 3-week follow-up 53% had completed their action plan [56]. A recent study of action planning using an online self-management support program reported that action plans were successfully completed (49%), partially completed (40%) or incomplete (11% of the time) [35].

Another caveat to consider is that the process of planning is more important that the actual plan itself. It is imperative to allow the patient, not the clinician, to determine the plan. For example, a patient with multiple poorly controlled chronic illnesses including depression may decide to focus his action plan around cleaning out his car rather than disease control such as dietary modification, medication adherence or exercise. The clinician may initially fail to view this as a good use of clinician time or healthcare resources since it seems unrelated to health. However, successful completion of an action plan is not the only objective of action planning. Building self-efficacy, which may lead to additional action planning around health, is more important [4,46]. The challenge is therefore for the clinician to take a step back, relinquish the “expert role,” and support the goal setting process regardless of the plan. In this example, successfully cleaning out his car may increase the patient’s self-efficacy to control other aspects of his life including diet and the focus of future plans may shift [4].

When to Use BAP

Opportunities for patient engagement in action planning occur when addressing chronic illness concerns as well as during discussions about health maintenance and preventive care. BAP can be considered as part of any routine clinical agenda unless patient preferences or clinical acuity preclude it. As with most clinical encounters, the flow is often negotiated at the beginning of the visit. BAP can be accomplished at any time that works best for the flow and substance of the visit, but a few patterns have emerged based on our experience.

BAP fits naturally into the part of the visit when the care plan is being discussed. The term “care plan” is commonly used to describe all of the care that will be provided until the next visit. Care plans can include additional recommendations for testing or screening, therapeutic adjustments and or referrals for additional expertise. Ideally the patients “agreed upon” contribution to their care should also be captured and documented in their care plan. This is often described as the patients “self-management goal.” For patients who are ready to make a specific plan to change behavior, BAP is an efficient way to support patients to craft an action plan that can then be incorporated into the overall care plan.

Another variation of when to use BAP is the situation when the patient has had a prior action plan and is being seen for a recheck visit. Discussing the action plan early in the visit agenda focuses attention on the work patients have put into following their plan. Descriptions of success lead readily to action plans for the future. Time spent discussing failures or partial success is valuable to problem solve as well as to affirm continued efforts to self-manage.

BAP can also be used between scheduled visits. The check-in portion of BAP is particularly amenable to follow-up by phone or by another supporter. A pre-arranged follow-up 1 to 2 weeks after creation of a new action plan [8] provides encouragement to patients working on their plan and also helps identify those who need more support.

Finally, BAP can be completed over multiple visits. For patients who are thinking about change but are not yet committed to planning, a brief suggestion about the value of action planning with a behavioral menu may encourage additional self-reflection. Many times patients return to the next visit with clear ideas about changes that would be important for them to make.

Fitting BAP into a 20-Minute Visit

Using BAP is a time-efficient way to provide self-management support within the context of a 20-minute visit with engaged patients who are ready to set goals for health. With practice, clinicians can often conduct all the steps within 3 to 5 minutes. However, patients and clinicians often have competing demands and agendas and may not feel that they have time to conduct all the steps. Thus, utilizing other members of the health care team to deliver some or all of BAP can facilitate implementation.

Teams have been creative in their approach to BAP implementation but 2 common models involve a multidisciplinary approach to BAP. In one model, the clinician assesses the patient readiness to make a specific action plan by asking Question 1, usually after the current status of key problems have been addressed and discussions begin about the interim plan of care. If the patient indicates interest, another staff member trained in BAP, such as an medical assistant, health coach or nurse, guides the development of the specific plan, completes the remaining steps and inputs the patient’s BAP into the care plan.

In another commonly deployed model, the front desk clerk or medical assistant helps to get the patient thinking by asking Question 1 and perhaps by providing a behavioral menu. When the clinician sees the patient, he follows up on the behavior change the patient has chosen and affirms the choice. Clinicians often flex seamlessly with other team members to complete the action plan depending on the schedule and current patient flow.

Regardless of how the workflows are designed, BAP implementation requires staff that can provide BAP with fidelity, effective communication among team members involved in the process and a standardized approach to documentation of the specific action plan, plan for check-in and notes about follow-up. Care teams commonly test different variations of personnel and workflows to find what works best for their particular practice.

Implementing BAP to Support PCMH Transformation

To support PCMH transformation substantial changes are needed to make care more proactive, more patient-centered and more accountable. One of the common elements for PCMH recognition regardless of sponsor is to enhance self-management support [20,57,58]. Practices pursuing PCMH designation are searching for effective evidence-based approaches to provide self-management support and guide action planning for patients. The authors suggest implementation of BAP as a potential strategy to enhance self-management support. In addition to facilitating meeting the actual PCMH criteria, BAP is aligned with the transitions in care delivery that are an important part of the transformation including reliance on team-based care and meaningful engagement of patients in their care [59,60].

In our experience, BAP is introduced incrementally into a practice initially focusing on one or two patient segments and then including more as resources allow. Successful BAP implementation begins with an organizational commitment to self-management support, decisions about which populations would benefit most from self-management support and BAP, training of key staff and clearly defined workflows that ensure reliable BAP provision.

BAP’s stepped-care design makes it easy to teach to all team members and as described above, team-based delivery of BAP functions well in those situations where clinicians and trained ancillary staff can “hand off” the process at any time to optimize the value to the patient while respecting inherent time constraints.

Documentation of the actual goal and follow-up is an important component to fully leverage BAP. Goals captured in a template generate actionable lists for action plan follow-up. Since EHRs vary considerably in their capacity to capture goals, teams adding BAP to their workflow will benefit from discussion of standardized documentation practices and forms.

Summary

Brief Action Planning is a self-management support technique that can be used in busy clinical settings to support patient self-management through patient-centered goal setting. Each step of BAP is based on principles grounded in evidence. Health care teams can learn BAP and integrate it into clinical delivery systems to support self-management for PCMH transformation.

Corresponding author: Damara Gutnick, MD, New York University School of Medicine, New York, NY, [email protected].

Financial disclosures: None.

From the New York University School of Medicine, New York, NY (Drs. Gutnick and Jay), University of Colorado Health Sciences Center, Denver, CO (Dr. Reims), University of British Columbia, BC, Canada (Dr. Davis), University College London, London, UK (Dr. Gainforth), and Stonybrook University School of Medicine, Stonybrook, NY (Dr. Cole [Emeritus]).

Abstract

• Objective: To describe Brief Action Planning (BAP), a structured, stepped-care self-management support technique for chronic illness care and disease prevention.
• Methods: A review of the theory and research supporting BAP and the questions and skills that comprise the technique with provision of a clinical example.
• Results: BAP facilitates goal setting and action planning to build self-efficacy for behavior change. It is grounded in the principles and practice of Motivational Interviewing and evidence-based constructs from the behavior change literature. Comprised of a series of 3 questions and 5 skills, BAP can be implemented by medical teams to help meet the self-management support objectives of the Patient-Centered Medical Home.
• Conclusion: BAP is a useful self-management support technique for busy medical practices to promote health behavior change and build patient self-efficacy for improved long-term clinical outcomes in chronic illness care and disease prevention.

Chronic disease is prevalent and time consuming, challenging, and expensive to manage [1]. Half of all adult primary care patients have more than 2 chronic diseases, and 75% of US health care dollars are spent on chronic illness care [2]. Given the health and financial impact of chronic disease, and recognizing that patients make daily decisions that affect disease control, efforts are needed to assist and empower patients to actively self-manage health behaviors that influence chronic illness outcomes. Patients who are supported to actively self-manage their own chronic illnesses have fewer symptoms, improved quality of life, and lower use of health care resources [3]. Historically, providers have tried to influence chronic illness self-management by advising behavior change (eg, smoking cessation, exercise) or telling patients to take medications; yet clinicians often become frustrated when patients do not “adhere” to their professional advice [4,5]. Many times, patients want to make changes that will improve their health but need support—commonly known as self-management support—to be successful.

Involving patients in decision making, emphasizing problem solving, setting goals, creating action plans (ie, when, where and how to enact a goal-directed behavior), and following up on goals are key features of successful self-management support methods [3,6–8]. Multiple approaches from the behavioral change literature, such as the 5 A’s (Assess, Advise, Agree, Assist, Arrange) [9], Motivational Interviewing (MI), and chronic disease self-management programs [10] have been used to provide more effective guidance for patients and their caregivers. However, the practicalities of these approaches in clinical settings have been questioned. The 5A’s, a counseling framework that is used to guide providers in health behavior change counseling, can feel overwhelming because it encompasses several different aspects of counseling [11,12]. Likewise, MI and adaptations of MI, which have been shown to outperform traditional “advice giving” in treatment of a broad range of behaviors and chronic conditions [13–16], have been critiqued since fidelity to this approach often involves multiple sessions of training, practice, and feedback to achieve proficiency [15,17,18]. Finally, while chronic disease self-management programs have been shown to be effective when used by peers in the community [10], similar results in primary care are not well established.

Given the challenges of providers practicing, learning, and using each of these approaches, efforts to develop an approach that supports patients to make behavioral changes that can be implemented in typical practice settings are needed. In addition, health delivery systems are transforming to team-based models with emphasis on leveraging each team member’s expertise and licensure [19]. In acknowledgement of these evolving practice realities, the National Committee for Quality Assurance (NCQA) included development and documentation of patient self-management plans and goals as a critical factor for achieving NCQA Patient-Centered Medical Home (PCMH) recognition [20]. Successful PCMH transformation therefore entails clinical practices developing effective and time efficient ways to incorporate self-management support strategies, a new service for many, into their care delivery systems often without additional staffing.

In this paper, we describe an evidence-informed, efficient self-management support technique called Brief Action Planning (BAP) [21–24]. BAP evolved into its current form through ongoing collaborative efforts of 4 of the authors (SC, DG, CD, KR) and is based on a foundation of original work by Steven Cole with contributions from Mary Cole in 2002 [25]. This technique addresses many of the barriers providers have cited to providing self-management support, as it can be used routinely by both individual providers and health care teams to facilitate patient-centered goal setting and action planning. BAP integrates principles and practice of MI with goal setting and action planning concepts from the self-management support, self-efficacy, and behavior change literature. In addition to reviewing the principles and theory that inform BAP, we introduce the steps of BAP and discuss practical considerations for incorporating BAP into clinical practice. In particular, we include suggestions about how BAP can be used in team-based clinical practice settings within the PCMH. Finally, we present a common clinical scenario to demonstrate BAP and provide resource links to online videos of BAP encounters. Throughout the paper, we use the word “clinician” to refer to professionals or other trained personnel using BAP, and “patient” to refer to those experiencing BAP, recognizing that other terms may be preferred in different settings.

What is BAP?

BAP is a highly structured, stepped-care, self-management support technique. Composed of a series of 3 questions and 5 skills (reviewed in detail below), BAP can be used to facilitate goal setting and action planning to build self-efficacy in chronic illness management and disease prevention [21–24]. The overall goal of BAP is to assist an individual to create an action plan for a self-management behavior that they feel confident that they can achieve. BAP is currently being used in diverse care settings including primary care, home health care, rehabilitation, mental health and public health to assist and empower patients to self-manage chronic illnesses and disabilities including diabetes, depression, spinal cord injury, arthritis, and hypertension. BAP is also being used to assist patients to develop action plans for disease prevention. For example, the Bellevue Hospital Personalized Prevention clinic, a pilot clinic that uses a mathematical model [26] to help patients and providers collaboratively prioritize prevention focus and strategies, systematically utilizes BAP as its self-management support technique for patient-centered action planning. At this time, BAP has been incorporated into teaching curriculums at multiple medical schools, presented at major national health care/academic conferences and is being increasingly integrated into health delivery systems across the United States and Canada to support patient self-management for NCQA-PCMH transformation. We have also developed a series of standardized programing to support fidelity in BAP skills development including a multidisciplinary introductory training curriculum, telephonic coaching, interactive web-based training tools, and a structured “Train the Trainer” curriculum [27]. In addition, a set of guidelines designed to ensure fidelity in BAP research has been developed [27].

Underlying Principles of BAP

BAP is grounded in the principles and practice of MI and the psychology of behavior change. Within behavior change, we draw primarily on self-efficacy and action planning theory and research. We discuss the key concepts in detail below.

The Spirit of MI

MI Spirit (Compassion, Acceptance, Partnership and Evocation) is an important overarching tenet for BAP. Compassionately supporting self-management with MI spirit involves a partnership with the patient rather than a prescription for change and the assurance that the clinician has the patients best interest always in mind (Compassion) [17]. Exemplifying “spirit” accepts that the ultimate choice to change is the patient’s alone (Acceptance) and acknowledges that individuals bring expertise about themselves and their lives to the conversation (Evocation). Adherence to “MI spirit” itself has been associated with positive behavior change outcomes in patients [5,28–32]. Demonstrating MI spirit throughout the change conversation is an essential foundational principle of BAP.

Action Planning and Self-Efficacy

In addition to the spirit of MI, BAP integrates 2 evidence-based constructs from the behavior change literature: action planning and self-efficacy [4,6,33–36]. Action planning requires that individuals specify when, where and how to enact a goal-directed behavior (eg, self-management behaviors). Action planning has been shown to mediate the intention-behavior relationship thereby increasing the likelihood that an individual’s intentions will lead to behavior change [37,38]. Given the demonstrated potential of action planning for ensuring individuals achieve their health goals, the BAP framework aspires to assist patients to create an action plan.

BAP also aims to build patients’ self-efficacy to enact the goals outlined in their action plans. Self-efficacy refers to a patient’s confidence in their ability to enact a behavior [33]. Several reviews of the literature have suggested a strong relationship between self-efficacy and adoption of healthy behaviors such as smoking cessation, weight control, contraception, alcohol abuse and physical activity [39–42]. Furthermore, Lorig et al demonstrated that the process of action planning itself contributes to enhanced self-efficacy [8]. BAP aims to build self-efficacy and ultimately change patients’ behaviors by helping patients to set an action plan that they feel confident in their ability to achieve.

Description of the BAP Steps

Three questions and 3 of the BAP skills (ie, SMART plan, eliciting a commitment statement, and follow-up) are applied during every BAP interaction, while 2 skills (ie, behavioral menu and problem solving for low confidence) are used as needed. The distinct functions and the evidence supporting the 3 questions and 5 BAP skills are described below.

Question 1: Eliciting a Behavioral Focus or Goal

Once engagement has been established and the clinician determines the patient is ready for self-management planning to occur, the first question of BAP can be asked: “Is there anything you would like to do for your health in the next week or two?” 

Although technically Question 1 is a closed-ended question (in that it can be answered “yes” or “no”), in actual practice it generates productive discussions about change. 

1) Have an Idea. A group of patients immediately present an idea that they are ready to do or are ready to consider doing. For these patients, clinicians can proceed directly to Skill 2—SMART Behavioral Planning; that is, asking patients directly if they are ready to turn their idea into a concrete plan. Some evidence suggests that further discussion, assessment, or even additional "motivational" exploration in patients who are ready to make a plan and already have an idea may actually decrease motivation for change [17, 32].

2) Not Sure. Another group of patients may want or need suggestions before committing to something specific they want to work on. For these patients, clinicians should use the opportunity to offer a Behavioral Menu (Skill 1).

3) No or Not at This Time. A third group of patients may not be interested or ready to make a change at this time or at all. Some in this group may be healthy or already self-managing effectively and have no need to make a plan, in which case the clinician acknowledges their active self-management and moves to the next part of the visit. Others in this group may have considerable ambivalence about change or face complex situations where other priorities take precedence. Clinicians frequently label these individuals as "resistant." The Spirit of MI can be very useful when working with these patients to accept and respect their autonomy while encouraging ongoing partnership at a future time. For example, a clinician may say “It sounds like you are not interested in making a plan for your health right now. Would it be OK if I ask you about this again at our next visit?” Pushing forward to make a "plan for change" when a patient is not ready decreases both motivation for change as well as the likelihood for a successful outcome [32].

Other patients may benefit from additional motivational approaches to further explore change and ambivalence. If the clinician does not have these skills, patients may be seamlessly transitioned to another resource within or external to the care team.

Skill 1: Offering a Behavioral Menu

If in response to Question 1 an individual is unable to come up with an idea of their own or needs more information, then offering a Behavioral Menu may be helpful [44,45]. Consistent with the “Spirit of MI,” BAP attempts to elicit ideas from the individual themselves; however, it is important to recognize that some people require assistance to identify possible actions. A behavioral menu is comprised of 2 or 3 suggestions or ideas that will ideally trigger individuals to discover an idea of their own. There are 3 distinct evidence-based steps to follow when presenting a Behavioral Menu.

1) Ask permission to offer a behavioral menu. Asking permission to share ideas respects patient autonomy and prevents the provider from inadvertently assuming an expert role. For example: “Would it be OK if I shared with you some examples of what some other patients I work with have done?”

2) Offer 2 to 3 general yet varied ideas all at once (Figure 2, entry 5). It helps to mention things that other patients have decided to do with some success. Using this approach avoids the clinician assuming too much about the patient or allowing the patient to reject the ideas. It is important to remember that the list is to prompt ideas, not to find a perfect solution [17]. For example: “One patient I work with decided to join a gym and start exercising, another decided to pick up an old hobby he used to enjoy doing and another patient decided to schedule some time with a friend she hadn’t seen in a while.”

3) Ask if any of the ideas appeal to the individual as something that might work for them or if the patient has an idea of his/her own (Figure 2, entry 5). Evocation from the Spirit of MI is built in with this prompt [17]. For example: “These are some ideas that have worked for other patients I work with, do they trigger any ideas that might work for you?”

Skill 2: SMART Planning

Once an individual decides on an area of focus, the clinician partners with the patient to clarify the details and create an action plan to achieve their goal. Given that individuals are more likely to successfully achieve goals that are specific, proximal, and achievable as opposed to vague and distal [46,47], the clinician works with patient to ensure that the patient’s goal is SMART (specific, measurable, achievable, relevant and time-bound). The term SMART has its roots in the business management literature [48] as an adaptation of Locke’s pioneering research (1968) on goal setting and motivation [49]. In particular, Locke and Latham’s theory of Goal Setting and Task performance, states that “specific and achievable” goals are more likely to be successfully reached [47,50].

We suggest helping the patient to make smart goals by eliciting answers to questions applicable to the plan, such as “what?” “where?” “when?” “how long?” “how often?” “how much?” and “when will you start?” [51]. A resulting plan might be “I will walk for 20 minutes, in my neighborhood, every Monday, Wednesday and Friday before dinner.”

Skill 3: Elicit a Commitment Statement

Once the individual has developed a specific plan, the next step of BAP is for the clinician to ask him or her to “tell back” the specifics of the plan. The provider might say something like, “Just to make sure we understand each other, would you repeat back what you’ve decided to do?” The act of “repeating back” organizes the details of the plan in the persons mind and may lead to an unconscious self-reflection about the feasibility of the plan [43,52], which then sets the stage for Question 2 of BAP (Scaling for Confidence). Commitment predicts subsequent behavior change, and the strength of the commitment language is the strongest predictor of success on an action plan [43,52,53]. For example saying “I will” is stronger than saying “I will try.”

Question 2: Scaling for Confidence

After a commitment statement has been elicited, the second question of BAP is asked. “How confident or sure do you feel about carrying out your plan on a scale from 0 to 10, where 0 is not confident at all and 10 is totally confident or sure?” Confidence scaling is a common tool used in behavioral interventions, MI, and chronic disease self-management programs [17,51]. Question 2 assesses an individual’s self-efficacy to complete the plan and facilitates discussion about potential barriers to implementation in order to increase the likelihood of success of a personal action plan.

For patients who have difficulty grasping the concept of a numerical scale, the word “sure” can be substituted for “confident” and a Likert scale including the terms “not at all sure,” “somewhat sure,” and “very sure” substituted for the numerical confidence ruler, ie, “How sure are you that you will be able to carry out your plan? Not at all sure, somewhat sure, or very sure?” Alternatively, people of different cultural backgrounds may find it easier to grasp the concept using familiar images or experiences. For example, Native Americans from the Southwest have adapted the scale to depict a series of images ranging from planting a corn seed to harvesting a crop or climbing a ladder, while in some Latino cultures the image of climbing a mountain (“How far up the mountain are you?”) is useful to demonstrate “level of confidence” concept [54].

Skill 4: Problem Solving for Low Confidence

When confidence is relatively low (ie, below 7), we suggest collaborative problem solving as the next step [8,51]. Low confidence or self-efficacy for plan completion is a concern since low self-efficacy predicts non-completion [8]. Successfully implementing the action plan, no matter how small, increases confidence and self-efficacy for engaging in the behavior [8].

There are several steps that a clinician follows when collaboratively problem-solving with a patient with low confidence (Figure 1).

• Recognize that a low confidence level is greater than no confidence at all. By affirming the strength of a patient’s confidence rather than negatively focusing on a low level of confidence, the provider emphasizes the patient’s strengths.

• Collaboratively explore ways that the plan could be modified in order to improve confidence. A Behavioral Menu can be offered if needed. For example, a clinician might say something like: “That’s great that your confidence level is a 5. A 5 is a lot higher than a 1. People are more likely to have success with their action plans when confidence levels are 7 or more. Do you have any ideas of how you might be able to increase your level confidence to a 7 or more?”

• If the patient has no ideas, ask permission to offer a Behavioral Menu: “Would it be ok to share some ideas about how other patients I’ve worked with have increased their confidence level?” If the patient agrees, then say... “Some people modify their plans to make them easier, some choose a less ambitious goal or adjust the frequency of their plan, and some people involve a friend or family member. Perhaps one of these ideas seems like a good one for you or maybe you have another idea?”

Question 3: Arranging Accountability

Once the details of the plan have been determined and confidence level for success is high, the next step is to ask Question 3: “Would you like to set a specific time to check in about your plan to see how things are going?” This question encourages a patient to be accountable for their plan, and reinforces the concept that the physician and care team consider the plan to be important. Research supports that people are more likely to follow through with a plan if they choose to report back their progress [43] and suggests that checking-in frequently earlier in the process is helpful [55]. Ideally the clinician and patient should agree on a time to check in on the plan within a week or two (Figure 2, entry 29).

Accountability in the form of a check-in may be arranged with the clinical provider, another member of the healthcare team or a support person of the patient’s choice (eg, spouse, friend). The patient may also choose to be accountable to themselves by using a calendar or a goal setting application on their smart phone device or computer.

Skill 5: Follow-up

The purpose of the check-in is for learning and adjustment of the plan as well as to provide support regardless of outcome. Checking-in encourages reflection on challenges and barriers as well as successes. Patients should be given guidance to think through what worked for them and what did not. Focusing just on “success” of the plan will be less helpful. If follow-up is not done with the care team in the near term, checking-in can be accomplished at the next scheduled visit. Patient portals provide another opportunity for patients to dialogue with the care team about their plan.

Experiential Insights from Clinical Experience Using BAP

The authors collective experience to date indicates that between 50% to 75% of individuals who are asked Question 1 go on to develop an action plan for change with relatively little need for additional skills. In other studies of action planning in primary care, 83% of patients made action plans during a visit, and at 3-week follow-up 53% had completed their action plan [56]. A recent study of action planning using an online self-management support program reported that action plans were successfully completed (49%), partially completed (40%) or incomplete (11% of the time) [35].

Another caveat to consider is that the process of planning is more important that the actual plan itself. It is imperative to allow the patient, not the clinician, to determine the plan. For example, a patient with multiple poorly controlled chronic illnesses including depression may decide to focus his action plan around cleaning out his car rather than disease control such as dietary modification, medication adherence or exercise. The clinician may initially fail to view this as a good use of clinician time or healthcare resources since it seems unrelated to health. However, successful completion of an action plan is not the only objective of action planning. Building self-efficacy, which may lead to additional action planning around health, is more important [4,46]. The challenge is therefore for the clinician to take a step back, relinquish the “expert role,” and support the goal setting process regardless of the plan. In this example, successfully cleaning out his car may increase the patient’s self-efficacy to control other aspects of his life including diet and the focus of future plans may shift [4].

When to Use BAP

Opportunities for patient engagement in action planning occur when addressing chronic illness concerns as well as during discussions about health maintenance and preventive care. BAP can be considered as part of any routine clinical agenda unless patient preferences or clinical acuity preclude it. As with most clinical encounters, the flow is often negotiated at the beginning of the visit. BAP can be accomplished at any time that works best for the flow and substance of the visit, but a few patterns have emerged based on our experience.

BAP fits naturally into the part of the visit when the care plan is being discussed. The term “care plan” is commonly used to describe all of the care that will be provided until the next visit. Care plans can include additional recommendations for testing or screening, therapeutic adjustments and or referrals for additional expertise. Ideally the patients “agreed upon” contribution to their care should also be captured and documented in their care plan. This is often described as the patients “self-management goal.” For patients who are ready to make a specific plan to change behavior, BAP is an efficient way to support patients to craft an action plan that can then be incorporated into the overall care plan.

Another variation of when to use BAP is the situation when the patient has had a prior action plan and is being seen for a recheck visit. Discussing the action plan early in the visit agenda focuses attention on the work patients have put into following their plan. Descriptions of success lead readily to action plans for the future. Time spent discussing failures or partial success is valuable to problem solve as well as to affirm continued efforts to self-manage.

BAP can also be used between scheduled visits. The check-in portion of BAP is particularly amenable to follow-up by phone or by another supporter. A pre-arranged follow-up 1 to 2 weeks after creation of a new action plan [8] provides encouragement to patients working on their plan and also helps identify those who need more support.

Finally, BAP can be completed over multiple visits. For patients who are thinking about change but are not yet committed to planning, a brief suggestion about the value of action planning with a behavioral menu may encourage additional self-reflection. Many times patients return to the next visit with clear ideas about changes that would be important for them to make.

Fitting BAP into a 20-Minute Visit

Using BAP is a time-efficient way to provide self-management support within the context of a 20-minute visit with engaged patients who are ready to set goals for health. With practice, clinicians can often conduct all the steps within 3 to 5 minutes. However, patients and clinicians often have competing demands and agendas and may not feel that they have time to conduct all the steps. Thus, utilizing other members of the health care team to deliver some or all of BAP can facilitate implementation.

Teams have been creative in their approach to BAP implementation but 2 common models involve a multidisciplinary approach to BAP. In one model, the clinician assesses the patient readiness to make a specific action plan by asking Question 1, usually after the current status of key problems have been addressed and discussions begin about the interim plan of care. If the patient indicates interest, another staff member trained in BAP, such as an medical assistant, health coach or nurse, guides the development of the specific plan, completes the remaining steps and inputs the patient’s BAP into the care plan.

In another commonly deployed model, the front desk clerk or medical assistant helps to get the patient thinking by asking Question 1 and perhaps by providing a behavioral menu. When the clinician sees the patient, he follows up on the behavior change the patient has chosen and affirms the choice. Clinicians often flex seamlessly with other team members to complete the action plan depending on the schedule and current patient flow.

Regardless of how the workflows are designed, BAP implementation requires staff that can provide BAP with fidelity, effective communication among team members involved in the process and a standardized approach to documentation of the specific action plan, plan for check-in and notes about follow-up. Care teams commonly test different variations of personnel and workflows to find what works best for their particular practice.

Implementing BAP to Support PCMH Transformation

To support PCMH transformation substantial changes are needed to make care more proactive, more patient-centered and more accountable. One of the common elements for PCMH recognition regardless of sponsor is to enhance self-management support [20,57,58]. Practices pursuing PCMH designation are searching for effective evidence-based approaches to provide self-management support and guide action planning for patients. The authors suggest implementation of BAP as a potential strategy to enhance self-management support. In addition to facilitating meeting the actual PCMH criteria, BAP is aligned with the transitions in care delivery that are an important part of the transformation including reliance on team-based care and meaningful engagement of patients in their care [59,60].

In our experience, BAP is introduced incrementally into a practice initially focusing on one or two patient segments and then including more as resources allow. Successful BAP implementation begins with an organizational commitment to self-management support, decisions about which populations would benefit most from self-management support and BAP, training of key staff and clearly defined workflows that ensure reliable BAP provision.

BAP’s stepped-care design makes it easy to teach to all team members and as described above, team-based delivery of BAP functions well in those situations where clinicians and trained ancillary staff can “hand off” the process at any time to optimize the value to the patient while respecting inherent time constraints.

Documentation of the actual goal and follow-up is an important component to fully leverage BAP. Goals captured in a template generate actionable lists for action plan follow-up. Since EHRs vary considerably in their capacity to capture goals, teams adding BAP to their workflow will benefit from discussion of standardized documentation practices and forms.

Summary

Brief Action Planning is a self-management support technique that can be used in busy clinical settings to support patient self-management through patient-centered goal setting. Each step of BAP is based on principles grounded in evidence. Health care teams can learn BAP and integrate it into clinical delivery systems to support self-management for PCMH transformation.

Corresponding author: Damara Gutnick, MD, New York University School of Medicine, New York, NY, [email protected].

Financial disclosures: None.