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Study Overview
Objective. To test the efficacy of a weight loss app with incorporated social support and self-monitoring of diet, physical activity, and weight compared to a commercially available diet and PA tracking app.
Design. 2-group, randomized controlled trial.
Setting and participants. From October 2014 to January 2015, potential study participants were recruited via university/worksite listserv announcements, flyers, electronic newsletters, newspaper advertisements, social media posts, and a local research fair in 2 cities in South Carolina. Exclusion criteria included body mass index (BMI) outside the range of 25.0 to 49.9 kg/m2, unable to attend required measurement sessions, unable to access a computer or internet for completing assessments, having a psychiatric illness, receiving treatment for drug or alcohol dependency, having an eating disorder, participating in another weight loss program, reporting weight loss of 10+ pounds in the past 6 months, being pregnant or planning on becoming pregnant during study, or breastfeeding, or endorsing items on the Physical Activity Readiness Questionnaire (PAR-Q) regarding having a heart condition, feeling chest pain during physical activity, experiencing chest pain, becoming dizzy/ever losing balance or consciousness, and not having a physician give consent to participate despite reporting joint problems or taking blood pressure medication. Those who were eligible were invited to an orientation to the study, signed consent, and completed baseline assessments.
Intervention. Participants were randomized to either the experimental group (theory-based podcasts plus the Social POD app) or the comparison group (theory-based podcasts plus a standard app [“Fat Secret” app]). Both groups attended a training session on how to access the podcasts and download and use their app, and also had their baseline height and weight taken by study staff. Both groups received 2 podcasts per week, tracked their diet, weight, and physical activity, completed weekly surveys to report use of their assigned tracking app, and had their weight measures taken after 3 months. Objective measures of podcast usage and app usage were collected by study staff (experimental group only).
Both apps had diet and physical activity tracking features, but the Social POD app also included notifications to track diet and physical activity, messages sent from frequent app users to re-engage infrequent app users, a newsfeed to view other participants app tracking activity, stars awarded to frequent users of the app, points awarded for tracking, and prizes for earning points distributed at the final session by study staff. The Fat Secret app did not have any social support components but included a recipe database for looking up recipes by category.
Main outcome measures. The primary outcome was between-group differences in kilograms lost at 3 months. Secondary outcomes included group change in BMI after 3 months, as well as group differences in self-reported caloric intake, caloric expenditure, social support, self-efficacy, and outcome expectations scores, controlled for baseline measures.
Main results. Of the potential participants that inquired about the study (n = 189), those found to be eligible (n = 78) were invited to an orientation. Of those that attended the orientation (n = 62), 51 were randomized after completing baseline assessments (n = 25 to experimental group with Social POD app, n = 25 to comparison group with Fat Secret app), and 42 completed final weight measurements after 3 months. Participants were mostly white (57%) females (82%) with a mean BMI of 34.7 ± 6.0 kg/m2 and mean age of 46.2 ± 12.4 years. Baseline characteristics were similar between groups except that more comparison group participants reported previously downloading an app to track their diet than experimental participants. Participation attrition was 12% (n = 3 in each group).
Experimental group participants lost significantly more weight (–5.3 kg [95% CI, –7.5 to –3.0]) than the comparison group (–2.23 kg [95% CI, –3.6 to 1.0; P = 0.02). Experimental group participants also had a greater reduction in mean BMI (–1.9 kg/m2 [95% CI, –2.6 to –1.2]) vs. the comparison group (mean –0.9 kg/m2 [95% CI, –1.4 to – 0.05], P = 0.02). While there were significant differences in positive outcome expectations between groups (P = 0.04), other secondary outcomes were not significant.
Conclusions. An intervention with theory-based podcasts, social support, and incentivized self-monitoring resulted in significantly greater weight loss than a comparison intervention with theory-based podcasts and a commercially available standard self-monitoring app. This study highlights key features to add to mobile health interventions for adult weight loss.
Commentary
Obesity prevalence rates have increased over the past several decades across all genders, ages, ethnicities, income levels, and education levels [1], and recent data show that over one-third of adults in the US are obese and over two-thirds are overweight [2,3]. Behavior or lifestyle modification, which incorporates (often tailored) diet, physical activity, and behavior therapy, is highly recommended as the first strategy for losing initial weight and sustaining weight management efforts [4,5]. Mobile health (mHealth) technologies and other web-based and technology-assisted approaches (eg, mobile applications or “apps”) to facilitate behavior change for weight loss and management have aimed to address many of the limitations posed by traditional face-to-face weight loss approaches [6–8]. Prevailing theories of health behavior change imply key intervention design features that may increase their likelihood of promoting and sustaining desired behavior changes, particularly those that impact self-efficacy, self-regulation, and social facilitation [9,10].
Despite the plethora of weight loss mobile apps available to the public, it remains unclear if these are guided by evidence-based behavior change strategies typically used in traditional programs and approaches [11,12]. Further, very few of these apps have been rigorously evaluated with scientific testing to determine true effectiveness and safety [13,14]. This study adds to the literature by evaluating a mobile app for weight loss (Social POD) that was developed by researchers and utilizes theory-based components to target specific constructs that lead to health behavior change. Additionally, while self-monitoring is commonly incorporated into most available weight loss/management apps [11], the Social POD mobile app also incorporates social support and motivational strategies, which are less often included. The findings from this study add to the limited literature that mobile phone app-based interventions may be useful tools for weight loss [13].
The authors outlined several strengths and limitations. Briefly, this study was particularly strengthened by its randomized assignment to equivalent intervention groups, the use of a researcher-developed experimental group app that targets several key theory-based constructs for behavior change, measurement of objective use of the intervention group app, a racially diverse sample (over one-third of participants in both groups identified as black), measurement of secondary psycho-social behavioral outcomes, significant efforts to ensure survey completion and compliance with the intervention (increase retention), as well efforts to decrease participation burden by limiting required in-person sessions.
However, several important aspects of the study limit the internal validity and generalizability of its findings. The study had a small sample size and included a highly educated study population. If possible, future studies should consider including a large, diverse population to enhance generalizability. Also, this study was limited to those with an Android device, and significant demographic differences between Android and iPhone users have been reported [15]. The comparison group reported significantly more prior downloading of a diet-tracking app compared to the experimental group, which may have impacted use of the comparison app. The extrinsic reward system built into the experimental group intervention could have impacted adherence to experimental app, and is likely not feasible in real-world application of the experimental group app. Findings may have been subject to recall bias and measurement error due to self-reporting of outcomes measures. Importantly, this was a short-term weight loss study, and long-term weight loss/maintenance data is needed to support findings since in the usual course of weight-loss therapy the greatest weight loss occurs within 6 months of treatment, after which weight is often regained, sometimes near original level [16].
Applications for Clinical Practice
With the increasing popularity of technology-assisted and mHealth applications for weight loss and other health behaviors, it is important for practitioners to be familiar with proven, theory-based approaches and advise patients accordingly. This study demonstrated that social support components added to self-monitoring components in a weight loss app can lead to significant weight loss compared to self-monitoring alone. Thus, those that offer obesity counseling should be mindful that tracking and controlling dietary and physical activity behaviors alone may not prove to be successful. Opportunities for social facilitation to support weight loss efforts should be discussed with patients, including sources of social influence, support and collaboration between individuals, families, and health care professionals.
—Katrina F. Mateo, MPH
1. Mitchell NS, Catenacci VA, Wyatt HR, Hill JO. Obesity: overview of an epidemic. Psychiatr Clin North Am 2011;34:717–32.
2. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008. JAMA 2010;303:235–41.
3. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014;311:806–14.
4. Wadden TA, Butryn ML, Wilson C. Lifestyle modification for the management of obesity. Gastroenterology 2007;132:2226–38.
5. Butryn ML, Webb V, Wadden TA. Behavioral treatment of obesity. Psychiatr Clin North Am 2011;34:841–59.
6. Okorodudu DE, Bosworth HB, Corsino L. Innovative interventions to promote behavioral change in overweight or obese individuals: a review of the literature. Ann Med 2015;47:179–85.
7. Taylor RW, Roy M, Jospe MR, et al. Determining how best to support overweight adults to adhere to lifestyle change: protocol for the SWIFT study. BMC Public Health 2015;15:861.
8. Laing BY, Mangione CM, Tseng C-H, et al. Effectiveness of a smartphone application for weight loss compared with usual care in overweight primary care patients: a randomized, controlled trial. Ann Intern Med 2014;161(10 Suppl):
S5–S12.
9. Teixeira PJ, Carraça E V, Marques MM, et al. Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Med 2015;13:84.
10. Ryan P. Integrated theory of health behavior change: background and intervention development. Clin Nurse Spec 2009;23:161–70.
11. Rivera J, McPherson A, Hamilton J, et al. Mobile apps for weight management: a scoping review. JMIR mHealth uHealth 2016;4:e87.
12. Pellegrini CA, Pfammatter AF, Conroy DE, Spring B. Smartphone applications to support weight loss: current perspectives. Adv Health Care Technol 2015;1:13–22.
13. Flores Mateo G, Granado-Font E, Ferré-Grau C, Montaña-Carreras X. Mobile phone apps to promote weight loss and increase physical activity: a systematic review and meta-analysis. J Med Internet Res 2015;17:e253.
14. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J Cardiovasc Nurs 28:320–9.
15. Smith A. Smartphone ownership 2013. Pew Research Center.
16. Jeffery RW, Drewnowski A, Epstein LH, et al. Long-term maintenance of weight loss: current status. Health Psychol 2000;19(1 Suppl):5–16.
Study Overview
Objective. To test the efficacy of a weight loss app with incorporated social support and self-monitoring of diet, physical activity, and weight compared to a commercially available diet and PA tracking app.
Design. 2-group, randomized controlled trial.
Setting and participants. From October 2014 to January 2015, potential study participants were recruited via university/worksite listserv announcements, flyers, electronic newsletters, newspaper advertisements, social media posts, and a local research fair in 2 cities in South Carolina. Exclusion criteria included body mass index (BMI) outside the range of 25.0 to 49.9 kg/m2, unable to attend required measurement sessions, unable to access a computer or internet for completing assessments, having a psychiatric illness, receiving treatment for drug or alcohol dependency, having an eating disorder, participating in another weight loss program, reporting weight loss of 10+ pounds in the past 6 months, being pregnant or planning on becoming pregnant during study, or breastfeeding, or endorsing items on the Physical Activity Readiness Questionnaire (PAR-Q) regarding having a heart condition, feeling chest pain during physical activity, experiencing chest pain, becoming dizzy/ever losing balance or consciousness, and not having a physician give consent to participate despite reporting joint problems or taking blood pressure medication. Those who were eligible were invited to an orientation to the study, signed consent, and completed baseline assessments.
Intervention. Participants were randomized to either the experimental group (theory-based podcasts plus the Social POD app) or the comparison group (theory-based podcasts plus a standard app [“Fat Secret” app]). Both groups attended a training session on how to access the podcasts and download and use their app, and also had their baseline height and weight taken by study staff. Both groups received 2 podcasts per week, tracked their diet, weight, and physical activity, completed weekly surveys to report use of their assigned tracking app, and had their weight measures taken after 3 months. Objective measures of podcast usage and app usage were collected by study staff (experimental group only).
Both apps had diet and physical activity tracking features, but the Social POD app also included notifications to track diet and physical activity, messages sent from frequent app users to re-engage infrequent app users, a newsfeed to view other participants app tracking activity, stars awarded to frequent users of the app, points awarded for tracking, and prizes for earning points distributed at the final session by study staff. The Fat Secret app did not have any social support components but included a recipe database for looking up recipes by category.
Main outcome measures. The primary outcome was between-group differences in kilograms lost at 3 months. Secondary outcomes included group change in BMI after 3 months, as well as group differences in self-reported caloric intake, caloric expenditure, social support, self-efficacy, and outcome expectations scores, controlled for baseline measures.
Main results. Of the potential participants that inquired about the study (n = 189), those found to be eligible (n = 78) were invited to an orientation. Of those that attended the orientation (n = 62), 51 were randomized after completing baseline assessments (n = 25 to experimental group with Social POD app, n = 25 to comparison group with Fat Secret app), and 42 completed final weight measurements after 3 months. Participants were mostly white (57%) females (82%) with a mean BMI of 34.7 ± 6.0 kg/m2 and mean age of 46.2 ± 12.4 years. Baseline characteristics were similar between groups except that more comparison group participants reported previously downloading an app to track their diet than experimental participants. Participation attrition was 12% (n = 3 in each group).
Experimental group participants lost significantly more weight (–5.3 kg [95% CI, –7.5 to –3.0]) than the comparison group (–2.23 kg [95% CI, –3.6 to 1.0; P = 0.02). Experimental group participants also had a greater reduction in mean BMI (–1.9 kg/m2 [95% CI, –2.6 to –1.2]) vs. the comparison group (mean –0.9 kg/m2 [95% CI, –1.4 to – 0.05], P = 0.02). While there were significant differences in positive outcome expectations between groups (P = 0.04), other secondary outcomes were not significant.
Conclusions. An intervention with theory-based podcasts, social support, and incentivized self-monitoring resulted in significantly greater weight loss than a comparison intervention with theory-based podcasts and a commercially available standard self-monitoring app. This study highlights key features to add to mobile health interventions for adult weight loss.
Commentary
Obesity prevalence rates have increased over the past several decades across all genders, ages, ethnicities, income levels, and education levels [1], and recent data show that over one-third of adults in the US are obese and over two-thirds are overweight [2,3]. Behavior or lifestyle modification, which incorporates (often tailored) diet, physical activity, and behavior therapy, is highly recommended as the first strategy for losing initial weight and sustaining weight management efforts [4,5]. Mobile health (mHealth) technologies and other web-based and technology-assisted approaches (eg, mobile applications or “apps”) to facilitate behavior change for weight loss and management have aimed to address many of the limitations posed by traditional face-to-face weight loss approaches [6–8]. Prevailing theories of health behavior change imply key intervention design features that may increase their likelihood of promoting and sustaining desired behavior changes, particularly those that impact self-efficacy, self-regulation, and social facilitation [9,10].
Despite the plethora of weight loss mobile apps available to the public, it remains unclear if these are guided by evidence-based behavior change strategies typically used in traditional programs and approaches [11,12]. Further, very few of these apps have been rigorously evaluated with scientific testing to determine true effectiveness and safety [13,14]. This study adds to the literature by evaluating a mobile app for weight loss (Social POD) that was developed by researchers and utilizes theory-based components to target specific constructs that lead to health behavior change. Additionally, while self-monitoring is commonly incorporated into most available weight loss/management apps [11], the Social POD mobile app also incorporates social support and motivational strategies, which are less often included. The findings from this study add to the limited literature that mobile phone app-based interventions may be useful tools for weight loss [13].
The authors outlined several strengths and limitations. Briefly, this study was particularly strengthened by its randomized assignment to equivalent intervention groups, the use of a researcher-developed experimental group app that targets several key theory-based constructs for behavior change, measurement of objective use of the intervention group app, a racially diverse sample (over one-third of participants in both groups identified as black), measurement of secondary psycho-social behavioral outcomes, significant efforts to ensure survey completion and compliance with the intervention (increase retention), as well efforts to decrease participation burden by limiting required in-person sessions.
However, several important aspects of the study limit the internal validity and generalizability of its findings. The study had a small sample size and included a highly educated study population. If possible, future studies should consider including a large, diverse population to enhance generalizability. Also, this study was limited to those with an Android device, and significant demographic differences between Android and iPhone users have been reported [15]. The comparison group reported significantly more prior downloading of a diet-tracking app compared to the experimental group, which may have impacted use of the comparison app. The extrinsic reward system built into the experimental group intervention could have impacted adherence to experimental app, and is likely not feasible in real-world application of the experimental group app. Findings may have been subject to recall bias and measurement error due to self-reporting of outcomes measures. Importantly, this was a short-term weight loss study, and long-term weight loss/maintenance data is needed to support findings since in the usual course of weight-loss therapy the greatest weight loss occurs within 6 months of treatment, after which weight is often regained, sometimes near original level [16].
Applications for Clinical Practice
With the increasing popularity of technology-assisted and mHealth applications for weight loss and other health behaviors, it is important for practitioners to be familiar with proven, theory-based approaches and advise patients accordingly. This study demonstrated that social support components added to self-monitoring components in a weight loss app can lead to significant weight loss compared to self-monitoring alone. Thus, those that offer obesity counseling should be mindful that tracking and controlling dietary and physical activity behaviors alone may not prove to be successful. Opportunities for social facilitation to support weight loss efforts should be discussed with patients, including sources of social influence, support and collaboration between individuals, families, and health care professionals.
—Katrina F. Mateo, MPH
Study Overview
Objective. To test the efficacy of a weight loss app with incorporated social support and self-monitoring of diet, physical activity, and weight compared to a commercially available diet and PA tracking app.
Design. 2-group, randomized controlled trial.
Setting and participants. From October 2014 to January 2015, potential study participants were recruited via university/worksite listserv announcements, flyers, electronic newsletters, newspaper advertisements, social media posts, and a local research fair in 2 cities in South Carolina. Exclusion criteria included body mass index (BMI) outside the range of 25.0 to 49.9 kg/m2, unable to attend required measurement sessions, unable to access a computer or internet for completing assessments, having a psychiatric illness, receiving treatment for drug or alcohol dependency, having an eating disorder, participating in another weight loss program, reporting weight loss of 10+ pounds in the past 6 months, being pregnant or planning on becoming pregnant during study, or breastfeeding, or endorsing items on the Physical Activity Readiness Questionnaire (PAR-Q) regarding having a heart condition, feeling chest pain during physical activity, experiencing chest pain, becoming dizzy/ever losing balance or consciousness, and not having a physician give consent to participate despite reporting joint problems or taking blood pressure medication. Those who were eligible were invited to an orientation to the study, signed consent, and completed baseline assessments.
Intervention. Participants were randomized to either the experimental group (theory-based podcasts plus the Social POD app) or the comparison group (theory-based podcasts plus a standard app [“Fat Secret” app]). Both groups attended a training session on how to access the podcasts and download and use their app, and also had their baseline height and weight taken by study staff. Both groups received 2 podcasts per week, tracked their diet, weight, and physical activity, completed weekly surveys to report use of their assigned tracking app, and had their weight measures taken after 3 months. Objective measures of podcast usage and app usage were collected by study staff (experimental group only).
Both apps had diet and physical activity tracking features, but the Social POD app also included notifications to track diet and physical activity, messages sent from frequent app users to re-engage infrequent app users, a newsfeed to view other participants app tracking activity, stars awarded to frequent users of the app, points awarded for tracking, and prizes for earning points distributed at the final session by study staff. The Fat Secret app did not have any social support components but included a recipe database for looking up recipes by category.
Main outcome measures. The primary outcome was between-group differences in kilograms lost at 3 months. Secondary outcomes included group change in BMI after 3 months, as well as group differences in self-reported caloric intake, caloric expenditure, social support, self-efficacy, and outcome expectations scores, controlled for baseline measures.
Main results. Of the potential participants that inquired about the study (n = 189), those found to be eligible (n = 78) were invited to an orientation. Of those that attended the orientation (n = 62), 51 were randomized after completing baseline assessments (n = 25 to experimental group with Social POD app, n = 25 to comparison group with Fat Secret app), and 42 completed final weight measurements after 3 months. Participants were mostly white (57%) females (82%) with a mean BMI of 34.7 ± 6.0 kg/m2 and mean age of 46.2 ± 12.4 years. Baseline characteristics were similar between groups except that more comparison group participants reported previously downloading an app to track their diet than experimental participants. Participation attrition was 12% (n = 3 in each group).
Experimental group participants lost significantly more weight (–5.3 kg [95% CI, –7.5 to –3.0]) than the comparison group (–2.23 kg [95% CI, –3.6 to 1.0; P = 0.02). Experimental group participants also had a greater reduction in mean BMI (–1.9 kg/m2 [95% CI, –2.6 to –1.2]) vs. the comparison group (mean –0.9 kg/m2 [95% CI, –1.4 to – 0.05], P = 0.02). While there were significant differences in positive outcome expectations between groups (P = 0.04), other secondary outcomes were not significant.
Conclusions. An intervention with theory-based podcasts, social support, and incentivized self-monitoring resulted in significantly greater weight loss than a comparison intervention with theory-based podcasts and a commercially available standard self-monitoring app. This study highlights key features to add to mobile health interventions for adult weight loss.
Commentary
Obesity prevalence rates have increased over the past several decades across all genders, ages, ethnicities, income levels, and education levels [1], and recent data show that over one-third of adults in the US are obese and over two-thirds are overweight [2,3]. Behavior or lifestyle modification, which incorporates (often tailored) diet, physical activity, and behavior therapy, is highly recommended as the first strategy for losing initial weight and sustaining weight management efforts [4,5]. Mobile health (mHealth) technologies and other web-based and technology-assisted approaches (eg, mobile applications or “apps”) to facilitate behavior change for weight loss and management have aimed to address many of the limitations posed by traditional face-to-face weight loss approaches [6–8]. Prevailing theories of health behavior change imply key intervention design features that may increase their likelihood of promoting and sustaining desired behavior changes, particularly those that impact self-efficacy, self-regulation, and social facilitation [9,10].
Despite the plethora of weight loss mobile apps available to the public, it remains unclear if these are guided by evidence-based behavior change strategies typically used in traditional programs and approaches [11,12]. Further, very few of these apps have been rigorously evaluated with scientific testing to determine true effectiveness and safety [13,14]. This study adds to the literature by evaluating a mobile app for weight loss (Social POD) that was developed by researchers and utilizes theory-based components to target specific constructs that lead to health behavior change. Additionally, while self-monitoring is commonly incorporated into most available weight loss/management apps [11], the Social POD mobile app also incorporates social support and motivational strategies, which are less often included. The findings from this study add to the limited literature that mobile phone app-based interventions may be useful tools for weight loss [13].
The authors outlined several strengths and limitations. Briefly, this study was particularly strengthened by its randomized assignment to equivalent intervention groups, the use of a researcher-developed experimental group app that targets several key theory-based constructs for behavior change, measurement of objective use of the intervention group app, a racially diverse sample (over one-third of participants in both groups identified as black), measurement of secondary psycho-social behavioral outcomes, significant efforts to ensure survey completion and compliance with the intervention (increase retention), as well efforts to decrease participation burden by limiting required in-person sessions.
However, several important aspects of the study limit the internal validity and generalizability of its findings. The study had a small sample size and included a highly educated study population. If possible, future studies should consider including a large, diverse population to enhance generalizability. Also, this study was limited to those with an Android device, and significant demographic differences between Android and iPhone users have been reported [15]. The comparison group reported significantly more prior downloading of a diet-tracking app compared to the experimental group, which may have impacted use of the comparison app. The extrinsic reward system built into the experimental group intervention could have impacted adherence to experimental app, and is likely not feasible in real-world application of the experimental group app. Findings may have been subject to recall bias and measurement error due to self-reporting of outcomes measures. Importantly, this was a short-term weight loss study, and long-term weight loss/maintenance data is needed to support findings since in the usual course of weight-loss therapy the greatest weight loss occurs within 6 months of treatment, after which weight is often regained, sometimes near original level [16].
Applications for Clinical Practice
With the increasing popularity of technology-assisted and mHealth applications for weight loss and other health behaviors, it is important for practitioners to be familiar with proven, theory-based approaches and advise patients accordingly. This study demonstrated that social support components added to self-monitoring components in a weight loss app can lead to significant weight loss compared to self-monitoring alone. Thus, those that offer obesity counseling should be mindful that tracking and controlling dietary and physical activity behaviors alone may not prove to be successful. Opportunities for social facilitation to support weight loss efforts should be discussed with patients, including sources of social influence, support and collaboration between individuals, families, and health care professionals.
—Katrina F. Mateo, MPH
1. Mitchell NS, Catenacci VA, Wyatt HR, Hill JO. Obesity: overview of an epidemic. Psychiatr Clin North Am 2011;34:717–32.
2. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008. JAMA 2010;303:235–41.
3. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014;311:806–14.
4. Wadden TA, Butryn ML, Wilson C. Lifestyle modification for the management of obesity. Gastroenterology 2007;132:2226–38.
5. Butryn ML, Webb V, Wadden TA. Behavioral treatment of obesity. Psychiatr Clin North Am 2011;34:841–59.
6. Okorodudu DE, Bosworth HB, Corsino L. Innovative interventions to promote behavioral change in overweight or obese individuals: a review of the literature. Ann Med 2015;47:179–85.
7. Taylor RW, Roy M, Jospe MR, et al. Determining how best to support overweight adults to adhere to lifestyle change: protocol for the SWIFT study. BMC Public Health 2015;15:861.
8. Laing BY, Mangione CM, Tseng C-H, et al. Effectiveness of a smartphone application for weight loss compared with usual care in overweight primary care patients: a randomized, controlled trial. Ann Intern Med 2014;161(10 Suppl):
S5–S12.
9. Teixeira PJ, Carraça E V, Marques MM, et al. Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Med 2015;13:84.
10. Ryan P. Integrated theory of health behavior change: background and intervention development. Clin Nurse Spec 2009;23:161–70.
11. Rivera J, McPherson A, Hamilton J, et al. Mobile apps for weight management: a scoping review. JMIR mHealth uHealth 2016;4:e87.
12. Pellegrini CA, Pfammatter AF, Conroy DE, Spring B. Smartphone applications to support weight loss: current perspectives. Adv Health Care Technol 2015;1:13–22.
13. Flores Mateo G, Granado-Font E, Ferré-Grau C, Montaña-Carreras X. Mobile phone apps to promote weight loss and increase physical activity: a systematic review and meta-analysis. J Med Internet Res 2015;17:e253.
14. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J Cardiovasc Nurs 28:320–9.
15. Smith A. Smartphone ownership 2013. Pew Research Center.
16. Jeffery RW, Drewnowski A, Epstein LH, et al. Long-term maintenance of weight loss: current status. Health Psychol 2000;19(1 Suppl):5–16.
1. Mitchell NS, Catenacci VA, Wyatt HR, Hill JO. Obesity: overview of an epidemic. Psychiatr Clin North Am 2011;34:717–32.
2. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008. JAMA 2010;303:235–41.
3. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014;311:806–14.
4. Wadden TA, Butryn ML, Wilson C. Lifestyle modification for the management of obesity. Gastroenterology 2007;132:2226–38.
5. Butryn ML, Webb V, Wadden TA. Behavioral treatment of obesity. Psychiatr Clin North Am 2011;34:841–59.
6. Okorodudu DE, Bosworth HB, Corsino L. Innovative interventions to promote behavioral change in overweight or obese individuals: a review of the literature. Ann Med 2015;47:179–85.
7. Taylor RW, Roy M, Jospe MR, et al. Determining how best to support overweight adults to adhere to lifestyle change: protocol for the SWIFT study. BMC Public Health 2015;15:861.
8. Laing BY, Mangione CM, Tseng C-H, et al. Effectiveness of a smartphone application for weight loss compared with usual care in overweight primary care patients: a randomized, controlled trial. Ann Intern Med 2014;161(10 Suppl):
S5–S12.
9. Teixeira PJ, Carraça E V, Marques MM, et al. Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Med 2015;13:84.
10. Ryan P. Integrated theory of health behavior change: background and intervention development. Clin Nurse Spec 2009;23:161–70.
11. Rivera J, McPherson A, Hamilton J, et al. Mobile apps for weight management: a scoping review. JMIR mHealth uHealth 2016;4:e87.
12. Pellegrini CA, Pfammatter AF, Conroy DE, Spring B. Smartphone applications to support weight loss: current perspectives. Adv Health Care Technol 2015;1:13–22.
13. Flores Mateo G, Granado-Font E, Ferré-Grau C, Montaña-Carreras X. Mobile phone apps to promote weight loss and increase physical activity: a systematic review and meta-analysis. J Med Internet Res 2015;17:e253.
14. Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J Cardiovasc Nurs 28:320–9.
15. Smith A. Smartphone ownership 2013. Pew Research Center.
16. Jeffery RW, Drewnowski A, Epstein LH, et al. Long-term maintenance of weight loss: current status. Health Psychol 2000;19(1 Suppl):5–16.