The Association Between Engagement and Weight Loss Through Personal Coaching and Cell Phone Interventions in Young Adults: Randomized Controlled Trial

Background Understanding how engagement in mobile health (mHealth) weight loss interventions relates to weight change may help develop effective intervention strategies. Objective This study aims to examine the (1) patterns of participant engagement overall and with key intervention components within each intervention arm in the Cell Phone Intervention For You (CITY) trial; (2) associations of engagement with weight change; and (3) participant characteristics related to engagement. Methods The CITY trial tested two 24-month weight loss interventions. One was delivered with a smartphone app (cell phone) containing 24 components (weight tracking, etc) and included prompting by the app in predetermined frequency and forms. The other was delivered by a coach via monthly calls (personal coaching) supplemented with limited app components (18 overall) and without any prompting by the app. Engagement was assessed by calculating the percentage of days each app component was used and the frequency of use. Engagement was also examined across 4 weight change categories: gained (≥2%), stable (±2%), mild loss (≥2% to <5%), and greater loss (≥5%). Results Data from 122 cell phone and 120 personal coaching participants were analyzed. Use of the app was the highest during month 1 for both arms; thereafter, use dropped substantially and continuously until the study end. During the first 6 months, the mean percentage of days that any app component was used was higher for the cell phone arm (74.2%, SD 20.1) than for the personal coaching arm (48.9%, SD 22.4). The cell phone arm used the apps an average of 5.3 times/day (SD 3.1), whereas the personal coaching participants used them 1.7 times/day (SD 1.2). Similarly, the former self-weighed more than the latter (57.1% days, SD 23.7 vs 32.9% days, SD 23.3). Furthermore, the percentage of days any app component was used, number of app uses per day, and percentage of days self-weighed all showed significant differences across the 4 weight categories for both arms. Pearson correlation showed a negative association between weight change and the percentage of days any app component was used (cell phone: r=−.213; personal coaching: r=−.319), number of apps use per day (cell phone: r=−.264; personal coaching: r=−.308), and percentage of days self-weighed (cell phone: r=−.297; personal coaching: r=−.354). None of the characteristics examined, including age, gender, race, education, income, energy expenditure, diet quality, and hypertension status, appeared to be related to engagement. Conclusions Engagement in CITY intervention was associated with weight loss during the first 6 months. Nevertheless, engagement dropped substantially early on for most intervention components. Prompting may be helpful initially. More flexible and less intrusive prompting strategies may be needed during different stages of an intervention to increase or sustain engagement. Future studies should explore the motivations for engagement and nonengagement to determine meaningful levels of engagement required for effective intervention. Trial Registration ClinicalTrials.gov NCT01092364; https://clinicaltrials.gov/ct2/show/NCT01092364 (Archived by WebCite at http://www.webcitation.org/72V8A4e5X)

[1]  C. Vandelanotte,et al.  Past, Present, and Future of eHealth and mHealth Research to Improve Physical Activity and Dietary Behaviors. , 2016, Journal of nutrition education and behavior.

[2]  Lisa V. Hampson,et al.  Adaptive designs in clinical trials: why use them, and how to run and report them , 2018, BMC Medicine.

[3]  Jon O. Ebbert,et al.  Managing Overweight and Obesity in Adults to Reduce Cardiovascular Disease Risk , 2014, Current Atherosclerosis Reports.

[4]  Ann Blandford,et al.  Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions. , 2016, American journal of preventive medicine.

[5]  Mick P Couper,et al.  Engagement and Retention: Measuring Breadth and Depth of Participant Use of an Online Intervention , 2010, Journal of medical Internet research.

[6]  Dori M. Steinberg,et al.  Weighing every day matters: daily weighing improves weight loss and adoption of weight control behaviors. , 2015, Journal of the Academy of Nutrition and Dietetics.

[7]  L. Burke,et al.  Association between Self-Weighing and Percent Weight Change: Mediation Effects of Adherence to Energy Intake and Expenditure Goals. , 2016, Journal of the Academy of Nutrition and Dietetics.

[8]  Iain Buchan,et al.  Who Self-Weighs and What Do They Gain From It? A Retrospective Comparison Between Smart Scale Users and the General Population in England , 2016, Journal of medical Internet research.

[9]  Tom Loney,et al.  Does exercise motivation predict engagement in objectively assessed bouts of moderate-intensity exercise? A self-determination theory perspective. , 2008, Journal of sport & exercise psychology.

[10]  Dori M. Steinberg,et al.  Electronic health (eHealth) interventions for weight management among racial/ethnic minority adults: a systematic review , 2014, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[11]  Victor J Stevens,et al.  Effects of comprehensive lifestyle modification on blood pressure control: main results of the PREMIER clinical trial. , 2003, JAMA.

[12]  Stephen Intille,et al.  Weight loss intervention for young adults using mobile technology: design and rationale of a randomized controlled trial - Cell Phone Intervention for You (CITY). , 2014, Contemporary clinical trials.

[13]  Lora E Burke,et al.  Self-monitoring dietary intake: current and future practices. , 2005, Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation.

[14]  Fangchao Liu,et al.  Mobile phone intervention and weight loss among overweight and obese adults: a meta-analysis of randomized controlled trials. , 2015, American journal of epidemiology.

[15]  Dori M. Steinberg,et al.  Engagement with eHealth Self-Monitoring in a Primary Care-Based Weight Management Intervention , 2015, PloS one.

[16]  Alan Bauck,et al.  Comparison of strategies for sustaining weight loss: the weight loss maintenance randomized controlled trial. , 2008, JAMA.

[17]  Carrie D. Patnode,et al.  Behavioral Counseling to Promote a Healthful Diet and Physical Activity for Cardiovascular Disease Prevention in Adults Without Known Cardiovascular Disease Risk Factors: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force , 2017, JAMA.

[18]  Gary G Bennett,et al.  Adherence to Self-Monitoring via Interactive Voice Response Technology in an eHealth Intervention Targeting Weight Gain Prevention Among Black Women: Randomized Controlled Trial , 2014, Journal of medical Internet research.

[19]  Gary G Bennett,et al.  Daily self-weighing and adverse psychological outcomes: a randomized controlled trial. , 2014, American journal of preventive medicine.

[20]  L. Burke,et al.  Using a personal digital assistant for self-monitoring influences diet quality in comparison to a standard paper record among overweight/obese adults. , 2011, Journal of the American Dietetic Association.

[21]  Susan A. Murphy,et al.  Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research , 2014, Translational behavioral medicine.

[22]  David W. McDonald,et al.  Activity sensing in the wild: a field trial of ubifit garden , 2008, CHI.

[23]  Alan Bauck,et al.  Associations of Internet Website Use With Weight Change in a Long-term Weight Loss Maintenance Program , 2010, Journal of medical Internet research.

[24]  Deborah F. Tate,et al.  The efficacy of a daily self-weighing weight loss intervention using smart scales and email , 2013, Obesity.

[25]  Stephen Intille,et al.  Adaptive intervention design in mobile health: Intervention design and development in the Cell Phone Intervention for You trial , 2015, Clinical trials.

[26]  Jeffrey K Aronson,et al.  A new taxonomy for describing and defining adherence to medications. , 2012, British journal of clinical pharmacology.

[27]  L. Burke,et al.  Patterns of self-weighing behavior and weight change in a weight loss trial , 2016, International Journal of Obesity.

[28]  G. Alkhaldi,et al.  The Effectiveness of Prompts to Promote Engagement With Digital Interventions: A Systematic Review , 2016, Journal of medical Internet research.

[29]  C. Ryan,et al.  Theoretical Perspectives of Adherence to Web-Based Interventions: a Scoping Review , 2018, International Journal of Behavioral Medicine.

[30]  Elroy J. Aguiar,et al.  Process Evaluation of the Type 2 Diabetes Mellitus PULSE Program Randomized Controlled Trial: Recruitment, Engagement, and Overall Satisfaction , 2017, American journal of men's health.

[31]  M. Sevick,et al.  Using mHealth technology to enhance self-monitoring for weight loss: a randomized trial. , 2012, American journal of preventive medicine.

[32]  Jan Seghers,et al.  Face-to-Face Versus Mobile Versus Blended Weight Loss Program: Randomized Clinical Trial , 2018, JMIR mHealth and uHealth.

[33]  Bryan C. Batch,et al.  Cell phone Intervention for You (CITY): A randomized, controlled trial of behavioral weight loss intervention for young adults using mobile technology , 2015, Obesity.

[34]  T. Skinner,et al.  Effective strategies for weight loss in post‐partum women: a systematic review and meta‐analysis , 2015, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[35]  Jerilyn K Allen,et al.  Mobile phone interventions to increase physical activity and reduce weight: a systematic review. , 2013, The Journal of cardiovascular nursing.

[36]  J. Wardle,et al.  The association between weight loss and engagement with a web-based food and exercise diary in a commercial weight loss programme: a retrospective analysis , 2011, The international journal of behavioral nutrition and physical activity.