Assessing the Contribution of Self-Monitoring Through a Commercial Weight Loss App: Mediation and Predictive Modeling Study

Background Electronic self-monitoring technology has the potential to provide unique insights into important behaviors for inducing weight loss. Objective The aim of this study is to investigate the effects of electronic self-monitoring behavior (using the commercial Lose It! app) and weight loss interventions (with differing amounts of counselor feedback and support) on 4- and 12-month weight loss. Methods In this secondary analysis of the Fit Blue study, we compared the results of two interventions of a randomized controlled trial. Counselor-initiated participants received consistent support from the interventionists, and self-paced participants received assistance upon request. The participants (N=191), who were active duty military personnel, were encouraged to self-monitor their diet and exercise with the Lose It! app or website. We examined the associations between intervention assignment and self-monitoring behaviors. We conducted a mediation analysis of the intervention assignment for weight loss through multiple mediators—app use (calculated from the first principal component [PC] of electronically collected variables), number of weigh-ins, and 4-month weight change. We used linear regression to predict weight loss at 4 and 12 months, and the accuracy was measured using cross-validation. Results On average, the counselor-initiated–treatment participants used the app more frequently than the self-paced–treatment participants. The first PC represented app use frequencies, the second represented calories recorded, and the third represented reported exercise frequency and exercise caloric expenditure. We found that 4-month weight loss was partially mediated through app use (ie, the first PC; 60.3%) and the number of weigh-ins (55.8%). However, the 12-month weight loss was almost fully mediated by 4-month weight loss (94.8%). Linear regression using app data from the first 8 weeks, the number of self–weigh-ins at 8 weeks, and baseline data explained approximately 30% of the variance in 4-month weight loss. App use frequency (first PC; P=.001), self-monitored caloric intake (second PC; P=.001), and the frequency of self-weighing at 8 weeks (P=.008) were important predictors of 4-month weight loss. Predictions for 12-month weight with the same variables produced an R2 value of 5%; only the number of self–weigh-ins was a significant predictor of 12-month weight loss. The R2 value using 4-month weight loss as a predictor was 31%. Self-reported exercise did not contribute to either model (4 months: P=.77; 12 months: P=.15). Conclusions We found that app use and daily reported caloric intake had a substantial impact on weight loss prediction at 4 months. Our analysis did not find evidence of an association between participant self-monitoring exercise information and weight loss. As 12-month weight loss was completely mediated by 4-month weight loss, intervention targets should focus on promoting early and frequent dietary intake self-monitoring and self-weighing to promote early weight loss, which leads to long-term success. Trial Registration ClinicalTrials.gov NCT02063178; https://clinicaltrials.gov/ct2/show/NCT02063178

Is It Possible to Predict Weight Loss After Bariatric Surgery?—External Validation of Predictive Models

Background Bariatric surgery is the most effective obesity treatment. Weight loss varies among patients, and not everyone achieves desired outcome. Identification of predictive factors for weight loss after bariatric surgery resulted in several prediction tools proposed. We aimed to validate the performance of available prediction models for weight reduction 1 year after surgical treatment. Materials and Methods The retrospective analysis included patients after Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) who completed 1-year follow-up. Postoperative body mass index (BMI) predicted by 12 models was calculated for each patient. The correlation between predicted and observed BMI was assessed using linear regression. Accuracy was evaluated by squared Pearson’s correlation coefficient (R2). Goodness-of-fit was assessed by standard error of estimate (SE) and paired sample t test between estimated and observed BMI. Results Out of 760 patients enrolled, 509 (67.00%) were women with median age 42 years. Of patients, 65.92% underwent SG and 34.08% had RYGB. Median BMI decreased from 45.19 to 32.53kg/m2 after 1 year. EWL amounted to 62.97%. All models presented significant relationship between predicted and observed BMI in linear regression (correlation coefficient between 0.29 and 1.22). The best predictive model explained 24% variation of weight reduction (adjusted R2=0.24). Majority of models overestimated outcome with SE 5.03 to 5.13kg/m2. Conclusion Although predicted BMI had reasonable correlation with observed values, none of evaluated models presented acceptable accuracy. All models tend to overestimate the outcome. Accurate tool for weight loss prediction should be developed to enhance patient’s assessment. Graphical abstract

Psychosocial Characteristics by Weight Loss and Engagement in a Digital Intervention Supporting Self-Management of Weight

There is substantial variability in weight loss outcomes. Psychosocial characteristics underlying outcomes require better understanding, particularly on self-managed digital programs. This cross-sectional study examines differences in psychosocial characteristics by weight loss and engagement outcome, and which characteristics are most associated with weight loss, on a self-managed digital weight loss program. Some underexplored psychosocial characteristics are included, such as flourishing, or a sense of meaning and purpose in life. A questionnaire was emailed to a random sample of 10,000 current users at week 5 in the program and 10,000 current users at week 17. The questionnaire was completed by 2225 users, and their self-reported weight and recorded program engagement data were extracted from the program’s database. Multiple comparison tests indicated that mental health quality of life, depression, anxiety, work-life balance, and flourishing differed by weight loss outcome at program end (week 17; ≥5%, 2–5%, below 2%) and by engagement tertile at program beginning and end (weeks 5 and 17). Only anxiety was associated with weight loss in a backward stepwise regression controlling for engagement and sociodemographic characteristics. Flourishing did not predict weight loss overall but predicted the weight loss outcome group. Our findings have implications for creating more effective interventions for individuals based on psychosocial characteristics and highlight the potential importance of anxiety in underexplored self-managed digital programs.