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

Abstract

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.

Keywords: apps; behavioral intervention; obesity; self-monitoring; weight loss.

Figure 1

Diagram of variables used in prediction models and mediation analysis.

Figure 2

Diagram representing our causal model for treatment assignment, mediators, and outcomes. Arrows show the direction of causation. Dashed arrows represent mediated effects, and solid arrows represent direct effects. a: Causal model for the 4-month weight loss outcome. b: Causal model for the 12-month weight loss outcome.

Figure 3

Number of days per week with at least one logging (by week and treatment groups) during the first 4 months.

Figure 4

The contribution of variable results (top) for the first 3 PCs from the 8-week PC analysis runs on a scale of 0 to 100. The darker and larger a circle, the more it contributed to a PC. The quality of the representation of variable results (bottom) are on a scale of 0 to 1. The darker and larger a circle, the more it is represented by a PC compared with other PCs. PC: principal component.