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Individual instances of nonadherence to reduced calorie dietary prescriptions, that is, dietary lapses, represent a key challenge for weight management. Just-in-time adaptive interventions (JITAIs), which collect and analyze data in real time to deliver tailored interventions during moments of need, may be well suited to promote weight loss by preventing dietary lapses. We developed OnTrack (OT), a smartphone application (app) that collects data on lapses and triggers of lapse, uses a continuously improving machine learning model to predict lapse risk, and delivers tailored interventions when risk is elevated. The current study evaluated the efficacy of OT against an active control in facilitating weight loss. Participants (N = 181) with overweight/obesity (MBMI = 34.32; 85.1% female; 73.5% White) were randomized to receive either the WW (formerly Weight Watchers) Beyond the Scale (BTS) digital program alone or WW plus OnTrack (WW + OT) for 10 weeks. In an unplanned, natural experiment, the WW program changed mid-way through the trial from BTS to a more flexible one, Freestyle (FS). A general linear model revealed a treatment condition × diet plan interaction (F1, 173 = 9.68, p = .002) such that OT demonstrated greater efficacy only among those receiving BTS (weight loss MWW + OT = 4.7%, standard error SE = .55 versus MWW = 2.6%, SE = .80). Compared to FS, BTS WW + OT participants also reported considerably higher satisfaction with the intervention, engagement was higher, and algorithm accuracy was superior. Overall, results offer qualified support for OT and generally for machine learning-powered JITAIs that facilitate weight loss by predicting and preventing dietary lapses.
Forman et al. (Fri,) studied this question.