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Behavioral weight loss (WL) trials show that, on average, participants regain lost weight unless provided long-term, intensive-and thus costly-intervention. Optimization solutions have shown mixed success. The artificial intelligence principle of "reinforcement learning" (RL) offers a new and more sophisticated form of optimization in which the intensity of each individual's intervention is continuously adjusted depending on patterns of response. In this pilot, we evaluated the feasibility and acceptability of a RL-based WL intervention, and whether optimization would achieve equivalent benefit at a reduced cost compared to a non-optimized intensive intervention. Participants (n = 52) completed a 1-month, group-based in-person behavioral WL intervention and then (in Phase II) were randomly assigned to receive 3 months of twice-weekly remote interventions that were non-optimized (NO; 10-min phone calls) or optimized (a combination of phone calls, text exchanges, and automated messages selected by an algorithm). The Individually-Optimized (IO) and Group-Optimized (GO) algorithms selected interventions based on past performance of each intervention for each participant, and for each group member that fit into a fixed amount of time (e.g., 1 h), respectively. Results indicated that the system was feasible to deploy and acceptable to participants and coaches. As hypothesized, we were able to achieve equivalent Phase II weight losses (NO = 4.42%, IO = 4.56%, GO = 4.39%) at roughly one-third the cost (1.73 and 1.77 coaching hours/participant for IO and GO, versus 4.38 for NO), indicating strong promise for a RL system approach to weight loss and maintenance.

Objective In the Mind Your Health Trial, acceptance‐based behavioral treatment (ABT) for obesity outperformed standard behavioral treatment (SBT) at posttreatment. This trial compared effects over 2 years of follow‐up. Methods Participants with overweight or obesity (n = 190) were randomized to 25 sessions of SBT or ABT over 1 year and assessed at months 12 (i.e., posttreatment), 24 (1 year posttreatment), and 36 (2 years posttreatment). Results Weight‐loss differences previously observed at 12 months attenuated by follow‐up, though a large difference was observed in the proportion of treatment completers who maintained 10% weight loss at 36 months (SBT = 17.1% vs. ABT = 31.6%; P = 0.04; intent‐to‐treat: SBT = 14.4% vs. ABT = 25.0%; P = 0.07). The amount of regain between posttreatment and follow‐up did not differ between groups. ABT produced higher quality of life at 24 and 36 months. Autonomous motivation and psychological acceptance of food‐related urges mediated the effect of condition on weight. No moderator effects were identified. Conclusions Overall, results suggest that infusing SBT for weight loss with acceptance‐based strategies enhances weight loss initially, but these effects fade in the years following the withdrawal of treatment. Even so, those receiving ABT were about twice as likely to maintain 10% weight loss at 36 months, and they reported considerably higher quality of life.

A major contributor to the obesity epidemic is the overconsumption of high-calorie foods, which is partly governed by inhibitory control, that is, the ability to override pre-prepotent impulses and drives. Computerized inhibitory control trainings (ICTs) have demonstrated qualified success at affecting real-world health behaviors, and at improving weight loss, particularly when repeated frequently over an extended duration. It has been proposed that gamification (i.e., incorporating game-like elements such as a storyline, sounds, graphics, and rewards) might enhance participant interest and thus training compliance. Previous findings from a mostly female sample did support this hypothesis; however, it might be expected that the effects of gamification differ by gender such that men, who appear more motivated by gaming elements, stand to benefit more from gamification. The present study evaluated whether gender moderated the effect of a gamified ICT on weight loss. Seventy-six overweight individuals received a no-sugar-added dietary prescription and were randomized to 42 daily and 2 weekly ICTs focused on sweet foods that were either gamified or nongamified. Results supported the hypothesis that gamification elements had a positive effect on weight loss for men and not women (p = .03). However, mechanistic hypotheses for the moderating effect (in terms of enjoyment, compliance, and improvements in inhibitory control) were generally not supported (p’s > .20). These results suggest that gamification of ICTs may boost weight loss outcomes for men and not women, but further research is needed to determine the specific mechanisms driving this effect and to arrive at gamification elements that enhance effects for both men and women.

Social comparison-based features are widely used in social computing apps. However, most existing apps are not grounded in social comparison theories and do not consider individual differences in social comparison preferences and reactions. This paper is among the first to automatically personalize social comparison targets. In the context of an m-health app for physical activity, we use artificial intelligence (AI) techniques of multi-armed bandits. Results from our user study (n=53) indicate that there is some evidence that motivation can be increased using the AI-based personalization of social comparison. The detected effects achieved small-to-moderate effect sizes, illustrating the real-world implications of the intervention for enhancing motivation and physical activity. In addition to design implications for social comparison features in social apps, this paper identified the personalization paradox, the conflict between user modeling and adaptation, as a key design challenge of personalized applications for behavior change. Additionally, we propose research directions to mitigate this Personalization Paradox.