This randomized trial demonstrated qualified support for the ability of a machine learning-powered, smartphone-based just-in-time, adaptive intervention to enhance weight loss over and above a commercial weight loss program.

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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.

In a sample of adolescents with poorly controlled type 1 diabetes, this study examined if delay discounting, the extent to which individuals prefer immediate over delayed rewards, was associated with severity of non-adherence and poor glycemic control, and if parental monitoring of diabetes management moderated those associations. Sixty-one adolescents (Mage=15.08years, SD=1.43) with poorly controlled type 1 diabetes completed a delayed discounting task and an HbA1c blood test. Adherence was assessed via self-monitoring of blood glucose (SMBG) data from adolescents’ glucometers. Parents completed a parental monitoring questionnaire. Greater delay discounting was associated with higher HbA1c, but not SMBG. Direct parent observation of diabetes tasks, but not indirect parental monitoring, moderated the link between greater delay discounting and higher HbA1c, with higher direct parent observation buffering the link between greater discounting and poorer glycemic control. Delay discounting may be a target for future interventions to improve HbA1c in youth with type 1 diabetes.