This pilot study provides initial support for the feasibility, acceptability, and preliminary efficacy of a remotely delivered acceptance-based behavioral intervention for postoperative weight regain.

These findings provide initial support for the use of a psychological acceptance-based intervention for weight regain in bariatric surgery patients.

Objective Evaluate the association between pre-treatment and during-treatment weight change. Evaluate differences in self-regulation between those who gain weight, remain weight stable, and lose weight pre-treatment. Methods Data from the first six months of a behavioral weight loss study were used. Participants (n=283) were weighed at two assessment points (screening visit and baseline) prior to the start of treatment and at every treatment session. Participants were divided into those who gained weight, remained weight stable, or lost weight between screening visit and the first treatment session. Results Pre-treatment weight change was not significantly associated with during-treatment change. Weight change from screening visit to month six was significantly different by category, with losses of 11% and 7% for those who lost and gained weight pre-treatment respectively. Weight change from first treatment session to month six was not different by category. Poorer self-regulation was associated with pre-treatment weight gain and better self-regulation with pre-treatment weight loss. Conclusions Pre-treatment weight change may not relate to success during behavioral weight loss treatment. Researchers should carefully consider when the “baseline” assessment takes place to reduce bias introduced by weight change during pre-treatment. Poorer self-regulation may place individuals at risk for weight gain prior to treatment.

No abstract

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.