Using Artificial Intelligence to Optimize Delivery of Weight Loss Treatment - Abstract Seventy percent of American adults are overweight or obese, presenting an unprecedented challenge to the nation’s health systems. Effective behavioral programs exist, but these programs are intensive, long-term and require highly-trained clinicians, making them prohibitively expensive and thus limiting disseminability. Approaches to decreasing costs include replacing highly-trained clinicians with paraprofessionals, reducing contact frequency, and/or automating intervention. However, although these alternative interventions result in considerably lower average weight losses, variability of weight loss is high. Specifically, and consistent with a Supportive Accountability Model, a substantial minority of participants in high-intensity interventions receive no benefit, while a subset of those receiving low-intensity interventions achieve clinically significant weight loss. An ideal weight loss treatment system would enhance outcomes and reduce costs by matching each participant to the intervention he/she needs, thus adapting to participants’ needs and conserving resources where they are not needed. Stepped care represents one such system, but has had mixed success and suffers from a number of shortcomings. The innovative artificial intelligence (AI) strategy of reinforcement learning (RL) provides rapidly and repeatedly-varying features of intervention, continuously learning which features provide optimal responses for which participants. Our team recently completed a pilot of an AI weight loss system in which overweight adults received a brief in-person weight loss intervention and then were randomly assigned to receive 3 months of non-optimized interventions (i.e., 12-minute phone calls) or an optimized combination of phone calls, text exchanges, and automated messages, selected based on each participants’ response to each intervention as determined by weight and behavioral data. As hypothesized, we achieved equivalent weight losses at a fraction of the time cost. The proposed study would recruit 320 overweight adults, provide 1 month of group-based behavioral weight loss treatment and then randomize participants to either continue to receive group-based behavioral weight loss in a remote format for 11 months (BWL-S) or to reinforcement learning-based treatment (BWL-AI). In line with our Supportive Accountability model, BWL-AI would vary modality, intensity and counselor skill based on continuously-monitored participant digital data. The proposed study--the first of its kind--would expand on our pilot in several ways including sample size, duration, and features of intervention selected by the AI system. Aims of this project are to test the hypotheses that weight loss outcomes in BWL-AI will be equivalent to or better than BWL-S, and that the cost per participant and per kg of lost weight will be less in BWL-AI than in BWL-S. Other include characterizing the AI system (in terms of interventions selected), assessing feasibility and acceptability of the refined AI system, evaluating psychological and demographic predictors of AI intervention selection and investigating differences between responders and non-responders in how the AI system allocates resources.
