Using Behavioral Economic Strategies to Address Obesity in Economically Disadvantaged Adults

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Obesity is a public health crisis among adults from economically disadvantaged backgrounds, with over 85% experiencing overweight or obesity and associated health ailments. To date, lifestyle interventions targeting this high-risk group have produced modest weight losses. Thus, effective interventions for this vulnerable population are urgently needed. New evidence from behavioral economics suggests that targeting lack of reinforcement and bias for the present may improve treatment outcomes in adults from disadvantaged backgrounds. Specifically, impoverished environments have been shown to have few sources of healthy reinforcement, which makes responding to basic sources of reinforcement (e.g., palatable food) more resistant to change. Moreover, all humans have been shown to have bias for the present, or a preference for immediate rewards (palatable food) over future rewards (improved health), and studies suggest that individuals from disadvantaged backgrounds have even greater bias for the present (perhaps due to life demands, stress, and cognitive load). Addressing these two processes (lack of reinforcement and bias for the present) in obesity treatment may uniquely meet the needs of this high-risk, underserved population and result in weight loss success. The proposed study will test the efficacy of a mHealth behavioral economics weight loss intervention that addresses lack of reinforcement and bias for the present. Lack of reinforcement will be addressed with small monetary reinforcers delivered at the beginning of treatment. Reinforcers will taper during the initial treatment period and eventually end. As reinforcers taper, participants will be trained in Episodic Future Thinking, which has been shown to reduce bias for the present and may improve longer-term weight loss outcomes. This two-pronged, phased approach that first addresses lack of reinforcement and then bias for the present is essential. Providing reinforcement immediately at treatment start is necessary to engage participants straightaway. Then, as participants are developing success experiences with weight loss, which naturally provides its own reinforcement (improved mood, health, appearance), reinforcers will taper. During this time, EFT training will begin. This novel behavioral economics mHealth intervention will be compared to a mHealth only intervention. The two interventions will be delivered primarily via a mobile platform, include treatment material tailored to this population, and be matched for contact. Thus, the only way the two interventions will differ is in the inclusion of behavioral economics strategies in BE mHealth. Our primary hypothesis is that the behavioral economics intervention will yield significantly better weight losses at month 12 (treatment end). Mediators (food reinforcement, bias for the present), moderators (stress, resilience, obesogenic environment), and cost-effectiveness will also be explored. If effective, this mHealth behavioral economics intervention would be a new and transformative intervention approach that significantly improves obesity treatment outcomes in a high-risk, underserved population.