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Data Science for Dynamic Intervention Decision-making Lab

National Institutes of Health (NIH) – R01-DK108678
Bonnie Spring and Inbal Nahum-Shani
 

Obesity’s high prevalence and costs make it a public health crisis, but current standard of care treatment impedes uptake and depletes resources by taking a one-size-fits-all approach. Guidelines recommend provision of expensive, burdensome treatment components (e.g., counseling, meal replacement) continuously to all consumers regardless of weight loss response. Stepped care that tries less costly evidence-based treatments first, reserving more resource-intensive treatments for suboptimal responders is a logical, equitable population health management strategy. However, stepped care approaches to obesity treatment have not yet incorporated inexpensive, widely available mHealth tools. It is unclear whether conjoint clinical and cost outcomes are better optimized by providing a low cost, low intensity, autonomously controlled mHealth treatment as the initial treatment with risk of nonresponse, or by providing a more costly, traditional obesity treatment with the potential to create a dependency that undermines autonomous motivation.

The potential pitfall of beginning with mHealth treatment is that long-term outcome may be poor if nonresponse to initially insufficient treatment allows demoralization to set in. To reduce that risk, we will identify nonresponders earlier than previously has been possible by applying a predictive model derived from our prior mHealth obesity research and will quickly reallocate nonresponders to augmented treatment. We propose to use a novel experimental approach, the SMART (Sequential Multiple Assignment Randomized Trial), to randomize 400 overweight/obese adults to one of two first line treatments, either (1) an app alone (APP), or (2) the app plus coaching (APP +C).

Those who do not respond to the first line treatment (i.e., evidenced by failure to lose weight) will be e-randomized to one of two subsequent augmentation tactics, either: (1) Modestly Step-Up: add another mHealth component (e.g., text messages), or (2) Vigorously Step-Up: add both a mHealth component (e.g., texts) and a more traditional component (e.g., coaching, meal replacement). Responders will continue with the same first line treatment for 12 weeks.

Assessments will occur at 3, 6, and 12 months to determine (1) whether mHealth or traditional obesity treatment (coaching) is the optimal first line treatment for overweight/obese adults; and (2) whether the optimal response to weight loss failure is to modestly or vigorously augment the first line treatment. As the first stepped care trial to integrate mHealth tools and implement our predictive model of weight loss failure, SMART will be the most temporally and resource efficient strategy evaluated to date.

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