Obesity’s high prevalence and costs make it a public health crisis, but 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. 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, SP4 identifies nonresponders earlier than previously has been possible by applying a predictive model derived from its prior mHealth obesity research and quickly reallocates nonresponders to augmented treatment. The overall objective of this study is to determine the best way to sequence the delivery of mHealth tools and traditional treatment components in a stepped program of obesity treatments. By sequentially delivering treatment components based on participant response and by the use of an innovative experimental approach, the Sequential Multiple Assignment Randomized Trial, this study permits achievement of the target outcome, weight loss, with least resource consumption and participant burden.
SP4 is interested in the personalization algorithms proposed by TR&D2, in particular the use of these stochastic algorithms to determine the randomization probabilities in the MRT. If the TR&D algorithms under Aims 1 and 2 are demonstrated to be robust, SP4 would be interested in including extra participants so as to conduct a feasibility study for use in informing SP4’s future research.
Emotional Context, Overeating, SP, TR&D2