Methodological and data-driven approach to infer durable behavior change from mHealth data

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Abstract Poor cancers, lengthy, achieve diet and physical activity (PA) behaviors, the most prevalent risk factors for cardiometabolic diseases and can be treated to prevent disease. However, most diet, PA, and weight loss interventions are costly, and burdensome. Theseinterventions could be more cost-efficient if we could tell when people a sustainable pattern of health behavior change so that treatment could be tapered and then stopped without behavioral relapse. Theories of habit formation might be assumed to address this problem, but they have not proved actionable to guide treatment decisions because they do not specify measurable criteria to reliably detect acquisition of a durable behavior pattern. Hence, we propose to identify behavior patterns that precede and predict maintenance of target-level behavioral improvement that persist after an intervention ends. The measurements needed to tell whether an intervention has durably entrained behavioral improvement are collected as part of diet, PA, and weight loss interventions. Specifically, participants continuously self-monitor their behavior digitally while assessments are relayed back to inform them about progress toward goals. We will analyze self-monitoring measures collected in 6 mHealth trials, conducted over 14 years among over 1,600 participants and more than 147,000 daily observations, to assess when an intervention has durably entrained targeted behaviors, as validated by their reliable persistence post- intervention. We will use location scale modeling to quantify change not only in the absolute level (location) of a behavior but also in its within-person variability (scale). We posit that the induction of durable behavior change requires both improvement in location (increases for healthy behaviors; decreases for unhealthy ones) and decrease in scale (i.e., increased behavioral consistency). Aim 1 will apply existing location scale methods to test the hypothesis that effective interventions will improve the location and reduce the scale of targeted behaviors across all trials. Because existing methods only measure scale at the group level and cannot measure the change in an individual's behavioral consistency that we need to personalize treatment adaptation, Aim 2 will extend location scale methods to enable individual estimation of the rate of change in behavioral consistency. Estimates derived from the new method will be analyzed to learn which parameters of behavior change during intervention are most associated with maintenance post-treatment. Finally, Aim 3 will apply machine learning to estimates from the extended location-scale mixed models to establish ranges and behavioral patterns that predict behavioral maintenance post-treatment. These resultswill inform behaviorinterventionscience and improve treatment efficiency by guiding real-timedecisions about the needed dosage and duration of behavioral treatments.