SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing

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PROJECT SUMMARY/ABSTRACT Medical professionals have recently put to rest the idea that there is an ideal weight loss diet for everyone. One cause for obesity is overeating, but we do not know what patterns and behaviors contribute to this problematic habit. Defining problematic eating behaviors that lead to energy imbalance is essential for treating obesity. Studies typically focus on a single putative causal mechanism of overeating such as stress or craving, not addressing the multiple features that co-occur with overeating. Hence, the factors that predict overeating episodes remain unknown, as do which of them contribute to an individual's consistency and variability of overeating. Given recent advancements in passive sensing, we now have the potential to detect problematic eating using seamlessly captured physiological features such as number of feeding gestures and swallows, and heart rate variability. Collecting detectable and predictable features that identify overeating will hone in on the patterns that interventionists may optimally target to help populations with obesity understand their eating habits and ultimately improve their ability to self-regulate their eating behaviors. Location-scale models will map the factors that most contribute to habit formation within subjects, providing interventionists with essential targets to guide behavior. The first aim is to collect sensor-based and ecological momentary assessment data (to assess factors not yet detectable through sensing) from adults with obesity and apply machine learning algorithms to identify a subset of features that detect overeating, as validated against ground truth of videotaped eating episodes and 24 hour dietary recall. Participants will wear a passive sensing sensor suite and respond to random and event-triggered prompts regarding each eating episode. Then, machine learning will determine the optimal feature subset that detect overeating episodes using Gradient Boosting Machines. In the second aim, hierarchical clustering techniques will cluster overeating episodes into theoretically meaningful and clinically known problematic behaviors related to overeating. The final aim is to build statistical models that explain the effect of detectable and clinically-known problematic features on new habit formation. These models will lay a foundation for optimization studies to discover evidence-based decision rules that can guide timely interventions to treat obesity by preventing overeating, and maintaining healthy eating behaviors.