Rationale and design of the SenseWhy project: A passive sensing and ecological momentary assessment study on characteristics of overeating episodes

Abstract

Objectives: Overeating interventions and research often focus on single determinants and use subjective or nonpersonalized measures. We aim to (1) identify automatically detectable features that predict overeating and (2) build clusters of eating episodes that identify theoretically meaningful and clinically known problematic overeating behaviors (e.g., stress eating), as well as new phenotypes based on social and psychological features.

Method: Up to 60 adults with obesity in the Chicagoland area will be recruited for a 14-day free-living observational study. Participants will complete ecological momentary assessments and wear 3 sensors designed to capture features of overeating episodes (e.g., chews) that can be visually confirmed. Participants will also complete daily dietitian-administered 24-hour recalls of all food and beverages consumed.

Analysis: Overeating is defined as caloric consumption exceeding 1 standard deviation of an individual's mean consumption per eating episode. To identify features that predict overeating, we will apply 2 complementary machine learning methods: correlation-based feature selection and wrapper-based feature selection. We will then generate clusters of overeating types and assess how they align with clinically meaningful overeating phenotypes.

Conclusions: This study will be the first to assess characteristics of eating episodes in situ over a multiweek period with visual confirmation of eating behaviors. An additional strength of this study is the assessment of predictors of problematic eating during periods when individuals are not on a structured diet and/or engaged in a weight loss intervention. Our assessment of overeating episodes in real-world settings is likely to yield new insights regarding determinants of overeating that may translate into novel interventions.

Keywords: behavioral phenotypes; detection; machine learning; overeating; prediction; wearable sensors.

Figure 1.

Study procedures from screening to trial conclusion. Procedures are listed in the top row and described in the bottom row. Abbreviations: BMI, body mass index; EMA, ecological momentary assessment.

Figure 2.

The 3 elements of an eating episode recording in the FoodTrck mobile app. Participants complete the “Decided to” element when they make the decision to eat, typically before the prepared food is in front of them. “Decided to” contains an EMA targeting stress, affect, and food source. Participants complete the “About to” element immediately before eating. “About to” involves taking a photo of the eating episode and entering a simple description. Participants complete “After” immediately after eating. “After” includes both an EMA and a photograph of any leftovers or empty plates. Abbreviation: EMA, ecological momentary assessment.

Figure 3.

The sequence of events during the in-wild phase. Meals from days 1–14 are captured via diet recall on the subsequent day.

Figure 4.

Example of an eating episode as characterized by the multi-sensor system.