Project Summary/Abstract
Energy intake (EI) plays a critical role in the etiology and prevention of prevalent and debilitating chronic
diseases such as overweight/obesity and type 2 diabetes. Self-monitoring is the cornerstone of the self-
regulation approach for reducing EI, but prevailing methods are burdensome and inaccurate which limits our
ability to understand eating patterns and intervene on them to improve health. There is a clear need for
innovative solutions that can unobtrusively monitor and reliably estimate EI in the context of daily life. For 10+
years, our group has researched the utility of a wrist-watch device (e.g., smartwatch) to passively monitor
eating behavior by measuring the acceleration and rotation of dominant-hand wrist motion of food being
brought to the mouth. Through several studies we have refined our approach for using patterns of wrist motion
to identify individual intake gestures ("bite" of food, "drink" of beverage) during meals/snacks. We have shown
that we can use intake gesture count to estimate meal-level EI by using advanced modeling to estimate
kilocalories per bite (KPB) and kilocalories per drink (KPD) (e.g., EI = #bites x KPB + #drinks x KPD). We are
on the cusp of making this approach widely available for clinical application, but our latest advances in sensor-
based EI estimation require validation before the method is truly viable in real-world settings. In this project we
will definitively address 3 final barriers: 1) Our approach must be validated across settings and among a highly
representative sample; 2) Our models that use intake gestures to estimate EI must account for varying
contexts, such as different types of foods or food sources, that could influence EI; and 3) We must maximize
acceptability of the measurement methods. The proposed study will validate our sensor-based EI estimation
methods among a diverse sample, across three settings (cafeteria, home-based, and free-living), incorporating
minimal user input on foods and beverages (e.g., high energy density foods, zero calorie beverages) and
contexts (e.g., food source, time of day), and using two different sensors (commercial smartwatch and smart
ring). We will conduct two controlled data collections in which a single meal is video recorded while participants
wear the smartwatch and smart ring: N=300 in a cafeteria and N=240 in participant homes. All participants
(N=540) will then wear both devices and complete remote food photography during 4 days of everyday life
(free living). We will evaluate sensor-based estimates of EI against ground truth captured using video (cafeteria
and home) and remote food photography method (free-living). We will use our findings to create a practical
platform to guide researchers/clinicians implementing a sensor-based EI self-monitoring protocol that
maximizes accuracy and acceptability (selecting wrist vs. ring sensor, type of user input, and length of self-
monitoring). Our platform will ultimately support work in precision nutrition by transforming how we develop and
evaluate health-related interventions, and ultimately improve the quality of interventions targeting EI.
