PROJECT SUMMARY/ABSTRACT
Binge eating, characterized by eating a large amount of food in a short period of time accompanied by
a sense of loss of control over eating, is a public health crisis. Negative affect is a well-established antecedent
for binge eating. The affect regulation model of binge eating posits that elevated negative affect increases
momentary risk for binge eating, as engaging in binge eating alleviates negative affect and reinforces the
behavior. The field’s existing capacity to identify moments of elevated negative affect, and thus risk for binge
eating, has largely relied on ecological momentary assessment (EMA). EMA involves the completion of
surveys in real time on one’s smartphone to report behavioral, cognitive, and emotional symptoms throughout
the day. Although EMA provides ecologically valid information about daily experiences, EMA surveys are often
delivered only 5-6 times per day, involve self-report of affect intensity, and are unable to assess physiological
arousal that accompanies affect. Wearable, psychophysiological sensors that measure markers of affect
arousal including heart rate, heart rate variability, and electrodermal activity, may augment EMA surveys to
improve our capacity to accurately detect risk for binge eating in real time. These sensors can objectively,
continuous, and passively measure biomarkers of nervous system arousal that coincide with affect, thus
allowing them to measure affective trajectories on a continuous timescale, detect changes in negative affect
before the individual is consciously aware of them, and reduce user burden to improve data completeness.
Despite their potential to improve the field’s capacity to detect risk for binge eating, the feasibility and
acceptability of these sensors among individuals with binge eating has not yet been established. Additionally, it
is unknown whether features extracted from these sensors can adequately distinguish between positive and
negative affect states, given that physiological arousal may occur during both negative and positive affect
states. The aims of the present study are: 1) test the hypothesis that sensor features will distinguish positive
and negative affect states in individuals with binge eating with > 60% accuracy; 2) test the hypothesis that a
machine learning algorithm using sensor data and EMA-reported negative affect data to predict the occurrence
of binge eating episodes will predict binge eating with greater accuracy than an algorithm using EMA-reported
negative affect alone; 3) use a mixed methods approach to evaluate acceptability and feasibility of wearable
sensors among individuals with binge eating. To do so, the present study will recruit 30 individuals with
clinically-significant binge eating who will wear Empatica E4 wristbands to passively measure heart rate and
electrodermal activity and report affect and binge eating on EMA surveys for four weeks. Participants with
binge eating (N = 30) and community eating disorder clinicians (N = 10) will also complete self-report
measures and focus groups to assess the feasibility, acceptability, and user preferences regarding the use of
sensors to power improved momentary interventions for binge eating.
