Project Details
Description
Abstract
Significance: Binge-eating behavior is characterized by the consumption of a large amount of food and
accompanied by the subjective experience of loss of control. Cognitive Behavioral Therapy has been shown to
be effective, but CBT and other options for binge eating are limited because treatment is not effective in all
patients, and because of high rates of relapse and attrition. A key reason for the limited effectiveness of
treatments is restrictive eating following a binge-eating episode, which is a good opportunity to deploy CBT
strategies designed to encourage a return to a normal eating schedule.
Hypothesis: It is hypothesized that a smartwatch app can be designed for use in treating binge eating disorder
using just-in-time adaptive interventions (JITAIs). Smartwatches with sophisticated motion sensors and
capable of deploying powerful machine learning algorithms can be trained to passively detect not only eating,
but the qualities that differentiate an individual’s binge eating behavior from his/her normal eating behavior.
CBT strategies can then be surfaced to the user after a binge eating episode to encourage the patient to
resume healthy eating patterns. Moreover, the device can be used to obtain an objective report of binge eating
episodes, as well as identify each user’s patterns of antecedents to the binge episodes.
Preliminary Data: The investigative team has trained a machine learning model capable of detecting eating in
free living situations, showing 23/23 accurately detected eating sessions in 60 hours of data captured across 7
participants. Further, it shows that two binge-eating sessions had differentiated characteristics in rate and
duration of eating from the normal eating sessions. The team also has deployed adaptive interventions
successfully in several projects relating to problematic eating. Patient and clinician survey respondents agree
the concept could be useful in averting binge-eating episodes.
Specific Aim 1: The eating detection model will be improved and validated using data from patients who
routinely binge eat. After this validation, it will be deployed across binge eating patients to determine the
identifying characteristics of binge eating. Specific Aim 2: Develop the smartwatch app as a JITAI system for
delivering CBT
, with consultation from expert clinicians and end users.
Specific Aim 3: The team will conduct
an initial feasibility, usability and acceptability test of the HabitAware device to determine next steps and
progress to a Phase II proposal.
Long-Term Goal: After developing passive means of identifying binge eating, we will study the antecedents to
binge episodes in a Phase II, which would allow us to predict binge episodes further in advance and divert the
patient away from the harmful behavior. We will also extend the app to share data with a clinician.
Status | Active |
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Effective start/end date | 8/1/23 → 7/31/24 |
Funding
- National Institute of Mental Health: $420,178.00
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