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.
Public Health Relevance Statement: Narrative Binge-eating behavior is a central feature of eating disorders (e.g., binge-eating disorder, bulimia nervosa, anorexia nervosa-binge-eating/purging subtype) and is associated with higher rates of psychopathology, psychosocial impairment, medical comorbidity, and more severe obesity, all of which pose significant public health problems. Passive detection of binge eating episodes combined with a context-based reminder intervention could assist a patient in resuming healthy eating patterns. We propose developing and testing a smartwatch app that identifies binge eating behavior and provides CBT strategies to the patient after the episode.
Project Terms: 21+ years old; Adult Human; adulthood; Adult; Algorithms; Behavior; Bulimia; Bulimia Nervosa; bulimic; Cognitive Therapy; Cognition Therapy; Cognitive Psychotherapy; cognitive behavior intervention; cognitive behavior modification; cognitive behavior therapy; cognitive behavioral intervention; cognitive behavioral modification; cognitive behavioral therapy; cognitive behavioral treatment; comorbidity; co-morbid; co-morbidity; Consultations; consultation; Eating; Food Intake; Eating Disorders; Feedback; Focus Groups; Food; Goals; Hand; hands; Maintenance; Motivation; Movement; body movement; Persons; National Institute of Mental Health; NIMH; Obesity; adiposity; corpulence; Morbid Obesity; Severe obesity; extreme obesity; Patients; Psychopathology; abnormal psychology; Public Health; Relapse; Running; Self Administration; Self Administered; Surveys; Survey Instrument; Technology; Testing; Time; Video Recording; Videorecording; video recording system; Wrist; Treatment outcome; Schedule; Treatment Effectiveness; Label; sensor; improved; Surface; Phase; Medical; Training; Stimulus; Individual; Respondent; randomized control trial; Randomized, Controlled Trials; Therapeutic; tool; Diagnostic; Binge Eating; compulsive eating; compulsive feeding; compulsive overeating; dietary restriction; diet restriction; restricted diet; Hour; Protocols documentation; Protocol; Pattern; Techniques; System; Location; psychosocial; Binge eating disorder; experience; success; novel; Participant; Agreement; Patient Self-Report; Self-Report; Devices; Reporting; Therapeutic Intervention; intervention therapy; Modeling; Intervention; Intervention Strategies; interventional strategy; Data; Detection; Strategic Planning; Validation; validations; Characteristics; Process; pilot trial; feeding; designing; design; new approaches; novel approaches; novel strategy; novel strategies; Outcome; Consumption; Eating Behavior; innovate; innovative; innovation; Impairment; Evidence based intervention; usability; week trial; treatment strategy; data sharing; Systems Development; Ecological momentary assessment; Healthy Eating; efficacy study; smartwatch; smart watch; participant engagement; patient engagement; machine learned algorithm; machine learning based algorithm; machine learning algorithm; adaptive intervention; motion sensor; restrictive eating; anorexia nervosa binge eating-purging subtype; anorexia nervosa binge-purge subtype; acceptability and feasibility; detection system; detection platform; feasibility testing; machine learning model; machine learning based model; assessment application; assessment app
