Technology Assisted Treatment for Binge Eating Behavior

暴食行为的技术辅助治疗

• 批准号: 10603975
• 负责人: Sameer Kumar
• 金额: $ 42.02万
• 依托单位: HABITAWARE, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10603975
• 关键词: Adult; Agreement; Algorithms; Behavior; Binge Eating; Binge eating disorder; Bulimia; Characteristics; Cognitive Therapy; Consultations; Consumption; Data; Detection; Devices; Diagnostic; Eating; Eating Behavior; Eating Disorders; Ecological momentary assessment; Evidence based intervention; Feedback; Focus Groups; Food; Goals; Hand; Healthy Eating; Hour; Impairment; Individual; Intervention; Label; Location; Maintenance; Medical; Modeling; Morbid Obesity; Motivation; Movement; National Institute of Mental Health; Obesity; Outcome; Participant; Patient Self-Report; Patients; Pattern; Persons; Phase; Process; Protocols documentation; Psychopathology; Public Health; Randomized, Controlled Trials; Relapse; Reporting; Respondent; Running; Schedule; Self Administration; Stimulus; Strategic Planning; Surface; Surveys; System; Systems Development; Techniques; Technology; Testing; Therapeutic; Therapeutic Intervention; Time; Training; Treatment Effectiveness; Treatment outcome; Validation; Video Recording; Wrist; acceptability and feasibility; adaptive intervention; anorexia nervosa binge-purge subtype; assessment application; comorbidity; data sharing; design; detection platform; dietary restriction; efficacy study; experience; feasibility testing; feeding; improved; innovation; machine learning algorithm; machine learning model; motion sensor; novel; novel strategies; patient engagement; pilot trial; psychosocial; restrictive eating; sensor; smart watch; success; tool; treatment strategy; usability; week trial

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.

摘要