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 意义：暴饮暴食行为的特征是大量进食， 伴随着失去控制的主观体验。认知行为疗法已被证明 有效，但CBT和其他暴饮暴食的选择是有限的，因为治疗并不是对所有人都有效。 患者，并且因为复发率和磨损率高。有效性有限的一个关键原因是， 在暴饮暴食之后，限制性饮食是一个很好的机会， 旨在鼓励恢复正常饮食计划的策略。 假设：假设智能手表应用程序可以设计用于治疗暴食症 及时适应性干预（JITAIs）。智能手表具有复杂的运动传感器， 能够部署强大的机器学习算法，可以被训练成不仅被动地检测进食， 而是将一个人的暴饮暴食行为与他/她的正常饮食行为区分开来的品质。 CBT策略可以在暴饮暴食事件后向用户呈现，以鼓励患者 恢复健康的饮食习惯。此外，该装置可用于获得暴饮暴食的客观报告 发作，以及识别每个用户的模式的前情，以狂欢发作。 初步数据：调查小组已经训练了一个机器学习模型，能够检测进食情况。 自由生活的情况下，显示23/23准确地检测到饮食会议在60小时的数据捕获的7 参与者此外，它表明，两个暴饮暴食会议有不同的特点， 从正常的进食时间。该团队还部署了适应性干预措施 成功地参与了几个与饮食问题有关的项目。患者和临床医生调查受访者同意 这个概念可能有助于避免暴饮暴食。 具体目标1：进食检测模型将使用以下患者的数据进行改进和验证： 经常暴饮暴食在此验证之后，它将在暴食患者中部署，以确定 确定暴饮暴食的特征具体目标2：将智能手表应用程序开发为JITAI系统， 提供CBT ，咨询专家临床医生和最终用户。 具体目标3：团队将进行 对可识别设备进行初步可行性、可用性和可接受性测试，以确定后续步骤， 第二阶段的提案。 长期目标：在开发出识别暴饮暴食的被动方法后，我们将研究 在第二阶段，这将使我们能够预测狂欢发作进一步提前和转移 患者远离有害行为。我们还将扩展应用程序，与临床医生共享数据。