Predicting Binge and Purge Episodes from Passive and Active Apple Watch Data Using a Dynamical Systems Approach

使用动态系统方法根据被动和主动 Apple Watch 数据预测狂欢和清除事件

• 批准号: 10215486
• 负责人: CYNTHIA M BULIK
• 金额: $ 70.6万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-23 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10215486
• 关键词: Apple; Apple watch; Autonomic nervous system; Behavior; Behavior Disorders; Behavioral; Binge Eating; Binge eating disorder; Bulimia; Caring; Cognition; Cognitive; Cognitive Therapy; Coping Skills; DNA; Data; Development; Eating Behavior; Eating Disorders; Feeling; Foundations; Funding; Genetic; Genetic study; Goals; Guilt; Impairment; Individual; Interruption; Intervention; Investments; Life; Location; Measurement; Measures; Modeling; Monitor; Moods; National Institute of Mental Health; Parents; Patient Self-Report; Patients; Pattern; Periodicity; Physical activity; Precision therapeutics; Recovery; Regulation; Risk; Saliva; Sampling; Science; Shame; Signal Transduction; Statistical Models; Structure; Testing; Therapeutic; Time; Transcend; Vomiting; Work; actigraphy; base; contextual factors; cost; data streams; dynamic system; evidence base; high risk; negative mood; nutrition; phenotypic data; positive mood; precision medicine; public health relevance; purge; purging behavior; sensor; standard of care; tool

PROJECT SUMMARY/ABSTRACT Bulimia nervosa (BN) and binge eating disorder (BED) are life-interrupting and associated with significant impairment. Via a unique opportunity that allowed us to adapt the widely used cognitive-behavioral based app Recovery Record for use on 1000 Apple Watches, we propose to optimize two domains of data being collected over a 30-day period in 1000 individuals with bulimia nervosa (BN) or binge-eating disorder (BED). This proposal augments a parent study [Binge Eating Genetics INitiative (BEGIN)], supported by NIMH (saliva kits for DNA at no cost). We will collect longitudinal passive sensor data via native applications in the Apple Watch and active data on binge-eating, purging, nutrition, mood, and cognitions using Recovery Record adapted for the Apple Watch. We will combine sensor-based measurements of autonomic nervous system (ANS) activity, actigraphy, and geolocation with active Recovery Record measures to characterize real world conditions under which individuals are more/less likely to binge and/or purge in their daily lives. Applying dynamical systems analytic approaches, both across and within individuals, we will identify stable, low-risk, and high-risk patterns that will enable the prediction of transition to high risk epochs that signal impending binge or purge episodes. Our work will provide an empirical foundation for transcending current cognitive- behavioral therapy approaches that are dependent on self-report (often retrospective) of high risk states, will enhance the understanding of eating disorders in terms of regulation, and will yield a personalized precision medicine approach to eating disorders treatment. Efficient and reliable quantitative characterization is the essential first step in the development of real-time interventions driven by automated recognition of individualized transitions into high-risk periods for disordered eating behaviors. Our aims are: 1) To predict the occurrence of binge eating and purging episodes in individuals with BN or BED with passive sensor data; 2) To test theoretically-derived regulatory models of binge eating and purging as reflected in differences in temporal patterns; and 3) To refine our capacity to predict high risk states by augmenting passive data with contextual factors collected by Recovery Record. This proposal optimizes the richness and longitudinal structure of the deep phenotypic data collected in BEGIN to lay the foundation for the next translational step in which we will develop personalized just-in-time interventions that can disrupt eating disorders behaviors in real time before they occur.

项目摘要/摘要 神经性暴食症(BN)和暴饮暴食障碍(BED)是干扰生活的因素，并与显著的 减损。通过一个独特的机会，使我们能够调整广泛使用的基于认知行为的应用程序 在1000块Apple Watch上的恢复记录，我们建议优化两个数据域 在30天内收集了1000名神经性暴食症(BN)或暴饮暴食障碍(BED)患者的数据。 这项建议增加了一项由NIMH(唾液)支持的父母研究[暴食遗传学倡议(Begin)] 免费的DNA试剂盒)。我们将通过Apple中的本机应用程序收集纵向被动传感器数据 使用恢复记录观看和活跃关于暴食、通便、营养、情绪和认知的数据 为Apple Watch改编的。我们将结合基于传感器的自主神经系统测量 (ANS)具有主动恢复记录措施的活动、活动和地理位置，以表征真实世界 个人在日常生活中更多/更不可能暴饮暴食和/或清洗的条件。施药 动态系统分析方法，无论是在个人之间还是在个人内部，我们都将识别稳定、低风险、 以及高风险模式，这将使过渡到预示即将到来的高风险时代的预测成为可能 狂欢或清洗插曲。我们的工作将为超越当前认知提供经验基础-- 依赖于对高风险状态的自我报告(通常是回顾)的行为治疗方法将 加强对饮食失调的理解，并将产生个性化的精确度 饮食失调的医学治疗方法。高效和可靠的定量表征是 开发由自动识别驱动的实时干预的关键第一步 个体化过渡到饮食行为紊乱的高危时期。我们的目标是：1)预测 BN患者或床上被动传感器数据患者暴饮暴食和大便发作的发生；2) 为了测试从理论上得出的暴饮暴食和排泄的调节模型，这反映在 时间模式；以及3)通过增加被动数据来提高我们预测高风险状态的能力 回收记录收集的上下文因素。这一建议优化了丰富性和纵向 Begin中收集的深层表型数据的结构为下一步的翻译奠定了基础 我们将开发个性化的及时干预措施，以扰乱饮食失调行为 在它们发生之前的实时时间。