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: 10021708
• 负责人: CYNTHIA M BULIK
• 金额: $ 70.72万
• 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/10021708
• 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 神经性贪食症（BN）和暴饮暴食障碍（BED）是生命的，并且与大量相关 损害。通过独特的机会，使我们能够适应广泛使用的基于认知行为的应用程序 恢复记录可在1000台Apple手表上使用，我们建议优化两个数据域 在1000名神经性贪食症（BN）或暴饮暴食（BED）中，收集了30天的时间。 该提案增加了父母研究[暴饮暴食遗传学计划（BEGIN）]，由NIMH支持（唾液 免费提供DNA的套件）。我们将通过Apple中的本机应用收集纵向被动传感器数据 使用恢复记录观看和主动数据 适用于Apple Watch。我们将结合基于传感器的自主神经系统的测量 （ANS）活动，动作法和地理位置以及主动恢复记录措施以表征现实世界 个人在日常生活中狂暴和/或清除的可能性更大。申请 无论跨个体内外，动态系统分析方法都将确定稳定，低风险， 和高风险模式，可以预测过渡到高风险时期，以信号即将来临 狂欢或清除情节。我们的工作将为超越当前认知的经验基础 - 依赖于高风险状态的自我报告（通常回顾性）的行为疗法方法，将 在监管方面增强对饮食失调的理解，并将产生个性化的精度 饮食失调治疗的医学方法。有效且可靠的定量表征是 由自动认可驱动的实时干预措施的基本第一步 个性化的过渡到高风险时期，以实现无序饮食行为。我们的目标是：1）预测 BN或带被动传感器数据的个体的个体中发生暴饮暴食和清除发作； 2） 测试理论上衍生的暴饮暴食和清除的调节模型，如 时间模式； 3）通过使用使用被增强被动数据来提高我们预测高风险状态的能力 通过恢复记录收集的上下文因素。该建议优化了丰富性和纵向 收集的深度表型数据的结构开始为下一个翻译步骤奠定基础 我们将开发个性化的即时干预措施，可以破坏饮食失调的行为 实时发生之前。