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

项目概要/摘要 神经性贪食症（BN）和暴饮暴食症（BED）是生活中断，并与显着 损伤通过一个独特的机会，使我们能够适应广泛使用的基于认知行为的应用程序， 恢复记录上使用1000苹果手表，我们建议优化两个域的数据， 收集了1000名神经性贪食症（BN）或暴食症（BED）患者30天的数据。 这项建议增加了由NIMH（唾液）支持的母研究[暴食遗传学启动（开始）] 免费提供DNA试剂盒）。我们将通过Apple中的本地应用程序收集纵向无源传感器数据 使用恢复记录观看和活动有关暴饮暴食，清除，营养，情绪和认知的数据 适用于Apple Watch。我们将联合收割机结合自主神经系统的传感器测量 (ANS)活动、活动记录和地理定位，以及用于表征真实的世界的活动恢复记录措施 个人在日常生活中更有可能/不太可能暴饮暴食和/或清除的条件。应用 动态系统分析方法，无论是跨和个人内，我们将确定稳定，低风险， 以及高风险模式，这些模式将能够预测向高风险时期的过渡， 暴饮暴食或清除发作。我们的工作将为超越当前的认知- 依赖于自我报告（通常是回顾性的）高风险状态的行为治疗方法，将 增强对饮食失调的理解，并将产生个性化的精确度。 饮食失调的治疗方法。高效可靠的定量表征是 这是开发实时干预措施的重要第一步， 个体化过渡到饮食失调行为的高风险时期。我们的目标是：1）预测 在具有被动传感器数据的BN或BED个体中暴饮暴食和排泄事件的发生; 2） 测试理论推导的暴饮暴食和净化的调节模型，反映在 时间模式; 3）通过增加被动数据来改善我们预测高风险状态的能力， 恢复记录收集的上下文因素。这一建议优化了丰富性和纵向 开始中收集的深层表型数据的结构，为下一步的翻译步骤奠定基础。 我们将开发个性化的及时干预措施，可以破坏饮食失调行为， 真实的时间。

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.