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

基本信息

批准号：
10452494
负责人：
CYNTHIA M BULIK
金额：
$ 69.57万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-23 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10452494
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

项目摘要

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.

项目概要/摘要神经性贪食症 (BN) 和暴食症 (BED) 会中断生命并与重大疾病相关损害。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用于 1000 个 Apple Watch 的恢复记录，我们建议优化两个数据域：在 30 天内收集了 1000 名神经性贪食症 (BN) 或暴食症 (BED) 患者的数据。该提案增强了一项由 NIMH（唾液）支持的家长研究 [暴食遗传学倡议 (BEGIN)] 免费 DNA 试剂盒）。我们将通过 Apple 中的本机应用程序收集纵向无源传感器数据使用恢复记录观察和活动有关暴饮暴食、通便、营养、情绪和认知的数据适合Apple Watch。我们将结合基于传感器的自主神经系统测量 (ANS) 活动、体动记录和地理定位，以及主动恢复记录措施来表征现实世界个人在日常生活中更有可能/不太可能暴饮暴食和/或排便的情况。正在申请动态系统分析方法，无论是在个体之间还是在个体内部，我们将识别稳定的、低风险的、和高风险模式，将能够预测向高风险时期的过渡，这些时期预示着即将到来暴饮暴食或清除事件。我们的工作将为超越当前的认知提供实证基础依赖于高风险状态的自我报告（通常是回顾性）的行为治疗方法，将增强对饮食失调调节的理解，并将产生个性化的精准度饮食失调治疗的医学方法。高效可靠的定量表征是开发由自动识别驱动的实时干预措施的重要第一步个体化地过渡到饮食失调行为的高风险期。我们的目标是：1）预测具有被动传感器数据的 BN 或 BED 个体暴食和清除事件的发生情况； 2）测试理论上衍生的暴食和清除的调节模型，反映在暴食和清除的差异上时间模式； 3）通过增强被动数据来提高我们预测高风险状态的能力恢复记录收集的背景因素。该提案优化了丰富度和纵向 BEGIN中收集的深层表型数据的结构为下一步转化奠定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实时进行。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Passive Sensor Data for Characterizing States of Increased Risk for Eating Disorder Behaviors in the Digital Phenotyping Arm of the Binge Eating Genetics Initiative: Protocol for an Observational Study.

DOI：
10.2196/38294
发表时间：
2022-06-02
期刊：
JMIR RESEARCH PROTOCOLS
影响因子：
1.7
作者：
Kilshaw, Robyn E.;Adamo, Colin;Butner, Jonathan E.;Deboeck, Pascal R.;Shi, Qinxin;Bulik, Cynthia M.;Flatt, Rachael E.;Thornton, Laura M.;Argue, Stuart;Tregarthen, Jenna;Baucom, Brian R. W.
通讯作者：
Baucom, Brian R. W.

Sociodemographic and clinical characteristics of treated and untreated adults with bulimia nervosa or binge-eating disorder recruited for a large-scale research study.

DOI：
10.1186/s40337-023-00846-4
发表时间：
2023-07-31
期刊：
JOURNAL OF EATING DISORDERS
影响因子：
4.1
作者：
Carrino, Emily A. A.;Flatt, Rachael E. E.;Pawar, Pratiksha S. S.;Sanzari, Christina M. M.;Tregarthen, Jenna P. P.;Argue, Stuart;Thornton, Laura M. M.;Bulik, Cynthia M. M.;Watson, Hunna J. J.
通讯作者：
Watson, Hunna J. J.