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.
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.
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DOI：
10.2196/38294
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发表时间：
2022-06-02
影响因子：
1.7
通讯作者：
Baucom, Brian R. W.
中科院分区：
文献类型：
--
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.
可以通过手机和其他数字设备轻松、高效、安全地收集的数据具有巨大的临床应用潜力。在这里，我们关注的是如何使用这些数据来完善和增强对暴食症（BED）和神经性贪食症（BN）的干预策略，这些疾病缺乏高效、持久和可获得的治疗方法。这些数据很容易以数字方式收集，但非常复杂，并提出了独特的方法挑战，需要创新的解决方案。我们描述了暴饮暴食遗传学计划的数字表型组成部分，该计划使用个人数字设备数据来捕捉暴饮暴食和清除发作的动态风险模式。特征数据签名最终将用于开发饮食失调病理的个性化模型和及时干预，以降低相关行为的风险。在这里，我们将重点介绍用于准备分析数据的方法，并讨论如何将这些方法推广到当前应用程序之外。北卡罗来纳大学生物医学机构审查委员会批准了所有研究程序。符合BED或BN诊断标准的参与者通过iphone和Apple watch上的Recovery Record应用程序提供了对饮食行为和感受的实时评估。在30天的时间里，研究人员从苹果手表上收集了生理活动（心率）和身体活动（步数）的连续被动测量数据。清理数据以解释用户和设备记录错误，包括重复条目和不可靠的心率和步长值。在参与者中，根据数据源的不同，在清理期间删除的数据点比例从<0.1%到2.4%不等。为了准备多变量时间序列分析的数据，我们使用了一种新的数据处理方法来处理数据源和设备之间的可变测量频率。这包括将心率、步数、感觉评级和饮食失调行为映射到同时的分钟级时间序列上，从而能够表征暴食和排空发作前后个人和群体层面的调节动态。已完成数据收集和清理。在2017年8月至2021年5月期间，1019名参与者提供了平均25天的数据，产生了3,419,937个心率值，1,635,993个步数，8274个狂欢或清除事件，以及85,200个感觉观察。分析将于2022年春季开始。我们提供了用于收集、清理和准备个人数字设备数据的方法的详细描述，这些数据来自一项大型纵向饮食失调研究的一个组成部分。结果将确定暴食和净化事件风险增加的数字签名，最终可能用于为BED和BN创建数字干预措施。我们的目标是提高个人数字设备数据处理和分析的透明度。ClinicalTrials.gov NCT04162574;https://clinicaltrials.gov/ct2/show/NCT04162574 derr1 - 10.2196/38294
Data that can be easily, efficiently, and safely collected via cell phones and other digital devices have great potential for clinical application. Here, we focus on how these data could be used to refine and augment intervention strategies for binge eating disorder (BED) and bulimia nervosa (BN), conditions that lack highly efficacious, enduring, and accessible treatments. These data are easy to collect digitally but are highly complex and present unique methodological challenges that invite innovative solutions. We describe the digital phenotyping component of the Binge Eating Genetics Initiative, which uses personal digital device data to capture dynamic patterns of risk for binge and purge episodes. Characteristic data signatures will ultimately be used to develop personalized models of eating disorder pathologies and just-in-time interventions to reduce risk for related behaviors. Here, we focus on the methods used to prepare the data for analysis and discuss how these approaches can be generalized beyond the current application. The University of North Carolina Biomedical Institutional Review Board approved all study procedures. Participants who met diagnostic criteria for BED or BN provided real time assessments of eating behaviors and feelings through the Recovery Record app delivered on iPhones and the Apple Watches. Continuous passive measures of physiological activation (heart rate) and physical activity (step count) were collected from Apple Watches over 30 days. Data were cleaned to account for user and device recording errors, including duplicate entries and unreliable heart rate and step values. Across participants, the proportion of data points removed during cleaning ranged from <0.1% to 2.4%, depending on the data source. To prepare the data for multivariate time series analysis, we used a novel data handling approach to address variable measurement frequency across data sources and devices. This involved mapping heart rate, step count, feeling ratings, and eating disorder behaviors onto simultaneous minute-level time series that will enable the characterization of individual- and group-level regulatory dynamics preceding and following binge and purge episodes. Data collection and cleaning are complete. Between August 2017 and May 2021, 1019 participants provided an average of 25 days of data yielding 3,419,937 heart rate values, 1,635,993 step counts, 8274 binge or purge events, and 85,200 feeling observations. Analysis will begin in spring 2022. We provide a detailed description of the methods used to collect, clean, and prepare personal digital device data from one component of a large, longitudinal eating disorder study. The results will identify digital signatures of increased risk for binge and purge events, which may ultimately be used to create digital interventions for BED and BN. Our goal is to contribute to increased transparency in the handling and analysis of personal digital device data. ClinicalTrials.gov NCT04162574; https://clinicaltrials.gov/ct2/show/NCT04162574 DERR1-10.2196/38294
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影响因子：
5.2
作者：
Torous J;Kiang MV;Lorme J;Onnela JP
通讯作者：
Onnela JP
影响因子：
7.4
作者：
Saeb S;Zhang M;Karr CJ;Schueller SM;Corden ME;Kording KP;Mohr DC
通讯作者：
Mohr DC
影响因子：
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作者：
Eagle, Nathan;Pentland, Alex (Sandy)
通讯作者：
Pentland, Alex (Sandy)
DOI：
10.1146/annurev-clinpsy-032816-044949
发表时间：
2017-05-08
影响因子：
18.4
作者：
Mohr DC;Zhang M;Schueller SM
通讯作者：
Schueller SM
影响因子：
4.4
作者：
Bulik, Cynthia M.;Butner, Jonathan E.;Deboeck, Pascal R.
通讯作者：
Deboeck, Pascal R.