Measuring Daily Activity Rhythms in Young Adults at Risk of Affective Instability Using Passively Collected Smartphone Data: Observational Study.
Measuring Daily Activity Rhythms in Young Adults at Risk of Affective Instability Using Passively Collected Smartphone Data: Observational Study.
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DOI：
10.2196/33890
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发表时间：
2022-09-14
影响因子：
2.2
通讯作者：
Satterthwaite T
中科院分区：
文献类型：
--
作者：
Ren B;Xia CH;Gehrman P;Barnett I;Satterthwaite T
昼夜节律的刺激与不良的健康结果有关。可以使用被动收集的智能手机数据来量化节律的规律性，以提供常规的临床相关生物标志物。本研究的目的是开发一个度量，以量化的活动节奏的规律性，并探讨常规和情绪之间的关系，以及人口统计学协变量，在门诊精神科队列。来自宾夕法尼亚大学费城儿童医院Lifespan脑研究所和宾夕法尼亚大学门诊精神病学诊所的38名年轻人的智能手机数据被被动感知，并与代表活动和休息状态的2状态连续时间隐马尔可夫模型相拟合。常规的规律性被建模为一天中的时间对状态转换概率的随机效应（即，一天中的时间和状态成员之间的关联）。根据连续时间隐马尔可夫模型计算规律性评分（活动节律指标），并对临床和人口统计学协变量进行回归。规律的活动节律与较长的睡眠时间（P=.009）、年龄（P=.001）和情绪（P=.049）相关。被动感知的活动节律监测是现有指标的替代方案，但不需要繁琐的基于调查的评估。基于智能手机数据的低负担被动感知指标是传统测量的有前途和可扩展的替代方案。
Irregularities in circadian rhythms have been associated with adverse health outcomes. The regularity of rhythms can be quantified using passively collected smartphone data to provide clinically relevant biomarkers of routine. This study aims to develop a metric to quantify the regularity of activity rhythms and explore the relationship between routine and mood, as well as demographic covariates, in an outpatient psychiatric cohort. Passively sensed smartphone data from a cohort of 38 young adults from the Penn or Children’s Hospital of Philadelphia Lifespan Brain Institute and Outpatient Psychiatry Clinic at the University of Pennsylvania were fitted with 2-state continuous-time hidden Markov models representing active and resting states. The regularity of routine was modeled as the hour-of-the-day random effects on the probability of state transition (ie, the association between the hour-of-the-day and state membership). A regularity score, Activity Rhythm Metric, was calculated from the continuous-time hidden Markov models and regressed on clinical and demographic covariates. Regular activity rhythms were associated with longer sleep durations (P=.009), older age (P=.001), and mood (P=.049). Passively sensed Activity Rhythm Metrics are an alternative to existing metrics but do not require burdensome survey-based assessments. Low-burden, passively sensed metrics based on smartphone data are promising and scalable alternatives to traditional measurements.