Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study.
Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study.
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DOI：
10.2196/jmir.4273
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发表时间：
2015-07-15
影响因子：
7.4
通讯作者：
Mohr DC
中科院分区：
文献类型：
--
作者：
Saeb S;Zhang M;Karr CJ;Schueller SM;Corden ME;Kording KP;Mohr DC
抑郁症是一种常见的，负担沉重的，经常复发的心理健康疾病，经常未被发现和治疗。移动的电话无处不在，并且具有越来越大的传感器补充，这些传感器在监测可能指示抑郁症状的行为模式方面可能有用。本研究的目的是探讨日常生活中的行为标记物的检测使用移动的手机全球定位系统（GPS）和使用传感器，并在确定抑郁症状的严重程度。从一般社区招募了总共40名成年参与者，让他们携带带有传感器数据采集应用程序（Purple Robot）的移动的手机2周。在这些参与者中，28人收到了足够的传感器数据进行分析。在2周的开始，参与者完成了自我报告的抑郁调查（PHQ-9）。行为特征是从GPS定位和手机使用数据中开发和提取的。来自GPS数据的许多特征与抑郁症状的严重程度有关，包括昼夜节律运动（24小时节律的规律性; r=-.63，P=.005），归一化熵（最喜欢的位置之间的移动性; r=-.58，P=.012）和位置方差（GPS移动性独立于位置; r=-.58，P=.012）。手机使用特征、使用持续时间和使用频率也相关（分别为r= 0.54，P= 0.011和r= 0.52，P= 0.015）。使用归一化熵特征和分类器区分有抑郁症状的参与者（PHQ-9评分≥5）和没有抑郁症状的参与者（PHQ-9评分<5），我们实现了86.5%的准确率。此外，使用相同特征估计参与者PHQ-9分数的回归模型获得的平均误差为23.5%。从移动的手机传感器数据中提取的特征，包括GPS和手机使用情况，提供了与抑郁症状严重程度密切相关的行为标记。虽然这些发现必须在具有确认的临床症状的参与者中进行更大规模的研究，但它们表明手机传感器提供了许多临床机会，包括持续监测高危人群，患者负担很小，以及可以提供及时外展的干预措施。
Depression is a common, burdensome, often recurring mental health disorder that frequently goes undetected and untreated. Mobile phones are ubiquitous and have an increasingly large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms. The objective of this study was to explore the detection of daily-life behavioral markers using mobile phone global positioning systems (GPS) and usage sensors, and their use in identifying depressive symptom severity. A total of 40 adult participants were recruited from the general community to carry a mobile phone with a sensor data acquisition app (Purple Robot) for 2 weeks. Of these participants, 28 had sufficient sensor data received to conduct analysis. At the beginning of the 2-week period, participants completed a self-reported depression survey (PHQ-9). Behavioral features were developed and extracted from GPS location and phone usage data. A number of features from GPS data were related to depressive symptom severity, including circadian movement (regularity in 24-hour rhythm; r=-.63, P=.005), normalized entropy (mobility between favorite locations; r=-.58, P=.012), and location variance (GPS mobility independent of location; r=-.58, P=.012). Phone usage features, usage duration, and usage frequency were also correlated (r=.54, P=.011, and r=.52, P=.015, respectively). Using the normalized entropy feature and a classifier that distinguished participants with depressive symptoms (PHQ-9 score ≥5) from those without (PHQ-9 score <5), we achieved an accuracy of 86.5%. Furthermore, a regression model that used the same feature to estimate the participants’ PHQ-9 scores obtained an average error of 23.5%. Features extracted from mobile phone sensor data, including GPS and phone usage, provided behavioral markers that were strongly related to depressive symptom severity. While these findings must be replicated in a larger study among participants with confirmed clinical symptoms, they suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach.