@Article{信息:doi 10.2196 / / jmir。4273，作者=“Saeb, Sohrab和Zhang, Mi和Karr, Christopher J和Schueller, Stephen M和Corden, Marya E和Kording, Konrad P和Mohr, David C”，标题=“手机传感器与日常生活行为中抑郁症状严重程度的相关性:探索性研究”，期刊=“J医学互联网研究”，年=“2015”，月=“7月”，日=“15”，卷=“17”，数=“7”，页=“e175”，关键词=“抑郁症;移动医疗(移动医疗);日常生活活动;聚类分析;背景:抑郁症是一种常见的、负担重的、经常复发的精神健康障碍，经常未被发现和治疗。移动电话无处不在，并且有越来越多的传感器，这些传感器可能有助于监测可能指示抑郁症状的行为模式。目的:本研究的目的是探讨使用手机全球定位系统(GPS)和使用传感器检测日常生活行为标记，及其在识别抑郁症状严重程度中的应用。方法:从普通社区招募40名成年参与者，携带带有传感器数据采集应用程序(Purple Robot)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。 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. Results: 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{\%}. Conclusions: 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. ", issn="1438-8871", doi="10.2196/jmir.4273", url="//www.mybigtv.com/2015/7/e175/", url="https://doi.org/10.2196/jmir.4273", url="http://www.ncbi.nlm.nih.gov/pubmed/26180009" }