期刊
DEPRESSION AND ANXIETY
卷 36, 期 1, 页码 72-81出版社
WILEY
DOI: 10.1002/da.22822
关键词
ambulatory; classification; depression; geographic positioning systems; mobile health (mHealth); monitoring; passive data collection; smartphones
资金
- National Institute of Mental Health [R34MH100466, T32MH073553]
- NATIONAL INSTITUTE OF MENTAL HEALTH [T32MH018261, T32MH073553, R34MH100466] Funding Source: NIH RePORTER
Background Smartphones provide a low-cost and efficient means to collect population level data. Several small studies have shown promise in predicting mood variability from smartphone-based sensor and usage data, but have not been generalized to nationally recruited samples. This study used passive smartphone data, demographic characteristics, and baseline depressive symptoms to predict prospective daily mood. Method Daily phone usage data were collected passively from 271 Android phone users participating in a fully remote randomized controlled trial of depression treatment (BRIGHTEN). Participants completed daily Patient Health Questionnaire-2. A machine learning approach was used to predict daily mood for the entire sample and individual participants. Results Sample-wide estimates showed a marginally significant association between physical mobility and self-reported daily mood (B = -0.04, P < 0.05), but the predictive models performed poorly for the sample as a whole (median R-2 similar to 0). Focusing on individuals, 13.9% of participants showed significant association (FDR 0.10) between a passive feature and daily mood. Personalized models combining features provided better prediction performance (median area under the curve [AUC] > 0.50) for 80.6% of participants and very strong prediction in a subset (median AUC > 0.80) for 11.8% of participants. Conclusions Passive smartphone data with current features may not be suited for predicting daily mood at a population level because of the high degree of intra- and interindividual variation in phone usage patterns and daily mood ratings. Personalized models show encouraging early signs for predicting an individual's mood state changes, with GPS-derived mobility being the top most important feature in the present sample.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据