用智能手环数据筛查大学生心理问题，效果可达七成以上。
Student Mental Health Screening via Fitbit Data Collected During the COVID-19 Pandemic
- 用心率、睡眠等生理数据训练模型，自动识别心理状态
- 焦虑筛查F1达0.79，抑郁和压力也超0.77，表现良好
- 适合关注心理健康监测的高校与数字健康研究者
大学生面临多重压力，导致焦虑和抑郁水平较高。可穿戴设备能提供无感采集的生理数据，用于精神疾病早期发现。然而，现有研究在心理量表、生理模态和时间序列参数方面仍显不足。本研究收集了本校学生在疫情期间的StudentMEH Fitbit数据集，全面评估不同Fitbit模态对抑郁、焦虑和压力的预测能力。结果表明，心率和睡眠等生理特征在筛查中表现优异：焦虑预测F1分数最高达0.79，心率对压力筛查达0.77，睡眠对抑郁筛查达0.78。研究凸显可穿戴设备在持续心理健康监测中的潜力，强调需根据具体心理问题选择最优数据聚合方式与生理模态。
原文摘要 · Abstract (English)
College students experience many stressors, resulting in high levels of anxiety and depression. Wearable technology provides unobtrusive sensor data that can be used for the early detection of mental illness. However, current research is limited concerning the variety of psychological instruments administered, physiological modalities, and time series parameters. In this research, we collect the Student Mental and Environmental Health (StudentMEH) Fitbit dataset from students at our institution during the pandemic. We provide a comprehensive assessment of the ability of predictive machine learning models to screen for depression, anxiety, and stress using different Fitbit modalities. Our findings indicate potential in physiological modalities such as heart rate and sleep to screen for mental illness with the F1 scores as high as 0.79 for anxiety, the former modality reaching 0.77 for stress screening, and the latter modality achieving 0.78 for depression. This research highlights the potential of wearable devices to support continuous mental health monitoring, the importance of identifying best data aggregation levels and appropriate modalities for screening for different mental ailments.
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