在大学生中检测抑郁症状,使用多模式被动传感数据和光梯度增强机:纵向试点研究
Jessica L Borelli1, Yuning Wang2, Frances Haofei Li1
1Department of Psychological Science, University of California, Irvine, Irvine, CA, United States.
JMIR formative research
|June 3, 2025
概括
使用可穿戴设备的被动感应可以在大学生中以中等准确度检测抑郁症状. 关键指标包括睡眠质量差和错过的移动互动,使早期干预成为可能.
科学领域:
- 数字健康数字健康
- 机器学习 机器学习
- 心理健康 心理健康
背景情况:
- 抑郁症是全球残疾的主要原因,影响高达30%的大学生.
- 早期发现抑郁症状对于及时干预和减轻负面后果至关重要.
- 被动传感技术为持续监测心理健康提供了一种低负担的方法.
研究的目的:
- 在大学生中使用整体机器学习模型检测抑郁症状.
- 为了利用被动传感数据进行心理健康监测.
主要方法:
- 采用了一个整体机器学习模型 (光梯度增强机器).
- 从28名本科生收集了数据,他们使用了Oura戒指,三星智能手表和AWARE移动应用程序.
- 包括睡眠,生理学,运动和屏幕时间在内的被动感应数据在19至22周的时间内收集.
主要成果:
- 增强光梯度的机器模型获得了F1得分0.744和Cohen kappa的0.474.
- 该模型在检测抑郁症状方面表现出中度一致.
- 睡眠质量和错过的移动互动是抑郁症状最重要的预测因素.
结论:
- 被动传感数据有可能实时,低成本检测大学生抑郁症状.
- 这种方法可以促进未来的心理健康预防和干预策略.
- 可穿戴设备和移动传感为监测学生心理健康提供了一个有希望的途径.
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