评估大规模数字感应抑郁和焦虑的可行性:数字心理健康研究
medRxiv : the preprint server for health sciences
|November 19, 2025
概括
使用智能手机数据的数字表型化提供了对心理健康的客观见解. 数字心理健康研究 (DMHS) 显示了参与者在收集抑郁和焦虑研究的纵向数据方面的高度参与.
科学领域:
- 数字健康数字健康
- 心理健康研究 心理健康研究
- 可穿戴技术是可穿戴的技术.
背景情况:
- 智能手机和可穿戴设备的被动传感器数据可以量化与抑郁症和焦虑症等心理健康状况相关的生理,行为和情绪状态.
- 用于心理健康评估的数字表型化领域需要强大的纵向研究来证明其实用性.
研究的目的:
- 描述数字心理健康研究 (DMHS) 的设计和实施.
- 评估收集用于心理健康研究的纵向数字表型数据的可行性和实用性.
- 建立使用数字现象型分析复杂和异质抑郁和焦虑症状的方法.
主要方法:
- 收集了多达12个月的iPhone和Apple Watches传感器数据,来自4000多名参与者.
- 开发了一种定期自我报告和基于采访的尺度的协议,以评估抑郁症,焦虑和感知压力.
- 采用了参与者招募,入学和协议开发的策略,优先考虑最小化的测量负担.
主要成果:
- 在DMHS中成功招募并注册了4000多名参与者的多样化样本.
- 在12个月的数据收集期间,参与者表现出高度的参与和坚持.
- 报告了来自数字表型的纵向症状轨迹的初步发现.
结论:
- 数字心理健康研究 (DMHS) 提供了一个有价值的数据集,通过数字表型识别来了解抑郁和焦虑.
- 这项研究证明了被动传感器数据在持续,客观的心理健康监测方面的潜力.
- 未来的研究可以利用DMHS数据来探索心理健康状况的复杂性和异质性.
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