区分常见的数字表型和自我报告参数来监测和预测抑郁症:范围审查
Lisa Busshart1, Milica Petrovic1, Rebeka Amin1
1Department of Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Research Center of German Foundation for Depression and Suicide Prevention, Heinrich-Hoffmann-Str. 10, Frankfurt am Main, 60528, Germany, 49 162 9464667.
JMIR mHealth and uHealth
|March 3, 2026
概括
本范围审查确定了监测抑郁症的关键数字表型参数. 它综合了基于传感器和自我报告的数据的证据,突出了11个经常用于更好的数字心理健康工具的指标.
科学领域:
- 数字健康数字健康
- 心理健康技术 心理健康技术
- 计算精神病学是一种计算精神病学.
背景情况:
- 数字健康干预越来越多地用于抑郁症自我管理.
- 这些工具利用自我监控和被动传感器数据来提供个性化的反.
- 在了解哪些数字参数最好监测和预测抑郁症结果方面存在差距.
研究的目的:
- 识别和合成常见的数字表型和自我报告参数,用于抑郁症监测和预测.
- 解决关于频繁使用和预测基于传感器和自我报告数据的知识差距.
- 在数字平台上绘制共同的参数,以跟踪抑郁症状的变化.
主要方法:
- 在四个主要数据库 (PubMed,Embase,Cochrane图书馆,Web of Science) 进行了范围审查.
- 包括2021年1月至2025年11月期间发表的文章,重点关注使用数字表型识别的抑郁症成年人.
- 采用了PRISMA-ScR指导方针和五阶段框架,使用Downs和Black仪器和MMAT进行质量评估.
主要成果:
- 包括19项涉及85,193名参与者的研究,主要使用基于智能手机/可穿戴设备的工具和被动传感.
- 确定了五个参数类别:身体活动/位置,行为模式,生理信号,睡眠和社交能力/自我报告.
- 11个关键指标,包括步数,心率变化,睡眠时间和情绪自我评价,最常被报告.
结论:
- 本综述为抑郁症监测和预测提供了数字参数的新综合,超越了模式特定的审查.
- 它在观察性,预测性和干预性研究中绘制了共享的数字标记,有助于可比性和未来模型开发.
- 研究结果支持设计可扩展的数字心理健康工具,并将数字表型化纳入临床实践.
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