睡眠和活动模式作为精神病学中的跨诊断行为生物标志物:从DeeP-DD研究的纵向观察研究
Dylan Hamitouche1,2, Tihare Zamorano1,3, Youcef Barkat1,4
1Douglas Mental Health University Institute, 6875 LaSalle boulevard, Verdun, QC, H4H 1R3, Canada, 1 514-239-0428.
JMIR formative research
|November 14, 2025
概括
穿戴式传感器Actigraphy在通过将睡眠和活动模式与患者报告的结果相关联来跟踪精神病症状严重程度方面显示出前途. 这些客观措施提供了一个可扩展的临床监测方法,超出了传统的自我报告尺度.
科学领域:
- 精神病学是一个精神病学.
- 数字健康数字健康
- 可穿戴技术可穿戴技术
背景情况:
- 精神病症状评级尺度依赖于自我报告,限制了监测和预测结果.
- 动图提供客观的,高分辨率的行为数据来评估症状波动.
- 之前关于精神病学中动图的研究集中在狭窄的群体上,阻碍了临床应用.
研究的目的:
- 在一个跨诊断样本中,研究动图学衍生的睡眠/活动特征与精神病症状严重程度之间的相关性.
- 在各种时间分辨率上识别临床相关的动图特征.
主要方法:
- 八名门诊患者 (年龄在18-52岁) 的可行性案例系列,使用基于手腕的动图,长达5个月.
- 症状严重程度通过自我和临床医生的评分尺度进行评估.
- 在每日,每周,每月和全日期数据中分析了个体内和个体间的相关性.
主要成果:
- 晚些时候的上升时间与更高的每周PHQ-9和GAD-7分数相关.
- 增加轻度体力活动与较低的每周/每月PHQ-9分数相关.
- 在整个研究期间,增加的久坐活动与较低的GAD-7分数相关.
结论:
- 像起床时间和体力活动这样的动图特征是精神病症状负担的潜在跨诊断标记.
- 跨时间尺度和诊断的一致关联表明临床监测的实用性.
- 需要在更大的队列和先进分析中进一步验证.
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