通过可穿戴传感器记录的睡眠障碍可以预测9年后抑郁症的严重程度
Nur Hani Zainal1, Peter F Hitchcock2
1National University of Singapore, Department of Psychology, Kent Ridge Campus, Singapore.
Journal of affective disorders
|October 17, 2025
概括
穿戴设备检测到的睡眠障碍可以在9年内预测主要抑郁症 (MDD) 症状. 这一发现表明,整合睡眠传感器可能会改善MDD预防和治疗策略.
科学领域:
- 精神病学和行为科学 精神病学和行为科学
- 计算神经科学是一种神经科学.
- 睡眠医学 睡眠医学
背景情况:
- 重度抑郁症 (MDD) 是一种普遍存在的疾病,具有重大公共卫生后果.
- 自主神经系统 (ANS) 失调和睡眠障碍被认为是MDD的潜在长期风险因素.
- 之前的研究受到了截面设计和次优方法的限制,阻碍了准确的时间推断和预测.
研究的目的:
- 利用机器学习 (ML) 在9年的时间内识别MDD症状的生理和睡眠预测因素.
- 通过使用先进的分析技术和全面的数据集来克服先前研究的局限性.
主要方法:
- 一组1054名社区成年人接受了基线评估,包括使用可穿戴设备进行心电图 (ECG) 和睡眠动图.
- 在基线和9年随访期间进行了临床采访,以评估精神症状.
- 八个ML模型使用81个基线变量进行训练,以预测9年的MDD严重程度,采用70-30训练测试分割和5倍交叉验证.
主要成果:
- 性能最高的模型,一个梯度增强机器,使用10个变量实现了强大的预测准确性 (R2 = 19.8%).
- 基线MDD,通用焦虑和恐慌症症状是9年MDD严重程度的显著预测因素.
- 关键的睡眠相关性包括更长的总睡眠时间,更低的睡眠效率,以及在睡眠阶段增加的觉醒时间. 可穿戴式心电图数据提供了有限的额外预测价值.
结论:
- 通过可穿戴动作图识别的睡眠障碍是长期MDD症状的显著预测因素,独立于基线严重程度和ANS指标.
- 将被动睡眠监测纳入常规临床评估中,可以提高MDD的预防和治疗.
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