在使用可穿戴睡眠和昼夜节律特征的情绪障碍患者中准确预测情绪发作
Dongju Lim1,2, Jaegwon Jeong3,4, Yun Min Song1,2
1Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea.
NPJ digital medicine
|November 18, 2024
概括
现在,仅使用可穿戴设备的睡眠和清醒数据,就可以预测患者的情绪发作. 这种方法,专注于睡眠模式和昼夜节律,增强情绪障碍管理.
科学领域:
- 数字健康与睡眠科学 数字健康与睡眠科学
- 计算精神病学是一种计算精神病学.
- 时间生物学 时间生物学
背景情况:
- 可穿戴设备提供被动数据收集 (睡眠,心率,步骤) 用于情绪障碍患者监测.
- 现有的情绪预测模型通常需要不同的数据类型,这阻碍了实际应用.
- 需要可访问,数据效率高的方法来预测情绪发作.
研究的目的:
- 开发和验证仅使用睡眠和清醒数据来预测情绪情节的预测模型.
- 评估睡眠和昼夜节律特征在预测抑郁,躁狂和低躁狂发作中的有效性.
- 探索昼夜阶段变化和情绪事件发生之间的关系.
主要方法:
- 数学建模应用于168名情绪障碍患者的纵向数据.
- 从可穿戴设备和智能手机衍生的睡眠和清醒数据中提取36个睡眠和昼夜节律特征.
- 训练模型对个人的睡眠和清醒史和过去情绪事件记录进行训练.
主要成果:
- 精确预测抑郁症 (AUC:0.80),躁狂症 (AUC:0.98) 和低躁狂症 (AUC:0.95) 情节的第二天.
- 每天的昼夜阶段变化成为关键预测因素:与抑郁症相关的延迟,躁狂的进展.
- 这些模型有效地利用睡眠和清醒数据以及先前的情绪情节史进行预测.
结论:
- 睡眠和清醒数据,当与患者病史相结合时,提供了一种可靠的方法来预测情绪发作.
- 循环节律的动态,特别是相位变化,是即将发生情绪发作的重要指标.
- 这种方法提供了一个有希望的,数据效率高的战略,通过早期预测来增强情绪障碍管理.
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