基于多层感知子的可穿戴设备与运动相关的心率变化预测大学生焦虑和抑郁症
Xiongfeng Li1, Limin Zou2, Haojie Li3
1Department of Physical Educantion, Xinzhou Normal University, Xinzhou 034000, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
运动期间的心率变化 (HRV) 有效预测大学生焦虑和抑郁. 这种非侵入性生物标志物显示出评估心理健康的前景,尽管得分预测的准确性需要提高.
科学领域:
- 运动医学 运动医学
- 心理学 心理学 心理学
- 生物医学工程 生物医学工程
背景情况:
- 大学生经历了显著的焦虑和抑郁.
- 心率变化 (HRV) 是生理和心理压力的潜在指标.
- 心理健康评估的非侵入性方法越来越重要.
研究的目的:
- 检查运动和恢复HRV与大学生焦虑和抑郁之间的关系.
- 用HRV数据评估多层感知神经网络对这些情绪状态的预测准确度.
主要方法:
- 845名大学生 (18-22岁) 完成了焦虑 (SAS) 和抑郁 (PHQ-9) 尺度.
- 在运动期间和之后收集的HRV数据.
- 多层感知神经网络用于HRV分析和预测.
主要成果:
- HRV预测了各种严重程度的焦虑,准确率为74.9%-89.3%.
- 在重度水平上,HRV预测抑郁症的准确率为82.1%-90.1%.
- 预测R平方值为焦虑的0.62和抑郁的0.41.
结论:
- 运动和恢复期间的HRV是学生焦虑和抑郁症的可行预测指标.
- 需要进一步的研究来提高基于HRV的心理得分预测的准确性.
- 与运动相关的HRV可以作为心理健康的非侵入性生物标志物.
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