现实世界智能手机数据预测缺血性中风和过时缺血性发作症状后的情绪,并可能构成数字终点:一项概念验证研究
Stephanie Zawada1, Jestrii Acosta2, Caden Collins2
1College of Medicine and Science, Mayo Clinic, Scottsdale, AZ.
Mayo Clinic proceedings. Digital health
|August 29, 2025
概括
智能手机的数据可以帮助预测中风患者的抑郁程度. 包括像加速度计和GPS这样的传感器数据, 提高了预测准确度,
科学领域:
- 神经学
- 数字健康
- 行为科学
背景情况:
- 缺血性中风和过渡性缺血性心脏病发作 (IS/ TIA) 可能导致抑郁.
- 客观的行为测量对于评估抑郁症的严重程度至关重要.
- 目前的抑郁症评估方法可能无法长度捕捉现实世界的行为.
研究的目的:
- 评估在IS/TIA患者中使用智能手机进行纵向数据收集的可行性.
- 为了确定智能手机的客观行为测量是否能预测抑郁症的严重程度.
- 为了比较不同智能手机数据组合的预测准确性.
主要方法:
- 使用Beiwe智能手机应用程序对IS/ TIA症状的参与者进行了超过8周的监测.
- 使用患者健康问卷 (PHQ) - 8和蒙哥马利- 阿斯伯格抑郁症评分表 (MADRS) 评估了抑郁症症状.
- 使用线性混合模型分析智能手机传感器数据 (加速度计,GPS) 和调查数据,以预测MADRS得分.
主要成果:
- 智能手机传感器成功捕获了IS/TIA患者的客观行为数据.
- PHQ-8分数与离家距离和在家度过的时间相关.
- 结合加速度计和GPS数据的预测模型在估计抑郁症严重程度方面显示出更高的准确性.
结论:
- 基于智能手机的客观行为测量对于IS/TIA患者来说是可行的.
- 包括被动传感器数据可以更好地预测抑郁症的严重程度.
- 这种方法可以促进IS/TIA患者的远程抑郁查和监测.
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