现实世界智能手机数据预测缺血性卒中和短暂性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

Mayo Clinic Proceedings. Digital health Pub Date : 2025-06-09 DOI:10.1016/j.mcpdig.2025.100240

Stephanie Zawada PhD, MS , Jestrii Acosta MS , Caden Collins BA , Oana Dumitrascu MD, MS , Ehab Harahsheh MBBS , Clinton Hagen MS , Ali Ganjizadeh MD , Elham Mahmoudi MD , Bradley Erickson MD, PhD , Bart Demaerschalk MD, MSc

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引用次数: 0

摘要

目的评估使用智能手机纵向收集客观行为测量的可行性，并确定其在多大程度上可以预测缺血性卒中和短暂性脑缺血发作（IS/TIA）症状患者的金标准抑郁严重程度。在2024年3月1日至2024年11月15日期间，在现实环境中使用Beiwe应用程序对具有IS/TIA症状的受试者进行8周或更长时间的监测。通过每周患者健康问卷(PHQ)-8调查、每月人员管理的Montgomery -Åsberg抑郁评定量表（MADRS）评估和每周智能手机传感器测量的平均值来跟踪抑郁症状。重复测量相关性建立了PHQ-8得分与客观行为测量之间的关联。为了研究智能手机数据预测MADRS分数的密切程度，使用了线性混合模型。结果入组受试者（n=54）中，35人（64.8%）完成研究。PHQ-8得分与离家距离（r=0.173）、在家时间（r= - 0.147）和PHQ-8给药时间（r=0.151）相关。使用人口统计数据和最新的PHQ-8评分，各模型预测抑郁严重程度的平均均方根误差仅为PHQ-8评分为1.64，同时包括加速度计和GPS数据为1.49，同时包括PHQ-8给药时间为1.36。结论智能手机传感器可捕获IS/TIA患者的客观行为测量。在预测模型中，抑郁严重程度评分的准确性随着额外智能手机传感器测量的纳入而提高。未来的研究应该在更大的队列中验证这种分散的、探索性的方法。我们的工作是向现实世界监测主动和被动数据迈出的一步，可以对is /TIA患者进行有效的抑郁症筛查，并提供数字移动性和响应时间端点。

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Real-World Smartphone Data Predicts Mood After Ischemic Stroke and Transient Ischemic Attack Symptoms and May Constitute Digital Endpoints: A Proof-of-Concept Study

Objective

To assess the feasibility of using smartphones to longitudinally collect objective behavior measures and establish the extent to which they can predict gold-standard depression severity in patients with ischemic stroke and transient ischemic attack (IS/TIA) symptoms.

Patients and Methods

Participants with IS/TIA symptoms were monitored in real-world settings using the Beiwe application for 8 or more weeks during March 1, 2024 to November 15, 2024. Depression symptoms were tracked via weekly Patient Health Questionnaire (PHQ)-8 surveys, monthly personnel-administered Montgomery–Åsberg Depression Rating Scale (MADRS) assessments, and weekly averages of smartphone sensor measures. Repeated measures correlation established associations between PHQ-8 scores and objective behavior measures. To investigate how closely smartphone data predicted MADRS scores, linear mixed models were used.

Results

Among enrolled participants (n=54), 35 completed the study (64.8%). PHQ-8 scores were associated with distance from home (r=0.173), time spent at home (r=−0.147) and PHQ-8 administration duration (r=0.151). Using demographic data and the most recent PHQ-8 scores, average root-mean-squared error for depression severity prediction across models was 1.64 with only PHQ-8 scores, 1.49 also including accelerometer and GPS data, and 1.36 also including PHQ-8 administration duration.

Conclusion

Smartphone sensors captured objective behavior measures in patients with IS/TIA. In predictive models, the accuracy of depression severity scores improved as measures from additional smartphone sensors were included. Future research should validate this decentralized, exploratory approach in a larger cohort. Our work is a step toward showing that real-world monitoring with active and passive data may triage patients with IS/TIA for efficient depression screening and provide digital mobility and response time endpoints.

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来源期刊

Mayo Clinic Proceedings. Digital health Medicine and Dentistry (General), Health Informatics, Public Health and Health Policy

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审稿时长

47 days