Zawada Stephanie, Acosta Jestrii, Collins Caden, Dumitrascu Oana, Harahsheh Ehab, Hagen Clinton, Ganjizadeh Ali, Mahmoudi Elham, Erickson Bradley, Demaerschalk Bart
College of Medicine and Science, Mayo Clinic, Scottsdale, AZ.
Division of Cerebrovascular Disease, Mayo Clinic, Scottsdale, AZ.
Mayo Clin Proc Digit Health. 2025 Jun 9;3(3):100240. doi: 10.1016/j.mcpdig.2025.100240. eCollection 2025 Sep.
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.
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.
Among enrolled participants (n=54), 35 completed the study (64.8%). PHQ-8 scores were associated with distance from home (=0.173), time spent at home (=-0.147) and PHQ-8 administration duration (=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.
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.
评估使用智能手机纵向收集客观行为指标的可行性，并确定这些指标能够预测缺血性中风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的程度。
在2024年3月1日至2024年11月15日期间，使用“倍未”应用程序在现实环境中对有IS/TIA症状的参与者进行了8周或更长时间的监测。通过每周的患者健康问卷（PHQ）-8调查、每月由工作人员进行的蒙哥马利-Åsberg抑郁评定量表（MADRS）评估以及智能手机传感器测量的每周平均值来跟踪抑郁症状。重复测量相关性确定了PHQ-8评分与客观行为指标之间的关联。为了研究智能手机数据对MADRS评分的预测程度，使用了线性混合模型。
在纳入的参与者（n = 54）中，35人完成了研究（64.8%）。PHQ-8评分与离家距离（= 0.173）、在家时间（= -0.147）和PHQ-8施测持续时间（= 0.151）相关。使用人口统计学数据和最新的PHQ-8评分，仅使用PHQ-8评分时，各模型抑郁严重程度预测的平均均方根误差为1.64，同时纳入加速度计和GPS数据时为1.49，同时纳入PHQ-8施测持续时间时为1.36。
智能手机传感器捕获了IS/TIA患者的客观行为指标。在预测模型中，随着纳入更多智能手机传感器的测量指标，抑郁严重程度评分的准确性有所提高。未来的研究应在更大的队列中验证这种分散式的探索性方法。我们的工作朝着表明利用主动和被动数据进行现实世界监测可对IS/TIA患者进行有效抑郁筛查分诊、并提供数字移动性和反应时间终点迈出了一步。
