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 (=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.

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.