Key result
A smartphone-based digital phenotyping model combining passive sensor data and active survey responses predicted depression severity with a root-mean-squared error of 1.33, improving upon predictions using self-reported scores alone.
Why the study?
Depression is prevalent in patients with cerebrovascular dysfunction, but screening is not routine and is often limited by subjective patient-reported surveys.
Cohort (n=54)
No
Absolute Event Rate: 1.33% vs 1.54%
Passive smartphone sensor data combined with self-reported surveys can accurately predict depression severity in patients recovering from cerebrovascular events, offering a potential digital biomarker for post-stroke depression.
May support smartphone-based objective depression screening post-stroke; hypothesis-generating and requires prospective validation before clinical use.
Though depression is prevalent in patients with cerebrovascular dysfunction, screening for symptoms is not routine and is often limited via subjective patient-reported surveys. Using smartphone sensors, we sought to evaluate the performance of objective behavior measures and self-report surveys at predicting depression severity in patients with cerebrovascular syndromes. Among enrolled participants (n = 54), 35 patients with ischemic stroke or transient ischemic attack symptoms were monitored in real-world settings using the Beiwe app for 8 or more weeks with adequate compliance. Depression symptoms were tracked via weekly Patient Health Questionnaire-8 (PHQ-8) surveys, monthly personnel-administered Montgomery-Asberg Depression Rating Scale (MADRS) assessments, and passive smartphone sensors. Across weeks, several passive measures were significantly associated with PHQ-8 scores. Personnel-assessed depression severity moderately correlated with self-reported scores. To estimate MADRS, we applied linear mixed models using passive data and PHQ-8 scores. Using antecedent PHQ-8 scores and demographic data, average root-mean-squared error (RMSE) for depression severity prediction across models was 1.54 with accelerometer data, 1.40 also including global position system (GPS) data, and 1.33 also including PHQ-8 open survey duration. Though future research should validate this decentralized approach in a larger cohort, real-world monitoring with active and passive data may triage cerebrovascular patients for efficient depression screening and provide novel mobility and response time outcome measures.
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Zawada et al. (2025) conducted a cohort in Cerebrovascular syndromes (n=54). Smartphone-based digital phenotyping (active and passive data) vs. Self-reported PHQ-8 scores alone was evaluated on Root-mean-squared error (RMSE) for predicting MADRS depression severity scores. A smartphone-based digital phenotyping model combining passive sensor data and active survey responses predicted depression severity with a root-mean-squared error of 1.33, improving upon predictions using self-reported scores alone.
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