Key result
Smartwatch PPG continuous tracking detects post-stroke anxiety with ~0.86 AUC accuracy.
Why the study?
Post-stroke anxiety affects 20-30% of stroke survivors, but traditional emotion assessment relies on subjective self-reports that fail to capture real-time fluctuations.
Does a smartwatch-based PPG emotion classifier accurately track continuous anxiety in post-stroke patients compared to GAD-7 scores?
Observational
Does a smartwatch-based PPG emotion classifier accurately track continuous anxiety in post-stroke patients compared to GAD-7 scores?
Effect estimate: AUC 0.859 (95% CI 0.664-0.992)
p-value: p=0.0086
A smartwatch-based self-supervised learning model using PPG signals can feasibly and continuously track anxiety in post-stroke patients, demonstrating good discrimination compared to standard GAD-7 surveys.
Should not yet change post-stroke anxiety assessment; leaves open prospective validation of wearable PPG classifiers.
Post-stroke anxiety (PSA) affects 20-30% of stroke survivors and significantly impacts quality of life (QoL) and rehabilitation outcomes. Traditional emotion assessment relies on subjective self-reports, limiting to capture real-time fluctuations in emotional states. This study proposes a self-supervised learning (SSL) framework combined with a transformer-based emotion classifier to enable continuous anxiety tracking in patients with stroke using smartwatch-derived photoplethysmography (PPG). The SSL model was pretrained on the VitalDB dataset using R-peak-to-peak intervals (RRIs) extracted from electrocardiography (ECG) signals. The pretrained encoder is then integrated into a transformer-based classifier trained on the Psychophysiology of Positive and Negative Emotions (POPANE) dataset with labeled emotional responses. The trained model was applied to smartwatch-derived pulse peak intervals (PPI) from patients with stroke, and evaluated against Generalized Anxiety Disorder-7 (GAD-7) scores and we evaluated it against GAD-7 scores using both group-level and 90-day longitudinal analyses, along with on-device feasibility on a Galaxy Watch6. Over the 30 days preceding the second survey, between-group differences were significant by Welch's t-test (p = 0.0086), and discrimination reached an AUC of 0.859 with a 95% confidence interval of 0.664-0.992. In 90-day monitoring, generalized estimating equations showed persistent divergence when groups were defined by the second survey, with significant differences across multiple weeks preceding the survey, consistent with the retrospective GAD-7 window. These findings indicate that ECG-pretrained cardiac representations can be translated to smartwatch PPG for unobtrusive, continuous anxiety tracking in stroke. The results provide feasibility-level evidence and motivate larger, multi-site prospective validation toward clinical deployment.
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Choi et al. (2025) conducted an observational in Stroke. Smartwatch-based continuous anxiety tracking using PPG vs. GAD-7 scores was evaluated on Discrimination of anxiety evaluated against GAD-7 scores (AUC 0.859, 95% CI 0.664-0.992, p=0.0086). A smartwatch-based continuous anxiety tracker using PPG signals successfully discriminated anxiety in stroke patients with an AUC of 0.859 (95% CI 0.664-0.992).
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