Wearable-based digital biomarker provides a valid alternative to traditional clinical measures for post-stroke upper-limb motor recovery

Existing clinical assessments for upper-limb motor rehabilitation post-stroke pose limitations as endpoints for clinical trials. This study aims to develop a wearable-based digital biomarker for assessing motor recovery using accelerometer data collected in naturalistic environments. The study analyzed approximately 23,000 hours of data from 215 participants, including subacute and chronic stroke survivors and healthy individuals. A novel analytical approach decomposed continuous accelerometer data into a lower-level unit of motor behaviors called movement segments, from which key features were extracted and aggregated using a linear mixed-effects model to produce a composite biomarker. The resulting digital biomarker demonstrated excellent interpretability, reliability, concurrent validity, discriminant validity, known-group validity, and responsiveness, enabling a nearly 66% reduction in the required sample size for clinical trials compared to traditional measures. These findings highlight its potential as a low-burden, scalable assessment tool for upper-limb motor recovery, with applications in both clinical trials and routine clinical practice.