Clinical feasibility of a wearable, conformable sensor patch to monitor motor symptoms in Parkinson's disease. (April 2019) - Boroojerdi, Babak; Ghaffari, Roozbeh; Mahadevan, Nikhil; Markowitz, Michael; Melton, Katie; Morey, Briana; Otoul, Christian; Patel, Shyamal; Phillips, Jake; Sen-Gupta, Ellora; Stumpp, Oliver; Tatla, Daljit; et al.
Abstract: Introduction: Clinical assessment of motor symptoms in Parkinson's disease (PD) is subjective and may not reflect patient real-world experience. This two-part pilot study evaluated the accuracy of the NIMBLE wearable biosensor patch (containing an accelerometer and electromyography sensor) to record body movements in clinic and home environments versus clinical measurement of motor symptoms. Methods: Patients (Hoehn & Yahr 2–3) had motor symptom fluctuations and were on a stable levodopa dose. Part 1 investigated different sensor body locations (six patients). In Part 2, 21 patients wore four sensors (chest, and most affected side of shin, forearm and back-of-hand) during a 2-day clinic- and 1-day home-based evaluation. Patients underwent Unified Parkinson's Disease Rating Scale assessments on days 1–2, and performed pre-defined motor activities at home on day 3. An algorithm estimated motor-symptom severity (predicted scores) using patch data (in-clinic); this was compared with in-clinic motor symptom assessments (observed scores). Results: The overall correlation coefficient between in-clinic observed and sensor algorithm-predicted scores was 0.471 ( p = 0.031). Predicted and observed scores were identical 45% of the time, with a predicted score within a ±1 range 91% of the time. Exact accuracy for each activity varied, ranging from 32% (pronation/supination) to 67% (rest-tremor-amplitude). Patients rated the patch easy-to-use and as providing valuable data for managing PD symptoms. Overall patch-adhesion success was 97.2%. The patch was safe and generally well tolerated. Conclusions: This study showed a correlation between sensor algorithm-predicted and clinician-observed motor-symptom scores. Algorithm refinement using patient populations with greater symptom-severity range may potentially improve the correlation. Highlights: Wearable, adhesive NIMBLE patch contains accelerometer and electromyograph sensors. NIMBLE patches can collect objective motor data in clinic and real-world settings. NIMBLE data correlate with observed Parkinson's disease motor symptom severity. NIMBLE data have potential to assist clinician management of Parkinson's disease. NIMBLE data might improve patient engagement with their treatment.