Clinical feasibility of a wearable, conformable sensor patch to monitor motor symptoms in Parkinson's disease

Babak Boroojerdi*, Roozbeh Ghaffari, Nikhil Mahadevan, Michael Markowitz, Katie Melton, Briana Morey, Christian Otoul, Shyamal Patel, Jake Phillips, Ellora Sen-Gupta, Oliver Stumpp, Daljit Tatla, Dolors Terricabras, Kasper Claes, John A. Wright, Nirav Sheth

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)70-76
Number of pages7
JournalParkinsonism and Related Disorders
Volume61
DOIs
StatePublished - Apr 2019

Funding

The authors thank the participants who contributed to the study. The authors thank Jeffrey B. Model (former employee of MC10, Inc.) for his contribution towards study concept and design, data collection and site support during the study, The authors acknowledge Nicole Meinel (Evidence Scientific Solutions, London, UK) and Meryl Mandle (Evidence Scientific Solutions, Philadelphia, PA) for writing assistance, which was funded by UCB Pharma (Brussels, Belgium). The authors acknowledge Elisabeth Dohin MD (UCB Pharma, Brussels, Belgium) for scientific and medical input into the data analyses and interpretation.The study was funded by UCB Pharma, Brussels, Belgium. Authors employed by UCB Pharma were involved in conduct of the research; in study design; in the collection, analysis, and interpretation of data; in writing the report; and preparation of the article and the decision to submit the article for publication.

Keywords

  • Actigraphy/instrumentation
  • Bio-sensing techniques/instrumentation
  • Outcomes
  • Parkinson's disease
  • Quantitative motor assessment
  • Wearable devices

ASJC Scopus subject areas

  • Neurology
  • Geriatrics and Gerontology
  • Clinical Neurology

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