Babak Boroojerdi1, Roozbeh Ghaffari2, Nikhil Mahadevan3, Michael Markowitz4, Katie Melton5, Briana Morey6, Christian Otoul7, Shyamal Patel8, Jake Phillips9, Ellora Sen-Gupta10, Oliver Stumpp11, Daljit Tatla12, Dolors Terricabras13, Kasper Claes14, John A Wright15, Nirav Sheth16. 1. UCB Pharma, Monheim am Rhein, Germany. Electronic address: Babak.Boroojerdi@ucb.com. 2. MC10 Inc., Lexington, MA, USA. Electronic address: rooz@northwestern.edu. 3. MC10 Inc., Lexington, MA, USA. Electronic address: nmdevan816@gmail.com. 4. UCB Pharma, Raleigh, NC, USA. Electronic address: Michael.Markowitz@ucb.com. 5. UCB Pharma, Raleigh, NC, USA. Electronic address: Katie.Melton@ucb.com. 6. MC10 Inc., Lexington, MA, USA. Electronic address: Briana.Morey@gmail.com. 7. UCB Pharma, Brussels, Belgium. Electronic address: Christian.Otoul@ucb.com. 8. MC10 Inc., Lexington, MA, USA. Electronic address: shyamal.patel@gmail.com. 9. MC10 Inc., Lexington, MA, USA. Electronic address: jphillmatic@gmail.com. 10. MC10 Inc., Lexington, MA, USA. Electronic address: esengupta@mc10inc.com. 11. UCB Medical Devices, Bulle, Switzerland. Electronic address: Oliver.Stumpp@ucb.com. 12. UCB Pharma, Raleigh, NC, USA. Electronic address: Daljit.Tatla@ucb.com. 13. UCB Pharma, Slough, UK. Electronic address: terricabras.dolors@gmail.com. 14. UCB Pharma, Brussels, Belgium. Electronic address: Kasper.Claes@ucb.com. 15. MC10 Inc., Lexington, MA, USA. Electronic address: jwright@mc10.com. 16. MC10 Inc., Lexington, MA, USA. Electronic address: rav_sheth@hotmail.com.
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.
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.
Authors: Diane Stephenson; Robert Alexander; Varun Aggarwal; Reham Badawy; Lisa Bain; Roopal Bhatnagar; Bastiaan R Bloem; Babak Boroojerdi; Jackson Burton; Jesse M Cedarbaum; Josh Cosman; David T Dexter; Marissa Dockendorf; E Ray Dorsey; Ariel V Dowling; Luc J W Evers; Katherine Fisher; Mark Frasier; Luis Garcia-Gancedo; Jennifer C Goldsack; Derek Hill; Janice Hitchcock; Michele T Hu; Michael P Lawton; Susan J Lee; Michael Lindemann; Ken Marek; Nitin Mehrotra; Marjan J Meinders; Michael Minchik; Lauren Oliva; Klaus Romero; George Roussos; Robert Rubens; Sakshi Sadar; Joseph Scheeren; Eiichi Sengoku; Tanya Simuni; Glenn Stebbins; Kirsten I Taylor; Beatrice Yang; Neta Zach Journal: Digit Biomark Date: 2020-11-26
Authors: F Elizabeth Godkin; Erin Turner; Youness Demnati; Adam Vert; Angela Roberts; Richard H Swartz; Paula M McLaughlin; Kyle S Weber; Vanessa Thai; Kit B Beyer; Benjamin Cornish; Agessandro Abrahao; Sandra E Black; Mario Masellis; Lorne Zinman; Derek Beaton; Malcolm A Binns; Vivian Chau; Donna Kwan; Andrew Lim; Douglas P Munoz; Stephen C Strother; Kelly M Sunderland; Brian Tan; William E McIlroy; Karen Van Ooteghem Journal: J Neurol Date: 2021-10-27 Impact factor: 6.682
Authors: Renuka Visvanathan; Damith C Ranasinghe; Kylie Lange; Anne Wilson; Joanne Dollard; Eileen Boyle; Katherine Jones; Michael Chesser; Katharine Ingram; Stephen Hoskins; Clarabelle Pham; Jonathan Karnon; Keith D Hill Journal: J Gerontol A Biol Sci Med Sci Date: 2022-01-07 Impact factor: 6.053