Remote smartphone monitoring of Parkinson’s disease and individual response to therapy

Abstract

Remote health assessments that gather real-world data (RWD) outside of clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here, we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson's Disease (PD). Within the first six months of study commencement, 960 participants had enrolled and performed at least 5 self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation, or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (AUC=0.8) and correlated with in-clinic evaluation of disease severity (r =0.71; p<1.8×10 -6 ) when compared with motor MDS-UPDRS). Although remote assessment requires careful consideration for accurate interpretation of RWD, our results support the use of smartphones and wearables in objective and personalized disease assessments.RWD offers the opportunity to improve our understanding and management of health and disease outside of the clinical setting. 1 An increasingly popular method for collecting RWD is the use of remote digital assessments that allow frequent sampling and have been used to aid in the diagnosis, treatment, and monitoring of multiple conditions, including atrial fibrillation and diabetes. 2,3 When used in population-based studies, remote assessment can increase understanding of heterogeneity in disease manifestation, the Bas Bloem currently serves as Editor-in-Chief for the Journal of Parkinson's disease, serves on the editorial board of Practical Neurology and Digital Biomarkers, has received honoraria from serving on the scientific advisory board for Zambon, Biogen, UCB and Walk with Path, has

A Smartphone Application as an Exploratory Endpoint in a Phase 3 Parkinson’s Disease Clinical Trial: A Pilot Study

“…In this study, we customized a smartphone research application (mPower) that has been previously used in an observational study of over 15,000 participants [13, 16]. The smartphone application had a set of 7 active tasks (finger tapping, standing still for 30 s, walking for 30 s, saying “aaah” for 10 s, assessments of rest and postural tremor, and a brief spatial memory test) designed to assess domains affected by Parkinson’s disease.…”

Advances in Traumatic Brain Injury Biomarkers

“…One of the shortcomings of current biomarker studies of mTBI is reporting values at a single, and varying, post-injury time-point [ 61 ]. The availability of digital devices provides an opportunity to explore and adopt digital biomarkers as objective quantifiable measures [ 69 , 70 ]. Although an experimental study of intentional falls from a wheelchair among able-bodied young adults using Apple Watch showed poor sensitivity for fall detection [ 71 ], numerous anecdotes of improved outcomes from hard falls due to detection by Apple Watch consistently make the news.…”

Quantitative Digitography Solves the Remote Measurement Problem in Parkinson’s disease

“…In fact, alternating finger tapping has been shown to be the most effective motor predictor for conversion from idiopathic REM sleep behavior disorder to Parkinson's disease, with deficits observed nine years prior to conversion 13 . The effectiveness of differentiating PD from controls has led to the widespread implementation of finger tapping on smart phone battery of assessments for PD 12,[14][15][16][17] . However, smartphone applications are limited to temporal metrics of tapping and position in 2D space of the fingers 15 .…”

“…The effectiveness of differentiating PD from controls has led to the widespread implementation of finger tapping on smart phone battery of assessments for PD 12,[14][15][16][17] . However, smartphone applications are limited to temporal metrics of tapping and position in 2D space of the fingers 15 .…”