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

Abstract

BackgroundAssessment of motor signs in Parkinson’s disease (PD) has required an in-person examination. However, 50% of people with PD do not have access to a neurologist. Wearable sensors can provide remote measures of some motor signs but require continuous data acquisition for several days. A major unmet need is reliable metrics of all cardinal motor signs, including rigidity, from a simple short active task that can be performed remotely or in the clinic.ObjectiveInvestigate whether thirty seconds of repetitive alternating finger tapping (RAFT) on a portable quantitative digitography (QDG) device, which measures amplitude and timing, produces reliable metrics of all cardinal motor signs in PDMethodsNinety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and clinical motor assessment. Eighteen individuals were followed longitudinally with repeated assessments for an average of three years and up to six years.ResultsQDG-RAFT metrics differentiated individuals with PD from controls and provided validated metrics for total motor disability (MDS-UPDRS III) and for rigidity, bradykinesia, tremor, gait impairment and freezing of gait (FOG). Additionally, QDG-RAFT tracked disease progression over several years off therapy, and differentiated akinetic rigid from tremor dominant phenotypes, as well as people with from those without FOG.ConclusionsQDG is a reliable technology, which will improve access to care, allows complex remote disease management, and accurate monitoring of disease progression over time in PD. QDG-RAFT also provides the comprehensive PD motor metrics needed for therapeutic trials.

Technological support for people with Parkinson’s disease: a narrative review

“…In the literature, different cueing strategies such as, continuous stimulation devices during movement 22 or activated only when needed, through autonomous real-time sensing algorithms to start stimulation [23][24][25][26] have shown to be efficacy. Moreover, devices have shown to predict disease course by rest tremor (RT) monitoring thanks to the study of upper limbs movements 27 , identify problems related to food intake 28 , detect problems through handwriting and digitography analysis 29,30 . Other studies [31][32][33][34][35] have focused on the definition of high-tech miniaturized and portable devices made of fabrics and materials that conform to the body.…”