Objective Monitoring of Motor Symptom Severity and their Progression in Parkinson's Disease Using a Digital Gait Device

Digital technologies for monitoring motor symptoms of Parkinson’s Disease (PD) have underwent astrong evolution during the past years. Although it has been shown for several devices that deriveddigital gait features can reliably discriminate between healthy controls and people with PD, the specifi cgait tasks best suited for monitoring motor symptoms and especially their progression, remain unclear.Furthermore, the potential benefi t as endpoint in a clinical trial context has not been investigated so far.
In this study we employed a digital gait device manufactured by Portabiles HCT, which has been used by339 patients within the LuxPark cohort (n = 161, Luxembourg) as well as within routine clinical care visitsat the University Medical Center Erlangen (n = 178, Erlangen, Germany). Linear (mixed) models were usedto assess the association of task-specifi c digital gait features with disease progression and motorsymptom severity measured by several clinical scores. Furthermore, we employed machine learning toevaluate whether digital gait assessments were prognostic for patient-level motor symptom progression.
Overall, digital gait features derived from Portabiles digital gait device were found to effectively monitormotor symptoms and their longitudinal progression. At the same time the prognostic performance ofdigital gait features was limited. However, we could show a strong reduction in required sample size, ifdigital gait features were employed as surrogates for traditional endpoints in a clinical trial context. Thus,Portabiles digital gait device provides an effective way to objectively monitor motor symptoms and theirprogression in PD. Furthermore, the digital gait device bears strong potential as an alternative and easilyassessable endpoint predictor in a clinical trial context.