Objective: We present the development and design of a comprehensive digital biomarker smartphone application (app) for remote and frequent assessments of motor symptom severity and progression in Parkinson’s disease (PD).

Background: PD symptoms vary within and across patients over time, complicating the benchmarking of symptom severity at infrequent clinic visits. Our previous studies with 44 PD patients and 35 controls demonstrated that daily testing with smartphone apps generates reliable, clinically valid, and sensitive symptomatic data in PD patients. We built upon these initial learnings to develop a smartphone app that comprehensively measures the core signs of PD remotely, continuously (i.e., daily) and objectively (i.e., via smartphone sensors).

Methods: The app design was based on our initial learnings, the available literature, analyses of longitudinal PPMI MDS-UPDRS item data, and consultation with movement disorders neurologists and patients. Tests of the cardinal signs of PD which were both amenable to smartphone testing and declined most in the first year of de novo PD (PPMI) were identified. Neurologists reviewed these tests and selected a final set based on estimated fidelity of clinical signs during unsupervised, in-home testing. Tests were programmed on the app. Finally, the app was reviewed by patients to ensure they understood and could complete all tasks.

Results: The testing suite encompasses the following active tests: sustained phonation, postural tremor, and rest tremor (tremor); reading and free speech, shape drawing, finger-tapping and pronation/supination (bradykinesia); balance and U-turn tests (rigidity/postural instability); and a modified version of the digitsymbol modalities test. Passive monitoring via smartphone and smartwatch estimated the effect of PD on daily motor behavior. Finally, the Timed Up and Go and short Berg Balance Scale are performed with the smartphone at clinic visits.

Conclusions: The present smartphone app relies on direct symptom measurement via smartphone sensors while patients perform a comprehensive suite of active and passive activities in an ecologically valid environment. Thus, this app should provide measures of symptom severity and progression that are complementary to established clinical outcome measures.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/a-comprehensive-digital-biomarker-active-testing-and-passive-monitoring-suite-for-the-remote-and-frequent-assessment-of-motor-symptom-progression-in-parkinsons-disease/