Affiliations: [a] Department of Computer Science & Engineering, University of Notre Dame, Notre Dame, IN, USA
| [b] School of Computing & Information Sciences, Florida International University, Miami, FL, USA
| [c] Department of Communicative Sciences & Disorders, St. Mary’s College, Notre Dame, IN, USA
Correspondence:
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Correspondence to: John Michael Templeton, Department of Computer Science & Engineering, University of Notre Dame, 384 Fitzpatrick Hall, Notre Dame, IN 46556, USA. E-mail: jtemplet@nd.edu.
Abstract: Background:Mobile devices and their capabilities (e.g., device sensors and human-device interactions) are increasingly being considered for use in clinical assessments and disease monitoring due to their ability to provide objective, repeatable, and more accurate measures of neurocognitive performance. These mobile-based assessments also provide a foundation for the design of intervention recommendations. Objective:The purpose of this work was to assess the benefits of various physical intervention programs as they relate to Parkinson’s disease (PD), its symptoms, and stages (Hoehn and Yahr (H&Y) Stages 1–5). Methods:Ninety-five participants (n = 70 PD; n = 25 control) completed 14 tablet-based neurocognitive functional tests (e.g., motor, memory, speech, executive, and multi-function) and standardized health questionnaires. 208 symptom-specific digital features were normalized to assess the benefits of various physical intervention programs (e.g., aerobic activity, non-contact boxing, functional strength, and yoga) for individuals with PD. While previous studies have shown that physical interventions improve both motor and non-motor PD symptoms, this paper expands on previous works by mapping symptom-specific neurocognitive functionalities to specific physical intervention programs across stages of PD. Results:For early-stage PD (e.g., H&Y Stages 1 & 2), functional strength activities provided the largest overall significant delta improvement (Δ= 0.1883; p = 0.0265), whereas aerobic activity provided the largest overall significant delta improvement (Δ= 0.2700; p = 0.0364) for advanced stages of PD (e.g., H&Y Stages 3–5). Conclusions:As mobile-based digital health technology allows for the collection of larger, labeled, objective datasets, new ways to analyze and interpret patterns in this data emerge which can ultimately lead to new personalized medicine programs.
