Developing Personalized Medicine Strategies to Increase Physical Activity in Parkinson's Disease (PD) Through Digital Health Technology

Objectives(s): To investigate whether: 1) Protocols using gamification to increase physical activity in patients with PD employing remote intervention and assessment will be feasible and acceptable to participants; 2) Individual NMS will predict responsiveness to a gamification intervention targeting physical activity levels-facilitating a personalized approach in future efficacy studies; 3) Changes in physical activity will be associated with changes in motor symptoms, measured using the wearable Parkinson's Kinetograph (PKG)

Research Design: This is a prospective observational study. Using administrative databases and review of medical records, we will identify and remotely enroll 85 Veterans with PD on stable medication regimens into a protocol including: 1) Two week assessment of baseline activity using Fitbits; 2) a 6-week gamification intervention program targeted to a 20% increase in average daily steps; 3) 4-week follow-up period after the intervention. At baseline and following the gamification period, we will assess a variety of NMS in PD using validated questionnaires. PD motor symptoms will be assessed using the PKG (bradykinesia and motor scores) at baseline, at the end of the gamification program and after follow-up..

Methodology: All survey assessments, activity monitoring and administration of the gamification intervention will be conducted remotely using Way to Health, a web-based digital health platform developed at the University of Pennsylvania. Participants will complete surveys at the beginning of the study that quantify various sociodemographic, clinical, and geographical factors using the Way to Health platform. All participants will be mailed a Fitbit Inspire, a wearable activity tracker to be worn on the hip for 30 days to record daily steps. Participatiants will also be mailed a Parkinson's Kinetograph (PKG) to wear at baseline, at the end of the gamification program and after follow-up. Participants will sync their Fitbit data to the Way to Health platform, that has been used for previous clinical trials at the CMCVAMC. Statistical analysis: Aim 1: We will assess feasibility of the intervention using descriptive statistics; Aim 2: We will examine the association of baseline disease characteristics with response to the intervention using linear mixed effects models Aim 3: We will examine the association between changes in physical activity and motor symptoms using linear mixed effects models.

Clinical relationships: Exercise and physical activity are potential "silver bullets" in the treatment of Parkinson's disease (PD). Exercise has efficacy as a symptomatic and potentially disease-modifying therapy. However, low levels of physical activity in community-dwelling populations with PD demonstrate that exercise is not yet being used as an effective therapy. Interventions to increase activity levels of PD patients under real world conditions will have tremendous and immediate translational impact. Personalized strategies using digital health technology to increase physical activity would be highly scalable, improving the symptoms and lives of countless people living with PD The COVID19 pandemic halted most human-subjects research because of the need for face to face recruitment and visits. Traditional models of face to face recruitment and assessment are unlikely to be viable during the funding period for the proposed studies, and, perhaps, beyond. Approaches to recruitment and assessment that circumvent the need for face to face contact will have high impact.

Impact/Significance: Personalized strategies using digital health technology to increase physical activity would be highly scalable, improving the symptoms and lives of countless people living with PD.