The Power of Access in Parkinson's Disease Care: A Retrospective Review of Telehealth Uptake During the COVID-19 Pandemic

Feasibility of regional center telehealth visits utilizing a rural research network in people with Parkinson's disease

Background

Impaired motor and cognitive function can make travel cumbersome for People with Parkinson's disease (PwPD). Over 50% of PwPD cared for at the University of Arkansas for Medical Sciences (UAMS) Movement Disorders Clinic reside over 30 miles from Little Rock. Improving access to clinical care for PwPD is needed.

Objective

To explore the feasibility of remote clinic-to-clinic telehealth research visits for evaluation of multi-modal function in PwPD.

Methods

PwPD residing within 30 miles of a UAMS Regional health center were enrolled and clinic-to-clinic telehealth visits were performed. Motor and non-motor disease assessments were administered and quantified. Results were compared to participants who performed at-home telehealth visits using the same protocols during the height of the COVID pandemic.

Results

Compared to the at-home telehealth visit group (n = 50), the participants from regional centers (n = 13) had similar age and disease duration, but greater disease severity with higher total Unified Parkinson's disease rating scale scores (Z = -2.218, p = 0.027) and lower Montreal Cognitive Assessment scores (Z = -3.350, p < 0.001). Regional center participants had lower incomes (Pearson's chi = 21.3, p < 0.001), higher costs to attend visits (Pearson's chi = 16.1, p = 0.003), and lived in more socioeconomically disadvantaged neighborhoods (Z = -3.120, p = 0.002). Prior research participation was lower in the regional center group (Pearson's chi = 4.5, p = 0.034) but both groups indicated interest in future research participation.

Conclusions

Regional center research visits in PwPD in medically underserved areas are feasible and could help improve access to care and research participation in these traditionally underrepresented populations.

Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson's Disease from Full-Body Kinematics

Characterizing motor subtypes of Parkinson's disease (PD) is an important aspect of clinical care that is useful for prognosis and medical management. Although all PD cases involve the loss of dopaminergic neurons in the brain, individual cases may present with different combinations of motor signs, which may indicate differences in underlying pathology and potential response to treatment. However, the conventional method for distinguishing PD motor subtypes involves resource-intensive physical examination by a movement disorders specialist. Moreover, the standardized rating scales for PD rely on subjective observation, which requires specialized training and unavoidable inter-rater variability. In this work, we propose a system that uses machine learning models to automatically and objectively identify some PD motor subtypes, specifically Tremor-Dominant (TD) and Postural Instability and Gait Difficulty (PIGD), from 3D kinematic data recorded during walking tasks for patients with PD (MDS-UPDRS-III Score, 34.7 ± 10.5, average disease duration 7.5 ± 4.5 years). This study demonstrates a machine learning model utilizing kinematic data that identifies PD motor subtypes with a 79.6% F1 score (N = 55 patients with parkinsonism). This significantly outperformed a comparison model using classification based on gait features (19.8% F1 score). Variants of our model trained to individual patients achieved a 95.4% F1 score. This analysis revealed that both temporal, spectral, and statistical features from lower body movements are helpful in distinguishing motor subtypes. Automatically assessing PD motor subtypes simply from walking may reduce the time and resources required from specialists, thereby improving patient care for PD treatments. Furthermore, this system can provide objective assessments to track the changes in PD motor subtypes over time to implement and modify appropriate treatment plans for individual patients as needed.

Potential clinical and economic benefits of remote deep brain stimulation programming

Deep brain stimulation (DBS) teleprogramming may help reducing travel-related and other financial burdens for patients and maintaining DBS care in special situations. To determine travel-related burdens of DBS patients and explore effects of COVID-19 on DBS care. Travel- and visit-related data of 319 patients were retrospectively analyzed for the first year, five years, and ten years after initiating DBS. Frequencies of in-person and telemedicine visits over the 18-month periods just before and after the outbreak of COVID-19 in Hungary were also compared. Average travel distance during an in-person visit was 415.2 ± 261.5 km, while average travel time was 342.1 ± 199.4 min. Travel costs for the first year, five years, and ten years were 151.8 ± 108.7, 461.4 ± 374.6, and 922.7 ± 749.1 Euros, respectively. Travel distance, age, and type and severity of disease could help identify patients who would particularly benefit from teleprogramming. We detected a significant decrease in the number of visits during COVID-19 pandemic (from 3.7 ± 2.1 to 2.4 ± 2.7; p < 0.001) which mainly resulted from the decreased frequency of in-person visits (3.6 ± 2.0 vs. 1.7 ± 1.8; p < 0.001). Our results support the introduction of DBS teleprogramming in Hungary which could save money and time for patients while maintaining a secure delivery of DBS.