An equation to calculate UPDRS motor severity for online and rural assessments of Parkinson's

Objective: Management of PD has largely been affected by COVID-19. Due to the restrictions posed by COVID-19, there has been a shift from in-person to online forms of assessment. This presents a challenge as not all motor symptoms can be assessed virtually. Two of the four cardinal symptoms of PD (rigidity and postural instability) cannot be assessed virtually using the gold-standard Unified Parkinson's Disease Rating Scale (UPDRS-III). As a result, an accurate total motor severity score can not be computed from the remaining subsections. Recently, one study stated that in order for accurate scores to be calculated, only three sections could be absent. Virtually, six sections are unable to be evaluated with online assessments. This inability to compute a total motor severity score may result in poor disease management. Thus, in this study a regression equation was developed to predict total motor severity scores from partial scores. Methods: Total motor severity scores (UPDRS-III) from N = 234 individuals with idiopathic Parkinson's were retrospectively analyzed. In order to conduct a linear regression analysis predictor and outcome variables were created. The variables were then used for the linear regression. The equation was then tested on an independent data set N = 1168. Results: The regression analysis resulted in the equation to predict total motor symptom severity of PD. Conclusions: In conclusion, the developed equation will be very useful for outreach in rural communities, as well as the continued remote management of PD during COVID-19 and beyond.

Automated summary

The management of Parkinson's disease has been affected by COVID-19 restrictions, leading to challenges in conducting virtual assessments and potentially impacting disease management. A regression equation was developed to predict total motor symptom severity scores from partial scores, which may be useful for remote management and outreach in rural communities.