ArticleParkinsonism & related disorders2022
An equation to calculate UPDRS motor severity for online and rural assessments of Parkinson's.
Article in Parkinsonism & related disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Accessible assessment of motor and cognitive symptoms in Parkinson's disease: integrating large datasets, machine-learning, and videoconferencing.NPJ digital medicine · 2026Article
- Characterizing motor-cognitive associations in Parkinson's disease using digital assessments.Frontiers in aging neuroscience · 2026Article
- Neurological Examination via Telemedicine: An Updated Review Focusing on Movement Disorders.Medicina (Kaunas, Lithuania) · 2024Review
- Online cognitive testing in Parkinson's disease: advantages and challenges.Frontiers in neurology · 2024Review
- Feasibility and Efficacy of Online Neuropsychological Assessment.Sensors (Basel, Switzerland) · 2023Article
- Telerehabilitation: A Practical Remote Alternative for Coaching and Monitoring Physical Kinetic Therapy in Patients with Mild and Moderate Disabling Parkinson's Disease during the COVID-19 Pandemic.Parkinson's disease · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveManagement 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.
methodsTotal 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.
resultsThe regression analysis resulted in the equation to predict total motor symptom severity of PD.
conclusionsIn 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.