ArticleNPJ digital medicine2025
A systematic review of real-world gait-related digital mobility outcomes in Parkinson's disease.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Wearable Sensors in Gait Assessment for Parkinson Disease and Stroke in Real-World Environments: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Effects of different types of deep brain stimulation on gait disorders in patients with Parkinson's disease: a network meta-analysis of randomized controlled trials.Frontiers in aging neuroscience · 2025Pooled it
- Meaningful digital mobility outcomes for people with Parkinson's disease: Insights grounded in diverse lived experience.Journal of Parkinson's disease · 2026Article
- A Smart Shoe System for Gait Analysis and Remote Monitoring in Parkinson's Disease-A Validation Study.Sensors (Basel, Switzerland) · 2026Article
- Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026Review
- Capacity Versus Performance of Volitional Head Turns After Mild Traumatic Brain Injury.The Journal of head trauma rehabilitation · 2026Article
- Characterizing motor-cognitive associations in Parkinson's disease using digital assessments.Frontiers in aging neuroscience · 2026Article
- Digital mobility assessment using wearable devices in people with Parkinson's disease: a feasibility and implementation study in Ghana (TraPCAf) compared with a UK cohort.Frontiers in aging neuroscience · 2026Article
- Knowledge structure and frontier trends of exercise interventions for Parkinson's disease: a bibliometric and visualized analysis.Frontiers in aging neuroscience · 2026Review
- Toward an understanding of real-world mobility in Parkinson's: insights from enhanced contextualisation using GPS-derived location and data-driven modeling of walking speed.Frontiers in aging neuroscience · 2026Article
- Deriving Motor States and Mobility Metrics from Gamified Augmented Reality Rehabilitation Exercises in People with Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Clinical assessments of Parkinson's disease (PD) focus on structured motor tasks, potentially underestimating real-world mobility impairment. Digital mobility outcomes (DMOs) from wearable devices, offer a promising alternative, but their clinical value remains unclear. This study reviewed the clinical utility of real-world DMOs in PD. Eight databases were searched (2000-2024). Eligibility included reports assessing real-world gait-based DMOs in PD. Study selection and data extraction were conducted independently by four reviewers. No meta-analysis was performed. Twenty-seven reports met inclusion criteria, reporting 21 DMOs. Walking speed was most common (92%), followed by step length (59%) and stride time (48%). Devices, assessment durations and walking bout definitions varied. DMOs differentiated PD from controls and real-world from supervised conditions. PD Subgroup analyses and associations with motor severity were inconsistent. Real-world DMOs offer valuable insight into PD mobility. Standardised protocols are needed for comparability. Further research should explore predictive utility and complex or composite DMOs. Trial Registration: PROSPERO 2025 CRD42021281213.
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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.