ArticleGait & posture2025
Identifying gait characteristics associated with freezing of gait in Parkinson's disease: An analysis of on and off medication states.
Article in Gait & posture, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Virtual reality training for gait disorders in Parkinson's disease: a bibliometric and visual analysis of hotspots and trends using CiteSpace and VOSviewer.Frontiers in neurologyPooled it
- Enhanced Gait Variability Index in Neurological and Geriatric Rehabilitation: A Scoping Review.NeuroRehabilitation · 2026Article
- Attentive Prototype Learning with Wearable Sensor Mutual Information for Fall Risk Stratification of Parkinson's Patients.Bioengineering (Basel, Switzerland) · 2026Article
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Authors and funding
8 authors.
Funding
Abstract
BACKGROUND &
aimFreezing of gait (FOG) is common in Parkinson's disease (PD) and increases fall risk. While medication may reduce FOG, many with PD still show abnormalities in unfrozen gait. This study aimed to identify gait characteristics in both ON- and OFF-PD medication states that are associated with FOG severity and those that can detect FOG presence.
methodsData were collected from 36 people with PD (15 with FOG, 21 without FOG), including demographics (age, sex), PD severity (MDS-UPDRS part III), Levodopa-Equivalent Daily Dose (LEDD), observed FOG severity (FOG Score), and temporal-spatial gait measures from an instrumented walkway. Gait was assessed in the OFF-state (>12-hour medication withdrawal) and ON-state (∼45 min after medication). Ridge regression was used to explore the relationship between OFF-state FOG severity and gait characteristics (eGVI, gait velocity, stride length (mean and coefficient of variation (CV)), cadence, stride time (mean and CV), single support time (mean and CV) in both states. Lasso regression identified characteristics most sensitive for detecting FOG.
resultsIn the ON-state, only single support time CV (β=0.90, p < 0.001) predicted FOG severity (R2 =0.80). In the OFF-state, MDS-UPDRS III (β=0.28, p = 0.136) and stride time CV (β=0.69, p = 0.002) predicted FOG severity (R2 =0.70). ON-state eGVI (β=0.12, p = 0.06), OFF-state stride length mean (β=-0.018, p = 0.77), and OFF-state velocity (β=-0.021, p = 0.70) distinguished between freezers and non-freezers with 88 % accuracy. DISCUSSION/
conclusionsThese results suggest that gait variability during unfrozen gait may reflect a subtle manifestation of FOG. Longitudinal studies should explore the development of FOG and associated gait changes over time.
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