ArticleFrontiers in bioengineering and biotechnology2025
Beyond clinical scales: an observational study on instrumental gait analysis and biomechanical patterns in patients with Parkinson's disease.
Article in Frontiers in bioengineering and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Use of Wearable Sensors to Assess Fall Risk in Neurological Disorders: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Pooled it
- Digital biomarkers derived from wearable sensors in neurorehabilitation: a scoping review.Frontiers in neurology · 2026Article
- Can neurologic music therapy make the difference when using immersive virtual reality in Parkinson disease motor training? Promising findings from a secondary analysis.Frontiers in rehabilitation sciences · 2026Article
- Enhancing gait training with anti-gravity treadmill 'Alter-G' in patients with Parkinson's disease.PloS one · 2026Article
- A Preliminary Mechanics-Informed Machine Learning Framework for Objective Assessment of Parkinson's Disease and Rehabilitation Outcomes.Diagnostics (Basel, Switzerland) · 2025Article
- Neural control meets biomechanics in the motor assessment of neurological disorders: a narrative review.Frontiers in neural circuits · 2025Review
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Authors and funding
11 authors.
Funding
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Abstract
Introduction: Parkinson's disease (PD), a common neurodegenerative disorder affecting motor functions, is associated with abnormal gait patterns characterized by altered kinematic, kinetic, and electrophysiological parameters. This observational study aims to instrumentally identify and quantify these gait dysfunctions in PD patients compared to normal values from healthy subjects. Methods: Sixty-nine PD patients underwent clinical and instrumental evaluations to assess gait. Demographic and clinical data were collected before motor assessment. Clinical scales evaluated the level of impairment, gait, balance, risk of falls and ability to complete activities of daily living. Instrumental evaluations were conducted using optoelectronic, force plates and electromyographic (EMG) systems in a motion analysis laboratory. Statistical analysis involved a non-parametric test to compare pathological and normal data, clustering methods to identify groups based on clinical evaluations, and a combination of non-parametric analysis and linear models to assess dependencies on clinical scales. Results: The results showed that PD patients had significant gait kinematic differences compared to normal values, with increased temporal and shortened spatial parameters. In addition, PD patients were grouped into four clusters based on clinical scales. While some gait features were influenced by clinical scales reflecting impairment, gait and balance, and independence, others were more affected by the perceived fear of falling (FoF). Discussion: In conclusion, the study identified specific biomechanical gait dysfunctions in kinematic, kinetic, and electrophysiological parameters in PD patients, undetectable by standard clinical scales. Additionally, higher FoF was associated with dysfunctional biomechanical patterns, independent of impairment severity, gait and balance dysfunction, or overall independence.
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