Concurrent validity of accelerometry to measure gait in Parkinsons Disease
Open accessAbstract
The Vitaport Activity Monitor (VAM) is an ambulatory monitoring device that uses accelerometer signals to assess the quality and quantity of walking and mobility related activities in home and community. The objective of this study was to investigate the concurrent validity of the VAM and GAITRite((R)) to measure gait speed, step length and step frequency during a test of functional gait that included single, dual and multiple task components for 12 people with Parkinsons Disease (PD) and 11 comparisons participating. For PD participants, ICCs were excellent for all gait variables (ICC (2, 2)=0.92-0.99, p< or =0.0001). For control participants, ICCs were good to excellent for all gait variables (ICC (2, 2)=0.74-0.94, p< or =0.05). Mean score differences between the two instruments were greatest for step frequency for both PD and comparisons. The VAM is useful for objective gait measurement in the home and community.
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Wearables were also used in Parkinson's disease to quantify sleep disturbances, gait measures, freezing of gait, missteps and falls, and physical activity levels.
“Wearables were also used to quantify sleep disturbances [30], gait measures [31, 32], freezing of gait [33, 34], missteps and fall [35, 36], and physical activity levels [37–39].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [31]
Measures derived from wearables quantifying tremor, dyskinesia, postural sway, and spatiotemporal gait characteristics discriminated well between individuals with Parkinson's disease and healthy controls.
“Different measures derived from wearables quantifying tremor, dyskinesia, postural sway, and spatiotemporal gait characteristics discriminated well between individuals with PD and healthy controls [20, 22, 24, 26, 31]; dyskinesia measures discriminated also between patients with and without dyskinesia [24, 26].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [31]
Good agreement was found between sway and spatiotemporal gait measures from wearables and other established technologies.
“In addition, good agreement was found between sway and spatiotemporal gait measures from wearables and other established technologies [23, 31, 32].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [31]
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- Authors
- Lord, S., Rochester, L., Baker, K., & Nieuwboer, A.
- Affiliations
- Northumbria University (GB)
- Glasgow Caledonian University (GB)
- KU Leuven (BE)
- Corresponding author
- Rochester, Lynn
- Venue
- Gait & Posture
- Publisher
- Elsevier BV
- Published
- 2008-02
- Volume
- 27
- Issue
- 2
- Pages
- 357-359
- ISSN
- 0966-6362 (ISSN-L)
- OpenAlex
- W1979626666
- Cited by
- 69
- References
- 14
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2022: 1 · 2021: 4 · 2020: 8 · 2019: 3 · 2018: 5 · 2017: 3 · 2016: 6 · 2015: 4 · 2014: 2 · 2013: 2 · 2012: 4
- FWCI
- 17.21
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Gait & Posture (publishedVersion)
- PubMed (publishedVersion)
- Northumbria Research Link (Northumbria University) (submittedVersion)
- Newcastle University ePrints (Newcastle Univesity)
- Topics
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- Parkinson's disease, accelerometry, GAITRite, concurrent validity, walking speed, step length, cadence, functional gait, ambulatory monitoring
- MeSH terms
- Acceleration, Aged, Disability Evaluation, Female, Gait, Humans, Male, Parkinson Disease, Case-Control Studies, Monitoring, Ambulatory, Statistics, Nonparametric
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