Criterion Validity of the StepWatch Activity Monitor as a Measure of Walking Activity in Patients After Stroke
Open accessAbstract
Objectives To test the validity of the StepWatch Activity Monitor (SAM) in subjects with stroke against 2 criterion standards, 3-dimensional gait analysis (3-DGA) and footswitches in a variety of indoor and outdoor walking conditions, including different speeds and different terrains, and to test the accuracy of the SAM when worn on the paretic limb.Design Criterion standard validation study.Setting Gait laboratory and outside course.Participants Twenty-five participants with physical disability after stroke.Interventions Not applicable.Main outcome measures The total step count measured simultaneously by SAM and either 3-DGA or footswitches for both paretic and nonparetic limbs.Results The total step count measured by the SAM and 3-DGA was highly correlated (nonparetic limb, r=.959; paretic limb, r=.896). The 95% limits of agreement (LOA) (derived from Bland-Altman analysis) between the SAM and 3-DGA were within +/-10 steps for SAMs worn on either the nonparetic or paretic limb. The total step count measured simultaneously by the SAM and footswitches was also highly correlated for each limb (nonparetic, r=.999; paretic, r=.963). The 95% LOA between the SAM and footswitches were +/-9 steps on the nonparetic limb but higher at +/-57 steps on the paretic limb. Further analysis showed that the measurement differences occurred during the outdoor component of the combined walk. The 95% LOA between footswitches on both limbs were not more than +/-9 steps for walking, suggesting that the error was accounted for by the SAM on the paretic limb, which both over- and underread the total step count in the outdoor walking conditions.Conclusions Criterion validity of the SAM to measure steps in both clinical and natural environments has been established when used on the nonparetic limb. However, more errors are apparent when the SAM is worn on the paretic limb while walking over a variety of outdoor terrains. Validation is recommended before use in patients with neurologic conditions affecting bilateral legs because there may be more error, particularly in outdoor activities.
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In stroke, good agreement was found between step counts derived from wearables compared to step counts from 3D gait analysis or video-based counts.
“In stroke, good agreement was found between step counts derived from wearables compared to step counts from 3D gait analysis [53] or video-based counts [54].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [53]
The StepWatch is accurate to within 1-2% of actual steps, even at very slow walking speeds and in obese individuals.
“The StepWatch is accurate to within 1–2% of actual steps, even at very slow walking speeds, and even in individuals who are obese [25].”
From Step Counting: A Review of Measurement Considerations and Health-Related Applications (2017) · cites this paper as [25]
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- Authors
- Mudge, S., Stott, N. S., & Walt, S. E.
- Affiliations
- University of Auckland (NZ)
- Corresponding author
- Mudge, Suzie
- Venue
- Archives of Physical Medicine and Rehabilitation
- Publisher
- Elsevier BV
- Published
- 2007-12
- Volume
- 88
- Issue
- 12
- Pages
- 1710-1715
- ISSN
- 0003-9993 (ISSN-L)
- OpenAlex
- W2054237895
- Cited by
- 105
- References
- 28
- Citations by year
- 2025: 4 · 2024: 8 · 2023: 5 · 2022: 7 · 2021: 5 · 2020: 6 · 2019: 6 · 2018: 8 · 2017: 7 · 2016: 8 · 2015: 2 · 2014: 3 · 2013: 7 · 2012: 7
- FWCI
- 5.41
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Archives of Physical Medicine and Rehabilitation (publishedVersion)
- PubMed (publishedVersion)
- Tuwhera (Auckland University of Technology) (submittedVersion)
- ResearchSpace (University of Auckland) (submittedVersion, cc-by)
- Funders
- Auckland District Health Board
- Topics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- wearable activity trackers, stroke, criterion validity, step count, paretic limb, foot switch, outdoor walking, terrain, measurement error, wearable sensors
- MeSH terms
- Stroke Rehabilitation, Adult, Aged, Female, Humans, Male, Middle Aged, Paresis, Walking, Monitoring, Ambulatory, Stroke
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