Wireless Accelerometry is Feasible in Acute Monitoring of Upper Limb Motor Recovery after Ischemic Stroke
Abstract
Background: Clinical deterioration in the acute stage of ischemic stroke powerfully predicts outcome and may serve as a marker for urgent intervention. However, accurate monitoring of acute stroke patients is hampered by the lack of validated continuous monitoring devices. We sought to assess the use of wireless accelerometry in this setting, hypothesizing that stroke patients would have a greater difference in movement between upper limbs than controls and that the magnitude of correlation between upper limb movements would be negatively associated with the National Institutes of Health Stroke Scale (NIHSS) score. Methods: In this pilot study, 20 patients with acute ischemic stroke and unilateral upper limb weakness and 10 controls were recruited from a comprehensive stroke centre. All subjects were fitted with two 3-axis accelerometers and underwent 24 h of continuous accelerometry recording of upper limb movements and repeat NIHSS assessments. The intra-class correlation coefficient (ICC), assessing the similarity (or otherwise) of spontaneous movements in each arm was calculated. The association between NIHSS (total and motor subset scores) and the magnitude of ICC was estimated by Spearman's rank correlation, receiver-operating characteristic curve analysis was performed and the optimal diagnostic threshold value of ICC was calculated. Results: The magnitude of the ICC was significantly associated with the baseline NIHSS score (p = 0.02) and non-significantly associated with the baseline NIHSS motor score (p = 0.08). At the optimal diagnostic threshold of ICC magnitude = 0.7, wireless accelerometry distinguished patients from controls with a sensitivity of 0.95, a specificity of 0.6 and a diagnostic odds ratio of 28.5. Conclusions: The wireless accelerometry system successfully detects a motor deficit in the setting of acute ischemic stroke, accurately differentiating patients from controls, and correlates well with the baseline NIHSS score. Its use is feasible in the acute stroke setting. Overall, it shows promise as a diagnostic tool to continuously monitor acute stroke patients but requires validation in a larger trial.
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In stroke, upper extremity activity, walking, and physical activity levels were investigated using step and activity counts.
“In stroke, upper extremity activity [40–51], walking, and physical activity levels were investigated in several studies using step and activity counts [52–62].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [42]
Upper extremity activity measures derived from accelerometers discriminated well between persons with stroke and healthy controls.
“Upper extremity activity measures derived from accelerometers discriminated well between persons with stroke and healthy controls [41, 42, 44, 50] as well as between patients with different impairment levels [40, 41].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [42]
Moderate correlations were found between arm activity measures (activity counts) and clinical assessments in individuals with acute stroke.
“Moderate correlations were found between arm activity measures (activity counts) and clinical assessments in individuals with acute stroke [40–42, 44].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [42]
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- Authors
- Le Heron, C., Fang, K., Gubbi, J., Churilov, L., Palaniswami, M., Davis, S., & Yan, B.
- Affiliations
- The University of Melbourne (AU)
- Venue
- Cerebrovascular Diseases
- Publisher
- S. Karger AG
- Published
- 2014
- Volume
- 37
- Issue
- 5
- Pages
- 336-341
- ISSN
- 1015-9770 (ISSN-L)
- 1421-9786
- OpenAlex
- W2033572293
- Cited by
- 15
- References
- 17
- Citations by year
- 2026: 1 · 2024: 1 · 2022: 2 · 2021: 4 · 2020: 3 · 2019: 2 · 2018: 1 · 2017: 1
- FWCI
- 0.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cerebrovascular Diseases (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Health Sciences › Medicine › Epidemiology › Acute Ischemic Stroke Management
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- acute ischemic stroke, upper limb motor recovery, NIHSS score, upper limb weakness, stroke severity assessment, intra-class correlation, stroke outcome prediction
- MeSH terms
- Stroke Rehabilitation, Aged, Aged, 80 and over, Arm, Brain Ischemia, Female, Humans, Male, Middle Aged, Movement, Pilot Projects, ROC Curve, Severity of Illness Index, Stroke, Wireless Technology, Accelerometry
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