ArticleBrain sciences2023
Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke.
Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
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Who cites it
7 citing papers in PubMed.
- A pilot study for self-guided, active robotic training of proprioception of the upper limb in chronic stroke.Journal of neuroengineering and rehabilitation · 2025Article
- The Role of Sensory Impairments on Recovery and Rehabilitation After Stroke.Current neurology and neuroscience reports · 2025Review
- A Novel Wrist Device for Characterizing the Components of Proprioceptive Acuity.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Robot-aided assessment and associated brain lesions of impaired ankle proprioception in chronic stroke.Journal of neuroengineering and rehabilitation · 2024Article
- The independence of impairments in proprioception and visuomotor adaptation after stroke.Journal of neuroengineering and rehabilitation · 2024Article
- Bibliometric and visualized analysis of the application of artificial intelligence in stroke.Frontiers in neuroscience · 2024Article
- Integrative Approaches in Acute Ischemic Stroke: From Symptom Recognition to Future Innovations.Biomedicines · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Proprioceptive impairments occur in ~50% of stroke survivors, with 20-40% still impaired six months post-stroke. Early identification of those likely to have persistent impairments is key to personalizing rehabilitation strategies and reducing long-term proprioceptive impairments. In this study, clinical, neuroimaging and robotic measures were used to predict proprioceptive impairments at six months post-stroke on a robotic assessment of proprioception. Clinical assessments, neuroimaging, and a robotic arm position matching (APM) task were performed for 133 stroke participants two weeks post-stroke (12.4 ± 8.4 days). The APM task was also performed six months post-stroke (191.2 ± 18.0 days). Robotics allow more precise measurements of proprioception than clinical assessments. Consequently, an overall APM Task Score was used as ground truth to classify proprioceptive impairments at six months post-stroke. Other APM performance parameters from the two-week assessment were used as predictive features. Clinical assessments included the Thumb Localisation Test (TLT), Behavioural Inattention Test (BIT), Functional Independence Measure (FIM) and demographic information (age, sex and affected arm). Logistic regression classifiers were trained to predict proprioceptive impairments at six months post-stroke using data collected two weeks post-stroke. Models containing robotic features, either alone or in conjunction with clinical and neuroimaging features, had a greater area under the curve (AUC) and lower Akaike Information Criterion (AIC) than models which only contained clinical or neuroimaging features. All models performed similarly with regard to accuracy and F1-score (>70% accuracy). Robotic features were also among the most important when all features were combined into a single model. Predicting long-term proprioceptive impairments, using data collected as early as two weeks post-stroke, is feasible. Identifying those at risk of long-term impairments is an important step towards improving proprioceptive rehabilitation after a stroke.
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Registered trials
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