Background: Despite tremendous advances in the treatment and management of stroke, restoring motor and functional outcomes after stroke continues to be a major clinical challenge. Given the wide range of approaches used in motor rehabilitation, several commentaries have highlighted the lack of a clear scientific basis for different interventions as one critical factor that has led to suboptimal study outcomes. Objective: To understand the content of current therapeutic interventions in terms of their active ingredients. Methods: We conducted an analysis of randomized controlled trials in stroke rehabilitation over a 2-year period from 2019-2020. Results: There were three primary findings: (i) consistent with prior reports, most studies did not provide an explicit rationale for why the treatment would be expected to work, (ii) most therapeutic interventions mentioned multiple active ingredients and there was not a close correspondence between the active ingredients mentioned versus the active ingredients measured in the study, and (iii) multimodal approaches that involved more than one therapeutic approach tended to be combined in an ad-hoc fashion, indicating the lack of a targeted approach. Conclusion: These results highlight the need for strengthening cross-disciplinary connections between basic science and clinical studies, and the need for structured development and testing of therapeutic approaches to find more effective treatment interventions.
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The Role of Sensory Impairments on Recovery and Rehabilitation After Stroke
Abstract Purpose of ReviewThe current review aims to address critical gaps in the field of stroke rehabilitation related to sensory impairment. Here, we examine the role and importance of sensation throughout recovery of neural injury, potential clinical and experimental approaches for improving sensory function, and mechanism-based theories that may facilitate the design of sensory-based approaches for the rehabilitation of somatosensation. Recent FindingsRecently, the field of neurorehabilitation has shifted to using more quantitative and sensitive measures to more accurately capture sensory function in stroke and other neurological populations. These approaches have laid the groundwork for understanding how sensory impairments impact overall function after stroke. However, there is less consensus on which interventions are effective for remediating sensory function, with approaches that vary from clinical re-training, robotics, and sensory stimulation interventions. SummaryCurrent evidence has found that sensory and motor systems are interdependent, but commonly have independent recovery trajectories after stroke. Therefore, it is imperative to assess somatosensory function in order to guide rehabilitation outcomes and trajectory. Overall, considerable work in the field still remains, as there is limited evidence for purported mechanisms of sensory recovery, promising early-stage work that focuses on sensory training, and a considerable evidence-practice gap related to clinical sensory rehabilitation.
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- Award ID(s):
- 1934650
- PAR ID:
- 10575671
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Current Neurology and Neuroscience Reports
- Volume:
- 25
- Issue:
- 1
- ISSN:
- 1528-4042
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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BackgroundStroke therapy is essential to reduce impairments and improve motor movements by engaging autogenous neuroplasticity. Traditionally, stroke rehabilitation occurs in inpatient and outpatient rehabilitation facilities. However, recent literature increasingly explores moving the recovery process into the home and integrating technology-based interventions. This study advances this goal by promoting in-home, autonomous recovery for patients who experienced a stroke through robotics-assisted rehabilitation and classifying stroke residual severity using machine learning methods. ObjectiveOur main objective is to use kinematics data collected during in-home, self-guided therapy sessions to develop supervised machine learning methods, to address a clinician’s autonomous classification of stroke residual severity–labeled data toward improving in-home, robotics-assisted stroke rehabilitation. MethodsIn total, 33 patients who experienced a stroke participated in in-home therapy sessions using Motus Nova robotics rehabilitation technology to capture upper and lower body motion. During each therapy session, the Motus Hand and Motus Foot devices collected movement data, assistance data, and activity-specific data. We then synthesized, processed, and summarized these data. Next, the therapy session data were paired with clinician-informed, discrete stroke residual severity labels: “no range of motion (ROM),” “low ROM,” and “high ROM.” Afterward, an 80%:20% split was performed to divide the dataset into a training set and a holdout test set. We used 4 machine learning algorithms to classify stroke residual severity: light gradient boosting (LGB), extra trees classifier, deep feed-forward neural network, and classical logistic regression. We selected models based on 10-fold cross-validation and measured their performance on a holdout test dataset using F1-score to identify which model maximizes stroke residual severity classification accuracy. ResultsWe demonstrated that the LGB method provides the most reliable autonomous detection of stroke severity. The trained model is a consensus model that consists of 139 decision trees with up to 115 leaves each. This LGB model boasts a 96.70% F1-score compared to logistic regression (55.82%), extra trees classifier (94.81%), and deep feed-forward neural network (70.11%). ConclusionsWe showed how objectively measured rehabilitation training paired with machine learning methods can be used to identify the residual stroke severity class, with efforts to enhance in-home self-guided, individualized stroke rehabilitation. The model we trained relies only on session summary statistics, meaning it can potentially be integrated into similar settings for real-time classification, such as outpatient rehabilitation facilities.more » « less
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