SENSory re-learning of the UPPer limb (SENSUPP) after stroke: development and description of a novel intervention using the TIDieR checklist.
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ABSTRACT: Background
Sensorimotor impairments of upper limb (UL) are common after stroke, leading to difficulty to use the UL in daily life. Even though many have sensory impairments in the UL, specific sensory training is often lacking in stroke rehabilitation. Thus, the aim of this paper is to provide a detailed description of the novel intervention "SENSory re-learning of the UPPer limb after stroke (SENSUPP)" that we have developed to improve functioning in the UL in persons with mild to moderate impairments after stroke.Methods
The SENSUPP protocol was designed using information from literature reviews, clinical experience and through consultation of experts in the field. The protocol integrates learning principles based on current neurobiological knowledge and includes repetitive intensive practice, difficulty graded exercises, attentive exploration of a stimulus with focus on the sensory component, and task-specific training in meaningful activities that includes feedback. For reporting the SENSUPP protocol, the Template for Intervention Description and Replication (TIDieR) checklist was used.Results
The essential features of the SENSUPP intervention comprise four components: applying learning principles based on current neurobiological knowledge, sensory re-learning (exercises for touch discrimination, proprioception and tactile object recognition), task-specific training in meaningful activities, and home-training. The training is performed twice a week, in 2.5-h sessions for 5 weeks.Conclusion
Since there is close interaction between the sensory and motor systems, the SENSUPP intervention may be a promising method to improve UL functioning after stroke. The TIDieR checklist has been very useful for reporting the procedure and development of the training.Trial registration
ClinicalTrials.gov NCT03336749 . Registered on 8 November 2017.
SUBMITTER: Carlsson H
PROVIDER: S-EPMC8259306 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAction observation (AO) is a physical rehabilitation approach that facilitates the occurrence of neural plasticity through the activation of the mirror-neural system, promoting motor recovery in people with stroke.ObjectivesTo assess whether AO enhances upper limb motor function in people with stroke.Search methodsWe searched the Cochrane Stroke Group Trials Register (last searched 18 May 2021), the Cochrane Central Register of Controlled Trials (18 May 2021), MEDLINE (1946 to 18 May 2021), Embase (1974 to 18 May 2021), and five additional databases. We also searched trial registries and reference lists.Selection criteriaRandomized controlled trials (RCTs) of AO alone or associated with physical practice in adults after stroke. The primary outcome was upper limb (arm and hand) motor function. Secondary outcomes included dependence on activities of daily living (ADL), motor performance, cortical activation, quality of life, and adverse effects.Data collection and analysisTwo review authors independently selected trials according to the predefined inclusion criteria, extracted data, assessed risk of bias using RoB 1, and applied the GRADE approach to assess the certainty of the evidence. The reviews authors contacted trial authors for clarification and missing information.Main resultsWe included 16 trials involving 574 individuals. Most trials provided AO followed by the practice of motor actions. Training varied between 1 day and 8 weeks of therapy, 10 to 90 minutes per session. The time of AO ranged from 1 minute to 10 minutes for each motor action, task or movement observed. The total number of motor actions ranged from 1 to 3. Control comparisons included sham observation, physical therapy, and functional activity practice.Primary outcomesAO improved arm function (standardized mean difference (SMD) 0.39, 95% confidence interval (CI) 0.17 to 0.61; 11 trials, 373 participants; low-certainty evidence); and improved hand function (mean difference (MD) 2.76, 95% CI 1.04 to 4.49; 5 trials, 178 participants; low-certainty evidence).Secondary outcomesAO did not improve ADL performance (SMD 0.37, 95% CI -0.34 to 1.08; 7 trials, 302 participants; very low-certainty evidence), or quality of life (MD 5.52, 95% CI -30.74 to 41.78; 2 trials, 30 participants; very low-certainty evidence). We were unable to pool the other secondary outcomes (motor performance and cortical activation). Only two trials reported adverse events without significant adverse effects.Authors' conclusionsThe effects of AO are small for arm function compared to any control group; for hand function the effects are large, but not clinically significant. For both, the certainty of evidence is low. There is no evidence of benefit or detriment from AO on ADL and quality of life of people with stroke; however, the certainty of evidence is very low. As such, our confidence in the effect estimate is limited because it will likely change with future research.
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Project description:BackgroundRobots can generate rich kinematic datasets that have the potential to provide far more insight into impairments than standard clinical ordinal scales. Determining how to define the presence or absence of impairment in individuals using kinematic data, however, can be challenging. Machine learning techniques offer a potential solution to this problem. In the present manuscript we examine proprioception in stroke survivors using a robotic arm position matching task. Proprioception is impaired in 50-60% of stroke survivors and has been associated with poorer motor recovery and longer lengths of hospital stay. We present a simple cut-off score technique for individual kinematic parameters and an overall task score to determine impairment. We then compare the ability of different machine learning (ML) techniques and the above-mentioned task score to correctly classify individuals with or without stroke based on kinematic data.MethodsParticipants performed an Arm Position Matching (APM) task in an exoskeleton robot. The task produced 12 kinematic parameters that quantify multiple attributes of position sense. We first quantified impairment in individual parameters and an overall task score by determining if participants with stroke fell outside of the 95% cut-off score of control (normative) values. Then, we applied five machine learning algorithms (i.e., Logistic Regression, Decision Tree, Random Forest, Random Forest with Hyperparameters Tuning, and Support Vector Machine), and a deep learning algorithm (i.e., Deep Neural Network) to classify individual participants as to whether or not they had a stroke based only on kinematic parameters using a tenfold cross-validation approach.ResultsWe recruited 429 participants with neuroimaging-confirmed stroke (< 35 days post-stroke) and 465 healthy controls. Depending on the APM parameter, we observed that 10.9-48.4% of stroke participants were impaired, while 44% were impaired based on their overall task score. The mean performance metrics of machine learning and deep learning models were: accuracy 82.4%, precision 85.6%, recall 76.5%, and F1 score 80.6%. All machine learning and deep learning models displayed similar classification accuracy; however, the Random Forest model had the highest numerical accuracy (83%). Our models showed higher sensitivity and specificity (AUC = 0.89) in classifying individual participants than the overall task score (AUC = 0.85) based on their performance in the APM task. We also found that variability was the most important feature in classifying performance in the APM task.ConclusionOur ML models displayed similar classification performance. ML models were able to integrate more kinematic information and relationships between variables into decision making and displayed better classification performance than the overall task score. ML may help to provide insight into individual kinematic features that have previously been overlooked with respect to clinical importance.
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