Adapting Tai Chi for upper limb rehabilitation post stroke : an exploratory feasibility and efficacy study
Bibliographic record
Abstract
Background and Purpose: Tai Chi (TC) has been reported as beneficial for improving balance post stroke, yet its use for upper limb (UL) rehabilitation remains unknown. The purpose of this study was to evaluate the feasibility and efficacy of TC on UL rehabilitation post stroke. Methods: Twelve chronic stroke survivors with a persistent paresis underwent 60-minute adapted TC sessions twice a week for eight weeks and a 4-week follow-up evaluation. A 10-minute TC home program was recommended for the days without sessions. TC level of performance, adapted movements used, attendance to the sessions and duration of self-practice at home were recorded. Shoulder pain (Visual Analogue Scale (VAS)), motor function of the paretic arm ((Fugl-Meyer Assessment upper-limb section (FMA-UL), Wolf Motor Function Test (WMFT)) and paretic arm use in daily life (Motor Activity Log (MAL)) were measured at baseline, post-treatment and follow-up. A feedback questionnaire was used to evaluate participants’ perception of the use of TC at follow-up. Results: Eleven participants completed the 8-week study. A clinical reasoning algorithm underlying the adaptation of TC was developed based on different functional levels of the participants. Participants with varying profiles including severely impaired UL, poor balance, shoulder pain, and severe spasticity were not only capable of practicing the adapted TC but attended all 16 sessions and practiced TC at home more than recommended (a total of 16.51±9.21 hours). The self-practice amount for subgroups with lower UL function, shoulder pain or moderate-to-severe spasticity, was similar to subgroups with higher functional UL, no shoulder pain, and minimal-to-no spasticity. Participants demonstrated significant improvement over time in the FMA-UL (p=.009), WMFT functional scale (p=.003), WMFT performance time (p=.048) and MAL Amount of Use scale (p=.02). Shoulder pain of four participants decreased following TC (VAS 5.5±3, 3±2.8, 2.5±2.5 for the pre, post and follow-up period respectively). Moreover, participants confirmed the usefulness and ease of practicing the adapted TC. Conclusion: Adapted TC is feasible, acceptable and effective for UL rehabilitation post stroke. Low UL function, insufficient balance, spasticity, and shoulder pain do not seem to hinder practicing TC. Further large-scale randomized trials evaluating TC for UL rehabilitation are warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".