Effects of a biomechanical-based Tai Chi program on gait and posture in people with Parkinson’s disease: study protocol for a randomized controlled trial
Bibliographic record
Abstract
Abstract Background Parkinson’s disease (PD) is associated with changes in gait and posture, which increases the rate of falls and injuries in this population. Tai Chi (TC) training enhances the movement capacity of patients with PD. However, the understanding of the effect of TC training on gait and postural stability in PD is lacking. This study aims to examine the effect of biomechanical-based TC training on dynamic postural stability and its relationship with walking performance. Methods/design A single-blind, randomized control trial of 40 individuals with early-stage PD was conducted (Hoehn and Yahr stages 1 to 3). Patients with PD will be randomly assigned to either the TC or control group. The TC group will participate in a biomechanical-based TC training program that is formed based on the movement analysis of TC and will be practiced thrice a week for 12 weeks. The control group will be required to engage in at least 60 min of regular physical activity (PA) on their own for three times per week for 12 weeks. The primary and secondary outcomes will be assessed at baseline and at 6 and 12 weeks after commencing the study protocol. The primary outcome measures will include dynamic postural stability indicated by the center of mass and center of pressure separation distance and clearance distance of the heel and toe measured during fixed-obstacle crossing. The secondary measures are gait speed, cadence, step length during level surface walking (simple task), and fixed-obstacle crossing (challenging task). The Unified Parkinson’s Disease Rating Scale, single leg-stance test with eyes open and closed, and three cognitive scores (Stroop Test, Trail Making Test Part B, and the Wisconsin Card Sorting Test) were also employed. Discussion This protocol could lead to the development of a biomechanics TC training program for the improvement of gait and postural stability among individuals with PD. The program could enhance the understanding of the effect of TC training on gait and postural stability and could help improve or preserve the postural stability, self-confidence, and active participation in social activities of the participants, thus enhancing their overall quality of life. Trial registration ClinicalTrials.gov NCT04644367. Registered on 25 November 2020.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".