A Tai chi and qigong mind-body program for low back pain: A virtually delivered randomized control trial
Bibliographic record
Abstract
Background: Mind-body treatments have the potential to manage pain, yet their effectiveness when delivered online for the treatment of low back pain (LBP) is unknown. We sought to evaluate whether a virtually delivered mind-body program integrating tai chi, qigong, and meditation (VDTQM) is effective for treating LBP. Methods: This randomized controlled trial compared VDTQM (n=175) to waitlist control (n=175). Eligible participants were at least 18 years old, had LBP for at least 6 weeks, were not pregnant, had not previously taken tai chi classes, and had not undergone spine surgery within 6 months. The treatment group received a 12-week VDTQM program in live online 60-minute twice-weekly group classes from September 2022 to December 2022. All participants continued their usual activities and care. Primary outcome was pain-related disability assessed by the Oswestry Disability Index (ODI) score. Secondary outcomes included pain intensity, sleep quality, and quality of life (QOL). Intent-to-treat analyses were conducted. Results: Of the 350 participants 278 (79%) were female, mean age was 58.8 years (range: 21-92), 244 (69.7%) completed the 8-week survey, 248 (70.9%) the 12-week, and 238 (68%) the 16 -week. No participants withdrew due to adverse treatment effects. Compared with control group, treatment group experienced statistically and clinically significant improvement in ODI score by -4.7 (95% CI: -6.24 to -3.16, p<.01), -6.42 (95% CI: -7.96 to -4.88, p<.01), and -8.14 (95% CI: -9.68 to -6.59, p<.01) points at weeks 8, 12, and 16, respectively. Treatment group also experienced statistically significant improvement at all time points in the other outcomes. Conclusions: clincaltrials.gov Identifier: NCT05801588.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".