Pilot testing the effectiveness of a community-based yoga program on health outcomes among adults living with chronic pain
Bibliographic record
Abstract
Chronic pain affects nearly 20% of Canadian adults and can severely impact health and well-being. Using prescription opioids is the most common pain management strategy, despite their long-term ineffectiveness and high dependency, tolerance, and addiction risks. Alternatively, exercise is a recommended non-pharmacological evidence-based strategy. Gentle movement exercises, including yoga, are efficacious in improving pain-related health outcomes (pain intensity; depressive symptoms; physical function) in randomized controlled trials. However, testing the effectiveness in real-world settings has not been done. Thus, this pilot study examined the effectiveness of an 8-week community-based yoga program on improving pain intensity, symptoms of depression, and physical function among adults living with chronic pain. Participants (n = 7; Mage = 54.14 ± 7.54 years) completed pre- and post-program paper surveys assessing all health outcomes. Wilcoxon signed rank tests revealed that symptoms of depression decreased significantly from pre- to post-program (p = .04), whereas changes in pain intensity and physical function were non-significant (p's > .05). Given the small sample size, effect sizes were also calculated. All health outcomes demonstrated large effects (r's = 0.42 to 0.54). This pilot study was the first to show that a community-based yoga program had a large, favorable impact on pain intensity, symptoms of depression, and physical function. Future replication research with larger samples is needed to determine if community-based yoga that targets adults living with chronic pain is an effective management strategy. If so, then this type of exercise should be made available to people living with pain in communities across Canada.Acknowledgments: The research was unfunded and the authors do not have conflicts of interest to disclose. The researchers extend a sincere 'thank you' to all of the participants who volunteered their time to make this research possible.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".