Exploration of Dance as an Intervention for Long-term Low Back Pain amongst an Older Population: Groove to Improve
Bibliographic record
Abstract
Low Back Pain (LBP) has been cited as one of the highest ranked causes of disability worldwide. So far exercise interventions have generally had low to moderate effects on pain, disability, psychological measures and function. Dance interventions have been utilised in a number of different conditions with good outcomes, however this has yet to be assessed in older individuals reporting long-term LBP. This preliminary exploratory single arm study took place within a Dance company environment with support from clinicians involved in the treatment of LBP. A routine of exercises built around the principles of dance were developed. 3 Groups (with a total of 46) participated in three separate 6-week (1x week 1.5 hours) programmes. All participants had to be 60 or over, with a history of LBP greater than 12 months and not actively under care. Outcome measures were taken at the start and end of the programme with a dropout of three. The outcomes selected were the VAS Numerical Pain distress scale, Quebec Back Pain Disability Questionnaire and SF-36. Outcomes were positive with the average VAS dropping from 5 to 3 and Quebec Back Pain Disability Questionnaire from 35 to 28 (30% reduction regarded as clinically significant change) therefore VAS was clinically significant, the Quebec Back Pain Disability questionnaire although improved, did not meet that threshold. The SF-36 across the domains showed using a meaningful change of 5 points saw significant changes across multiple sub-scales. In conclusion the utilisation of dance programmes could be considered as an option for the management of long-term LBP in older adults. Further work is suggested to evaluate other demographics and in comparative studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".