Does leisure time physical activity protect against low back pain? Systematic review and meta-analysis of 36 prospective cohort studies
Bibliographic record
Abstract
Background There are plausible mechanisms whereby leisure time physical activity may protect against low back pain (LBP) but there have been no quality systematic reviews and meta-analyses of the subject. Objective This review aims to assess the effect of leisure time physical activity on non-specific LBP. Methods Literature searches were conducted in PubMed, Embase, Web of Science, Scopus and Google Scholar databases from their inception through July 2016. Methodological quality of included studies was evaluated. A random-effects meta-analysis was performed, and heterogeneity and publication bias were assessed. Results Thirty-six prospective cohort studies (n=158 475 participants) qualified for meta-analyses. Participation in sport or other leisure physical activity reduced the risk of frequent or chronic LBP, but not LBP for > 1 day in the past month or past 6–12 months. Risk of frequent/chronic LBP was 11% lower (adjusted risk ratio (RR)=0.89, CI 0.82 to 0.97, I 2 =31%, n=48 520) in moderately/highly active individuals, 14% lower (RR=0.86, CI 0.79 to 0.94, I 2 =0%, n=33 032) in moderately active individuals and 16% lower (RR=0.84, CI 0.75 to 0.93, I 2 =0%, n=33 032) in highly active individuals in comparison with individuals without regular physical activity. For LBP in the past 1–12 months, adjusted RR was 0.98 (CI 0.93 to 1.03, I 2 =50%, n=32 654) for moderate/high level of activity, 0.94 (CI 0.84 to 1.05, I 2 =3%, n=8549) for moderate level of activity and 1.06 (CI 0.89 to 1.25, I 2 =53%, n=8554) for high level of activity. Conclusions Leisure time physical activity may reduce the risk of chronic LBP by 11%–16%. The finding, however, should be interpreted cautiously due to limitations of the original studies. If this effect size is proven in future research, the public health implications would be substantial.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.044 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".