Exercise for the prevention and treatment of low back, pelvic girdle and lumbopelvic pain during pregnancy: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The purpose of this review was to investigate the relationship between prenatal exercise, and low back (LBP), pelvic girdle (PGP) and lumbopelvic (LBPP) pain. DESIGN: Systematic review with random effects meta-analysis and meta-regression. DATA SOURCES: Online databases were searched up to 6 January 2017. STUDY ELIGIBILITY CRITERIA: Studies of all designs were eligible (except case studies and reviews) if they were published in English, Spanish or French, and contained information on the population (pregnant women without contraindication to exercise), intervention (subjective or objective measures of frequency, intensity, duration, volume or type of exercise, alone ["exercise-only"] or in combination with other intervention components [eg, dietary; "exercise + co-intervention"]), comparator (no exercise or different frequency, intensity, duration, volume and type of exercise) and outcome (prevalence and symptom severity of LBP, PGP and LBPP). RESULTS: The analyses included data from 32 studies (n=52 297 pregnant women). 'Very low' to 'moderate' quality evidence from 13 randomised controlled trials (RCTs) showed prenatal exercise did not reduce the odds of suffering from LBP, PGP and LBPP either in pregnancy or the postpartum period. However, 'very low' to 'moderate' quality evidence from 15 RCTs identified lower pain severity during pregnancy and the early postpartum period in women who exercised during pregnancy (standardised mean difference -1.03, 95% CI -1.58, -0.48) compared with those who did not exercise. These findings were supported by 'very low' quality evidence from other study designs. CONCLUSION: Compared with not exercising, prenatal exercise decreased the severity of LBP, PGP or LBPP during and following pregnancy but did not decrease the odds of any of these conditions at any time point.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".