The Influence of Physical Activity during Pregnancy on Maternal Pain and Discomfort: A Meta-Analysis
Bibliographic record
Abstract
Pregnant women may experience pain and discomfort during pregnancy, especially in areas such as the lower back and pelvic girdle. Pain in pregnancy is associated with poor quality of life, and because it is a common occurrence, pregnant women may be offered several resources to prevent discomforts throughout pregnancy, such as engaging in physical activity. This study was a meta-analysis of randomised controlled trials (prospectively registered in Prospero, registration number: CRD42023451320) aimed to assess the effects of physical activity during pregnancy on maternal pain and discomfort. We analysed 16 randomised clinical trials. The results of these analyses indicate that women who performed physical activity had significantly less intensity of pain (z = <2.69, p = <0.007; SMD = −0.66, 95% CI = −1.13, −0.18, I2 = <91%, Pheterogeneity = <0.001) and a reduction observed in the disability questionnaire (z = <2.37, p = <0.02; SMD = −0.80, 95% CI = −1.47, −0.14, I2 = <91%, Pheterogeneity = <0.001), and overall reduced general pain (z = <3.87, p = <0.001; SMD = −0.56, 95% CI = −0.84, −0.27, I2 = <86%, Pheterogeneity = <0.001) than women who did not practice physical activity during pregnancy. In conclusion, physical activity during pregnancy could effectively help to diminish pain intensity, reduce disability due to pain, and generally reduce pain.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.062 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".