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Record W4390338747 · doi:10.3390/jpm14010044

The Influence of Physical Activity during Pregnancy on Maternal Pain and Discomfort: A Meta-Analysis

2023· review· en· W4390338747 on OpenAlexaff
Miguel Sánchez‐Polán, Taniya S. Nagpal, Dingfeng Zhang, Cristina Silva‐José, Rocío Montejo, Rubén Barakat

Bibliographic record

VenueJournal of Personalized Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicPregnancy-related medical research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicinePregnancyPhysical therapyRandomized controlled trialPelvic painPhysical activityQuality of life (healthcare)Clinical trialObstetricsInternal medicineSurgeryNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.677
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.003
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.151
GPT teacher head0.447
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2023
Admission routes1
Has abstractyes

Explore more

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