Assessment of physical fitness during pregnancy: validity and reliability of fitness tests, and relationship with maternal and neonatal health – a systematic review
Bibliographic record
Abstract
Background: Physical fitness (PF) is a powerful marker of health throughout the lifespan. In pregnant women, higher PF is associated with better maternal and fetal health, better delivery outcomes and earlier postpartum recovery. The assessment of PF during pregnancy requires special considerations to preserve fetal and maternal health; thus, providing a compilation of the most frequently used fitness tests, and assessing their validity, reliability, and association with maternal and neonatal health-related outcomes is of scientific and clinical interest. Objectives: To systematically review studies evaluating one or more components of PF in pregnant women, to answer two research questions: 1) What fitness tests have been previously employed in pregnant women? and 2) What is the validity and reliability of these tests and their relationship with health-related outcomes? Data Sources: PubMed and Web of Science. Methods: Two independent reviewers systematically examined the articles in each database. The information from the included articles was summarized by a single researcher. Results: A total of 149 articles containing a sum of 191 fitness tests were included. Among the 191 fitness tests, 99 (i.e.,52%) assessed cardiorespiratory fitness through 75 different protocols, 28 (15%) assessed muscular fitness through 16 different protocols, 14 (7%) assessed flexibility through 13 different protocols, 45 (24%) assessed balance through 40 different protocols, 2 assessed speed with the same protocol and 3 were multidimensional tests using one protocol. A total of 19 articles with 23 tests (13%) assessed either validity (n=4), reliability (n=6), or the relationship of PF with health-related outcomes (n=16). Conclusion: PF during pregnancy has been assessed through a wide variety of protocols, mostly lacking validity and reliability data.
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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 | 0.020 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".