Associations Between Walking in the Third Trimester of Pregnancy and Maternal Mental Health During the COVID-19 Pandemic
Bibliographic record
Abstract
Prenatal physical activity (PA) has well-established benefits for maternal mental health. However, PA levels are generally low among pregnant individuals and were even lower during the COVID-19 pandemic. Since walking is the most popular form of prenatal PA, we aimed to examine associations between walking in the third trimester of pregnancy and mental health symptoms of depression, anxiety, pregnancy-related anxiety and perceived stress during the pandemic. Relevant pandemic-related factors (e.g., COVID-19 waves, population density) associated with walking were also studied. Pregnant individuals were recruited across Quebec (Canada) between October 2020 and September 2022, as part of the Resilience and Perinatal Stress during the Pandemic (RESPPA) study. Analyses were conducted on data collected via online questionnaires during the third trimester (n = 1086). Results revealed that higher levels of walking were significantly associated with lower symptoms of generalized anxiety (β = −0.06, p = 0.035), and perceived stress (β = −0.07, p = 0.007). Living in a more densely populated area, living with fewer children at home and having a university degree were associated with higher levels of walking. Those who completed their questionnaire in the second pandemic wave also reported higher levels of walking. Our results highlight the potential of walking in the third trimester to support maternal mental health.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".