Physical activity and prenatal depression: going beyond statistical significance by assessing the impact of reliable and clinical significant change
Bibliographic record
Abstract
BACKGROUND: Previous literature supports exercise as a preventative agent for prenatal depression; however, treatment effects for women at risk for prenatal depression remain unexplored. The purpose of the study was to examine whether exercise can lower depressive symptoms among women who began pregnancy at risk for depression using both a statistical significance and reliable and clinically significant change criteria. METHODS: This study is a secondary analysis of two randomized controlled trials that followed the same exercise protocol. Pregnant women were allocated to an exercise intervention group (IG) or control group (CG). All participants completed the Center for Epidemiological Depression (CES-D) scale at gestational week 9-16 and 36-38. Women with a baseline score ⩾16 were included. A clinically reliable cut-off was calculated as a 7-point change in scores from pre- to post-intervention. RESULTS: Thirty-six women in the IG and 25 women in the CG scored ⩾16 on the CES-D at baseline. At week 36-38 the IG had a statistically significant lower CES-D score (14.4 ± 8.6) than the CG (19.4 ± 11.1; p < 0.05). Twenty-two women in the IG (61%) had a clinically reliable decrease in their post-intervention score compared to eight women in the CG (32%; p < 0.05). Among the women who met the reliable change criteria, 18 (81%) in the IG and 7 (88%) in the CG had a score <16 post-intervention, with no difference between groups (p > 0.05). CONCLUSIONS: A structured exercise program might be a useful treatment option for women at risk for prenatal depression.
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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 | 0.030 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".