To HIIT or not to HIIT? The question pregnant women may be searching for online: a descriptive observational study
Bibliographic record
Abstract
AIMS: An increasingly popular exercise modality for women is high-intensity interval training (HIIT). Limited research has assessed HIIT during pregnancy, and as a result, pregnant women may inquire about HIIT on their own through online searches. The purpose of this study was to systematically search and critically evaluate online resources that women may access when inquiring about performing HIIT during pregnancy. METHODS: as a reference, we identified evidence-based safety recommendations that apply to all prenatal exercise regimes. All selected links were assessed for whether or not they included evidence-based exercise and pregnancy safety recommendations. Descriptive analyses were performed to report the frequency of each construct. RESULTS: Seventy-six links were retrieved, and 33 relevant links were selected for inclusion. The majority of the retrieved links recommended that women should consult a healthcare provider before beginning any exercise programme (67%), and modify the intensity and types of exercises in the active HIIT bout based on general pregnancy-related changes (73%) and individual comfort level (55%). Just under half of the links recommended modifying intensity based on prepregnancy activity level (46%), offered trimester-specific recommendations (42%), and only 12% mentioned contraindications to exercise. CONCLUSION: Publicly accessible information online on HIIT during pregnancy does not routinely adhere to evidence-based safety recommendations for prenatal exercise. Further research on HIIT during pregnancy and public dissemination of findings is required.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".