Associations entre l'utilisation de l'application SmartMoms Canada et le contrôle du gain de poids gestationnel, les habitudes de vie et les issues centrées sur le patient chez les femmes enceintes
Bibliographic record
Abstract
Nearly 49% of Canadian pregnant individuals exceed their gestational weigh gain (GWG) recommendations. Lifestyle habit (LsH) interventions improve adherence to a recommended GWG, but their large-scale adoption is limited by the resources they require. Our aims were therefore to assess associations between, on one hand, the engagement with a mobile health (mHealth) intervention aimed at Canadian pregnant women, the SmartMoms Canada app, and, on the other hand, GWG recommendations adherence (vs. the Canadian population, or based on level of user engagement), and changes in LsH, depressive symptoms, quality of life (QoL), and sleep measures during pregnancy in app users. Methods. Prospective, uncontrolled interventional study design. Seventy-five persons with an uncomplicated pregnancy were assessed in the early- (12th–20th week), mid- (24th–28th week), and late-pregnancy (36th–40th week), when physical activity (PA) levels (self-reported [Godin’s questionnaire] or measured [Fitbit Charge 2/Luxe tracker]), dietary intakes (food log), sleep quality (Pittsburgh index), depressive symptoms (Edinburgh’s scale), and QoL (Short-Form 36 questionnaire) were measured. GWG (Withing scale) and app usage (min/week) were repeatedly monitored. 95%CI were built around proportions (adequate, insufficient, or excessive GWG compared to Canadian population). Multinomial logistic regressions, independent 2-sample t-tests (early-to-late changes in outcomes between a higher engagement group [≥median] and a lower engagement group [
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".