The short-term effect of a mHealth intervention on gestational weight gain and health behaviors: The SmartMoms Canada pilot study
Bibliographic record
Abstract
Gestational weight gain (GWG) has been shown to impact several maternal-infant outcomes. Since healthcare provider guidance on weight gain and healthy behaviors alone has failed to help women to meet guidelines during pregnancy, a practical adjunctive approach is to deliver evidence-based behavior change programs through mobile interventions. The present study aimed to assess the short-term effect of the SmartMoms Canada app to promote adequate GWG and healthy behaviors. Twenty-nine pregnant women were recruited in this app-based intervention trial to test whether a higher app usage (≥ 3.8 min·week −1 ) between 12–20 gestational weeks and 24–28 gestational weeks improved GWG , diet, physical activity, and sleep, compared to women with a lower app usage (< 3.8 min·week −1 ). Two-way mixed ANOVA for repeated measures was used to estimate the effect of the app usage and time, as well as their interaction on GWG and healthy behaviors. The likelihood ratio was used to examine the association between app usage categorization and GWG classification. Cramer's V statistic was used to estimate the effect size for interpretation of the association. Pregnant women using the SmartMoms Canada app more frequently had a higher moderate-to-vigorous physical activity (MVPA) daily average when compared with women with a lower usage (mean difference: 17.84 min/day, 95% CI: 2.44; 33.25). A moderate effect size (28.6% vs . 15.4%; Cramer's V = 0.212) was found for the association between app categorization and rate of GWG , representing a greater adherence to the GWG guidelines in women in the higher app usage group vs . the lower app usage group. Considering other physical activity, diet, and sleep variables, no app categorization effect was observed. A short-term higher usage of SmartMoms Canada app has a positive effect on objectively-measured MVPA .
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".