Development and pilot evaluation of a pregnancy-specific mobile health tool: a qualitative investigation of SmartMoms Canada
Bibliographic record
Abstract
BACKGROUND: Mobile technology is ubiquitous. Women of childbearing age have embraced health information technology for pregnancy-related counsel as prenatal care provider communication is increasingly scarce and brief. Pregnant women and new mothers place high value in the use of online sources to support their pregnancy information needs. In Canada, over 300,000 women are pregnant annually, with approximately 60% exceeding evidence-based weight gain recommendations. Mobile health (mHealth) tools, such as mobile applications (app), have the potential to reduce excessive gestational weight gain, offering pregnant women trustworthy guidance, ultimately improving the health outcomes of mothers and infants. Therefore, the primary aim of this study was to implement a qualitative, descriptive research design to assess the receptiveness, functionality, and future prospective of the SmartMoms Canada mHealth app. METHODS: Two focus groups (n = 13) involving both currently pregnant and recently postpartum women were organized on the same day. Focus groups were transcribed verbatim and thematic analysis was undertaken using manual coding and NVivo software. Participants who took part in the focus groups (n = 13) and those who could not attend (n = 4) were asked to complete a Likert-scale survey. All survey responses (n = 17) were analyzed using simple tabulation and percentage analysis. RESULTS: Participants were technologically proficient and interacted with several mHealth tools prior to testing the SmartMoms Canada app. Six major themes emerged from thematic analysis: knowledge of pregnancy-specific mHealth services, knowledge and attitudes of weight gain guidelines, weight tracking, strengths of the app, critique and lastly, future suggestions for the app. CONCLUSIONS: Our thematic analysis found that women positively viewed the future potential of our app and offered constructive feedback to improve the next version. Participants sought more personalization and enhanced app interactivity, along with promotion of overall maternal health including nutrition and mental health, in addition to weight tracking.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".