Participants’ Engagement and Satisfaction With a Smartphone App Intended to Support Healthy Weight Gain, Diet, and Physical Activity During Pregnancy: Qualitative Study Within the HealthyMoms Trial
Bibliographic record
Abstract
BACKGROUND: Excessive gestational weight gain (GWG) is common and associated with negative health outcomes for both mother and child. Mobile health-delivered lifestyle interventions offer the potential to mitigate excessive GWG. The effectiveness of a smartphone app (HealthyMoms) was recently evaluated in a randomized controlled trial. To explore the users' experiences of using the app, a qualitative study within the HealthyMoms trial was performed. OBJECTIVE: This qualitative study explored participants' engagement and satisfaction with the 6-month usage of the HealthyMoms app. METHODS: ; university degree attainment: 13/19, 68%; primiparous: 11/19, 58%) who received the HealthyMoms app in a randomized controlled trial completed semistructured exit interviews. The interviews were audiorecorded and fully transcribed, coded, and analyzed using thematic analysis with an inductive approach. RESULTS: Thematic analysis revealed a main theme and 2 subthemes. The main theme, "One could suit many: a multifunctional tool to strengthen women's health during pregnancy," and the 2 subthemes, "Factors within and beyond the app influence app engagement" and "Trust, knowledge, and awareness: aspects that can motivate healthy habits," illustrated that a trustworthy and appreciated health and pregnancy app that is easy to use can inspire a healthy lifestyle during pregnancy. The first subtheme discussed how factors within the app (eg, regular updates and feedback) were perceived to motivate both healthy habits and app engagement. Additionally, factors beyond the app were described to both motivate (eg, interest, motivation, and curiosity) and limit (eg, pregnancy-related complications, lack of time) app engagement. The second subtheme reflected important aspects, such as high trustworthiness of the app, increased knowledge, and awareness from using the app, which motivated participants to improve or maintain healthy habits during pregnancy. CONCLUSIONS: The HealthyMoms app was considered a valuable and trustworthy tool to mitigate excessive GWG, with useful features and relevant information to initiate and maintain healthy habits during pregnancy. TRIAL REGISTRATION: ClinicalTrials.gov NCT03298555; https://clinicaltrials.gov/ct2/show/NCT03298555. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/13011.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 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".