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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».