A Digital Lifestyle App for Hypertension During Pregnancy: Mixed Methods Intervention Development Study Using the Person-Based Approach
Notice bibliographique
Résumé
Background: Chronic hypertension affects 1%-5% of pregnancies, increasing women's risks of adverse pregnancy outcomes and life-long cardiovascular disease risk. Therefore, care management during pregnancy includes close monitoring of blood pressure and medication. Healthy dietary and physical activity behaviors have proven beneficial effects on blood pressure outside and during pregnancy. However, little is known about the best way to support women with chronic hypertension during pregnancy to adopt such behaviors, which could improve pregnancy outcomes, as well as future cardiovascular health. Objective: This study aims to develop and optimize a digital lifestyle intervention-the DAPHNY (Diet and Activity for Pregnancy Hypertension) app-with those who have experienced chronic hypertension during pregnancy. Methods: Guided by the person-based approach to intervention development, a review of literature and continuous expert input, including from patient and public representatives, informed the planning stage. This was followed by focus groups with maternity health professionals (n=23) and think-aloud interviews with women who had experience of chronic hypertension during pregnancy (n=11). A content analysis, underpinned by theoretical modeling using the capability opportunity motivation-behavior model, informed 3 logic models to visualize modifications for meaningful engagement with an intervention and sustained behavior change. The intervention was modified iteratively, leading to a first version of the digital intervention that was tested by women (n=10) to further optimize acceptability and engagement. App use data and user engagement patterns were captured. Results: An evidence-based, theoretically informed lifestyle app, named DAPHNY, was developed. Key features included in logic models and implemented into a first version of the app comprised supportive messaging to acknowledge challenges of hypertensive pregnancy, goal setting and progress reports for feedback on behaviors, information about health consequences to shape knowledge, credible source endorsement, and a reward or recognition system to acknowledge effort had been made. Engagement with the DAPHNY app during user testing demonstrated variability across users, with a mean of 13 (SD 6.84) sessions per participant. Session duration was variable, with a median of 36 seconds (range: 5 seconds to 5 minutes, 20 seconds). Action-based pages, including recording blood pressure (40 sessions) and step count (39 sessions), were accessed more frequently than informational pages, which required a deeper level of app engagement. Conclusions: Development of the DAPHNY app, underpinned by an established behavioral framework for developing digital interventions, provided new data insights about how to support women with chronic hypertension to engage in healthy behaviors, a currently overlooked aspect of blood pressure management. Future iterations should focus on increasing engagement and supporting implementation through streamlined content and integration with existing health systems and self-monitoring data. Rigorous, larger-scale studies including comprehensive process evaluation would determine potential clinical effectiveness, implementation strategies, and impact for women and health care professionals.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».