Designing a Digital Intervention to Increase Human Milk Feeding Among Black Mothers: Qualitative Study of Acceptability and Preferences
Notice bibliographique
Résumé
BACKGROUND: Breastfeeding rates among US mothers, particularly Black or African American mothers, fall short of recommended guidelines. Despite the benefits of human milk, only 24.9% of all infants receive human milk exclusively at 6 months. OBJECTIVE: Our team previously explored the key content areas a mobile health intervention should address and the usability of an initial prototype of the Knowledge and Usage of Lactation using Education and Advice from Support Network (KULEA-NET), an evidence-based mobile breastfeeding app guided by preferences of Black or African American parents. This study aimed to identify the preferences and acceptability of additional features, content, and delivery methods for an expanded KULEA-NET app. Key social branding elements were defined to guide app development as a trusted adviser. The study also aimed to validate previous findings regarding approaches to supporting breastfeeding goals and cultural tailoring. METHODS: We conducted a qualitative study using in-depth interviews and focus groups with potential KULEA-NET users. A health branding approach provided a theoretical framework. We recruited 24 participants across 12 interviews and 2 focus groups, each with 6 participants. The Data methods aligned with qualitative research principles and concluded once saturation was reached. Given the focus on cultural tailoring, team members who shared social identities with study participants completed data collection and coding. Two additional team members, 1 with expertise in social branding and 1 certified in lactation, participated in the thematic analysis. RESULTS: All participants identified as Black or African American mothers, and most interview participants (7/12, 58%) engaged in exclusive breastfeeding. In total, 4 themes were recognized. First, participants identified desired content, specifying peer support, facilitated access to experts, geolocation to identify resources, and tracking functions. Second, delivery of content differentiated platforms and messaging modality. Third, functionality and features were identified as key factors, highlighting content diversity, ease of use, credibility, and interactivity. Finally, appealing aspects of messaging to shape a social brand highlighted support and affirmation, inclusivity and body positivity, maternal inspiration, maternal identity, social norms, and barriers to alignment with aspirational maternal behaviors as essential qualities. Crosscutting elements of themes included a desire to communicate with other mothers in web-based forums and internet-based or in-person support groups to help balance the ideal medical recommendations for infant feeding with the contextual realities and motivations of mothers. Participants assigned high value to personalization and emphasized a need to achieve both social and factual credibility. CONCLUSIONS: This formative research suggested additional elements for an expanded KULEA-NET app that would be beneficial and desired. The health branding approach to establish KULEA-NET as a trusted adviser is appealing and acceptable to users. Next steps include developing full app functionality that reflects these findings and then testing the updated KULEA-NET edition in a randomized controlled trial.
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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 | 0,015 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».