Designing a Digital Intervention to Increase Human Milk Feeding Among Black Mothers: Qualitative Study of Acceptability and Preferences
Bibliographic record
Abstract
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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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".