Theory-Based Design and Development of a Socially Connected, Gamified Mobile App for Men About Breastfeeding (Milk Man)
Bibliographic record
Abstract
BACKGROUND: Despite evidence of the benefits of breastfeeding, <15% of Australian babies are exclusively breastfed to the recommended 6 months. The support of the father is one of the most important factors in breastfeeding success, and targeting breastfeeding interventions to the father has been a successful strategy in previous research. Mobile technology offers unique opportunities to engage and reach populations to enhance health literacy and healthy behavior. OBJECTIVE: The objective of our study was to use previous research, formative evaluation, and behavior change theory to develop the first evidence-based breastfeeding app targeted at men. We designed the app to provide men with social support and information aiming to increase the support men can offer their breastfeeding partners. METHODS: We used social cognitive theory to design and develop the Milk Man app through stages of formative research, testing, and iteration. We held focus groups with new and expectant fathers (n=18), as well as health professionals (n=16), and used qualitative data to inform the design and development of the app. We tested a prototype with fathers (n=4) via a think-aloud study and the completion of the Mobile Application Rating Scale (MARS). RESULTS: Fathers and health professionals provided input through the focus groups that informed the app development. The think-aloud walkthroughs identified 6 areas of functionality and usability to be addressed, including the addition of a tutorial, increased size of text and icons, and greater personalization. Testers rated the app highly, and the average MARS score for the app was 4.3 out of 5. CONCLUSIONS: To our knowledge, Milk Man is the first breastfeeding app targeted specifically at men. The development of Milk Man followed a best practice approach, including the involvement of a multidisciplinary team and grounding in behavior change theory. It tested well with end users during development. Milk Man is currently being trialed as part of the Parent Infant Feeding Initiative (ACTRN12614000605695).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".