Evaluation of Breastfeeding App Features: Content Analysis Study
Bibliographic record
Abstract
BACKGROUND: While a variety of health apps abound, less than half of adults in the United States report using a health app, despite the ubiquity of smartphones among users aged 18 to 49 years. Several studies have examined the use of breastfeeding apps; however, less is known about the types of features found on these apps and what factors might influence app ratings. OBJECTIVE: This paper seeks to characterize breastfeeding apps, assess whether apps with higher user ratings differ from apps with lower user ratings in their tracking and nontracking features, and analyze whether the type and number of features predict user star ratings and whether an app is higher- or lower-rated. METHODS: Using a cross-sectional design, a convenience sample of breastfeeding apps was culled from the Apple App Store (iOS) and Google Play Store (Android). Content analysis of the apps (N=82) was conducted using a schema of 87 items, which was then compiled into 9 topical indices for breastfeeding, bottle feeding, solid foods, infant health, infant care, technical characteristics, informatics, informational characteristics, and interactivity. Analysis consisted of descriptive statistics, the Mann-Whitney U test, and Spearman rank correlations. Linear regression and binary logistic regression analyses were conducted to determine which features predicted user star ratings. RESULTS: On average, users rated breastfeeding apps 4.4 of 5 stars. Two-thirds of apps (n=54) were higher rated (≥4.5 stars), and one-third (n=28) were lower rated (<4.5 stars). Higher-rated apps offered more tracking features for breastfeeding, bottle feeding, solid foods, infant health, and infant care than lower-rated apps. The breastfeeding, solid-food, and technical indices explained 17% of user star ratings. For each additional breastfeeding and solid-food feature, we can expect to see a 27% and 35% increase, respectively, in user star ratings. Additionally, as the number of solid-food features increased, the odds that the app is higher rated increased 1.58 times. CONCLUSIONS: Our findings suggest user ratings are driven in part by tracking features, specifically those related to breastfeeding and solid foods. The proliferation of mobile health apps offers opportunities for parents and caregivers to track behaviors associated with infant feeding and other health metrics in a dynamic, detailed, and comprehensive manner. Hence, breastfeeding apps have the potential to promote and support breastfeeding among users.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".