Content and Quality of Infant Feeding Smartphone Apps: Five-Year Update on a Systematic Search and Evaluation
Bibliographic record
Abstract
BACKGROUND: Parents use apps to access information on child health, but there are no standards for providing evidence-based advice, support, and information. Well-developed apps that promote appropriate infant feeding and play can support healthy growth and development. A 2015 systematic assessment of smartphone apps in Australia about infant feeding and play found that most apps had minimal information, with poor readability and app quality. OBJECTIVE: This study aimed to systematically evaluate the information and quality of smartphone apps providing information on breastfeeding, formula feeding, introducing solids, or infant play for consumers. METHODS: The Google Play store and Apple App Store were searched for free and paid Android and iPhone Operating System (iOS) apps using keywords for infant feeding, breastfeeding, formula feeding, and tummy time. The apps were evaluated between September 2018 and January 2019 for information content based on Australian guidelines, app quality using the 5-point Mobile App Rating Scale, readability, and suitability of health information. RESULTS: A total of 2196 unique apps were found and screened. Overall, 47 apps were evaluated, totaling 59 evaluations for apps across both the Android and iOS platforms. In all, 11 apps had affiliations to universities and health services as app developers, writers, or editors. Furthermore, 33 apps were commercially developed. The information contained within the apps was poor: 64% (38/59) of the evaluations found no or low coverage of information found in the Australian guidelines on infant feeding and activity, and 53% (31/59) of the evaluations found incomplete or incorrect information with regard to the depth of information provided. Subjective app assessment by health care practitioners on whether they would use, purchase, or recommend the app ranged from poor to acceptable (median 2.50). Objective assessment of the apps' engagement, functionality, aesthetics, and information was scored as acceptable (median 3.63). The median readability score for the apps was at the American Grade 8 reading level. The suitability of health information was rated superior or adequate for content, reading demand, layout, and interaction with the readers. CONCLUSIONS: The quality of smartphone apps on infant feeding and activity was moderate based on the objective measurements of engagement, functionality, aesthetics, and information from a reliable source. The overall quality of information on infant feeding and activity was poor, indicated by low coverage of topics and incomplete or partially complete information. The key areas for improvement involved providing evidence-based information consistent with the Australian National Health and Medical Research Council's Infant Feeding Guidelines. Apps supported and developed by health care professionals with adequate health service funding can ensure that parents are provided with credible and reliable resources.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".