How Do Infant Feeding Apps in China Measure Up? A Content Quality Assessment
Bibliographic record
Abstract
BACKGROUND: Globally, with the popularization of mobile phones, the number of health-related mobile phone apps has skyrocketed to 259,000 in 2016. In the digital era, people are accessing health information through their fingertips. In China, there are several apps that claim to provide infant feeding and nutrition guidance. However, the quality of information in those apps has not been extensively assessed. OBJECTIVE: We aimed to assess the quality of Chinese infant feeding apps using comprehensive quality assessment criteria and to explore Chinese mothers' perceptions on apps' quality and usability. METHODS: We searched for free-to-download Chinese infant feeding apps in the iTunes and Android App Stores. We conducted a comprehensive assessment of the accountability, scientific basis, accuracy of information relevant to infant feeding, advertising policy, and functionality and carried out a preliminary screening of infant formula advertisements in the apps. In addition, we also conducted exploratory qualitative research through semistructured interviews with Chinese mothers in Shanghai to elicit their views about the quality of apps. RESULTS: A total of 4925 apps were screened, and 26 apps that met the selection criteria were evaluated. All 26 apps were developed by commercial entities, and the majority of them were rated poorly. The highest total score was 62.2 (out of approximately 100) and the lowest was 16.7. In the four quality domains assessed, none of them fulfilled all the accountability criteria. Three out of 26 apps provided information covering the three practices from the World Health Organization's infant feeding recommendations. Only one app described its advertising policy in its terms of usage. The most common app functionality was a built-in social forum (19/26). Provision of a website link was the least common functionality (2/26). A total of 20 out of 26 apps promoted infant formula banner advertisements on their homepages. In addition, 12 apps included both e-commerce stores and featured infant formula advertisements. In total, 21 mothers were interviewed face-to-face. Mothers highly valued immediate access to parenting information and multifunctionality provided by apps. However, concerns regarding incredible information and commercial activities in apps, as well as the desire for information and support offered by health care professionals were expressed. CONCLUSIONS: The findings provide valuable information on Chinese infant feeding apps. The results are concerning, particularly with the relative absence of scientific basis and credibility and the large number of commercial advertisements that are displayed. Apps do seem to be able to provide an opportunity for mothers to access health information and support; it is time for tighter controls on content and advertisements. Ongoing app research and development should focus on implementation of a standard framework, which would drive the development of high-quality apps to support healthy infant feeding through cooperation among academics, health professionals, app users, app developers, and government bodies.
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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 | 0.034 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".