Mobile Phone Apps for Low-Income Participants in a Public Health Nutrition Program for Women, Infants, and Children (WIC): Review and Analysis of Features
Bibliographic record
Abstract
BACKGROUND: Since 1972, the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) has been proven to improve the health of participating low-income women and children in the United States. Despite positive nutritional outcomes associated with WIC, the program needs updated tools to help future generations. Improving technology in federal nutrition programs is crucial for keeping nutrition resources accessible and easy for low-income families to use. OBJECTIVE: This review aimed to analyze the main features of publicly available mobile phone apps for WIC participants. METHODS: Keyword searches were performed in the app stores for the 2 most commonly used mobile phone operating systems between December 2017 and June 2018. Apps were included if they were relevant to WIC and excluded if the target users were not WIC participants. App features were reviewed and classified according to type and function. User reviews from the app stores were examined, including ratings and categorization of user review comments. RESULTS: A total of 17 apps met selection criteria. Most apps (n=12) contained features that required verified access available only to WIC participants. Apps features were classified into categories: (1) shopping management (eg, finding and redeeming food benefits), (2) clinic appointment management (eg, appointment reminders and scheduling), (3) informational resources (eg, recipes, general food list, tips about how to use WIC, links to other resources), (4) WIC-required nutrition education modules, and (5) other user input. Positive user reviews indicated that apps with shopping management features were very useful. CONCLUSIONS: WIC apps are becoming increasingly prevalent, especially in states that have implemented electronic benefits transfer for WIC. This review offers new contributions to the literature and practice, as practitioners, software developers, and health researchers seek to improve and expand technology in the program.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".