What’s Cooking? A Content and Quality Analysis of Food Preparation Mobile Applications (P16-050-19)
Bibliographic record
Abstract
To assess the nutrition content and overall quality of mobile apps for children that focus on food preparation. These apps are games that require the user to cook, prepare and decorate virtual foods. A systematic search of the Canadian Apple App and Google Play Stores was conducted using 16 unique search terms related to nutrition, education, and children. Apps were included for analysis if they were rated appropriate for children, if the app had been updated since January 2016 and was in English. App titles, developers and descriptions were screened to identify apps eligible for analysis. App content was assessed by classifying foods according to the Canadian Food Guide categorizations. App quality was evaluated using the Mobile Application Rating Scale (MARS), which ranges from 1 (lowest quality) to 5 (highest quality). All screening and analysis were conducted by two independent reviewers with a third reviewer to resolve disagreements. A total of 2575 unique apps were identified. After screening, 142 were included in the analysis. Apps were most likely to contain the following foods: Dairy products (73%), candy/frozen desserts (71%), refined grains (68%) fruits (61%), lean meats (52%), desserts/baked goods (49%), vegetables (44%), sugar-sweetened beverages (38%) and processed meats (33%). Apps were least likely to include fish (14%), plant-based proteins (10%), and whole grains (4%). Although the appearance of fruits in apps was high, in 55% of apps fruit was shown in combination with desserts, chocolate and candy, and rarely on their own (7%). Apps were more likely to include sweet, high-sugar foods and/or sugar-sweetened beverages (86%) over savoury, high-sodium foods (40%). No app directly provided nutrition information and only 3% of apps included healthy eating messages. The mean MARS score was 3.6 (range 2.5–4.8), indicating moderate quality overall. Children’s food preparation games were moderate quality and include many foods that are not recommended by dietary guidelines. Given the popularity of these games, collaborations between app developers and nutritionists could enhance the quality and content of food preparation apps by incorporating a variety of foods recommended by current guidelines and healthy eating messages. Ontario Research Excellence Fund.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".