Exploring Children's Knowledge of Healthy Eating, Digital Media Use, and Caregivers’ Perspectives to Inform Design and Contextual Considerations for Game-Based Interventions in Schools for Low-Income Families in Lima, Peru: Survey Study
Bibliographic record
Abstract
BACKGROUND: The prevalence of overweight and obesity in schoolchildren is increasing in Peru. Given the increased use of digital media, there is potential to develop effective digital health interventions to promote healthy eating practices at schools. This study investigates the needs of schoolchildren in relation to healthy eating and the potential role of digital media to inform the design of game-based nutritional interventions. OBJECTIVE: This study aims to explore schoolchildren's knowledge about healthy eating and use of and preferences for digital media to inform the future development of a serious game to promote healthy eating. METHODS: A survey was conducted in 17 schools in metropolitan Lima, Peru. The information was collected virtually with specific questions for the schoolchild and their caregiver during October 2021 and November 2021 and following the COVID-19 public health restrictions. Questions on nutritional knowledge and preferences for and use of digital media were included. In the descriptive analysis, the percentages of the variables of interest were calculated. RESULTS: We received 3937 validated responses from caregivers and schoolchildren. The schoolchildren were aged between 8 years and 15 years (2030/3937, 55.8% girls). Of the caregivers, 83% (3267/3937) were mothers, and 56.5% (2223/3937) had a secondary education. Only 5.2% (203/3937) of schoolchildren's homes did not have internet access; such access was through WiFi (2151/3937, 54.6%) and mobile internet (1314/3937, 33.4%). In addition, 95.3% (3753/3937) of schoolchildren's homes had a mobile phone; 31.3% (1233/3937) had computers. In relation to children's knowledge on healthy eating, 42.2% (1663/3937) of schoolchildren did not know the recommendation to consume at least 5 servings of fruits and vegetables daily, 46.7% (1837/3937) of schoolchildren did not identify front-of-package warning labels (FOPWLs), and 63.9% (2514/3937) did not relate the presence of an FOPWL with dietary risk. Most schoolchildren (3100/3937, 78.7%) preferred to use a mobile phone. Only 38.3% (1509/3937) indicated they preferred a computer. In addition, 47.9% (1885/3937) of caregivers considered that the internet helps in the education of schoolchildren, 82.7% (3254/3937) of caregivers gave permission for schoolchildren to play games with digital devices, and 38% (1495/3937) of caregivers considered that traditional digital games for children are inadequate. CONCLUSIONS: The results suggest that knowledge about nutrition in Peruvian schoolchildren has limitations. Most schoolchildren have access to the internet, with mobile phones being the device type with the greatest availability and preference for use. Caregivers' perspectives on games and schoolchildren, including a greater interest in using digital games, provide opportunities for the design and development of serious games to improve schoolchildren's nutritional knowledge in Peru. Future research is needed to explore the potential of serious games that are tailored to the needs and preferences of both schoolchildren and their caregivers in Peru in order to promote healthy eating.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".