Gamification of nutrition: A preliminary study on the impact of gamification on nutrition knowledge, attitude, and behaviour of adolescents in Nigeria
Bibliographic record
Abstract
BACKGROUND:: In Nigeria and many parts of sub-Saharan Africa, the availability of foods that are high in salt, sugar, and saturated fat is steadily increasing. This has led to an increase in the consumption of such foods among Nigerians, particularly among adolescents. AIM:: This pilot study was undertaken to understand whether, and how, gamification of nutrition can have an impact on addressing the problem of unhealthy eating among Nigerian adolescents. METHODS:: Gamification of nutrition through board games, clubs and vouchers was introduced in three secondary schools in Abuja, Nigeria over a span of three to four months. Semi-structured focus groups were conducted with grade 11 and 12 students in the three secondary schools. Participants were asked about their perceptions of the intervention and how it influenced their eating behaviour, attitudes and knowledge about nutrition. RESULTS:: A total of 31 students participated in four focus groups. Participants reported that the intervention shifted their perceptions and preferences, leading them to alter their behaviour by incorporating more nutritious foods (such as fruits and vegetables) into their diet and engaging in more physical activity. Five themes emerged from the analyses: improved eating behaviour; increased physical activity; improved overall well-being; increased nutrition knowledge; and influencing others. CONCLUSIONS:: The results from the focus groups suggest that gamification of nutrition can lead to improvements in dietary behaviour among adolescents over the short-term. More studies are needed to evaluate the long-term effects of nutrition interventions that use gamification techniques.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".