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Record W3015853593 · doi:10.2196/16431

Nutritional Education and Promotion of Healthy Eating Behaviors Among Mexican Children Through Video Games: Design and Pilot Test of FoodRateMaster

2020· article· en· W3015853593 on OpenAlexvenueno aff
Ismael Edrein Espinosa‐Curiel, Edgar Efrén Pozas-Bogarin, Jorge Luis Lozano-Salas, Juan Martı́nez-Miranda, Edwin Emeth Delgado-Pérez, Lizeth Stefania Estrada-Zamarron

Bibliographic record

VenueJMIR Serious Games · 2020
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsPromotion (chess)Test (biology)Video gamePsychologyPerceptionHealthy foodCognitionHealthy eatingMedicineObesityGerontologyDevelopmental psychologyPhysical activityPhysical therapyMultimediaPsychiatryFood science

Abstract

fetched live from OpenAlex

BACKGROUND: Childhood obesity has risen dramatically in recent decades, reaching epidemic levels. Children need guidance on and support for maintaining a healthy diet and physical activity to ensure that they grow appropriately and develop healthy eating habits. Serious video games have shown positive effects on promoting the nutritional knowledge, and eating attitudes and behaviors of children; however, research about the usefulness of such games with younger children (8-10 years old) is sparse. OBJECTIVE: The objective of this study was to design and test the serious video game FoodRateMaster targeting children between 8 and 10 years old. The game includes nutritional information and behavior change techniques to help children improve their knowledge of healthy and unhealthy foods, increase their intake of healthy food, and reduce their intake of ultraprocessed food. In addition, FoodRateMaster was designed as an active game to promote physical activity. METHODS: An interdisciplinary team developed FoodRateMaster following an iterative methodology based on a user-centered design. A total of 60 participants (mean age 9 years, SD 0.8; 53% male) completed 12 individual gaming sessions in 6 weeks. A food knowledge questionnaire and a food frequency questionnaire were completed before and after game play. In addition, 39 of the participants' parents answered a parent perception questionnaire after the game play. RESULTS: Participants showed increased food knowledge from pregame (mean 56.9, SD 10.7) to postgame play (mean 67.8, SD 10.7; P<.001). In addition, there was a greater self-reported frequency in the consumption of cauliflower and broccoli (P<.001) and corn quesadillas (P<.001). They also indicated a lower self-reported intake of 10 unhealthy foods, including french fries (P=.003), candy and chocolate (P<.001), sweet soft cakes (P=.009), and soft drinks (P=.03). Moreover, most of the parents who answered the parent perception questionnaire agreed that their children showed greater interest in explaining why they should avoid some unhealthy foods (67%, 26/39), in distinguishing between healthy and unhealthy foods (64%, 25/39), and in the intake of fruits (64%, 25/39) and vegetables (59%, 23/39). Finally, 14 parents stated that they introduced some changes in their children's diet based on the comments and suggestions they received from their children. CONCLUSIONS: In an initial evaluation, children between 8 and 10 years old indicated an increased level in nutritional knowledge and their self-reported frequency intake of two healthy foods, and a decreased level in their self-reported intake of 10 unhealthy foods after playing FoodRateMaster. Moreover, the participants' parents agreed that FoodRateMaster positively influenced their children's attitudes toward several healthy eating behaviors. These results support that health games such as FoodRateMaster are viable tools to help young children increase their food knowledge and improve dietary behaviors. A follow-up randomized controlled trial will be conducted to assess the medium- and long-term effects of FoodRateMaster.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.332
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations49
Published2020
Admission routes1
Has abstractyes

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