MétaCan
Menu
Back to cohort
Record W4284974870 · doi:10.2196/39400

Feasibility and Usefulness of Evidence-Based Gaming to Deliver Health Messages to Tweens in a Classroom Setting

2022· article· en· W4284974870 on OpenAlexvenueno aff
Dana Purcell, Lavar Johnson, Kate Killion, Shane J. Sacco, Carolyn A. Lin, Valerie B. Duffy

Bibliographic record

VenueIproceedings · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingPsychologyDemographicsLoginApplied psychologyMedical educationSocial psychologyMedicineComputer science

Abstract

fetched live from OpenAlex

Background Our interdisciplinary team developed a publicly available online game—Eat and Move as I Like (EAMAIL)—for tweens based on the MyPlate evidence-based representation of the Dietary Guidelines. Objective We aimed to test the feasibility of using EAMAIL in a classroom setting to promote engagement and self-awareness and motivate healthier diet behaviors in tweens. Methods Teachers in one middle school offered EAMAIL on school Chromebooks (institutional review board–approved). The researcher introduced EAMAIL’s login instructions, including nonidentifiable usernames, basic demographics, and home zip codes. Children were instructed to enter EAMAIL’s Story Mode, which had 5 MyPlate-food group levels; children caught healthy foods in color-matching buckets and avoided sweets. Each level delivers informational and motivational messages, asking users to report liking or disliking food groups and making dietary improvements on 7-point facial hedonic scales (from Love it to It’s okay to Hate it). At game completion, children rated the game based on whether it made them want to eat better and play again. Aligned with the Design, Play, and Experience Framework, the researcher made observations to assess child engagement, feelings about the game and the messages, and the motivation to make dietary improvements. Children were encouraged to complete the Story Mode before advancing to Free Play Mode, which had greater game challenges and 15-second interruptions every 2 to 3 minutes to deliver physical activity and health messages. Finally, each child completed a 13-item online survey to assess game-playing experiences, the desire to play again, new knowledge learning, and whether the game motivated healthier behaviors. Results EAMAIL was administered to five 30-minute classes involving 54 children (age: mean 11.6 years; female: 75%; White: 58%) and 105 users, and 1187 games were played. By the highest user level reached in Story Mode, 10% of users completed level 1 (Grains), 14% completed level 2 (Vegetables), 11% completed level 3 (Healthier Protein), 17% completed level 4 (Fruits), 15% completed level 5 (Dairy), and 31% completed all levels. Across users’ highest levels, Healthier Protein, on average, was the most liked, and Vegetables was the least liked. Most reported at least Like it to eating more fruits and vegetables (82%), vegetables (73%), healthier protein (79%), fruits (84%), and dairy (80%). All users responded to end-game questions; 64% reported at least Like it to “The game made me want to eat better” and “I would like to play the game again.” These responses were unchanged for most users who completed Story Mode and entered Free Play Mode (n=24); 6 reported worse and 3 reported better. From the postgame online survey, somewhat agreed to strongly agreed was reported by 76% of children with regard to learning about healthy eating and by 50% with regard to the game being fun, the game having positive attributes (pace, challenge, and flow), and whether they would share their game experiences. Researcher observations were consistent with children’s online responses. Conclusions EAMAIL appears feasible for teaching tweens in classroom settings about MyPlate, encouraging self-reflection, and motivating healthier eating, with Story Mode maximizing health promotion messages and engagement. Conflicts of Interest None declared.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.043
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.160
GPT teacher head0.362
Teacher spread0.202 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIproceedingsSame topicChild Development and Digital TechnologyFrench-language works237,207