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Record W2945645598 · doi:10.2196/13051

Effects of the FIT Game on Physical Activity in Sixth Graders: A Pilot Reversal Design Intervention Study

2019· article· en· W2945645598 on OpenAlexvenueno aff
Damon Joyner, Heidi J. Wengreen, Sheryl Aguilar, Gregory J. Madden

Bibliographic record

VenueJMIR Serious Games · 2019
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUtah State University
KeywordsIntervention (counseling)PsychologyMathematics educationDesign studyPhysical activityApplied psychologyDevelopmental psychologyPhysical therapyEngineeringMedicineArchitectural engineering

Abstract

fetched live from OpenAlex

BACKGROUND: The FIT Game is a low-cost intervention that increases fruit and vegetable consumption in elementary school children. For this study, the FIT Game was adapted into an intervention designed to increase children's physical activity at school. OBJECTIVE: We aimed to evaluate if the FIT Game could increase children's physical activity relative to their baseline levels. METHODS: A total of 29 participants were recruited from a sixth-grade classroom. An ABAB reversal design was used. Participants wore an accelerometer while at school during pre/postintervention baseline (A) and intervention (B) phases. During the FIT Game intervention, daily physical activity goals encouraged the class to increase their median daily step count above the 60th percentile of the previous 10 days. When daily goals were met, game-based accomplishments were realized. RESULTS: Children met their activity goals 80% of the time during the intervention phases. Physical activity at school increased from a median of 3331 steps per day during the baseline to 4102 steps during the FIT Game phases (P<.001, Friedman test). CONCLUSIONS: Preliminary evidence showed that playing the FIT Game could positively influence children's physical activity at school.

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 score0.573
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.023
GPT teacher head0.308
Teacher spread0.285 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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