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Exploring the Relationship between Diet and TV, Computer and Video Game Use in a Group of Canadian Children

2014· article· en· W2131357677 on OpenAlexafffundvenueabout
Dona Tomlin, Heather McKay, Martina Forster, Ryan E. Rhodes, Joan Higgins, Patti‐Jean Naylor

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of Victoria
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BCHeart and Stroke Foundation of Canada
KeywordsMedicineVideo gameGroup (periodic table)AdvertisingMultimedia

Abstract

fetched live from OpenAlex

Increased screen-time has been linked to unhealthy dietary practices but most studies have looked primarily at television viewing or an amalgam. Therefore the purpose of this study was to investigate the relationship between specific screen-time (TV, computer, video game) and a selection of healthy dietary intake measures (calories, carbohydrate, fat, sugar, fruit, vegetables, fibre and sugar-sweetened beverages (SSB)) in a group of Canadian children. We used single day sedentary and dietary recalls to assess sedentary behaviour and diet in 1423 children (9.90 (0.58) y; 737 girls, 686 boys) from the Action Schools! BC Dissemination study. Correlations and multiple regression analyses were used to explore sedentary behaviour-diet relationships. TV and video game use were correlated with higher calories, fat, sugar and SSB consumption (r = 0.07 to 0.09; p <.01) and lower fibre intake (r = -0.05 to -0.06; p <.05). TV use was also correlated with lower fruit and vegetable intake. Regression analyses showed that when controlling for other variables, only TV and video game use predicted sugar and SSB consumption (β =.06 to.08; p <.05). Computer use was correlated with calories but did not significantly predict any of the measures of dietary intake. Although screen time was significantly associated with less healthy eating profiles, it did not account for much variance in dietary behaviour of these children.

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)0.001
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 score0.033
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.085
GPT teacher head0.309
Teacher spread0.223 · 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

Citations8
Published2014
Admission routes4
Has abstractyes

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