A model linking video gaming, sleep quality, sweet drinks consumption and obesity among children and youth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is a growing need to curb paediatric obesity. The aim of this study is to untangle associations between video-game-use attributes and obesity as a first step towards identifying and examining possible interventions. Cross-sectional time-lagged cohort study was employed using parent-child surveys (t1) and objective physical activity and physiological measures (t2) from 125 children/adolescents (mean age = 13.06, 9-17-year-olds) who play video games, recruited from two clinics at a Canadian academic children's hospital. Structural equation modelling and analysis of covariance were employed for inference. The results of the study are as follows: (i) self-reported video-game play duration in the 4-h window before bedtime is related to greater abdominal adiposity (waist-to-height ratio) and this association may be mediated through reduced sleep quality (measured with the Pittsburgh Sleep Quality Index); and (ii) self-reported average video-game session duration is associated with greater abdominal adiposity and this association may be mediated through higher self-reported sweet drinks consumption while playing video games and reduced sleep quality. Video-game play duration in the 4-h window before bedtime, typical video-game session duration, sweet drinks consumption while playing video games and poor sleep quality have aversive associations with abdominal adiposity. Paediatricians and researchers should further explore how these factors can be altered through behavioural or pharmacological interventions as a means to reduce paediatric obesity.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it