Screen-based sedentary behavior and associations with functional strength in 6–15 year-old children in the United States
Bibliographic record
Abstract
BACKGROUND: Physical strength is associated with improved health outcomes in children. Heavier children tend to have lower functional strength and mobility. Physical activity can increase children's strength, but it is unknown how different types of electronic media use impact physical strength. METHODS: Data from the NHANES National Youth Fitness Survey (NNYFS) from children ages 6-15 were analyzed in this study. Regression models were conducted to determine if screen-based sedentary behaviors (television viewing time, computer/video game time) were associated with strength measures (grip, leg extensions, modified pull-ups, plank) while controlling for potential confounders including child age, sex, BMI z-score, and days per week with 60+ minutes of physical activity. Grip strength and leg extensions divided by body weight were analyzed to provide measures of relative strength together with pull-ups and plank, which require lifting the body. RESULTS: The results from the regression models showed the hypothesized inverse association between TV time and all strength measures. Computer time was only significantly inversely associated with the ability to do one or more pull-ups. CONCLUSIONS: This study shows that television viewing, but not computer/videogames, is inversely associated with measures of child strength while controlling for child characteristics and physical activity. These findings suggest that "screen time" may not be a unified construct with respect to strength outcomes and that further exploration of the potential benefits of reducing television time on children's strength and related mobility is needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".