Association of Physical Fitness Indicators with Health Profile and Lifestyle of Children
Bibliographic record
Abstract
Objective: High physical fitness (PF) level is a significant health determinant in children and adolescents so that it is important to identify the factors affecting PF in this population. Despite available studies highlighting the relationship between PF and characteristics of children, there is still a need to uncover how the health status and lifestyle of children impact different PF indicators. Thus, the purpose of this study is to investigate the relationship between physical fitness, and the health profile and lifestyle of children. Methods: This study was conducted with 110 (58 girls; age 11.85±0.35) adolescents between February and March 2020. The preditors of PF which were gender, body mass index, physical activity level (PAL) measured via Physical Activity Questionnaire (PAQ), motivation measured via Participation Motivation Questionnaire (PMQ), sleep time, and tablet usage time regressed against PF related outcome measured using 6 Minutes Walk test (6MWT), T-Test, vertical jump test and broad jump test (BJT). Results: There were significant associations between T-test performance, and gender, BMI (being obese), and PAL. PAL and gender were also significant predictors for 6MWT and BJT respectively. PF was not significantly associated with motivation, sleep, and table usage time. A high level of physical activity, being male, and low BMI score resulted in better PF performance. Conclusion: The health profile and lifestyle of adolescents may estimate the significant proportion of variabilities observed in physical fitness levels in adolescents.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".