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.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".