Sports Participation As A Protective Factor Of Metabolic Syndrome In Youth
Bibliographic record
Abstract
School-based intervention programs based on sports participation (SP) may be an important strategy to prevent and control metabolic syndrome risk factors and associated co-morbidities. PURPOSE: This study aims to investigate the influence of SP on a metabolic syndrome risk score (zMetS), whilst controlling for age, sex, maturation, cardiorespiratory fitness (CRF) and socioeconomic status (SES). METHODS: A sample of 197 Portuguese youth (93 boys) aged 9 to 16 years were studied. The zMetS was computed as the sum of the standardized scores of five components: systolic blood pressure, waist circumference, fasting blood glucose, triglycerides and high-density lipoprotein cholesterol. SES was determined using the Portuguese school social support system. Maturity offset was obtained by the Mirwald method. SP was recorded by Baecke questionnaire, and performance on the 1-mile run was used to estimate CRF. Robust multiple linear regressions were used in all calculations. RESULTS: Regression analysis showed that SP (β=−0.273, 95%CI: −0.504 to −0.042) significantly predicted zMetS (p<0.05). It was also found that individuals who had better CRF levels, later maturity, and who were younger had better metabolic profiles (p<0.05). Girls (zMetS=−0.062±1.016) also had better metabolic profiles than boys (zMetS=0.073±0.975). CONCLUSIONS: The findings suggest that SP is associated with metabolic syndrome risk factors in Portuguese youth, as a protective factor. Thus, school-based intervention programs focusing on sports practice may help to prevent increases in metabolic syndrome risk factors.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".