Examination of Lifestyle Behaviors and Cardiometabolic Risk Factors in University Students Enrolled in Kinesiology Degree Programs
Bibliographic record
Abstract
Preventing physical inactivity and weight gain during college is critical in decreasing lifelong obesity and associated disease risk. As such, we sought to compare cardiometabolic risk factors and lifestyle behaviors between college students enrolled in kinesiology and non-kinesiology degree programs to assess whether health and exercise degree programs may influence health behaviors and associated disease risk outcomes. Anthropometrics, fasting blood glucose, insulin, lipid profiles and HbA1c%, blood pressure, and peak oxygen consumption (V[Combining Dot Above]O2peak) were assessed in 247 healthy college students. The homeostasis model assessment of insulin sensitivity (HOMA) was calculated using glucose and insulin levels. Self-reported physical activity from the Paffenbarger questionnaire was collected to estimate the average caloric expenditure due to different types of physical activities. Despite no significant differences in body mass index or waist circumference between groups, kinesiology majors presented with ∼20% lower fasting insulin levels and HOMA (p = 0.01; p < 0.01, respectively) relative to nonmajors. Kinesiology majors reported increased weekly participation in vigorous-intensity sport and leisure activities and, on average, engaged in >300 metabolic equivalent-h·wk, whereas non-kinesiology majors engaged in <300 MET-h wk (p = 0.01). Our data suggest that students enrolled in kinesiology degree programs display improved healthy behaviors and associated outcomes (parameters of glucose homeostasis). Practical outcomes of this research indicate that implementing components of a comprehensive kinesiology curriculum encourages improved health behaviors and associated cardiometabolic risk factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".