Effect of a Health-Based-Physical Activity Intervention on University Students’ PA Behaviors and Perception
Bibliographic record
Abstract
As part of their mission to educate students holistically, colleges and universities are expected to provide resources to help students avoid unhealthy lifestyle choices like sedentary living.In this regard, research indicates that sedentary lifestyle behaviours have been linked to various adverse physiological health outcomes.In this study, a health-based intervention was designed to encourage female college students to be physically active (PA) on a regular basis.The BMI, PBF, body image, and exercise self-efficacy were all studied.Using a pre-test-post-test experimental design, 157 female university students, out of 192 enrolled in five sections of a physical fitness and wellness course, voluntarily participated in a 12-week brisk-walking intervention.Tanita BC-420 MA, Body-Image Measure, and Exercise Scale were used to collect data at the beginning and end of the intervention during the fall of 2018 related to physically active adherence, BMI, PBF, body image, and exercise self-efficacy.The results of the current study showed that the strategy pursued with university students contributed to increasing levels of physical activity, reducing body weight, and reducing body fat in all study variables, while there was no change between the pre-and post-tests in the body image variable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".