Identifying Changes in Physical Activity, Body Weight, and Body Composition in First Year University Students
Bibliographic record
Abstract
Objective To identify changes in physical activity, body weight, and body composition as students' transition from high school to university and undergo their first year of university. Methods Subjects included male and female students entering their first year at Brock University, aged 17–20 years, with no previous university or college experience. Data was collected over 2 academic years (2014–15 and 2015–16) at 2 time points: September and April. To analyze the changes in physical activity, students filled out an Activity Questionnaire assessing the physical activity they engaged in, and another questionnaire measuring interpersonal, intrapersonal and structural factors that promoted or dissuaded them from being physically active. Participants also underwent body composition testing. Results There was an overall average weight gain of 2.0 kg (4.4 lbs) ( p <0.001) across the sample, with males gaining an average of 3.5 kg (7.7 lbs) ( p <0.001) and females gaining an average of 1.4 kg (3.1 lbs) ( p <0.001). Females demonstrated significant increases in fat mass ( p <0.001) and lean mass ( p <0.001), whereas males demonstrated significant increases in lean mass ( p <0.001) only. These changes were accompanied by significant reductions in physical activity output, including energy expenditure, light, moderate, and vigorous activity minutes, and MET minutes per week among females. Males experienced similar significant reductions in all variables except vigorous activity minutes. Perceived intrapersonal, interpersonal, and structural barriers to physical activity participation significantly increased favouring reductions in physical activity for both males and females. Conclusions Weight gain occurred among first year students at Brock University, characterized by increased fat and lean mass in females and increased lean mass in males. This difference illustrates the importance of measuring body composition and not just body weight to assess these changes. Reductions in overall physical activity were observed across the sample highlighting the importance of developing interventions to promote physical activity among first year students. These interventions should focus on reducing the barriers that affect students' physical activity participation including peer influence, gender, age, ethnic background, and perceived body image.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".