Analyzing Exercise Behaviors during the College Years: Results from Latent Growth Curve Analysis
Bibliographic record
Abstract
UNLABELLED: The objective of this study is to analyze changes in the predictors of physical activity behavior among college students. The Theory of Planned Behavior served as its theoretical framework. METHODS: Among an initial sample of 417 college students, 195 participants completed a validated questionnaire measuring attitudes, subjective norms, perceived control, intentions and self-reported physical activity, at the beginning and end of each of 3 college semesters. Latent growth curve modeling analyses were conducted to examine the relationships between the trajectories of changes in PA, intentions, attitudes, subjective norms, and perceived control. RESULTS: Good fit indices supported the validity of the proposed longitudinal model (CFI > .97, RMSEA < .05). Changes in perceived control (γ = 0.57) were significantly linked with changes in intentions (p < .05). Perceived control (γ = 0.28) and intention growth (γ = 0.36) predicted behavior changes (p < .05). No gender differences were observed on attitudes, subjective norms and perceived control (p > .10). However, girls tend to have higher growth parameters on intentions and physical activity (p < .05). In summary, intentions and physical activity has significantly increased over 3 college semesters (growth parameters significant at p < .05). CONCLUSIONS: This study demonstrated that attitudes and perceived control are key determinants regarding the intentions of being active. On a longer term perspective, future physical activity interventions should focus on the enhancement of students' perceived control. Such educational context should help in promoting the adoption of an active lifestyle during college.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".