Distinct trajectories of leisure time physical activity and predictors of trajectory class membership: a 22 year cohort study
Bibliographic record
Abstract
UNLABELLED: or BACKGROUND: Prospective studies linking social factors to long term patterns of physical activity are lacking. In this 22 year longitudinal study, we seek to identify long term patterns of involvement in leisure time physical activity (LTPA), and explore socioeconomic and demographic predictors of distinct LTPA trajectories. METHODS: Among 2102 individuals aged 18-60 years in 1981 who participated in the 1981 Canada Fitness Survey/1988 Campbell's Survey of Well-Being, 1186 (56.4%) completed questionnaires for the 2002/04 follow-up. Complete data on LTPA at all 3 surveys were available for 884 participants. Latent class growth analysis was used to identify major classes of LTPA trajectories; predictors of class membership were identified using polytomous logistic regression. RESULTS: Four latent classes were identified: inactive, increasers, active, and decreasers (53%, 26%, 12%, and 9% of participants, respectively). Women, older participants, those with lower household income, and with lower educational attainment, were significantly less likely to follow active (Vs. inactive) trajectories of LTPA. Disadvantaged groups with respect to education and income were also significantly more likely to follow decreasing (Vs. active) trajectories. CONCLUSION: There is a need for continued efforts to increase overall population levels of LTPA, particularly among socially disadvantaged groups with respect to income and education, who are most likely to experience unfavorable trajectories of LTPA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".