Longitudinal Associations Between e-Cigarette Use, Cigarette Smoking, Physical Activity, and Recreational Screen Time in Canadian Adolescents
Bibliographic record
Abstract
INTRODUCTION: This study examined longitudinal associations between e-cigarette use, cigarette smoking, physical activity, and recreational screen time (ST) in a cohort of Canadian adolescents (ages 14-17 years; grades 9-12). AIMS AND METHODS: Data from 5951 adolescents who participated in COMPASS Year 4 (2015-2016; baseline) and Year 6 (2017-2018; follow-up) were used. Exposures included e-cigarette use and cigarette smoking. Outcomes included cutpoints for moderate- to vigorous-physical activity (MVPA; ≥60 min/d), muscular strengthening exercises (MSE; ≥3 time/wk), participation in sport (SP; intramural or competitive), and recreational screen time (ST; ≤430 min/day). Generalized linear mixed models were performed. RESULTS: e-Cigarette use (16.6% vs. 39.2%), cigarette smoking (0.9% vs. 4.7%), and dual use (0.8% vs. 4.1%) increased from baseline to follow-up. SP (70.8% vs. 61.3%) and the prevalence of meeting MVPA (49.8% vs. 42.1%) and MSE cutpoints (54.0% vs. 45.3%) decreased from baseline to follow-up. Recreational ST remained similar from baseline to follow-up. New e-cigarette use at follow-up was associated with maintenance of SP and meeting MVPA and MSE cutpoints, but also with increased ST. New cigarette smoking at follow-up was associated with maintaining high ST and low SP. Cigarette smoking at baseline and follow-up was associated with maintaining high ST, low MSE, and low SP. Cigarette smoking cessation at follow-up was associated with increasing MVPA and MSE, decreasing ST, and maintaining low SP. CONCLUSION: Given the clustering and co-occurring unhealthy behavioral patterns, intervention strategies to promote healthy lifestyles should take a holistic approach, by targeting multiple behavioral changes simultaneously. IMPLICATIONS: This investigation highlighted that, unhealthy behaviors, particularly e-cigarette use, cigarette smoking, and excessive use of screens, tend to co-occur among Canadian adolescents. Therefore, intervention strategies to promote healthy lifestyles should take a holistic approach, by targeting multiple behavioral changes simultaneously particularly in school and community settings. As an exception, new and stable e-cigarette use appears to co-occur with achieving sufficient levels of physical activity. Increasing awareness about the risk of e-cigarette use may target population groups that are physically and socially active (eg, athletes, sport teams).
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".