Walk or Run to Quit: a 3-year evaluation of a physical activity-based smoking cessation intervention
Bibliographic record
Abstract
Abstract Introduction Walk or Run to Quit was a national program targeting smoking cessation through group-based running clinics. Increasing physical activity may facilitate smoking cessation as well as lead to additional health benefits beyond cessation. Aim To evaluate the impact of Walk or Run to Quit over 3 years. Methods Adult male and female participants (N = 745) looking to quit smoking took part in 156 running-based cessation clinics in 79 locations across Canada. Using a pre-post design, participants completed questionnaires assessing physical activity, running frequency and smoking at the beginning and end of the 10-week program and at 6-months follow-up. Carbon monoxide testing pre- and post- provided an objective indicator of smoking status and coach logs assessed implementation. Results 55.0% of program completers achieved 7-day point prevalence (intent-to-treat = 22.1%) and carbon monoxide significantly decreased from weeks 1 to 10 (P < 0.001). There was an increase in physical activity and running from baseline to end-of-program (P's<0.001). At 6-month follow-up, 28.9% of participants contacted self-reported prolonged 6-month abstinence (intent-to-treat = 11.4%) and 35.6% were still running regularly. Conclusions Although attrition was a concern, Walk or Run to Quit demonstrated potential as a scalable behaviour change intervention that targets both cessation and physical activity.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".