Walk it off: Examining the effects of exercise on self-control and cravings in smokers
Bibliographic record
Abstract
Smoking is the leading cause of preventable death (Canadian Cancer Society, 2016). Although millions of Canadians try to quit each year, 95% of their quit attempts fail within a year (CDCP, 2011). Quitting requires self-control (SC)—a limited mental resource that leaves one susceptible to task failure upon depletion (Muraven & Baumeister, 2000). SC depletion increases lapse behaviour in smokers and cravings in deprived smokers (Heckman, 2014). A single bout of moderate-intense exercise alleviates cravings in abstinent smokers (Roberts, 2012). Unfortunately, the mechanism by which exercise exerts its effect is not well understood. We hypothesize that exercise reduces cravings by replenishing depleted SC resources during cessation. Thirteen smokers (M age = 43.15; 8 female) were randomized into 15 minutes of moderate-intensity exercise or passive control (read a magazine) conditions. A handgrip task was used to measure SC four times (baseline, post 24-h quit, post thought suppression task, and post exercise manipulation). Cigarette cravings were also assessed at these time points. Results showed no SC depletion effect after 24-h abstinence. However, a SC depletion effect was found after the thought suppression task (p < .05). Cravings were elevated (p < .05) and remained high following abstinence and thought suppression and then were reduced only for those who exercised (p < .05). Although not significant (p > .05), SC depletion continued for those in control condition but remained stable for those in the exercise condition. Preliminary data do not support the tenet that abstinence-induced cravings are reduced by exercise through SC replenishment pathways.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".