Do anxiety sensitivity and impulsivity interact in predicting exercise involvement in emerging adult drinkers and cannabis users?
Bibliographic record
Abstract
Anxiety sensitivity (AS; i.e. fear of arousal-related sensations) and impulsivity (i.e. tendency to act quickly without regard for longer-term consequences) represent risks for low physical activity participation. Theoretically, higher impulsivity may exacerbate the negative exercise behaviours of high AS individuals given the tendency of impulsive individuals to favour immediate (e.g. watching TV) over delayed rewards (e.g. the benefits of exercise). Our goal was to investigate the main and interactive effects of AS and impulsivity on physical activity levels at varying exercise intensities. Participants were 178 emerging adults (Mean age = 21.9; 68.8% women). Higher AS was associated with less engagement in vigorous intensity exercise. Moderator analyses revealed an AS x impulsivity interaction: high AS predicted significantly less engagement in moderate intensity exercise at low impulsivity levels and marginally more engagement in moderate intensity exercise at high impulsivity levels. Finally, higher impulsivity was associated with more time spent walking. Cognitive behavioural therapy for high AS, or teaching individuals with high AS to focus on immediate, external rewards of exercise, may help them engage in more physical activity. Given the wide-ranging physical and mental health benefits of exercise involvement, developing effective strategies to increase such involvement in high AS individuals is vital.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".