An Examination of the Interconnectedness of Exercise Habit, Stress, and Exercise Behaviours
Bibliographic record
Abstract
Exercise habit relates to higher exercise (Lutz et al., 2010), but it is unclear if stress impacts this relationship. Although the relationship between exercise and stress is well documented, little research has examined how stress can impact the habit-exercise link. According to the Physical Activity Model (PAM; Nigg et al., 2008), stress diminishes psychological factors linked to exercise, such as self-efficacy (one’s belief about their ability to cope with difficulties; Bandura & Schunk, 1981). Given the positive links between exercise habit and self-efficacy, and based on contentions within the PAM, we examined if heightened stress moderated the effect of habit on exercise. Carleton University undergraduate students (N = 141; Mage = 19.37 years, SD = 3.89; 73% female) completed self-report surveys at two time points across seven days. Moderation analyses were performed using the PROCESS Macro (Hayes, 2022). Habit at time one related to exercise at time one (r = .44, p < .05) and time 2 (r = .31, p < .05). Exercise at time 1 related to exercise at time two (r = .54, p < .05). Stress did not moderate the link between habit and change in exercise behaviours (p = .75). It is unclear if null results stem from small sample sizes or use self-report questionnaires that may have recall bias. Measuring stress within the same context for all participants may be key. Reschke et al. (2024) did this when they measured stress during a university’s exam season, which is something for future research to consider.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".