Physical activity and anxiety: Dose-response versus mindset?
Bibliographic record
Abstract
There is a large body of evidence suggesting an inverse relationship between physical activity and anxiety (WHO, 2022). In explaining this relationship, researchers typically think in terms of dose-response (Kim et al., 2020). However, the relationship between physical activity and anxiety may be more complex, based on emerging research proposing that about half of the psychological benefits of physical activity may result from the belief that one is being active (Lindheimer et al., 2015). As one example, individuals who held a mindset that the amount of physical activity they engaged in was adequate for health reported better perceived health, regardless of actual activity levels (Zahrt & Crum, 2020). Following this line of research, the current study examined which had a stronger relationship with anxiety – physical activity or mindset about the adequacy of one’s activity for health. University students (N= 666) completed an online questionnaire assessing aerobic activity (PASB-Q, Fowles et al, 2017), perceived activity adequacy mindset (Zahrt & Crum, 2020), and generalized anxiety symptoms (GAD-7, Spitzer et al., 2006). Results from a hierarchical regression revealed a significant relationship between activity and anxiety (β= -0.094, p = 0.013) while controlling for gender on step 1. However, activity disappeared (β = -.005, p = .907) as a predictor of anxiety when mindset was entered on step 2 (β = -.213, p =
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".