Unravelling the Relationship Between Exercise-Induced Affective Responses and Daily Physical Activity in People with Chronic Diseases
Bibliographic record
Abstract
Objectives: Exercise-induced affective responses (ARs) may encourage people with chronic diseases (PCD) to engage in future physical activity (PA).However, research has predominantly examined average ARs rather than specific moments, and mechanisms linking ARs to subsequent PA remain unclear.This study examines whether AR timing during exercise differentially predicts daily moderate-to-vigorous PA (MVPA) in PCD, exploring two pathways: the affect processing pathway (via remembered pleasure, anticipated pleasure and affective attitudes) and the self-efficacy pathway (via self-efficacy towards exercise and PA). Design: Prospective correlational design.Methods: 109 adults (Mage=66.6years, 79% women) with chronic diseases reported ARs four times during exercise.Post-exercise measures included remembered and forecasted pleasure, self-efficacy, and confounders (body mass index, perceived exertion).Affective attitudes and self-efficacy towards PA were assessed 24h later.Daily MVPA was measured using accelerometers over seven days.Results: Only the last AR significantly predicted daily MVPA (β=.24, p=.041), though this became non-significant after adjusting for confounders.ARs were associated with affective attitudes via remembered and anticipated pleasure, but affective attitudes did not predict MVPA (partial affect processing pathway).Conversely, the self-efficacy pathway was fully supported, with significant associations from ARs to daily MVPA via self-efficacy towards exercise and PA.However, while statistically significant, the effect size of this indirect pathway was small and potentially negligible (β = .05). Conclusions: Exercise-induced ARs do not directly predict subsequent daily MVPA in PCD.This challenges the assumption that positive experiences during a supervised exercise session spontaneously translate into higher PA in everyday life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".