Examining the utility of exercise-related cognitive errors in predicting physical activity across 12-week fitness programmes
Bibliographic record
Abstract
Social cognitive theories often assume that information is acted upon in a rational manner. While theorists acknowledge biased perceptions exist, their theories do not include factors representing these inaccurate perceptions. Exercise-related cognitive errors (ECEs) may be such a factor that is associated with physical activity adherence beyond established predictors, like self-efficacy or past behaviour. The purpose of this study was to examine whether ECEs change across participants’ 12-week fitness programmes and examine whether ECEs predicted moderate-to-vigorous physical activity (MVPA) and self-regulatory efficacy (SRE). A sample of N = 93 adults (Mage = 54.8, SD = 12.8 years; 86% female, 14% male) who signed up for various group fitness classes were recruited to participate in this study. ECEs did not change across the 12-week fitness programmes (p > .05). Mid-programme ECEs significantly predicted post-programme SRE and MVPA beyond pre-programme SRE and MVPA (R2MVPA = 0.44, p < .001, R2 self-efficacy = 0.65, p < .001). The path model fit the data, further supporting these associations (CFI = .994, Χ2 = 7.74, p = .17). Those who missed at least one class reported higher ECEs (M = 4.58, SD = 1.49) compared to those who did not miss any classes (M = 3.55, SD = 1.50; t = 2.35, p < .05, Cohen’s d = 0.69). While certain analyses were limited by a small sample size, ECEs might be a useful concept that represents biased or inaccurate thinking useful in understanding physical activity attendance. Findings implicate ECEs as a factor that may predict non-adherence in physical activity interventions, highlighting biased thinking as a potential mechanism to target in interventions.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".