Personality Correlates of Physical Activity
Bibliographic record
Abstract
PURPOSE: Personality traits and physical activity (PA) have been researched sporadically over the last 30 years, but there has been a recent resurgence in personality work based on improved psychometrics and more convincing evidence for trait validity. This review unites the literature on major personality traits and PA while providing meta-analytic summaries of the findings where appropriate. METHODS: Overall, 33 studies containing 35 independent samples, ranging from 1969–2006 met our review inclusion criteria and were obtained through online databases and detailed reference-list searching. RESULTS: Extraversion (r = .23), neuroticism (r = −.11) and conscientiousness (r = .20) were identified as correlates of PA using random effects meta-analytic procedures correcting for sampling bias and attenuation of measurement error. The five-factor model traits of openness to experience/intellect and agreeableness, as well as Eysenck's psychoticism trait were not associated with PA. Potential moderators of personality and PA relations such as gender, age, culture/country, design, and instrumentation were inconclusive given the small number of studies. Still, the existing evidence was suggestive that personality and PA relations are relatively invariant to these factors. Studies examining personality and different PA modes suggested differences by traits such as extraversion, but more research is needed to make any conclusions. CONCLUSIONS: Extraversion, neuroticism, and conscientiousness are reliably associated with PA (small effect size), but future research using multivariate analyses, personality-channelled PA interventions, longitudinal designs, and objective PA measurement is recommended.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".