The Sport & Concussion Research on Executive Systems (SCoRES) study: a quantitative and qualitative examination of executive function, sport and physical activity in young adults
Notice bibliographique
Résumé
Introduction: The cognitive benefits of physical activity and sport on executive function are of interest to researchers, especially considering the subsequent interventions that could be implemented. However, researchers frequently use different approaches to conceptualize and measure executive function, often using only a few computerized tasks. My Master’s thesis, therefore, aims to investigate the associations between executive function and physical activity and sport through two different studies to obtain a robust evaluation of executive function: 1) A quantitative study examining physical activity, sport and concussion history in university students and their effect on executive function, and 2) A qualitative study exploring the executive functions engaged during a game from elite university athletes’ experiences. Methods: Quantitative Study. Canadian university students (n=247) completed an online study with nine computerized executive function tasks and a behavioural self-report of executive function, as well as questions assessing weekly physical activity, athletic status, and concussion history. Structural equation modelling and linear regression were conducted to predict executive function based on age, sex, physical activity, athletic status and concussion history. Qualitative study. Canadian university athletes (n=19) participated in semi-structured interviews via Zoom to determine the executive functions engaged during a game. Thematic analysis was used to analyze the interview transcripts. Results: Quantitative Study. The three-factor model of executive function had an overall good fit: χ2 = 66.38, df = 51, p = 0.07, CFI = 0.95, TLI = 0.93, RMSEA = 0.04 [90% CI: 0.00–0.06], SRMR = 0.05. No direct relationship was found between the factors and age, physical activity, concussion history, or athletic status. Sex was significantly related to inhibition, b = 0.52, p = 0.02, such that males had greater inhibition. Physical activity (b = 0.09, p < .01), concussion history (b = 3.29, p < .05) and athletic status (b = -4.01, p < .05) were found to be significant predictors in the regression predicting self-reported executive functioning. Qualitative study. Three themes were generated: 1) Engaging in pre-play or pre-game planning, organization and decision making, 2) Engaging in mid-play problem solving and purposive action and 3) Engaging in post-play or post-game information processing, emotional control and effective performance. Conclusions: The quantitative study found that physical activity, athletic status and concussion history were predictive of subjective but not objective executive function. The qualitative study determined that athletes engage many executive functions that are dependent on the timing of both the play and the game (pre-, mid- or post-). Real-life behavioural manifestations of executive functioning are more challenging to assess and measure but may be better predicted by and relate to life factors such as physical activity and sport participation. The results of my thesis provide support for future research to utilize and develop more unique and ecologically valid methods of measuring EF in the field of physical activity and sport.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».