41 Concussion History, Physical Activity, and Athletic Status Predict Subjective but not Objective Executive Functioning
Notice bibliographique
Résumé
Objective: Factors such as physical activity and sports participation may have a positive effect on executive functioning. However, people involved in sports are at a higher risk of experiencing a concussion, which may have a detrimental effect. Previous research has yet to investigate those combined negative and positive effects while also utilizing a comprehensive assessment of executive function. This study aims precisely to examine the effects of physical activity, athletic status and concussion history on subjective (e.g., questionnaire) and objective measures (e.g., latent variables) of three well-established components of executive function (i.e., inhibiting, shifting, and updating) in young adults. Participants and Methods: 247 Canadian university students (ages 18 - 25; 83% female) completed a remote assessment of executive function involving nine computerized tasks and a behavioural self-report, in addition to demographic questionnaires and items assessing weekly physical activity, athletic status, and concussion history. A linear regression analysis was used to assess the effects of the predictor variables (age, sex, concussion history, physical activity and athletic status) on subjective reporting of executive functioning using the Executive Function Index. Furthermore, structural equation modelling (SEM) was used to predict objective executive function using a three-factor model (shifting, updating, inhibition). Results: The three-factor measurement model of executive function fit the data adequately: x2 = 26.10, df = 17, p = 0.07, CFI = 0.97, TLI = 0.95, RMSEA = 0.05 [90% CI: 0.00-0.09], SRMR = 0.04. Then, the three-factor SEM of executive function also fit the data adequately: X2 = 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. Using SEM, no direct relationship was found between the factors of executive function and the predictor variables (i.e., age, physical activity, concussion history, and athletic status). Sex was significantly related to inhibition (b = 0.52, p = 0.02), such that males had greater inhibition. For the regression, 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 for the Executive Function Index. Conclusions: Concussion history, physical activity, and athletic status were all predictive of subjective but not objective measures of executive function. Interestingly, these findings align with previous research that demonstrated performance-based executive function measures often do not align with self-report measures, which may suggest they are complementary but measure slightly different aspects of the underlying executive function construct. Mixed findings in the extant literature regarding sex differences and executive function require continued research to understand better the relationship and mechanisms behind the sex differences in inhibition. In summary, these findings offer support for the differentiation between subjective and objective measures of executive function when investigating their relationship with physical activity, sport participation, concussion history, age and sex.
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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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».