Conscientiousness Versus Executive Function as Predictors of Health Behaviors and Health Trajectories
Notice bibliographique
Résumé
We welcome the paper by Bogg and Roberts [1] describingthe potential role of conscientiousness in explaining health-related outcomes. However, we think that this discussioncould be more focused by examining conceptually similarconstructs that have documented patterns of connection withthese same outcomes. Executive function (EF), for example,is a set of cognitive processes—subsuming behavioral inhi-bition, working memory, and set shifting—that assist ingoal-directed behavior, temporal organization of responses,and future-oriented actions in general [2]. Prior studies havefound that individual differences in EF predict medicationadherence, health behavioral trajectories, and longevity[3–5]. In a recent study utilizing an age-stratified communi-ty sample collected from an urban region in western Canada(N=208; age range 19–89), we assessed EF and frequencyof fatty food consumption and found that stronger EF(whether measured by Stroop performance or Go–No Goperformance) predicted less frequent consumption of suchfoods, an effect that was independent of demographics, IQ,and BMI [6].Given the potential conceptual overlap between con-scientiousness and EF, we undertook a reanalysis ofthis dataset, which also included a measure of the BigFive dimensions of personality (the BFI), as well asaccelerometer-assessed physical activity. When enteringthe Big Five variables as a single block in a linear regres-sion analysis, conscientiousness was indeed a significantpredictor of physical activity behavior (β=.156, p=.045),butnotfattyfoodconsumptionfrequency(β=−.118,p=.132).Interestingly, conscientiousness was not the most importantpersonality predictor of these outcomes; openness thestrongest predictor in absolute terms. More importantly,when conscientiousness and EF were entered in a com-petitive test, EF was the only significant predictor ofunique variability in each behavior (Tables 1 and 2).Finally, when predicting a composite index of bothhealth behaviors combined, EF was a significantly strongerpredictor (β=.368, p<.001) than was conscientiousness(β=.187,p=.011; z=1.868,p=.031).We believe that although conscientiousness may be apotentially useful heuristic for thinking about health-related behaviors, risks, and outcomes, because there existsempirical overlap with EF—specifically, behavioral inhibi-tion, the most “pure” facet of EF [2]—some of this overlapcould be responsible for the association between conscien-tiousness and outcomes of interest (e.g., health behaviorperformance). On a theoretical level, this may suggest thatsome sub-facets of conscientiousness are more predictive ofhealth outcomes than others partially because the globalconstruct itself is not a necessary part of explanatory (orpredictive) models.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».