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Enregistrement W2008596551 · doi:10.1007/s12160-012-9466-2

Conscientiousness Versus Executive Function as Predictors of Health Behaviors and Health Trajectories

2013· letter· en· W2008596551 sur OpenAlexaffabout
Peter A. Hall, Geoffrey T. Fong

Notice bibliographique

RevueAnnals of Behavioral Medicine · 2013
Typeletter
Langueen
DomainePsychology
ThématiqueMental Health Research Topics
Établissements canadiensUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésConscientiousnessPsychologyOpenness to experiencePersonalityStroop effectHealth psychologyBig Five personality traitsClinical psychologyDevelopmental psychologyCognitionSocial psychologyExtraversion and introversionMedicinePublic healthPsychiatry

Résumé

récupéré en direct d'OpenAlex

We welcome the paper by Bogg and Roberts [1] describing the potential role of conscientiousness in explaining health-related outcomes. However, we think that this discussion could be more focused by examining conceptually similar constructs that have documented patterns of connection with these same outcomes. Executive function (EF), for example, is a set of cognitive processes—subsuming behavioral inhibition, working memory, and set shifting—that assist in goal-directed behavior, temporal organization of responses, and future-oriented actions in general [2]. Prior studies have found that individual differences in EF predict medication adherence, health behavioral trajectories, and longevity [3–5]. In a recent study utilizing an age-stratified community sample collected from an urban region in western Canada (N = 208; age range 19–89), we assessed EF and frequency of fatty food consumption and found that stronger EF (whether measured by Stroop performance or Go–No Go performance) predicted less frequent consumption of such foods, an effect that was independent of demographics, IQ, and BMI [6]. Given the potential conceptual overlap between conscientiousness and EF, we undertook a reanalysis of this dataset, which also included a measure of the Big Five dimensions of personality (the BFI), as well as accelerometer-assessed physical activity. When entering the Big Five variables as a single block in a linear regression analysis, conscientiousness was indeed a significant predictor of physical activity behavior (β = .156, p = .045), but not fatty food consumption frequency (β = −.118, p = .132). Interestingly, conscientiousness was not the most important personality predictor of these outcomes; openness the strongest predictor in absolute terms. More importantly, when conscientiousness and EF were entered in a competitive test, EF was the only significant predictor of unique variability in each behavior (Tables 1 and 2). Finally, when predicting a composite index of both health behaviors combined, EF was a significantly stronger predictor (β = .368, p<.001) than was conscientiousness (β = .187, p=.011; z = 1.868, p = .031). Executive function versus conscientiousness as predictors of accelerometer-assessed physical activity N = 208; age stratified community sample; mean age = 45.21 years; executive function assessed using a composite of Stroop performance (% correct, incongruent trials) and Go-NoGo reaction times; physical activity assessed via tri-axial accelerometer worn for 7 days Executive function versus conscientiousness as predictors of accelerometer-assessed physical activity N = 208; age stratified community sample; mean age = 45.21 years; executive function assessed using a composite of Stroop performance (% correct, incongruent trials) and Go-NoGo reaction times; physical activity assessed via tri-axial accelerometer worn for 7 days Executive function versus conscientiousness as predictors of 2-week fatty food consumption N = 208; age stratified community sample; mean age = 45.21 years; executive function assessed using a composite of Stroop performance (% correct, incongruent trials) and Go-NoGo reaction times; fatty food consumption assessed via fatty food items from the NCI Fat Screener completed for two consecutive weeks Executive function versus conscientiousness as predictors of 2-week fatty food consumption N = 208; age stratified community sample; mean age = 45.21 years; executive function assessed using a composite of Stroop performance (% correct, incongruent trials) and Go-NoGo reaction times; fatty food consumption assessed via fatty food items from the NCI Fat Screener completed for two consecutive weeks We believe that although conscientiousness may be a potentially useful heuristic for thinking about health-related behaviors, risks, and outcomes, because there exists empirical overlap with EF—specifically, behavioral inhibition, the most “pure” facet of EF [2]—some of this overlap could be responsible for the association between conscientiousness and outcomes of interest (e.g., health behavior performance). On a theoretical level, this may suggest that some sub-facets of conscientiousness are more predictive of health outcomes than others partially because the global construct itself is not a necessary part of explanatory (or predictive) models. However, this aside, there are some advantages of EF on an epistemic level, in that its measurement does not require self-referencing. In order to score highly on a measure of conscientiousness, one must endorse being goal-oriented and following through on one's intentions. Consistent performance of health-related behaviors requires these same things, such that one could very well be considering such behaviors when deciding on a response to items contained in any self-report measure of conscientiousness. Executive function, however, does not suffer from these same measurement problems, and yet dovetails seamlessly with social–cognitive perspectives on self-regulatory process, and provides many avenues for intervention beyond personality change [7]. In summary, we think that the link between conscientiousness and health outcomes is possibly an important one. However, we suggest that there is overlap between conscientiousness and executive function, and that the latter may be a more parsimonious (and powerful) explanatory variable for many health-related phenomena of interest. Careful reconsideration of the conscientiousness dimension from a social neuroscience perspective may be a useful direction forward.

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,027

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,300
Tête enseignante GPT0,515
Écart entre enseignants0,214 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations27
Publié2013
Routes d'admission2
Résumé présentoui

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