Mind Over Matter: Understanding the Relationship Between Memory Self‐Efficacy, Cognition and Brain Health in Older Adults with Probable Mild Cognitive Impairment
Notice bibliographique
Résumé
Background In our aging population, cognitive decline and brain health are critical areas of concern for healthy aging. Evidence has shown that personality factors such as self‐efficacy, one's personal perceived ability to perform a specific task, directly impacts components of healthy aging, including total brain volume. However, it is unknown whether memory self‐efficacy, specifically, might also be associated with brain function and structure. Methods A cross‐sectional pilot study of community dwelling older women with probable Mild Cognitive Impairment (Montreal Cognitive Assessment score <26) were asked to evaluate their global memory self‐efficacy using two questionnaires: Memory Self‐Efficacy Questionnaire (MSEQ‐4) and the Multifactorial Memory Questionnaire (MMQ). Participants were asked to complete various standardized cognitive tests: Alzheimer's Dementia Assessment Scale – Cognition (ADAS‐cog), Digit Span, Auditory Verbal Learning Test, Stroop and Trail Making Test. Participants also performed an associative memory task during an fMRI scan. High resolution T1 weighted structural imaging was obtained from a 3T SIEMENS scanner. Multivariate linear regression models were constructed for cognitive and brain health measures in relation to the memory self‐efficacy measures. Covariates of the models included age and current physical activity level. Results We report that the MMQ subscale of Mistakes and Ability (MMQ‐A) was the strongest measure in accounting for variance after including covariates. The final model for ADAS‐cog accounted for 65% of the variance, with the MMQ‐A score accounting for 44%. For structural brain measures, total brain volume, white matter and grey matter volumes, the final model accounted for 70%, 98% and 13% for each of the listed measures, with MMQ‐A accounting for 52%, 63% and 9% respectively. Other measures of global memory self‐efficacy, MSEQ‐4 and MMQ subscale of feelings of contentment (MMQ‐C), were also seen to have correlations to ADAS scores and structural brain measures, but could not account for the same level of variance as the MMQ‐A. Conclusion Based on the results collected it appears that one's perceived self‐efficacy of memory mistakes and ability is associated with measures of cognition and brain health. Based on this data our research has the potential to progress into a longitudinal study of observing the relationship between changes in memory self‐efficacy and brain health and cognition, as well as progression to collaborative clinical studies in memory self‐efficacy modification for healthy aging. Support or Funding Information Funding: Natural Sciences and Engineering Research Council of Canada This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| 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 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 ».