Cognition and Functional Capacity: An Initial Comparison of Veteran and Non-Veteran Older Adults
Notice bibliographique
Résumé
INTRODUCTION: The U.S. Military Veterans aged 65 and older comprise an estimated 43% of the 22 million living Veterans in the United States. Veterans have high rates of physical, psychiatric, and social challenges, but it is not known whether Veteran status confers additional risk for cognitive or functional impairments in later life. Thus, this investigation specifically compared older Veterans with their non-Veteran peers in cognitive functioning and performance-based functional capacity. MATERIALS AND METHODS: Participants (N = 110; 29 Veterans and 81 non-Veterans) were part of a larger longitudinal study on biopsychosocial functioning in independently living older adult residents of a Continuing Care Senior Housing Community. The University of California San Diego Institutional Review Board approved the study and all participants provided written informed consent. Participants provided demographic and mental health information and were administered a comprehensive neuropsychological battery. Functional capacity was assessed using the UCSD Performance-Based Skills Assessment-Brief (UPSA-B), which uses financial and communication role-plays to assess everyday functioning skills. Neuropsychological scores were appropriately normed prior to analysis. Multivariate Analyses of Variances with post hoc t-tests and an Analysis of Covariance were used to examine neuropsychological and functional capacity differences, respectively, between Veterans and non-Veterans. RESULTS: Veterans did not differ from non-Veterans in educational attainment (16.4 years versus 15.5 years, P = 0.110), but they were significantly older (mean age 86.9 years ± 5.7, versus 81.74 years ± 6.53; P < 0.001) and were more likely to be male (X2 [1, N = 110] = 62.39, P < 0.001). Thus, though neuropsychological norms already accounted for demographic differences in our participants, age and sex were controlled in the Analysis of Covariance predicting UPSA-B score from Veteran status. Results suggested that, compared to non-Veterans, Veterans had significantly worse performance in the list learning portion of a test of verbal memory (Hopkins Verbal Learning Test-Revised, Total Recall; t = 2.56, P = 0.012, d = 0.56). Veterans and non-Veterans did not significantly differ in performance on the delayed recall portion of the verbal learning test and did not differ on a cognitive screening test (Montreal Cognitive Assessment) or on measures of premorbid intellectual functioning (Wide Range Achievement Test-4 Reading), language (Boston Naming Test, Verbal Fluency), visual memory (Brief Visuospatial Memory Test-Revised), attention/working memory (WAIS-IV Digit Span), processing speed (WAIS-IV Digit Symbol Coding), executive function (Delis-Kaplan Executive Function System Trails and Color-Word Test), or functional capacity (UPSA-B). Because our examination of multiple outcomes might have inflated Type I error, we performed a post hoc adjustment of P values using Benjamini-Hochberg procedures and the group difference in verbal learning remained significant. CONCLUSIONS: Despite largely similar function in most domains, Veterans performed significantly more poorly in verbal list learning than their non-Veteran peers. Additional attention should be given to the understanding, assessment, and possible treatment of learning and memory differences in older Veterans, as this may be an area in which Veteran status confers additional risk or vulnerability to decline. This is the first study to compare objective neuropsychological and functional performance between older (age 65+) US Veterans and non-Veterans.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,001 | 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.
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