2022/12/05 by Irene Fernández, Adrián García-Mollá, Adrián García‐Mollá +3 · 29 citations
Medicine · Psychology · Social Sciences · #Aging and Gerontology Research #Association (psychology) #Cognition #Cognitive test #Dementia and Cognitive Impairment Research #Demography #Developmental psychology #Effects of sleep deprivation on cognitive performance #Gerontology #Medicine #Neuropsychology #Population #Psychology #Structural equation modeling #Technology Use by Older Adults #Verbal fluency test
paper · pdf · doi:10.1016/j.archger.2022.104891
published in Archives of Gerontology and Geriatrics 107, 104891 (Elsevier BV)
openalex publication_date 2022/12/05 · crossref created 2022/12/05 · crossref issued 2023/04/01 · crossref published 2023/04/01 · crossref published-print 2023/04/01 · crossref deposited 2025/09/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02 · crossref indexed 2026/08/05
THEORETICAL BACKGROUND: A challenge of the ageing of the population is cognitive performance, given its association to optimal ageing. Documented predictors of cognition have included socio-demographics, education or physical factors. However, the association of social and intellectual activity participation to cognition has been less studied. AIM: This study presents a predictive model of cognitive functioning including these alternative factors as well as more seminal ones to explain cognition in old age. MATERIALS AND METHODS: The sample was composed by 45475 older adult participants in the 8th Wave of the Survey of Health, Aging and Retirement in Europe, that took place between 2019 and 2020. A correlational design was specified to test the effects of age, gender, years of education, physical inactivity, number of chronic diseases, social activity participation and intellectual activity participation on temporal orientation, numeracy, verbal fluency and memory. A completely a priori Structural Equation Model with latent variables was tested. RESULTS: The sample had an average of 70 years of age, was well-educated and physically active and engaged in reading. There was a higher proportion of females. The model showed an optimal fit to the data, explaining 8.7%-36.0% of the different cognitive components' variance. Age, years of education and intellectual activity displayed the largest effects across the cognitive domains. CONCLUSIONS: Findings suggest that social and intellectual activity participation are of relative importance to predict cognition in old age, even when considering other well-documented factors affecting older adults' cognitive functioning.