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Socioeconomic, emotional, and physical execution variables as predictors of cognitive performance in a Spanish sample of middle-aged and older community-dwelling participants

Published online by Cambridge University Press:  29 June 2017

Mari Feli González*
Affiliation:
Department of Developmental and Educational Psychology, University of Santiago de Compostela, Rúa Xosé María Suárez Núñez, s/n. Campus Vida, Santiago de Compostela, ES 15782, Spain
David Facal
Affiliation:
Department of Developmental and Educational Psychology, University of Santiago de Compostela, Rúa Xosé María Suárez Núñez, s/n. Campus Vida, Santiago de Compostela, ES 15782, Spain
Onésimo Juncos-Rabadán
Affiliation:
Department of Developmental and Educational Psychology, University of Santiago de Compostela, Rúa Xosé María Suárez Núñez, s/n. Campus Vida, Santiago de Compostela, ES 15782, Spain
Javier Yanguas
Affiliation:
Matia Instituto Gerontológico – INGEMA, Camino de los Pinos, n° 27 bajo, San Sebastián, ES 20009, Spain
*
Correspondence should be addressed to: Mari Feli González, Department of Developmental and Educational Psychology, University of Santiago de Compostela, Rúa Xosé María Suárez Núñez, s/n. Campus vida, Santiago de Compostela, ES 15782, Spain. Phone: +34 881813717; Fax: +34 881813901. Email: mari.feli.gonzalez@gmail.com.

Abstract

Background:

Cognitive performance is not easily predicted, since different variables play an important role in the manifestation of age-related declines. The objective of this study is to analyze the predictors of cognitive performance in a Spanish sample over 50 years from a multidimensional perspective, including socioeconomic, affective, and physical variables. Some of them are well-known predictors of cognition and others are emergent variables in the study of cognition.

Methods:

The total sample, drawn from the “Longitudinal Study Aging in Spain (ELES)” project, consisted of 832 individuals without signs of cognitive impairment. Cognitive function was measured with tests evaluating episodic and working memory, visuomotor speed, fluency, and naming. Thirteen independent variables were selected as predictors belonging to socioeconomic, emotional, and physical execution areas. Multiple linear regressions, following the enter method, were calculated for each age group in order to study the influence of these variables in cognitive performance.

Results:

Education is the variable which best predicts cognitive performance in the 50–59, 60–69, and 70–79 years old groups. In the 80+ group, the best predictor is objective economic status and education does not enter in the model.

Conclusions:

Age-related decline can be modified by the influence of educational and socioeconomic variables. In this context, it is relevant to take into account how easy is to modify certain variables, compared to others which depend on each person's life course.

Type
Research Article
Copyright
Copyright © International Psychogeriatric Association 2017 

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References

Adam, S., Bonsang, E., Grotz, C. and Perelman, S. (2013). Occupational activity and cognitive reserve: implications in terms of prevention of cognitive aging and Alzheimer's disease. Clinical Interventions in Aging, 8, 377390. doi: 10.2147/CIA.S39921 CrossRefGoogle ScholarPubMed
Allerhand, M., Gale, C. R. and Deary, I. J. (2014). The dynamic relationship between cognitive function and positive well-being in older people: a prospective study using the english longitudinal study of aging. Psychology and Aging, 29, 306318. doi:10.1037/a0036551 CrossRefGoogle ScholarPubMed
Baltes, P. B. and Mayer, K. U. (2001). The Berlin Aging Study: Aging from 70 to 100 2nd edn., New York: Cambridge University Press Google Scholar
Benito-Leon, J., Mitchell, A. J, Hernandez-Gallego, J. and Bermejo-Pareja, F. (2013). Obesity and impaired cognitive functioning in the elderly: a population-based cross-sectional study (NEDICES). European Journal of Neurology, 20, 899906 doi:10.1111/ene.12083 CrossRefGoogle ScholarPubMed
Benton, A., Hamsher, K., Rey, G. L. and Sivan, A. B. (1994). Multilingual Aphasia Examination. Iowa City, IA: AJA Associates.Google Scholar
Bielak, A. A. M., Hughes, T. F., Small, B. J. and Dixon, R. A. (2007). It's never too late to engage in lifestyle activities: significant concurrent but not change relationships between lifestyle activities and cognitive speed. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 62, 331339. doi:10.1093/geronb/62.6.P331 CrossRefGoogle Scholar
Charles, S. and Carstensen, L. L. (2010). Social and emotional aging. Annual Review of Psychology, 61, 383409.CrossRefGoogle ScholarPubMed
Crimmins, E. M., Kim, J. K., Langa, K. M. and Weir, D. R. (2011). Assessment of cognition using surveys and neuropsychological assessment: the health and retirement study and the aging, demographics, and memory study. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 66, i162–171. doi.org/10.1093/geronb/gbr048 CrossRefGoogle ScholarPubMed
Demakakos, P., Nazroo, J., Breeze, E. and Marmot, M. (2008). Socioeconomic status and health: the role of subjective social status. Social Science & Medicine, 67, 330340. doi:10.1016/j.socscimed.2008.03.038 CrossRefGoogle ScholarPubMed
Dipietro, L., Caspersen, C. J., Ostfeld, A. M. and Nadel, E. R. (1993). A survey for assessing physical activity among older adults. Medicine & Science in Sports & Exercise, 25, 628642.CrossRefGoogle ScholarPubMed
Driscoll, I. et al. (2011). Weight change and cognitive function: findings from the women's health initiative study of cognitive aging. Obesity (Silver Spring), 19, 15951600. doi:10.1038/oby.2011.23 CrossRefGoogle ScholarPubMed
Facal, D., Juncos-Rabadan, O., Pereiro, A. X. and Lojo-Seoane, C. (2014). Working memory span in mild cognitive impairment. Influence of processing speed and cognitive reserve. International Psychogeriatrics, 26, 615625. doi: 10.1017/S1041610213002391 CrossRefGoogle ScholarPubMed
Fried, L. P. et al. (2001). Frailty in older adults: evidence for a phenotype. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 56, 146156.CrossRefGoogle ScholarPubMed
Garibotto, V. et al. (2008). Education and occupation as proxies for reserve in aMCI converters and AD: FDG-PET evidence. Neurology, 71, 13421349. doi:10.1212/01.wnl.0000327670.62378.c0 CrossRefGoogle Scholar
Gerstorf, D., Ram, N., Röcke, C., Lindenberger, U. and Smith, J. (2008). Decline in life satisfaction in old age: longitudinal evidence for links to distance-to-death. Psychology and Aging, 23, 154168. doi:10.1037/0882-7974.23.1.154 CrossRefGoogle ScholarPubMed
González, M. F., Facal, D., Juncos-Rabadán, O. and Yanguas, J. (2015). Creación de un índice de rendimiento cognitivo en el estudio longitudinal ELES. Revista Española de Geriatría y Gerontología, 50, 56.Google Scholar
Goodglass, H. and Kaplan, E. (1996). Test De Vocabulario De Boston. Madrid: Editorial Médica Panamericana.Google Scholar
Gottesman, R. F. et al. (2014). Impact of differential attrition on the association of education with cognitive change over 20 years of follow-up: the ARIC neurocognitive study. American Journal of Epidemiology, 179, 956966. doi:10.1093/aje/kwu020 CrossRefGoogle ScholarPubMed
Haan, M. N., Al-Hazzouri, A. Z. and Aiello, A. E. (2011). Life-span socioeconomic trajectory, nativity, and cognitive aging in Mexican Americans: the sacramento area latino study on aging. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 66, 102110. doi:10.1093/geronb/gbq071 CrossRefGoogle Scholar
Huppert, F., Gardener, E. and McWilliams, B. (2006). Cognitive function. In Banks, J., Breeze, E., Lessof, C. and Nazroo, J. (eds.), Retirement, Health and Relationships of the Older Population in England: The 2004 English Longitudinal Study of Ageing (pp. 217230). London: The Institute for Fiscal Studies.Google Scholar
International Wellbeing Group. (2006). Personal Wellbeing Index – Adult (PWI-A). Manual. Melbourne: Australian Centre on Quality of Life, Deakin University, 36 p. Available at: http://www.deakin.edu.au/research/acqol/instruments/wellbeing_index.htm; last accessed May 27, 2014.Google Scholar
Karlamangla, A. S., Miller-Martinez, D., Aneshensel, C. S., Seeman, T. E., Wight, R. G. and Chodosh, J. (2009). Trajectories of cognitive function in late life in the United States: demographic and socioeconomic predictors. American Journal of Epidemiology, 170, 331342. doi: 10.1093/aje/kwp154 CrossRefGoogle ScholarPubMed
Lobo, A. et al. (1999). Revalidación y normalización del mini-examen cognoscitivo (primera versión en castellano del mini-mental status examination) en la población geriátrica. Medicina Clinnica (Barcelona), 112, 767774.Google Scholar
Lojo-Seoane, C., Facal, D., Guardia-Olmos, J. and Juncos-Rabadán, O. (2014). Structural model for estimating the influence of cognitive reserve on cognitive performance in adults with subjective memory complaints. Archives of Clinical Neuropsychology, 29, 245255. doi:10.1093/arclin/acu007 CrossRefGoogle ScholarPubMed
Lyu, J., Lee, C. M. and Dugan, E. (2014). Risk factors related to cognitive functioning: a cross-national comparison of U.S. and Korean older adults. The International Journal of Aging and Human Development, 79, 81101.CrossRefGoogle ScholarPubMed
MacDonald, S. W., DeCarlo, C. A. and Dixon, R. A. (2011). Linking biological and cognitive aging: toward improving characterizations of developmental time. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 66, 5970. doi: 10.1093/geronb/gbr039 CrossRefGoogle ScholarPubMed
Middleton, L. E., Mitnitski, A., Fallah, N., Kirkland, S. A. and Rockwood, K. (2008). Changes in cognition and mortality in relation to exercise in late life: a population based study. PLoS One, 3, e3124. doi:10.1371/journal.pone.0003124 CrossRefGoogle ScholarPubMed
Peña-Casanova, J. et al. (2009). Spanish multicenter normative studies (NEURONORMA Project): methods and sample characteristics. Archives of Clinical Neuropsychology, 24, 307–19. doi:10.1093/arclin/acp027.CrossRefGoogle ScholarPubMed
Pereiro-Rozas, A. X., Juncos-Rabadán, O., Facal, D. and Pérez-Fernández, A. (2014). Cognitive diversity in middle-aged and elderly adults: the role of education. Educational Gerontology, 40, 4052. doi:10.1080/03601277.2013.768075 CrossRefGoogle Scholar
Pinto, J. M. and Neri, A. L. (2013). Factors associated with low life life satisfaction in community-dwelling elderly: FIBRA study. Cadernos de Saude Publica, 29, 24472458.CrossRefGoogle ScholarPubMed
Rabbitt, P. et al. (2004). The university of manchester longitudinal study of cognition in normal healthy old age, 1983 through 2003. Aging, Neuropsychology and Cognition, 11, 245279.CrossRefGoogle Scholar
Rey, A. (1964). L'examen clinique en Psychologia. Paris: Presses universitaires de France.Google Scholar
Salthouse, T. A. (2010). Selective review of cognitive aging. Journal of the International Neuropsychological Society, 16, 754760.CrossRefGoogle ScholarPubMed
Sánchez-Benavides, G. et al. (2016). One-year reference norms of cognitive change in spanish old adults: data from the NEURONORMA sample. Archives of Clinical Neuropsychology, 31, 378388. doi:10.1093/arclin/acw018 CrossRefGoogle ScholarPubMed
Singh-Manoux, A., Marmot, M. G., Glymour, M., Sabia, S., Kivimäki, M. and Dugravot, A. (2011). Does cognitive reserve shape cognitive decline?. Annals of Neurology, 70, 296304. doi.org/10.1002/ana.22391 CrossRefGoogle ScholarPubMed
Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47, 20152028. doi:10.1016/j.neuropsychologia.2009.03.004 CrossRefGoogle ScholarPubMed
Sterne, J. A. et al. (2009). Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ, 338, b2393. doi: 10.1136/bmj.b2393 CrossRefGoogle ScholarPubMed
Taekema, D. G., Gussekloo, J., Maier, A. B., Westendorp, R. G. J. and de Craen, A. J. M. (2010). Handgrip strength as a predictor of functional, psychological and social health. A prospective population-based study among the oldest old. Age Ageing, 39, 331337. doi:10.1093/ageing/afq022 CrossRefGoogle Scholar
Vemuri, P. et al. (2014). Association of lifetime intellectual enrichment with cognitive decline in the older population. JAMA Neurology, 71, 10171024. doi:10.1001/jamaneurol.2014.963 CrossRefGoogle ScholarPubMed
Wechsler, D. (1999). Adaptación española de la Wechsler Adult Intelligence Scale-III. Madrid: TEA Ediciones.Google Scholar
Yaffe, K. et al. (2009). Predictors of maintaining cognitive function in older adults: the health ABC study. Neurology, 72, 20292035. doi: 10.1212/WNL.0b013e3181a92c36 CrossRefGoogle ScholarPubMed
Yu, F., Ryan, L. H., Schaie, K. W., Willis, S. L. and Kolanowski, A. (2009). Factors associated with cognition in adults: the seattle longitudinal study. Research in Nursing y Health, 32, 540550. doi.org/10.1002/nur.20340 CrossRefGoogle ScholarPubMed