Investigating the role of Big Five personality traits in relation to various health outcomes has been extensively studied. The impact of “Big Five” on physical health is here explored for older Europeans with a focus on examining age groups differences. The study sample included 378,500 respondents derived from the seventh data wave of Survey of Health, Aging and Retirement in Europe (SHARE). The physical health status of older Europeans was estimated by constructing an index considering the combined effect of well-established health indicators such as the number of chronic diseases, mobility limitations, limitations with basic and instrumental activities of daily living, and self-perceived health. This index was used for an overall physical health assessment, for which the higher the score for an individual, the worst health level. Then, through a dichotomization process applied to the retrieved Principal Component Analysis scores, a two-group discrimination (good or bad health status) of SHARE participants was obtained as regards their physical health condition, allowing for further con-structing logistic regression models to assess the predictive significance of “Big Five” and their protective role for physical health. Results showed that neuroti-cism was the most significant predictor of physical health for all age groups un-der consideration, while extraversion, agreeableness and openness were not found to significantly affect the self-reported physical health levels of midlife adults aged 50 up to 64. Older adults aged 65 up to 79 were more prone to open-ness, whereas the oldest old individuals aged 80 up to 105 were mainly affected by openness and conscientiousness.
KeywordsBig Five Personality TraitsPhysical HealthOlder EuropeansSHAREPrincipal Component Analysis
Costa, P.T. and McCrae, R.R. (2008) The Revised Neo Personality Inventory (NEO-PI-R). In: Boyle, G.J., Matthews, G. & Saklofske, D.H., Eds., The SAGE Handbook of Personality Theory and Assessment, Vol. 2, SAGE Publications Ltd, New York, 179-198. https://doi.org/10.4135/9781849200479.n9
Power, R.A. and Pluess, M. (2015) Heritability Estimates of the Big Five Personality Traits Based on Common Genetic Variants. Translational Psychiatry, 5, e604. https://doi.org/10.1038/tp.2015.96
Shi, J., Yao, Y., Zhan, C., Mao, Z., Yin, F. and Zhao, X. (2018) The Relationship between Big Five Personality Traits and Psychotic Experience in a Large Non-Clinical Youth Sample: The Mediating Role of Emotion Regulation. Frontiers in Psychiatry, 9, Article No. 648. https://doi.org/10.3389/fpsyt.2018.00648
Jerram, K.L. and Coleman, P.G. (1999) The Big Five Personality Traits and Reporting of Health Problems and Health Behaviour in Old Age. British Journal of Health Psychology, 4, 181-192. https://doi.org/10.1348/135910799168560
Goodwin, R.D. and Friedman, H.S. (2006) Health Status and the Five-Factor Personality Traits in a Nationally Representative Sample. Journal of Health Psychology, 11, 643-654. https://doi.org/10.1177/1359105306066610
Löckenhoff, C.E., Duberstein, P.R., Friedman, B. and Costa Jr., P.T. (2011) Five-Factor Personality Traits and Subjective Health among Caregivers: The Role of Caregiver Strain and Self-Efficacy. Psychology and Aging, 26, 592-604. https://doi.org/10.1037/a0022209
Linkievicz, N.M., Sgnaolin, V., Engroff, P., Behr Gomes Jardim, G. and Cataldo Neto, A. (2022) Association between Big Five Personality Factors and Medication Adherence in the Elderly. Trends in Psychiatry and Psychotherapy, 44, e20200143. https://doi.org/10.47626/2237-6089-2020-0143
Adachi, T., Tsunekawa, Y. and Tanimura, D. (2022) Association between the Big Five Personality Traits and Medication Adherence in Patients with Cardiovascular Disease: A Cross-Sectional Study. PLOS ONE, 17, e0278534. https://doi.org/10.1371/journal.pone.0278534
Axelsson, M., Lötvall, J., Cliffordson, C., Lundgren, J. and Brink, E. (2013) Self-Efficacy and Adherence as Mediating Factors between Personality Traits and Health-Related Quality of Life. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care & Rehabilitation, 22, 567-575. https://doi.org/10.1007/s11136-012-0181-z
Lyon, K.A., Elliott, R., Ware, K., Juhasz, G. and Brown, L.J.E. (2021) Associations between Facets and Aspects of Big Five Personality and Affective Disorders: A Systematic Review and Best Evidence Synthesis. Journal of Affective Disorders, 288, 175-188. https://doi.org/10.1016/j.jad.2021.03.061
Weston, S.J. and Jackson, J.J. (2015) Identification of the Healthy Neurotic: Personality Traits Predict Smoking after Disease Onset. Journal of Research in Personality, 54, 61-69. https://doi.org/10.1016/j.jrp.2014.04.008
Hampson, S.E. (2012) Personality Processes: Mechanisms by Which Personality Traits “Get outside the Skin”. Annual Review of Psychology, 63, 315-339. https://doi.org/10.1146/annurev-psych-120710-100419
Clark, E.M., Williams, R.M., Park, C.L., Schulz, E., Williams, B.R. and Knott, C.L. (2019) Explaining the Relationship between Personality and Health in a National Sample of African Americans: The Mediating Role of Social Support. Journal of Black Psychology, 45, 339-375. https://doi.org/10.1177/0095798419873529
Kersting, K. (2003) Personality Changes for the Better with Age. Monitor on Psychology, 34, 14. https://www.apa.org/monitor/julaug03/personality https://doi.org/10.1037/e301162003-010
Donnellan, M.B. and Lucas, R.E. (2008) Age Differences in the Big Five across the Life Span: Evidence from Two National Samples. Psychology and Aging, 23, 558-566. https://doi.org/10.1037/a0012897
Kawamoto, T., Oshio, A., Abe, S., Tsubota, Y., Hirashima, T., Ito, H. and Tani, I. (2015) Age and Gender Differences of Big Five Personality Traits in a Cross-Sectional Japanese Sample. Japanese Journal of Developmental Psychology, 26, 107-122. https://psycnet.apa.org/record/2015-33363-003
Baek, Y., Martin, P., Siegler, I.C., Davey, A. and Poon, L.W. (2016) Personality Traits and Successful Aging: Findings from the Georgia Centenarian Study. The International Journal of Aging and Human Development, 83, 207-227. https://doi.org/10.1177/0091415016652404
Kang, W. (2023) Personality Predicts Self-Rated Health: Considering Age Differences. Frontiers in Psychology, 14, Article ID: 1143077. https://doi.org/10.3389/fpsyg.2023.1143077
Israel, S., Moffitt, T.E., Belsky, D.W., Hancox, R.J., Poulton, R., Roberts, B., Thomson, W.M. and Caspi, A. (2014) Translating Personality Psychology to Help Personalize Preventive Medicine for Young Adult Patients. Journal of Personality and Social Psychology, 106, 484-498. https://doi.org/10.1037/a0035687
Bergmann, M., Scherpenzeel, A. and Börsch-Supan, A. (2019) SHARE Wave 7 Methodology: Panel Innovations and Life Histories. Munich Center for the Economics of Aging (MEA), Munich.
Börsch-Supan, A., Brandt, M., Hunkler, C., Kneip, T., Korbmacher, J., Malter, F., Schaan, B., Stuck, S. and Zuber, S. (2013) Data Resource Profile: The Survey of Health, Ageing and Retirement in Europe (SHARE). International Journal of Epidemiology, 42, 992-1001. https://doi.org/10.1093/ije/dyt088
Börsch-Supan, A. (2022) Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 7. Release Version: 8.0.0. SHARE-ERIC. Data Set.
Andridge, R.R. and Little, R.J. (2010) A Review of Hot Deck Imputation for Survey Non-Response. International Statistical Review, 78, 40-64. https://doi.org/10.1111/j.1751-5823.2010.00103.x
Stuck, S., Zuber, S., Kotte, M., Franzese, F., Gruber, S., Birkenbach, T., Pflüger, S., Atzendorf, J., Brändle C. and Gerum, M. (2022) SHARE Release Guide 8.0.0. Munich Center for the Economics of Aging, Munich. https://share-eric.eu/fileadmin/user_upload/Release_Guides/SHARE_release_guide_8-0-0.pdf
Gumà, J. (2021) What Influences Individual Perception of Health? Using Machine Learning to Disentangle Self-Perceived Health. SSM-Population Health, 16, Article ID: 100996. https://doi.org/10.1016/j.ssmph.2021.100996
Pearson, K. (1901) LIII. On Lines and Planes of Closest Fit to Systems of Points in Space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2, 559-572. https://doi.org/10.1080/14786440109462720
Hotelling, H. (1933) Analysis of a Complex of Statistical Variables into Principal Components. Journal of Educational Psychology, 24, 417-441. https://doi.org/10.1037/h0071325
Tabachnick, B.G., Fidell, L.S. and Ullman, J.B. (2013) Using Multivariate Statistics. Vol. 6, Pearson, Boston, 497-516. https://ebook.upgrisba.ac.id/ebook/komputer-informasi-referensi-umum/6th-edition-using-multivariate-statistics-pearson/download
Jolliffe, I.T. and Cadima, J. (2016) Principal Component Analysis: A Review and Recent Developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374, Article ID: 20150202. https://doi.org/10.1098/rsta.2015.0202
Kolenikov, S. and Angeles, G. (2009) Socioeconomic Status Measurement with Discrete Proxy Variables: Is Principal Component Analysis a Reliable Answer? Review of Income and Wealth, 55, 128-165. https://doi.org/10.1111/j.1475-4991.2008.00309.x
Kolenikov, S. and Angeles, G. (2004) The Use of Discrete Data in PCA: Theory, Simulations, and Applications to Socioeconomic Indices. Carolina Population Center, University of North Carolina, Chapel Hill, Vol. 20, 1-59. https://www.measureevaluation.org/resources/publications/wp-04-85.html
Perkins, N.J. and Schisterman, E.F. (2006) The Inconsistency of “Optimal” Cutpoints Obtained Using Two Criteria Based on the Receiver Operating Characteristic Curve. American Journal of Epidemiology, 163, 670-675. https://doi.org/10.1093/aje/kwj063
MacCallum, R.C., Zhang, S., Preacher, K.J. and Rucker, D.D. (2002) On the Practice of Dichotomization of Quantitative Variables. Psychological Methods, 7, 19-40. https://doi.org/10.1037/1082-989X.7.1.19
Nelson, S.P., Ramakrishnan, V., Nietert, P.J., Kamen, D.L., Ramos, P.S. and Wolf, B.J. (2017) An Evaluation of Common Methods for Dichotomization of Continuous Variables to Discriminate Disease Status. Communications in Statistics—Theory and Methods, 46, 10823-10834. https://doi.org/10.1080/03610926.2016.1248783
Le, C.T. (2009) Applied Categorical Data Analysis and Translational Research. John Wiley & Sons, Hoboken. https://lccn.loc.gov/98014782
Hosmer Jr., D.W and Lemeshow, S. (2000) Applied Logistic Regression. 2nd Edition, John Wiley & Sons, Inc., New York. https://doi.org/10.1002/0471722146
Freedman, D.A. (2009) Statistical Models: Theory and Practice. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511815867
Truett, J., Cornfield, J. and Kannel, W. (1967) A Multivariate Analysis of the Risk of Coronary Heart Disease in Framingham. Journal of Chronic Diseases, 20, 511-524. https://doi.org/10.1016/0021-9681(67)90082-3
Atherton, O.E., Robins, R.W., Rentfrow, P.J. and Lamb, M.E. (2014) Personality Correlates of Risky Health Outcomes: Findings from a Large Internet Study. Journal of Research in Personality, 50, 56-60. https://doi.org/10.1016/j.jrp.2014.03.002
Lever, J., Krzywinski, M. and Altman, N. (2017) Points of Significance: Principal Component Analysis. Nature Methods, 14, 641-643. https://doi.org/10.1038/nmeth.4346
d’Aspremont, A., Ghaoui, L., Jordan, M. and Lanckriet, G. (2004) A Direct Formulation for Sparse PCA Using Semidefinite Programming. Advances in Neural Information Processing Systems, 49, 434-448. https://doi.org/10.2139/ssrn.563524
Krause, N., Shaw, B.A. and Cairney, J. (2004) A Descriptive Epidemiology of Lifetime Trauma and the Physical Health Status of Older Adults. Psychology and Aging, 19, 637-648. https://doi.org/10.1037/0882-7974.19.4.637
Al Senany, S. and Al Saif, A. (2015) Assessment of Physical Health Status and Quality of Life among Saudi Older Adults. Journal of Physical Therapy Science, 27, 1691-1695. https://doi.org/10.1589/jpts.27.1691
Gobbens, R.J. (2018) Associations of ADL and IADL Disability with Physical and Mental Dimensions of Quality of Life in People Aged 75 Years and Older. PeerJ, 6, e5425. https://doi.org/10.7717/peerj.5425
Ćwirlej-Sozańska, A., Wiśniowska-Szurlej, A., Wilmowska-Pietruszyńska, A. and Sozański, B. (2019) Determinants of ADL and IADL Disability in Older Adults in Southeastern Poland. BMC Geriatrics, 19, Article No. 297. https://doi.org/10.1186/s12877-019-1319-4
Mourão, L.F., Xavier, D.A.N., Neri, A.L. and Luchesi, K.F. (2016) Association Study between Natural Chronic Diseases of Aging and Swallowing Changes Referred by Community Elderly. Audiology-Communication Research, 21, e1657.
Josefsson, K., Andersson, M. and Erikstedt, A. (2016) Older Adults’ Self-Rated Health and Differences by Age and Gender: A Quantitative Study. Healthy Aging Research, 5, 1-10. https://doi.org/10.1097/01.HXR.0000511864.85548.25
Huang, I.C., Lee, J.L., Ketheeswaran, P., Jones, C.M., Revicki, D.A. and Wu, A.W. (2017) Does Personality Affect Health-Related Quality of Life? A Systematic Review. PLOS ONE, 12, e0173806. https://doi.org/10.1371/journal.pone.0173806
Lahey, B.B. (2009) Public Health Significance of Neuroticism. American Psychologist, 64, 241-256. https://doi.org/10.1037/a0015309
Hudek-Knežević, J. and Kardum, I. (2009) Five-Factor Personality Dimensions and 3 Health-Related Personality Constructs as Predictors of Health. Croatian Medical Journal, 50, 394-402. https://doi.org/10.3325/cmj.2009.50.394
Turiano, N.A., Pitzer, L., Armour, C., Karlamangla, A., Ryff, C.D. and Mroczek, D.K. (2012) Personality Trait Level and Change as Predictors of Health Outcomes: Findings from a National Study of Americans (MIDUS). Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 67, 4-12. https://doi.org/10.1093/geronb/gbr072
Chapman, B.P., Roberts, B. and Duberstein, P. (2011) Personality and Longevity: Knowns, Unknowns, and Implications for Public Health and Personalized Medicine. Journal of Aging Research, 2011, Article ID: 759170. https://doi.org/10.4061/2011/759170
Cheng, C.H.E., Weiss, J.W. and Siegel, J.M. (2015) Personality Traits and Health Behaviors as Predictors of Subjective Wellbeing among a Multiethnic Sample of University-Attending Emerging Young Adults. International Journal of Wellbeing, 5, 21-43. https://doi.org/10.5502/ijw.v5i3.2
Lehmann, R., Denissen, J.J., Allemand, M. and Penke, L. (2013) Age and Gender Differences in Motivational Manifestations of the Big Five from Age 16 to 60. Developmental Psychology, 49, 365-383. https://doi.org/10.1037/a0028277