Skip to main content

Determinants of Subjective Wellbeing Trajectories in Older Adults: A Growth Mixture Modeling Approach

Abstract

Subjective wellbeing (SWB) is a core component of healthy aging to be promoted among older adults. This study aims to analyze whether there are subgroups with different trajectories in the main components of SWB (i.e. positive affect, negative affect, and life satisfaction) within the older population, and identify potential determinants of these heterogeneous trajectories. We analyzed data on 1,189 Spanish older adults aged 50 +, collected as part of a nationwide representative longitudinal survey. We used a growth mixture modeling approach to identify heterogeneous trajectories within each SWB component, and logistic and multinomial regressions to explore the associated determinants. In addition to a predominant trajectory with above neutral, relatively stable scores on each SWB outcome, we found an additional trajectory with worse scores throughout all older adulthood for all SWB components, alongside a trajectory with a better life satisfaction. Depression, loneliness, disability, income, education, marital status, physical activity, and occupational status were found to be significant determinants of the membership to different trajectories. Our results suggest that there is no unitary trajectory of SWB in the older population regarding any of its components. Moreover, they point at the appropriateness of programs aimed at promoting or counteracting the aspects that may respectively prevent or facilitate pertaining to the trajectories with worst long-term outcomes as an effective way of enhancing healthy aging.

This is a preview of subscription content, access via your institution.

Fig. 1

Data Availability

Data, analytic methods, and study materials are available from the corresponding author upon reasonable request.

References

  1. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association.

    Book  Google Scholar 

  2. Ayuso-Mateos, J. L., Miret, M., Caballero, F. F., Olaya, B., Haro, J. M., Kowal, P., et al. (2013). Multi-country evaluation of affective experience: Validation of an abbreviated version of the day reconstruction method in seven countries. PLoS ONE, 8(4), e61534. https://doi.org/10.1371/journal.pone.0061534.

    Article  Google Scholar 

  3. Baselmans, B. M. L., van de Weijer, M. P., Abdellaoui, A., Vink, J. M., Hottenga, J. J., Willemsen, G., et al. (2019). A genetic investigation of the well-being spectrum. Behavior Genetics, 49(3), 286–297. https://doi.org/10.1007/s10519-019-09951-0.

    Article  Google Scholar 

  4. Blanchflower, D. G., & Oswald, A. J. (2008). Is well-being U-shaped over the life cycle? Social Science and Medicine, 66(8), 1733–1749. https://doi.org/10.1016/j.socscimed.2008.01.030.

    Article  Google Scholar 

  5. Bottan, N. L., & Truglia, R. P. (2011). Deconstructing the hedonic treadmill: Is happiness autoregressive? The Journal of Socio-Economics, 40(3), 224–236.

    Article  Google Scholar 

  6. Bull, F. C., Maslin, T. S., & Armstrong, T. (2009). Global physical activity questionnaire (GPAQ): Nine country reliability and validity study. Journal of Physical Activity and Health, 6(6), 790–804. https://doi.org/10.1123/jpah.6.6.790.

    Article  Google Scholar 

  7. Cantril, H. (1965). The pattern of human concerns. New Brunswick, NY: Rutgers University Press.

    Google Scholar 

  8. Carstensen, L. L., Fung, H. H., & Charles, S. T. (2003). Socioemotional selectivity theory and the regulation of emotion in the second half of life. Motivation and Emotion, 27(2), 103–123.

    Article  Google Scholar 

  9. Carstensen, L. L., Turan, B., Scheibe, S., Ram, N., Ersner-Hershfield, H., Samanez-Larkin, G. R., et al. (2011). Emotional experience improves with age: Evidence based on over 10 years of experience sampling. Psychology and Aging, 26(1), 21–33. https://doi.org/10.1037/a0021285.

    Article  Google Scholar 

  10. Cohen, S., Doyle, W. J., Skoner, D. P., Rabin, B. S., & Gwaltney Jr., J. M., (1997). Social ties and susceptibility to the common cold. JAMA, 277(24), 1940–1944.

    Article  Google Scholar 

  11. de la Fuente, J., Caballero, F. F., Sanchez-Niubo, A., Panagiotakos, D. B., Prina, M. A., Arndt, H., et al. (2018). Determinants of health trajectories in England and the US: An approach to identify different patterns of healthy aging. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences. https://doi.org/10.1093/gerona/gly006.

    Article  Google Scholar 

  12. de la Fuente, J., Moreno-Agostino, D., de la Torre-Luque, A., Prina, M. A., Haro, J. M., Caballero, F. F., et al. (2019). Development of a combined sensory-cognitive measure based on the common cause hypothesis: heterogeneous trajectories and associated risk factors. The Gerontologist. https://doi.org/10.1093/geront/gnz066.

    Article  Google Scholar 

  13. Deaton, A. (2008). Income, health, and well-being around the world: Evidence from the Gallup World Poll. Journal of Economic Perspective, 22(2), 53–72. https://doi.org/10.1257/jep.22.2.53.

    Article  Google Scholar 

  14. Diener, E., Lucas, R. E., & Scollon, C. N. (2009). Beyond the hedonic treadmill: Revising the adaptation theory of well-being. In F. A. Huppert, N. Baylis, & B. Keverne (Eds.), The science of well-being (pp. 103–118). Berlin: Springer.

    Chapter  Google Scholar 

  15. Dockray, S., Grant, N., Stone, A. A., Kahneman, D., Wardle, J., & Steptoe, A. (2010). A comparison of affect ratings obtained with ecological momentary assessment and the day reconstruction method. Social Indicators Research, 99(2), 269–283.

    Article  Google Scholar 

  16. Dolan, P., Kudrna, L., & Stone, A. (2017). The measure matters: An investigation of evaluative and experience-based measures of wellbeing in time use data. Social Indicators Research, 134(1), 57–73. https://doi.org/10.1007/s11205-016-1429-8.

    Article  Google Scholar 

  17. Dolan, P., & Metcalfe, R. (2012). Measuring subjective wellbeing: Recommendations on measures for use by national governments. Journal of Social Policy, 41(2), 409–427. https://doi.org/10.1017/S0047279411000833.

    Article  Google Scholar 

  18. Dolan, P., Peasgood, T., & White, M. (2008). Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being. Journal of Economic Psychology, 29(1), 94–122.

    Article  Google Scholar 

  19. European Commission (2016). European framework for action on mental health and wellbeing. Brussels: EU Joint Action on Mental Health and Wellbeing.

    Google Scholar 

  20. Filus, A., Junghaenel, D. U., Schneider, S., Broderick, J. E., & Stone, A. A. (2018). Age effects of frames of reference in self-reports of health, well-being, fatigue and pain. Applied Research in Quality of Life. https://doi.org/10.1007/s11482-018-9663-7.

    Article  Google Scholar 

  21. Frijters, P., & Beatton, T. (2012). The mystery of the U-shaped relationship between happiness and age. Journal of Economic Behavior & Organization, 82(2), 525–542. https://doi.org/10.1016/j.jebo.2012.03.008.

    Article  Google Scholar 

  22. Haro, J. M., Arbabzadeh-Bouchez, S., Brugha, T. S., de Girolamo, G., Guyer, M. E., Jin, R., et al. (2006). Concordance of the composite international diagnostic interview version 3.0 (CIDI 3.0) with standardized clinical assessments in the WHO World Mental Health surveys. International Journal of Methods in Psychiatric Research, 15(4), 167–180.

    Article  Google Scholar 

  23. Hughes, M. E., Waite, L. J., Hawkley, L. C., & Cacioppo, J. T. (2004). A short scale for measuring loneliness in large surveys: Results from two population-based studies. Research on Aging, 26(6), 655–672. https://doi.org/10.1177/0164027504268574.

    Article  Google Scholar 

  24. Jivraj, S., Nazroo, J., Vanhoutte, B., & Chandola, T. (2014). Aging and subjective well-being in later life. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 69(6), 930–941. https://doi.org/10.1093/geronb/gbu006.

    Article  Google Scholar 

  25. Kahneman, D., & Deaton, A. (2010). High income improves evaluation of life but not emotional well-being. Proceedings of the National Academy of Sciences of the United States of America, 107(38), 16489–16493. https://doi.org/10.1073/pnas.1011492107.

    Article  Google Scholar 

  26. Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. A. (2004). A survey method for characterizing daily life experience: The day reconstruction method. Science, 306(5702), 1776–1780. https://doi.org/10.1126/science.1103572.

    Article  Google Scholar 

  27. Lam, J., & García-Román, J. (2019). Solitary day, solitary activities, and associations with well-being among older adults. The Journals of Gerontology: Series B. https://doi.org/10.1093/geronb/gbz036.

    Article  Google Scholar 

  28. Lanza, S. T., & Cooper, B. R. (2016). Latent class analysis for developmental research. Child Development Perspectives, 10(1), 59–64. https://doi.org/10.1111/cdep.12163.

    Article  Google Scholar 

  29. Lucas, R. E. (2007). Adaptation and the set-point model of subjective well-being: Does happiness change after major life events? Current Directions in Psychological Science, 16(2), 75–79. https://doi.org/10.1111/j.1467-8721.2007.00479.x.

    Article  Google Scholar 

  30. Luciano, J. V., Ayuso-Mateos, J. L., Aguado, J., Fernandez, A., Serrano-Blanco, A., Roca, M., et al. (2010). The 12-item World Health Organization Disability Assessment Schedule II (WHO-DAS II): A nonparametric item response analysis. BMC Medical Research Methodology, 10, 45. https://doi.org/10.1186/1471-2288-10-45.

    Article  Google Scholar 

  31. Martin-Maria, N., Miret, M., Caballero, F. F., Rico-Uribe, L. A., Steptoe, A., Chatterji, S., et al. (2017). The impact of subjective well-being on mortality: A meta-analysis of longitudinal studies in the general population. Psychosomatic Medicine, 79(5), 565–575. https://doi.org/10.1097/PSY.0000000000000444.

    Article  Google Scholar 

  32. Mazzucchelli, T. G., Kane, R. T., & Rees, C. S. (2010). Behavioral activation interventions for well-being: A meta-analysis. The Journal of Positive Psychology, 5(2), 105–121. https://doi.org/10.1080/17439760903569154.

    Article  Google Scholar 

  33. Miret, M., Caballero, F. F., Chatterji, S., Olaya, B., Tobiasz-Adamczyk, B., Koskinen, S., et al. (2014). Health and happiness: Cross-sectional household surveys in Finland, Poland and Spain. Bulletin of the World Health Organization, 92(10), 716–725. https://doi.org/10.2471/BLT.13.129254.

    Article  Google Scholar 

  34. Miret, M., Caballero, F. F., Olaya, B., Koskinen, S., Naidoo, N., Tobiasz-Adamczyk, B., et al. (2017). Association of experienced and evaluative well-being with health in nine countries with different income levels: A cross-sectional study. Global Health, 13(1), 65. https://doi.org/10.1186/s12992-017-0290-0.

    Article  Google Scholar 

  35. Moreno-Agostino, D., Stone, A. A., Schneider, S., Koskinen, S., Leonardi, M., Naidoo, N., et al. (2019). Are retired people higher in experiential wellbeing than working older adults? A time use approach. Emotion. https://doi.org/10.1037/emo0000637.

    Article  Google Scholar 

  36. Muthen, B., & Muthen, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism, Clinical and Experimental Research, 24(6), 882–891.

    Article  Google Scholar 

  37. Pinquart, M., & Schindler, I. (2007). Changes of life satisfaction in the transition to retirement: A latent-class approach. Psychology and Aging, 22(3), 442.

    Article  Google Scholar 

  38. Pinquart, M., & Sorensen, S. (2000). Influences of socioeconomic status, social network, and competence on subjective well-being in later life: A meta-analysis. Psychology and Aging, 15(2), 187–224. https://doi.org/10.1037//0882-7974.15.2.187.

    Article  Google Scholar 

  39. Proust-Lima, C., Philipps, V., & Liquet, B. (2017). Estimation of extended mixed models using latent classes and latent processes: The R package lcmm [curvilinearity; dynamic prediction; Fortran 90; growth mixture model; joint model; psychometric tests; R]. Journal of Statistical Software, 78(2), 56. https://doi.org/10.18637/jss.v078.i02.

    Article  Google Scholar 

  40. R Core Team. (2017). R: A language and environment for statistical computing. St. Louis: R Foundation for Statistical Computing.

    Google Scholar 

  41. Ram, N., & Grimm, K. J. (2009). Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups. International Journal of Behavioral Development, 33(6), 565–576. https://doi.org/10.1177/0165025409343765.

    Article  Google Scholar 

  42. Richards, J., Jiang, X., Kelly, P., Chau, J., Bauman, A., & Ding, D. (2015). Don't worry, be happy: Cross-sectional associations between physical activity and happiness in 15 European countries. BMC Public Health, 15, 53. https://doi.org/10.1186/s12889-015-1391-4.

    Article  Google Scholar 

  43. Shankar, A., Rafnsson, S. B., & Steptoe, A. (2015). Longitudinal associations between social connections and subjective wellbeing in the English Longitudinal Study of Ageing. Psychol Health, 30(6), 686–698. https://doi.org/10.1080/08870446.2014.979823.

    Article  Google Scholar 

  44. StataCorp. (2015). Stata statistical software: Release 14. College Station, TX: StataCorp LP.

    Google Scholar 

  45. Steptoe, A., Deaton, A., & Stone, A. A. (2015). Subjective wellbeing, health, and ageing. Lancet, 385(9968), 640–648. https://doi.org/10.1016/S0140-6736(13)61489-0.

    Article  Google Scholar 

  46. Stone, A. A., Schneider, S., Krueger, A., Schwartz, J. E., & Deaton, A. (2018). Experiential wellbeing data from the American Time Use Survey: Comparisons with other methods and analytic illustrations with age and income. Social Indicators Research, 136(1), 359–378.

    Article  Google Scholar 

  47. Stone, A. A., Schwartz, J. E., Broderick, J. E., & Deaton, A. (2010). A snapshot of the age distribution of psychological well-being in the United States. Proceedings of the National Academy of Sciences of the United States of America, 107(22), 9985–9990. https://doi.org/10.1073/pnas.1003744107.

    Article  Google Scholar 

  48. United Nations. (2017). World population ageing report 2017. New York: United Nations.

    Book  Google Scholar 

  49. Vanhoutte, B. (2014). The multidimensional structure of subjective well-being in later life. Journal of Population Ageing, 7(1), 1–20. https://doi.org/10.1007/s12062-014-9092-9.

    Article  Google Scholar 

  50. Winkelmann, R. (2009). Unemployment, social capital, and subjective well-being. Journal of Happiness Studies, 10(4), 421–430. https://doi.org/10.1007/s10902-008-9097-2.

    Article  Google Scholar 

  51. World Health Organization. (2012). Strategy and action plan for healthy ageing in Europe, 2012–2020. Copenhagen: World Health Organization Regional Office for Europe.

    Google Scholar 

  52. World Health Organization. (2015). World report on ageing and health. Geneva: World Health Organization.

    Google Scholar 

  53. World Health Organization. (2017). Global strategy and action plan on ageing and health. Geneva: World Health Organization.

    Google Scholar 

Download references

Acknowledgements

This work was supported by the European Community’s Seventh Framework Programme (Grant No. 223071); Instituto de Salud Carlos III (Grant Nos. PS09/00295, PS09/01845, PI12/01490, PI13/00059, PI16/00218, PI16/01073); the Spanish Ministry of Economy and Competitiveness ACI Promociona (Grant No. ACI2009-1010); the Spanish Ministry of Education, Culture, and Sport (Grant No. FPU15/02634 to D.M.A., FPU16/03276 to J.F.); the Sara Borrel postdoctoral program from Instituto de Salud Carlos III (Grant No. CD18/00099 to E.L.), and Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Marta Miret.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 28 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Moreno-Agostino, D., de la Torre-Luque, A., de la Fuente, J. et al. Determinants of Subjective Wellbeing Trajectories in Older Adults: A Growth Mixture Modeling Approach. J Happiness Stud 22, 709–726 (2021). https://doi.org/10.1007/s10902-020-00248-2

Download citation

Keywords

  • Latent class
  • Day reconstruction method
  • Healthy aging
  • Longitudinal study