Terminal decline in well-being: The role of multi-indicator constellations of physical health and psychosocial correlates.

Abstract

Well-being is often relatively stable across adulthood and old age, but typically exhibits pronounced deteriorations and vast individual differences in the terminal phase of life. However, the factors contributing to these differences are not well understood. Using up to 25-year annual longitudinal data obtained from 4,404 now-deceased participants of the nationwide German Socio-Economic Panel Study (SOEP; age at death: M = 73.2 years; SD = 14.3 years; 52% women), we explored the role of multi-indicator constellations of sociodemographic variables, physical health and burden factors, and psychosocial characteristics. Expanding earlier reports, structural equation model (SEM) trees allowed us to identify profiles of variables that were associated with differences in the shape of late-life well-being trajectories. Physical health factors were found to play a major role for well-being decline, but in interaction with psychosocial characteristics such as social participation. To illustrate, for people with low social participation, disability emerged as the strongest correlate of differences in late-life well-being trajectories. However, for people with high social participation, whether or not an individual had spent considerable time in the hospital differentiated high versus low and stable versus declining late-life well-being. We corroborated these results with variable importance measures derived from a set of resampled SEM trees (so-called SEM forests) that provide robust and comparative indicators of the total interactive effects of variables for differential late-life well-being. We discuss benefits and limitations of our approach and consider our findings in the context of other reports about protective factors against terminal decline in well-being.

How far reaches the power of personality? Personality predictors of terminal decline in well-being.

“…Converging with prior evidence, we found that people who died at younger ages, were less socially active, and suffered from disability or a compromised ability to carry out Instrumental Activities of Daily Living reported lower levels of well-being close to death (see Brandmaier et al, 2017;Windsor et al, 2014). Moving beyond previous reports and demonstrating the power of personality to predict important life outcomes across the entire life span, our results further revealed that personality traits predict late-life well-being above and beyond these well-established predictors.…”

“…The measure has extensively been used in research (Fujita & Diener, 2005;Headey et al, 2010;Lucas, Clark, Georgellis, & Diener, 2003) and taps into cognitive-evaluative facets (rather than affective facets) of wellbeing. To make sure our report is immediately comparable to earlier publications examining terminal decline in well-being (e.g., Brandmaier et al, 2017;, we standardized responses to a T metric (M = 50; SD = 10) with the total 2002 SOEP sample serving as a reference (M = 6.90, SD = 1.81).…”

“…Please note that the larger SOEP data set has been extensively used in psychological research, with prior studies investigating, for example, the development of personality across the life span (Denissen, Ulferts, Lüdtke, Muck, & Gerstorf, 2014;Lucas & Donnellan, 2011;Specht, Egloff, & Schmukle, 2011), psychosocial and physical health predictors of terminal well-being decline (e.g., Brandmaier et al, 2017;, or associations between personality and well-being from young adulthood to young old age (Specht et al, 2013).…”

Score-Guided Structural Equation Model Trees

“…There are currently two software packages for the statistical programming language R that allow fitting SEM trees. One is the semtree package (Brandmaier et al, 2013b) that has been widely applied in the literature (Brandmaier et al, 2013a(Brandmaier et al, , 2016(Brandmaier et al, , 2017(Brandmaier et al, , 2018Jacobucci et al, 2017;Usami et al, 2017Usami et al, , 2019de Mooij et al, 2018;Ammerman et al, 2019;Serang et al, 2020;Simpson-Kent et al, 2020). The other software implementation is the partykit package (Hothorn and Zeileis, 2015).…”

Score-Guided Structural Equation Model Trees

“…SEM trees as first presented by Brandmaier, Oertzen, McArdle, and Lindenberger (2013), discussed in the methodological literature (Brandmaier, Driver, & Voelkle, 2018;Brandmaier, Prindle, McArdle, & Lindenberger, 2016;Jacobucci, Grimm, & McArdle, 2017;Serang et al, 2020;Usami, Hayes, & McArdle, 2017;Usami, Jacobucci, & Hayes, 2019), and used for data analysis (Ammerman, Jacobucci, & McCloskey, 2019;Brandmaier, Ram, Wagner, & Gerstorf, 2017;Mooij, Henson, Waldorp, & Kievit, 2018;Simpson-Kent et al, 2020) are another popular method for exploring heterogeneity in SEMs that can be considered as a compromise between MGSEMs and latent class models. SEM trees are a data-driven approach that automatically searches through all available covariates to identify groups with similar SEM parameter values.…”