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Lower executive functioning predicts steeper subsequent decline in well-being only in young-old but not old-old age
IHLE, Andreas, et al.
Abstract
Objectives: From a longitudinal perspective, the direction of the relationship between cognitive functioning and well-being in old age, both conceptually and empirically, is still under debate.
Therefore, we aimed to disentangle the different longitudinal relationship patterns proposed and whether those differed between young-old and old-old adults. Methods: We used latent change score modeling based on longitudinal data from 1,040 older adults (M = 74.54 years at Time 1 [T1], median = 73 years) to analyze reciprocal lead–lag relationships over 6 years in executive functioning (trail making test [TMT] completion time) and well-being (life satisfaction), taking into account chronological age, sex, education, leisure activities, and chronic diseases. Results: In young-old adults (
IHLE, Andreas, et al. Lower executive functioning predicts steeper subsequent decline in well-being only in young-old but not old-old age. International Journal of Behavioral Development, 2021, vol. 45, no. 2, p. 97-108
DOI : 10.1177/0165025420937076
Available at:
http://archive-ouverte.unige.ch/unige:147584
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This is the accepted manuscript version of the following publication: Ihle, A., Ghisletta, P., Gouveia, É. R., Gouveia, B. R., Oris, M., Maurer, J., & Kliegel, M. (2020). Lower Executive Functioning Predicts Steeper Subsequent Decline in Well-Being Only in Young-Old But Not Old-Old Age. International Journal of Behavioral Development.
DOI: 10.1177/0165025420937076. This article has been published in a revised form in the International Journal of Behavioral Development
https://journals.sagepub.com/doi/full/10.1177/0165025420937076?casa_token=ntrHPoE9 8GIAAAAA%3AIJ4XaZ0zvAW34cBqmLwfXHd8CXh9uEiSI2Fi9xYgUrsQanc2ibMmd oMP9ydp_8LHJ0x1f8k0P9d9. This version is free to view and download for private research and study only. Not for re-distribution or re-use. © SAGE
Lower Executive Functioning Predicts Steeper Subsequent Decline in Well-Being Only in Young-Old But Not Old-Old Age
Andreas Ihle1, 2, 3, PhD Paolo Ghisletta1, 3, 4, PhD Élvio R. Gouveia2, 5, 6, PhD Bruna R. Gouveia2, 6, 7, 8, PhD Michel Oris2, 3, PhD
Jürgen Maurer3, 9, PhD Matthias Kliegel1, 2, 3, PhD
2
1 Department of Psychology, University of Geneva, Geneva, Switzerland
2 Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Geneva, Switzerland
3 Swiss National Centre of Competence in Research LIVES – Overcoming vulnerability: Life course perspectives, Lausanne and Geneva, Switzerland
4 Distance Learning University Switzerland, Sierre, Switzerland
5 Department of Physical Education and Sport, University of Madeira, Funchal, Portugal
6 LARSyS, Interactive Technologies Institute, Funchal, Portugal
7 Health Administration Institute, Secretary of Health of the Autonomous Region of Madeira, Funchal, Portugal
8 Saint Joseph of Cluny Higher School of Nursing, Funchal, Portugal
9 Department of Economics, University of Lausanne, Lausanne, Switzerland
Correspondence: Andreas Ihle, CIGEV, University of Geneva,
Boulevard du Pont d'Arve 28, 1205 Geneva, Switzerland. E-mail: Andreas.Ihle@unige.ch Phone: +41 22 37 98308
Short Title: Executive Functioning and Decline in Well-Being in Old Age
3 Abstract
Objectives: From a longitudinal perspective, the direction of the relationship between cognitive functioning and well-being in old age, both conceptually and empirically, is still under debate. Therefore, we aimed to disentangle the different longitudinal relationship patterns proposed and whether those differed between young-old and old-old adults.
Methods: We used latent change score modeling based on longitudinal data from 1,040 older adults (M = 74.54 years at time 1 [T1], median = 73 years) to analyze reciprocal lead-lag relationships over six years in executive functioning (Trail Making Test [TMT]
completion time) and well-being (life satisfaction), taking into account chronological age, sex, education, leisure activities, and chronic diseases.
Results: In young-old adults (< 73 years) longer TMT completion time at T1 (i.e., lower executive functioning status) significantly predicted steeper subsequent decline in well- being. This was not the case for old-old adults (>= 73 years), for whom this relationship was significantly different from that of the young-old (moderation effect). In either group, well- being status at T1 did not predict changes in TMT completion time.
Discussion: Lower executive functioning may predict a subsequent decline in well- being in young-old adults only. Wider implications in a context of promotion of healthy aging are discussed.
Keywords: aging; well-being; executive functioning; health; longitudinal change
Word counts: Abstract=198; main text=5,029; full document=9,771
4 Introduction
One of the major aims of gerontological research is to better understand how
individuals can maintain relatively good health in old age (Hicks & Siedlecki, 2017). With its first world report on aging and health, the World Health Organization promotes a global paradigm shift in aging research. It redefines “healthy aging” that is not anymore simply characterized by the absence of diseases. Instead, this revised understanding of healthy aging focuses on the dynamic interplay of intrinsic capacities, environments, and their interactions in producing functional abilities in given contexts, irrespective of the presence of diseases (Beard et al., 2016).
Two psychological domains that are important in the context of healthy aging are cognitive functioning and well-being (Braun, Schmukle, & Kunzmann, 2017; Steptoe, Deaton, & Stone, 2015; Wilson et al., 2013). Cross-sectional empirical evidence suggests a relationship between cognitive functioning and well-being in old age (Clarke, Marshall, Black, & Colantonio, 2002; Lawton et al., 1999; Woods et al., 2015). Yet, from a longitudinal perspective, the direction of this relationship, both conceptually and empirically, is still under debate. To identify possible intervention targets to promote healthy aging it is therefore necessary to understand whether one of the two domains influences the other by partially determining its trajectories across advanced age, whether that influence might be reciprocated, and which might be possible factors, such as different phases in aging, determining individual differences in such dynamics.
Specifically, in concepts of successful aging, cognitive functioning is considered as an important capacity determining well-being maintenance in old age (Charles & Hong, 2016;
Rowe & Cosco, 2016). For example, good cognitive functioning helps to maintain
independently managing the routines of an individual’s instrumental activities of daily living, which in turn are crucial for sustaining well-being in old age (Woods et al., 2015). Empirical longitudinal evidence for the predictive role of cognitive functioning for well-being comes for
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example from studies reporting that better cognitive performance status in terms of spatial abilities and processing speed predicts higher subsequent well-being status in terms of life satisfaction three years later (Enkvist, Ekstrom, & Elmstahl, 2013). These correlative findings dovetail with experimental studies demonstrating effects of cognitive interventions on
subsequent increases in well-being, as shown for self-representations of intrapersonal affective and emotional states reflecting a sense of subjective well-being (Castel, Lluch, Ribas, Borras, & Molto, 2017) and psychological wellness (Chan, Cheung, Yeung, & Lee, 2018). Yet, other studies report mixed results. For example, Castro-Lionard et al. (2011) observed that better performance in some cognitive abilities, such as in one reasoning and one memory test, predicts higher subsequent well-being status in terms of life satisfaction six years later, while better performance in another memory test predicts lower subsequent life satisfaction six years later. Comijs, Dik, Aartsen, Deeg, and Jonker (2005) found in a longitudinal study over a period of six years in three waves that steeper decline in cognitive performance, as measured through performance changes in the Mini-Mental State
Examination over two subsequent 3-year periods, predicts lower well-being in terms of feelings of loneliness, while for life satisfaction no longitudinal association was found.
Moreover, other studies do not find evidence for a longitudinal relationship between cognitive performance and well-being. For example, Braun et al. (2017) reported that cognitive status, as determined by standard psychometric tests of fluid cognitive abilities, was unrelated to longitudinal change in subjective well-being in terms of life satisfaction, aging satisfaction, and nonagitation across 2 measurements spanning a period of 12 years.
Another conceptual perspective highlights the role of well-being in determining cognitive aging trajectories. For example, a positive self-evaluation of aging might be instrumental for an individual’s development (Kornadt, Voss, & Rothermund, 2017) and for having positive consequences for different outcomes of healthy aging, such as cognitive functioning and health through motivational and activity engagement pathways (Hicks &
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Siedlecki, 2017; Vallet et al., 2018; Wurm, Tomasik, & Tesch-Romer, 2010; Zuber, Ihle, Blum, Desrichard, & Kliegel, 2019). Moreover, a further conceptual view proposes a reciprocal relationship between cognitive functioning and well-being, i.e., both domains are influencing each other (Lawton, 1983; Rowe & Cosco, 2016). Empirically investigating possible reciprocal relationship patterns between cognitive performance and well-being, Wilson et al. (2013) followed individuals over five years with annual assessments. They found that better cognitive performance status in terms of global cognition, as a composite score based on 19 tests measuring perceptual speed and memory, at a given evaluation predicts higher subsequent well-being status in terms of purpose in life at the subsequent evaluation and that in turn higher purpose in life also predicts better subsequent cognition.
To disentangle these different relationship patterns, Gerstorf, Lövdén, Röcke, Smith, and Lindenberger (2007) found based on 13-year longitudinal data across six waves that higher well-being status in terms of life satisfaction, aging satisfaction, and nonagitation predicts a reduced subsequent decline in cognitive functioning in terms of perceptual speed, with time lags of 2 years, while the authors did not observe evidence for the opposite pattern of perceptual speed predicting subsequent changes in well-being. Gerstorf et al. concluded that future studies ought to more thoroughly investigate whether the role of well-being for determining cognitive decline holds across different periods of the adult lifespan and whether the opposite relationship of cognitive functioning influencing change in well-being may also be at play. Remarkably, until now, more than one decade later, possible differences in relationship patterns between cognitive functioning and well-being, depending on different phases in aging, have not been empirically tested in depth.
Hence, the present study focused on investigating such potentially differential patterns across old age. For this purpose, we adopted the model of third versus fourth age (Baltes, 1998; Baltes & Smith, 2003) that is important with regard to the healthy aging perspective.
Specifically, this model postulates discontinuity in aging processes and therefore conceptually
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distinguishes between young-old versus old-old age that differ qualitatively. In this regard, in young-old age, individuals are still able to compensate age-related losses in for example health. In contrast, in old-old age, such compensation is no longer possible. Thus, the model of third versus fourth age (Baltes, 1998; Baltes & Smith, 2003) argues that aging in advanced age is not a continuous process, but that instead one needs to qualitatively differentiate
between young-old and old-old age due to discontinuity in aging. Empirically, this conceptual distinction between young-old and old-old age has been adopted in many research areas including psychology, epidemiology, physiology, demography, and sociology (e.g., Abdel- Ghany & Sharpe, 1997; Adams, Roberts, & Cole, 2011; Alterovitz & Mendelsohn, 2013;
Ansah et al., 2015; Bodner, Palgi, & Kaveh, 2013; Buch et al., 1999; Calero, Perez-Diaz, Gonzalez, & Calero-Garcia, 2013; Gavazzi, Mallaret, Couturier, Iffenecker, & Franco, 2002;
Kvavilashvili, Cockburn, & Kornbrot, 2013; Menec & Chipperfield, 1997; Vaupel et al., 1998; Wright & Holliday, 2007; Wu et al., 2015; Yoshimura, Yamada, Kajiwara, Nishiguchi,
& Aoyama, 2013; Zinke et al., 2014).
Yet, given the postulation that young-old versus old-old age differ qualitatively, rather than differing only on the single quantitative dimension ‘age’ per se, it has been a challenge to empirically approach the likely age ranges for those possible qualitative shifts in order to identify the mark that distinguishes between young-old and old-old age. For example, a demographic, population-based approach that may be limited to developed countries only represents the chronological age at which 50% of the birth cohort are no longer alive. This method would place the beginning of old-old age in developed countries at about 75-80 years (Olshansky, Carnes, & Desesquelles, 2001; Oris & Lerch, 2009; Vaupel et al., 1998). A major shortcoming of such demographic approaches is that they consider overall mortality, but not the individual’s abilities. For instance, for studies aiming to compare older adults regarding certain outcomes it remains difficult to stratify age groups according to reference categories such as ‘young-old’ and ‘old-old’ age. In fact, there is no consensus regarding the exact cutoff
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age to divide young-old from old-old age. Consequently, researchers have often to return to
‘practical’ strategies such as splitting the available old age sample into two halves. Therefore, across studies comparing young-old and old-old adults there is a large variety in sample stratification criteria. For example, in prior research the old-old age group was stratified being at least (the young-old age group being respectively below) 70 years (e.g., Buch et al., 1999), 71 years (e.g., Kvavilashvili et al., 2013), 75 years (e.g., Abdel-Ghany & Sharpe, 1997;
Alterovitz & Mendelsohn, 2013; Gayzur et al., 2014; Wright & Holliday, 2007; Yoshimura et al., 2013), 80 years (e.g., Adams et al., 2011; Ansah et al., 2015; Bodner et al., 2013; Kliegel
& Jäger, 2006; Menec & Chipperfield, 1997; Zinke et al., 2014), 81 years (e.g., Calero et al., 2013; Wu et al., 2015), or even more than 85 years (e.g., Gavazzi et al., 2002). Yet, the commonly accepted approach in this regard is to divide the available old age sample into two halves based on a median split, which we applied for the present study.
Study Goals
To the best of our knowledge, potentially differential patterns between young-old and old-old age in the longitudinal relationship between executive functioning and well-being have not been comprehensively tested so far. Therefore, in the present study, based on a large sample of 1,040 older adults that were followed up over six years, we aimed to disentangle different longitudinal relationship patterns (i.e., unidirectional relationships, executive functioning predicting a subsequent change in well-being or well-being predicting a subsequent change in executive functioning, versus a reciprocal relationship) and to test whether these patterns differed between young-old and old-old adults. For this purpose, we used latent change score modeling (McArdle, 2009) to analyze status and subsequent change over six years in executive functioning as measured through Trail Making Test (TMT) completion time and well-being in terms of life satisfaction. We took into account
chronological age and sex as well as education, leisure activities, and chronic diseases, as key predictors of aging trajectories in cognitive functioning and well-being (Charles & Hong,
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2016; Rowe & Cosco, 2016; Stern, 2012). To test whether the longitudinal relationship patterns differed between young-old and old-old adults, we divided the sample into two groups based on a median split of 73 years.
Study Hypotheses
In line with the postulated discontinuity in the model of third versus fourth age
(Baltes, 1998; Baltes & Smith, 2003), we expect differential patterns in young-old and old-old adults. Specifically, we hypothesize that a longitudinal relationship between executive
functioning and well-being should emerge in young-old age only, in which individuals are still able to compensate. In particular, we expect that in young-old adults, inter-individual differences in executive functioning predict inter-individual differences in subsequent intra- individual change in well-being. Such pattern seems reasonable because, in line with the Model of Selective Optimization with Compensation (SOC; Baltes & Baltes, 1989), empirical evidence suggests that resources, for example in terms of cognitive functioning, facilitate compensation processes (Lang, Rieckmann, & Baltes, 2002), which in turn are predictive of multiple outcomes of healthy aging, such as well-being (Freund & Baltes, 1998; Ihle, Borella et al., 2015). We only investigated those age comparisons that are relevant for our hypotheses and did not carry out additional statistical tests concerning age comparisons of other
parameters.
Methods Participants
We analyzed data from 1,040 individuals who participated in the two waves of the Vivre-Leben-Vivere (VLV) survey (Ihle, Oris et al., 2015; Oris et al., 2016). Respondents were first interviewed during 2011 (time 1; T1) and again in 2017 (Time 2; T2) using face-to- face computer-assisted personal interviewing (CAPI) and paper-pencil questionnaires. Mean age of these respondents at T1 was 74.54 years (SD = 6.64, range 64-96). All participants gave their written informed consent for inclusion in the study before participating. The
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present study was conducted in accordance with the Declaration of Helsinki and the study protocol had been approved by the ethics commission of the Faculty of Psychology and Social Sciences of the University of Geneva (project identification codes: CE_FPSE_14.10.2010 and CE_FPSE_05.04.2017).
Materials Well-Being
At both time points, we administered the Satisfaction with Life Scale (SWLS; Diener et al., 1985). Participants rated the following five statements ‘In most ways my life is close to my ideal.’; ‘The conditions of my life are excellent.’; ‘I am satisfied with my life.’; ‘So far I have gotten the important things I want in life.’; and ‘If I could live my life over, I would change almost nothing.’ using a seven-point Likert-type scale ranging from -3 (‘strongly disagree’) to +3 (‘strongly agree’). For analyses, we modeled latent well-being factors indicated by the first four SWLS items (i.e., not including the fifth item) due to several
reasons: First, in the model with five indicators the fifth had a low R² (a.k.a., squared multiple correlation, which means, it shared little variance with the others, which all had higher R²), which is a common finding for this scale (e.g., Pavot, Diener, & Suh, 1998). Second, the model with all five indicators fit the data less well than the model with four indicators. Third, besides these methodological reasons, conceptually the fifth indicator is hypothetical in nature, rather than an evaluation of participants’ actual life as with the four other items (for a discussion see e.g. Pavot et al., 1998). Thus, due to these methodological and conceptual reasons, we modeled latent well-being factors indicated by the first four SWLS items only (i.e., not including the fifth item). In structural equation modeling, selecting and leaving out an item weakly related to the others (and thus to their common underlying latent variable) is a common approach (e.g., Bollen, 1989).
11 Trail Making Test Completion Time
At both time points, we administered the Trail Making Test part A (TMT A; Reitan, 1958). After one exercise trail (connecting the numbers from 1 to 8), participants had to connect the numbers from 1 to 25 as fast as possible and without error in ascending order. The TMT A completion time was the time in seconds needed to correctly connect the 25 numbers.
In addition, we administered at both time points the Trail Making Test part B (TMT B;
Reitan, 1958). After one exercise trail (connecting 1-A-2-B-3-C-4-D), participants had to connect the numbers 1 to 13 in ascending order and the letters A to L in alphabetic order while alternating between numbers and letters (i.e., 1-A-2-B-3-C ... 12-L-13) as fast as possible and without error. The TMT B completion time was the time in seconds needed to correctly connect the 25 numbers / letters.
Education
We asked participants to indicate the total time in years they had spent for formal education, comprising primary school, secondary school, and university.
Leisure Activities
Participants reported at T1 the leisure activities they were regularly engaging in, such as going to the cinema, going to conferences, journeys, artistic activities, table games, and municipality activities. For analyses, we summed up the overall number of leisure activities reported by participants at T1.
Chronic Diseases
Participants reported at T1 the chronic diseases they suffered from, such as heart diseases of ischemic or organic pathogenesis, primary arrhythmias, pulmonary heart diseases, hypertension, and peripheral vascular diseases. For analyses, we summed up the overall number of chronic diseases participants suffered from at T1 as a global indicator of
individuals’ multimorbidity (see, e.g. Rozzini et al., 2002, for a validation; Ihle et al., 2018).
12 Statistical Analyses
We conducted latent change score modeling (McArdle, 2009) using the R package lavaan (Rosseel, 2012). The specification of our latent change score model is illustrated in Figure 1. Specifically, we modeled latent well-being factors at T1 and T2 (indicated by the first four SWLS items at T1 and T2, respectively) as well as a latent variable representing the change in well-being between T1 and T2. We also modeled latent executive functioning factors of TMT completion time at T1 and T2 (indicated by TMT parts A and B at T1 and T2, respectively) as well as a latent variable representing the change in TMT completion time between T1 and T2. We included the following relationships between the latent variables: the latent executive functioning factor at T1 predicting latent change in executive functioning and latent change in well-being, the latent well-being factor at T1 predicting latent change in well- being and latent change in TMT completion time, the correlation between the latent well- being and executive functioning factors at T1, and the correlation between latent change in well-being and latent change in TMT completion time. Moreover, we included several T1- covariates that predicted latent change in well-being and TMT completion time and were correlated to the latent well-being and executive functioning factors at T1: chronological age, sex, years of education, the number of leisure activities, and the number of chronic diseases.
The covariates were allowed to intercorrelate. To test for moderation effects by age group (i.e., differential relationships in young-old versus old-old adults), we distinguished the sample into two groups of age using a median-split approach (young-old adults < 73 years versus old-old adults ≥ 73 years). For latent variables of well-being and TMT completion time we correspondingly enforced strong factorial invariance across age groups and across time on the respective factor loadings, with intercepts of all indicators being fixed to zero to assure that the same executive functioning / well-being factor was assessed in both age groups at both time points (i.e., strong metric invariance; Meredith & Teresi, 2006).
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We estimated all relationships separately for both age groups simultaneously in a model that comprised both moderator subgroups without any equality constraint. We
evaluated model fit of this moderator model as follows: Given that with large study samples the χ² test often indicates a significant deviation of the model matrix from the covariance matrix despite good model fit (Hu & Bentler, 1999), we inspected several additional fit indices. Specifically, we used the following criteria: Comparative Fit Index (good models:
CFI > .95), Incremental Fit Index (good models: IFI > .95), Root Mean Square Error of Approximation (good models: RMSEA < .06), and Standardized Root Mean Square Residual (good models: SRMR < .08; Hu & Bentler, 1999).
For analyses, we standardized covariates that have big scales, i.e. years of education and number of leisure activities so that the reported raw estimates (b) can be interpreted in terms of SDs. We did not standardize the number of chronic diseases because it allowed interpreting the reported raw estimates in terms of effects ‘for each additional chronic disease’. We divided completion times in TMT A and TMT B by 60 (i.e., representing minutes instead of seconds) to obtain a comparable scaling of well-being and TMT
completion time (Kline, 1998). We did not standardize well-being scores nor completion time in TMT A or TMT B so that the reported raw estimates can be interpreted in points and minutes, respectively.¹ For model estimation, we used full information maximum likelihood.
Results Descriptive Statistics
Table 1 shows descriptive statistics of all analyzed measures for the overall sample as well as separately for young-old and old-old adults.
Latent Change Score Modeling
The latent change score model provided a good statistical account of the data (χ² = 448.94, df = 192, p < .001, CFI = .95, IFI = .95, RMSEA = .05, SRMR = .05).
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Table 2 shows parameter estimates for descriptive statistics in the latent change score model in terms of latent means and standard deviations. With respect to age group
comparisons regarding these patterns, young-old and old-old adults did not differ with respect to mean well-being at T1 (Δχ² = 2.14, Δdf = 1, p = .143). Old-old adults showed steeper decline in well-being compared to young-old adults (Δχ² = 5.47, Δdf = 1, p = .019). Moreover, old-old adults showed longer TMT completion time at T1 (i.e., lower executive functioning status) compared to young-old adults (Δχ² = 55.05, Δdf = 1, p < .001). Old-old adults showed greater increase in TMT completion time (i.e., steeper executive functioning decline)
compared to young-old adults (Δχ² = 12.61, Δdf = 1, p < .001).
Table 3 shows parameter estimates for relationships of well-being and TMT
completion time. Specifically, in line with our predictions, in young-old adults longer TMT completion time at T1 (i.e., lower executive functioning status) significantly predicted steeper subsequent decline in well-being (b = -0.38, p < .05). This was not the case for old-old adults (p > .05; with a significant difference in size of this relationship between both age groups, Δχ²
= 8.89, Δdf = 1, p < .01).² Figure 2 illustrates this interaction by displaying estimated mean change in well-being between T1 and T2 at shorter (-1 SD) and longer (+1 SD) TMT
completion time at T1 (i.e., higher and lower executive functioning status, respectively) as a function of age group (young-old versus old-old). Moreover, in young-old adults steeper decline in well-being was significantly predicted by older age (b = -0.16, p < .05) but not by sex, education, activities, nor diseases. In comparison, in old-old adults we found significantly steeper decline in well-being in women (compared to men, b = -0.16, p < .01). Age,
education, activities, and diseases did not predict change in well-being in old-old adults.
In young-old adults greater decrease in TMT completion time (i.e., steeper executive functioning improvements) were significantly predicted by more activities (b = -0.03, p < .05) but not by level of well-being at T1, nor by age, sex, education, or diseases. In old-old adults greater increase in TMT completion time (i.e., steeper executive functioning decline) was
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significantly predicted by older age (b = 0.06, p < .05) and fewer activities (b = -0.05, p < .05) but not by level of well-being at T1, nor by education or diseases. Moreover, in old-old adults we found significantly greater increase in TMT completion time (i.e., steeper executive functioning decline) in men (compared to women, b = -0.08, p < .05).
There was no change-change association of executive functioning and well-being in either age group. In young-old adults (but not in old-old adults) there was a significant level- level association of shorter TMT completion time at T1 (i.e., better executive functioning status) being significantly correlated with higher well-being at T1 (r = -.16, p < .05; see Table 3 for an overview regarding all relationships of latent level of well-being / executive
functioning in the latent change score model).
Discussion
In this study we aimed to empirically disentangle different longitudinal relationship patterns between executive functioning and well-being in old age and whether those differed between young-old and old-old adults. Using latent change score modeling, in line with our predictions, we found that in young-old adults lower executive functioning status at T1 (i.e., indicated by longer TMT completion time) predicted steeper subsequent decline in well- being, while this was not the case for old-old adults. Accordingly, there was a significant moderation of the relationship between executive functioning and subsequent well-being decline by age group. Moreover, well-being status at T1 did not predict changes in TMT completion time in either age group.
Conceptually important, with respect to the debated more general question which of the two domains, cognitive functioning and well-being, serves as predictor of aging pathways in the other domain, present data did not support the unidirectional relationship account of well-being determining cognitive aging trajectories, as shown for performance changes in perceptual speed (Gerstorf et al., 2007). We neither found evidence for the conceptual view proposing a reciprocal relationship between cognitive functioning and well-being, i.e., that
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both domains may influence each other over time (Lawton, 1983; Rowe & Cosco, 2016).
Instead, present data speak for a mechanism in which lower executive functioning may predict a subsequent decline in well-being. Notably, this relationship seems to emerge in young-old adults only. Hence, besides the large heterogeneity regarding cognitive and well- being measures and longitudinal time frames captured in prior research on the debated longitudinal relationship between cognitive functioning and well-being, we propose age- group differences as a key factor to at least partly explain these differences observed.
Thereby, present findings have important conceptual implications for current
gerontological models of healthy aging highlighting the individual’s capacities for producing functional abilities in given contexts irrespective of the presence of diseases (Beard et al., 2016). Specifically, good cognitive functioning in general is seen as a crucial capacity (among others) for sustaining well-being in old age because it helps for example to continue pursuing the activities valued by an individual (Beard et al., 2016; Rowe & Cosco, 2016; Steptoe et al., 2015). Moreover, good cognitive functioning in general helps to maintain independently managing the routines of an individual’s instrumental activities of daily living, which in turn are crucial for sustaining well-being in old age (Woods et al., 2015). Notably, present findings suggest that the link between executive functioning and subsequent changes in well-being seems to emerge for young-old adults only. Conceptually, this dovetails with the model of third versus fourth age (Baltes, 1998; Baltes & Smith, 2003), which qualitatively
distinguishes between young-old and old-old age and postulates that, in contrast to old-old age, in young-old age individuals are still able to compensate age-related losses. In this context, in young-old adults inter-individual differences in executive functioning seem to be a leading indicator of inter-individual differences in subsequent intra-individual change in well- being. In contrast, in old-old adults the individual’s executive functioning capacity seem to be no longer decisive with regard to their subsequent well-being development. We assume that perhaps if well-being already shows a relatively steep decline (as probably for the old-old
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adults in our sample) it can dot be compensated anymore in old-old age, not even by good executive functioning. These explanations are in line with the model of third versus fourth age (Baltes, 1998; Baltes & Smith, 2003) and further dovetail with prior evidence suggesting that well-being shows terminal decline (Gerstorf et al., 2008) as well as our observations that old- old adults showed a much steeper decline in well-being than young-old adults and that in young-old adults (but not in old-old adults) better executive functioning status at T1 (i.e., indicated by shorter TMT completion time) was correlated with higher well-being at T1 (i.e., level-level association). In the long term, present findings suggest that lower executive functioning leads to steeper subsequent decline in well-being trajectories in young-old adults only.
One may debate whether age should be treated as a continuous variable or as a grouping variable as in the present analyses. Besides the already outlined conceptual
reasoning for breaking the sample into two age groups, we had methodological reasons to use a median-split approach to capture in our model the postulation that young-old versus old-old age differ qualitatively, rather than differing only on the single quantitative dimension ‘age’
per se. Specifically, if we had analyzed the data as a single group without any age split we would have argued for a linear relationship between executive functioning and well-being.
But, by introducing a multiple-group perspective, we allowed age to interact with the dynamic effects of interest in a spline-like way. That is, the cross-lagged parameters thereby are
different according to a critical age threshold, so that the relation between executive functioning and well-being is no longer linear, but qualitatively differs according to young- old versus old-old age groups. To illustrate this reasoning, we conducted an additional analysis with a model based on a single group including the full age range without any age split. In this analysis, we did not find any longitudinal relationship between executive
functioning and well-being.³ Thus, a single-group analysis clouds the significant relationship between executive functioning and subsequent change in well-being found in young-old
18
adults in the age-split model. This further corroborates the applied median-split not being simply a median on the single quantitative dimension ‘age’ per se, but instead an attempt to methodologically consider the qualitative differences between young-old and old-old age due to discontinuity in aging conceptually proposed.
Present findings also have important practical implications in a context of promotion of healthy aging. In line with experimental studies (Castel et al., 2017; Chan et al., 2018), the present results suggest that (besides other modifiable factors such as lifestyle; Gouveia et al., 2017) enhancing executive functioning may be an avenue to promote well-being, particularly in young-old adults. Moreover, we found that chronic diseases did not predict changes in executive functioning, nor well-being, but were correlated with baseline well-being status.
Thus, well-being might also be a direct reflection of an assessment individuals make about their health, although health may not influence directly executive functioning. In this context, disease prevention might be another angle to promote well-being. This seems reasonable because health constraints may hinder pursuing an active lifestyle, which has been found to mediate the association between health issues and well-being outcomes (Paggi, Jopp, &
Hertzog, 2016). This dovetails with present observations that activity engagement was correlated with better baseline well-being status (see also e.g. Gouveia et al., 2017).
Interestingly, the present observation that both young-old and old-old adults showed a decline in well-being seems in contrast to observations that well-being in Western societies typically increases in old age (Steptoe et al., 2015). Yet, the present finding that old-old adults showed a much steeper decline in well-being compared to young-old adults dovetails with prior research suggesting that well-being shows terminal decline (Gerstorf et al., 2008).
Conceptually, this suggests that the determinants for well-being trajectories in old age may be multifaceted and not simply reflect the individual’s abilities, but likely also (among others) the individual’s life course and social inequalities (Stowe & Cooney, 2015), which have been also adopted in current gerontological models of healthy aging emphasizing the dynamic
19
interplay of the individual’s capacities with the given contexts (Beard et al., 2016). Thus, an additional avenue for better understanding individuals’ well-being trajectories across old age may concern possible gender differences. For instance, in old-old adults we found steeper well-being decline in women (compared to men). In contrast, there were no gender
differences in young-old adults. One possible pathway could be that lifelong accumulating gender inequalities in health and activities may not until very late adulthood result in
disadvantageous well-being trajectories in women as soon as their main buffering resources, such as social networks, collapse (Alvarado, Zunzunegui, Beland, Sicotte, & Tellechea, 2007;
Katz & Calasanti, 2015). Future longitudinal research might further explore such gender- specific pathways and their role for well-being trajectories in detail.
We acknowledge that the present correlative study does not allow drawing causal inferences. We assessed the SWLS as one of the most common indicators of well-being (Veenhoven, 2007). We assessed the TMT as a sensitive measure of inter-individual differences in intra-individual cognitive change (e.g., Chen et al., 2001). Yet, we
acknowledge that future longitudinal studies will have to examine whether the present pattern of results holds also for other dimensions of well-being as well as other cognitive abilities, such as episodic memory, working memory, and a broader range of executive functions.
In the end, we conclude that present data speak for a mechanism in which lower executive functioning may predict a subsequent decline in well-being. Notably, this relationship seems to emerge in young-old adults only.
Footnotes
¹ We checked for differential scaling by additionally estimating the model on
standardized executive functioning and well-being scores. The pattern of results was identical.
Specifically, in young-old adults longer TMT completion time at T1 (i.e., lower executive functioning status) significantly predicted steeper subsequent decline in well-being (b = -0.17,
20
p < .05). This was not the case for old-old adults (b = 0.14, p > .05; with a significant difference in size of this relationship between both age groups, Δχ² = 8.89, Δdf = 1, p < .01).
² In additional analyses, we constrained the variance of the latent change variables invariant across both age groups (respectively for executive functioning and well-being).
Compared to the unconstrained model, setting the variance of the two latent change variables invariant across both age groups revealed a significant decrease in model fit (Δχ² = 9.33, Δdf = 2, p < .01). Thus, the two age groups had different variances in change. Yet, most
importantly, regarding our main hypothesis we found the same pattern of results. Specifically, in young-old adults longer TMT completion time at T1 (i.e., lower executive functioning status) significantly predicted steeper subsequent decline in well-being (b = -0.37, p < .05).
This was not the case for old-old adults (b = 0.29, p > .05). Again, there was a significant difference in size of this relationship between both age groups (Δχ² = 8.69, Δdf = 1, p < .01).
³ We conducted an additional analysis with a model based on a single group including the full age range without any age split. In this analysis, we did not find any longitudinal relationship between executive functioning and well-being. Specifically, TMT completion time status at T1 did not predict changes in well-being (b = -0.01, p > .05). Well-being status at T1 did not predict changes in TMT completion time (b = 0.00, p > .05). Moreover,
regarding latent correlations, there was no significant level-level association at T1 (r = -.08, p
> .05), nor a significant change-change association of executive functioning and well-being (r
= .04, p > .05).
Acknowledgements
The authors thank the participants of the VLV study, as well as all members of the LIVES project IP213 and LINK institute who contributed to the realization of the VLV study.
The authors are grateful to the Swiss National Science Foundation for its financial assistance.
21 Funding
This work was supported by the Swiss National Centre of Competence in Research LIVES – Overcoming vulnerability: life course perspectives, granted by the Swiss National Science Foundation [grant number: 51NF40-185901]; and LARSyS - Portuguese national funding agency for science, research and technology (FCT) Pluriannual funding 2020-2023 [grand number: UIDB/50009/2020 to ERG and BRG].
Conflict of interest The Authors declare that there is no conflict of interest.
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31 Table 1
Descriptive statistics of analyzed measures
Variable Overall (N = 1,040) Young-old (n = 499) Old-old (n = 541)
1. Satisfaction with Life Scale item 1 (at time
1) [rating -3 to +3] 1.39 (1.12) 1.45 (1.10) 1.33 (1.15)
2. Satisfaction with Life Scale item 2 (at time
1) [rating -3 to +3] 1.61 (1.16) 1.70 (1.15) 1.52 (1.17)
3. Satisfaction with Life Scale item 3 (at time
1) [rating -3 to +3] 1.88 (0.97) 1.90 (0.98) 1.86 (0.96)
4. Satisfaction with Life Scale item 4 (at time
1) [rating -3 to +3] 1.60 (1.12) 1.67 (1.04) 1.53 (1.19)
5. Satisfaction with Life Scale item 1 (at time
2) [rating -3 to +3] 1.22 (1.16) 1.36 (1.10) 1.08 (1.20)
6. Satisfaction with Life Scale item 2 (at time
2) [rating -3 to +3] 1.42 (1.21) 1.55 (1.16) 1.29 (1.24)
7. Satisfaction with Life Scale item 3 (at time
2) [rating -3 to +3] 1.69 (1.13) 1.79 (1.07) 1.60 (1.17)
8. Satisfaction with Life Scale item 4 (at time
2) [rating -3 to +3] 1.52 (1.15) 1.61 (1.06) 1.43 (1.22)
9. Trail Making Test part A completion time
(at time 1) [minutes] 0.93 (0.40) 0.84 (0.37) 1.00 (0.42)
10. Trail Making Test part B completion time
(at time 1) [minutes] 1.93 (0.74) 1.75 (0.69) 2.10 (0.76)
11. Trail Making Test part A completion time
(at time 2) [minutes] 0.93 (0.41) 0.81 (0.29) 1.06 (0.46)
12. Trail Making Test part B completion time
(at time 2) [minutes] 1.82 (0.76) 1.61 (0.60) 2.08 (0.85)
13. Chronological age (at time 1) [years] 74.54 (6.64) 68.85 (2.30) 79.80 (4.72)
14. Sex men: 50.8%
women: 49.2%
men: 48.7%
women: 51.3%
men: 52.7%
women: 47.3%
15. Education [years] 13.36 (3.97) 13.68 (4.13) 13.06 (3.79)
16. Number of leisure activities (at time 1)
[number] 9.78 (2.93) 10.61 (2.67) 9.03 (2.96)
17. Number of chronic diseases (at time 1)
[number] 1.93 (1.58) 1.68 (1.44) 2.16 (1.67)
Note: Descriptive statistics for the four analyzed items of the Satisfaction with Life Scale at time 1 and time 2, completion time in Trail Making Test parts A and B at time 1 and time 2, chronological age at time 1, sex, years of education, the number of leisure activities at time 1, and the number of chronic diseases at time 1, in terms of means (standard deviations are given in parentheses) as well as sample proportions, for the overall sample as well as separately for young-old and old-old adults.
32 Table 2
Parameter estimates for descriptive statistics in the latent change score model
Young-old (n = 499) Old-old (n = 541) Δχ² Latent level well-being (at time 1) [points] 1.46*** [1.38 to 1.53]
(0.72*** [0.66 to 0.77])
1.39*** [1.31 to 1.46]
(0.69*** [0.63 to 0.74])
2.14 ns
Latent change in well-being [points]
-0.08* [-0.14 to -0.02]
(0.58*** [0.53 to 0.63]) {-0.11}
-0.18*** [-0.24 to -0.12]
(0.58*** [0.52 to 0.63]) {-0.26}
5.47*
Latent level Trail Making Test completion time (at time 1) [minutes]
0.84*** [0.81 to 0.87]
(0.26*** [0.23 to 0.29])
1.00*** [0.97 to 1.04]
(0.29*** []0.25 to 0.32)
55.05***
Latent change in Trail Making Test completion time [minutes]
-0.04* [-0.07 to -0.01]
(0.24*** [0.19 to 0.27]) {-0.15}
0.05** [0.01 to 0.09]
(0.30*** [0.24 to 0.34]) {0.19}
12.61***
Note: Left panel: Parameter estimates for descriptive statistics of well-being and Trail Making Test completion time in the latent change score modelin terms of latent means (standard deviations are given in parentheses) [95% confidence intervals for latent means and standard deviations are given in square brackets] {Cohen’s d is given in braces as a quantification of latent change}. Right panel: Between-group comparison statistic. Δχ² = difference in model fit between an unconstrained model and a constrained model in which the respective latent means were constrained to be equal in young-old and old-old adults (i.e., likelihood ratio test with Δdf = 1).
*** p < .001; ** p < .01; * p < .05; ns = non-significant, p > .05.
33 Table 3
Parameter estimates for relationships in the latent change score model
Young-old (n = 499)
Old-old (n = 541) Latent change in well-being predicted by
Latent level well-being (at time 1) -0.34*** -0.26***
Latent level Trail Making Test completion time (at
time 1) -0.38* 0.27 ns
Chronological age (at time 1) -0.16* -0.05 ns
Sex (0 = men; 1 = women) 0.01 ns -0.16**
Education -0.02 ns -0.01 ns
Number of leisure activities (at time 1) 0.02 ns 0.05 ns Number of chronic diseases (at time 1) -0.01 ns -0.02 ns Latent change in Trail Making Test completion time predicted by Latent level Trail Making Test completion time (at
time 1) -0.58*** -0.28**
Latent level well-being (at time 1) 0.02 ns -0.03 ns
Chronological age (at time 1) 0.06 ns 0.06*
Sex (0 = men; 1 = women) 0.01 ns -0.08*
Education 0.00 ns 0.02 ns
Number of leisure activities (at time 1) -0.03* -0.05*
Number of chronic diseases (at time 1) 0.02 ns 0.02 ns Latent level well-being (at time 1) correlated with
Chronological age (at time 1) -.02 ns .07 ns
Sex (0 = men; 1 = women) .03 ns -.12*
Education .03 ns .04 ns
Number of leisure activities (at time 1) .19*** .12*
Number of chronic diseases (at time 1) -.27*** -.19***
Latent level Trail Making Test completion time (at time 1) correlated with
Chronological age (at time 1) .13* .23***
Sex (0 = men; 1 = women) .07 ns -.02 ns
Education -.21*** -.22***
Number of leisure activities (at time 1) -.18** -.30***
Number of chronic diseases (at time 1) .08 ns .07 ns Latent correlations
Level-level -.16* .01 ns
Change-change .12 ns -.04 ns
Factor loadings
Satisfaction with Life Scale item 1 (at time 1) 1.00 1.00 Satisfaction with Life Scale item 2 (at time 1) 1.13*** 1.13***
Satisfaction with Life Scale item 3 (at time 1) 1.29*** 1.29***
Satisfaction with Life Scale item 4 (at time 1) 1.15*** 1.15***
Satisfaction with Life Scale item 1 (at time 2) 1.00 1.00 Satisfaction with Life Scale item 2 (at time 2) 1.13*** 1.13***
Satisfaction with Life Scale item 3 (at time 2) 1.29*** 1.29***
Satisfaction with Life Scale item 4 (at time 2) 1.15*** 1.15***
Trail Making Test part A completion time (at time
1) 1.00 1.00
Trail Making Test part B completion time (at time
1) 2.13*** 2.13***
Trail Making Test part A completion time (at time
2) 1.00 1.00
Trail Making Test part B completion time (at time
2) 2.13*** 2.13***
Residual variances
Satisfaction with Life Scale item 1 (at time 1) 0.56*** 0.69***
Satisfaction with Life Scale item 2 (at time 1) 0.59*** 0.65***
