Variability Modifies Life Satisfaction’s Association With Mortality Risk in Older Adults

Abstract

Greater life satisfaction is associated with greater longevity, but its variability across time has not been examined relative to longevity. We investigated whether mean life satisfaction across time, variability in life satisfaction across time, and their interaction were associated with mortality over 9 years of follow-up. Participants were 4,458 Australians initially at least 50 years old. During the follow-up, 546 people died. After we adjusted for age, greater mean life satisfaction was associated with a reduction in mortality risk, and greater variability in life satisfaction was associated with an increase in mortality risk. These findings were qualified by a significant interaction such that individuals with low mean satisfaction and high variability in satisfaction had the greatest risk of mortality over the follow-up period. In combination with mean life satisfaction, variability in life satisfaction is relevant for mortality risk among older adults. Considering intraindividual variability provides additional insight into associations between psychological characteristics and health.

Keywords

life satisfaction well-being mortality longevity intraindividual variability within-person stability

Life satisfaction is associated with beneficial health outcomes, including increased longevity (e.g., Bowling & Grundy, 2009; Collins, Glei, & Goldman, 2009; Koivumaa-Honkanen et al., 2000; Wiest, Schüz, Webster, & Wurm, 2011). For example, in men and women ages 25 to 74 at study initiation, risk of mortality during 12 years of follow-up was reduced by more than 30% among those with higher life satisfaction levels, even after the researchers controlled for health behaviors and body mass index (Lacruz, Emeny, Baumert, & Ladwig, 2011). However, most of these studies used a single assessment of life satisfaction to predict mortality risk (cf. Collins et al., 2009). There is a growing recognition that individual differences exist in the extent to which psychological characteristics remain stable or vary over time (Mroczek, Almeida, Spiro, & Pafford, 2006), and assessing life satisfaction at a single point in the life course cannot account for such variability. Although life satisfaction is typically considered relatively consistent across time, it may change in response to life circumstances. Some people may adapt more readily to new situations (and thus appear to have relatively stable life satisfaction) and others may not adapt as quickly or at all (Fujita & Diener, 2005; Lucas & Donnellan, 2007). This is especially true in the context of major life events such as unemployment or divorce (Luhmann, Hofmann, Eid, & Lucas, 2012).

Given that life satisfaction may vary across time, we investigated whether intraindividual variability in life satisfaction might be related to risk of mortality over a 9-year follow-up period. Past work suggests that variability in psychological characteristics may be as important as mean levels, particularly with regard to mortality risk (Eizenman, Nesselroade, Featherman, & Rowe, 1997; Mroczek et al., 2015). Moreover, greater variability in life satisfaction is correlated with meaningful outcomes, such as less personal control, less social support, greater daily hassles, and worse physical health (Gadermann & Zumbo, 2007).

Thus, we had three goals in this research. First, to expand beyond the well-established finding that greater life satisfaction at a single point in time is related to reduced mortality risk, we investigated whether greater mean life satisfaction derived from repeated assessment across 9 years was associated with reduced mortality risk. Second, we investigated whether variability in levels of life satisfaction over time were associated with mortality risk. We hypothesized that individuals with greater variability in life satisfaction would have greater risk of mortality during the follow-up period. Third, and perhaps most critically, we examined whether variability in life satisfaction would modify the relationship between mean life satisfaction and mortality risk (or whether mean life satisfaction would modify the relationship between variability in life satisfaction and mortality risk). Following the example of prior work in this area, we considered relevant covariates that might confound the association (e.g., sociodemographic factors, health conditions) or be on the pathway linking life satisfaction to mortality (e.g., health behaviors; Chida & Steptoe, 2008).

Method

Sample

Participants were from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Survey was established in 2001 (Wave 1) to investigate social and economic issues in a national probability sample of Australian households. At Wave 1, 19,914 individuals from 7,682 households were assessed. Interviews were conducted annually thereafter with adult participants. In the current study, we use participant data through 2010 (Wave 9), which was the most recent year available when we started the research project. From the original sample of 19,914, we excluded the 14,789 individuals younger than 50 years old because most adult deaths (91%) occurred in older individuals. We also excluded 667 adults who were 50 years and older and for whom we had fewer than two assessments of life satisfaction. Thus, the present research was based on data from participants who were 50 years or older at Wave 1 and for whom we had complete data on life satisfaction and date of death (N = 4,458). Compared with the people included in the sample, those excluded because they had fewer than two assessments of life satisfaction were more likely to be men, were less educated, tended to be more physically inactive, and were more likely to die during the follow-up period.

Of the 4,458 eligible participants, 2,347 (52.65%) were women. The average baseline age of participants was 63.32 years (SD = 9.83, range = 50–92 years), and 13.23% had a bachelor’s degree or higher level of education. Participants were generally healthy: 61.75% were free from long-term health conditions, 49.81% had never smoked cigarettes, and 48.56% engaged in physical activity at least three times per week. All participants provided informed consent, and the relevant institutional review boards approved the research.

Measurement

Life satisfaction

Participants reported their life satisfaction between two and nine times using a commonly used single-item measure of life satisfaction: “All things considered, how satisfied are you with your life?” (Bjørnskov, 2010). Responses ranged from 0 to 10 (higher numbers indicated greater life satisfaction). Most participants (62%) reported life satisfaction at all nine waves. Each participant’s mean and standard deviation for life satisfaction across time was calculated using all available scores. The standard deviation can serve as a reliable and straightforward indicator of the amount of spread within a set of scores (Eid & Diener, 1999). The mean life satisfaction in the sample overall was 8.17 (SD = 1.23, range = 0.33–10.00). The mean standard deviation of life satisfaction in the sample overall was 0.95 (SD = 0.63, range = 0.00–5.29). Participants’ means and standard deviations for life satisfaction across time were moderately correlated, r = −.44, p < .0001. Both the mean and standard deviation of life satisfaction were standardized (M = 0, SD = 1) before inclusion in the statistical models.

Covariates

Sociodemographic covariates included age, gender (women or men; women was the reference), and education (less than a high school diploma, high school diploma or equivalent, some college or vocational training, or bachelor’s degree or more; less than a high school diploma was the reference). Health-related covariates included long-term health conditions, disabilities, or impairments (none or ≥ 1; none was the reference), cigarette smoking status (never smoker, former smoker, or current smoker; never smoker was the reference), and physical activity (none, moderate: ≤ 1 to 2 times per week, or high: ≥ 3 times per week; none was the reference).

Given that depression is associated with increased risk of mortality (Schulz et al., 2000; Wulsin, Vaillant, & Wells, 1999), we controlled for depressive symptoms to ensure that life satisfaction was not simply a marker of the absence of depression. Depressive symptoms were assessed with the five-item Mental Health subscale of the Short-Form Health Survey (SF-36; Ware & Sherbourne, 1992). Higher scores indicated more depressive symptoms, M = 75.75, SD = 17.33, range = 0–100. The internal consistency of the five items was good, α = .81. Because some items on the measure of life satisfaction potentially overlap with positively worded items on the SF-36, a three-item composite excluding the two positively worded items was also calculated; analyses with this scale yielded findings nearly identical to those presented and are not discussed further. All categorical covariates were dummy-coded before they were included in the analysis. All covariates were self-reported at Wave 1.

Mortality

Date of death (month and year) was assessed by interviews with surviving members of the household or other contacts. When the respondent was known to be deceased but date of death was unknown, the period of death was approximated by using the fieldwork period during which the death occurred (e.g., death between Waves 4 and 5). We retained only the year of death (not month) and coded deaths between Waves 1 and 2 as year 2002, between Waves 2 and 3 as year 2003, and so forth.

Statistical analyses

SAS 9.3 was used for all statistical analyses. In preliminary analyses, we investigated associations between mean life satisfaction or the standard deviation of life satisfaction with covariates using correlations, t tests, and analyses of variance.

We used Cox proportional-hazards regression analyses to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Missing values for covariates were estimated through multiple imputation, and estimates were pooled from five imputed data sets. In initial descriptive analyses, we separately examined the association of mortality risk with both mean life satisfaction across time and the standard deviation of life satisfaction across time before undertaking more complex analyses that included their interaction. These initial analyses also allowed us to replicate the well-established association between higher levels of life satisfaction and a reduction in mortality risk and to provide information for future researchers seeking to synthesize the literature. The primary Cox proportional-hazards regression analyses included the interaction between mean life satisfaction and variability in life satisfaction across time, as well as the lower-order effects. In both the initial and the primary analyses, we first adjusted for age (Model 1) and then additionally adjusted for sociodemographic covariates (age, gender, education) and health conditions (Model 2). Model 3 additionally included health behaviors that could be on the pathway linking life satisfaction with mortality (smoking status, physical activity) and Model 4 included depressive symptoms.

The assumption of proportional hazards was tested in an extended Cox model that included time-dependent variables (Kleinbaum & Klein, 2010). The time-dependent variable for age was statistically significant, so we reran the initial and primary analyses including time-dependent age. Results with time-dependent age were practically identical to those presented here (for a comparison of the findings, see Table S1 in the Supplemental Material available online).

We used sensitivity analyses to investigate how clustering by household, personality traits, and terminal decline affected the primary findings. In analyses that accounted for clustering by household, HRs remained unchanged and conclusions about statistical significance were identical to those for the primary findings, so we do not discuss them further. In addition, because certain personality traits are related to mortality risk (Wilson, de Leon, Bienias, Evans, & Bennett, 2004), in our most fully adjusted model we controlled for the Big Five personality traits, which were first assessed in Wave 5 of HILDA (Watson & Wooden, 2010). Using five different approaches, we also investigated terminal decline (i.e., life satisfaction decreases rapidly close to death; Gerstorf, Ram, Estabrook, et al., 2008). First, we used multilevel models to determine whether there was evidence of terminal decline in our sample. Second, among people who died, we evaluated the magnitude of the terminal decline effect by centering time around the year of death rather than the study baseline. This approach allowed us to see changes in life satisfaction as death approached. Third, we examined how terminal decline contributed to our results by calculating the detrended residual in life satisfaction after the effects of total and individual change over time (the latter of which included effects of terminal decline) were removed. Fourth, we tested whether variability in the detrended residual in life satisfaction predicted mortality risk in the Cox proportional-hazards regression models. Finally, we examined whether life satisfaction assessed at Wave 1 (when life satisfaction was the least likely to be subject to terminal decline because Wave 1 was the point furthest from death) was associated with mortality risk.

Results

Preliminary analyses

Higher mean life satisfaction was significantly associated with older age, earning less than a high school diploma, having fewer health conditions, not smoking, and being more physically active (ps < .05); however, there was no association with gender (p > .05). Greater variability in life satisfaction was significantly associated with older age, being a woman, earning less than a high school diploma, having more health conditions, smoking, and being less physically active (ps < .05). We categorized participants as high (top 25%), moderate (middle 50%), or low (bottom 25%) in mean life satisfaction and in variability in life satisfaction and determined the number of people in each of the nine possible combinations (Table 1).

Table 1.

Distribution of Participants by Level of Mean Life Satisfaction and Standard Deviation of Life Satisfaction (N = 4,458)

Mean life satisfaction	Standard deviation of life satisfaction
Mean life satisfaction	Low (bottom 25%)	Moderate (middle 50%)	High (top 25%)
Low (bottom 25%)	84	462	610
Moderate (middle 50%)	492	1,303	460
High (top 25%)	413	602	32

Life satisfaction and mortality risk

During the 9-year follow-up, 546 (12%) of the participants died. After we adjusted for age at baseline (Model 1), we found that each 1-SD increase in mean life satisfaction (i.e., a difference of 1.23 scale points in the individual mean) was significantly associated with an 18% reduction in mortality risk. This relationship persisted when we also controlled for gender, education, and health conditions (Model 2), but it was attenuated and became only marginally significant when health behaviors were added (Model 3). When depressive symptoms were further included, the association remained unchanged but nonsignificant (Model 4; Table 2). Note that depressive symptoms were not significantly associated with mortality risk in the fully adjusted model (HR = 1.00, 95% CI = [0.99, 1.01]).

Table 2.

Hazard Ratios for the Association Between Mortality Risk and Mean Life Satisfaction Over Time, Standard Deviation of Life Satisfaction Over Time, and Their Interaction

Predictor	Model 1	Model 2	Model 3	Model 4
Mean life satisfaction	0.82*** [0.76, 0.89]	0.87** [0.80, 0.95]	0.93^† [0.86, 1.01]	0.93 [0.84, 1.02]
Standard deviation of life satisfaction	1.20*** [1.11, 1.29]	1.15** [1.06, 1.24]	1.08^† [1.00, 1.17]	1.08^† [0.99, 1.18]
Mean Life Satisfaction × Standard Deviation of Life Satisfaction	0.92* [0.87, 0.97]	0.91** [0.86, 0.96]	0.91** [0.86, 0.96]	0.91** [0.86, 0.96]

Note: The values in square brackets are 95% confidence intervals. There were 4,458 participants; of these, 546 died during follow-up. Model 1 adjusted for age. Model 2 adjusted for demographic characteristics (age, gender, and education) and health conditions. Model 3 adjusted for demographic characteristics, health conditions, and health behaviors (smoking status and physical activity). Model 4 adjusted for demographic characteristics, health conditions, health behaviors, and depressive symptoms. The results for the interaction are from the primary models, which controlled for lower-order effects (i.e., mean life satisfaction and standard deviation of life satisfaction).

†

p ≤ .10. *p ≤ .01. **p ≤ .001. ***p ≤ .0001.

When we included the standard deviation of life satisfaction in a separate model adjusting for age (Model 1), we found that each 1-SD increase in the variability of life satisfaction (i.e., a difference of 0.63 points in the individual standard deviation) was significantly associated with a 20% increase in mortality risk. The association was unchanged when we also controlled for gender, education, and health conditions (Model 2), but it became only marginally significant when we included health behaviors (Model 3). Adding depressive symptoms to the model did not alter these findings (Model 4; Table 2).

The initial findings regarding mean life satisfaction and variability in life satisfaction were qualified by a significant interaction effect when we controlled for age and the lower-order effects (HR = 0.92; 95% CI = [0.87, 0.97]; Model 1). Figure 1 shows the predicted slopes for high, moderate, and low mean life satisfaction at high (+1 SD), moderate, and low variability levels (−1 SD) after we controlled for age. Simple main-effect analyses showed that the risk of mortality over the follow-up period was significantly associated with variability at low mean life satisfaction (HR = 1.13, 95% CI = [1.03, 1.24]), but not with moderate mean life satisfaction (HR = 1.04, 95% CI = [0.94, 1.16]) or high mean life satisfaction (HR = 0.96, 95% CI = [0.84, 1.10]). Individuals with the highest mortality risk had the lowest mean life satisfaction and the greatest variability in life satisfaction. In contrast, individuals with high mean life satisfaction tended to have reduced mortality risk, regardless of the variability in their life satisfaction. The interaction between mean life satisfaction and variability in life satisfaction remained statistically significant—with the magnitude of effect relatively unchanged—after we also controlled for gender, education, health conditions, health behaviors, and depressive symptoms (Table 2, Models 2–4). In addition, Figure S1 in the Supplemental Material shows the survival curves for the four most extreme groups of participants: those with high mean and high standard deviation, those with high mean and low standard deviation, those with low mean and high standard deviation, and those with low mean and low standard deviation.

Fig. 1.

Model 1 results from the primary Cox proportional-hazards regression analyses. Predicted hazard ratios for mortality risk are plotted as a function of mean life satisfaction for participants with high, moderate, and low standard deviations of life satisfaction.

When the Big Five personality traits were added to the fully adjusted model with the interaction between mean life satisfaction and variability in life satisfaction (and their lower-order effects), results remained mostly the same for the interaction effect (HR without personality traits = 0.91, 95% CI = [0.86, 0.96]; HR with personality traits = 0.90, 95% CI = [0.85, 0.95]). Extraversion was the only personality trait to show a significant association with mortality risk (HR = 0.88, 95% CI = [0.80, 0.96]).

Finally, additional analyses (presented in the Supplemental Material) tested the presence of terminal decline. Terminal decline was observed; however, variability in the detrended residual (i.e., net variability with the variance attributable to terminal decline removed) was highly correlated with variability in the raw score for life satisfaction. When we substituted variability in the detrended residual for raw-score variability among participants who died, the primary findings regarding mortality risk were replicated. Furthermore, when we substituted baseline life satisfaction (i.e., at the point furthest from death) for mean life satisfaction, the mortality-risk findings were replicated, which is further evidence that the associations were not due to reverse causality.

Discussion

This is the first study to consider effects of life satisfaction on risk of mortality when life satisfaction is summarized across as many as nine repeated assessments. Researchers in one other study repeatedly assessed life satisfaction in the context of mortality, but only three repeated assessments were available (Collins et al., 2009). We initially found that higher mean life satisfaction across 9 years was associated with a reduction in mortality risk over the follow-up period. Moreover, greater variability in life satisfaction across 9 years was associated with an increase in mortality risk. The more dramatic the lability in life satisfaction across time, the greater one’s mortality risk. No other study has investigated how variability in life satisfaction is related to longevity. Although some variability in psychological well-being may have beneficial effects in that it signals an individual’s ability to adapt (Gruber, Kogan, Quoidbach, & Mauss, 2013; Röcke & Brose, 2013), our initial findings suggest that high levels of variability (which are perhaps more indicative of instability) are detrimental to longevity.

However, these initial findings were qualified by an interaction such that the effects of mean life satisfaction on mortality risk depended on variability in those levels or vice versa (i.e., the effects of variability in life satisfaction on mortality risk depend on mean life satisfaction). This result is consistent with previous findings regarding associations between variability in psychological functioning and mortality (Mroczek et al., 2015). Specifically, we found that individuals who had relatively low mean life satisfaction that also varied greatly across time tended to have the highest mortality risk during the follow-up period. In contrast, individuals who had relatively high mean life satisfaction had a reduced mortality risk, regardless of how life satisfaction varied over time. That is, the effect of variability seemed to matter only among individuals with relatively low mean life satisfaction. The interaction between the mean and standard deviation of life satisfaction over time held after controlling for numerous covariates. It is noteworthy that individuals with low mean life satisfaction levels were also more likely to exhibit higher variability in those levels relative to people with higher mean life satisfaction levels.

There has been no research, as yet, on the interplay between mean life satisfaction and variability in life satisfaction over time in relation to an objective health outcome such as mortality. Investigators have speculated that variability can signal poor emotion regulation and an inability to adapt to the environment (Röcke & Brose, 2013). Our findings support this perspective, indicating not only that persistently low life satisfaction over time may reduce longevity, but also that high variability in those levels may be particularly detrimental when cycling in the lower ranges of life satisfaction. Such conditions might occur when individuals repeatedly encounter distressing events in their lives—for example, losing jobs or the deaths of loved ones. Life circumstances that are continually in flux may prevent people from getting used to their conditions. In other words, the process of hedonic adaptation—whereby effects of unfavorable or favorable events on well-being diminish over time—does not have the chance to occur because the context is changing (Frederick & Loewenstein, 1999). Although variety in positive experiences may help prolong positive thoughts and feelings (Sheldon & Lyubomirsky, 2012), variety in negative experiences may be damaging because hedonic adaptation and coping processes are delayed or never have a chance to occur (Röcke & Brose, 2013).

As with any research, the current study has limitations. Participants were Australians who were 50 years or older, so generalization to other cultural and age groups is not warranted. Only all-cause mortality was available, so we were unable to distinguish among causes of death. In addition, because few participants had very low levels of life satisfaction, a restricted range or a ceiling effect is possible (Diener & Diener, 1996). Another potential limitation is whether the variability in life satisfaction captured with the standard deviation was meaningful or was due to measurement error within a single-item measure. However, split-half reliability suggested that the standard deviation of life satisfaction was a stable individual difference (r = .46). Future research could investigate other conceptualizations of intraindividual variability, especially with approaches that capture rate of change (Eid & Diener, 1999; Estabrook, Grimm, & Bowles, 2012). In addition, although we were able to control for the Big Five personality traits without substantive change to the associations under investigation, the traits were assessed partway through the follow-up period. The preferred methodology for future research would be to adjust for baseline personality traits. Another possible limitation is that a third variable may be operating. For example, negative life events or declining health may precede death and may be associated with changes in life satisfaction. Although such speculations cannot be confirmed with HILDA data, further examination is warranted with additional life satisfaction assessments and explanatory variables.

These limitations are balanced by many strengths, including prospective and repeated measurement of life satisfaction across 9 years, limited attrition, validated measures of potential confounders and pathways, and careful attention to the issue of terminal decline (Gerstorf, Ram, Rocke, Lindenberger, & Smith, 2008). Although terminal decline was present among the individuals who died during follow-up, it is unlikely that the reported associations are attributable to a decline in life satisfaction in the few years immediately preceding death.

This research adds to the relatively scarce body of work regarding intraindividual variability (Biesanz, West, & Kwok, 2003) and the effects of long-term well-being on mortality risk. Findings broadly demonstrate not only that psychological attributes themselves may be critical correlates of health, but also that variability in psychological attributes over time may be meaningful, especially in combination with mean levels. This is consistent with research suggesting that variability in psychological characteristics by itself (or in combination with mean levels) is relevant for mortality risk (Mroczek & Spiro, 2007), health behaviors (Ong et al., 2013), depression and anxiety (Gruber et al., 2013), and self-reported health (Turiano et al., 2012). Furthermore, extreme variability in psychological states is often associated with mental-health disorders (e.g., bipolar depression). Thus, although some level of adaptability and range is likely to be important for healthy psychological functioning (Kashdan & Rottenberg, 2010), extreme cycling may be intrinsically harmful or may serve as a marker of dysregulation in emotional or behavioral domains that influence health.

Healthy People 2020 highlights the critical impact of mental health on physical health. Moreover, increasing data demonstrate that mental-health problems are associated with the prevalence and progression of many chronic diseases, including diabetes, heart disease, and cancer (U.S. Department of Health and Human Services, 2010). In contrast, positive mental-health constructs, such as life satisfaction, appear to protect against disease (e.g., Boehm & Kubzansky, 2012). Thus, it may be beneficial for clinicians to explicitly consider the role of mental health in relation to physical health. Although clinicians often focus on symptoms of depression or anxiety, the current study suggests that it may also be important to investigate the development and maintenance of positive well-being, such as life satisfaction. Moreover, clinicians may want to consider the role of variability in positive well-being in relation to physical health.

Limited research has investigated whether interventions with an emphasis on positive psychological functioning translate into health benefits (e.g., Huffman et al., 2011; Ogedegbe et al., 2012; Peterson et al., 2012), and no research has investigated whether interventions related to variability may have effects. However, the findings reported here indicate not only that variability in life satisfaction plays a critical role in understanding associations with health and longevity, but also that a single cross-sectional snapshot of life satisfaction may not tell the whole story. In sum, when considering the potential impact of mental health on aging and longevity, it may be important to go beyond a focus on mental-health problems to consider not only positive psychological functioning (e.g., life satisfaction), but also within-person stability of such functioning over time as well as their interaction (Röcke & Brose, 2013).

Footnotes

Acknowledgements

We thank Brent Roberts for insightful discussions about this topic. The findings reported here were previously presented at the annual meetings of the American Psychosomatic Society (March 2014, San Francisco, CA) and the Gerontological Society of America (November 2014, Washington, DC).

Declaration of Conflicting Interests

The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.

Funding

Support for this research was provided by the Robert Wood Johnson Foundation through a grant to the Positive Psychology Center of the University of Pennsylvania (“Exploring the Concept of Positive Health,” Martin Seligman, project director). Additional support was provided by National Institute on Aging Grant K02-033629. The Household, Income and Labour Dynamics in Australia Survey was initiated and is funded by the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this article are those of the authors and should not be attributed to either FaHCSIA or the Melbourne Institute.

Supplemental Material

Additional supporting information can be found at

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