PERSONALITY AND SELF-RATED HEALTH

Running Head: PERSONALITY AND SELF-RATED HEALTH

Do Neuroticism and Conscientiousness Interact with Health Conditions in Predicting 4-

Year Changes in Self-Rated Health among Swedish Older Adults?

Georg Henning1, Anne Ingeborg Berg2, Anja Cengia1, Isabelle Hansson2, Svenja M. Spuling1,

& Markus Wettstein1,3

1 German Centre of Gerontology, Berlin, Germany

2 Department of Psychology and Centre for Ageing and Health (AgeCap), University of

Gothenburg, Gothenburg, Sweden

3 Network Aging Research, Heidelberg University, Heidelberg, Germany

© 2021, American Psychological Association. This paper is not the copy of record and may not exactly replicate the final, authoritative version of the article. Please do not copy or cite without authors' permission. The final article will be available, upon publication, via its DOI:

10.1037/pag0000626

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Author Note

Correspondence concerning this paper should be addressed to Georg Henning,

German Centre of Gerontology, Manfred-von-Richthofen Straße 2, 12101 Berlin, Germany.

Email: [email protected]

Georg Henning was supported by a grant from the German Research Foundation

(DFG, project number 441444293). The HEARTS study was supported by the grants Dnr

2013-2291 and Dnr 2013-2300 from the Swedish Research Council for Health, Working Life and Welfare (FORTE).

Our study design and the hypotheses were pre-registered on the OSF server

(https://osf.io/53hq7). The SPSS syntax for the preparation of the dataset, as well as the

MPlus Syntax for the analyses can be found on the OSF server as well. The preprint is available at the psyarxiv server (https://psyarxiv.com/vt8fa).

This dataset can be made available upon request and in accordance with applicable laws. For further information about accessibility of data, contact [email protected]

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Abstract

Health conditions such as higher disease burden, pain or lower functional health are associated with poorer self-rated health (SRH) in older age. Poorer SRH, in turn, is a predictor of morbidity and mortality. Personality traits are associated with SRH as well, but little is known about the interaction of personality and health conditions. In the present pre-registered analyses, we used five annual waves of the Health, Aging and Retirement Transitions in

Sweden (HEARTS) study (n = 5,823, M(age) = 63.09, SD = 2.01) to investigate the associations of personality (neuroticism and conscientiousness) and physical health indices

(disease burden, pain, functional limitations) with levels and change in SRH. In addition, we tested personality x health interaction effects. We found that higher neuroticism and lower conscientiousness were related to lower levels of SRH, but not to change in SRH after controlling for the health indices. Personality did not moderate the effect of health indices on levels and change in SRH. Exploratory analyses showed that high scores of neuroticism may augment the association of increased pain and functional limitations with declines in SRH.

Additional studies with other samples are needed to test if this result can be replicated. Taken together, our findings provide only weak evidence for interaction effects of personality and physical health factors on SRH. More research is needed to understand the interplay of physical and psychological factors in shaping individual SRH.

Keywords: Self-rated health; personality; Adaptation

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Introduction

Self-rated health is often assessed in empirical studies as a proxy for objective health conditions and diseases (Graf & Patrick, 2016; Löckenhoff, Sutin, Ferrucci, & Costa Jr,

2008). The routine integration of self-rated health (SRH) in health questionnaires and surveys is not solely justified on the grounds that including a single item (“How would you rate your health?”) is more parsimonious and less time-consuming than more extensive checklists. This kind of single-item assessment of SRH also seems sufficient to meaningfully predict mortality and other important health outcomes such as functional declines, with substantial predictor effects of SRH over and above supposedly more “objective” health indicators such as medical diagnoses (Bond et al., 2006; Burström & Fredlund, 2001; DeSalvo, Bloser, Reynolds, He, &

Muntner, 2006; Idler & Benyamini, 1997; Lima-Costa, Cesar, Chor, & Proietti, 2011;

Tamayo-Fonseca et al., 2013; Vie, Hufthammer, Holmen, Meland, & Breidablik, 2014).

Poorer SRH is related to different health indicators such as pain or multimorbidity

(Galenkamp, Braam, Huisman, & Deeg, 2011; Svedberg, Bardage, Sandin, & Pedersen,

2006). However, this correlation decreases with advancing age (French, Sargent-Cox, &

Luszcz, 2012; Pinquart, 2001). Interestingly, a good SRH does not necessarily go hand in hand with freedom from diseases or limitations, especially in older age (Jopp, Park, Lehrfeld,

& Paggi, 2016; Spuling, Wurm, Tesch-Römer, & Huxhold, 2015; Wettstein, Schilling, &

Wahl, 2016). This phenomenon has been labelled the “disability paradox” (Albrecht &

Devlieger, 1999). It can be seen in some analogy to the “well-being paradox” (Kunzmann,

Little, & Smith, 2000): Despite declining resources and various loss experiences in old age, older adults usually report high well-being levels and do not differ from younger adults with regard to their well-being. Social downward comparisons, that is comparing one’s situation with others who are worse off, seem to be one major adaptive mechanism that contributes to positive perceptions of one’s own health and well-being in later life (Frieswijk, Buunk,

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Steverink, & Slaets, 2004; Heidrich & Ryff, 1993; Keciour & Spini, 2006). Individuals might also anticipate declining health with advancing age and decrease their health expectations and standards accordingly when getting older, which corresponds to “response shift” processes that contribute to maintaining good SRH into old age (Sprangers & Schwarz, 1999; Spuling et al., 2017).

Given the established relevance of SRH as a predictor of important developmental outcomes including longevity, it is important to understand what determines an individual’s self-evaluation of health. As Graf and Patrick (2016) point out, “Understanding the developmental mechanisms and multiple influences underlying change, however, may help further the use of SAH [self-assessed health] as a life-long health-promotion tool” (p. 177).

Specifically, more needs to be known about when and for whom health limitations result in poorer SRH and which personal characteristics contribute to favourable SRH even when health restrictions have set in. Jylhä (2009) presented a multidimensional model of SRH, highlighting that SRH scores result from a complex individual evaluation process, which includes defining important components of one’s health and choosing a comparison group as well as the cultural norms in expressing feelings on health. Not only physical health, but also psychological factors such as well-being and optimism explain inter-individual differences in

SRH, and their impact on SRH seems to get stronger in older age (French et al., 2012;

Pinquart, 2001; Spuling, Huxhold, & Wurm, 2015). Among these psychological factors, personality traits seem to show particularly strong associations with SRH (Bogg & Roberts,

2013; Kööts-Ausmees et al., 2016; Löckenhoff, Terracciano, Ferrucci, & Costa Jr, 2012;

Olshansky et al., 2012; Reiss, Eccles, & Nielsen, 2014; Shanahan, Hill, Roberts, Eccles, &

Friedman, 2014).

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Neuroticism, Conscientiousness and Self-Rated Health

In the present study, we focus on the role of conscientiousness and neuroticism, which are the strongest and most consistent predictors of different health outcomes among the five- factor personality traits (Jokela et al., 2020; Lahey, 2009; Roberts, Walton, & Bogg, 2005;

Turiano et al., 2011). According to Löckenhoff et al. (2008), there are two main pathways through which personality can influence SRH. First, personality traits have behavioural consequences (Löckenhoff et al., 2008; Roberts et al., 2005). For example, high conscientiousness is associated with better health behaviours (e.g., less smoking, less heavy drinking), which in turn are associated with better health and longevity (Bogg & Roberts,

2004; Turiano, Chapman, Gruenewald, & Mroczek, 2015). In contrast, higher neuroticism is related to worse health behaviours, resulting in a higher risk of mortality (Graham et al., 2017;

Mroczek, Spiro, & Turiano, 2009). Second, personality can have a direct impact on SRH by affecting the subjective interpretation of one’s health status and symptoms (Löckenhoff et al.,

2008) – higher neuroticism has, for example, been associated with a higher likelihood of reporting symptoms without a physiological basis (Costa Jr & McCrae, 1987; Feldman,

Cohen, Doyle, Skoner, & Gwaltney Jr, 1999; Jorm et al., 1993), and to a stronger reaction to experimentally induced pain (Banozic et al., 2018).

With regard to empirical evidence, higher levels of neuroticism have indeed been found to be associated with poorer SRH (Kööts-Ausmees et al., 2016; Löckenhoff et al.,

2012; Svedberg et al., 2006). Higher conscientiousness, in contrast, seems to have a protective effect on SRH (Friedman, Kern, Hampson, & Duckworth, 2014; Hampson et al., 2016;

Turiano et al., 2011). However, most of these associations have been found in cross-sectional studies. The few studies investigating personality as a predictor of change in SRH showed inconsistent findings (Hampson et al., 2016; Löckenhoff et al., 2012; Stephan et al., 2020).

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Health Conditions and SRH: The Moderating Role of Personality

Jylhä (2009) states that self-evaluations of health status are based on various interactions between different physical and psychological factors. Personality may in this sense serve as a psychological resource that counteracts (or aggravates) the impact of physical health conditions on SRH. In the present study, we therefore investigate whether personality moderates the extent to which individual differences in physical health relate to SRH status as well as to change in SRH. We include three health dimensions that are meaningfully associated with SRH – disease burden (Spuling, Huxhold, et al., 2015; Spuling, Wurm, et al.,

2015), pain (Reyes-Gibby, Aday, & Cleeland, 2002), and functional limitations (Hoeymans,

Feskens, Kromhout, & Van Den Bos, 1997; Manderbacka, Lundberg, & Martikainen, 1999).

A recent article found that these three factors seem to be the most important health factors for self-rated health apart from mental health (Lazarevič & Brandt, 2020). By differentiating between different health domains, we take the multidimensionality of health into account

(Spiro III, 2001) and also investigate the robustness of associations between personality and

SRH by controlling for various physical health indicators.

There are several reasons why personality traits might act as a moderator of associations between health conditions and SRH. Higher neuroticism scores imply a heightened stress susceptibility and reactivity (Bolger & Schilling, 1991; Hampson, 2012;

Lahey, 2009; Mroczek & Almeida, 2004; Suls & Martin, 2005). This means that individuals with higher neuroticism scores may interpret health-related stressors such as disease burden, pain, and functional limitations as more problematic, given their increased tendencies to health worries, symptom reporting, and rumination (Costa Jr & McCrae, 1987; Denovan,

Dagnall, & Lofthouse, 2019; Goubert, Crombez, and Van Damme; 2004)).

Individuals with higher scores on neuroticism also seem to have more problems in coping with stressors in general (Connor-Smith & Flachsbart, 2007; Gunthert, Cohen, & 7 PERSONALITY AND SELF-RATED HEALTH

Armeli, 1999), including current health problems, which may also lead to worse health behaviours. For example, higher neuroticism is related to a worse medication adherence

(Axelsson, Brink, Lundgren, & Lötvall, 2011; Emilsson et al., 2011; Jerant, Chapman,

Duberstein, Robbins, & Franks, 2011). Rhodes, Courneya, and Bobick (2001) found that among patients with breast cancer, higher neuroticism was related to a more maladaptive exercise behaviour. There is also some evidence that neuroticism may increase the negative consequences of certain health conditions – for example, higher neuroticism seems to aggravate the negative effects of sensory impairment on cognitive abilities (Gaynes, Shah,

Leurgans, & Bennett, 2013; Wettstein, Kuźma, Wahl, & Heyl, 2016) and on functional health

(Wettstein, Wahl, & Heyl, 2018). Consequently, because of the higher likelihood of maladaptive health behaviours, inadequate coping, and higher stress reactivity among those with higher neuroticism scores, we assume that individuals scoring higher on neuroticism may experience more negative consequences in terms of poorer SRH and steeper declines in SRH when they are affected by disease burden, pain, or functional limitations than persons scoring lower on neuroticism.

Higher conscientiousness, in contrast, has been conceptualized as a general “stress buffer” (Shanahan et al., 2014) and might thus also buffer negative effects of health-related stressors such as diseases, pain, or functional limitations on SRH. Higher conscientiousness is associated with a better health-specific decision making style (Flynn & Smith, 2007), better health behaviours (Friedman et al., 2014; Hill & Roberts, 2011; Reiss et al., 2014), and the use of adaptive coping strategies (Connor-Smith & Flachsbart, 2007). Moreover, many studies show the benefits of high conscientiousness in dealing with certain health issues such as diabetes, as higher conscientiousness scores seem to be associated with better adherence to treatment plans, self-care behaviour, and physical activity (Brickman, Yount, Blaney,

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Rothberg, & De-Nour, 1996; Fisher, Hessler, Masharani, & Strycker, 2014; Skinner, Bruce,

Davis, & Davis, 2014; Wheeler, Wagaman, & McCord, 2012). Given their ability to deal with occurring health problems, highly conscientious individuals may thus have more resources available which they can invest to counteract these health problems and their progression. In consequence, their SRH may be less affected by different health conditions compared to individuals with lower conscientiousness scores. Hence, higher conscientiousness may weaken and compensate for the negative impact of disease burden, pain, and functional limitations on SRH as well as on change in SRH.

Few studies have tested for interactions between personality and other predictors of

SRH. Authors have rather tended to consider these different predictors separately without taking their interplay into account. Löckenhoff et al. (2008) found significant associations of neuroticism and conscientiousness with SRH in a healthy sample, but these associations were not significant in a medically challenged sample, which implies that there might indeed be an interaction between health conditions and personality in their predictor effects on SRH.

However, this study used cross-sectional data, so it remains unclear to what extent personality and health conditions might interact as predictors of changes of SRH.

Research Aims and Hypotheses

In the present study, we investigate the role of neuroticism and conscientiousness for level and change of SRH over four years in a population-based sample of Swedes in their 60’s

(Lindwall et al., 2017). We also examine the interaction effects of personality with different physical health indicators (i.e., disease burden, pain, and functional limitations). Following theoretical notions and empirical evidence, we have the following hypotheses:

H1: Higher initial neuroticism scores are associated with worse SRH at baseline (H1a) and steeper decline in SRH over time (H1b).

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H2: Higher initial conscientiousness scores are associated with better SRH at baseline (H2a) and less steep decline in SRH over time (H2b).

H3: For people with higher initial neuroticism scores, the negative associations of higher initial disease burden (H3a), pain (H3b), and functional limitations (H3c) with baseline SRH are stronger.

H4: For people with higher initial neuroticism scores the negative associations of higher initial disease burden (H4a), pain (H4b), and functional limitations (H4c) with trajectories of

SRH are stronger.

H5: For people with higher initial conscientiousness scores, the negative associations of higher initial disease burden (H5a), pain (H5b), and functional limitations (H5c) with baseline SRH are weaker.

H6: For people with higher initial conscientiousness scores, the negative associations of higher initial disease burden (H6a), pain (H6b), and functional limitations (H6c) with trajectories of SRH are weaker.

Fig. 1 illustrates our hypotheses.

[please insert Figure 1 here]

Methods

All methods, analyses, protocols and the syntax used in this paper were pre-registered and can be found on the Open Science Framework Server (https://osf.io/ek9cn/? view_only=a5ab0f7bb79f4d40b5ff093d55c786bf).

Sample

Our analyses are based on an ongoing longitudinal cohort study, the HEalth, Ageing, and Retirement Transitions in Sweden study (HEARTS; Lindwall et al., 2017). The HEARTS 10 PERSONALITY AND SELF-RATED HEALTH study is based on a nationally representative sample of 14,990 older adults aged 60-66 years at baseline (birth year 1949 to 1955), which was drawn from the Swedish population registry

SPAR (Statens personadressregister). Ethical approval for the HEARTS study was granted from the ethical approval board of the University of Gothenburg (Dnr: 970-14). A total of

5,913 individuals (mean age = 63.1 years, SD = 2.0 years) completed the first questionnaire in

2015 (i.e., response rate ~ 39%), and were thus included for the consecutive follow-up assessments. Compared to data on the Swedish population aged 60-66 in 2015 (Statistics

Sweden, 2021), the HEARTS sample includes more women (53.85% in HEARTS vs. 50.21% in the population, χ²(1) = 30.56, p < .001, OR = 1.16). Age composition did not differ significantly (χ²(6) = 9.99, p = .125). Furthermore, the HEARTS dataset includes more individuals who went to University (40.78%, compared to 33.18% in the population, χ²(1) =

140.97, p < .001; OR = 1.37).

Annual follow-ups on the baseline sample were conducted each spring in the period

2016–2020 (W2: N = 4,651; W3: N = 4,320, W4: N = 4,033, W5: N = 3,935, W6 = 3,914).

The survey was mainly completed online, but a paper version was distributed to non- responders with the second reminder. The dataset was used in other published papers, but without substantial overlap to the scope and the analyses of the present paper.

In the present paper, we used data from the first five waves of HEARTS (2015–2019) since wave six was not yet completed and available at the time of data analysis. We included all participants regardless of baseline characteristics, but excluded 90 participants who did not contribute any data on SRH which resulted in a final sample size of n = 5,823 participants.1

The final model with interaction terms (personality x health indices) included only n =

5,582, as the standard procedure for latent interactions in Mplus (xwith command) includes

1 This is a small deviation from the pre-registration, in which we stated to include all participants. 11 PERSONALITY AND SELF-RATED HEALTH only those with nonmissing data on the variables used for the interaction (i.e., health indices and personality).

Given study attrition, the longitudinal sample might be selective with regard to health and personality. A comparison of participants with only one assessment of SRH and those with two or more waves of SRH can be found in the supplementary material (Table S1). We conclude that, although the longitudinal sample reported better baseline SRH and physical health than those who contributed only one data point, effect sizes of all differences in the study variables were consistently below d =. 31 and thus quite small according to common effect size classifications (Cohen, 1992).

Measures

Self-rated health. SRH was assessed in all five waves with a single-item question.

Participants were asked to rate their present health status on a 6-point Likert scale (very bad =

1, very good = 6).

Personality. Neuroticism and conscientiousness were assessed at baseline with the

Mini-IPIP scale (International Personality Item Pool; Donnellan, Oswald, Baird, & Lucas,

2006). The scale is intended to assess the Big Five traits broadly, without further differentiating between facets within each personality trait. It has been shown to have good criterion validity and to tap into nearly the same facets as the original large IPIP-FFM

(Donnellan et al., 2006). The scale included 4 items per trait on a scale from “completely wrong” (1) to “completely true” (5). Items for neuroticism were “I have frequent mood swings”, “I am relaxed most of the time” (reversed), “I get upset easily” and “I seldom feel blue” (reversed). Items for conscientiousness were “I get chores done right away", “I often forget to put things back in their proper place” (reversed), “I like order”, and “I make a mess of things” (reversed). Higher values on each scale indicate higher neuroticism or higher conscientiousness, respectively. Reliability (Cronbach’s α) for neuroticism and 12 PERSONALITY AND SELF-RATED HEALTH conscientiousness at baseline was α = .61 and α = .62, respectively. The scale has been used in previous HEARTS publications that investigated personality as a predictor of change in life satisfaction in the retirement transition (Hansson et al., 2020; Henning, Hansson, Berg,

Lindwall, & Johansson, 2017).

Health indices. The three health indices disease burden, pain, and functional limitations were based on two symptom lists available in the HEARTS questionnaire, which were partly adapted from the Swedish National public health survey of 2013 (Statistics

Sweden, 2013). In the present study, we excluded all mental health symptoms (e.g., tiredness, fear / anxiety) because of their potential overlap with neuroticism. One list had symptoms coded as "1 No”, “2 Yes, symptom and mild discomfort”, “3 Yes, symptom and severe discomfort". The other list coded as “1 No”, “2 Yes, symptom but no discomfort”, “3 Yes, symptom and mild discomfort”, “4 Yes, symptom and severe discomfort”. We harmonized the lists by re-coding the second list, so symptoms without or with little discomfort were merged into one category. Furthermore, we recoded all items to 0-2, so having no symptoms equals 0. Disease burden included 17 conditions (e.g., diabetes, cancer, recurrent gastrointestinal problems), the pain index included four items (pain in neck or shoulders, back pain, hip pain or sciatica, headache or migraine) and functional limitations included three items (visual impairment, reduced ability to walk, hearing impairment). All items can be found in the supplementary material (Table S2). Sum scores were used (range 0-34 for disease burden, range 0-8 for pain, range 0-6 for functional limitations), with higher scores representing higher disease burden, more/stronger pain, and more functional limitations, respectively. For the main analyses, we used the baseline measures as time-invariant predictors. For the additional exploratory analyses, we included measures from all five waves as time-varying predictors.

13 PERSONALITY AND SELF-RATED HEALTH

Covariates. Covariates were all measured at baseline and include age (at baseline), gender (0 = male, 1 = female), retirement status and education. Retirement status was measured with the item “Are you retired (i.e., have started to receive old age pension)?”.

Possible answers were “1 No”, “2 Yes - but continue working and do not perceive myself as a pensioner”, “3 Yes - continue working but perceive myself as a pensioner” and “4 Yes, "full- time retired”. Only those choosing the fourth response option were coded as retired.

Education was measured with the item “What is your highest degree of education?”. Options were “1 Not completed primary school/less than 9 years of schooling”, “2 Primary school”, “3

Vocational training”, “4 Secondary school (3- or 4-years)”, “5 Some higher education (not at university level)”, “6 University - no degree”, “7 University – degree”. We distinguished between 0 = no higher education (1-4), and 1 = tertiary/higher education (5-7).

Analyses

All analyses were based on structural equation modelling techniques using Mplus version 8.4 (Muthén & Muthén, 2020). The robust full information maximum likelihood estimator (MLR) was used in all models to account for missing data (Enders, 2010). Overall model fit was evaluated based on a combination of indices, including the comparative fit index (CFI), the standardized root mean residual (SRMR), and the root mean square error of approximation (RMSEA). In case of the CFI, values above .90 indicate acceptable model fit, whereas values of .08 and lower are seen as acceptable for SRMR and RMSEA (Marsh,

2007). We grand-mean-centered all personality items as well as the health indices and age to facilitate interpretability of the means of intercept and slope2. We report unstandardized and standardized estimates, using the STDYX standardization available in Mplus.

2 In the pre-registration, we had planned to standardize our predictors, but this was not necessary, because the most recent version of Mplus offers standardized estimates for latent moderated structural equation models. 14 PERSONALITY AND SELF-RATED HEALTH

In a first step, we specified latent univariate measurement models of neuroticism and conscientiousness by conducting confirmatory factor analyses based on the item scores for each trait, checking for appropriate model fit.

Next, we ran a series of latent growth curve models (Ram & Grimm, 2007) on SRH and added predictors in a stepwise fashion. We started with a model without any predictors and compared models with a linear and a quadratic slope to a model with a linear slope component only, based on (Satorra-Bentler scaled) χ² values as well as other fit indices including CFI, RMSEA and SRMR.

We then included neuroticism and conscientiousness (measured at baseline) as time- invariant latent predictors of the intercept and slope(s) of SRH. This allowed us to test hypotheses H1a-b and H2a-b. We added covariates (age, gender, education, retirement status) as predictors of the intercept and slope(s) to test the robustness of effects of personality over and above the socio-demographic factors. Means of both personality factors were set to zero for better interpretability of their estimated predictor effects.

In the next model, we added disease burden, pain, and functional limitations

(measured at baseline) as time-invariant manifest predictors of intercept and slope(s). This model tested the robustness of the effects of personality over and above health conditions.

In the final model, we added latent interactions (Klein & Moosbrugger, 2000;

Maslowsky, Jager, & Hemken, 2015) between the three health indices (disease burden, pain, and functional limitations) and the two personality traits (neuroticism and conscientiousness, i.e. six interaction terms) as predictors of intercept and linear slope of SRH, to test H3 to H6, using the xwith command in Mplus (Maslowsky et al., 2015; Muthén & Muthén, 2020).

Latent interaction terms can be interpreted like manifest interactions, but the conventional fit indices are not available in this modelling procedure. Hence, a -2 Log-Likelihood test was computed, comparing a model without interactions to the model with interaction terms. A 15 PERSONALITY AND SELF-RATED HEALTH significant difference indicates that adding interaction terms resulted in a better model fit and thus accounted for a substantial additional proportion in the variance of the outcome (SRH).

In (pre-registered) exploratory analyses we further investigated the role of personality in the longitudinal relationship of health indices changes and SRH change. We computed three bivariate growth curve models to specify the relationship of SRH with disease burden, pain, and functional limitations, respectively. In a first step, we investigated how level and change in these domains were correlated. In the next step, we added conscientiousness and neuroticism as predictors of level and slope of SRH, regressed the level of SRH on the intercept of each health index, and the slope of SRH on level and slope of each health index.

In a final model, we added personality x health index slope interactions on the slope of SRH, to test if the effect of changes in health indices on changes in SRH was moderated by individual personality traits.3

The alpha level was set to 0.05. In the model with interaction effects, we adjusted our alpha for the interaction effects by dividing it by 6 (i.e., the overall number of interaction terms specified; adjusted α = .008), because we had unspecific hypotheses on the personality x health interaction effects regarding the three different health indices and to avoid inflation

3 . In our preregistration, we had planned to simultaneously include personality x health index intercept interaction effects in addition to the slope interactions as predictors of the slope of SRH in one single model. However, such a model did not converge. If only personality x intercept interactions were included, the model converged. However, we were mainly interested in personality as a moderator of slope-slope associations, i.e. of associations between change in health indices and change in SRH, and the intercept x personality interaction effects were already included in the previous models.

16 PERSONALITY AND SELF-RATED HEALTH of Type I error of significant interaction terms. For the same reason, we divided our alpha for interaction effects by the number of models (α = 0.05 / 3 = .017).

In models with predictors, we report standardized effects and 95% confidence intervals. Effect sizes were classified as small, moderate or large according to Cohen’s (1992) classification.

Results

Descriptive Statistics

Table 1 shows descriptive statistics of the study sample. Participants perceived themselves as comparably healthy – on average, they rated their health between 4 (fair) and 5

(good) in all waves.

[please insert Table 1 here]

A correlation table including all study variables can be found in the supplementary material

(Table S3). The baseline correlations of (manifest) neuroticism and conscientiousness with

SRH were small to medium in size in our sample (neuroticism: r = -.32, p < .001; conscientiousness: r = .15, p < .001)

Measurement Model for Personality

Neuroticism. The fit indices from the measurement model of neuroticism were not acceptable in terms of RMSEA (RMSEA = .130, 90%CI[.115; .147]) but showed a good fit in terms of CFI and SRMR (CFI = .916, SRMR = .046). Allowing an inter-correlation of the residuals of the two reversed items4 improved model fit significantly (Δχ² (1) = 166.51, p

<.001) and led to a very good model fit (CFI = .999, SRMR = .005, RMSEA = .024,

90%CI[.003; .050]). This implies a reversed item bias, generating higher correlations among recoded items. Other commonly used scales (e.g. the Rosenberg Self-Esteem Scale, see

4”I am relaxed most of the time“ and ”I seldom feel blue“. 17 PERSONALITY AND SELF-RATED HEALTH

DiStefano & Motl, 2006; Lindwall et al., 2011; Weijters, Baumgartner, & Schillewaert, 2013) show comparable method effects when using balanced scales (i.e., both positively and negatively worded items).

Conscientiousness. The measurement model of conscientiousness showed appropriate fit without additional amendments (CFI = .958, SRMR = .022, RMSEA = .076,

90%CI[.061;.093]).

Trajectories of SRH

Parameter estimates for the linear and quadratic latent growth curve models of SRH can be found in the supplementary materials (Table S4). The model with a (negative) linear and a (small and positive) quadratic slope had a very good model fit (CFI = .998, SRMR

= .018, RMSEA= .018, 90%CI[.008;.028]). Setting the quadratic slope and its variance to zero resulted in a model with a very good fit as well (CFI = .993, SRMR = .013,

RMSEA= .028, 90%CI[.021;.036]). Although there was a significant loss of model fit in the linear change model compared to the quadratic change model (Δχ² (4) = 38.10, p < .001), we chose the linear change model to reduce complexity and to increase model parsimony, because both models had very good and comparable values on the global model fit indices.

Moreover, in the quadratic change model, both linear and quadratic mean slope estimates were non-significant. Additionally, the RMSEA confidence intervals for both models overlapped, so that the linear change model does not seem to provide a substantially worse model fit in terms of RMSEA than a quadratic change model.

In the linear change model, there was a significant, but small decrease in SRH over time (B = -0.02, 95%CI[-0.03;-0.02], p < .001). Both intercept (σ² = 0.79, 95%CI[0.75;0.83]; p < .001) and slope (σ² = 0.02, 95%CI[0.02;0.02], p < .001) had significant variances, indicating substantial inter-individual differences in SRH intercepts and changes.

18 PERSONALITY AND SELF-RATED HEALTH

Personality Traits as Predictors of Self-Rated Health (hypotheses 1 and 2)

In the next step, we added neuroticism and conscientiousness as predictors of the intercept and slope of SRH. The model fit was good (CFI = .962, SRMR = .046, RMSEA

= .039, 90%CI[.036; .042]). Parameter estimates can be found in the supplementary materials

(Table S5). In line with H1a and H2a, there was a moderate negative association between neuroticism and SRH at baseline (b =-0.35, 95%CI[-0.38;-0.31], p < .001), and a small positive association between conscientiousness and SRH at baseline (b = 0.16,

95%CI[0.12;0.19], p < .001).

In contrast to H1b, higher neuroticism was related to a less steep decline in SRH (b =

0.13, 95%CI[0.08;0.19], p < .001), but the effect was rather small. In contrast to H2b, higher conscientiousness was not significantly associated with the slope of SRH (b = 0.00, 98%CI[-

0.06;0.05], p = .968). Figures 2 and 3 illustrate average trajectories of SRH for persons with high and low neuroticism as well as with high and low conscientiousness. Figure 2 shows that individuals with high neuroticism had a disadvantage with regard to SRH at baseline compared to those with low neuroticism. Although their trajectory of SRH was more stable than the trajectory of those with low neuroticism scores, a gap in SRH between both groups remained after 4 years. The two personality traits together accounted for 16% of the variance in baseline SRH, but only for 2% of the variance in the linear slope of SRH.

[please insert Figure 2 here]

[please insert Figure 3 here]

After controlling for the effects of covariates (age, gender, education, retirement status), all described associations of personality with level and change in SRH remained significant. The estimates for this model including covariates can also be found in the supplementary material (Table S6).

19 PERSONALITY AND SELF-RATED HEALTH

Personality traits and health indices as predictors of self-rated health

As a next step, we included disease burden, pain, and functional limitations in addition to the personality traits (and covariates) as predictors of level and change in SRH (Table 2).

Model fit was good (CFI = 0.962, SRMR = .034, RMSEA = .031, 90%CI[.029; .033]). The effects of personality on baseline levels of SRH remained significant but was attenuated; hence neuroticism and conscientiousness seem to play a role in the prediction of initial SRH over and above self-reported health conditions. However, the effect of neuroticism on change in SRH was no longer significant. Higher disease burden (b = -0.34, 95%CI[-0.37;-0.32], p

< .001), more pain (b = -0.32, 95%CI [-0.34;-0.29], p < .001) and more functional limitations

(b = -0.12 95%CI[-0.15;-0.10], p < .001) were associated with poorer SRH at baseline.

Surprisingly, more pain (b = 0.17, 95%CI[0.12;0.22], p < .001), and higher disease burden (b = 0.09, 95%CI[0.04;0.14], p = .004), were significantly related to a less steep decline in SRH, although the effects were rather small. In total, the predictors accounted for

51% of the variance in levels of SRH and 10% variance in change in SRH.

[please insert Table 2 here]

Interaction Effects of Personality Traits and Health Indices (hypotheses 3-6) In the final model, interactions of personality with the three health indices were included. The parameters of this model can be found in Table 3. To assess if adding interaction effects improved the model fit, we compared models via a Satorra-Bentler corrected likelihood ratio test.5 The difference in log-likelihood was not significant (ΔLL (12)

= 20.96, p = .051), thus, adding interaction effects did not improve the model fit significantly.

R² differed only slightly between models (ΔR² = 0.01 for both intercept and slope). We found

5 As a comparison model, we did not use the model from Table 2, but a model with the same (reduced) sample size as only those with full data on the three health indices could be included (n = 5,582). 20 PERSONALITY AND SELF-RATED HEALTH the same significant main predictor effects as in the original model. None of the interaction effects was significant at the adjusted alpha level (all p > .008).

[please insert Table 3 here]

Exploratory Analyses: Longitudinal Relations of Health Indices and Self-Rated Health

As an additional step, we ran three bivariate latent growth curve models to investigate whether changes in the health indices and changes in SRH are interrelated (see Table S7-S9 in the supplementary materials). Both disease burden (Mslope = 0.11, 95%CI[0.10;0.12], p < .001) and functional limitations (Mslope = 0.08, 95%CI[0.07;0.09], p < .001) increased over time, but pain levels declined (Mslope = -0.02, 95%CI[-0.03;-0.01], p < .001). Declines in SRH were significantly related to increases in disease burden (r = -.53, 95%CI[-.62;-.45], p < .001), in pain (r = -.64, 95%CI[-.74;-.55], p < .001), and in functional limitations (r = -.35,

95%CI[-.44;-.26], p < .001), and associations were in a small to medium effect size range.

In the next step, we added baseline neuroticism and conscientiousness to the models.

Both intercept and slope of SRH were regressed on neuroticism and conscientiousness as well as on the intercepts of the respective health indices. The slope of SRH was additionally regressed on the slopes of the respective health indices. Lower neuroticism and higher conscientiousness were related to higher levels of SRH in all three models. Higher neuroticism was significantly associated with less steep declines in SRH in the model with functional limitations, but not in the models on disease load or pain.

We then added neuroticism x slope (i.e., change in the three health indices) and conscientiousness x slope interaction effects as predictors of change in SRH to the models.

For the model with disease burden, adding interaction effects did not increase the model fit significantly (TRd (2) = 1.56, p = .458). For the models with pain (TRd (2) = 9.48, p = .008) and functional limitations (TRd (2) = 10.50, p = .002), however, there was a significant

21 PERSONALITY AND SELF-RATED HEALTH improvement in model fit. All model parameters can be found in the supplementary material

(Tables S10-S12).

There was a significant negative interaction effect of neuroticism and change in pain

(b = -0.26 [-0.41;-0.10], p = .006) on change in SRH, which indicated that the negative association of change in pain and change in SRH was exacerbated in those with high neuroticism scores. The effect was small but remained significant based on our adjusted alpha level. Figure 4 illustrates this relationship. The relationship of change in pain and change in

SRH is weaker for those with lower neuroticism. The model accounted for 39% of the variance in the slope of SRH when interactions were not included, and 57% in the model with interactions.

[please insert Figure 4 here]

There was an interaction effect of neuroticism and change in functional limitations (b

= -0.22, [-0.36;-0.08], p = .012). The effect was small, but significant based on our adjusted alpha level. We illustrate the effect in Figure 5. An increase in functional limitations over time was associated with a decrease in SRH, but less so for individuals with a lower neuroticism score. The model accounted for 17% of the variance in the slope of SRH when interactions were not included, and 29% in the model with interactions.

[please insert Table 5 here]

Discussion

In the present study, we investigated the role of personality (neuroticism and conscientiousness) and different health conditions (disease burden, pain, and functional limitations) as well as of their interaction as predictors of level and 4-year change in SRH in

Swedish older adults. Following theory and previous results, we expected that higher neuroticism would be related to lower SRH levels and steeper declines in SRH, whereas 22 PERSONALITY AND SELF-RATED HEALTH higher conscientiousness would be related to higher SRH levels and a less steep decline in

SRH. Furthermore, we assumed interaction effects in the way that higher levels of disease burden, pain or functional limitations would show less detrimental effects on SRH and SRH change for those with lower neuroticism and higher conscientiousness scores.

Our results confirmed our hypotheses that lower neuroticism and higher conscientiousness are related to a better SRH. These associations were of small to moderate size and remained significant when controlling for the effects of disease burden, pain, and functional limitations and can thus be regarded as robust, although their effect sizes decreased in the adjusted models. Personality seems to play a role in predicting self-evaluation of health status over and above physical health. Effects were similar to previous studies among older adults, although the effect of neuroticism may be particularly strong in our sample

(Löckenhoff et al., 2012; Stephan et al., 2020; Wettstein et al., 2017). Notably, the causal direction of these cross-sectional associations remains unclear – although personality may influence SRH, in particular via health behaviours (Bogg & Roberts, 2004) or via stress regulation, declines in physical health or SRH may also lead to changes in personality

(Mueller et al., 2016; Mueller, Wagner, Smith, Voelkle, & Gerstorf, 2018; Wettstein, Tauber,

Wahl, & Frankenberg, 2017), given that personality is plastic and changeable across the entire life span (Mõttus, Johnson, & Deary, 2012; Mõttus, Johnson, Starr, & Deary, 2012; Mueller et al., 2016; Wagner, Ram, Smith, & Gerstorf, 2016).

In contrast to our hypotheses, higher neuroticism was positively associated with change in SRH, but the effect was small and only significant when not controlling for the health indices. Some studies suggest that neuroticism can have positive effects under specific conditions (e.g. Friedman, 2000; Hooker, Choun, Mejía, Pham, & Metoya, 2013; Weston &

Jackson, 2018). For example, individuals with high neuroticism may be more sensitive to physical symptoms and health threats in general and thus more likely to seek help in face of

23 PERSONALITY AND SELF-RATED HEALTH serious health conditions (Weston & Jackson, 2018), which may positively influence SRH in the long run. Nevertheless, based on the literature on personality and SRH, there are more likely alternative explanations such as regression to the mean or adaptation processes

(Stephan et al., 2020). Individuals with higher scores in neuroticism at baseline reported lower

SRH, and there was a negative correlation of intercept and slope in SRH in our models, which means that those who felt less healthy at baseline were more likely to reveal less decline in

SRH over the study period. A substantial proportion of participants may either recover from or adjust to certain health conditions over time (Schilling, Wahl, Horowitz, Reinhardt, &

Boerner, 2011; Schilling & Wahl, 2006; Sprangers & Schwartz, 1999; Spuling, Wolff, &

Wurm, 2017), both by medical and psychological means, including “response shift” mechanisms (Sprangers & Schwartz, 1999; Spuling et al., 2017) or social downward comparisons (Frieswijk et al., 2004; Heidrich & Ryff, 1993) resulting in increasing or at least more stable SRH trajectories compared to other individuals with better initial SRH.

Nevertheless, the small advantage in change in SRH did not make up for the overall disadvantage in SRH of participants scoring higher on neuroticism, and this disadvantage was smaller, but still observable after four years. Conscientiousness was not significantly related to change in SRH.

In our sample, personality explained only 2% of the slope variance of SRH. Those small associations with change are in line with at least some other studies, which also showed that personality is related to level, but not or only weakly related to change in SRH in older age (Löckenhoff et al., 2012; Stephan et al., 2020). Personality traits might thus be associated with stable between-person differences in SRH, but they do not predict change in SRH, at least in our sample of young-old individuals and over the restricted observational period encompassing four years. Stephan and colleagues (Stephan et al., 2020) further argue that

“personality is most predictive of substantial changes toward worse self-rated health rather

24 PERSONALITY AND SELF-RATED HEALTH than more subtle shifts in self-rated health over time” (p. 7). Such substantial deterioration of

SRH might have occurred only in very few cases in our young-old study sample over the limited 4-year observational interval. Longitudinal effects of personality on self-rated health might thus get stronger in other life phases, such as old or advanced old age (Mueller,

Wagner, & Gerstorf, 2017), when general health-related vulnerabilities increase, or they may emerge only over longer time intervals (Wettstein et al., 2017). It might also be change in personality over time rather than personality as assessed at only one point in time only that predicts subsequent SRH (Magee et al., 2013).

Contrary to our expectations, the effect of health indices on level of or change in SRH does not seem to depend on levels of neuroticism or conscientiousness. Low neuroticism and high conscientiousness do not seem to buffer the negative effects of disease burden, pain, and functional limitations on SRH. Hence, personality and health conditions seem to affect SRH independently, at least when they are considered as time-invariant predictors. This is in line with previous results addressing other health outcomes which showed, for instance, that most of the Big Five personality traits do not interact with physical health in their prediction of change in cognitive health (Wettstein, Wahl, Siebert, & Schröder, 2019).

Nevertheless, taking a dynamic time perspective, exploratory analyses showed that neuroticism may weaken the ability to deal with or adjust to a deterioration in physical health.

Specifically, increases in pain and functional limitations were more strongly associated with declines in SRH for those with high versus low neuroticism scores, although effect sizes were small. This may be a consequence of less adaptive coping behaviours in those with higher neuroticism (Connor-Smith & Flachsbart, 2007; Gunthert et al., 1999) or a higher tendency to worry and engage in catastrophic thinking (Denovan et al., 2019) when health restrictions set in or progress. This means that whereas the effects of baseline health conditions on SRH are not moderated by neuroticism, lower neuroticism might help to better adjust to worsening

25 PERSONALITY AND SELF-RATED HEALTH health conditions over time and to maintain high levels of SRH despite these health deteriorations. This is in line with other studies which reported that higher neuroticism seems to augment the detrimental effects of chronic conditions on outcomes of cognitive abilities and health (Gaynes et al., 2013; Wettstein, Kuźma, et al., 2016; Wettstein et al., 2018). It is also in line with studies showing that pain is perceived as more problematic in patients with high neuroticism (Goubert et al., 2004).

More research is needed to replicate the moderating role of neuroticism on (long-term) associations between health conditions and self-rated health in independent samples and in pre-registered analyses. Future research is also required to understand and identify possible mechanisms underlying these effects. If replicated, those findings could be important for the development of interventions. Among the Big Five traits, neuroticism is the one that seems to be best modifiable based on interventions (Jackson, Weston, & Schultz, 2017a). Therefore, interventions to reduce neuroticism could help older adults to prevent or reduce negative effects of health restrictions on ratings of self-rated health and well-being so that declines in

SRH, an important “life-long health-promotion tool” (Graf & Patrick, 2014) also in old and very old age, can be prevented. Of course, such interventions should complement rather than replace general efforts of prevention of morbidity and treatment to avoid the onset and progression of pain, chronic diseases, and functional limitations.

Strengths and Limitations

This study is one of the first to investigate interactions of personality traits and health indices in the prediction of level and change in SRH among older adults. So far, most studies on the association between personality and SRH were based on cross-sectional samples. A further strength of the present study is the large and representative sample which allows for generalization of findings to the population of young-old adults and which provides the statistical power to detect small effects. We consider the inclusion of different major health 26 PERSONALITY AND SELF-RATED HEALTH indices and the availability of these health indices across all measurement occasions as a strength of this study, allowing for investigation of how change in different health conditions relates to change in self-rated health.

Limitations of the present study include the restricted age range limited to young-old individuals and the fact that women and participants with higher education were overrepresented compared to the general population of Swedish older adults, which limits the representativeness of Another possible problem may be the common-method bias – SRH, personality and health indices were all based on self-reports. This may affect the results as, for example, those with higher neuroticism may also be more likely to report their diseases and rate them as more limiting than those with lower levels of neuroticism. Informant-rated personality (Kööts-Ausmees et al., 2016) or more “objective” health indicators such as physician ratings (Chapman, Duberstein, Sörensen, & Lyness, 2006; Wettstein et al., 2019), activities of daily living or grip strength (Mueller et al., 2016) should be employed by future studies to investigate whether effects can be replicated when these different assessment methods are applied. Additional health information which was not available in this study, such as pain medication, should also be considered by future research, as the impact of the health indices on SRH might depend on such factors. Furthermore, we only used a short personality scale. Generally, a short scale is easier to include in large-scale surveys, but usually shows weaker psychometric qualities than longer versions (e.g. Credé. Harms,

Niehorster, & Gaye-Valentine, 2012; Ziegler, Kemper, & Kruyen, 2014). However, the mini-

IPIP reliability estimates were acceptable, given that each scale comprised only 4 items. With regard to personality, short scales only measure some facets of personality, but miss others.

The conscientiousness sub-scale of the mini-IPIP seems to rather assess orderliness than other facets like self-efficacy or cautiousness (Donnellan et al., 2006), which may have particular effects on SRH via health behaviours. The neuroticism sub-scale correlates most strongly with

27 PERSONALITY AND SELF-RATED HEALTH the facets anxiety, depression and vulnerability from the IPIP-NEO, but is only weakly related to immoderation, which may be related to SRH via worse health behaviours. Future studies should thus include longer scales and consider the effects of different facets of neuroticism and conscientiousness on SRH. Finally, as we were interested in personality traits as stable time-invariant dispositions, we did not consider how personality may change in response to

SRH, or how personality change might predict and precede SRH change, although personality is plastic and changeable across the entire life span, including later life, so that it is likely that personality-SRH associations are reciprocal (Jackson, Weston, & Schultz, 2017b; Mueller et al., 2018; Wettstein et al., 2017). Future research should take these potentially bidirectional longitudinal personality-SRH associations into account.

Conclusion

Neuroticism and conscientiousness are associated with SRH in young-old age, but, according to our findings, as a stable between-person association rather than a predictor of within-person SRH change. Neither of the personality traits seem to moderate the impact of health conditions on levels or change in SRH, but we found some evidence that, from a time- varying perspective, higher neuroticism may worsen the negative effects of increase in pain and in functional limitations on SRH changes. Lower neuroticism could thus help to maintain positive perceptions of one’s own health, even when health conditions deteriorate. More research is needed to better understand the longitudinal dynamic of the relation between health and personality as well as to identify additional factors that might act as mediators or moderators in this association.

28 PERSONALITY AND SELF-RATED HEALTH

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X

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Tables. Table 1. Descriptive statistics of the study variables. M (SD) / % Age Wave 1 (60-66, n = 5,766) 63.09 (2.01)

Gender (n =5,745):

Male: 2.652 (46.15%)

Female 3.093 (53.85%) Education (n =5,763):

Tertiary Education 2,904 (50.30%)

No Tertiary Education 2,859 (49.70%) Retirement Wave 1 (% retired, n = 5,701) 1,253 (21.93%) SRH Wave 1 (1-6, n = 5,645) 4.64 (1.06) SRH Wave 2 (1-6, n = 4,534) 4.67 (1.00) SRH Wave 3 (1-6, n = 4,246) 4.63 (1.00) SRH Wave 4 (1-6, n = 3,990) 4.64 (1.00) SRH Wave 5 (1-6, n = 3,882) 4.60 (0.95) Disease Burden Wave 1 (sum score, 0-34, n = 5,630) 2.99 (2.51) Disease Burden Wave 2 (sum score, 0-34, n = 4,543) 3.02 (2.50) Disease Burden Wave 3 (sum score, 0-34, n = 4,253) 3.06 (2.45) Disease Burden Wave 4 (sum score, 0-34, n = 3,992) 3.20 (2.49) Disease Burden Wave 5 (sum score, 0-34, n = 3,895) 3.30 (2.53) Pain Wave 1 (sum score, 0-8, n = 5,625) 1.94 (1.64) Pain Wave 2 (sum score, 0-8, n = 4,536) 1.84 (1.56) Pain Wave 3 (sum score, 0-8, n = 4,246) 1.78 (1.52) Pain Wave 4 (sum score, 0-8, n = 3,980) 1.78 (1.50) Pain Wave 5 (sum score, 0-8, n = 3,893) 1.82 (1.53) Functional Limitations Wave 1 (sum score, 0-6, n = 5.590) 1.16 (0.86) Functional Limitations Wave 2 (sum score, 0-6, n = 4,530) 1.34 (0.97) Functional Limitations Wave 3 (sum score, 0-6, n = 4,237) 1.36 (0.97) Functional Limitations Wave 4 (sum score, 0-6, n = 3,972) 1.42 (1.00) Functional Limitations Wave 5 (sum score, 0-6, n = 3,887) 1.46 (0.98) Neuroticism Wave 1 (manifest mean, 1-5, n = 5,251) 2.36 (0.81) Conscientiousness Wave 1 (manifest mean, 1-5, n = 5,255) 4.00 (0.72) n = 5,823. Higher SRH scores indicate better health.

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Table 2. Personality and health indices as predictors of level and change in self-rated health.

Intercept Slope b [ 95%CI] b [ 95%CI] Intercept 5.13 [4.99;5.27]*** -0.11 [-0.18;-0.04]* Personality Neuroticism -0.17 [-0.20;-0.14]*** 0.06 [0.01;0.12] Conscientiousness 0.10 [0.08;0.13]*** 0.02 [-0.04;0.07] Health Disease Burden -0.34 [-0.36;-0.31]*** 0.09 [0.04;0.14]** Pain -0.32 [-0.34;-0.29]*** 0.17 [0.12;0.22]*** Functional Limitations -0.13 [-0.15;-0.10]*** 0.01 [-0.04;0.05] Socio-Demographic Predictors Age 0.07 [-0.20;-0.10]*** -0.13 [-0.17;-0.08)*** Gender 0.02 [-0.01;0.04] 0.01 [-0.03;0.08] Education 0.05 [0.03;0.07]*** -0.01 [-0.05;0.03] Retirement -0.02 [-0.04;0.00] -0.09 [-0.13;-0.04]**

Residual Variance 0.49 [0.47;0.51]*** 0.90 [0.88;0.93]*** Covariance Intercept- -0.23 [-0.29;-0.17]***

Slope N = 5,823. *p < .05 ** p < .01 *** p < .001. R² (intercept) = 0.51, SE = 0.01, p < .001.

R²(slope) = 0.10; SE = 0.02, p < .001.

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Table 3. Personality and health interactions for level and change in self-rated health. Intercept Slope

b[95%CI] b[95%CI] Intercept 5.16 [5.02;5.31]*** -0.14 [-0.22;-0.06]*** Neuroticism -0.17 [-0.20;-0.14]*** 0.06 [0.00;0.11]

(N) Conscientiousness 0.10 [0.07;0.13]*** 0.02 [-0.04;0.08]

(C) Disease Burden -0.34 [-0.37;-0.32]*** 0.09 [0.04;0.15]** Pain -0.32 [-0.34;-0.29]*** 0.17 [0.12;0.22]*** Functional Limitations -0.12 [-0.15;-0.10]*** 0.01 [-0.04;0.05] Neuroticism x Disease Burden 0.04 [0.01;0.08]* -0.08 [-0.15;-0.02]* N x Pain 0.00 [-0.04;0.03] 0.09 [0.02;0.17]* N x Functional Limitations -0.03 [-0.06;0.00] 0.00 [-0.07;0.06] C x Disease Burden 0.05 [0.00;0.09] -0.08 [-0.15;-0.02]* C x Pain -0.05 [-0.09;-0.01] 0.08 [0.01;0.16] C x Functional limitations -0.03 [-0.07;0.00] 0.04 [-0.03;0.12] Age 0.08 [0.05;0.10]*** -0.12 (-0.17;-0.08]*** Gender 0.02 [0.00;0.04] 0.02 [-0.03;0.06] Education 0.05 [0.03;0.07]*** -0.01 [-0.05;0.03] Retirement -0.02 [-0.04;0.00] -0.08 [-0.13;-0.04]**

Residual Variance 0.48 [0.46;0.50]*** 0.89 [0.86;0.92]*** Covariance Intercept-Slope -0.21 [-0.28;-0.14]*** N = 5,823. *p < .05 ** p < .01 *** p < .001. R²(intercept) = 0.52, SE = 0.01, p < .001. R²

(slope) = 0.11, SE = 0.02, p < .001.

Figure Captions Figure 1. Hypothesized associations between personality traits (neuroticism and conscientiousness), physical health indicators (disease burden, pain, and functional limitations), and self-rated health (SRH).

Figure 2. The association of neuroticism with level and change in self-rated health over four years. Figure 3. The association of conscientiousness with level and change in self-rated health over four years.

Figure 4. Interaction effect of the slope of pain and neuroticism on the slope of self-rated health. 39 PERSONALITY AND SELF-RATED HEALTH

Figure 5. Interaction effect of the slope of functional limitations and neuroticism on the slope of self-rated health.

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