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. Author manuscript; available in PMC: 2022 Dec 1.
Published in final edited form as: Aging Ment Health. 2020 Nov 12;25(12):2191–2199. doi: 10.1080/13607863.2020.1839857

Neuroticism Predicts Informant Reported Cognitive Problems Through Health Behaviors

Rachel D Best 1, Patrick J Cruitt 1, Thomas F Oltmanns 1, Patrick L Hill 1
PMCID: PMC8767481  NIHMSID: NIHMS1758752  PMID: 33183066

Abstract

Objective:

Personality traits have been linked to cognitive impairment, though work is needed to understand the mechanisms involved. Research also needs to consider alternative markers of cognitive impairment, such as informant report measures. The aim of the current study was to examine the role of health behaviors and social engagement as mediators for the relationship between personality and informant reported cognitive problems. It was expected that neuroticism would predict cognitive problems through negative health behaviors, while conscientiousness might predict cognitive problems through positive health behaviors.

Methods:

Using data from the St. Louis Personality and Aging Network study at three time points, spanning approximately 2.27 years (N=829, M age=65.95), correlations were computed between the Big Five personality traits and health behaviors at wave 1, social engagement at wave 2, and informant reported cognitive problems at wave 3. Mediation tests examined whether health behaviors and social engagement explained the relationships found between personality and informant reported cognitive problems.

Results:

Findings showed that neuroticism at wave 1 significantly predicted informant reported cognitive problems at wave 3 and that health behaviors, specifically wellness maintenance, partially explained this relationship. No significant associations were found between informant reported cognitive problems and conscientiousness, agreeableness, extraversion, openness, or social engagement.

Conclusion:

This study supports claims that neuroticism predicts later cognitive problems and expands on previous literature by demonstrating this relationship using an informant report measure. Furthermore, we found that health behaviors, and specifically wellness maintenance, account for some of the relationship between neuroticism and informant reported cognitive problems.

Introduction

Although rates of non-normative cognitive impairment may have diminished in recent years, the onset of cognitive impairment in older persons remains a prominent problem in the United States. Approximately 14.7% of U. S. adults over the age of 70 meet diagnostic criteria for dementia (Fishman, 2017). The Alzheimer’s Association calculated that the United States would spend $290 billion on healthcare for patients with Alzheimer’s or other dementias in 2019 (Alzheimer’s Association, 2019a). The largest risk factor for cognitive decline is age, which is problematic given that the number of older adults in the U.S. and other developed countries is steadily increasing: by 2030, twenty percent of the U.S. population is expected to be over 65 (Vincent & Velkoff, 2010). Although rates of cognitive impairment are decreasing, the at-risk population is growing steadily, and it is likely that cognitive impairment will remain a costly problem.

Thus far, the most promising treatment is prevention. In an effort to stop cognitive impairment before it has manifested, investigators have searched for mitigating factors. Two of the strongest predictive factors that have been linked to cognitive impairment are health behaviors and social engagement (Colcombe et al., 2006; Smith, 2016). Physical activity engagement is associated with lower rates of cognitive impairment (Colcombe et al., 2006; Soni et al., 2019). Eating a healthy diet also appears protective against cognitive impairment (Kang, Ascherio & Grodsteim, 2005; Smyth et al. 2015). Additionally, social engagement has been linked to reduced risk for cognitive impairment, assessed as social integration (Béland, Zunzunegui, Alvarado, Otero, & del Ser, 2005), social network size (Crooks, Lubben, Petitti, Little & Chiu, 2008, though see Krueger et al., 2009), social activity (Krueger et al., 2009), contact frequency (Schwartz, Khalaila, & Litwin, 2019), and social support (Krueger et al., 2009; Yeh & Liu, 2003). Given that these two factors have shown associations with better cognitive performance and reduced risk for decline, it is informative to consider individual differences that predict older adults’ propensity for health behavior and social engagement. Toward this end, the current study considered the role of personality traits on symptoms for informant reported cognitive problems through health behaviors and social engagement.

Personality, Health Behaviors, and Social Engagement

Most of the literature on personality and health behavior has focused on the Big Five personality traits. These five traits include (1) extraversion - a propensity towards assertiveness and gregariousness, (2) agreeableness - a propensity towards friendliness and being trusting of others, (3) conscientiousness - a tendency to be organized and industriousness, (4) neuroticism - a proclivity towards anxiety and emotional lability, and (5) openness to experience - a tendency to be more willing to try new things and seek out intellectually engaging activities (John, Naumann, & Soto, 2008; John & Srivastava, 1999). Of these traits, the most consistent predictors of health behavior are conscientiousness and neuroticism (Hampson, 2012), which tend to hold opposite associations with health behavior. Meta-analytic work shows that conscientious individuals are more likely to engage in health promoting behaviors, such as getting exercise and better diet, and avoid health-deteriorating behaviors, such as drug use and activities that involve potential for self-harm (Bogg & Roberts, 2004). Moreover, conscientiousness likely promotes health behavior in different ways across the lifespan (Shanahan, Hill, Roberts, Eccles, & Friedman, 2014). For instance, in adulthood, higher conscientiousness is likely associated with avoiding risks in general and, particularly in middle-to-older adulthood, aligned with health maintenance and appropriate management of health conditions.

Conversely, higher neuroticism tends to be associated with greater risk for poor health behaviors such as unprotected sexual activity and substance use (see Lahey, 2009 for a review). Again, some differences in the specific health behaviors impacted may be found across the lifespan. However, neuroticism still predicts smoking behavior (Terracciano & Costa, 2008) and poorer, riskier decision-making (Denburg et al., 2009) among older adults. As such, the trait again appears to hold clear relevance for predicting negative health behaviors across lifespan. The other three traits are less consistently associated with health-promoting or health-risk behaviors.

However, they do tend to play an important role with respect to social engagement. Extraversion and agreeableness, in particular, appear to play important roles with respect to social interactions. Extraverted individuals attract more social attention (Ashton, Lee, & Paunonen, 2002), and enjoy social settings more than others (Lucas & Diener, 2001). Agreeable individuals tend to be more focused on having positive social interactions and avoiding conflicts (Graziano & Tobin, 2009). Agreeableness is defined with respect to whether individuals comply with others, are sympathetic to others’ concerns, and tend to produce altruistic actions (Costa & McCrae, 1992a). In addition to these two ‘social’ traits, research also suggests that conscientiousness plays a role in promoting positive adult social role commitments, such as community engagement, work behaviors, and romantic relationships (Lodi-Smith & Roberts, 2007). Higher neuroticism also predicts lower romantic relationship satisfaction (Malouff, Thorsteinsson, Schutte, Bhullar, & Rooke, 2010; Schaffhuser, Allemand, & Martin, 2014). The association between openness to experience and social engagement is less clear, but the trait is positively correlated with social well-being both concurrently and over time (Hill, Turiano, Mroczek, & Roberts, 2012).

Personality and Cognitive Impairment

Given these associations with factors that promote cognitive resilience, it is unsurprising that previous research has linked personality to risk for cognitive impairment (Chapman et al., 2012; Curtis, Windsor & Soubelet, 2015; Low, Harrison, & Lackersteen, 2013). For instance, high neuroticism and low conscientiousness have been consistently associated with greater risk for cognitive impairment (Chapman et al., 2012; Low, Harrison, & Lackersteen, 2013; Soubelet & Salthouse, 2011; Terracciano, Stephan, Luchetti, Albanese & Sutin, 2017). High openness is often, but not always, associated with less risk for cognitive impairment (Curtis, Windsor, & Soubelet, 2015; Gregory, Nettelback & Wilson, 2010; Low, Harrison, & Lackersteen, 2013). There have also been mixed findings regarding the predictive nature of extraversion and agreeableness on cognitive impairment (Curtis, Windsor, & Soubelet, 2015; Low, Harrison, & Lackersteen, 2013).

This literature comes with two primary caveats. First, studies have differed widely with respect to ways in which cognitive outcomes are measured, often varying between laboratory tests of cognitive performance and clinical markers of non-normative decline. Largely missing from this literature is a consideration of how personality may predict observer reports of cognitive difficulties, a common early warning sign for cognitive impairment (e.g., Galvin et al., 2005). Informant reported cognitive problems have previously been shown to accurately predict clinical measures of cognitive decline, including diagnosis of Alzheimer’s Disease (Carr, Gray, Baty, & Morris, 2000; Gruters et al., 2019; Rabin et al., 2012; Valech et al., 2015), and informant report is a particularly useful measure because it is a relatively easy and efficient measure to administer. Second, few studies have formally tested mediational explanations for the associations between personality and cognitive functioning (though see Jackson, Hill, Payne, Parisi, & Stine-Morrow, 2019). Researchers have speculated on why personality may predict cognitive impairment (Hill & Payne, 2017; Payne & Lohani, 2019), but more work is needed to understand the routes by which personality leads to cognitive resilience (or decline).

Current Study

The current study attempts to better understand the relationship between personality, health behaviors, social engagement, and cognitive problems. Personality has been linked to both health behaviors and social engagement, two of the strongest predictors of cognitive impairment (Smith, 2016). Accordingly, it is possible that the relationship between personality and cognitive impairment is influenced by personality’s impact on health behaviors and social engagement. We hypothesized that the Big Five personality traits at wave 1 would be correlated with informant reported cognitive problems at wave 3, with low conscientiousness and high neuroticism being the strongest predictors (H1). Our second hypothesis was that higher scores for both health behaviors and social engagement at waves 1 and 2 respectively would be correlated with lower informant reported cognitive problems (H2). Our third and fourth hypotheses were that higher conscientiousness and lower neuroticism would be correlated with higher health behaviors (H3) and that agreeableness and extraversion would be correlated with higher social engagement (H4). Our fifth hypothesis was that health behaviors would act as a mediator for the relationship between both conscientiousness and neuroticism and informant reported cognitive problems, such that neuroticism would predict less engagement in health behaviors and more cognitive problems, and conscientiousness would predict greater engagement in health behaviors and less cognitive problems (H5). Lastly, we hypothesized that social engagement would act as a mediator for the relationship for both extraversion and agreeableness and informant reported cognitive problems, such that both extraversion and agreeableness would predict more social engagement and less cognitive problems (H6).

Additionally, we were interested to see how personality influences different categories of health behaviors, and which health behaviors in particular are predictive of cognitive problems. Towards this end, we analyzed four components of health behavior – wellness maintenance, accident control, traffic risk, and substance abuse – that come from the frequently employed Health Behavior Checklist (Vickers, Conway & Hervig, 1990). Previous work has already demonstrated that each domain is associated to differing magnitudes with the Big Five personality traits (Booth-Kewley & Vickers, 1994). Moreover, that research points to the need to consider all aspects of health behavior for understanding the impact of personality, and the current study heeds that advice by investigating health behaviors seemingly more linked to cognitive functioning (e.g., wellness maintenance and substance use) as well as those seemingly less conceptually linked (like traffic risk). As such, this study provides one of the first investigations into whether the four different components of health behaviors are unique explanations for the link between personality and cognitive problems.

Methods

Participants

Data were collected as part of the St. Louis Personality and Aging Network (SPAN) study, an ongoing longitudinal study that examines how personality affects later life outcomes. The SPAN study has investigated personality using multiple approaches over the years, and for the current work, we focused specifically on the Big Five traits because these constructs have been more frequently investigated with respect to the mediators of interest. For readers interested in alternative assessments from SPAN, such as non-normal range personality traits or narrative approaches to personality, we would alert them to other papers have considered these constructs (Author Citation, under review). The first phase of the study began in 2007 when 1,630 participants aged 55 to 64 years were recruited from the St. Louis area (see Oltmanns, Rodrigues, Weinstein, & Gleason, 2014). Data for the current analyses come from three waves of data collection later in the study (hereafter referred to as waves 1, 2, and 3), with the first wave occurring approximately seven years after the baseline assessment. Data collection for wave 1 and 3 occurred in-lab approximately 2.27 years apart and consisted of questionnaires and interviews regarding the participant’s personality, health, and life events. Wave 2 was collected in between the two in-lab waves of data collection and consisted of questionnaires that participants completed online or through the mail. All procedures were approved by the relevant institutional review board as being consistent with ethical guidelines.

Out of the original 1,630 participants, 829 provided relevant data at wave 1, when they were between the ages of 60 and 73 (M = 65.95, SD = 2.92). The current sample was demographically similar to the full baseline sample and representative of the St. Louis metropolitan area. Women composed 56% of the sample (n = 464), with the remaining 44% identifying as men (n = 365). In terms of racial identity, 74% identified as White/Caucasian (n = 612) and 24% as Black/African American (n = 201), with most of the remaining 2% identifying as biracial or other. Participants ranged in level of education from less than high school to a doctoral or professional degree, with the median participant having earned a 4-year college degree.

Each participant was also asked to nominate another person who knew them well enough to describe their personality to serve as an informant. Relevant informant data for the current analyses came from wave 3 (n = 498). Informants were spouses/partners (57%), other family members (19%), friends (19%), or neighbors/co-workers/other (4%). On average, informants had known participants for 40.2 years (SD = 14.9, range = 1 to 73). At baseline, informants were restricted to individuals who talked with the participant at least once a month and saw the participant face-to-face at least once a year (Oltmanns, Rodrigues, Weinstein, & Gleason, 2014). Although a small number of informants no longer met these criteria at wave 3 (n = 5), we decided to retain them for the current analyses. Informants completed questionnaires online or through the mail at each wave of data collection.

Measures

Descriptive statistics for all variables used in the current analyses are presented in Table 1.

Table 1.

Descriptive Statistics for Personality Traits, Health Behaviors, Social Engagement, and Informant Reported Cognitive Problems

Variable N Mean SD Range
Personality Traits
 Agreeableness 774 129.6 15.3 79–177
 Conscientiousness 774 123.8 17.9 51–175
 Extraversion 774 106.1 19.8 46–161
 Neuroticism 774 72.1 21.3 9–168
 Openness 774 112.7 19.2 41–172
Health Behaviors
 Accident Control 771 20.6 3.9 5–30
 Wellness Maintenance 771 36.7 6.1 10–50
 Traffic Risk 771 26.2 4.3 12–35
 Substance Use 770 15.0 2.6 4–20
Social Engagement 725 2.1 0.7 1–5
Cognitive Problems
 Raw Score 498 0.6 1.3 0–8
 AD8 ≥ 2 498 14.5%
 AD8 < 2 498 85.5%
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NEO Personality Inventory-Revised (Costa & McCrae, 1992b).

The NEO Personality Inventory-Revised (NEO PI-R) is a self-report questionnaire with 240 items that measures the Big Five personality traits (Conscientiousness, Openness, Agreeableness, Extraversion, and Neuroticism). Participants completed the NEO PI-R at wave 1. Item responses are on a Likert-style scale ranging from 1 (strongly disagree) to 5 (strongly agree). Coefficient alphas for the domains in the current sample ranged from .87 (agreeableness) to .93 (neuroticism), with a median of .90.

Health Behavior Checklist (Vickers, Conway, & Hervig, 1990).

The HBC is a self-report questionnaire that includes 40 statements, on which participants are asked to rate how much they agree with the statement from “Disagree strongly” (1) to “Agree strongly” (5). We calculated scores for the four original subscales (wellness maintenance, accident control, traffic risk, and substance abuse). Example items include: “I eat a balanced diet” and “I exercise to stay healthy” (wellness maintenance), “I have a first aid kit in my home” and “I fix broken things in my home right away” (accident control), “I wear a seat belt when in a car” and “I carefully obey traffic rules so I won’t have accidents” (traffic risk), and “I do not drink alcohol” and “I don’t smoke” (substance abuse). The HBC was assessed at wave 1. Coefficient alphas for the four subscales in the current sample were .40 (substance abuse), .68 (accident control), and .74 (wellness maintenance and traffic risk).

Social Adjustment Scale (Weissman & Bothwell, 1976; Weissman, Olfson, Gameroff, Feder, & Fuentes, 2001).

The Social and Leisure subscale of the Social Adjustment Scale (SAS) was used to assess social functioning and engagement at wave 2 (n = 725). This subscale was administered because it could be completed regardless of the individual participant’s social roles, unlike other subscales (e.g., those focused on work or parental roles). We used five items from this subscale, such as, “How many times in the last 2 weeks have you gone out socially with other people, for example, visited friends; gone to movies, bowling, church, or restaurants; or invited friends to your home?”, that are rated on a Likert-style scale with varying response options. Participants respond to the items regarding their functioning over the past 2 weeks. Scores on the SAS are coded such that higher scores indicate poorer adjustment. Coefficient alphas for the Social and Leisure subscale of the SAS in the current sample was .70.

The Ascertain Dementia Eight-Item Informant Questionnaire (Galvin et al., 2005).

The AD8 is an eight-item informant report measure of change in cognitive ability that asks for yes or no responses to eight items regarding whether “there has been a change in the last several years caused by cognitive (thinking and memory) problems.” Sample statements include, “Repeats questions, stories, or statements” and “Trouble remembering appointments.” The AD8 has been validated as a screening measure for mild dementia in both community-based (Galvin et al., 2005) and clinical samples (Galvin, Roe, Xiong, & Morris, 2006). In a different community sample, also drawn from the St. Louis metropolitan area, a score of two or more demonstrated 85% sensitivity and 86% specificity in discriminating between individuals with a Clinical Dementia Rating (CDR) of 0 and a CDR of 0.5 or greater (Galvin et al., 2005). Out of the 498 participants with AD8 data at wave 3, 72 (14%) obtained a score of two or higher. Coefficient alpha for the AD8 in the current sample at wave 3 was .79.

Analytic Plan

We ran Pearson correlations between the Big Five personality traits at wave 1, the four HBC subscales at wave 1, social engagement at wave 2, and informant reported cognitive problems at wave 3. We used the psych package in R to run bootstrapping tests of mediation model, entering the four categories of health behaviors at wave 1 as mediators for the relationship between the Big Five personality traits at wave 1 and informant reported cognitive problems at wave 3 (Revelle, 2017). We separately tested social engagement at wave 2 as a mediator for the relationship between the Big Five personality traits at wave 1 and informant reported cognitive problems at wave 3. In both cases, all participants with relevant data for estimating any one leg of the pathway were included in the mediation analyses, using pairwise deletion to deal with individuals who were missing one leg.

Results

Table 2 presents the Pearson correlations between the Big Five personality traits, the four categories of health behaviors, social engagement, and informant reported cognitive problems. The results show that H1 and H2 were only partially supported. Consistent with previous literature, we found a small positive correlation between neuroticism at wave 1 and informant reported cognitive problems at wave 3 (r(478) = 0.14, p < .005). Additionally, there was a small, negative correlation between wellness maintenance at wave 1 and informant reported cognitive problems at wave 3 (r(477) = −0.14, p < .005). Surprisingly, no other significant correlations were found between the other four personality traits or social engagement and informant reported cognitive problems. However, all five personality traits were significantly related to both the wellness maintenance and accident control categories of health behaviors as well as social engagement in the directions that we hypothesized for H3 and H4.

Table 2.

Correlations Between the Personality, Health Behavior, Social Engagement, and Informant Reported Cognitive Problems

Accidenta Wellnessa Traffica Substanceb Social Engagement AD8d AD8 ≤ 2
Correlations of the Big Five traits (predictors) with potential mediators and the cognitive problems outcome
Agreeableness .14* .21* .27* .17* −.20* −.05 −.03
Conscientiousness .28* .27* .11* .10* −.22* −.09 −.06
Extraversion .26* .31* −.09 −.05 −.42* −.08 −.08
Neuroticism −.26* −.20* −.01 −.03 .28* .14* .12
Openness .11* .31* −.06 −.08 −.26* −.08 −.07
Correlations between the potential mediators and the cognitive problems outcome
Accident Control − .01 .01
Wellness Maintenance .32* − −.14* −.15*
Traffic Risk .10 −.01 − .03 .10
Substance Use .20* .17* .23* − .08 .09
Social Engagement −.12* −.26* .09 .01 − .01 .03
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Note.

a

Ns range from 719 to 771.

b

Ns range from 718 to 770.

c

Ns range from 718 to 722.

d

Ns range from 458 to 480.

*

p ≤ .005. Higher scores on the Social Adjustment Scale indicate poorer adjustment.

Table 3 presents the results for models testing whether the four categories of health behaviors at wave 1 mediate the relationships between the Big Five personality traits at wave 1 and informant reported cognitive problems at wave 3. We also tested a separate model with social engagement at wave 2 as a mediator for the relationship between the Big Five personality traits at wave 1 and informant reported cognitive problems at wave 3 (Table 4). There was a small positive association between neuroticism at wave 1 and informant reported cognitive problems at wave 3 (B = 0.13, SE = 0.03, t = 3.77, p < .001). H5 was partially supported: wellness maintenance mediated the relationship between neuroticism and informant reported cognitive problems (Figure 1; indirect effect estimate = 0.04, sd = 0.01, 95% CI: 0.00 to 0.09). This mediation model remained significant when predicting for probable cognitive problems (AD8 ≥ 2; indirect effect estimate = 0.04, sd = 0.01, 95% CI: 0.00 to 0.09). Although these effect sizes are quite small, they are significant and worth studying; cognitive impairment is a detrimental and pervasive problem that has no known cure, and accordingly, all findings that could potentially help with prevention should be explored. None of the other four personality traits (openness, extraversion, agreeableness, and conscientiousness) showed significant indirect effects through health behaviors on informant reported cognitive problems (H5 was partially unsupported). Moreover, social engagement did not mediate the relationship between neuroticism and informant reported cognitive problems (H6 was unsupported).

Table 3.

Results from Bootstrapped Tests of Mediation with Health Behaviors Included as Simultaneous Mediators

Personality trait Mediator Standardized indirect estimate (95% CI)
Agreeableness Wellness −0.02 (−0.05, 0.00)
Accident 0.01 (−0.02, 0.05)
Traffic 0.00 (−0.02, 0.03)
Substance 0.01 (−0.00, 0.04)
Conscientiousness Wellness −0.04 (−0.09, 0.00)
Accident 0.01 (−0.01, 0.02)
Traffic 0.00 (−0.01, 0.02)
Substance 0.03 (−0.01, 0.06)
Extraversion Wellness −0.04 (−0.09, 0.00)
Accident 0.00 (−0.02, 0.01)
Traffic 0.00 (−0.01, 0.01)
Substance 0.02 (−0.00, 0.06)
Neuroticism Wellness 0.04* (0.00, 0.08)
Accident 0.00 (−0.01, 0.01)
Traffic 0.00 (−0.01, 0.01)
Substance −0.02 (−0.05, 0.00)
Openness Wellness −0.02 (−0.05, -0.00)
Accident 0.00 (−0.01, 0.01)
Traffic 0.00 (−0.01, 0.01)
Substance 0.01 (−0.00, 0.03)
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Note

*

indicates p < .05.

Table 4.

Results from Bootstrapped Tests of Mediation with Social Engagement Included as a Mediator

Personality Trait Unstandardized Indirect Effect Estimate (95% CI)
Agreeableness .00 (−0.03 to 0.03)
Conscientiousness .00 (−0.03 to 0.03)
Extraversion .01 (−0.04 to 0.06)
Neuroticism −.01 (−0.04 to 0.03)
Openness .00 (−0.03 to 0.04)
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Figure 1.

Figure 1

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Wellness Maintenance, But None of the Other Categories of Health Behaviors, Significantly Mediates the Relationship Between Neuroticism and Informant Reported Cognitive Problems

Exploratory Analyses

Although we did not preregister our hypotheses with plans to include covariates in the models, on the recommendation of a reviewer we reran the mediation models with age, gender, race, and years of education as simultaneous covariates (Supplementary Table 2). The results for the relationship between neuroticism and informant reported cognitive problems remained the same (B = 0.13, SE = 0.03, t = 3.77, p < .001). The estimate and confidence interval for the indirect effect of neuroticism through wellness maintenance on informant cognitive problems was very similar, although the confidence interval included zero (indirect effect estimate = 0.03, sd = 0.02, 95% CI: −0.01 to 0.08). The stability of the estimate suggests the existence of a small effect, even though the results no longer meet the threshold for statistical significance when controlling for covariates.

Discussion

These results provide new information regarding the role of health behaviors and social engagement as mechanisms that may account for the connection between personality and cognitive problems. Our study is unique in that we used informant report as a measure of cognitive problems. Informant report may be more reliable than self-report when measuring cognitive problems because participants who become cognitively impaired may lack the insight to report accurately regarding their own behaviors and abilities (Alzheimer’s Association, 2019b). Additionally, compared to clinical and laboratory measures, informant report is a much more time and cost-efficient way to screen for cognitive problems. Neuroticism was the only Big Five personality trait that predicted informant reported cognitive problems. This finding supports the previous literature that links neuroticism to cognitive impairment through other measures, such as laboratory tests of cognitive performance or a clinical diagnosis of Alzheimer’s Disease or Mild Cognitive Impairment (Chapman et al., 2012; Low, Harrison, & Lackersteen, 2013; Soubelet & Salthouse, 2011; Terracciano et al., 2017). Our results also expand on the previous literature by showing that the link between neuroticism and cognitive problems is maintained when using the AD8 as a measure of cognitive problems.

Additionally, we found that health behaviors, specifically those included on the wellness maintenance subscale of the HBC, mediate the relationship between neuroticism and cognitive problems. Wellness maintenance items (such as “I exercise to stay healthy” and “I watch my weight”) predicted informant reported cognitive problems. This was the only HBC subscale that independently predicted subsequent informant reported cognitive problems. Subscales tapping accident control, traffic risk, and substance use were not significantly associated with risk for cognitive problems. This is one of the first studies to compare different facets of health behaviors in their prediction of cognitive problems, and our findings can be used to target health behavior interventions more narrowly on the wellness maintenance components. Moreover, the current study found that only the wellness maintenance category mediated the relationship between neuroticism and informant reported cognitive problems. It should also be noted, however, that a main effect between neuroticism and cognitive problems remains even when accounting for health behaviors. Therefore, the relationship between cognitive problems and neuroticism is partially, but not fully, attributable to the wellness maintenance aspect of health behaviors.

In contrast to findings reported in the previous literature (Chapman et al., 2012; Curtis, Windsor, & Soubelet, 2015; Gregory, Nettelbeck, & Wilson, 2010; Low, Harrison, & Lackersteen, 2013; Soubelet & Salthouse, 2011; Terracciano et al., 2017), we did not find significant relationships between cognitive problems and either conscientiousness or openness. Inconsistences between our results and those from other studies may, of course, be related to differences among studies in terms of sampling methods, demographic characteristics of the participants (e.g., age, sex, and race), or methods used to measure personality. Another possibility is that conscientiousness and openness show no association with cognitive problems when using an informant report measure. Indeed, one direction for future research is to reconcile the different findings across studies in the literature, with respect to how personality differs in its predictive value depending on the outcome of interest. That said, the current findings are aligned with previous work in suggesting that neuroticism appears the most consistent predictor of proxy-reported symptoms of cognitive problems (Sutin, Stephan, Luchetti, & Terracciano, 2019); indeed, although that work also found associations with conscientiousness, only neuroticism was associated with later risk across seven different symptoms of dementia. Furthermore, in line with past work (Bogg & Roberts, 2004), we did find a significant relationship between conscientiousness and all four categories of health behaviors. Openness was also related to three out of four categories of health behaviors. These associations add to our understanding of why these traits appear to lead adults to longer lives (e.g., Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007; Turiano, Spiro, & Mroczek, 2012), but limited evidence was found for their value in explaining associations with cognitive problems.

Surprisingly, we did not find any support for a relationship between social engagement and informant reported cognitive problems. The assessment of social engagement is a complex issue. In our study, we focused on the social and leisure subscale of the SAS-SR. In particular, we used those items that tapped level of social interaction (e.g., “How often in the past 2 weeks have you gone out socially with other people?”). We excluded some items which are focused overtly on personality rather than amount of social contact with others (e.g., “How often have you felt shy or uncomfortable with people in the past 2 weeks?”). Inconsistences between our results and those from other studies may be related to differences in the way that social engagement was measured. In further pursuit of these issues, it will be valuable to consider subjective elements of the social environment, such as loneliness and social isolation, which have been shown to predict non-normative cognitive decline (Cacioppo & Hawkley, 2009; Wilson et al., 2007). Loneliness has been shown to be largely independent of the actual quantity of interaction with other people. Furthermore, consideration of these issues could better help explain the role of neuroticism in relation to cognitive problems. Indeed, it has been suggested that social isolation influences cognitive decline in part due to increased negativity and depressive thoughts (Cacioppo & Hawkley, 2009), which are hallmarks of neuroticism (John & Srivastava, 1999).

These data may be useful in the process of designing preventative interventions. In line with personalized treatments, and the notion that personality could be used to help target samples for efforts to change (Hagger-Johnson & Whiteman, 2008), the current study would suggest targeting interventions that promote health behaviors towards people who score high in neuroticism. For example, providers may need to engage in motivational interviewing techniques when making recommendations for patients who are high on neuroticism, to enhance follow-through on wellness maintenance behaviors such as exercise. That said, neuroticism also retained a main effect on cognitive problems after controlling for health behaviors. This suggests the need both to find alternative mechanisms that explain the influence of neuroticism, and perhaps to develop interventions aimed at personality change. As noted earlier, it is worth considering negative social variables as another explanatory mechanism, as well as whether decreased cognitive engagement may play a role, as neuroticism has been associated with reduced daily engagement in intellectual activities (Aschwanden, Luchetti, & Allemand, 2019). With respect to personality interventions, meta-analytic research has shown that interventions aimed to alter personality traits are possible, and that neuroticism may be the Big Five trait for which interventions hold the most promise, particularly more clinically-focused programs (Roberts et al., 2017). Though this route is clearly more difficult, it also may lead to significant benefits given that changes in neuroticism are predictive of mortality risk in older adulthood (Mroczek & Spiro, 2007).

Limitations and Future Directions

These findings are not without their limitations, which can help inform future research. First, though using informant report as a measurement was a novel addition to the literature and has clear benefits, it also does not provide an objective or clinician-based measure for cognitive functioning. Unfortunately, the data set we used did not include any cognitive screening measures that would yield a diagnosis of cognitive impairment. It did include an immediate recall test which has been described in other work focusing on the relationship between the pathological extremes of personality and cognitive problems (Author Citation, under review). On the recommendation of a reviewer, we conducted exploratory analyses with memory performance as the outcome and found the same significant mediational pathway from neuroticism through wellness behaviors1. Future research should consider how personality associations with cognitive problems differ when employing self-report, observer report, and laboratory measures of cognitive problems. Studies that validate observer report by comparing it to objective measures of cognitive impairment would be particularly useful. Second, it would be valuable to supplement the current data with observer reports of health behavior, given the propensity for self-report biases. Third, the current study was limited in its employment of only a single assessment of cognitive functioning; however, the AD8 is a difficult measure to use as a repeated assessment of cognitive difficulties, given that the framing of questions involves asking whether there have been significant changes over the past several years. In repeated assessments, an observer’s report of “no change” could be interpreted as cognitive maintenance or continued issues depending on how the observer interprets the question framing. There is also the possibility of reverse causality; cognitive problems prior to wave 1 may lead to higher neuroticism (Islam et al., 2019; Terracciano et al., 2018). More research should examine the reciprocal relationship. Yet another limitation is the small effect size and the lack of significance when accounting for the covariates. Lastly, the study assessed personality and health behaviors concurrently at wave 1, but ideally, these would be captured at different timepoints to avoid biases and better clarify directionality. Future research should attempt to assess cognitive change over time and may wish to consider adapting the text to better capture observer reports from the previous measurement occasion.

Our current study reinforces findings from previous research that show a link between neuroticism and cognitive problems. More importantly, the data help explain the nature of this relationship: health behaviors—specifically wellness maintenance—explain some, but not all, of the relationship between neuroticism and cognitive problems. This information can be utilized to develop more effective preventative interventions. Specifically, interventions can be targeted at people who are high in neuroticism to increase their wellness maintenance. Alternatively, they can be aimed at decreasing neuroticism in older adults before it has the opportunity to manifest in worse health behaviors. Additionally, our results should encourage future research to understand what accounts for the rest of the relationship between neuroticism and cognitive impairment.

Supplementary Material

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Footnotes

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These results are available upon request from the author.

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