Personality and the Leading Behavioral Contributors of Mortality

Abstract

Objective:
Personality traits predict both health behaviors and mortality risk across the life course. However, there are few investigations that have examined these effects in a single study. Thus, there are limitations in assessing if health behaviors explain why personality predicts health and longevity.

Method:
Utilizing 14-year mortality data from a national sample of over 6,000 adults from the Midlife in the United States Study, we tested whether alcohol use, smoking behavior, and waist circumference mediated the personality-mortality association.

Results:
After adjusting for demographic variables, higher levels of Conscientiousness predicted a 13% reduction in mortality risk over the follow-up. Structural equation models provided evidence that heavy drinking, smoking, and greater waist circumference significantly mediated the Conscientiousness-mortality association by 42%.

Conclusion:
The current study provided empirical support for the health-behavior model of personality-Conscientiousness influences the behaviors persons engage in and these behaviors affect the likelihood of poor health outcomes. Findings highlight the usefulness of assessing mediation in a structural equation modeling framework when testing proportional hazards. In addition, the current findings add to the growing literature that personality traits can be used to identify those at risk for engaging in behaviors that deteriorate health and shorten the life span.

Full text

Health Psychology
Personality and the Leading Behavioral Contributors of
Mortality
Nicholas A. Turiano, Benjamin P. Chapman, Tara L. Gruenewald, and Daniel K. Mroczek
Online First Publication, December 23, 2013. doi: 10.1037/hea0000038
CITATION
Turiano, N. A., Chapman, B. P., Gruenewald, T. L., & Mroczek, D. K. (2013, December 23).
Personality and the Leading Behavioral Contributors of Mortality. Health Psychology.
Advance online publication. doi: 10.1037/hea0000038

Personality and the Leading Behavioral Contributors of Mortality
Nicholas A. Turiano
University of Rochester School of Medicine and Dentistry and
Center on Aging and the Life Course, West Lafayette, Indiana
Benjamin P. Chapman
University of Rochester School of Medicine and Dentistry
Tara L. Gruenewald
University of Southern California
Daniel K. Mroczek
Northwestern University
Objective: Personality traits predict both health behaviors and mortality risk across the life course.
However, there are few investigations that have examined these effects in a single study. Thus, there are
limitations in assessing if health behaviors explain why personality predicts health and longevity.
Method: Utilizing 14-year mortality data from a national sample of over 6,000 adults from the Midlife
in the United States Study, we tested whether alcohol use, smoking behavior, and waist circumference
mediated the personality–mortality association. Results: After adjusting for demographic variables,
higher levels of Conscientiousness predicted a 13% reduction in mortality risk over the follow-up.
Structural equation models provided evidence that heavy drinking, smoking, and greater waist circum-
ference significantly mediated the Conscientiousness–mortality association by 42%. Conclusion: The
current study provided empirical support for the health-behavior model of personality—
Conscientiousness influences the behaviors persons engage in and these behaviors affect the likelihood
of poor health outcomes. Findings highlight the usefulness of assessing mediation in a structural equation
modeling framework when testing proportional hazards. In addition, the current findings add to the
growing literature that personality traits can be used to identify those at risk for engaging in behaviors
that deteriorate health and shorten the life span.
Keywords: Big Five, personality, health-behavior model, mediation, mortality
Supplemental materials: http://dx.doi.org/10.1037/hea0000038.supp
Individual differences in personality traits have emerged as
important influences on disease and comorbidity over the life
course, as well as longevity (Hampson & Friedman, 2008). For
example, higher levels of Conscientiousness predict greater lon-
gevity (Kern & Friedman, 2008). With these associations now
replicated across diverse samples, the more important question is
no longer what traits predict longevity, but why do personality
traits predict how long someone lives? In other words, what are the
mediating pathways connecting personality to mortality risk? The
current study examined this question by testing whether three
health-related behaviors recorded over a 14-year period would
explain why personality predicts mortality.
Most investigations of the Big Five personality traits (i.e., Neu-
roticism, Extraversion, Openness to experience, Agreeableness,
Conscientiousness) focus on Conscientiousness, which reflects the
propensity to be goal-directed, responsible, and in control of
impulses, because of the consistent finding that higher levels of
Conscientiousness confer a protective effect against earlier mor-
tality. This effect has been found in diverse samples in terms of
age, sex, health status, and country of origin, and has been con-
firmed through several meta-analyses (r⫽.11, range ⫽0.01–
0.38; Kern, & Friedman, 2008; Jokela et al., 2013).
Findings for Neuroticism (e.g., frequent experience of negative
emotions and emotional instability) are mixed. Some find a positive
association with mortality risk (Christensen et al., 2002; Denollet,
Sys, & Brutsaert, 1995; Mroczek, Spiro, & Turiano, 2009; Ploubidis
& Grundy, 2009; Shipley, Weiss, Der, Taylor, & Deary, 2007; Ter-
racciano, Löckenhoff, Zonderman, Ferrucci, & Costa, 2008; Wilson,
Mendes de Leon, Bienas, Evans, & Bennett, 2004), others find a
negative association (Korten et al., 1999; Weiss & Costa, 2005), and
some report no association (Almada et al., 1991; Friedman et al.,
1995; Iwasa et al., 2008; Maier & Smith, 1999).
Nicholas A. Turiano, Department of Psychiatry, University of Rochester
School of Medicine and Dentistry and Center on Aging and the Life
Course, West Lafayette, Indiana; Benjamin P. Chapman, Department of
Psychiatry, University of Rochester School of Medicine and Dentistry;
Tara L. Gruenewald, Davis School of Gerontology, University of Southern
California; Daniel K. Mroczek, Department of Psychology, Weinberg
College of Arts & Sciences and Department of Medical Social Sciences,
Feinberg School of Medicine, Northwestern University.
This research was supported by Grant T32-MH018911-23 from the
United States Department of Health and Human Services, National Insti-
tutes of Health (NIH), National Institute of Mental Health and Grants T32
AG025671-02, P01 AG20166, and K08 AG031328 from the NIH National
Institute on Aging.
Correspondence concerning this article should be addressed to Nicholas
A. Turiano, 300 Crittenden Blvd., Box PSYCH, Rochester, NY 14642-
8409. E-mail: Nicholas_Turiano@urmc.rochester.edu
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2013, Vol. 32, No. 12, 000 0278-6133/13/$12.00 http://dx.doi.org/10.1037/hea0000038
1

Meta-analyses have confirmed one particular aspect of Agree-
ableness (the tendency to be hostile and aggressive or not) predicts
increased mortality risk (Miller, Smith, Turner, Guijarro, & Hallet,
1996), but studies employing a more comprehensive measure of
Agreeableness, for the most part, have not found an association
(Weiss & Costa, 2005). Few studies have found a positive asso-
ciation between longevity and Extraversion (inclination to be
outgoing, expressive, and sociable; Ploubidis & Grundy, 2009;
Wilson et al., 2004). Last, earlier investigations focusing on Open-
ness to experience (the tendency to be imaginative and creative)
have found that it is generally unrelated to health and longevity
(Christensen et al., 2002; Maier & Smith, 1999; Mccann, 2005;
Weiss & Costa, 2005; Wilson et al., 2004), but more recent
investigations have suggested a protective effect of Openness and
related facets (Iwasa et al., 2008; Taylor et al., 2009; Turiano,
Spiro, & Mroczek, 2012).
With ample evidence accumulating, it is important to identify
the mechanisms through which personality traits are linked to
mortality. The health-behavior model (HBM) of personality is the
leading behavioral theory that suggests levels of certain personal-
ity traits are associated with either engagement or abstinence of
certain health behaviors that ultimately impact health over the life
course (Friedman, 2000; Smith, 2006). Support for this hypothesis
is found in observations that lower levels of Conscientiousness and
higher levels of Neuroticism have each been linked to negative
behaviors, such as smoking tobacco, excessive alcohol use, illicit
drug use, and unhealthy eating habits (Bogg & Roberts, 2004;
Hopwood et al., 2007; Kashdan, Vetter, & Collins, 2005; Malouff,
Thorsteinsson, & Schutte, 2006; Mroczek et al., 2009; Munafò,
Zetteler, & Clark, 2007; Terracciano, Löckenhoff, Crum, Bien-
venu, & Costa, 2008). These unhealthy behaviors are also among
the leading behavioral causes of mortality (United States Depart-
ment of Health & Human Services, 2013; Mokdad, Marks, Stroup,
& Gerberding, 2004). One would expect these behaviors to explain
a significant portion of variance in the personality–mortality asso-
ciation, however, there has been little explicit investigating of
these pathways, let alone support for a strong mediating role in the
handful of studies that have examined such links.
One of the main obstacles to testing mechanisms is methodolog-
ical in nature because well-established tests of mediation, (e.g.,
causal steps approach, Sobel test) are appropriate for either con-
tinuous or categorical outcomes. However, when an outcome such
as mortality includes both discrete (e.g., dead or alive) and con-
tinuous (e.g., survival time) information, these types of modeling
approaches are not as straightforward. Thus, in reviewing prior
studies, it is difficult to determine whether including health-related
behaviors in models does in fact explain why certain traits are
predictive of mortality risk (see Appendix in the online supple-
mental materials for review of prior studies). Some previous stud-
ies did not explicitly examine potential behavioral mediation of the
personality–mortality association because that was not the aim of
that particular study, even though relevant behaviors were included
in it. Other investigations have set out to examine particular
behavioral mediators, but without providing both unadjusted and
health-behavior-adjusted models, it becomes difficult to determine
if adjusting for health behaviors does reduce the personality–
mortality association (Korten et al., 1999; Weiss & Costa, 2005;
Jonassaint et al., 2007). Likewise, studies that do compare multiple
models but include both health behaviors and other factors related
to health behaviors (e.g., education, self-rated health) but also
influencing outcomes through other pathways (e.g., access to
health care, environmental constraints) limits the ability to partial
out the unique effects of just health behaviors (Martin, Friedman,
& Schwartz, 2007; Kern, Friedman, Martin, Reynolds, & Luong,
2009). The studies that have examined health behaviors in a
separate model have found generally low reductions of the per-
sonality effects on mortality (e.g., 0% to 26% reductions; see
Appendix in the online supplemental materials). Adjusting for
health behaviors explains on average 12% (0% to 21% range) of
the variance in the Conscientiousness–mortality association and
13% (0% to 26% range) of the variance in the Neuroticism–
mortality association, with some evidence that smoking (Mroczek
et al., 2009) and physical activity (Shipley et al., 2007) may be the
stronger pathways accounting for the personality–mortality asso-
ciation.
A second major limitation with earlier work is that, although a
reduction in personality–mortality association after adjusting for
health behaviors suggests mediation, no statistical tests of media-
tion were conducted. Taylor et al., (2009) formally tested media-
tion through structural equation modeling with a discrete outcome
(e.g., dead/alive), but found no evidence to support the role of
smoking and body-mass index (BMI) as pathways between Open-
ness and Conscientiousness with mortality risk. However, analysis
of a discrete outcome along with continuous information (e.g.,
length of life) may provide greater power to examine the potential
mediating role of health behaviors in these associations. Recently
developed methods have extended the use of proportional hazards
in structural equation modeling frameworks (Asparouhov, Masyn,
& Muthén, 2006; Muthén & Masyn, 2005). Ploubidis and Grundy
(2009) were among the first to apply this modeling technique in a
large UK sample by calculating the predictive effects for each
personality trait on each of the mediators (e.g., alcohol use, smok-
ing, psychological distress, and somatic health) and the effect of
each mediator on mortality risk, which ultimately allowed them to
estimate statistical significance tests for each indirect effect. The
authors found that higher Neuroticism was associated with in-
creased risk of dying largely through psychological distress and
somatic health, but to a lesser extent by smoking. Higher Extra-
version was associated with an increased risk of dying, which was
partially explained by higher prevalence of smoking.
This earlier study laid the methodological groundwork for our
own work, documenting a significant indirect effect through smok-
ing, which explained approximately 11% of why lower levels of
Conscientiousness predicted an increased mortality risk in a sam-
ple of older men from the Normative Aging Study (Turiano, Hill,
Roberts, Spiro, & Mroczek, 2012). However, to date, this meth-
odology has not been utilized in a large U.S. sample with multiple
behaviors being tested as possible mediators of the personality–
mortality association.
Study Aims
The overall objective of the current study was to further our
understanding of the personality–mortality link by assessing if
health behaviors statistically mediated this association. Our first
goal was to extend findings of an earlier investigation (Chapman,
Fiscella, Kawachi, & Duberstein, 2010) of the personality–
mortality in the National Survey of Midlife in the United States
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2TURIANO, CHAPMAN, GRUENEWALD, AND MROCZEK

(MIDUS). This previous study found that higher levels of Neurot-
icism predicted increased odds of dying over the first 10 years of
the MIDUS study: 179 confirmed deaths reported by the National
Death Index (NDI) search as of 2004, whereas Agreeableness was
associated with an increased risk of dying when Conscientiousness
levels were low. The current study extends the mortality follow-up
to a 14-year period (580 confirmed deaths through the 2009 NDI
mortality update) as well as survival-time information so that both
death status (e.g., dead or alive) and survival time (e.g., how long
someone lived) could be jointly estimated in a proportional haz-
ards modeling framework.
Our second goal was to test whether age and sex emerged as
potential moderating factors of the personality–mortality associa-
tion. Although findings are by no means consistent, emerging
evidence does suggest that associations between personality traits
and mortality may differ between men and women and in some
cases may be in opposite directions (Friedman, Kern, & Reynolds,
2010; Korten et al., 1999; Taylor et al., 2009; Ploubidis & Grundy,
2009). For example, higher levels of Neuroticism may predict an
increased mortality risk in women, but a decreased risk for men.
We also tested trait by trait interactions because of a previous
Conscientiousness ⫻Agreeableness interaction predicting mortal-
ity in the MIDUS study (Chapman, Fiscella, Kawachi, & Duber-
stein, 2010) and mounting evidence that a Conscientiousness ⫻
Neuroticism interaction is an important predictor of both mortality
(Friedman et al., 2010) and other important health outcomes (Tu-
riano, Mroczek, Moynihan, & Chapman, 2013).
Our third goal was to formally test if health behaviors did
mediate the personality–mortality association by testing propor-
tional hazards in a structural equation modeling framework. We
examined whether alcohol and tobacco use, and waist circumfer-
ence (a proxy for both diet and exercise behavior) were mediators
because these are among the leading behavioral contributors to
mortality (Mokdad et al., 2004). This modeling technique allows
us to simultaneously test multiple mediating pathways which is
essential to ensure confounded mediation is avoided (Babyak,
2009) because there are likely multiple behavioral pathways that
explain the personality-health association (Hampson & Friedman,
2008). Overall, the large national sample and updated mortality
information will allow greater generalizability of study findings
and the long follow-up duration will permit a more extensive test
of how the Big Five personality traits are related to longevity.
Method
Sample
The MIDUS is an interdisciplinary longitudinal study examin-
ing midlife development (for review, see Brim, Ryff, & Kessler,
2004). Over 7,000 participants were recruited in 1994 –1996 (N⫽
7,108) from a nationally representative random-digit-dialing sam-
ple of noninstitutionalized adults between the ages 25–75. Once
potential participants consented to the study, they completed an
approximate 30-min telephone survey and were mailed additional
questionnaires. These self-completed questionnaires took approx-
imately 2 hr to complete and were sent back to the study team
when completed. If surveys were not returned, participants were
contacted and sent new questionnaires. The current sample drew
from the 6,325 participants who completed both the phone and the
self-administered questionnaires at MIDUS 1 in 1995–1996. To be
included in the current analysis, participants needed to complete
the following measures: demographics such as age, sex, race,
marital status, and education; the Big Five personality question-
naire; and questions regarding alcohol, smoking, and body indices.
Comparing those with full versus incomplete MIDUS 1 data,
participants completing only the telephone questionnaire (N⫽
783), were significantly younger (t⫽10.19, p⬍.001), had higher
average levels of alcohol use (t⫽6.13, p⬍.001), and were
slightly more likely to be male (␹
2
⫽17.03, p⬍.001).
Covariates
All models were adjusted for age, sex, race, marital status, and
education since these variables have known associations with
mortality risk. Education was coded based on the highest level
obtained as of 1995–1996. A 12-point scale was constructed rang-
ing from 1, no schooling or some grade school, to 12, professional
degrees such as PhD or MD. A dummy code was constructed for
both race and marital status. Caucasians were contrasted against all
other races, and those married were contrasted against those who
were unmarried. Descriptive information on the demographic vari-
ables can be found in Table 1.
Personality
The key predictor variables were assessed via the self-
administered adjectival measures of the Big Five examined at
MIDUS 1 (Prenda & Lachman, 2001). Respondents were asked
how much each of 25 adjectives described themselves on a scale
ranging from 1 (not at all)to4(a lot). The adjectives were as
follows: moody, worrying, nervous, calm (Neuroticism); outgoing,
friendly, lively, active, talkative (Extraversion); creative, imagina-
tive, intelligent, curious, broad-minded, sophisticated, adventurous
(Openness); organized, responsible, hardworking, careless (Con-
scientiousness); helpful, warm, caring, softhearted, sympathetic
(Agreeableness). A mean was calculated from the adjectives for
each trait, after reversing the appropriate items.
The MIDUS Big Five scale was developed from a combination
of existing personality-trait lists and inventories (for review, see
Lachman & Weaver, 1997). The scales have good construct va-
lidity (Mroczek & Kolarz, 1998) and all five traits significantly
correlated with the NEO trait scales (Prenda & Lachman, 2001).
Cronbach alphas for the personality traits were as follows: Agree-
ableness ⫽.80; Conscientiousness ⫽.58; Extraversion ⫽.76;
Neuroticism ⫽.74; Openness ⫽.77.
Alcohol Use
Participants answered questions about their alcohol use through
the phone questionnaire portion of MIDUS 1. Participants were
asked the following question: “During the year you drank most,
about how many drinks would you usually have on the days that
you drank?” Because there were a handful of participants who
reported extremely high alcohol intake, we Winsorized the top 1%
of responses and set them equal to 30 drinks. In addition, based on
prior findings of a “j”-shaped relationship with alcohol use and
mortality risk (Di Castelnuovo et al., 2006), we created three
groups between which to distinguish: (a) nondrinkers (11%), (b)
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3
PERSONALITY, BEHAVIOR, AND MORTALITY

average drinkers (41%; drinking 1–2 alcoholic beverages per
drinking occasion), and (c) heavier drinkers (48%; averaging 3 or
more alcoholic drinks per drinking occasion). Dummy codes were
created to contrast average drinkers (referent) from nondrinkers
and heavy drinkers.
Smoking
Participants answered a series of questions about whether they
had ever had a cigarette, ever smoked regularly (at least a few
cigarettes every day), if they were currently smoking, or if they
had quit smoking. We were able to create three groups from this
series of questions: (a) those who never smoked during their lives;
(b) those who had smoked but were currently not smoking as of
1995–1996; and (c) those who were currently smoking cigarettes
in 1995–1996.
Waist Circumference
Waist circumference was utilized as a proxy variable for both
eating and exercise habits because it is a strong predictor of
mortality risk (Bigaard et al., 2005; Jacobs et al., 2010). Partici-
pants were asked to measure the circumference of their waists in
inches using a tape measure mailed to them along with the MIDUS
self-administered questionnaire.
Vital Status
Mortality data on participants was obtained through an NDI
search through January, 2010. Survival time for decedents was the
interval from the date of MIDUS 1 completion (1995–1996) to the
date of their death (censor date January 15, 2010). Since only
month and year of death were provided by NDI, every deceased
participant was given the 15th day of the month as his or her day
of death. Participants who were still alive (censored observations)
had survival times that equaled the length of the follow-up (cen-
sored January 15, 2010). The mean survival time for decedents
was 8.01 years (SD ⫽3.90, range ⫽.20 –14 years).
Data Analysis
A series of proportional hazards models (i.e., Cox models) was
conducted to examine the association between the Big Five per-
sonality traits and mortality risk using Mplus Version 6.0 software
(Muthén & Muthén, 1998 –2010). Proportional hazards modeling
is the most appropriate method when examining mortality as an
outcome because it takes into account continuous survival times,
varying ages at entry into the study, and occurrence of a discrete
outcome event (Cox, 1972). Cox models yield estimates (hazard
ratios; HRs) of how much a standard deviation increase in a
predictor increases or decreases the chances of dying over the
specified follow-up period. All predictors were converted into
standard deviation units for ease of interpretation.
Estimating mediation in proportional hazards modeling through
an SEM framework is an ideal approach because it allows for an
assessment of both the direct and indirect effects on continuous
survival time (Asparouhov et al., 2006). With a maximum likeli-
hood robust (MLR) estimator and Monte Carlo integration, the
program calculates indirect effects comparable to the Sobel
method. Specifically, a product-of-coefficients approach computes
the ratio of the path from the predictor to the mediator and the path
from the mediator to the outcome to its standard error. We consider
it important that this technique provides standard errors, confi-
Table 1
Descriptive Statistics
Variables
Deceased (n⫽580) Alive (n⫽5,745) Total sample (N⫽6,325)
Mean (SD) or % Mean (SD) or % Mean (SD) or % Range
Age 59.90 (11.02) 45.03 (12.40) 46.38 (13.00) 20–75
Education 6.04 (2.52) 6.85 (2.48) 6.77 (5.76) 1–12
Sex
Female 46% 52% 52% 0–1
Male 54% 48% 48% 0–1
Race
Caucasian 91% 91% 91% 0–1
Other 9% 9% 9% 0–1
Marital status
Married 58% 66% 66% 0–1
Unmarried 42% 34% 34% 0–1
Conscientiousness 3.37 (0.45) 3.43 (0.44) 3.42 (0.44) 1–4
Neuroticism 2.17 (0.66) 2.25 (0.66) 2.24 (0.66) 1–4
Extraversion 3.17 (0.58) 3.20 (0.56) 3.20 (0.56) 1–4
Agreeableness 3.53 (0.47) 3.49 (0.49) 3.50 (0.49) 1–4
Openness 2.98 (0.55) 3.02 (0.52) 3.02 (0.53) 1–4
Smoking status
Never 27% 44% 43% 1–3
Former 34% 25% 25% 1–3
Current 39% 31% 32% 1–3
Years smoked 23.33 (18.86) 9.63 (12.82) 10.87 (14.04) 0–61
Alcohol use 3.56 (4.75) 3.23 (3.60) 3.26 (3.72) 0–30
Med (IQR) 2 (1–4) 2 (1–4) 2 (1–4)
Waist circumference 37.36 (5.95) 35.20 (5.07) 35.40 (5.76) 14–66
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4TURIANO, CHAPMAN, GRUENEWALD, AND MROCZEK

dence intervals, and significance tests. With respect to the latter,
the significance test of the indirect effect permits the statistical
interpretation of mediation.
Model 1 tested the baseline-unadjusted model that includes all
of the Big Five personality traits. Two-way interactions among
personality traits, sex, and age were tested as potential moderators
of the personality–mortality association. All interactions were
screened in the baseline model and if any were significant, they
would be included in subsequent models. Model 2 included de-
mographic variables considered confounds which included age,
sex, race, education, and marital status. The final fully adjusted
path model included each of the mediators in a single model. It is
essential to include all four mediators in the same model for two
main reasons: (a) the significance of the total indirect effect (sum
of all specific indirect effects) determines whether the full set of
variables together explain the personality–mortality association
and (b) it allows an experimenter to determine whether specific
indirect effects are a significant net of the effect of other media-
tors; in other words, it takes into account the correlation between
multiple indirect effects (Preacher & Hayes, 2008).
Results
Descriptive data for the full sample, for those deceased, and for
those alive are presented in Table 1. Over the 14-year follow-up,
580 participants died (approximately 9% of the sample). Testing
mean differences among those who survived versus those who
died over follow-up showed that the deceased were significantly
older (t⫽29.28, p⬍.001), more likely to be male (␹
2
⫽9.82, p⬍
.001), less educated (t⫽7.88, p⬍.001), and less likely to be
married (␹
2
⫽19.16, p⬍.001). In terms of personality traits,
those who died during the follow-up period were significantly
lower in Conscientiousness (t⫽3.04, p⬍.01) and lower on
Neuroticism (t⫽2.79, p⬍.01).
Correlations between each of the mediator variables revealed
modest, but significant, positive associations. Specifically, alcohol
use was positively related to both smoking (r⫽.20), and waist
circumference (r⫽.11). Smoking was positively associated with
waist circumference (r⫽.10). Those who died during the
follow-up period reported higher average alcohol consumption in
the past (t⫽2.16, p⬍.05), had greater waist circumference (t⫽
8.46, p⬍.001), and were more likely to be former (␹
2
⫽14.35,
p⬍.01) or current (␹
2
⫽44.40, p⬍.01) smokers, compared with
those who never smoked.
The results for both the baseline and demographic adjusted
models are reported in Table 2. According to Model 1, which
included all the Big Five personality factors, higher levels of
Conscientiousness and Neuroticism were related to a decreased
hazard of dying, whereas higher Agreeableness was associated
with an increased hazard of dying. There was a trend for higher
levels of Extraversion to protect against mortality risk. Openness
was not a significant predictor of mortality hazard.
Model 2 adjusted for demographic variables (age, sex, race,
education, and marital status). Increasing age, being male, being
unmarried, and having fewer years of education were all signifi-
cantly associated with an increased hazard of dying over the
14-year follow-up. After adjusting for these demographic con-
founders, the Conscientiousness effect remained significant, albeit
reduced in magnitude by about 19%. Specifically, every standard
deviation increase in Conscientiousness was associated with a 13%
decreased hazard of dying over the 14-year follow-up. The
strength of the effects for Neuroticism and Agreeableness were
substantially reduced (100% and 63% respectively) and rendered
nonsignificant after adjusting for the demographic confounds. Sen-
sitivity analyses indicated that age was the specific factor that
accounted for the relationships of Neuroticism and Agreeableness
with mortality risk. The Extraversion effect did not change (re-
mained a trend). None of the two-way interactions among traits
with age or sex approached statistical significance.
Test of Mediation
Since Conscientiousness was the only personality trait that
predicted mortality after adjusting for demographic factors, all
other traits were dropped from the mediation analysis. Figure 1
displays the fully adjusted path model that tests the indirect effects
between Conscientiousness and mortality through each of the
mediators. Although not shown on the path diagram, the model
adjusts for age, sex, race, education, and marital status; for ease of
interpreting the direction of effects, Conscientiousness scores were
reverse-coded so higher scores reflected those scoring lower in
Conscientiousness. The total effect of Conscientiousness on mor-
tality (sum of paths [a1
ⴱ
b1], [a2
ⴱ
b2], [a3
ⴱ
b3], [a4
ⴱ
b4], [a5
ⴱ
b5], [c])
was significant. The full indirect effect (sum of paths [a1
ⴱ
b1],
[a2
ⴱ
b2], [a3
ⴱ
b3], [a4
ⴱ
b4], [a5
ⴱ
b5]) was also significant, suggesting
that Conscientiousness predicted mortality risk through the set of
mediators tested. Examination of the indirect effects for each
specific mediator revealed that Conscientiousness predicted mor-
tality risk through previous alcohol use (sum of paths [a1
ⴱ
b1],
[a2
ⴱ
b2]), smoking status
1
(sum of paths [a3
ⴱ
b3], [a4
ⴱ
b4]), and
1
Sensitivity analyses indicated that the findings were similar when
either the number of years someone smoked, or the average number of
cigarettes smoked in a given day during a year in life the person smoked
most heavily were used instead of smoking category as mediators.
Table 2
Baseline Proportional Hazards Models Predicting Mortality
Predictors
Model 1 Model 2
Hazard ratio
[95% CI]
Hazard ratio
[95% CI]
Conscientiousness 0.84 [0.77, 0.92]
ⴱⴱⴱ
0.88 [0.80, 0.96]
ⴱⴱ
Neuroticism 0.84 [0.77, 0.91]
ⴱⴱⴱ
0.99 [0.91, 1.09]
Extraversion 0.90 [0.81, 1.00]
ⴱ
0.90 [0.81, 1.01]
ⴱ
Agreeableness 1.24 [1.12, 1.38]
ⴱⴱⴱ
1.11 [0.99, 1.24]
ⴱ
Openness 0.93 [0.85, 1.03] 1.09 [0.99, 1.22]
ⴱ
Age 3.27 [2.96, 3.62]
ⴱⴱⴱ
Sex (Male) 1.59 [1.33, 1.90]
ⴱⴱⴱ
Minority status (Caucasian) 1.09 [0.80, 1.59]
Marital status (unmarried) 1.61 [1.35, 1.92]
ⴱⴱⴱ
Education 0.82 [0.75, 0.90]
ⴱⴱⴱ
AIC 100,020.49 8,959.16
Note. AIC ⫽Akaike Information Criterion.
ⴱ
p⬍.10.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001.
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5
PERSONALITY, BEHAVIOR, AND MORTALITY

waist circumference
2
(path [a5
ⴱ
b5]. Lower levels of Conscien-
tiousness predicted heavier average alcohol use in the past, greater
odds of being a former or current smoker, and a greater waist
circumference—which led to an increased hazard of dying over the
14-year follow-up. Examination of the total indirect effect revealed
that this set of health behaviors significantly mediated the
Conscientiousness–mortality effect, explaining 42% of this asso-
ciation according to the mediated proportion (Ditlevsen, Chris-
tensen, Lynch, Damsgaard, & Keiding, 2005; Kaufman, Ma-
cLehose, Kaufman, & Greenland, 2005; MacKinnon & Fairchild,
2009).
Discussion
The current study extended earlier work examining the associ-
ations between each of the Big Five personality traits and mortality
risk in a national U.S. sample of adults aged 25–74. We did not
replicate earlier findings in the MIDUS of a positive association
between Neuroticism and mortality risk, nor a positive association
between Agreeableness and mortality risk in the context of low
Conscientiousness (Chapman et al., 2010). However, examining a
longer follow-up period and time to mortality occurrence, we
found that higher levels of Conscientiousness predicted a de-
creased hazard of dying. In addition, using a parsimonious test of
mediation we found that the reason those participants who scored
lower in Conscientiousness were more likely to die was partly
because they had greater levels of central adiposity, engaged in
higher average levels of alcohol use during their lives, and were
more likely to have smoked during their lives. These results not
only provide direct support of the HBM of personality for Con-
scientiousness, but they also highlight the efficacy of using per-
sonality traits as predictors of identifying individuals at risk of
engaging in unhealthy behaviors that ultimately contribute to ear-
lier mortality.
The general consensus from prior studies investigating the
personality–mortality association was that health-related behaviors
such as smoking and alcohol use only slightly attenuated (if at all)
the relationship between Conscientiousness and mortality
(Hagger-Johnson et al., 2012; Martin et al., 2007; Taylor et al.,
2009; Terracciano, Löckenhoff, Zonderman, et al., 2008; Wilson et
2
Sensitivity analyses indicated that the findings were similar when a
measure of BMI was used instead of waist circumference.
a1
096(088
1 04)
b1
Non-Alcohol User
Heavy Alcohol
0
.
96
(0
.
88
-
1
.
04)
1.34 (1.04-1.75)*
Heavy
Alcohol
User
a2
1.17 (1.11-1.24)***
c
b
2
1.42 (1.16-1.75)***
Conscientiousness
Mortality Risk
c
1.07 (0.98-1.15)
a3 b3
Former Smoker
1.11 (1.05-1.08)*** 1.45 (1.16-1.81)***
a4
1.07 (1.00-1.18)*
b4
3.44 (2.74-4.33)***
Current Smoker
a5
0.12 (0.09- 0.14)***
b5
1.19 (1.08-1.31)***
Wai st
Circumference
Figure 1. Fully adjusted path model adjusting for age, sex, race, education, and marital status. Specific path
estimates with 95% CI limits are as follows: total effect (HR ⫽0.25, CI [0.13, 0.36]
ⴱⴱⴱ
); full indirect effect
(HR ⫽0.18, CI [0.09, 0.27]
ⴱⴱⴱ
); alcohol-use indirect effect (HR ⫽0.04, CI [0.01, 0.09]
ⴱ
); smoking indirect
effect ⫽(HR ⫽0.12, CI [0.04, 0.20]
ⴱⴱ
); waist-circumference indirect effect (HR ⫽0.02, CI [0.01, 0.03]
ⴱⴱⴱ
);
ⴱ
p⬍.05.
ⴱⴱ
p⬍.01.
ⴱⴱⴱ
p⬍.001. HR ⫽hazard ratio.
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6TURIANO, CHAPMAN, GRUENEWALD, AND MROCZEK

al., 2004). Across these prior studies, between 0 and 21% of the
variance in the Conscientiousness–mortality association was ac-
counted for by health behaviors. Counter to this prior work, our
study demonstrated that health-related behaviors explain a more
substantial amount of variance, roughly 42% of the
Conscientiousness–mortality association. Since the mediated pro-
portion effect was calculated exactly the same as in previous
studies, we can conclude that the larger amount of variance ex-
plained by health-related behaviors is not an artifact of the meth-
odology used in this study. Rather, the size of the mediating effect
is likely to be driven by the specific sample studied. We are unable
to discern from our current study exactly what is unique about the
MIDUS study population, but it is possible that the relationship
between personality and mortality is much stronger in some sam-
ples, leaving more variance to be explained by health behaviors.
Alternatively, certain samples on the whole may endorse lower
levels of Conscientiousness, thus resulting in a stronger association
between personality and detrimental health behaviors.
Another unique contribution of the current study was our ability
to use a more parsimonious test of mediation (Asparouhov et al.,
2006), allowing us to specifically identify how personality was
related to each mediator and how each mediator was related to
mortality. Moreover, instead of inferring whether these behaviors
did in fact explain a significant amount of variance in the
personality–mortality association, we were able to construct a
statistical significance test for mediation via indirect-effect esti-
mation for each behavioral mediator and the overall set of behav-
iors. Thus, we can conclude, statistically, that the mediated effect
was large enough to be considered statistically different than zero
(Kaufman et al., 2005; MacKinnon & Fairchild, 2009). Overall,
our study demonstrated the usefulness of statistical advances in
mediation analysis in understanding how individual differences in
personality traits lead to either engagement in health-detrimental
or health-protective behaviors, and how those behaviors influence
how long someone lives.
Although the current study did not find any evidence for age,
sex, or trait-by-trait interactions as moderators of the personality–
mortality association, future work should still examine such factors
as a standard model-fitting procedure. Testing and reporting such
interactions is useful because it is no longer safe to assume that
certain traits will be either protective or detrimental in all circum-
stances. We should continue to question under which conditions
(i.e., trait-by-trait interactions), for whom (i.e., males vs. females),
and at what times over the life course (i.e., age) are traits related
to health outcomes. This specificity will bolster the idea that
psychological factors such as personality are likely important
processes involved in determining health and longevity.
Limitations
Even in light of the innovative statistical methods and long-term
follow-up applied for the current study, there are qualifications that
must be discussed. Since the majority of participants in the
MIDUS sample were Caucasian and well-educated, generalizabil-
ity of study findings are limited. However, this study is one of the
first investigations utilizing a large U.S. sample to test the behav-
ioral pathways connecting personality to mortality. The study
could also be strengthened if we had information on the cause of
death. We might expect the relationship between Conscientious-
ness and mortality to be stronger for some specific causes of death
such as cardiovascular disease, given the role that tobacco, alcohol
use, and higher levels of central adiposity play in contributing to
the development of this condition (Lloyd-Jones et al., 2010).
There are also limitations with the measures of personality and
health behaviors. First, there was a relatively low internal consis-
tency of the Conscientiousness measure. This is due to the small
number of items used to assess this trait. A tradeoff of the short
Conscientiousness measure was to maximize coverage of the
broad trait without selecting multiple items with high redundancy,
which would have contributed to participant burden from the large
number and scope of questionnaires participants completed. De-
spite the moderate level of internal consistency, the Midlife De-
velopment Inventory scale of Conscientiousness has been shown
to have high test–retest reliability and good construct validity
(Mroczek & Kolarz, 1998) with high correlation (r⫽.81) with the
more expansive NEO personality measure (Lachman & Weaver,
1997). Second, although we examined some of the leading behav-
ioral contributors to early mortality (Mokdad et al., 2004; U.S.
Department of Health & Human Services, 2013), there are other
behaviors and physiological factors that could be examined as
mediating variables. For example, specific questions about diet,
exercise, and health-care utilization are just a few other behaviors
that could shed light on how personality is related to health.
Likewise, more detailed questions assessing both life-time and
current alcohol, tobacco, and drug-use behaviors would provide a
more nuanced analysis. More detailed measures of substance-
abuse patterns from ecological momentary assessment techniques
found in many daily diary studies could also provide more nuanced
information on substance-abuse patterns not captured in retrospec-
tive questionnaires (Shiffman, 2009). Even so, as measured, health
behaviors in the current study were associated with large hazard
ratios— demonstrating the long-term impact prior behavior has on
health and longevity.
It is also important to interpret the current study findings in light
of limitations with the temporal ordering of the variables mea-
sured. An optimal mediation study design assumes that individual
differences in personality traits cause certain behaviors, and that
these behaviors then lead to poorer health and an increased risk of
dying. However, establishing evidence for this causal ordering is
difficult to demonstrate and actually may be untestable in most
situations (MacKinnon, Fairchild, & Fritz, 2007). For example, in
the current study, the mediating variables of alcohol use and
smoking behavior were behaviors that occurred before the mea-
surement of personality—a common problem with the majority of
prior studies investigating these health-behavior pathways
(Hagger-Johnson et al., 2012; Martin et al., 2007; Taylor et al.,
2009; Terracciano, Löckenhoff, Zonderman, et al., 2008; Wilson et
al., 2004). This common limitation is compounded in the current
study because the MIDUS questionnaires did not record the actual
age when alcohol consumption was at its peak. But personality is
generally considered to be a relatively stable trait, lending confi-
dence that personality assessments measured at any given point in
adulthood reflect an enduring pattern of trait experience in previ-
ous phases of adulthood. However, there are known individual
differences in the stability of personality traits (Roberts, Walton, &
Viechtbauer, 2006). Some persons do change in personality and
these changes in personality can impact behavior (Turiano et al.,
2012) and health outcomes (Mroczek & Spiro, 2007). Thus, the
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7
PERSONALITY, BEHAVIOR, AND MORTALITY

assumption that later personality can be used as a proxy for
personality earlier in life may be tenuous.
An important objective of future research is to examine life
course patterns of personality and health behavior. Repeated as-
sessments of these constructs across adulthood are essential for
more carefully untangling the potential bidirectional relationships
between personality and health behavior and their influences on
health (Hampson, 2012). Our findings suggest that personality
may shape engagement in certain forms of health-damaging be-
haviors. However, we also know that engagement in some detri-
mental health behaviors, like smoking, may also shape normative
changes in personality (Welch & Poulton, 2009). Also, poor health
may affect trajectories of personality development. Life-course
assessment of these constructs would shed light on temporal-
precedence assumptions needed to establish support for mediation
and enhance our understanding of how personality and behavior
influences health across the life course.
Conclusion
There have been many important investigations over the past
two decades into how personality influences health. Friedman
(2000) described the need to investigate health behaviors as me-
diators of the personality–mortality association. Our understanding
of exactly how personality influences health and longevity has
improved with new developments in longitudinal and mediation
methodology. The current study provides evidence that engaging
in detrimental health behaviors explains at least part of the reason
that individuals scoring lower on Conscientiousness are more
likely to die—providing credence for the HBM of personality.
Although the clinical application of these findings is still in its
infancy, there is growing interest in using personality assessment
in medical practice to identify individuals at risk for poor health
and engagement in detrimental health-related behaviors, and to
target personality as a point of intervention (Chapman et al., 2011;
Moffitt et al., 2011). Using personality traits as a means to uncover
chains of risk from dispositions to behavior and health/illness is an
exciting possibility in the move toward personalized medicine.
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Received March 20, 2013
Revision received October 3, 2013
Accepted October 4, 2013 䡲
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
10 TURIANO, CHAPMAN, GRUENEWALD, AND MROCZEK