ArticlePDF Available

Personality and the Leading Behavioral Contributors of Mortality


Abstract and Figures

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.
Content may be subject to copyright.
Health Psychology
Personality and the Leading Behavioral Contributors of
Nicholas A. Turiano, Benjamin P. Chapman, Tara L. Gruenewald, and Daniel K. Mroczek
Online First Publication, December 23, 2013. doi: 10.1037/hea0000038
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:
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:
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Health Psychology © 2013 American Psychological Association
2013, Vol. 32, No. 12, 000 0278-6133/13/$12.00
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-
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
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
(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.
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 (t10.19, p.001), had higher
average levels of alcohol use (t6.13, p.001), and were
slightly more likely to be male (
17.03, p.001).
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.
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)
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
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.
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
Deceased (n580) Alive (n5,745) Total sample (N6,325)
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
Female 46% 52% 52% 0–1
Male 54% 48% 48% 0–1
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
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
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).
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 (t29.28, p.001), more likely to be male (
9.82, p
.001), less educated (t7.88, p.001), and less likely to be
married (
19.16, p.001). In terms of personality traits,
those who died during the follow-up period were significantly
lower in Conscientiousness (t3.04, p.01) and lower on
Neuroticism (t2.79, 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 (t2.16, p.05), had greater waist circumference (t
8.46, p.001), and were more likely to be former (
p.01) or current (
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
b1], [a2
b2], [a3
b3], [a4
b4], [a5
b5], [c])
was significant. The full indirect effect (sum of paths [a1
b2], [a3
b3], [a4
b4], [a5
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
b2]), smoking status
(sum of paths [a3
b3], [a4
b4]), and
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
Model 1 Model 2
Hazard ratio
[95% CI]
Hazard ratio
[95% CI]
Conscientiousness 0.84 [0.77, 0.92]
0.88 [0.80, 0.96]
Neuroticism 0.84 [0.77, 0.91]
0.99 [0.91, 1.09]
Extraversion 0.90 [0.81, 1.00]
0.90 [0.81, 1.01]
Agreeableness 1.24 [1.12, 1.38]
1.11 [0.99, 1.24]
Openness 0.93 [0.85, 1.03] 1.09 [0.99, 1.22]
Age 3.27 [2.96, 3.62]
Sex (Male) 1.59 [1.33, 1.90]
Minority status (Caucasian) 1.09 [0.80, 1.59]
Marital status (unmarried) 1.61 [1.35, 1.92]
Education 0.82 [0.75, 0.90]
AIC 100,020.49 8,959.16
Note. AIC Akaike Information Criterion.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
waist circumference
(path [a5
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,
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
Sensitivity analyses indicated that the findings were similar when a
measure of BMI was used instead of waist circumference.
1 04)
Non-Alcohol User
Heavy Alcohol
1.34 (1.04-1.75)*
1.17 (1.11-1.24)***
1.42 (1.16-1.75)***
Mortality Risk
1.07 (0.98-1.15)
a3 b3
Former Smoker
1.11 (1.05-1.08)*** 1.45 (1.16-1.81)***
1.07 (1.00-1.18)*
3.44 (2.74-4.33)***
Current Smoker
0.12 (0.09- 0.14)***
1.19 (1.08-1.31)***
Wai st
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]
); full indirect effect
(HR 0.18, CI [0.09, 0.27]
); alcohol-use indirect effect (HR 0.04, CI [0.01, 0.09]
); smoking indirect
effect (HR 0.12, CI [0.04, 0.20]
); waist-circumference indirect effect (HR 0.02, CI [0.01, 0.03]
p.001. HR hazard ratio.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
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.
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
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
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.
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.
Almada, S. J., Zonderman, A. B., Shekelle, R. B., Dyer, A. R., Daviglus,
M. L., Costa, P. T., & Stamler, J. (1991). Neuroticism and cynicisms and
risk of death in middle-aged men: The Western Electric Study. Psycho-
somatic Medicine, 53, 165–175.
Asparouhov, T., Masyn, K., & Muthén, B. (2006, August). Continuous
time survival in latent variable models. In Proceedings of the Joint
Statistical Meeting (pp. 180 –187), American Statistical Association,
Biometrics Section, Seattle, Washington. Retrieved from http://www
Babyak, M. A. (2009). Understanding confounding and mediation. Evi-
dence Based Mental Health, 12, 68 –71. doi:10.1136/ebmh.12.3.68
Bigaard, J., Frederiksen, K., Tjonneland, A., Thomsen, B. L., Overvad, K.,
Heitmann, B. L., & Sorensen, T. I. (2005). Waist circumference and
body composition in relation to all-cause mortality in middle-aged men
and women. International Journal of Obesity, 29, 778 –784. doi:
Bogg, T., & Roberts, B. W. (2004). Conscientiousness and health-related
behaviors: A meta- analysis of the leading behavioral contributors to
mortality. Psychological Bulletin, 130, 887–919. doi:10.1037/0033-
Brim, O. G., Ryff, C. D., & Kessler, R. C. (2004). How healthy are we? A
national study of well-being at midlife. Chicago, IL: Chicago University
Chapman, B. P., Fiscella, K., Kawachi, I., & Duberstein, P. R. (2010).
Personality, socioeconomic status, all-cause mortality in the United
States. American Journal of Epidemiology, 171, 83–92. doi:10.1093/aje/
Chapman, B. P., Roberts, B. W., & Duberstein, P. (2011). Personality and
longevity: Knowns, unknowns, and implications for public health and
personalized medicine. Journal of Aging Research, 2011, 1–24. Online
publication. doi:10.4061/2011/759170
Christensen, A. J., Ehlers, S. L., Wiebe, J. S., Moran, P. J., Raichle, K.,
Ferneyhough, K., & Lawton, W. J. (2002). Patient personality and
mortality: A 4-year prospective examination of chronic renal insuffi-
ciency. Health Psychology, 21, 315–320. doi:10.1037/0278-6133.21.4
Cox, D. R. (1972). Regression models and life tables. Journal of the Royal
Statistical Society, 34, 187–220.
Denollet, J., Sys, S. U., & Brutsaert, D. L. (1995). Personality and mortality
after myocardial infraction. Psychosomatic Medicine, 57, 582–591.
Di Castelnuovo, A., Costanzo, S., Bagnardi, V., Donati, M. B., Iacoviello,
L., & de Gaetano, G. (2006). Alcohol dosing and total mortality in men
and women: An updated meta-analysis of 34 prospective studies. Ar-
chives of Internal Medicine, 166, 2437–2445. doi:10.1001/archinte.166
Ditlevsen, S., Christensen, U., Lynch, J., Damsgaard, M. T., & Keiding, N.
(2005). A structural equation modeling approach for estimating the
proportion of exposure effect on outcome explained by an intermediate
variable. Epidemiology, 16, 114 –120. doi:10.1097/01.ede.0000147107
Friedman, H. S. (2000). Long-term relations of personality and health:
Dynamisms, mechanisms, tropisms. Journal of Personality, 68, 1089 –
1107. doi:10.1111/1467-6494.00127
Friedman, H. S., Kern, M., & Reynolds, C. A. (2010). Personality and
health, subjective well-being, and longevity. Journal of Personality, 78,
179 –216. doi:10.1111/j.1467-6494.2009.00613.x
Friedman, H. S., Tucker, J. S., Tomlinson-Keasey, C., Martin, L. R.,
Wingard, D. L., & Criqui, M. H. (1995). Psychosocial and behavioral
predictors of longevity: The aging and death of the “Termites”. Ameri-
can Psychologist, 50, 69 –78. doi:10.1037/0003-066X.50.2.69
Hagger-Johnson, G., Sabia, S., Nabi, H., Brunner, E., Kivimaki, M.,
Shipley, M., & Singh-Manoux, A. (2012). Low conscientiousness and
risk of all-cause, cardiovascular and cancer mortality over 17 years:
Whitehall II cohort study. Journal of Psychosomatic Research, 73,
98 –103. doi:10.1016/j.jpsychores.2012.05.007
Hampson, S. E. (2012). Personality processes: Mechanisms by which
personality traits “get outside the skin”. Annual Review of Psychology,
63, 315–339. doi:10.1146/annurev-psych-120710-100419
Hampson, S. E., & Friedman, H. S. (2008). Personality and health: A
lifespan perspective. In O. P. John, R. Robins, & L. Pervin (Eds.), The
handbook of personality (3rded., pp. 770 –794). New York, NY: Guil-
ford Press.
Hopwood, C. J., Morey, L. C., Skodol, A. E., Stout, R. L., Yen, S., Ansell,
E.B.,...McGlashan, T. H. (2007). Five-factor model of personality
traits associated with alcohol-related diagnoses in a clinical sample.
Journal of Studies on Alcohol and Drugs, 68, 455– 460.
Iwasa, H., Masui, Y., Gondo, Y., Inagaki, H., Kawaai, C., & Suzuki, T.
(2008). Personality and all-cause mortality among older adults dwelling
in a Japanese community: A five-year population-based prospective
cohort study. The American Journal of Geriatric Psychiatry, 16, 399 –
405. doi:10.1097/JGP.0b013e3181662ac9
Jacobs, E. J., Newton, C. C., Wang, Y., Patel, A. V., McCullough, M. L.,
Campbell, P. T.,...Gapstur, S. M. (2010). Waist circumference and
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
all-cause mortality in a large US cohort. Archives of Internal Medicine,
170, 1293–1301. doi:10.1001/archinternmed.2010.201
Jokela, M., Batty, G. D., Nyberg, S. T., Virtanen, M., Nabi, H., Sing-
Manoux, A., & Kivimaki, M. (2013). Personality and all-cause mortal-
ity: Individual-participant meta-analysis of 3,947 deaths in 76,150
adults. American Journal of Epidemiology, 178, 667– 675. doi:10.1093/
Jonassaint, C. R., Boyle, S. H., Williams, R. B., Mark, D. B., Siegler, I. C.,
& Barefoot, J. C. (2007). Facets of openness predict mortality in patients
with cardiac disease. Psychosomatic Medicine, 69, 319 –322. doi:
Kashdan, T. B., Vetter, C. J., & Collins, R. L. (2005). Substance use in
young adults: Associations with personality and gender. Addictive Be-
haviors, 30, 259 –269. doi:10.1016/j.addbeh.2004.05.014
Kaufman, J. S., MacLehose, R. F., Kaufman, S., & Greenland, S. (2005).
The mediation proportion: To the editor. Epidemiology, 16, 710. doi:
Kern, M. L., & Friedman, H. S. (2008). Do conscientious individuals live
longer? A quantitative review. Health Psychology, 27, 505–512. doi:
Kern, M. L., Friedman, H. S., Martin, L. R., Reynolds, C. A., & Luong, G.
(2009). Conscientiousness, career success, and longevity: A lifespan
analysis. Annals of Behavioral Medicine, 37, 154 –163. doi:10.1007/
Korten, A. E., Jorm, A. F., Jiao, Z., Letenneur, L., Jacomb, P. A., Hen-
derson, A. S., Christensen, H., & Rodgers, B. (1999). Health, cognitive,
and psychosocial factors as predictors of mortality in an elderly com-
munity sample. Journal of Epidemiology and Community Health, 53,
83– 88.
Lachman, M., & Weaver, S. L. (1997). The Midlife Development Inventory
(MIDI) personality scales: Scale construction and scoring (Tech. Rep.
No. 1). Waltham, MA: Department of Psychology, Brandeis University.
Lloyd-Jones, D. M., Hong, Y., Labarthe, D., Mozaffarian, D., Appel, L. J.,
Van Horn, L.,...Rosamond, W. D. (2010). Defining and setting
national goals for cardiovascular health promotion and disease reduc-
tion. Circulation, 121, 586 – 613. doi:10.1161/CIRCULATIONAHA
MacKinnon, D. P., & Fairchild, A. J. (2009). Current directions in medi-
ation analysis. Current Directions in Psychological Science, 18, 16 –20.
MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation
analysis. Annual Review of Psychology, 58, 593– 614. doi:10.1146/
Maier, H., & Smith, J. (1999). Psychological predictors of mortality in old
age. The Journals of Gerontology: Series B. Psychological Sciences and
Social Sciences, 54, P44 –P54. doi:10.1093/geronb/54B.1.P44
Malouff, J. M., Thorsteinsson, E. B., & Schutte, N. S. (2006). The five-
factor model of personality and smoking: A meta-analysis. Journal of
Drug Education, 36, 47–58. doi:10.2190/9EP8-17P8-EKG7-66AD
Martin, L. R., Friedman, H. S., & Schwartz, J. E. (2007). Personality and
mortality risk across the life span: The importance of conscientiousness
as a biopsychosocial attribute. Health Psychology, 26, 428 – 436. doi:
Mccann, S. J. H. (2005). Longevity, Big Five personality factors, and
health behaviors: Presidents from Washington to Nixon. Journal of
Psychology: Interdisciplinary and Applied, 139, 273–288. doi:10.3200/
Miller, T. Q., Smith, T. W., Turner, C. W., Guijarro, M. L., & Hallet, A. J.
(1996). A meta-analytic review on research on hostility and physical
health. Psychological Bulletin, 119, 322–348. doi:10.1037/0033-2909
Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J.,
Harrington, H.,...Caspi, A. (2011). A gradient of childhood self-
control predicts health, wealth, and public safety. PNAS: Proceedings of
the National Academy of Sciences of the United States of America, 108,
Mokdad, A. H., Marks, J., Stoup, D. F., & Gerberding, J. L. (2004). Actual
causes of death in the United States, 200. JAMA: Journal of American
Medical Association, 291, 1238 –1245. doi:10.1001/jama.291.10.1238
Mroczek, D. K., & Kolarz, C. M. (1998). The effect of age on positive and
negative Affect: A developmental perspective on happiness. Journal of
Personality and Social Psychology, 75, 1333–1349. doi:10.1037/0022-
Mroczek, D. K., & Spiro, A., III. (2007). Personality change influences
mortality in older men. Psychological Science, 18, 371–376. doi:
Mroczek, D. K., Spiro, A., & Turiano, N. (2009). Do health behaviors
explain the effect of neuroticism on mortality? Journal of Research in
Personality, 43, 653– 659. doi:10.1016/j.jrp.2009.03.016
Munafò, M. R., Zetteler, J. I., & Clark, T. G. (2007). Personality and
smoking status: A meta-analysis. Nicotine & Tobacco Research, 9,
405– 413. doi:10.1080/14622200701188851
Muthén, B., & Masyn, K. (2005). Discrete-time survival mixture analysis.
Journal of Educational and Behavioral Statistics, 30, 27–58. doi:
Muthén, L. K., & Muthén, B. O. (1998 –2010). MPlus user’s guide. (6th
ed.). Los Angeles, CA: Authors.
Ploubidis, G. B., & Grundy, E. (2009). Personality and all-cause mortality:
Evidence for indirect links. Personality and Individual Differences, 47,
203–208. doi:10.1016/j.paid.2009.02.022
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling
strategies for assessing and comparing indirect effects in multiple me-
diator models. Behavior Research Methods, 40, 879 – 891. doi:10.3758/
Prenda, K. M., & Lachman, M. E. (2001). Planning for the future: A life
management strategy for increasing control and life satisfaction in
adulthood. Psychology and Aging, 16, 206 –216. doi:10.1037/0882-7974
Roberts, B. W., Walton, K., & Viechtbauer, W. (2006). Patterns of mean-
level change in personality traits across the life course: A meta-analysis
of longitudinal studies. Psychological Bulletin, 132, 1–25. doi:10.1037/
Shiffman, S. (2009). Ecological momentary assessment (EMA) in studies
of substance use. Psychological Assessment, 21, 486 – 497. doi:10.1037/
Shipley, B. A., Weiss, A., Der, G., Taylor, M. D., & Deary, I. J. (2007).
Neuroticism, extraversion, and mortality in the UK health and lifestyle
survey: A 21-year prospective cohort study. Psychosomatic Medicine,
69, 923–931. doi:10.1097/PSY.0b013e31815abf83
Smith, T. W. (2006). Personality as risk and resilience in physical health.
Current Directions in Psychological Science, 15, 227–231. doi:10.1111/
Taylor, M. D., Whiteman, M. C., Fowkes, G. R., Lee, A. J., Allerhand, M.,
& Deary, I. J. (2009). Five factor model personality traits and all-cause
mortality in the Edinburgh artery study cohort. Psychosomatic Medicine,
71, 631– 641. doi:10.1097/PSY.0b013e3181a65298
Terracciano, A., Löckenhoff, C. E., Crum, R. M., Bienvenu, O. J., & Costa,
P. T. (2008). Five-factor model personality profiles of drug users. BMC
Psychiatry, 8, no pagination. Online publication. doi:10.1186/1471-
Terracciano, A., Löckenhoff, C. E., Zonderman, A. B., Ferrucci, L., &
Costa, P. T. (2008). Personality predictors of longevity: Activity, emo-
tional stability, and conscientiousness. Psychosomatic Medicine, 70,
621– 627. doi:10.1097/PSY.0b013e31817b9371
Turiano, N. A., Hill, P. L., Roberts, B. W., Spiro, III, A., & Mroczek, D. K.
(2012). Smoking mediates the association between conscientiousness
and mortality. Journal of Research in Personality, 46, 719 –724.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Turiano, N. A., Mroczek, D. K., Moynihan, J., & Chapman, B. P. (2013).
Big 5 personality traits and interleukin-6: Evidence for “healthy Neu-
roticism” in a US population sample. Brain, Behavior, and Immunity, 28,
83– 89. doi:10.1016/j.bbi.2012.10.020
Turiano, N. A., Sprio III, A., & Mroczek, D. K. (2012). Openness to
experience and mortality in men: Analysis of trait and facets. Journal of
Aging and Health, 24, 654 – 672. doi:10.1177/0898264311431303
United States Department of Health and Human Services, Office of Dis-
ease Prevention and Health Promotion. (2013). Healthy people 2020.
Washington, DC: Author. Retrieved from
Weiss, A., & Costa, Jr., P. T. (2005). Domain and facet personality
predictors of all-cause mortality among Medicare patients aged 65 to
100. Psychosomatic Medicine, 67, 724 –733. doi:10.1097/01.psy
Welch, D., & Poulton, R. (2009). Personality influences on change in
smoking behavior. Health Psychology, 28, 292–299. doi:10.1037/
Wilson, R. S., Mendes de Leon, C. F., Bienas, J. L., Evans, D. A., &
Bennett, D. A. (2004). Personality and mortality in old age. The Journals
of Gerontology: Series B. Psychological Sciences and Social Sciences,
59, P110 –P116. doi:10.1093/geronb/59.3.P110
Received March 20, 2013
Revision received October 3, 2013
Accepted October 4, 2013
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
... Assessments of personality are prospectively related to health outcomes such as stroke, dementia, heart disease, and cancer Weston et al., 2015). These personality-health associations predict outcomes decades in advance (Friedman et al., 2010), with different assessment methods Turiano et al., 2013), and across the life span (Hill et al., 2011;Turiano et al., 2015). Personality is believed to influence health through mechanisms such as perceptions of health, disease progression, and health behaviors (Friedman et al., 2010;Roberts et al., 2009;Weston & Jackson, 2016), but health behaviors are arguably the most well-studied. ...
... Neuroticism has been associated with negative health outcomes (Lahey, 2009), such as higher BMI (Brummett et al., 2006) and increased rates of physical disorders (Goodwin & Friedman, 2006), although studies have typically focused on mental health over physical health outcomes (Friedman et al., 2010;Ozer & Benet-Martínez, 2006). Lower levels of agreeableness predict poorer physical and subjective health (Turiano et al., 2015), premature mortality , arthritis (Weston et al., 2015), and heart disease (Miller et al., 1996). Extraversion often shows both smaller positive and negative effects on health (Friedman et al., 2010;Munafò et al., 2007). ...
... These personality-health associations are thought to be partly driven by the initiation and maintenance of behaviors that directly impact health, known as health behaviors (Turiano et al., 2015). More specifically, people with higher levels of certain traits (e.g., conscientiousness) are more likely to engage in behaviors that promote positive health (e.g., exercise, healthy diet) and, conversely, refrain from risk behaviors (e.g., substance abuse, unprotected sex) that make poorer health outcomes more likely (Bogg & Roberts, 2004;Kern & Friedman, 2011;Lodi-Smith et al., 2010). ...
Objective: Personality influences many aspects of the health process. It is unclear to what extent self- and informant-reports of the Big Five offer incremental validity for the prediction of inflammatory biomarkers and whether inflammation provides a unique pathway between personality and indicators of physical health, independent of health behaviors. Method: Using data from older adults (N = 1,630) enrolled in the St. Louis Personality and Aging Network study, we tested whether self- and informant-reported Big Five traits show unique associations with inflammation (IL-6, CRP, TNF-α). Further, we tested whether inflammation and health behaviors indirectly link personality to health-related quality of life, body mass index, and chronic disease burden using longitudinal mediation in a structural equation modeling framework. Results: Self-reports, informant-reports, and general trait factors of personality predicted future inflammatory biomarker levels (unstandardized regression coefficients ranged -.08 to .07 for self, -.13 to -.10 for informants, and -.16 to -.11 for general). Additionally, all assessment methods of personality were associated with the indicators of physical health through biomarker and health behavior pathways. Effects were primarily found for conscientiousness and neuroticism; IL-6 and CRP were the biomarkers with the most indirect effects; and indirect paths overall emerged more frequently through health behaviors, but this varied by outcome. Conclusions: Self- and informant-reports provided unique predictive validity of inflammatory biomarkers. Findings highlight the benefits of using of multiple assessments of personality and the importance of examining multiple, distinct pathways by which personality might influence health to understand the mechanisms underlying this relationship more fully. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
... Parent agreeableness was positively related to the adolescent's self-report health status. Higher levels of agreeableness are sometimes associated with better physical and subjective health (Turiano et al., 2015;c.f., Wright et al., 2022), though this is reasoned to occur through physiological pathways as opposed to health behavior pathways, making it unlikely that the parent's agreeableness would then impact their child's health in a similar way to parent conscientiousness. One study that found better glycemic control in diabetic children that had more agreeable mothers suggested that it was the high level of care and involvement of the mother in the child's care that led to the association (Vollrath et al., 2007). ...
Full-text available
A parent has immense control over the experiences of their offspring. Given that personality is associated with important life outcomes – mediated through decision-making processes – it is likely that parental personality impacts their child’s life outcomes. While “compensatory” influences from another person have been identified in romantic partners, the impact of parent’s personality on their child’s outcomes has not been thoroughly examined. Using a response surface analysis approach, we will look at the associations of adolescent personality with the personality of their parents for the prediction of adolescent outcomes (Ndyads = 9,395). Parent personality predicted outcomes for their child, above and beyond their child’s traits. There were few instances of child-parent personality interactions, suggesting the mechanisms linking child and parent traits to child outcomes are largely independent. Findings suggest parents’ personalities offer a unique influence on the outcomes for children while they develop and navigate through adolescence.
... Personality traits are relatively enduring patterns of thoughts, feelings and behaviors 9 . Existing models and research indicate that the personality traits defined by the Five-Factor Model (FFM) 10 contribute to health across adulthood [11][12][13] . In particular, personality may be a valuable predictor of fatigue because it is associated with a range of health-related and behavioral factors that cause fatigue. ...
Full-text available
The present study examined the cross-sectional and longitudinal associations between the five major personality traits and fatigue. Participants were adults aged 16–104 years old (N > 40,000 at baseline) from the Health and Retirement Study, the National Social Life, Health, and Aging Project, the Wisconsin Longitudinal Study graduate and sibling samples, the National Health and Aging Trends Survey, the Longitudinal Internet Studies for the Social Sciences and the English Longitudinal Study of Ageing. Personality traits, fatigue, demographic factors, and other covariates were assessed at baseline, and fatigue was assessed again 5–20 years later. Across all samples, higher neuroticism was related to a higher risk of concurrent (meta-analytic OR = 1.73, 95% CI 1.62–1.86) and incident (OR = 1.38, 95% CI 1.29–1.48) fatigue. Higher extraversion, openness, agreeableness, and conscientiousness were associated with a lower likelihood of concurrent (meta-analytic OR range 0.67–0.86) and incident (meta-analytic OR range 0.80–0.92) fatigue. Self-rated health and physical inactivity partially accounted for these associations. There was little evidence that age or gender moderated these associations. This study provides consistent evidence that personality is related to fatigue. Higher neuroticism and lower extraversion, openness, agreeableness, and conscientiousness are risk factors for fatigue.
... Individuals who score high on neuroticism also experience more negative sensations and feelings in their daily activities, leading to feeling older than one's age. Other theoretical models postulate that personality predicts health-related behaviors (Turiano et al., 2015) that are known to contribute to subjective age. For example, lower neuroticism, higher extraversion, openness, and conscientiousness motivate more frequent physical activity , which has been prospectively related to feeling younger . ...
Subjective age is associated with health-related outcomes across adulthood. The present study examined the cross-sectional and longitudinal associations between personality traits and subjective age. Participants (N > 31,000) were from the Midlife in the United States Study (MIDUS), the Health and Retirement Study (HRS), the National Health and Aging Study (NHATS), the Wisconsin Longitudinal Study Graduate (WLSG) and Siblings (WLSS) samples, and the English Longitudinal Study of Aging (ELSA). Demographic factors, personality traits, and subjective age were assessed at baseline. Subjective age was assessed again in the MIDUS, the HRS, and the NHATS, 4 to almost 20 years later. Across the samples and a meta-analysis, higher neuroticism was related to an older subjective age, whereas higher extraversion, openness, agreeableness, and conscientiousness were associated with a younger subjective age. Self-rated health, physical activity, chronic conditions, and depressive symptoms partially mediated these relationships. There was little evidence that chronological age moderated these associations. Multilevel longitudinal analyses found similar associations with the intercept and weak evidence for an association with the slope in the opposite of the expected direction: Lower neuroticism and higher extraversion, agreeableness, and conscientiousness were related to feeling relatively older over time. The present study provides replicable evidence that personality is related to subjective age. It extends existing conceptualization of subjective age as a biopsychosocial marker of aging by showing that how old or young individuals feel partly reflects personality traits. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Most investigations in the structure of personality traits do not adequately address age; instead, they presuppose a constant structure across the lifespan. Further, few studies look at the structure of personality traits a-theoretically, often neglecting to examine the relationship among indicators within a trait (convergence) and across traits (divergence). Using a network approach, the present study examines (1) age differences in divergence and convergence, (2) the similarity between the Big Five and network structures, and (3) the consistency of network structure across age groups in a large, cross-sectional sample. Results indicate that convergence shows early gains in adolescence with few differences across the lifespan, while divergence mostly weakens across adulthood. The result of these age-related differences is that Big Five indicators only parallel the Big Five structure among young but not older adults. The structure of young adults tends to be quite similar while the network structures of older adults appear to greatly differ from one another. These results suggest that older adults have a different structure of personality than younger adults and suggest that future research should not assume consistency in personality structure across the lifespan.
Stress is implicated in models of personality and health as a mechanism that explains why traits like conscientiousness and neuroticism are associated with long‐term health outcomes. Evidence for an association between personality and cortisol, a biological marker of stress, however, has been inconsistent. This study examined the association between Five‐Factor Model personality traits and 24‐hour urinary cortisol (operationalized as a ratio of urinary free cortisol to creatinine) measured up to 12 times over intervals as long as 30 years in the Baltimore Longitudinal Study of Aging (Mage=61.21, SD=15.46; 49% female). There was a modest association between conscientiousness and lower mean‐level cortisol that was attenuated only slightly in the fully‐adjusted model. Neuroticism and the other traits were unrelated to cortisol levels, and none of the traits was related to cortisol change over time. The null association for neuroticism suggests that its relation with long‐term health may be primarily through pathways other than cortisol. The modest association between conscientiousness and 24‐hour urinary cortisol replicates a previous finding with a longer‐term measure of cortisol measured from hair, which calls for more research on the robustness and replicability of this finding. Cortisol may be one pathway through which conscientiousness is associated with health outcomes. This article is protected by copyright. All rights reserved.
Health‐Risk‐Behaviors (HRBs) are considered significant antecedent conditions of adolescents' poor health and mortality. Prevention of any adverse health outcome requires an in‐depth understanding of the risk and protective factors associated with its development and maintenance. Among other potential causal pathways, the “neuroticism‐ HRBs‐adverse health” link has been supported in previous studies. Trait neuroticism has been associated with poor health and HRBs, but several moderators were also observed, which might transform neuroticism into a desirable phenomenon, that is, healthy neuroticism, that leads to better health. Conscientiousness is one such potential moderator; however, the moderating effect of conscientiousness in the neuroticism‐HRBs link has not been explored extensively among adolescents, especially in India; therefore, no conclusive evidence is available. Thus, the present study was planned to explore the moderating effect of conscientiousness in the relationship between neuroticism and HRBs among adolescents. The study was conducted in India and its cross‐sectional sample, procured through a multi‐stage stratified random sampling, consists of 648 (364 males) adolescents (Mage = 16.08). Participants provided relevant information on standardized questionnaires. Moderated regression analysis was applied to test the stated hypotheses. Individuals high on neuroticism and low on conscientiousness reported more indulgence in health‐risk behaviors than individuals high on both neuroticism and conscientiousness. It indicates that a higher level of conscientiousness may reduce the negative impact that neuroticism has on HRBs. The findings imply that the assessment of conscientiousness and strategies to increase the same should be part of interventional programs to achieve adolescents' wellbeing.
Full-text available
Objective The purpose of this study was to compare the effectiveness of time out, cognitive regeneration, and cognitive diffusion techniques in children's self-control. Methods This research was a quasi-experimental study with a multi-group post-test design with a control group. The population consisted of all elementary students of Hamadan city in the academic year 2017-2018. The sample consisted of 60 children who were selected by multistage cluster random sampling and were randomly assigned to four experimental and control groups. The data collection instrument was the amount of time spent not eating sweets up to 10 minutes. Results The results of analysis of variance showed that there was a significant difference between groups (p < 0.01). Post hoc test results showed that time out and cognitive restructuring techniques had a significant effect on increasing self-control scores of the participants (ρ < 0.01). But the impact of cognitive diffusion on self-control was not significant (ρ = 0.08). Also, time out could increase self-control more than cognitive restructuring in the participants. Conclusion According to the results, cognitive deprivation and time out techniques can be used to increase the level of self-control in children.
Health-Risk-Behaviors (HRBs) are significant antecedent conditions of adolescents’ poor health and mortality. Prevention of avoidable adverse health outcomes requires an in-depth understanding of the factors associated with such outcomes. Among other possible pathways, the ‘Neuroticism- HRBs-adverse health’ link has been supported in previous studies. However, more extensive exploration of this link is required to identify the underlying modifiable risk factors. In the present study, one such factor, namely, emotion regulation difficulties, was explored to see its mediating effect in the relationship between neuroticism and HRBs—the first two constructs of the mentioned link. In this quantitative study, a total of 759 adolescents belonging to the Indian state of Punjab (Males= 402; M(age)=16.08) provided relevant information on a set of standardized questionnaires. Mediation analysis supported the major hypothesis of the present study. The results suggest that emotion regulation difficulty may be a significant mediator in the neuroticism-HRBs link. One’s difficulty in regulating emotions might be an underlying mechanism through which high neuroticism increases the probability of indulging in HRBs, resulting in adverse health outcomes. The study implies that the assessment of emotion regulation difficulties should be included in interventional programs aimed at achieving adolescents’ wellbeing, and early intervention may avoid progression toward adverse health outcomes in adulthood.
Full-text available
Background: Moderate consumption of alcohol is inversely related with coronary disease, but its association with mortality is controversial. We performed a meta-analysis of prospective studies on alcohol dosing and total mortality. Methods: We searched PubMed for articles available until December 2005, supplemented by references from the selected articles. Thirty-four studies on men and women, for a total of 1 015 835 subjects and 94 533 deaths, were selected. Data were pooled with a weighed regression analysis of fractional polynomials. Results: A J-shaped relationship between alcohol and total mortality was confirmed in adjusted studies, in both men and women. Consumption of alcohol, up to 4 drinks per day in men and 2 drinks per day in women, was inversely associated with total mortality, maximum protection being 18% in women (99% confidence interval, 13%-22%) and 17% in men (99% confidence interval, 15%-19%). Higher doses of alcohol were associated with increased mortality. The inverse association in women disappeared at doses lower than in men. When adjusted and unadjusted data were compared, the maximum protection was only reduced from 19% to 16%. The degree of association in men was lower in the United States than in Europe. Conclusions: Low levels of alcohol intake (1-2 drinks per day for women and 2-4 drinks per day for men) are inversely associated with total mortality in both men and women. Our findings, while confirming the hazards of excess drinking, indicate potential windows of alcohol intake that may confer a net beneficial effect of moderate drinking, at least in terms of survival.
Full-text available
The current study investigated if the Big 5 personality traits predicted interleukin-6 (IL-6) levels in a national sample over the course of 5 years. In addition, interactions among the Big 5 were tested to provide a more accurate understanding of how personality traits may influence an inflammatory biomarker. Data included 1,054 participants in the Midlife Development in the U.S. (MIDUS) biomarkers subproject. The Big 5 personality traits were assessed in 2005-06 as part of the main MIDUS survey. Medication use, comorbid conditions, smoking behavior, alcohol use, body mass index, and serum levels of IL-6 were assessed in 2005-2009 as part of the biomarkers subproject. Linear regression analyses examined personality associations with IL-6. A significant Conscientiousness∗Neuroticism interaction revealed that those high in both Conscientiousness and Neuroticism had lower circulating IL-6 levels than people with all other configurations of Conscientiousness and Neuroticism. Adjustment for health behaviors diminished the magnitude of this association but did not eliminate it, suggesting that lower comorbid conditions and obesity may partly explain the lower inflammation of those high in both Conscientiousness and Neuroticism. Our findings suggest, consistent with prior speculation, that average to higher levels of Neuroticism can in some cases be associated with health benefits-in this case when it is accompanied by high Conscientiousness. Using personality to identify those at risk may lead to greater personalization in the prevention and remediation of chronic inflammation.
Personality may influence the risk of death, but the evidence remains inconsistent. We examined associations between personality traits of the five-factor model (extraversion, neuroticism, agreeableness, conscientiousness, and openness to experience) and the risk of death from all causes through individual-participant meta-analysis of 76,150 participants from 7 cohorts (the British Household Panel Survey, 2006-2009; the German Socio-Economic Panel Study, 2005-2010; the Household, Income and Labour Dynamics in Australia Survey, 2006-2010; the US Health and Retirement Study, 2006-2010; the Midlife in the United States Study, 1995-2004; and the Wisconsin Longitudinal Study's graduate and sibling samples, 1993-2009). During 444,770 person-years at risk, 3,947 participants (54.4% women) died (mean age at baseline = 50.9 years; mean follow-up = 5.9 years). Only low conscientiousness-reflecting low persistence, poor self-control, and lack of long-term planning-was associated with elevated mortality risk when taking into account age, sex, ethnicity/nationality, and all 5 personality traits. Individuals in the lowest tertile of conscientiousness had a 1.4 times higher risk of death (hazard ratio = 1.37, 95% confidence interval: 1.18, 1.58) compared with individuals in the top 2 tertiles. This association remained after further adjustment for health behaviors, marital status, and education. In conclusion, of the higher-order personality traits measured by the five-factor model, only conscientiousness appears to be related to mortality risk across populations.
We describe a general multivariate, multilevel framework for continuous time survival analysis that includes joint modeling of survival time variables and continuous and categorical observed and latent variables. The proposed framework is implemented in the Mplus software pack- age. The survival time variables are modeled with non- parametric or parametric proportional hazard distribu- tions and include right censoring. The proposed model- ing framework includes finite mixtures of Cox regression models with and without class-specific baseline hazards, multilevel Cox regression models, and multilevel frailty models. We illustrate the framework with several sim- ulation studies. Comparison is made with discrete time survival models. We also investigate the eect of ties on the proposed estimation method. Simulation studies are conducted to compare the methods implemented in Mplus with those implemented in SAS.
The analysis of censored failure times is considered. It is assumed that on each individual are available values of one or more explanatory variables. The hazard function (age-specific failure rate) is taken to be a function of the explanatory variables and unknown regression coefficients multiplied by an arbitrary and unknown function of time. A conditional likelihood is obtained, leading to inferences about the unknown regression coefficients. Some generalizations are outlined. LIFEtables are one of the oldest statistical techniques and are extensively used by medical statisticians and by actuaries. Yet relatively little has been written about their more formal statistical theory. Kaplan and Meier (1958) gave a comprehensive review of earlier work and many new results. Chiang in a series of papers has, in particular, explored the connection with birth-death processes; see, for example, Chiang (1968). The present paper is largely concerned with the extension of the results of Kaplan and Meier to the comparison of life tables and more generally to the incorporation of regression-like arguments into life-table analysis. The arguments are asymptotic but are relevant to situations where the sampling fluctuations are large enough to be of practical importance. In other words, the applications are more likely to be in industrial reliability studies and in medical statistics than in actuarial science. The procedures proposed are, especially for the two-sample problem, closely related to procedures for combining contingency tables; see Mantel and Haenzel (1959), Mantel (1963) and, especially for the application to life tables, Mantel (1966). There is also a strong connection with a paper read recently to the Society by R. and J. Peto (1972). We consider a population of individuals; for each individual we observe either the time to "failure" or the time to ccloss" or censoring. That is, for the censored individuals we know only that the time to failure is greater than the censoring time. Denote by T a random variable representing failure time; it may be discrete or continuous. Let F(t) be the survivor function, %(t) = pr (T2 t)
The analysis of censored failure times is considered. It is assumed that on each individual are available values of one or more explanatory variables. The hazard function (age‐specific failure rate) is taken to be a function of the explanatory variables and unknown regression coefficients multiplied by an arbitrary and unknown function of time. A conditional likelihood is obtained, leading to inferences about the unknown regression coefficients. Some generalizations are outlined.
This study examined the relationship between conscientiousness and mortality over 18 years and whether smoking behavior mediated this relationship. We utilized data from the Veterans Affairs Normative Aging Study on 1349 men who completed the Goldberg (1992) adjectival markers of the Big Five. Over the 18-year follow-up, 547 (41%) participants died. Through proportional hazards modeling in a structural equation modeling framework, we found that higher levels of conscientiousness significantly predicted longer life, and that this effect was mediated by current smoking status at baseline. Methodologically, we also demonstrate the effectiveness of using a structural equation modeling framework to evaluate mediation when using a censored outcome such as mortality.
It is time to bury the old models of personality and health and replace them with theories and models that employ the most modern concepts from personality psychology. It has long been understood that some individuals are more prone to illness and premature mortality than are others. Indeed, assumptions about variations in disease proneness form part of the basis for clinical judgments by medical practitioners about their individual patients, the predictions of epidemiologists and insurance companies about health trends and costs, and much targeted preventive medical screening. Yet the extant models, both implicit and explicit, of the links between individual differences and health generally have relied on primitive and incomplete conceptions. Although some threats to a person's life and health are truly random, most of the threats to well-being are a function of various biopsychosocial characteristics of the individual. In principle, anyone may catch the flu or suffer a myocardial infarction, but individuals vary tremendously in the likelihood that they will achieve good health and longevity. That is, there is astonishing variation in whether one is vulnerable to various 770 diseases and whether one is likely to recover quickly from any diseases that take hold. A person does not contract the flu without exposure to an influenza virus, but persons vary tremendously as to whether they are exposed to the virus, whether they are infected after exposure, and how they respond to the illness if infected. In other words, understanding the likelihood of disease for the individual is often as important as knowing the general causes of disease. Much of this variation can be captured by a concept that encapsulates the biopsychosocial nature of the individual across time, namely the modern concept of personality. (PsycINFO Database Record (c) 2012 APA, all rights reserved)