Temperament, Childhood Illness Burden, and Illness Behavior in.docx

Temperament, Childhood Illness Burden, and Illness Behavior
in
Early Adulthood
Brittany L. Sisco-Taylor
University of California, Riverside
Robin P. Corley, Michael C. Stallings,
and Sally J. Wadsworth
University of Colorado, Boulder
Chandra A. Reynolds
University of California, Riverside
Objective: Illness behaviors— or responses to bodily
symptoms—predict individuals’ recovery and
functioning; however, there has been little research on the early
life personality antecedents of illness
behavior. This study’s primary aims were to evaluate (a)
childhood temperament traits (i.e., emotionality
and sociability) as predictors of adult illness behaviors,
independent of objective health; and (b) adult
temperament traits for mediation of childhood temperament’s
associations. Method: Participants in-
cluded 714 (53% male; 350 adoptive family and 364 control
family) children and siblings from the
Colorado Adoption Project (CAP; Plomin & DeFries, 1983).
Structural regression analyses evaluated
paths from childhood temperament to illness behavior (i.e.,
somatic complaints, sick days, and medica-
tion use) at two adulthood assessments (CAP years 21 and 30).

Analyses controlled for participant age,
sex, family type (adoptive or control), adopted status, parent
education/occupation, and middle childhood
illnesses, doctor visits, and life events stress. Results: Latent
illness behavior factors were established
across 2 adulthood assessments. Multilevel path analyses
revealed that higher emotionality (fearfulness)
in adulthood— but not childhood temperament—predicted
higher levels of illness behavior at both
assessments. Lastly, lower emotionality-fearfulness partially
mediated the effect of higher childhood
sociability on adult illness behavior. Conclusions: Results
suggest the importance of childhood illness
experiences and adult emotionality (fearfulness) in shaping
illness behavior in early adulthood. They also
suggest a small, protective role of childhood sociability on
reduced trait fearfulness in adulthood. These
findings broaden our understanding of the prospective links
between temperament and illness behavior
development, suggesting distinct associations from early life
illness experiences.
Keywords: illness behavior, temperament, burden of illness,
young adult, health promotion
Supplemental materials:
http://dx.doi.org/10.1037/hea0000759.supp
In 2013, United States health care expenditures reached $2.9
trillion, with an average personal health cost of $9,255 per
capita
(National Center for Health Statistics, 2014). Such daunting ex-
penditures point to a need for increased efficiency in the
delivery
and utilization of health services. As a first step, however, the
process of illness must be better understood. In other words,

what
psychological and behavioral processes occur before people
seek
(or choose not to seek) formal health services? Illness
behavior—a
psychosocial construct defined as individuals’ perceptions,
evalu-
ations, and responses to symptoms that signify illness
(Mechanic,
1962)—provides a framework for examining who is more likely
to
react to bodily sensations, and under what circumstances.
Illness behaviors are associated with individual health outcomes
such as reported pain levels, disease functioning (Schüssler,
1992;
This article was published Online First May 23, 2019.
Brittany L. Sisco-Taylor, Department of Psychology, University
of
California, Riverside; Robin P. Corley, Institute for Behavioral
Genetics,
University of Colorado, Boulder; Michael C. Stallings, Institute
for Be-
havioral Genetics and Department of Psychology and
Neuroscience, Uni-
versity of Colorado, Boulder; Sally J. Wadsworth, Institute for
Behavioral
Genetics, University of Colorado, Boulder; Chandra A.
Reynolds, Depart-
ment of Psychology, University of California, Riverside.
This research was supported by the National Institutes of
Health, includ-

ing HD010333 (Sally J. Wadsworth) and AG046938 (Chandra
A. Reyn-
olds and Sally J. Wadsworth). Brittany L. Sisco-Taylor was
partly sup-
ported by a Ruth L. Kirschstein National Research Service
Award (NRSA)
award, F31AG052273, funded by the National Institute on
Aging of the
National Institutes of Health. The content is solely the
responsibility of the
authors and does not necessarily represent the official views of
the National
Institutes of Health. The authors gratefully acknowledge the
dedicated
research staff and the generosity of the CAP families
participating in the
study across several years.
Correspondence concerning this article should be addressed to
Brittany
L. Sisco-Taylor, who is now at Department of Population Health
Sciences,
University of Utah School of Medicine, 295 Chipeta Way, Room
1S112,
Salt Lake City, UT 84108. E-mail: [email protected]
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Health Psychology
© 2019 American Psychological Association 2019, Vol. 38, No.
7, 648 – 657
0278-6133/19/$12.00 http://dx.doi.org/10.1037/hea0000759
648
mailto:[email protected]
http://dx.doi.org/10.1037/hea0000759
Crane & Martin, 2002), timely detection of life-threatening ill-
nesses (e.g., cancer, van Osch, Lechner, Reubsaet, de Nooijer,
&
de Vries, 2007), and return to work (Broadbent, Ellis, Gamble,
&
Petrie, 2006; Keefe, Crisson, Maltbie, Bradley, & Gil, 1986).
Examples of illness behavior range from denial of symptoms to
symptom monitoring and preoccupation; from stoicism to
expres-
sion of negative affect (e.g., complaining, grimacing); and from
delayed medical care-seeking to care-seeking for minor physical
complaints, or absenteeism from work and avoiding social
obliga-
tions. Because illness behavior describes not only how people
respond to their symptoms, but also what they choose not to do
(i.e., delaying treatment), both extremes of responding must be
targeted for optimizing health care delivery. Illness behaviors
are
also distinct from primary health behaviors: Whereas primary
health behaviors refer to the preventative actions of healthy or
symptomless individuals (e.g., diet, screenings), illness
behaviors
encompass individuals’ observable responses— or lack there-
of—to bodily symptoms and self-appraisals as “ill”. Illness

behav-
ior is not only a function of biological risks, but it is also influ-
enced by psychosocial factors. Access to health care, cultural
norms, prior experiences, social support, and personality, along
with symptom qualities (e.g., ambiguity, visibility), account for
variability in illness behaviors (e.g., Egan & Beaton, 1987;
Hagger
& Orbell, 2003; Mechanic, 1995; Shannon, 1977). Most studies
on
illness behavior, however, have examined one aspect at a time
(e.g., symptom reports, health utilization) and have considered
cross-sectional or short-term associations with psychosocial
fac-
tors in adulthood (Sirri, Fava, & Sonino, 2013). Furthermore,
although prior longitudinal work has examined associations of
childhood personality and illness with adults’ self-rated
(Hampson,
Goldberg, Vogt, & Dubanoski, 2007) and objective health
(Kubzansky, Martin, & Buka, 2009), these early life factors’
associations with adult illness behaviors as a unifying,
multivariate
construct has received little empirical attention. The present
study
examined middle-childhood illness burden and temperament as
antecedents of a latent illness behavior factor in early
adulthood, as
well as mediation by adult temperament traits.
Personality Development and Illness Behavior
The four most common amplifiers of bodily symptoms—
attention,
mood, beliefs, and social circumstances (e.g., interpersonal con-
flict, stress)—are entirely psychosocial in nature (Barsky,
1988).
Because the construct of personality theoretically encompasses

stable individual differences in the attention, mood, and beliefs
that shape behavior, it represents a promising target for research
on
illness behavior development. In particular, the Big Five trait of
neuroticism is posited to influence the ways that symptoms are
perceived and labeled (Costa & McCrae, 1987; Leventhal, Lev-
enthal, & Contrada, 1998), and these perceptions, in turn,
predict
illness responses. For example, greater neuroticism is associated
with an increased internal bodily focus (Costa & McCrae,
1987),
reduced internal locus of control in the face of health threats,
and
higher perceived vulnerability to disease (Gerend, Aiken, West,
&
Erchull, 2004). From this research, it follows that people who
score higher on neuroticism are more responsive to their symp-
toms. Conversely, the Big Five trait of extraversion might
decrease
illness behaviors, because highly extraverted individuals have
more difficulty shifting their attention from external to internal
stimuli (Pennebaker & Brittingham, 1982) and are less likely
than
introverts to accept illness-related restrictions on social
activity.
For example, individuals scoring high on extraversion are rela-
tively more likely to complain to others about pain, yet they
also
report less distress and pain sensitivity (Harkins, Price, &
Braith,
1989). Thus, extraverts might be more likely than introverts to
engage their support systems when ill, but less likely to notice
symptoms. Despite empirical support for concurrent
associations
of adult personality with a range of illness behaviors, the

emergent
relationships of these personality influences on illness behavior
are
not well understood (Crane & Martin, 2002; Schüssler, 1992).
No
studies to date have evaluated the predictive role of these traits
in
early life on adulthood illness behavior development.
The Present Study
To understand the role of personality in the development of
illness behavior, a life span approach is useful for examining
when, and to what extent, individuals’ traits contribute over
time.
Thus, the current study leverages prospective data from the Col-
orado Adoption Project (CAP; Plomin & DeFries, 1983) to
exam-
ine illness behavior as a developmental process, addressing the
emergence of middle childhood temperament traits and illnesses
as
predictors of early adult illness behavior. The temperament
traits
of emotionality and sociability are viewed as moderately
heritable,
stable precursors of neuroticism and extraversion (Buss &
Plomin,
2014; Goldsmith et al., 1987). Temperament more generally is
viewed as a key aspect of personality and encompasses the
afore-
mentioned symptom amplifiers of attention and mood. Although
personality traits are characterized by more specific beliefs and
values, temperament reliably predicts these cognitions (Rothbart
&
Bates, 2006). Empirical findings on the links between tempera-
ment and Big Five personality traits in adulthood suggest two

underlying common affective-motivational factors of
extraversion
and negative affect (Evans & Rothbart, 2007). Other
longitudinal
work predicting adult Big Five traits from middle childhood im-
pulsivity and inhibition found that these two dimensions
account
for more than 30% of variability in adult personality (Deal, Hal-
verson, Havill, & Martin, 2005). Thus, although this study
focuses
on temperament, it includes standard, reliable measures of two
traits with demonstrated concurrent and prospective links with
adult personality (Shiner & DeYoung, 2013). Furthermore, the
exploration of temperament in middle childhood is of particular
interest, as this is a time period in which children begin to
differ-
entiate themselves from others in terms of their psychological
traits (Harter, 2012).
Apart from childhood personality, a widespread literature also
shows that early childhood health has enduring associations
with
chronic disease and physical functioning in adolescence and
early
adulthood (Case, Fertig, & Paxson, 2005; Haas, 2008). These
results are underscored by a theoretical framework of
cumulative
risks or ‘insults’ (Kuh & Ben-Shlomo, 2004), which posits that
childhood biopsychosocial risk factors may accumulate across
the
life span to influence adult health and behavior; thus, the
current
study adjusted for parent reports of childhood illnesses to deter-
mine the extent to which child temperament associations
remained
after accounting for concurrently measured, objective health.

This
study’s hypotheses were as follows: (a) higher emotionality in
childhood would predict higher levels of adult illness behavior;
(b)
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649ILLNESS BEHAVIOR IN EARLY ADULTHOOD
childhood sociability would also predict illness behavior,
although
there was no hypothesis of directionality given the mixed
findings
in the literature; and (c) adult emotionality and sociability
would
partially mediate the relationship between childhood
temperament
and illness behavior in adulthood. Given the moderate stability
of
temperament across the life span (Buss & Plomin, 2014), the
strength of childhood temperaments’ associations was expected
to
decrease once proximal adult temperament traits were added to
the
predictive model. Finally, greater childhood illness burden was

expected to predict higher levels of adult illness behavior.
Method
Participants
Study participants were child members of adoptive families or
matched control families from the Colorado Adoption Project
(CAP; Plomin & DeFries, 1983), begun in 1977. The CAP is a
longitudinal, adoption study of genetic and environmental influ-
ences on behavioral development. The CAP provides rich, pro-
spective data on behavioral development and health across a key
developmental transition into early adulthood. Because clinical
studies on illness behavior may be biased toward clinic-based
samples who have already sought out health care (i.e., people
who
tend to fall along the higher extreme of the illness behavior
continuum), the CAP therefore represents a unique opportunity
to
examine the life span development of this construct within a
population-based sample of U.S. adults. CAP proband
participants
(adopted and matched control children) and their siblings were
assessed almost annually from infancy to approximately 21
years
of age (i.e., across CAP assessment years 1 to 21), with a more
recent (year 30) assessment completed in 2011 including a
subset
of participants between 30 and 35 years of age. In the present
study, the phrases “CAP assessment year” or “CAP year” refer
to
the measurement occasion, and do not necessarily reflect
partici-
pants’ actual ages. Across CAP years 7–15, all participants
com-
pleted assessments based on their current grade in school,

begin-
ning with first grade, such that all of the children were around
the
same age at a particular assessment year (i.e., in third grade at
assessment year 9). At CAP year 16, participants completed the
assessment as close to their 16th birthday as possible. Thus,
there
was not a wide range of ages at testing, and siblings were rarely
administered the same tests during a single visit. Of particular
relevance to this study, repeated measures of self-reported
temper-
ament were available across CAP assessment years 9 –16 and
21,
and illness behaviors were assessed at CAP assessment years 21
and 30.
The complete CAP sample consists of 493 families (247 adop-
tive; 246 control). Within adoptive families, there were both ad-
opted and biological children. Adoptive and control families
were
matched on the adopted or control child’s gender, number of
children, and the father’s age, education, and occupational
status
(see Plomin & DeFries, 1983). Adopted children were recruited
from local social services in Colorado and placed into their
adop-
tive homes, on average, 29 days after birth (range � 2 to 172
days). Prior to adoption, they received foster care (Rhea,
Bricker,
Corley, Defries, & Wadsworth, 2013). The majority of the CAP
sample self-identifies as Caucasian (95% control parents, 90%
adoptive parents) and the remaining as Hispanic/Latino or Asian
American. The sample was of slightly higher socioeconomic
status
compared to the U.S. average at the time the CAP was initiated;

however, its variability is comparable to U.S. norms (Rhea,
Bricker, Wadsworth, & Corley, 2013, 2013). Ethical approval
for
the CAP study was provided by the University of Colorado,
Boulder and University of California, Riverside Institutional
Re-
view Boards.
Within these families, 714 adopted and matched control
children
and their siblings were included in the analysis sample from
those
invited to participate in the childhood CAP assessments (53%
male; 350 from adoptive families and 364 controls).1 Of the 350
participants from adoptive families, 30 were biological children
and 320 were adopted. Participants were nested within 477
fami-
lies, each including up to three siblings (51% single-child).
Anal-
yses included all individuals with demographic data (e.g.,
family
type, age, sex), and substantive data (i.e., temperament, illness
behavior) at the CAP year 9, year 21 or year 30 assessments. Of
the 714 participants, 88% (n � 625) had data at the year 9
assessment, 77% (n � 551) had data at the year 21 assessment,
and
39% (n � 275) had data at the year 30 assessment. Of the 625
individuals with data at year 9, 80% (n � 501) also had longitu-
dinal data at year 21, and 40% (n � 247) had longitudinal data
at
year 30. Some of the missing data at the adulthood assessments
is
the result of attrition; however, there were some assessments in
which siblings of the original probands were not recruited to
participate (Rhea, Bricker, Wadsworth, et al., 2013). For the
year

30 assessment, a smaller subset of participants was randomly
selected for recruitment due to funding constraints.
In terms of attrition analyses, those who participated at CAP
year 21 had parents with, on average, higher education (t(708)
�
2.57, p � .010) and occupational prestige (t(708) � 3.05, p �
.002).
Nonadopted participants and those from control families were
also
more likely to have data at year 21 (�2[1] � 5.84, p � .016,
and
�2[1] � 4.66, p � .031, N � 714, respectively). Data were
assumed to be missing at random (MAR), and maximum likeli-
hood estimation was applied which ensures the validity of
results
under this assumption. All predictive analyses were adjusted for
parent reports of their child’s past-year illnesses, doctor visits,
and
life events stress, parents’ highest reported education and
occupa-
tional status, as well as age, sex, family type, and adopted
status.
The emphasis for the current study was not to compare adoptees
and nonadoptees, and we did not have reason to expect illness
behaviors to substantially differ by adopted status.
Nevertheless,
we accounted for adopted status in statistical analyses to
improve
the accuracy of model parameters. We controlled for both
family
type and adopted status, because adoptive families also included
biological (nonadopted) children.
Measures

Temperament (CAP years 9, 21). Child temperament was
measured using the Colorado Childhood Temperament Inventory
(CCTI; Rowe & Plomin, 1977). The CCTI was derived from the
EAS survey (Buss & Plomin, 2014), and measures four key
temperament dimensions: Emotionality, Activity, Sociability,
and
Impulsivity (Plomin, Corley, Caspi, Fulker, & DeFries, 1998).
The
1 Two individuals were excluded from the analysis given a
complex
adoptive history.
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650 SISCO-TAYLOR ET AL.
present study included the Emotionality and Sociability CCTI
scales from CAP year 9. Repeated measures of child self-
reported
temperament, as measured with the CCTI, were included across
CAP years 9 –16. Year 9 was chosen because this was the first
middle childhood wave in which children provided self-reports
of
their temperament.2 Adult temperament was measured with the

Emotionality-Activity-Sociability Temperament/-Impulsivity
Sur-
vey (EAS-I; Buss & Plomin, 1975). The EAS-I further divides
emotionality into two subscales: Fearfulness and Anger. The
pres-
ent study included Emotionality-Anger, Emotionality-
Fearfulness,
and Sociability EASI scales from CAP year 21. Scales were
previously validated and show good reliabilities (CCTI
subscales,
median � � .80; EASI, median � � .72; see Hubert, Wachs,
Peters-Martin, & Gandour, 1982).
Illness behavior (CAP years 21, 30). All illness behavior
measures were gathered at CAP years 21 and 30 as part of a
larger
telephone-based questionnaire. Illness behavior was
operational-
ized as any measure reflecting participants’ evaluations and re-
sponses to bodily symptoms, regardless of whether these
physical
complaints were corroborated by an objective health measure.
This
contrasts with physical health, which refers to participants’
phys-
ical well-being, or the absence of bodily symptoms and any
acute
or chronic health conditions. Measures included in this study
are
discussed in detail below.
Somatic complaints. The first indicator of illness behavior
was a checklist of bodily symptoms, reflecting participants’ dis-
ease or symptom preoccupation (cf., Illness Behavior Question-
naire/IBQ; Pilowsky & Spence, 1975). In the current study, par-
ticipants indicated how often they experienced 9 common

symptoms with no definable diagnoses (e.g., dizziness, nausea,
stomachaches), each on a 6-point scale from never (1) to daily
(6).
A confirmatory factor analysis constrained loadings to be equal
across waves without loss of fit; this yielded an invariant
“somatic
complaint” factor (see online supplemental material).
Sick days. This item indexed participants’ investment in the
sick role, asking their “frequency of missed school/work due to
illness” on a 6-point scale from never (1) to daily (6).
Medication use. This self-report item assessed participants’
“frequency of taking medication for emotion/nerve problems”,
on
a 6-point scale from never (1) to daily (6). This item indexed
participants’ somatic (rather than psychological) orientation to-
ward illness, denial of life stresses, and affective disturbance
(general anxiety; cf., Illness Behavior Questionnaire/IBQ; Pi-
lowsky & Spence, 1975).
Control Variables
Parent-report data on participants’ past-year illness burden,
number of doctor visits, and life events stress from the CAP
year
9 assessment, the highest-reported parental education and
occupa-
tional prestige, as well as participants’ actual age at each CAP
assessment year (9, 21, 30), sex (�.50 � male; �.50 � female),
family type (�.50 � adoptive; �.50 � control), and adopted
status (0 � nonadoptee; 1 � adoptee), were examined as
potential
covariates. Illness burden was a composite of parents’ reports of
the total frequency of any health problems their children had
experienced in the past year: that is, sum of International

Classifica-
tion of Diseases and Related Health Problems symptom codes
(ICD-9; http://www.icd9data.com/2011/Volume1/default.htm),
each
multiplied by frequency of occurrence. Previous research
suggests
that parent report of children’s doctor visits and medical chart
reviews are generally in strong agreement (Craig, Cox, & Klein,
2002). Past-year life events stress was measured via the Social
Readjustment Rating Scale (SSRS; Coddington, 1972). Parents
indicated which of 33 life events had occurred during the
previous
year and how upsetting the event was for their child from 0 (not
at
all) to 3 (very much). These ratings were then summed. Partici-
pants’ exact age at each CAP assessment year were entered to
account for possible age effects and the distance between mea-
surement occasions. These variables were centered at ages 9,
21,
and 30 years, respectively. Two variables were created
reflecting
adoptive or control parents’ highest reported levels of education
and occupational attainment (National Opinion Research Center/
NORC scores) at CAP intake and year 7 assessments (i.e., the
highest scores between the two assessments, regardless of
whether
they came from the mother or father).
Statistical Procedures
Model-fitting was conducted using Mplus (Version 8, Muthén &
Muthén, 2012). A confirmatory factor analysis (CFA) using
robust
weighted least squares estimation (WLSMV) was conducted to
test

and validate a factor indexed by somatic complaints, staying
home
from school or work due to illness, and medication use (see
online
supplemental material). All models accounted for data
dependency
(i.e., nesting within families). The final illness behavior factor
included somatic complaints (factor score), sick days, and medi-
cation use.
The primary analyses applied multilevel, latent path regres-
sion models using robust maximum likelihood estimation
(MLR). Full-information modeling of all data was applied to
reduce any possible attrition bias. Models were evaluated at
both adulthood assessments (CAP years 21 & 30). In the first
step (Model 1), illness behavior was regressed on year 9 emo-
tionality and sociability, adjusting for covariates, and the fit
was evaluated with a comparison model in which the two paths
between year 9 emotionality and sociability to adult illness
behavior were dropped, but otherwise identical to Model 1.
Next, we fitted a mediation model (Model 2) with year 21
emotionality and sociability traits added to Model 1 as media-
tors of child temperament traits on adult illness behavior (see
Figure 1), and compared its fit with three nested models, in
which (a) the two paths from year 9 temperament traits to adult
illness behavior were dropped; (b) the six paths from child to
adult temperament traits were dropped; and (c) the three paths
from adult temperament traits to illness behavior were dropped,
but all otherwise identical to Model 2. Nested models were
compared using the likelihood ratio test formula specified by
Muthén and Muthén (2010; http://www.statmodel.com/chidiffs
.html) for MLR estimation, and mediation was evaluated with
the MODEL CONSTRAINT command. Grand-mean centering
was used for all predictors, except life events stress and demo-
graphics. Age was treated as a covariate, where year 9 age was
regressed out of all outcomes (i.e., adult temperament, illness

2 Models including Emotionality and Sociability CCTI
measures from
CAP year 10 were also evaluated, and results were similar to
year 9.
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651ILLNESS BEHAVIOR IN EARLY ADULTHOOD
http://www.icd9data.com/2011/Volume1/default.htm
http://www.statmodel.com/chidiffs.html
http://www.statmodel.com/chidiffs.html
behavior), and age at year 21 or year 30 was regressed out of
illness behavior (see Supplemental figure).
Results
Descriptive analyses are presented in Table 1. The average
age of participants across the three assessments was 9.48 years
at CAP year 9 (SD � 0.37), 21.54 years at CAP year 21 (SD �
0.74), and 31.86 years at CAP year 30 (SD � 1.28), respec-

tively. Natural logarithmic transformations were applied to
childhood doctor visits and illness burden variables to adjust for
significant positive skew and reported pairwise correlations
(see Table 2) were based on transformed values. Correlations
between child temperament and observed adult illness behavior
items were modest to small (rs ranged from �.12 to .12).
Notably, child emotionality was positively associated with year
21 somatic complaints (r(468) � .12, p � .008), whereas child
sociability was negatively associated (r(466) � �.12, p �
.010). Stronger associations emerged for the year 21 tempera-
ment dimensions (rs ranged from �.15 to .40). Higher
emotionality-fearfulness at year 21 was positively associated
with all three illness behavior indicators at CAP year 21 (n �
518 to 533; rs � .18 to .40, all ps � .001), and with somatic
complaints and medication use at CAP year 30 (r(258) � .19,
p � .002, and r(260) � .24, p � .001, respectively). Higher
emotionality-anger at year 21 was positively …
Who’s Addicted to the Smartphone and/or the Internet?
Bernd Lachmann
Ulm University
Éilish Duke
University of London
Rayna Sariyska
Ulm University
Christian Montag
Ulm University and University of Electronic Science and
Technology of China

Over the past few years, a growing amount of research has
considered the links between personality and
overuse (pathological use) of the Internet. Given the partial
overlap between problematic Internet and
smartphone use (PIU and PSU, respectively), the present study
seeks to investigate whether the same
personality traits can be linked to overuse of both platforms. A
total of 612 participants (177 males/435
females, mostly students) completed questionnaires assessing
both PIU and PSU, and the NEO Five
Factor Inventory (NEO-FFI) to measure the Five-Factor Model
of Personality and the Self-Directedness
scale of the Temperament and Character Inventory. Our results
indicate the existence of a common
personality structure linked to both PIU and PSU. Interestingly,
the associations between personality and
PIU were higher than those concerning PSU. Low Self-
Directedness appears to be the best predictor of
overuse on both digital platforms. Therefore, lower willpower
anchored in the personality trait Self-
Directedness may reflect the core of digital additive tendencies.
Public Policy Relevance Statement
The present study suggests the presence of a common
personality structure linked to both problematic
Internet use and problematic smartphone use. In this regard
especially, low Self-Directedness seems
to be the best predictor of problematic digital use.
Keywords: personality, Self-Directedness, Internet addiction,
smartphone addiction
The study of problematic smartphone use (PSU) is much
younger than that of its sibling, problematic Internet use (PIU;
among others, originating in the work of Young, 1998b). This is

understandable, as the first commercially successful smartphone
is
a relatively recent introduction, originating with the launch of
the
Apple iPhone in 2007 by Steve Jobs. Since then, the smartphone
has become a runaway success. Nearly 2 billion people
worldwide
currently own a smartphone (cited by Miller, 2012), and people
use this powerful technical device for many daily tasks
including
surfing the web, navigating a new city, communicating via
classic
phone calls, short message services, or newer communication
channels such as Whats-App and Facebook. Given the many
advantages of smartphones, it is important not to (over-)
patholo-
gize everyday life, including smartphone usage (e.g., see the
discussion of problematic Internet use by Kardefelt-Winther,
2014). Nevertheless, a growing body of research suggests the
existence of a dark side of smartphone usage (Lee, Chang, Lin,
&
Cheng, 2014; Montag, Kannen, et al., 2015), with some work
even
highlighting its potentially addictive nature (Duke & Montag,
2017a; Kwon, Kim, Cho, & Yang, 2013; Kwon, Lee, et al.,
2013;
Lin et al., 2015).
From this perspective, one can distinguish between generalized
(addictive behavior to the Internet in general) and specific (ad-
dicted to an application on the Internet) Internet addiction
(Brand,
2017). Davis (2001) points out that individuals suffering from
generalized Internet addiction could not have developed their
dysfunctional behavior (e.g., shopping, gambling, etc.) without

the
Internet, that is, the problematic Internet use itself determines
subsequent specific problem behaviors. On the other hand, indi-
viduals suffering from specific Internet addiction are using the
Internet only as instrument to satisfy their needs (e.g.,
shopping,
gambling, and gaming) but are not dependent on the Internet per
se. The same problematic behavior could exist in the real world,
outside of cyberspace. The phenomenon of social or peer
pressure
This article was published Online First November 20, 2017.
Bernd Lachmann, Institute of Psychology and Education, Ulm
Univer-
sity; Éilish Duke, Department of Psychology, Goldsmiths,
University of
London; Rayna Sariyska, Institute of Psychology and Education,
Ulm
University; Christian Montag, Institute of Psychology and
Education, Ulm
University, and Key Laboratory for NeuroInformation/Center
for Informa-
tion in BioMedicine, School of Life Science and Technology,
University of
Electronic Science and Technology of China.
The position of CM is funded by a Heisenberg grant, awarded to
him by
the German Research Foundation (DFG, MO2363/3-2).
Moreover, the
study is funded by a grant on computer and Internet gaming
awarded to
CM by the German Research Foundation (DFG, MO2363/2-1).
Correspondence concerning this article should be addressed to

Christian
Montag, Institute of Psychology and Education, Ulm University,
Helm-
holtzstr. 8/1, 89081 Ulm. E-mail: [email protected]
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Psychology of Popular Media Culture © 2017 American
Psychological Association
2019, Vol. 8, No. 3, 182–189 2160-4134/19/$12.00
http://dx.doi.org/10.1037/ppm0000172
182
mailto:[email protected]
http://dx.doi.org/10.1037/ppm0000172
can further aggravate PIU (Wu, Ko, Wong, Wu, & Oei, 2016;
Zhu,
Zhang, Yu, & Bao, 2015), for example, when playing online
games or using social network sites, mainly due to the fear of
missing out (Gil, Chamarro, & Oberst, 2015).
It is also evident that smartphones often interrupt everyday life
and are associated with time distortion while engaged in smart-
phone use (Duke & Montag, 2017a; Lin et al., 2015).
Problematic

smartphone use (PSU) may also lead to a decrease in
productivity
(Montag & Walla, 2016). In some countries, law enforcement
has
banned smartphone use in situations such as driving a car,
owing
to the distraction of drivers from traffic and the potential for
creating dangerous situations (Coben & Zhu, 2013; Falkner,
2011).
Given that many users prolong their smartphone sessions even
when in the relative privacy of their bedrooms (Montag,
Kannen,
et al., 2015), it comes as no surprise that PSU is often
accompanied
by poor sleep quality (Yogesh, Abha, & Priyanka, 2014) and in
some cases, lower work engagement the next morning (Lanaj,
Johnson, & Barnes, 2014). In the context of well-being and
smart-
phone use, a relatively recent study highlights the importance of
including and assessing the motivation underlying people’s use
of
their smartphones (Ohly & Latour, 2014). Also related to well-
being a recent study finds evidence for an association between
PIU, life satisfaction, and commuting (during commuting the
Internet will be accessed mostly via portable devices like smart-
phones): A more negative attitude towards commuting was asso-
ciated with higher PIU and lower life satisfaction levels (Lach-
mann, Sariyska, Kannen, Stavrou, & Montag, 2017). This short
summary of current literature highlights the potential negative
effects of PSU in daily life and underlines the timeliness of the
current research.
Two theoretical models of Internet addiction have recently been
published. In their consideration of Internet gaming disorder,
Dong
and Potenza (2014), propose a model that emphasizes the

influence
of craving on the use of Internet games. Based on the work of
Davis (2001), a more general model of Internet addiction has
been
developed by Brand, Young, and Laier (2014), which has
become
the basis for the Interaction of Person-Affect-Cognition-
Execution
(I-PACE) model (Brand, Young, Laier, Wölfling, & Potenza,
2016). In this model, the authors focus more on specific types
of
Internet addiction like shopping or gambling than generalized
Internet addiction (of note they use the term Internet use
disorder).
Generalized Internet addiction may be described as a situation
in
which an individual is addicted to the Internet in general rather
than to a specific application of the Internet (Brand, 2017). Of
relevance to the current study, a key predisposing factor for the
development of a generalized Internet addiction within this
model
is personality (Brand et al., 2016).
With respect to PIU, a large body of research has been con-
ducted, which demonstrates the importance of a number of per-
sonality dimensions in predicting PIU (see review by Montag &
Reuter, 2015)1. The study of personality is important because it
describes rather stable characteristics of a person, manifesting
in
typical emotional reactions, cognitive thinking patterns, and be-
havior in everyday life (Montag & Panksepp, 2017). Moreover,
personality is linked to important real-life variables, such as
health
behavior (Bogg & Roberts, 2004), longevity (Jackson,
Connolly,
Garrison, Leveille, & Connolly, 2015), and vulnerability for af-

fective disorders (Lahey, 2009). Among the studied (and often
highlighted) factors in the field of Internet addiction, high Self-
Directedness, a personality trait describing persons with high
will-
power and who are reasonably content with themselves, might
represent a resilience factor against PIU (Montag et al., 2011;
Montag, Jurkiewicz, & Reuter, 2010; Sariyska et al., 2014). Be-
yond these results, several other research findings indicate that
the
personality dimensions Neuroticism (positively linked; Hardie
&
Tee, 2007) and Conscientiousness (negatively linked; Montag et
al., 2010) must be mentioned to understand PIU and PSU.
Recently, a questionnaire has been published to assess smart-
phone addiction: Kwon, Kim, et al. (2013) and Kwon, Lee, et al.
(2013) have also demonstrated that there is an overlap between
Internet and smartphone addiction but that this overlap is far
from
perfect. In their questionnaire, several facets of PSU are consid-
ered, including daily life disturbance, positive anticipation of
smartphone usage, withdrawal symptoms in absence of the
smart-
phone, cyberspace-oriented relationships, and problematic use
of
smartphone and development of tolerance (see Kwon, Lee et al.,
2013, p. 5). Interestingly, in the Kwon study, it appeared that
the
overlap between PIU and PSU is about r � .40. Thus, 16% of
the
variance in both concepts overlaps (i.e., .402). Although this
over-
lap might not seem excessively high, it underlines a certain
resem-
blance between PIU and PSU (note: imagine a smartphone

without
access to the Internet; it virtually would be worthless). Given
the
high number of findings describing the association between PIU
and personality, one could ask the question if the cause for the
observed overlap possibly can manifest in a similar personality
structure of PIU and PSU.
Therefore, the question arises whether the personality traits
linked to Internet addiction are also linked to smartphone addic-
tion. To answer this question, we collected data on Internet
addic-
tion, smartphone addiction, and personality to search for similar
underlying correlation patterns. This enabled us to investigate
whether the same personality variables were associated with
both
PIU and PSU and also allowed us to examine the strength of
these
associations. Beyond that, the presence of similar patterns
between
personality variables and PIU/PSU implicated the existence of a
possible trait underlying both PIU and PSU. The personality
struc-
ture of this trait was further examined to see whether similar
patterns emerged between the personality variables and both
PIU
and PSU, as any such finding would support the assumption that
the same personality traits could be linked to both Internet and
smartphone addiction.
Based on previous research, we predicted that low Self-
Directedness, low Conscientiousness, and high Neuroticism
would
be linked to higher problematic Internet use. Given the partial
overlap between Internet and problematic smartphone use, we
expected that the same patterns would be visible between these

personality traits and PSU. Finally, we assumed a common
under-
lying trait for PIU and PSU that should be affected by the same
personality variables.
1 Please note, that there is some controversy in the research
over how
best to refer to problematic Internet use (PIU). We use the terms
PIU and
Internet addiction somewhat synonymously, given that the
inventory we
used to assess PIU is called the Internet Addiction Test (please
see method
section of the current paper). This controversy has not been
made easier by
the inclusion of a distinct form of PIU–Internet Gaming
Disorder—in
section III of DSM–5 (Petry & O’Brien, 2013; Pontes &
Griffiths, 2014).
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183PERSONALITY AND DIGITAL ADDICTIVE
TENDENCIES

Method
Participants
In the present study, N � 612 (177 males and 435 females)
participants contributed data, whereof 572 (160 males and 412
females) owned a smartphone. All participants are part of the
Ulm
Gene Brain Behavior Project and part of the data has been pub-
lished in the context of an Affective Neuroscience Framework
earlier (Montag, Sindermann, Becker, & Panksepp, 2016; note
that
this paper deals with a different topic and only the smartphone
addiction scale (SAS) data have been presented with respect to
correlations of another questionnaire not of relevance for the
present study). The mean age of the sample was 23.55 years (SD
�
5.92). Participants were recruited in a university context, so
most
of the sample consists of students. All participants completed
several questionnaires dealing with personality and technology
use. For the purposes of the present study, participants provided
information on their problematic Internet and smartphone use
(questionnaires are described below). They also completed
several
questionnaires to assess personality (for more detail, see
below).
All participants provided written consent before participation in
the study. The study was approved by the ethics committee at
Ulm
University.
Questionnaires
All participants completed Young’s Internet Addiction Test
(IAT; Young, 1998a). This questionnaire consists of 20 items,

answered on a 5-point Likert scale, ranging from rarely (1) to
always (5). Items used in the IAT are, for example, “How often
do
you try to hide how long you’ve been online?” or “How often do
you find that you stay online longer than you intended?” Our
German translation of the IAT has been used in several of
earlier
studies, such as Montag et al. (2011) or Sariyska et al. (2014),
Sariyska, Reuter, Lachmann, and Montag (2015). The internal
consistency of the questionnaire in the present sample was very
high (� � .88). Scoring the measure requires summing up of the
20 items. Higher scores indicate higher addictive tendencies to-
ward the Internet. The possible range of scores is between 20
and
100 points.
The SAS has been published by Kwon, Lee, et al. (2013) and
consists of 33 items, answered on a 6-point Likert scale,
ranging
from strongly disagree (1) to strongly agree (6). Items used
within
the questionnaire are, for example, “My life would be empty
without my smartphone.” The questionnaire has been translated
twice (forward- and back-translation): first from English to Ger-
man and second from German to English language by two inde-
pendent psychologists. The internal consistencies of our German
translation are very high (� � .98). Similar to the IAT, scoring
the
SAS requires summing the individual items, with higher scores
representing greater addictive tendencies toward the
smartphone.
The possible range of scores is between 33 and 198 points.
To assess the Five-Factor Model of personality, we
administered
the NEO Five-Factor Inventory by Costa and McCrae (1992) in

German, as translated by Borkenau and Ostendorf (1993). This
questionnaire consists of 60 items scored on a 5-point Likert
scale,
ranging from strongly disagree (1) to strongly agree (5). The
Five-Factor Model of personality assesses Openness for Experi-
ence (Cronbach’s � � .75; sample item: “I am intrigued by the
patterns I find in art and nature”), Conscientiousness (� � .85;
“I
keep my belongings neat and clean”), Extraversion (� � .79; “I
like to have a lot of people around me”), Agreeableness (� �
.79;
“I try to be courteous to everyone I meet”), and Neuroticism (�
�
.86; “I often feel inferior to others”). Higher scores indicate
higher
scores on each dimension. Some items need to be recoded
before
the scores can be summed up.
Finally, given its relevance for a better understanding of
Internet
addiction (Sariyska et al., 2014), we asked participants to
answer
the items measuring Self-Directedness (e.g., “I usually am free
to
choose what I will do” or “My behavior is strongly guided by
certain goals that I have set for my life”) from the Temperament
and Character Inventory by Cloninger, Svrakic, and Przybeck
1993 (German translation by Cloninger & Richter, 1999). These
items are answered with either “yes” (1) or “no” (0). Internal
consistencies for the Self-Directedness scale were satisfying (�
�
.87). Higher scores indicate higher ratings on the dimension of
Self-Directedness. As with the NEO Five-Factor Inventory,
some

items required recoding before the scores were added.
Statistical Analyses
Owing to skewed distributions of IAT and SAS variables, we
used Spearman’s correlations to analyze the associations
between
the variables of interest. Gender effects were tested with Mann–
Whitney U tests. Although cut-off points for the distinction of
“problematic” or “addict” status have been mentioned in some
work (Widyanto & McMurran, 2004), we refrain from doing so
here. Debate remains over the precision of such cut-off values,
and
we understand the scores/diagnosis as a continuum. The correla-
tions between personality variables and PIU/PSU were further
investigated using Fisher’s z test. As the results indicated the
particular importance of the personality dimension Self-
Directedness, we conducted a hierarchical regression analysis,
which included the investigation of a composite trait called
prob-
lematic digital use, derived from a principal component analysis
(PCA). The extraction criterion for the PCA was, according to
Kaiser-Guttman, an Eigenvalue greater than 1. We also analyzed
the correlation patterns of the subdimensions of Self-
Directedness
in relation to PIU and PSU. All analyses have been computed in
SPSS 22.
Results
Data Inspection
Visual inspection revealed skewed distributions for the
variables
IAT and SAS. Because the variables were non-normally distrib-
uted, we decided to use nonparametric testing. The distributions

are depicted in Figure 1. We did not find any outliers on any
variables.
Age, Gender and IAT/SAS
Gender was significantly associated with IAT scores (U �
32978.50, p � .005) but not the SAS (U � 31976.00, p � .582).
On the IAT scale, males reported higher scores than females
(IAT:
males M � 32.45; SD � 10.20 vs. females M � 29.84; SD �
7.83;
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184 LACHMANN, DUKE, SARIYSKA, AND MONTAG
SAS: males M � 66.88; SD � 27.20 vs. females M � 64.58; SD
�
23.69). Age was associated with both IAT (rho � �.09, p �
.031)
and the SAS (rho � �.16, p � .001). Mean and median scores
for
the scales are as follows (SAS: M � 65.22, SD � 24.72 and
Median � 61.00; IAT: M � 30.59, SD � 8.66 and Median �
28.00).

Personality and IAT/SAS
First (and in line with the works by Kwon, Kim, et al., 2013;
Kwon, Lee, et al., 2013), a moderate association between the
SAS
and IAT was observed (rho � .53, p � .001). All other correla-
tions between personality and the two technology use variables
are
depicted in Table 1. Fishers’ z test was used to compare the
correlations between personality variables and IAT/SAS scores.
Significantly higher correlations for the IAT compared with the
SAS score were found for Extraversion (z � �2.4, p � .008),
Agreeableness (z � 1.8, p � .039), Conscientiousness (z � 2.1,
p � .023), and Self-Directedness (z � 1.8, p � .037). Openness
showed a significantly inverse correlation with the SAS and was
not related to the IAT score. Although some of the correlations
between SAS, IAT, and personality (Neuroticism,
Agreeableness,
Conscientiousness and Self-Directedness) are in the same direc-
tion, other correlations are unique (e.g., Extraversion and IAT).
For reasons of completeness, we also provide the correlation
patterns (including Fisher’s z tests) for males and females sepa-
rately in Table 2 though these patterns are largely similar for
both
genders. The strongest correlations appear between Self-
Directedness and both SAS (rho � �.33, p � .001) and IAT
(rho � �.41, p � .001).
Principal Component Analysis of IAT and SAS and
Regression Model
A (unrotated) PCA of IAT and SAS sum scores revealed one
underlying composite trait with an Eigenvalue of � � 1.55, ex-
plaining 77.6% of the variance of both addiction questionnaires
(no other Eigenvalue � 1). We call this composite trait

“problem-
atic digital use”. As a follow-up analysis, we inserted this trait
as
a dependent variable in a hierarchical regression model. As
inde-
pendent variables, we included demographic variables (age and
gender) in the first block, due to their significant associations
with
PIU and/or PSU and their general well-known role in both con-
structs. Given the robustness of the association between Self-
Directedness and PIU/PSU, this variable was entered in the
second
block. Big Five personality traits were inserted in the third
block.
Demographic variables alone explained 2.6% of the variance,
Self-Directedness added a further 15.6% to the model, and the
Big
Five variables an increment of 5.0% of problematic digital use.
The model that accounts for most of the variance (F(8,563) �
21.25,
p � .001), explains a total of 23.2% variance (Low). Self-
Directedness, (low) Conscientiousness, (low) Agreeableness,
(high) Extraversion, (low) Openness; and (high) Neuroticism
were
the predictors of the model, as age and gender did not achieve
significance in the final model (Table 3).
Self-Directedness and SAS/IAT:
A Close Look
The analysis in this results section demonstrates the importance
of (low) Self-Directedness for a better understanding of digital
Figure 1. Distibution of the Internet Addiction (left) and
Smartphone Addiction (right) Test scores are
presented. See the online article for the color version of this

figure.
Table 1
Common Personality Relationships to Internet Addiction Test
(IAT)/Smartphone Addiction Scale (SAS) Scores
Sample Neuroticism Extraversion Openness Agreeableness
Conscientiousness Self-Directedness
SAS N � 572 .21�� .01 �.14�� .11�� .23��
�.33��
Fisher’s z ns z � �2.4, p � .008 z � �2.9, p � .002 z � 1.8, p
�
.039
z � 2.1, p � .023 z � 1.8, p � .037
IAT N � 612 .26�� .13�� .03 �.21�� .34��
�.41��
Note. Spearman correlations are presented. Significant
associations common to both SAS and IAT are bold.
�� p � .01. �� p � .001.
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185PERSONALITY AND DIGITAL ADDICTIVE
TENDENCIES
overuse. As Self-Directedness is composed of several subdimen-
sions, we consider the subscales of this trait and their individual
associations with both IAT and SAS. The character trait Self-
Directedness is composed of the subscales Responsibility versus
Blaming (SD1), Purposefulness versus Lack of Goal Direction
(SD2), Resourcefulness versus Inertia (SD3), Self-Acceptance
ver-
sus Striving (SD4) and Congruent Second Nature versus Bad
Habits (SD5). For a more detailed discussion, please see the
work
by Kose (2003). As can be seen in Table 4, all subscales are
significantly associated with both forms of problematic digital
use.
Hence, no individual facet appears to be of special relevance,
rather the complete personality dimension of Self-Directedness
is
an important factor in problematic digital use.
Discussion
The present study investigated whether the same personality
traits are related to both PIU and PSU. This research question is
of
importance, because both PIU and PSU are moderately, though
not
perfectly, associated with each other. Therefore, we
investigated
whether one of the most prominent personality constructs linked
to
PIU—namely (low) Self-Directedness—would also predict
higher

PSU. Our study revealed that low Self-Directedness is indeed
associated with higher PSU and PIU, therefore, clearly
contribut-
ing toward the shared variance of both constructs. Furthermore,
we
extracted a common trait (problematic digital use) underlying
both
PIU and PSU. This trait was determined by the same personality
variables as PIU and PSU, especially by (low) Self-
Directedness.
People with lower Self-Directedness can be described as
dissatis-
fied with their personalities, not able to achieve their planned
goals
and have lower will-power. Given the importance of Self-
Directedness in the better understanding of PIU in previous
studies
(Montag et al., 2010, 2011 and Sariyska et al., 2014), the
present
study shows that these findings can also be extended to PSU.
Moreover, the frequently observed association between PIU and
Self-Directedness has been replicated again in a different
sample
in the present study.
Our findings highlight the importance of considering
personality
variables when investigating factors associated with Internet ad-
diction, as outlined in the I-PACE model of Internet addiction
(Brand et al., 2016). Although this model is theoretically
plausible,
it requires additional empirical support (Brand, 2017). With the
present study, we can contribute some empirical evidence (in
the
context of personality) toward the validity of this model.

As with the personality-addiction associations discussed above,
in the present study Fisher’s z test revealed that the associations
between personality and PSU are a bit weaker compared with
the
relationships with PIU, which may have something to do with
the
slightly different topics investigated: although a smartphone
with-
out an online connection is rather useless, it can be used in this
manner (and therefore only a moderate overlap with PIU can be
expected); generalized PIU assesses, in broad terms, one’s own
addictive tendencies, going beyond the rather small domain of
smartphone usage. These differences are mirrored in the results
of
our gender analysis. As the literature has provided evidence
(not
uniformly, but often) for a more “male Internet addict” (Ko,
Yen,
Yen, Chen, & Chen, 2012; Lachmann, Sariyska, Kannen,
Cooper,
& Montag, 2016; Shaw & Black, 2008), the present study shows
that this may again only be true for the broad term of PIU, but
not
PSU, where we could not find significant gender differences in
our
sample. This ultimately may be related to some channels being
prominent on a smartphone, but not on desktop computer, such
as
the social communication channel WhatsApp. In a recent study,
we were able to show that these channels are used more
frequently
by females compared with males (Montag, Błaszkiewicz,
Sariyska,
et al., 2015). We do not want to follow this point further
because

it was not the main focus of the manuscript and we did not set
up
a hypothesis with respect to gender issues in digital overuse.
At this point in the discussion, we also want to highlight the
less
prominent, though still important, links between personality
traits
of the Five-Factor Model of personality and both PIU and PSU.
In
line with earlier studies (Hardie & Tee, 2007; Montag et al.,
2010,
Table 2
Personality and the Internet Addiction Test (IAT)/Smartphone
Addiction Scale (SAS) Scores Distinguished by Gender
Sample Neuroticism Extraversion Openness Agreeableness …

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