Personality types and subjective well-being among people living with HIV: a latent profile analysis

Abstract

Purpose
We examined whether three types of personality (i.e. resilient, undercontrolled and overcontrolled) based on the Big Five personality taxonomy could be replicated among people living with HIV (PLWH). We also aimed to establish significant sociodemographic and clinical covariates of profile membership and verify whether these profiles are related to the subjective well-being (SWB) of participants.

Methods
770 PLWH participated in this study. The Big Five personality traits were evaluated with the NEO-FFI questionnaire. SWB was operationalised by satisfaction with life (Satisfaction with Life Scale) and positive and negative affects (PANAS-X). Moreover, sociodemographic and clinical variables were collected.

Results
Latent profile analysis was used to identify personality types among participants. Instead of the three profiles most frequently reported in the literature, we identified a four-profile model (the resilient, undercontrolled, overcontrolled and the average profile type) as the best fit to the data. These profiles did not differ with regard to sociodemographic and clinical covariates. However, significant differences in SWB across profiles were noted, i.e. the highest SWB was observed among members of the resilient profile, and overcontrollers and undercontrollers were almost equally regarded as second best in SWB level, whereas the average profile consists of PLWH with the worst SWB.

Conclusion
Identifying personality types in clinical settings enables more comprehensive understanding of interrelations between personality and health. Regarding PLWH, the typological approach may shed new light on ambiguous results devoted to the role of personality in well-being of these patients.

Full text

Vol.:(0123456789)
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Quality of Life Research (2020) 29:57–67
https://doi.org/10.1007/s11136-019-02288-5
Personality types andsubjective well‑being amongpeople living
withHIV: alatent prole analysis
MarcinRzeszutek1 · EwaGruszczyńska2
Accepted: 27 August 2019 / Published online: 10 September 2019
© The Author(s) 2019
Abstract
Purpose We examined whether three types of personality (i.e. resilient, undercontrolled and overcontrolled) based on the
Big Five personality taxonomy could be replicated among people living with HIV (PLWH). We also aimed to establish
significant sociodemographic and clinical covariates of profile membership and verify whether these profiles are related to
the subjective well-being (SWB) of participants.
Methods 770 PLWH participated in this study. The Big Five personality traits were evaluated with the NEO-FFI question-
naire. SWB was operationalised by satisfaction with life (Satisfaction with Life Scale) and positive and negative affects
(PANAS-X). Moreover, sociodemographic and clinical variables were collected.
Results Latent profile analysis was used to identify personality types among participants. Instead of the three profiles most
frequently reported in the literature, we identified a four-profile model (the resilient, undercontrolled, overcontrolled and
the average profile type) as the best fit to the data. These profiles did not differ with regard to sociodemographic and clinical
covariates. However, significant differences in SWB across profiles were noted, i.e. the highest SWB was observed among
members of the resilient profile, and overcontrollers and undercontrollers were almost equally regarded as second best in
SWB level, whereas the average profile consists of PLWH with the worst SWB.
Conclusion Identifying personality types in clinical settings enables more comprehensive understanding of interrelations
between personality and health. Regarding PLWH, the typological approach may shed new light on ambiguous results devoted
to the role of personality in well-being of these patients.
Keywords HIV/AIDS· Personality types· Typological approach
Introduction
For at least two decades, there has been ongoing debate
between proponents of the traditional, dimensional approach
to the study of personality traits [e.g. 1, 2] and their oppo-
nents, advocating for broader implementation of the typo-
logical perspective, which operationalises personality not
via interindividual differences across isolated traits, but in
terms of broader personality types that characterise each per-
son [e.g. 3–5]. More specifically, the authors representing
this latter standpoint argue that the dimensional approach is
lacking in describing interrelated configurations within per-
sonality structure as well as their dynamics [6], which is one
of the core elements across various personality definitions,
starting even from the classic conceptualisations of this term
[7]. The typological approach to personality is obviously not
a new idea and has a long history in personality psychology,
but it relied mainly on theoretically vague constructs devoid
of empirical evidence [8]. However, over the last 20years,
several authors have built a solid empirical basis to under-
stand personality as a functional whole, going beyond a set
of separately analysed dimensions [9]. With regard to such
understanding of personality, the most common typology
was first provided by Robins etal. [4], who distinguished
three personality types relying on ego-resiliency and ego-
control theory (see [10]): in other words, resilient type (e.g.
* Marcin Rzeszutek
marcin.rzeszutek@psych.uw.edu.pl
Ewa Gruszczyńska
egruszczynska@swps.edu.pl
1 Faculty ofPsychology, University ofWarsaw, Stawki 5/7,
00-183Warsaw, Poland
2 Faculty ofPsychology, University ofSocial Sciences
andHumanities, Chodakowska 19/31, 03-815Warsaw,
Poland
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58 Quality of Life Research (2020) 29:57–67
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‘self-confident, emotionally stable, energetic’), undercon-
trolled type (‘stubborn, active, impulsive’) and overcon-
trolled type (‘sensitive, introverted and dependable’). In
the subsequent years, this typology was replicated in many
samples with various methods and personality theories [e.g.
11, 12], including Big Five personality traits [3, 13].
As far as Big Five taxonomy is concerned, the resilient
type is characterised by high extraversion and conscientious-
ness, low neuroticism and relatively high values on the other
traits; the overcontrolled type reveals especially high neu-
roticism and conscientiousness, low extraversion and open-
ness; and the undercontrolled type obtains predominantly
low scores in conscientiousness and agreeableness [3, 14].
In addition, several studies found that overcontrolled and
undercontrolled individuals have internalising problems
(e.g. depression, anxiety, shyness and low sociability) and
externalising problems (e.g., aggression, attention problems,
but high sociability), respectively, whereas resilient people
are usually free from both these tendencies [e.g. 9, 12].
Until now, almost all studies on personality types have
been conducted in non-clinical samples [8, 9], thus very lit-
tle is known whether these three types of personality are
recognisable also in the clinical settings among individuals
struggling with chronic disease and related psychological
distress [15]. It is especially important regarding studies sug-
gesting that personality is more strongly associated with sub-
jective health indicators (e.g. distress, quality of life) com-
pared with objective medical parameters [16]. Moreover,
identifying personality types in clinical samples may shed
new light on inconclusive findings on the link between per-
sonality, health and well-being [17]. For example, extraver-
sion consists of two facets, both of which have contradictory
effects on health outcomes. Namely, whereas positive affect
is usually predictor of good health and reduced risk of ill-
ness [18], sensation seeking is mostly associated with risky
health behaviours and substance use [19]. Furthermore, it
was observed that extraversion might be differently linked
to health depending on its link with other Big Five traits,
particularly with conscientiousness [11].
The relationship between personality, health and well-
being is of special importance with regard to people living
with HIV (PLWH), who despite great progress in HIV treat-
ment and increasing life expectancy [20], are still faced with
intense HIV-related distress [21, 22]. Particularly, PLWH
are constantly reporting lower levels of well-being, not only
in comparison with the general population [23] but also
against other chronic illnesses [24]. When looking for factors
associated with well-being of these patients, an interesting
trend emerges associated with the changing nature of this
illness with time, from fatal disease in the past to manage-
able chronic health problem at present [25]. Namely, com-
pared with older studies pointing to the major role of clinical
variables [e.g. 26, 27], an increasing number of studies have
recently reported that psychosocial factors outweighed the
role of medical factors as predictors of PLWH well-being
[e.g. 28, 29]. Out of these psychosocial factors, personality
traits postulated by the Big Five theory may play a major
role [30–32].
Some authors observed the differences between the
selected Big Five traits (i.e. higher neuroticism and lower
conscientiousness) among PLWH compared with the general
US population [33]. It is still an open question whether the
differences in personality may be the reason for undertaking
risky health behaviours, as a potential pathway to HIV infec-
tion [34]. However, the above-mentioned studies were based
on the traditional dimensional approach, and thus provided
sometimes equivocal findings with respect to the relation-
ship between personality and various dimensions of well-
being [e.g. extraversion, 31 vs. 32]. Thus, implementing the
typological approach may bring new understanding to the
ambiguous results concerning the relationship of personality
traits with various aspects of functioning of PLWH.
Current study
In line with the reasoning set out above, the aim of our study
was threefold. First, we wanted to verify whether the most
often recognised three types of personality (i.e. resilient,
undercontrolled and overcontrolled) could also be identi-
fied among the clinical sample of PLWH. Additionally, we
investigated if there are differences in Big Five personal-
ity traits between PLWH and the Polish general population.
Second, we examined which sociodemographic and clinical
variables are significantly associated with the obtained per-
sonality types. Finally, we tested if these types of personality
are related to the subjective well-being (i.e. satisfaction with
life and positive and negative affects), after controlling for
sociodemographic and clinical correlates.
Method
Participants andprocedure
Participants were recruited from the State Hospital of
infectious diseases outpatient clinic. The following eligi-
bility criteria were implemented: 18years of age or older,
confirmed medical diagnosis of HIV+ and having received
antiretroviral treatment in the clinic where the study was
organised. The exclusion criteria were HIV-related cogni-
tive disorders, as screened by medical doctors. Of the 843
patients eligible for the study, 72 declined to participate,
which gives a participation rate of 91%. Thus, 771 adults
with a medically confirmed diagnosis of HIV infection pro-
vided informed consent to participate in the study. After
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59Quality of Life Research (2020) 29:57–67
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the informed consent was obtained, the study participants
completed a paper version of the questionnaires. The study
was approved by the local ethics commission. One person
was excluded from the final dataset due to high percentage
of missing answers. Table1 describes sociodemographic
and clinical characteristics for the final sample of 770 par-
ticipants in detail.
Measures
Personality dimensions
Personality was measured using the NEO-Five Factor Inven-
tory (NEO-FFI) proposed by Costa and McCrae [35]. The
NEO-FFI consists of 60 items (12 per trait), and participants
respond to each item on a five-point scale from 0 (strongly
disagree) to 4 (strongly agree). Higher summarised scores
imply higher levels of each trait. The Cronbach’s alpha coef-
ficients for the current study were .82 for neuroticism (N),
.69 for extraversion (E), .61 for openness to experience (O),
.71 for agreeableness (A) and .53 for conscientiousness (C).
The Cronbach’s alpha obtained in the official adaptation of
NEO-FFI [36] in the general Polish sample were .80 for
neuroticism (N), .77 for extraversion (E), .68 for openness
to experience (O), .68 for agreeableness (A) and .82 for con-
scientiousness (C).
Subjective well‑being indicators
Subjective well-being was evaluated using the Satisfaction
with Life Scale [SWLS; 37] together with the Positive and
Negative Affects [PANAS-X; 38], according to the con-
ceptualisation proposed by Diener. He defined subjective
well-being as individual cognitive and affective evalua-
tions of person’s own life [39]. The SWLS measures over-
all satisfaction with life. It is composed of five items on a
seven-point scale ranging from 1 (strongly disagree) to 7
(strongly agree). Thus, a higher total score indicates higher
level of life satisfaction. Cronbach’s alpha coefficient in the
studied sample was .87. The affective component of sub-
jective well-being describes an experience of longer-lasting
emotional responses, including both positive and negative
affects. Thus, 20 descriptions of feelings and emotions from
the PANAS-X were used: 10 for positive affect (e.g. ‘proud’,
‘excited’) and 10 for negative affect (e.g. ‘depressed’,
‘stressed’). Participants rated their answers on a five-point
response scale from 1 (not at all) to 5 (strongly). The Cron-
bach’s alpha coefficients obtained in this study were .86 for
the positive affect scale and .91 for the negative affect scale.
Table 1 Sociodemographic and clinical variables in the studied sam-
ple (N = 770)
M mean, SD standard deviation
Variable N (%)
Gender
Male 599 (77.8%)
Female 171 (22.2%)
Age in years (M ± SD) 38.58 ± 10.31
Marital status
In relationship 440 (57.1%)
Single 330 (42.9%)
Education
Elementary 33 (4.3%)
Basic vocational 79 (10.3%)
Secondary 270 (35.1%)
University degree 388 (50.4%)
Employment
Full employment 548 (71.2%)
Unemployment 101 (13.1%)
Retirement 24 (3.1%)
Sickness Allowance 97 (12.5%)
Financial status (from 1 = very low to 5 = very
high)
2.50 ± 0.94
Sexual orientation
Heterosexual 282 (36.6%)
Homosexual 413 (53.6%)
Bisexual 75 (9.7%)
Place of infection
Home country 694 (90.1%)
Abroad 76 (9.9%)
Mode of infection
Sex with men 525 (68.2%)
Sex with women 85 (11.0%)
Drugs 97 (12.6%)
Medical procedures 8 (1.0%)
Others 54 (7.0%)
HIV/AIDS status
HIV+ only 629 (81.7%)
HIV/AIDS 140 (18.2%)
HIV infection duration in years (M ± SD) 8.07 ± 7.57
Antiretroviral treatment duration in years (M ± SD) 6.27 ± 5.86
CD4 count (M ± SD) 504.63 ± 238.65
Viremia
Detectable 193 (25.1%)
Undetectable 518 (67.3%)
Don’t know 58 (7.5%)
Addiction
Yes 117 (15.2%)
No 653 (84.8%)
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60 Quality of Life Research (2020) 29:57–67
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Data analysis
A comparison between the general population and PLWH
on every Big Five personality dimension was made with the
one-sample t test. Next, we used latent profile analysis to the
identified types of people who are at the same time highly
similar on personality traits within their group and highly
dissimilar across the groups [40]. Analysis was performed
on standardised values (z-scores), with reverse values for
N to facilitate interpretation, which in such case should be
understood as emotional stability [ES; see 5]. Models from
one- to five-profile solutions were examined; as there have
been only a few studies on clinical samples, the optimal
solution could be different from the most popular in the
healthy populations (i.e. three personality profiles).
To choose between competing models, we used a vari-
ety of indicators. For Akaike’s information criterion (AIC),
Bayesian information criterion (BIC) and the sample-size
adjusted BIC (SABIC), the lowest values indicate a model
with the best fit [41]. The Vuong–Lo–Mendell–Rubin likeli-
hood ratio test (VLMR) and the adjusted Lo–Mendell–Rubin
likelihood ratio test (LMR) directly compare neighbouring
k − 1 and k profile models; significant p-values suggest that
the k profile model fits the data better than a model with one
profile less [42]. Entropy is an index of classification accu-
racy, and values closer to 1 indicate better profile separa-
tion [43]. Finally, a size of the smallest profile is a practical
criterion since a profile covering less than 5% of the sample
may be hard to replicate. However, in clinical samples, even
such small-size profiles may reflect rare but meaningful
subgroups; thus the final decision on a number of profiles
should be based on thorough inspection.
After establishing a number of profiles (here personality
types), a bias-adjusted three-step procedure [43] was used
for both testing their significant correlates (auxiliary vari-
ables; maximum likelihood method) and relationship with
distal outcomes in terms of SWB dimensions (Bayesian
hierarchical clustering method) [43]. Additionally, since
this automatic procedure does not allow directly for control
of correlates when examining the profile membership as a
predictor of SWB, we repeated it manually as recommended
by Asparouhov and Muthén [44]. Namely, after establish-
ing profile membership in step 1, we specified the posterior
profile membership probability as logistic function of the
correlates in step 2; [44] and such values were used then in
further analysis of relationship with distal outcomes. Thus,
they can be interpreted as adjusted analysis controlled for the
potential sociodemographic and clinical confounders. The
analyses were performed by means of IBM SPSS Statistics
version 25 [45], Mplus version 8.2 [46] and Latent Gold
version 5.1.0.19007.
Results
Descriptive statistics andcomparison
withthegeneral population onBig Five traits
Table2 provides the basic descriptive statistics for our sam-
ple and results of comparison with the general population.
The population data are taken from an official adaptation
and standardisation sample on NEO-FFI [36; N = 2041,
mean age = 27.51 ± 13.25years, 52% women, 11% univer-
sity degree]. PLWH are on average lower on four out of
five dimensions: E, O, A and C, and the same as general
population on N.
Due to these differences, latent profile analysis was car-
ried out twice: once on data standardised by sample means,
and then on data standardised by population means. The
Table 2 Descriptive statistics
and one-sample t test for
comparison with population
means on big five dimensions
N neuroticism, E extraversion, O openness to experience, A agreeableness, C conscientiousness, SWL satis-
faction with life, PA positive affect, NA negative affect
Variables Sample
N = 770
Population
N = 2041 t p Cohen’s d
M SD Min–max Skewness Kurtosis MSD
Personality
N 22.44 8.78 0–46 − .10 − .42 22.79 7.87 − 1.10 ns 0.04
E 23.47 5.73 8–39 − .14 − .02 27.79 6.86 − 20.93 < .001 0.75
O 25.34 5.92 10–41 .27 − .40 27.80 6.31 − 11.54 < .001 0.42
A 27.93 6.47 8–44 − .03 − .45 28.68 5.76 − 3.24 .001 0.12
C 26.52 4.99 0–41 − .39 2.26 29.40 7.25 − 16.02 < .001 0.58
Subjective wellbeing
SWL 19.13 6.43 5–35 − .02 − .64
PA 3.32 0.72 1.2–5 − .18 − .34
NA 2.23 0.90 1–5 .67 − .24
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61Quality of Life Research (2020) 29:57–67
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first analysis results allow for interpretation in the sample-
relative terms only, whereas in the second case they can be
related to the population-specific values, and hence treated
as more absolute. For example, a higher profile obtained
in the first analysis means only higher for a given sample,
whereas in the second case, it is higher ‘in general’, i.e. also
in relation to the averages for the general population.
Personality proles: Sample‑standardised
personality dimensions
The results of latent profile analysis are presented in Table3.
The four-profile solution is better fitted to the data than the
assumed three-profile model: namely, the former has lower
values of AIC, BIC and SABIC, higher entropy and sig-
nificant values of LMR and VLMR likelihood ratio tests.
For the latter the values are insignificant when comparing
models with four and five profiles that additionally points to
the 4-profile solution. The main difference between three-
and four-profile models arises due to extraction of a profile
with the lowest C (see Fig.1). Thus, profile 1 (6% of the
sample, 47 participants) may be considered equivalent to
undercontrollers, but the most frequent characteristics of
this type include being low on O and A, which is not the
case here. PLWH in this profile are very low on C (below
two standard deviations) and rather introvert. Profile 2 (39%,
300 participants) resembles a resilient profile, with all the
dimensions being above the sample average, with C being
the only exception. Profile 3 (42%, 320 participants) is an
average profile but with a tendency to be rather below the
sample mean. Profile 4 (13%, 103 participants) can be identi-
fied as overcontrollers: high on C, low on ES but—which is
untypical for this profile—not low on E.
Personality proles: Population‑standardised
personality dimensions
Congruently, a solution with four profiles can be regarded as
optimal (see Table3, bottom panel). The obtained profiles
are highly similar to those for the previous analysis in terms
of counts, shape and posteriors probabilities of belonging to
a given profile (i.e. correlation from .98 to 1 indicates almost
perfect overlap). The interpretation should be attuned mainly
for the resilient profile; now it is more like an average profile
albeit with higher than typical ES (see profile 2 in Fig.1 and
profile 4 in Fig.2). The previous average profile now turns
into low profile (i.e. coherently below average; profile 3 in
Fig.1 and profile 2 in Fig.2) and the differences in C for the
other two profiles are less pronounced. Nevertheless, due to
the similarities in the results of both analyses, in particular,
the very high correlations between the probabilities of being
a member of the corresponding profiles, further analyses will
be based on the sample-standardised solution only (Fig.1).
Sociodemographic andclinical correlates ofproles
The sociodemographic and clinical variables presented in
Table1 were included in the analysis. We found no sig-
nificant relationship with a profile membership in terms of
gender, age, education, employment, self-assessed finan-
cial status, sexual orientation or current romantic relation-
ship status. Concerning clinical variables, the personality
profiles also did not differ significantly in terms of CD4
count, self-declared viremia, AIDS stage or the presence
of addiction. The only exceptions were mode and place
of HIV infection. Specifically, a different pattern was
observed for undercontrollers: less-frequent infection due
Table 3 Summary of model selection indices of latent profile analysis
BIC Bayesian information criterion, AIC Akaike’s information criterion, SABIC sample-size adjusted BIC, LMR Lo–Mendell–Rubin likelihood
ratio test, VLMR Vuong–Lo–Mendell–Rubin likelihood ratio test
Model BIC AIC SABIC No of
parameters
Entropy LMR VLMR Smallest class
Value pValue p% of Nfrequency
Sample-based standarisation
1-Class 10,992 10,946 10,961 10
2-Class 10,725 10,650 10,674 16 .61 300.53 .05 81.82 .05 47.99 370
3-Class 10,555 10,453 10,485 22 .67 204.30 < .001 − 51.73 < .001 18.57 143
4-Class 10,318 10,318 10,359 28 .76 143.70 < .001 2.04 < .001 6.10 47
5-Class 10,417 10,259 10,309 34 .75 68.67 ns 77.166 ns 7.01 55
Population-based standarisation
1-Class 10,382 10,335 10,350 10
2-Class 10,110 10,036 10,059 16 .61 303.88 .04 76.25 .04 49.61 382
3-Class 9946 9843 9876 22 .66 199.51 < .001 − 41.74 < .001 18.57 143
4-Class 9829 9699 9740 28 .76 153.11 .002 7.01 .001 6.10 47
5-Class 9799 9641 9691 34 .75 68.18 ns 111.14 ns 6.36 49
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62 Quality of Life Research (2020) 29:57–67
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to sex with men (47 vs. 70% average for all the other pro-
files), more-frequent infection due to sex with women (28
vs. 11%), and more frequently reported ‘other sources of
infection’ (13 vs. 7%) as well as being infected outside
the country (none in undercontrollers in comparison to
approximately 10% in all the other profiles). Thus, under-
conntrollers differ significantly from other personality pro-
files in terms of both mode and place of infection, but they
did not differ in terms of sociodemographic variables and
indicators of disease control and progression.
Additionally, since duration of HIV infection was strongly
correlated with years of ART (.82, p < .001), we repeated
the analysis with only one of these variables at a time. It did
not change the general pattern of their similar values across
profiles.
Fig. 1 Results of latent profile analysis on the sample-based stand-
ardisation: four profiles of Big Five personality dimensions. ES
emotional stability (reverse scores of Neuroticism), E extraversion,
O openness to experiences, A agreeableness, C conscientiousness.
Profile 1—undercontrolled type; profile 2—resilient type; profile 3—
average type; profile 4—overcontrolled type
Fig. 2 Results of latent profile analysis on the population-based standardisation: four profiles of Big Five personality dimensions. ES emotional
stability (reverse scores of Neuroticism), E extraversion, O openness to experiences, A Agreeableness, C conscientiousness
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63Quality of Life Research (2020) 29:57–67
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Proles’ membership andSWB
As presented in Table4, SWB differs across personality pro-
files. The highest SWB is observed among members of resil-
ient profile: they differ significantly on each SWB dimension
from all the others profiles, for which the picture is unclear
(see note on pairwise comparisons). Namely, overcontrollers
and undercontrollers can be almost equally regarded as the
second best, whereas average profile is related to the low-
est SWB. When this analysis was adjusted for relationship
between the profile membership and correlates, the patterns
of results remained the same (bottom panel of table).
Discussion
The results of our study showed significant differences on
four personality traits between HIV-infected participants and
the general population—the observed effect size was strong
for E, medium for C and O and small for A, with no effect
for N. This finding is an interesting contribution to the HIV
literature, as no such comparison has been conducted to date.
Thus, our result may add to the long but less-conclusive
debate if a specific personality profile of PLWH exists in
comparison with the general population. Specifically, the
authors argue whether certain personality dimensions may
be linked to premorbid mood disorders and associated risky
behaviours that act as potential pathways to HIV infection
[34, 47] or that changes in some personality dimensions may
be the result of ongoing adaptation to potentially fatal and
still very stigmatising disease [22, 48]. However, similar to
the studies mentioned above, we did not control for the cau-
sality with respect to the link between personality and socio-
medical variables, and without any longitudinal studies on
this topic to date, the possibility to interpret personality-risk
associations among HIV-positive compared with HIV-nega-
tive individuals is very limited [48].
Thus, it seems that the major result obtained in this study
deals with identifying personality profiles among PLWH,
which also have not yet been published in the HIV/AIDS
literature. Particularly, we failed to identify the most fre-
quently reported three profiles [3, 4], but we managed to
extract a four-profile model on both standardisations (i.e.
sample-specific and population level). Namely, the resilient
type (emotionally stable, extravert, open to experience and
agreeable) and, similar to the latter, albeit with a signifi-
cantly lower profile, the average type both together cover
almost 81% of our sample. In contrast, only 13% of partici-
pants could be classified as overcontrollers (i.e. high on C,
low on ES, but—which is untypical for such profile—not
low on E). Finally, only 6% of our sample could be identified
as undercontrollers; however, in the literature, the most fre-
quently reported profile for this type is low also on A, which
is not the case here (i.e. in our sample, PLWH representing
this type are very low only on C and, additionally, low on E,
which is also not typical).
It should be noted that a similar four-profile model was
obtained in other studies, albeit in non-clinical settings
[5]. Even if our sample cannot be considered as randomly
coming from the general population in terms of personality
dimensions, it remains internally heterogeneous with respect
to personality traits. Thus, our results may shed new light on
Table 4 Relationship between
profiles and subjective well-
being (means)—overall Wald
test
a All the pairwise comparisons between profiles significant at least at p < .05. Exceptions are: profile 1 ver-
sus profile 3 (Wald = 3.38. df = 1. p = .07) and profile 1 versus profile 4 (Wald = 1.48. df = 1. p = .22) for
Satisfaction with life; profile 1 versus profile 3 (Wald = 0.22. df = 1. p = .64). for Positive affect; profile 3
versus profile 4 (Wald = 1.41. df = 1. p = .24) for Negative affect
b All the pairwise comparisons between profiles significant at least at p < .05. Exceptions are: profile 1 ver-
sus profile 3 (Wald = 2.99. df = 1. p = .08) and profile 1 versus profile 4 (Wald = 1.38. df = 1. p = .24) for
Satisfaction with life; profile 1 versus profile 3 (Wald = 0.45. df = 1. p = .50) for Positive affect; profile 3
versus profile 4 (Wald = 1.03. df = 1. p = .31) for Negative affect
Subjective wellbeing Profile 1 Profile 2 Profile 3 Profile 4 Wald
Undercontrollers
n1 = 47
Resilient
n2 = 300
Average
n3 = 320
Overcontrollers
n4 = 103
Value p
M M M M
Without adjustment for profile membership correlatesa
Satisfaction with life 17.28 23.72 15.33 18.71 203.79 < .001
Positive affect 2.89 3.77 2.96 3.35 163.10 < .001
Negative affect 2.15 1.64 2.68 2.53 185.43 < .001
With adjustment for profile membership correlatesb
Satisfaction with life 17.29 23.79 15.42 18.69 202.21 < .001
Positive affect 2.89 3.77 2.96 3.34 163.98 < .001
Negative affect 2.17 1.63 2.66 2.54 182.78 < .001
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64 Quality of Life Research (2020) 29:57–67
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two inconclusive issues of personality characteristics among
PLWH. Specifically, we observed that participants in almost
all personality profiles did not differ in terms of all studied
socio-medical correlates. Only the undercontrolled type was
different with respect to the mode of HIV infection (i.e. less-
frequent infection due to sex with men, more frequent infec-
tion due to sex with women and more frequently reported
‘other sources of infection’, which is a category separate
from these related to drug abusing or hospital infections)
as well as being infected outside the country. This result is
intriguing, as it suggests that personality is unrelated espe-
cially to HIV-related clinical variables, which is opposed
to several studies conducted so far [30, 31, 48]. However, it
should be noted that these studies were internally inconsist-
ent with each other, highlighting sometimes contradictory
findings on the role of the same trait with various medical
outcomes of PLWH (e.g. extraversion, neuroticism, open-
ness or conscientiousness). Thus, it seems that personality
types are much more nuanced, though an insignificant pic-
ture of this association was compared with simply basing
on single and isolated traits. Alternatively, the finding con-
nected to different behaviours of undercontrolled type with
regard to the mode and place of HIV infection may be inter-
preted in the light of classic theory that the relation between
personality and health is explained by health behaviours,
which mediates its association [49]. In a more recent review,
Shuper etal. [50] underlined that personality has a strong
effect on risky sexual behaviours among PLWH. One should
remember that what characterised the undercontrolled type
mostly was the lowest level of conscientiousness. Several
meta-analytic reviews documented a positive link between
this trait and health-promoting behaviours in the general
population [51], including studies conducted with PLWH
[52].
The second ambiguous topic deals with the link between
personality and well-being for PLWH [31, 32]. In our study,
the highest SWB was observed among members of the resil-
ient profile, that differed significantly on each SWB dimen-
sion from all the others profiles, for which the picture was
less clear. Namely, overcontrollers and undercontrollers were
almost equally regarded as the second best in the level of
SWB, whereas the average profile consists of PLWH with
the worst SWB. The highest SWB in the resilient profile is
in line with other studies documenting many positive psy-
chosocial outcomes among people representing this type,
yet conducted in non-clinical settings only [11]. However,
in our sample, the resilient profile was in fact an average
profile in terms of population means, which may serve as an
explanation why we noted the highest SWB for this profile.
Although causality is not proven here, an intriguing finding
is that a typical personality for a given society is related to
better SWB, even for PLWH. In other words, being more like
an average on each personality dimension played the major
role in SWB level among participants, neither specific per-
sonality traits nor its constellation [e.g. conscientiousness,
48 or extraversion, 32].
It should be underlined that standardisation may matter
for interpretation of the results, especially in the context of
clinical samples, which may differ on personality traits from
the general population [for PLWH, 33] and regarding the
explorative nature of LPA, where extracted profiles may be
strongly sample-related [39]. We did not know of any other
study that considered this possible source of bias. We have
attempted to overcome it by referring our results to the per-
sonality profiles most frequently reproduced thus far [9] and
to population-based standardisation.
In addition, overcontrollers and undercontrollers,
although representing reverse profiles, were very similar in
terms of SWB. Nevertheless, it has to be stressed that these
profiles do not entirely resemble those reported in the lit-
erature [4]. The main differences regard the levels of E and
A. Thus, there is a need to conduct further research on these
personality types among PLWH. The same applies to the
last, the sample-average type, but below average in terms
of population means. This group consisted of 42% of the
sample; however, hardly anything specific can be said about
this group without falling into speculative remarks.
Strengths andlimitations
This study has several strengths, including a large size of
clinical sample, two methods of standardisation (i.e. sam-
ple and population) and the person-oriented approach
to personality. However, a few limitations should also be
noted. Firstly, the cross-sectional design precludes causal
interpretations. Secondly, our sample was composed mostly
of highly functioning PLWH, with unequal distribution of
gender and sexual orientation (mostly homosexual men and
heterosexual women). However, this specific gender and
sexual distribution reflects the current distribution of these
variables among PLWH in Poland [53] and in most Euro-
pean countries [54]. Also, it should be mentioned that we
obtained relatively low reliability of the NEO-FFI subscales,
which could be related to the sample specificity. Finally,
due to ethical and legal issues related to data protection (i.e.
third-party access to medical records), we based our analysis
only on self-reported clinical variables.
Conclusion
Identifying personality types in clinical settings ena-
bles more comprehensive understanding of interrelations
between personality and health [15, 16]. However, additional
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65Quality of Life Research (2020) 29:57–67
1 3
studies are required to determine whether these types of per-
sonality are universal as well as why some studies failed to
extract them or obtain types that do not entirely resemble
those reported in the literature [8], which was also the case
of our study. Concerning PLWH, the typological approach
to the study of personality may clarify many ambiguous
results devoted to the role of personality traits across vari-
ous aspects of functioning of PLWH, including the issue of
well-being of these patients.
Acknowledgements This study was founded by the National Science
Center, Poland (Research Project No. 2016/23/D/HS6/02943).
Funding This study was created as the result of the research project
(2016/23/D/HS6/02943) financed by the National Science Centre in
Poland.
Compliance with ethical standards
Conflict of interest The corresponding author declares that he has no
conflicts of interest. The second author declares that she has no con-
flicts of interest.
Ethical approval All procedures performed in studies involving human
participants were in accordance with the ethical standards of the insti-
tutional and/or national research committee and with the 1964 Helsinki
declaration and its later amendments or comparable ethical standards.
Open Access This article is distributed under the terms of the Crea-
tive Commons Attribution 4.0 International License (http://creat iveco
mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribu-
tion, and reproduction in any medium, provided you give appropriate
credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
References
1. Costa, P., Herbst, J., McCrae, R., Samuels, J., & Ozer, D. (2002).
The replicability and utility of three personality types. European
Journal of Personality, 16, 73–s87. https ://doi.org/10.1002/
per.448.
2. McCrae, R., Herbst, J., & Costa, P. T. (2001). Effects of acquies-
cence on personality factor structures. In R. Riemann, F. Osten-
dorf, & F. Spinath (Eds.), Festschrift in honor of Alois Angleitner
(pp. 217–232). Berlin: Pabst Science Publishers.
3. Asendorpf, J., Borkenau, P., Ostendorpf, F., & van Aken, M.
(2001). Carving personality description at its joints: Confirma-
tion of three replicable personality prototypes for both child and
adults. European Journal of Personality, 15, 169–198. https ://doi.
org/10.1002/per.408.
4. Robins, R., John, O., Caspi, A., Moffitt, T., & Stouthamer-
Loeber, M. (1996). Resilient, overcontrolled, and undercon-
trolled boys: Three replicable personality types. Journal of
Personality and Social Psychology, 70, 157–171. https ://doi.
org/10.1037/0022-3514.70.1.157.
5. Specht, J., Luhmann, M., & Geiser, C. (2014). On the consistency
of personality types across adulthood: Latent profile analyses in
two large-scale panel studies. Journal of Personality and Social
Psychology, 107, 540–556. https ://doi.org/10.1037/a0036 863.
6. Asendorpf, J., & Denissen, J. (2006). Predictive validity of per-
sonality types versus personality dimensions from early childhood
to adulthood: Implications for the distinction between core and
surface traits. Merrill-Palmer Quarterly, 52, 486–513. https ://doi.
org/10.1353/mpq.2006.0022.
7. Allport, G. (1937). Personality: A psychological interpretation.
New York, NY: Holt.
8. Donnellan, M., & Robins, R. (2010). Resilient, overcontrolled,
and undercontrolled personality types: Issues and controversies.
Social and Personality Psychology Compass, 3, 1–14. https ://doi.
org/10.1111/j.1751-9004.2010.00313 .x.
9. Asendorpf, J. (2013). Person-centered approaches to personality.
In M. Mikulincer, P. R. Shaver, M. L. Cooper, & R. J. Larsen
(Eds.), APA handbooks in psychology. APA handbook of personal-
ity and social psychology, Personality processes and individual
differences (Vol. 4, pp. 403–424). Washington, DC: American
Psychological Association.
10. Block, J., Block, J. (1980). The role of ego-control and ego-
resiliency in the organization of behavior. In W. A. Collins (Ed.),
Development of cognition, affect, and social relations: Minnesota
symposia on child psychology (Vol. 13, pp. 39–101). Hillsdale,
NJ: Erlbaum.
11. Chapman, B., & Goldberg, L. (2011). Replicability and 40-year
predictive power of childhood ARC types. Journal of Personality
and Social Psychology, 101, 593–606. https ://doi.org/10.1037/
a0024 289.
12. Steca, P., Alessandri, G., & Caprara, G. (2010). The utility of a
well-known personality typology in studying successful aging:
Resilients, undercontrollers, and overcontrollers in old age. Per-
sonality and Individual Differences, 48, 442–446. https ://doi.
org/10.1016/j.paid.2009.11.016.
13. Schnabel, K., Asendorpf, J., & Ostendorf, F. (2002). Replicable
types and subtypes of personality: German NEO-PI-R versus
NEO-FFI. European Journal of Personality, 16, 7–24. https ://
doi.org/10.1002/per.445.
14. Avdeyeva, T., & Church, A. (2005). The cross-cultural generaliz-
ability of personality types: A Philippine study. European Journal
of Personality, 19, 475–499. https ://doi.org/10.1002/per.555.
15. Berry, J., Elliott, T., & Rivera, P. (2007). Resilient, undercon-
trolled, and overcontrolled personality prototypes among persons
with spinal cord injury. Journal of Personality Assessment, 89,
292–302. https ://doi.org/10.1080/00223 89070 16298 13.
16. Kinnunen, M., Metsäpelto, R., Feldt, T., Kokko, K., Tolvanen,
A., Kinnunen, U., etal. (2012). Personality profiles and health:
Longitudinal evidence among Finnish adults. Scandinavian
Journal of Psychology, 53, 512–522. https ://doi.org/10.111
1/j.1467-9450.2012.00969 .x.
17. Strickhouser, J., Zell, E., & Krizan, Z. (2017). Does personality
predict health and well-being? A metasynthesis. Health Psychol-
ogy, 36, 797–810. https ://doi.org/10.1037/hea00 00475 .
18. Steptoe, A., Wardle, J., & Marmot, M. (2005). Positive affect
and healthrelated neuroendocrine, cardiovascular, and inflamma-
tory processes. Proceedings of the National Academy of Sciences
of the United States of America, 102, 6508–6512. https ://doi.
org/10.1073/pnas.04091 74102 .
19. Zuckerman, M., & Kuhlman, D. (2000). Personality and risk-
taking: Common biosocial factors. Journal of Personality, 68,
999–1029. https ://doi.org/10.1111/1467-6494.00124 .
20. Samji, H., Cescon, A., Hogg, R., Modur, S., & Althoff, K. (2013).
Closing the gap: Increases in life expectancy among treated HIV-
positive individuals in the United States and Canada. PLoS ONE,
18, 144–156. https ://doi.org/10.1371/journ al.pone.00813 55.
21. Bogart, L., Wagner, G., Galvan, F., Landrine, H., & Klein, D.
(2011). Perceived discrimination and mental health symptoms
among black men with HIV. Cultural Diversity and Ethnic Minor-
ity Psychology, 17, 295–302. https ://doi.org/10.1037/a0024 056.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

66 Quality of Life Research (2020) 29:57–67
1 3
22. Rendina, H., Brett, M., & Parsons, J. (2018). The critical role
of internalized HIV-related stigma in the daily negative affec-
tive experiences of HIV-positive gay and bisexual men. Journal
of Affective Disorders, 227, 289–297. https ://doi.org/10.1016/j.
jad.2017.11.005.
23. Miners, A., Phillips, A., Kreif, N., Rodger, A., Speakman, A.,
Fisher, M., etal. (2014). Health-related quality-of-life of people
with HIV in the era of combination antiretroviral treatment: A
cross-sectional comparison with the general population. Lancet
HIV. https ://doi.org/10.1016/s2352 -3018(14)70018 -9.
24. Psaros, C., O’Cleirigh, C., Bullis, J., Markowitz, S., & Safren,
S. (2013). The influence of psychological variables on health
related quality of life among HIV positive individuals with a
history of intravenous drug use. Journal of Psychoactive Drugs,
45, 304–312. https ://doi.org/10.1080/02791 072.2013.82503 0.
25. Deeks, S., Lewin, S., & Havlir, D. (2013). The end of AIDS:
HIV infection as a chronic disease. Lancet. https ://doi.
org/10.1016/S0140 -6736(13)61809 -7.
26. Burgoyne, R., & Saunders, D. (2001). Quality of life among
urban Canadian HIV/AIDS clinic outpatients. International
Journal of STD and AIDS, 12, 505–512. https ://doi.org/10.1328/
a0356 4056.
27. Lubeck, D., & Fries, J. (1997). Assessment of quality of life in
early stage HIV-infected persons: Data from the AIDS Time-
oriented Health Outcome Study (ATHOS). Quality of Life
Research, 6, 494–506. https ://doi.org/10.1023/A:10184 04014
821.
28. Oberjé, E., Dima, A., van Hulzen, A., Prins, J., & de Bruin, M.
(2015). Looking beyond health-related quality of life: Predictors
of subjective well-being among people living with HIV in the
Netherlands. AIDS and Behavior, 19, 1398–1407. https ://doi.
org/10.1007/s1046 1-014-0880-2.
29. Rzeszutek, M., & Gruszczyńska, E. (2018). Positive and nega-
tive affect change among people living with HIV: A one-year
prospective study. International Journal of Behavioral Medi-
cine, 1, 28–37. https ://doi.org/10.1007/s1252 9-018-9741-0.
30. Burgess, A., Carretero, M., Elkington, A., Pasqual-Marsettin,
E., Lobacaro, C., & Catalan, J. (2000). The role of personality,
coping style and social support in health related quality of life
in HIV infection. Quality of Life Research, 9, 423–437. https ://
doi.org/10.1023/A:10089 18719 749.
31. Lockenhoff, C., Ironson, G., O’Cleirigh, C., & Costa, P.
(2009). Five-Factor model personality traits, spirituality, reli-
giousness, and mental health among people living with HIV.
Journal of Personality, 77, 1411–1436. https ://doi.org/10.111
1/j.1467-6494.2009.00587 .x.
32. Penedo, F., Gonzalez, J., Dahn, J., Antoni, M., Malow, R.,
Costa, P., etal. (2003). Personality, quality of life and HAART
adherence among men and women living with HIV/AIDS.
Journal of Psychosomatic Research, 54, 271–278. https ://doi.
org/10.1016/S0022 -3999(02)00482 -8.
33. O’Cleirigh, C., Perry, N., Taylor, W., Coleman, J., Costa, P.,
Mayer, K., etal. (2018). Personality traits and adaptive HIV
disease management: Relationships with engagement in care
and condomless anal intercourse among highly sexually active
sexual minority men living with HIV. LGBT Health, 5, 257–
263. https ://doi.org/10.1089/lgbt.2016.0065.
34. Moore, D., Atkinson, J., Akiskal, H., Gonzalez, R., & Wolf-
son, T. (2005). Temperament and risky behaviors: A pathway
to HIV? Journal of Affective Disorders, 85, 191–200. https ://
doi.org/10.1016/S0165 -0327(03)00193 -9.
35. Costa, P., & McCrae, R. (1992). Revised NEO personality inven-
tory (NEO-PI-R) and NEO five-factor inventory (NEO-FFI)
professional manual. Odessa, FL: Psychological Assessment
Resources.
36. Zawadzki, B., Strelau, J., Szczepaniak, P., Śliwińska, M. (1998).
Inwentarz osobowości NEO-FFI Costy i McCrae (Adaptacja
polska – podręcznik) [NEO-FFI inventory by Costa and
McCrae. Polish adaptation – manual]. Warsaw: Laboratory of
Psychological Tests of the Polish Psychological Association.
37. Diener, E., Emmons, R., Larsen, R., & Griffin, S. (1985). The
satisfaction with life scale. Journal of Personality Assessment,
49, 71–75. https ://doi.org/10.1207/s1532 7752j pa490 1_13.
38. Watson, D., Clark, L., & Tellegen, A. (1988). Development and
validation of brief measures of positive and negative affect. The
PANAS scales. Journal of Personality and Social Psychology,
54, 1063–1070.
39. Diener, E., Heintzelman, S., Kushlev, K., Tay, L., Wirtz, D.,
Lutes, L., etal. (2016). Findings all psychologists should know
from the new science on subjective well-being. Canadian Psy-
chology. https ://doi.org/10.1037/cap00 00063 .
40. Collins, L., & Lanza, S. (2013). Latent class and latent transi-
tion analysis: With applications in the social, behavioral, and
health sciences (Vol. 718). Hoboken: Wiley.
41. Vermunt, J. (2010). Latent class modelling with covariates:
Two improved three-step approaches. Political Analysis, 18,
450–469. https ://doi.org/10.1093/pan/mpq02 5.
42. Nylund, K., Asparouhov, T., & Muthén, B. (2007). Deciding
on the number of classes in latent class analysis and growth
mixture modeling: A Monte Carlo simulation study. Structural
Equation Modeling: A Multidisciplinary Journal, 14, 535–569.
https ://doi.org/10.1080/10705 51070 15753 96.
43. Bakk, Z., & Vermunt, J. (2016). Robustness of stepwise latent
class modeling with continuous distal outcomes. Structural
Equation Modeling: A Multidisciplinary Journal, 23, 20–31.
https ://doi.org/10.1080/10705 511.2014.95510 4.
44. Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in
mixture modeling: Three-step approaches using Mplus. Struc-
tural Equation Modeling: A Multidisciplinary Journal, 21,
329–341. https ://doi.org/10.1080/10705 511.2014.91518 1.
45. IBM Corp. (2017). Released. IBM SPSS Statistics for Windows,
Version 25.0. Armonk, NY: IBM Corp.
46. Muthén, L., Muthén, B. Mplus user’s guide. (1998–2017).
Eighth Edition. Los Angeles, CA: Muthén & Muthén.
47. Trobst, K., Herbst, J., Masters, H., & Costa, P. (2002). Personal-
ity pathways to unsafe sex: Personality, condom use and HIV
risk behaviors. Journal of Research in Personality, 36, 117–133.
https ://doi.org/10.1006/jrpe.2001.2334.
48. Perretta, P., Akiskal, H., Nisita, C., Lorenzetti, C., Zaccagnini,
E., Della Santa, M., etal. (1998). The high prevalence of bipolar
II and associated cyclothymic and hyperthymic temperaments
in HIV-patients. Journal of Affective Disorders, 50, 215–224.
https ://doi.org/10.1016/S0165 -0327(98)00111 -6.
49. Adler, N., & Matthews, K. (1994). Health psychology: Why
do some people get sick and some stay well? Annual Review
of Psychology, 45, 229–259. https ://doi.org/10.1146/annur
ev.ps.45.02019 4.00130 5.
50. Shuper, P., Joharchi, N., & Rehm, J. (2014). Personality as a
predictor of unprotected sexual behavior among people living
with HIV/AIDS: A systematic review. AIDS and Behavior, 18,
398–410. https ://doi.org/10.1007/s1046 1-013-0554-5.
51. Bogg, T., & Roberts, B. (2004). Conscientiousness and health-
related behaviors: A meta-analysis of the leading behavioral
contributors to mortality. Psychological Bulletin, 130, 887–919.
https ://doi.org/10.1037/0033-2909.130.6.887.
52. O’Cleirigh, C., Ironson, G., Weiss, A., & Costa, P. (2007). Con-
scientiousness predicts disease progression (CD4 number and
viral load) in people living with HIV. Health Psychology, 26,
473–480. https ://doi.org/10.1037/0278-6133.26.4.473.
53. Firląg-Burkacka, E., Siwak, E., Kubicka, J., Kowalska, J.,
Gizińska, J. (2016). Epidemiologia zakażeń HIV w Polsce
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

67Quality of Life Research (2020) 29:57–67
1 3
na tle sytuacji w Europie i na świecie [Epidemiology of HIV
infections in Poland against the background of the situation
in Europe and the world]. In: A. Pluta, E. Łojek, B. Habrat,
A. Horban (Eds.). Life and aging with HIV. Interdisciplinary
approach (pp. 15–23). Warsaw: Warsaw University Publishing
House.
54. The Joint United Nations Programme on HIV/AIDS (UNAIDS)
Report. (2017).http://www.unaid s.org/en/resou rces/docum
ents/2017/2018.
Publisher’s Note Springer Nature remains neutral with regard to
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