Access to this full-text is provided by Frontiers.
Content available from Frontiers in Psychology
This content is subject to copyright.
Frontiers in Psychology | www.frontiersin.org 1 July 2019 | Volume 10 | Article 1664
ORIGINAL RESEARCH
published: 23 July 2019
doi: 10.3389/fpsyg.2019.01664
Edited by:
Sabrina Cipolletta,
University of Padova, Italy
Reviewed by:
Serena Giunta,
University of Palermo, Italy
Rytis Pakrosnis,
Vytautas Magnus University,
Lithuania
*Correspondence:
Ewa Gruszczyńska
egruszczynska@swps.edu.pl
Specialty section:
This article was submitted to
Health Psychology,
a section of the journal
Frontiers in Psychology
Received: 20 April 2019
Accepted: 02 July 2019
Published: 23 July 2019
Citation:
Gruszczyńska E and Rzeszutek M
(2019) Trajectories of Health-Related
Quality of Life and Perceived Social
Support Among People Living With
HIV Undergoing Antiretroviral
Treatment: Does Gender Matter?
Front. Psychol. 10:1664.
doi: 10.3389/fpsyg.2019.01664
Trajectories of Health-Related
Quality of Life and Perceived Social
Support Among People Living With
HIV Undergoing Antiretroviral
Treatment: Does Gender Matter?
EwaGruszczyńska1
* and MarcinRzeszutek 2
1 Faculty of Psychology, SWPS University of Social Sciences and Humanities, Warsaw, Poland, 2 Faculty of Psychology,
University of Warsaw, Warsaw, Poland
The study examined the trajectories of health-related quality of life (HRQoL) and perceived
social support (PSS) among people living with HIV (PLWH), with a special focus on gender
differences. The participants included 252 PLWH (18% female) undergoing antiretroviral
therapy. HRQoL (WHO Quality of Life-BREF; WHOQOL Group, 1998) and PSS (Berlin
Social Support Scales; Schulz and Schwarzer, 2003) were measured three times at
six-month intervals. Using a univariate approach, three trajectories of HRQoL and four
trajectories of PSS were identied. Gender and relationship status were signicant
covariates for PSS only, with overrepresentation of single women in the increasing
trajectory. The dual trajectory approach revealed a match in the decrease of HRQoL and
PSS, but only for 31% of the sample. In fact, decreasing PSS co-occurred with increasing
as well as stable HRQoL. There was no signicant gender effect in this regard. Although
a clear correspondence for decreasing trajectories exists, the ndings also highlight a
discrepancy between HRQoL and PSS changes that are unrelated to gender.
Keywords: health-related quality of life, perceived social support, people living with HIV, gender differences,
latent class growth curve
TRAJECTORIES OF HEALTH-RELATED QUALITY OF LIFE
AND PERCEIVED SOCIAL SUPPORT AMONG PEOPLE LIVING
WITH HIV UNDERGOING ANTIRETROVIRAL TREATMENT:
DOES GENDER MATTER?
Over the last two decades, cutting-edge progress has occurred in the treatment and prevention
of HIV infection, which has not only signicantly reduced mortality and morbidity among
people living with HIV (PLWH; Samji et al., 2013; Cohen et al., 2016) but has also been
attributed to signaling the end of the HIV/AIDS epidemic (Deeks et al., 2013; Carrico,
2019). However, the enormous progress in bio-medical care for PLWH did not directly
translate to the improvement of their psychological well-being. More specically, both in the
past (e.g., Bing et al., 2001) and at present, PLWH systematically declare lower levels of
Gruszczyńska and Rzeszutek Trajectories of Health-Related Quality of Life
Frontiers in Psychology | www.frontiersin.org 2 July 2019 | Volume 10 | Article 1664
well-being and higher psychological distress than the general
population (Miners et al., 2014), and especially worse health-
related quality of life (HRQoL) with respect to other chronic
diseases (Psaros et al., 2013).
Although a large number of studies have been conducted
on factors related to the HRQoL of these patients (e.g., Burgoyne
and Saunders, 2001; Hansen et al., 2009; Herrmann etal., 2013;
Bucciardini et al., 2014; Lifson et al., 2017; Mitchell et al.,
2017; Torres et al., 2018), they failed not only to provide a
consistent picture of the variables associated with the HRQoL
of PLWH but also to produce a convincing answer to the
aforementioned issue of low well-being among PLWH, especially
at a time when their life expectancies are similar to the general
population (Degroote et al., 2014). e only visible trend deals
with the fact that clinical factors have been historically considered
as the predominant predictor of the well-being of PLWH (Lubeck
and Fries, 1997), while there is now a growing recognition of
psychosocial factors as the major determinant of their quality
of life (Chida and Vedhara, 2009; Cooper et al., 2017). Some
authors underline that the plethora of inconclusive results in
research on HRQoL among PLWH is derived from methodological
shortcomings among most studies on this topic. Notably, the
literature is dominated by cross-sectional frameworks using the
variable-centered approach, which focuses only on the average
values for an entire study sample and neglects the heterogeneity
of well-being among PLWH and its relationship with
sociodemographic, clinical, and psychological characteristics
(Oberjé etal., 2015; Rzeszutek and Gruszczyńska, 2018). erefore,
the present study aims to overcome these methodological
drawbacks by applying a longitudinal design accompanied by
a person-centered perspective that allows for the identication
of subgroups with dierent levels of HRQoL and their changes
during time. Specically, we aim to enrich the literature by
focusing on the relatively understudied issue of gender-based
dierences in HRQoL among PLWH.
Surprisingly, although the global amount of HIV-infected
women is similar to the number of HIV-infected men, both
of which continue to grow worldwide (European Centre for
Disease Prevention and Control/WHO Regional Oce for
Europe, 2017), the majority of studies on HRQoL in HIV
infection were conducted on male populations only (e.g., Jia
et al., 2004; Liu et al., 2006; Song et al., 2016; Emuren et al.,
2017). us, research on HRQoL among HIV-infected women
remains scarce (e.g., McDonnell et al., 2000; Gielen et al.,
2001). In addition, authors examining gender dierences in
HRQoL in this patient group consequently observed lower
HRQoL among HIV-infected women than HIV-infected men
(e.g., Campsmith etal., 2003; Mrus etal., 2005; Chandra etal.,
2009). ere are several hypotheses on this consistent nding
that have pointed to the limited access to antiretroviral treatment
(ART) in some world regions (Penniman et al., 2007; Aziz
and Smith, 2011), more intense HIV-related stigma, and an
associated higher rate of mental disorders among HIV-infected
women than HIV-infected men (Campbell et al., 2006; Machtinger
et al., 2012; Geary et al., 2014). On the other hand, several
studies have demonstrated that females living with HIV exhibit
far greater adherence to treatment than their male counterparts
and therefore report better HIV-related clinical outcomes
(Collazos et al., 2007; Nicastri et al., 2007; Bor et al., 2015).
In considering the aforementioned ndings, it seems that no
comprehensive explanation for the observed gender dierences
in HRQoL among PLWH exists to date. As such, the present
study examines one variable that may provide important context
on this topic – social support (Carvalhal, 2010).
HIV/AIDS is a chronic disease that promotes
multidimensional psychological distress among PLWH, which
is now primarily associated with persistent HIV-related stigma
and social isolation (Rendina et al., 2018). Many studies have
observed that social support – particularly perceived social
support (PSS) – is one of the most important assets in coping
with HIV infection and related distress (e.g., Turner-Cobb
et al., 2002; Gonzalez et al., 2004; Earnshaw et al., 2015).
More specically, perceiving a high availability of support may
enhance adjustment to HIV infection directly through improved
adherence to treatment (e.g., Ashton etal., 2005; Alemu etal.,
2012) and also indirectly through buering the eect of
HIV-related stigma on mental functioning and quality of life
among these patients (Bekele et al., 2013; Breet et al., 2014).
Although the benecial eects of perceived support on
quality of life of PLWH are widely known, some authors have
recently become increasingly skeptical regarding this
unambiguously optimistic picture and have highlighted several
methodological shortcomings of existing studies conducted
using a cross-sectional framework, which is the most common
(Qiao et al., 2014). Consequently, it is dicult to solve the
egg or chicken dilemma in research on the link between perceived
social support and HRQoL among PLWH. Notably, existing
studies have been conducted in small samples (since the
implementation of a repeated measurement design among
PLWH may be challenging; Burgoyne and Renwick, 2004) or
using only a baseline assessment of PSS as a predictor for
HRQoL changes (Jia et al., 2005). us, the current literature
has not examined possible heterogeneity of the dual trajectories
of HRQoL and PSS. Furthermore, another topic that remains
understudied in the literature deals with gender dierences in
both PSS and HRQoL among PLWH (Gordillo et al., 2009).
Current Study
In considering the aforementioned research gaps, the aim of
our study was three-fold. First, we aimed to examine whether
heterogeneity of univariate change of HRQoL and PSS exists
among PLWH, and if these trajectories are also gender-related,
both with and without other possible sociodemographic and
clinical covariates. en, the probability of following a given
pattern of the dual trajectories of HRQoL and PSS was explored.
Specically, wewere interested in the co-occurrence of trajectories
with the same direction of change, under the assumption of
existing cross-sectional studies that a decrease or an increase
in HRQoL corresponds to relevant changes in PSS. Finally,
weexamined whether any gender dierences in joint probability
for dual trajectories existed, supposing that combinations of
changes may not be equally distributed due to gender-related
patterns of social exchange as well as the social consequences
of being diagnosed with HIV.
Gruszczyńska and Rzeszutek Trajectories of Health-Related Quality of Life
Frontiers in Psychology | www.frontiersin.org 3 July 2019 | Volume 10 | Article 1664
MATERIALS AND METHODS
Participants and Procedure
e participants were 252 persons with conrmed HIV-positive
results undergoing antiretroviral therapy in an outpatient clinic.
e majority of them were men, which is typical based on
the gender-related prevalence rate of HIV infection in Europe
and the United States (Joint United Nations Programme on
HIV/AIDS (UNAIDS), 2018). Detailed characteristics of the
sample are provided in Tab l e 1 .
e study design was longitudinal, with three measurements
at 6-month intervals. Aer written informed consent was
obtained from a participant, they lled in the self-descriptive
questionnaire provided. For the next two measurements, they
were approached during their control visit in the outpatient
clinic aer establishing the date via phone or email, based on
their preference. All longitudinal data were collected by trained
research assistants using a “paper-and-pencil” approach.
Participation in the study was voluntary. e study was approved
by the institutional ethics committee.
Measures
Health-related quality of life was assessed using the WHO
Quality of Life-BREF (WHOQOL-BREF), developed under a
WHO initiative for cross-cultural assessment (WHOQOL Group,
1998). e tool consists of 26 items to measure four domains:
physical health, psychological health, social relationships, and
environment. Each item is rated on a ve-point Likert scale
(scores ranged from 1 to 5), and raw scores were used. Since
correlations between domains in our study were stable and
moderate (from 0.48 to 0.69), and followed the research indicating
a possibility of assessing global HRQoL using this tool (Harsha
et al., 2016), the overall indicator was obtained by summing
and averaging all item scores. Higher values indicate a higher
quality of life. e reliability, measured by the Cronbach’s α
coecient, was 0.93, 0.92, and 0.92, from the rst to third
wave, respectively.
Perceived social support was measured using the relevant
subscale of eight items from the Berlin Social Support Scales
developed by Schulz and Schwarzer (2003). The answers
are provided on a Likert-type scale, from 1 (not true at
all) to 4 (entirely true), then summed and averaged. The
higher scores indicate higher PSS. The Cronbach’s α coefficient
was 0.93, 0.92, and 0.92, for first, second, and third
measurement, respectively.
Data Analysis
We started the analysis with univariate latent class growth
curve models to examine how HRQoL and PSS changed in
our sample during the study period. From 1- to 5-class
solutions were tested separately for HRQoL and PSS, and
error variances and covariances were freely estimated across
classes. e optimal solution was identied on the basis of
several criteria widely identied in the literature (Nylund
et al., 2007). Namely, we used Akaike as well as Bayesian
information criterion (AIC and BIC, respectively), including
the sample size-adjusted BIC (SABIC). e model with lower
values was favored. Next, entropy as a measure of accuracy
of classication was taken into consideration; in this case,
the model with higher values was favored (Celeux and
Soromenho, 1996). Finally, sample proportion per class was
analyzed, since classes with very few individuals may besample-
specic and dicult to replicate. e practical rule is to favor
a model with a fewer number of classes when at least one
class has a frequency of less than 5% of the sample size
(Hipp and Bauer, 2006). Time was coded as 0 for the rst
measurement, 0.5 for the second, and 1 for the third (Biesanz
et al., 2004). Both linear and quadratic trends were explored,
but wedid not present them further since all quadratic terms
were revealed as insignicant.
To identify covariates of trajectories, the bias-adjusted three-
step analysis (Vermunt and Magidson, 2016) was implemented
in order (1) to separate modeling trajectories from their
relationship with other variables and (2) to correct for
probabilistic classication to classes. Namely, when univariate
models were established (e.g., the number, shape, and membership
of trajectories were xed), weexamined whether any dierences
existed in predicting this membership based on sociodemographic
and clinical variables. We started with gender and then added
other covariates to determine if they would modify the
gender eect.
In the next step, joint probabilities were computed since
we used two sets of trajectories (one for HRQoL and the other
for PSS). Specically, we were interested in the probability of
belonging to a given trajectory of HRQoL when simultaneously
being a member of a given trajectory of PSS. Finally, by means
of multinomial logistic regression, we assessed whether the
probability of being a member of each combination of HRQoL
and PSS trajectories was the same for women and men, both
with and without additional covariates. All analyses were performed
using IBM SPSS Statistics version 25 (IBM Corp, 2016) and
LatentGOLD version 5.1 (Vermunt and Magidson, 2016).
TABLE 1 | Sociodemographic and clinical variables in the studied sample
(N=252).
Variable N (%)
Gender
Male 208 (82.5)
Female 44 (17.5)
Age in years (M±SD) 39.03±10.40
Marital status
Married 147 (58.3)
Single 105 (41.7)
Education
Basic vocational 109 (43.3)
Secondary and university degree 143 (56.7)
HIV/AIDS status
HIV+ only 215 (85.3)
HIV/AIDS 37 (14.7)
HIV infection duration in years (M±SD) 7.23±6.23
Antiretroviral treatment (ART) duration in
years (M±SD)
5.82±5.25
CD4 count 575.48±2248.89
M, mean; SD, standard deviation.
Gruszczyńska and Rzeszutek Trajectories of Health-Related Quality of Life
Frontiers in Psychology | www.frontiersin.org 4 July 2019 | Volume 10 | Article 1664
RESULTS
Descriptive Statistics and Missing Values
Table 2 presents basic descriptive statistics for repeated measures
of HRQoL and PSS. e dropout due to longitudinal design
was 41% of the sample between the rst and last measurements.
Missing data analysis suggested that the pattern of missingness
can betreated as random (Little’s MCAR test: χ2= 53.32, df=54,
p = 0.50); therefore, the option that included all available data
was chosen, with missing values for indicators being handled
by the maximum likelihood function (Vermunt and Magidson,
2016). Furthermore, regarding sociodemographic and clinical
variables, there were also no signicant dierences between
completers and non-completers. However, the result for gender
was on the edge of signicance (χ2 = 3.95, df = 1, p = 0.05),
suggesting a tendency of higher dropout among women than men.
Heterogeneity of Change: HRQoL and PSS
Univariate Trajectories
e model t criteria indicate that model with three trajectories
was the best tted to the HRQoL data (see Tab l e 3). Specically,
although all the informative criteria indices scored lower
with every added class, the drop in value became smaller.
For models with more than three classes, the smallest class had
only four members, which suggests the existence of outliners.
Finally, entropy was relatively stable across all models, indicating
that the perfect classication of participants was challenging.
Average posterior probabilities were 0.79, 0.93, and 0.81 for classes
1, 2, and 3, respectively. is solution is plotted in Figure 1.
e starting points diered signicantly across trajectories
(overall Wald statistics =14101.81, p < 0.001; for all pairwise
comparisons p<0.001). e rst class was the most numerous,
containing 45.6% of the sample, for whom HRQoL signicantly
decreased during the study period (slope = −0.13, z = 2.55,
p < 0.02). e second class, with the lowest and most stable
HRQoL (slope = −0.08, z = −0.86, ns), was represented by
29.4% of the sample. Finally, the third class included 25% of
PLWH and exhibited the highest and increasing HRQoL
(slope = 0.12, z = 1.98, p < 0.05).
For PSS, the 4-class solution was chosen as the best tted
to the data due to the lowest BIC and SABIC values, and
highest accuracy of classication, while retaining a reasonable
size for the smallest class. Moreover, this decision was supported
by visibly worse performance on all these criteria by the 5-class
model (see Tabl e 3 ). Average posterior probabilities ranged
from 0.78 for class 2 to 0.89 for class 1. Figure 2 presents
the obtained trajectories. e majority of the sample (63.1%)
was allocated to the rst class and exhibited decreasing PSS
trajectory (slope = −0.19, z = −2.21, p < 0.05) with a middle
starting point. e second class consisted of PLWH (14.3% of
the sample) with a slightly higher starting point and increasing
PSS (slope = 0.21, z = 2.36, p < 0.05). e highest and stable
trajectory was represented by 15.1% of the sample, identied
as class 3. On the other side was the smallest class 4 (7.5%
of the sample) with the lowest and stable PSS, albeit with a
noticeable tendency to decrease (slope= −0.23, z= −1.08, ns).
Gender as a Covariate of Univariate
Trajectories
When gender was added to the models as the only predictor
of class membership, it was insignicant for both HRQoL
TABLE 3 | Summary of model selection indices of latent class growth curve analysis: Unconditional univariate models for health-related quality of life (HRQoL) and
perceived social support (PSS).
Model BIC AIC SABIC Number of
parameters
Entropy Smallest class
N (%) Frequency
HRQoL
1-Class 3467.27 3456.69 3457.76 3
2-Class 3334.58 3309.88 3312.39 7 0.61 39.3 99
3-Class 3285.87 3247.04 3251.00 11 0.63 25.0 63
4-Class 3262.65 3209.71 3215.09 15 0.64 1.6 4
5-Class 3256.24 3189.18 3196.00 19 0.62 1.6 4
PSS
1-Class 4039.37 4028.78 4029.86 3
2-Class 3824.25 3799.55 3802.06 7 0.84 26.2 60
3-Class 3751.05 3712.22 3716.18 11 0.62 15.9 40
4-Class 3727.09 3674.15 3679.54 15 0.71 7.5 19
5-Class 3738.32 3671.26 3678.08 19 0.57 15.1 38
BIC, Bayesian information criterion; AIC, Akaike’s information criterion; SABIC, sample-size adjusted BIC.
TABLE 2 | Descriptive statistics for health-related quality of life (HRQoL) and
perceived social support (PSS).
Variable Range Mean SD Skewness Kurtosis
HRQoL
Time 1 1.42–4.92 3.75 0.57 −0.82 0.99
Time 2 1.58–4.92 3.74 0.56 −0.59 0.61
Time 3 1.27–4.81 3.66 0.58 −0.65 1.47
PSS
Time 1 1–4 2.31 0.68 −0.84 0.561
Time 2 1–3 2.26 0.71 −0.75 −0.37
Time 3 1–3 2.22 0.72 −0.75 −0.29
SD, standard deviation; sample size for Time 1, Time 2, and Time 3 was 252, 201, 149,
respectively.
Gruszczyńska and Rzeszutek Trajectories of Health-Related Quality of Life
Frontiers in Psychology | www.frontiersin.org 5 July 2019 | Volume 10 | Article 1664
(W = 4.71, ns) and PSS (W = 3.25, ns). When all the other
covariates were included in the mode (i.e., age, education,
relationship status, CD4 count, duration of being diagnosed
with HIV infection, duration of antiretroviral therapy and being
in the AIDS stage) for HRQoL, trajectories of gender remained
insignicant. Signicant eects were observed for age (W=7.11,
p < 0.05) and education (W = 8.77, p < 0.02). Specically,
PLWH in the decreasing HRQoL trajectory were older than
the other two trajectories (41.2 vs. 36.8 and 37.7years of age,
respectively), and PLWH in the increasing HRQoL trajectory
were better educated than those with the stable HRQoL trajectory.
For PSS, both gender (W=674.39, p<0.001) and relationship
status (W = 9.02, p < 0.05) were signicant correlates of PSS
trajectories. us, an interaction of these variables was examined,
revealing a signicant eect between classes 1 and 2 (W=196.56,
p < 0.001). Compared to the decreasing trajectory, there was
overrepresentation of single women in the increasing PSS trajectory.
Dual Trajectories of HRQoL and PSS:
Gender Differences
Table 4 illustrates joint probability while accounting for both
sets of heterogonous trajectories (i.e., for HRQoL and PSS
simultaneously). It is evident that no clear correspondence exists
between the trajectories of HRQoL and PSS. Although the
highest probability was noted for the dual decreasing trajectory
(0.31), the relatively high probability also points to the
simultaneous membership of stable HRQoL and decreasing PSS.
Likewise, approximately 11% of PLWH followed the increasing
HRQoL and decreasing PSS trajectories concurrently. However,
no members in the lowest and stable PSS reported an increase
in HRQoL.
Finally, considering joint probability, gender dierences were
examined with control for all the other sociodemographic and
clinical variables. Wefocused on the dual trajectories combining
the decreasing PSS and all HRQoL trajectories, since for all
the other combinations the frequency was below 10% of the
sample (see Tab l e 4 ). e results of multinomial logistic
regression with dual decreasing trajectories as a reference
category showed no signicant eect of gender alone (χ2= 3.88,
df =2, p = 0.14) nor of any of the remaining variables when
added to the model (χ2 = 20.12, df = 16, p = 0.22).
DISCUSSION
e rst aim of our study was partly achieved (i.e., wemanaged
to observe the heterogeneity of change in HRQoL and PSS
among PLWH), but no systematic gender dierences were
found with regard to these trajectories. Specically, weidentied
three classes of HRQoL, and their members diered in terms
of age and education only; therefore, clinical variables did
not predict class membership. us, our study ts the current
trend of HRQoL research, which points to the diminishing
role of HIV-related clinical factors as determinants of quality
of life for PLWH (Cooper et al., 2017). In a time of great
progress in HIV/AIDS treatment and prevention HIV, infection
has lost its fatal character and became a chronic and manageable
health problem (Deeks etal., 2013). erefore, it is unsurprising
that socially valid resources such as education can be a
signicant predictor of HRQoL trajectories, which is in line
with other studies (albeit cross-sectional and variable-centered;
e.g., Degroote et al., 2014; O’Leary et al., 2014). Moreover,
we observed that older age was related to decreasing HRQoL
trajectories, even if they started from a relatively high level.
is corresponds to existing research on elderly PLWH who
compared to younger PLWH faced more problems and
impediments in their daily functioning due to higher HIV-related
stigma (Emlet, 2006) as well as diculties in distinguishing
physical HIV symptoms from those associated with aging
(Guaraldi et al., 2011; Morgan et al., 2012).
However, one of the most important (yet null) results
deals with the lack of gender dierences in HRQoL change
among our participants. Specically, our nding may revise
the long-lasting and relatively persistent trend in the literature,
which points to lower HRQoL among female PLWH than
male PLWH based on cross-sectional data only (e.g.,
Campsmith et al., 2003; Mrus et al., 2005; Solomon et al.,
2008; Chandra et al., 2009). Some authors observed that
gender dierences in HRQoL within this patient group may
be apparent, i.e., they disappeared aer careful adjustment
of the results with regard to some clinical (e.g., longer illness
duration; Ruiz-Perez et al., 2009) or sociodemographic data
(worse employment and education status; Rzeszutek, 2017).
In other words, lower quality of life among female PLWH
FIGURE 2 | Results of latent class growth curve analysis for perceived social
support (unconditional model).
FIGURE 1 | Results of latent class growth curve analysis for health-related
quality of life (unconditional model).
Gruszczyńska and Rzeszutek Trajectories of Health-Related Quality of Life
Frontiers in Psychology | www.frontiersin.org 6 July 2019 | Volume 10 | Article 1664
does not necessarily reect their more dicult or dierent
adjustment to HIV/AIDS in comparison to male PLWH but
may rather be a result of other factors that have not been
carefully controlled for in other studies (Bogart et al., 2011;
Rzeszutek, 2017).
However, the results for PSS do not support this explanation.
Namely, the null eect for gender became signicant only
when other covariates were included in the model, resulting
in gender and relationship status being identied as correlates
of PSS trajectory membership. For a single woman, there
was a higher probability of belonging to the increasing PSS
trajectory than to the decreasing one, even if the starting
points of both trajectories were only slightly dierent. Notably,
this is inconsistent with results concerning female PLWH
(Li etal., 2016), but it corresponds to research demonstrating
that relationship status may have dierent consequences for
men and women, with men beneting more from marriage
(Nock, 1998). Among PLWH, men also benet more from
social support, while women are more likely to seek it
(Gordillo et al., 2009; Bekele et al., 2013). us, being a
single woman is not necessarily a disadvantageous condition
in this context. Such individuals may eectively receive
support from other sources that do not require HIV disclosure,
and they may also be less prone to abuse from an intimate
partner (Machtinger et al., 2012). However, we lack data
on the relationship status of these women at the time of
diagnosis; therefore, it cannot be excluded that they had
been infected by a partner in a heterosexual relationship.
e decision to besingle could thus bea deliberate consequence
of this mode of transmission – the most frequent among
women in Europe and the United States (Crepaz etal., 2017;
European Centre for Disease Prevention and Control/WHO
Regional Oce for Europe, 2017). Nonetheless, since no
relevant published data exist with which to compare this
gender-relationship status interaction, it could represent a
sample-specic association. As such, this topic requires
further research.
e obtained combinations of dual trajectories added to a
complexity of change in HRQoL and PSS among PLWH.
Although the probability of being a member of dual decreasing
trajectories was the highest, only 31% of the sample could
be assigned to this group. us, some factors may indeed
be responsible for the simultaneous change of both HRQoL
and PSS; however, they do not respond in the same manner
across the entire sample, since co-occurrence of the matching
change direction in both variables was rather modest. is is
especially pronounced within decreasing PSS, where weidentied
three combinations of dual trajectories: (1) congruent decreasing
PSS and HRQoL, (2) decreasing PSS and increasing HRQoL,
and (3) decreasing PSS with stable HRQoL. erefore, a few
non-exclusive explanations are possible for this mixture of
HRQoL and PSS change.
First, it is likely that an interrelation exists between PSS
and HRQoL (Burgoyne and Renwick, 2004) causing a downward
spiral over time. In this case, some general factors may broadly
aect the functioning of PLWH. e natural candidates are
those related to sociodemographic resources and clinical
characteristics; however, this group did not dier in this respect
from the other combinations of trajectories. e primary
explanation for this null eect could be that we did not assess
the change of these characteristics, only baselines. Nevertheless,
this result is congruent with most studies, which show its
modest role in the functioning of PLWH aer the inception
of ART (Torres et al., 2018). Second, for some PLWH, an
increase in HRQoL may result in reduced perceived support
since it is no longer needed to the same extent. Also, this
shi in perception may additionally serve to conserve self-
ecacy (Warner etal., 2011), which is of particular importance
for patients with chronic diseases that have a stigmatized
social reception, thereby further improving quality of life (Li
et al., 2011). Finally, in the third case (i.e., for PLWH with
decreasing PSS and stable HRQoL), these two processes may
have dierent temporal dynamics, as a change in PSS likely
proceeded changes in HRQoL (Jia et al., 2005). It would
berather exceptional to maintain such an incongruent dynamic-
static status in light of ndings that suggest cross-sectionally
lower PSS is related to lower HRQoL among PLWH (Douaihy
and Singh, 2001; Burgoyne and Renwick, 2004). Interestingly,
being a member of each of these groups remained unrelated
not only to gender but also to the other sociodemographic
and clinical variables.
ere are several strengths of this study, including the
longitudinal and person-centered approach with a relatively
large clinical sample and three measurement points. However,
certain limitations must benoted. First, this is a correlational
study based on self-descriptive data; thus, no causal interpretation
is allowed. Additionally, the separation of the univariate trajectory
classes for HRQoL was only acceptable. Moreover, since the
HRQoL measurement covered social domain, signicant overlap
with social support is likely to occur, which may lead to an
overestimation of the relationship between these variables.
Even if conceptually relevant, only weak and highly similar
correlation has been noted across all the domains1. is indicates
already well-recognized dierence among perceived social
support, satisfaction with social support, and their correlates
and outcomes (Vangelisti, 2009). Next, although the sample
reects the gender-related prevalence of HIV infection in
1
We would like to thank the anonymous reviewer for this comment. e time-
averaged correlations with PSS for each domain of HRQoL were as follows:
0.25 for somatic domain, 0.40 for psychological domain, 0.40 for social domain,
and 0.43 for environmental domain.
TABLE 4 | Joint probability of membership for dual trajectories of health-related
quality of life (HRQoL) and perceived social support (PSS).
HRQoL PSS
Decreasing Increasing High
stable
Low
stable
Total
Decreasing 0.32 0.05 0.06 0.03 0.46
Stable 0.20 0.03 0.01 0.05 0.29
Increasing 0.11 0.06 0.08 0.00 0.25
Total 0.63 0.14 0.15 0.08 1
Gruszczyńska and Rzeszutek Trajectories of Health-Related Quality of Life
Frontiers in Psychology | www.frontiersin.org 7 July 2019 | Volume 10 | Article 1664
Europe and United States, the study could be underpowered
to detect gender dierences. Furthermore, since there was a
tendency for higher dropout among women, a recruitment
bias cannot be excluded, though it was not observed in a
careful examination of the general pattern of missingness.
Finally, it must be underlined that the ndings are restricted
only to PLWH who are formally diagnosed and under medical
treatment, which is a typical characteristic for most studies
with clinical samples.
Despite these limitations, our study adds to the HIV/AIDS
literature by investigating the heterogeneity of change in
HRQoL and PSS with a special emphasis on gender dierences.
e present study demonstrates the complexity of dual changes
and identies groups of PLWH with mismatched HRQoL
and PSS trajectories. e limited role of baseline
sociodemographic and clinical characteristics of PLWH in
predicting these changes was also highlighted, particularly a
lack of signicant dierences between men and women. In
fact, the ndings may suggest that gender is no longer a
crucial factor beyond their HRQoL and PSS change if aer
being diagnosed access to treatment is equal.
DATA AVAILABILITY
e datasets for this study will not bemade publicly available
because although anonymized, it concerns sensitive issues (being
infected with HIV). e informed consent did not include
the consent to the publication of the data.
ETHICS STATEMENT
All procedures performed in this study were in accordance
with the ethical standards of the Research Ethics Committee
of the University of Economics and Human Sciences, Warsaw,
Poland, and with the 1964 Helsinki declaration and its later
amendments or comparable ethical standards. Informed consent
was obtained from all individual participants included in the
study. e protocol was approved by the Research Ethics
Committee of the University of Economics and Human Sciences,
Warsaw, Poland.
AUTHOR CONTRIBUTIONS
EG and MR conceived the study, designed the study, supervised
the data collection and database organization, conducted the
interpretation of the data, draed, and revised the manuscript.
EG conducted the statistical analysis. EG and MR approved
the submitted version of the manuscript.
FUNDING
is study was funded by the National Science Center, Poland,
research project no. 2016/23/D/HS6/02943. Open access of this
article was nanced by the Ministry of Science and Higher
Education in Poland under the 2019–2022 program “Regional
Initiative of Excellence”, project number 012/RID/2018/19.
REFERENCES
Alemu, H., Haile, D., Tsui, A., Ahmed, S., and Shewamare, A. (2012). Eect
of depressive symptoms and social support on weight and CD4 count increase
at HIV clinic in Ethiopia. AIDS Care 24, 866–876. doi: 10.1080/0954
0121.2011.648160
Ashton, E., Vosvick, M., Chesney, M., Gore-Felton, C., Koopman, C., O’Shea, K.,
et al. (2005). Social support and maladaptive coping as predictors of the
change in physical health symptoms among persons living with HIV/AIDS.
AIDS Patient Care STDs 19, 587–598. doi: 10.1089/apc.2005.19.587
Aziz, M., and Smith, K. (2011). Challenges and successes in linking HIV-
infected women to care in the United States. Clin. Infect. Dis. 52, 231–237.
doi: 10.1093/cid/ciq047
Bekele, T., Rourke, S., Tucker, R., Greene, S., Sobota, M., Koornstra, J., et al.
(2013). Direct and indirect eects of perceived social support on health-
related quality of life in persons living with HIV/AIDS. AIDS Care 25,
337–346. doi: 10.1080/09540121.2012.701716
Biesanz, J. C., Deeb-Sossa, N., Papadakis, A. A., Bollen, K. A., and Curran, P. J.
(2004). e role of coding time in estimating and interpreting growth curve
models. Psychol. Methods 9, 30–52. doi: 10.1037/1082-989X.9.1.30
Bing, E. G., Burnam, M. A., Longshore, D., Fleishman, J. A., Sherbourne, C.
D., London, A. S., et al. (2001). Psychiatric disorders and drug use among
human immunodeciency virus infected adults in the United States. Arch.
Gen. Psychiatry 58, 721–728. doi: 10.1001/archpsyc.58.8.721
Bogart, L. M., Wagner, G. J., Galvan, F. H., Landrine, H., Klein, D. J., and
Sticklor, L. A. (2011). Perceived discrimination and mental health symptoms
among black men with HIV. Cult. Divers. Ethn. Minor. Psychol. 17, 295–302.
doi: 10.1037/a0024056
Bor, J., Rosen, S., Chimbindi, N., Haber, N., Herbst, K., Mutevedzi, T., et al.
(2015). Mass HIV treatment and sex disparities in life expectancy: demographic
surveillance in rural South Africa. PLoS Med. 12:e1001905. doi: 10.1371/
journal.pmed.1001905
Breet, E., Kagee, A., and Seedat, S. (2014). HIV-related stigma and symptoms
of post-traumatic stress disorder and depression in HIV-infected individuals:
does social support play a mediating or moderating role? AIDS Care 26,
947–951. doi: 10.1080/09540121.2014.901486
Bucciardini, R., Pugliese, K., Weimer, L., Digregorio, M., Fragola, V., and
Mancini, M. (2014). Relationship between health-related quality of life
measures and high HIV viral load in HIV-infected triple-class-experienced
patients. HIV Clin. Trials 15, 176–183. doi: 10.1310/hct1504-176
Burgoyne, R., and Renwick, R. (2004). Social support and quality of life over
time among adults living with HIV in the HAART era. Soc. Sci. Med. 58,
1353–1366. doi: 10.1016/S0277-9536(03)00314-9
Burgoyne, R., and Saunders, D. (2001). Quality of life among urban Canadian
HIV/AIDS clinic outpatients. Int. J. STD AIDS 12, 505–512. doi: 10.1328/
a03564056
Campbell, C., Nair, S., and Maimane, M. (2006). AIDS stigma, sexual moralities
and the policing of women and youth in South Africa. Fem. Rev. 83,
132–138. doi: 10.1057/palgrave.fr.9400285
Campsmith, M., Nakashima, A., and Davidson, A. (2003). Self-reported health-
related quality of life in persons with HIV infection: results from a multi-site
interview project. Health Qual. Life Outcomes 1:12. doi: 10.1186/1477-7525-1-12
Carrico, A. W. (2019). Getting to zero: targeting psychiatric comorbidities as
drivers of the HIV/AIDS epidemic. Int. J. Behav. Med. 26, 1–2. doi: 10.1007/
s12529-019-09771-w
Carvalhal, A. (2010). Are women a dierent group of HIV-infected individuals?
Arch. Womens Ment. Health 13, 181–183. doi: 10.1007/s00737-010-0167-1
Celeux, G., and Soromenho, G. (1996). An entropy criterion for assessing the
number of clusters in a mixture model. J. Classif. 13, 195–212. doi: 10.1007/
BF01246098
Gruszczyńska and Rzeszutek Trajectories of Health-Related Quality of Life
Frontiers in Psychology | www.frontiersin.org 8 July 2019 | Volume 10 | Article 1664
Chandra, P., Satyanarayana, V., Satishchandra, P., Satish, K., and Kumar, M.
(2009). Do men and women with HIV dier in their quality of life? A
study from South India. AIDS Behav. 13, 110–117. doi: 10.1007/s10461-
008-9434-9
Chida, Y., and Vedhara, K. (2009). Adverse psychosocial factors predict poorer
prognosis in HIV disease: a meta-analytic review of prospective investigations.
Brain Behav. Immun. 23, 434–445. doi: 10.1016/j.bbi.2009.01.013
Cohen, M. S., Chen, Y. Q., McCauley, M., Gamble, T., Hosseinipour, M. C.,
Kumarasamy, N., et al. (2016). Antiretroviral therapy for the prevention of
HIV-1 transmission. N. Engl. J. Med. 375, 830–839. doi: 10.1056/
NEJMoa1600693
Collazos, J., Asensi, V., and Carto, J. (2007). Sex dierences in the clinical,
immunological and virological parameters of HIV-infected patients treated
with HAART. AIDS 21, 835–843. doi: 10.1097/QAD.0b013e3280b0774a
Cooper, V., Clatworthy, J., Harding, R., Whetham, J., and Consortium, E. (2017).
Measuring quality of life among people living with HIV: a systematic review
of reviews. Health Qual. Life Outcomes 15:220. doi: 10.1186/s12955-017-0778-6
Crepaz, N., Dong, X., Chen, M., and Hall, H. I. (2017). Examination of HIV
infection through heterosexual contact with partners who are known to
be HIV infected in the United States. AIDS 31, 1641–1644. doi: 10.1097/
QAD.0000000000001526
Deeks, S., Lewin, S., and Havlir, D. (2013). e end of AIDS: HIV infection
as a chronic disease. Lancet 382, 1525–1533. doi: 10.1016/S0140-
6736(13)61809-7
Degroote, S., Vogelaers, D., and Vandijck, D. (2014). What determines health-
related quality of life among people living with HIV: an updated review
of the literature. Arch. Public Health 72:40. doi: 10.1186/2049-3258-72-40
Douaihy, A., and Singh, N. (2001). Factors aecting quality of life in patients
with HIV infection. AIDS Read. 11, 444–449.
Earnshaw, V., Lang, S., Lippitt, M., Jin, H., and Chaudoir, S. (2015). HIV
stigma and physical health symptoms: do social support, adaptive coping,
and/or identity centrality act as resilience resources? AIDS Behav. 19, 41–49.
doi: 10.1007/s10461-014-0758-3
Emlet, C. (2006). A comparison of HIV stigma and disclosure patterns between
older and younger adults living with HIV/AIDS. AIDS Patient Care STDs
20, 350–358. doi: 10.1089/apc.2006.20.350
Emuren, L., Welles, S., Evans, A. A., Polansky, M., Okulicz, J. F., Macalino,
G., et al. (2017). Health-related quality of life among military HIV patients
on antiretroviral therapy. PLoS One. 12:e0178953. doi: 10.1371/journal.
pone.0178953
European Centre for Disease Prevention and Control/WHO Regional Oce
for Europe (2017). HIV/AIDS surveillance in Europe 2017–2016 data. Stockholm:
ECDC.
Geary, C., Parker, W., Rogers, S., Haney, E., Njihia, C., and Haile, A. (2014).
Gender dierences in HIV disclosure, stigma, and perceptions of health.
AIDS Care 26, 1419–1425. doi: 10.1080/09540121.2014.921278
Gielen, A., McDonnell, K., Campo, P., and Faden, R. (2001). Quality of life
among women living with HIV: the importance violence, social support,
and self-care behaviors. Soc. Sci. Med. 52, 315–322. doi: 10.1016/
S0277-9536(00)00135-0
Gonzalez, J., Penedo, F., Antoni, M., Dura, R., Pherson-Baker, S., and Ironson, G.
(2004). Social support, positive states of mind, and HIV treatment adherence
in men and women living with HIV/AIDS. Health Psychol. 23, 413–418.
doi: 10.1037/0278-6133.23.4.413
Gordillo, V., Fekete, E., Platteau, T., Antoni, M., Schneiderman, N., and
Nostlinger, C. (2009). Emotional support and gender in people living with
HIV: eects on psychological well-being. J. Behav. Med. 32, 523–531. doi:
10.1007/s10865-009-9222-7
Guaraldi, G., Orlando, G., and Zona, S. (2011). Premature age-related co-
morbidities among HIV-infected persons compared with the general population.
Clin. Infect. Dis. 53, 1120–1126. doi: 10.1093/cid/cir627
Hansen, N., Vaughan, E., Cavanaugh, C., Connell, C. M., and Sikkema, K.
(2009). Health-related quality of life in bereaved HIV-positive adults:
relationships between HIV symptoms, grief, social support, and axis II
indication. Health Psychol. 28, 249–257. doi: 10.1037/a0013168
Herrmann, S., McKinnon, E., Hyland, N., Lalanne, C., Mallal, S., Nolan, D.,
et al. (2013). HIV-related stigma and physical symptoms have a persistent
inuence on health-related quality of life in Australians with HIV infection.
Health Qual. Life Outcomes 11:56. doi: 10.1186/1477-7525-11-56
Hipp, J. R., and Bauer, D. J. (2006). Local solutions in the estimation of growth
mixture models. Psychol. Methods 11, 36–53. doi: 10.1037/1082-989X.11.1.36
IBM Corp (2016). IBM SPSS statistics for windows. Version 24. Armonk, NY:
IBM Corp (Released 2016).
Jia, H., Uphold, C. R., Wu, S., Chen, G. J., and Duncan, P. W. (2005). Predictors
of changes in health-related quality of life among men with HIV infection
in the HAART era. AIDS Patient Care STDs 19, 395–405. doi: 10.1089/
apc.2005.19.395
Jia, H., Uphold, C., Wu, S., Reid, K., Findley, K., and Duncan, P. (2004).
Health-related quality of life among men with HIV infection: eects of
social support, coping, and depression. AIDS Patient Care STDs 18, 594–603.
doi: 10.1089/apc.2004.18.594
Joint United Nations Programme on HIV/AIDS (UNAIDS) (2018). UNAIDS
DATA 2018. Available at: http://www.unaids.org/sites/default/les/media_asset/
unaids-data-2018_en.pdf (Accessed March, 19 2019).
Li, X., Huang, L., Wang, H., Fennie, K. P., He, G., and Williams, A. B. (2011).
Stigma mediates the relationship between self-ecacy, medication adherence,
and quality of life among people living with HIV/AIDS in China. AIDS
Patient Care STDs 25, 665–671. doi: 10.1089/apc.2011.0174
Li, L., Lin, C., Liang, L. J., and Ji, G. (2016). Exploring coping and social
support with gender and education among people living with HIV in China.
AIDS Behav. 20, 317–324. doi: 10.1007/s10461-015-1232-6
Lifson, A. R., Grund, B., Gardner, E. M., Kaplan, R., Denning, E., Engen, N.,
et al. (2017). Improved quality of life with immediate versus deferred initiation
of antiretroviral therapy in early asymptomatic HIV infection. AIDS 31,
953–963. doi: 10.1097/QAD.0000000000001417
Liu, C., Ostrow, D., Detels, R., Hu, Z., Johnson, L., Kingsley, L., et al. (2006).
Impacts of HIV infection and HAART use on quality of life. Qual. Life
Res. 15, 941–949. doi: 10.1007/s11136-005-5913-x
Lubeck, D., and Fries, J. (1997). Assessment of quality of life in early stage
HIV-infected persons: data from the AIDS time-oriented health outcome
study (ATHOS). Qual. Life Res. 6, 494–506. doi: 10.1023/A:1018404014821
Machtinger, E., Wilson, T., Haberer, J., and Weiss, D. (2012). Psychological
trauma and PTSD in HIV-positive women: a meta-analysis. AIDS Behav.
16, 2091–2100. doi: 10.1007/s10461-011-0127-4
McDonnell, K., Gielen, A., Wu, A., Campo, P., and Faden, R. (2000). Measuring
health related quality of life among women living with HIV. Qual. Life Res.
9, 931–940. doi: 10.1023/A:1008909919456
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 1, e32–e40. doi: 10.1016/S2352-3018(14)70018-9
Mitchell, M., Nguyen, T., Isenberg, S., Maragh-Bass, A., Keruly, J., and Knowlton, A.
(2017). Psychosocial and service use correlates of health-related quality of
life among a vulnerable population living with HIV/AIDS. AIDS Behav. 21,
1580–1587. doi: 10.1007/s10461-016-1589-1
Morgan, E., Iudicello, J., and Weber, E. (2012). Synergistic eects of HIV
infection and older age on daily functioning. J. Acquir. Immune Dec. Syndr.
61, 341–348. doi: 10.1097/QAI.0b013e31826bfc53
Mrus, J., Williams, P., Tsevat, J., Cohn, S., and Wu, A. (2005). Gender dierences
in health-related quality of life in patients with HIV/AIDS. Qual. Life Res.
14, 479–491. doi: 10.1007/s11136-004-4693-z
Nicastri, E., Leone, S., Angeletti, C., Palmesano, L., Sarmati, L., Chiesi, A.,
et al. (2007). Sex issues in HIV-1-infected persons during highly active
antiretroviral therapy: a systematic review. J. Antimicrob. Chemother. 60,
724–732. doi: 10.1093/jac/dkm302
Nock, S. (1998). Marriage in men’s lives. Oxford, New York.
Nylund, K., Asparouhov, T., and Muthén, B. O. (2007). Deciding on the number
of classes in latent class analysis and growth mixture modeling: a Monte
Carlo simulation study. Struct. Equ. Model. 14, 535–569. doi: 10.1080/
10705510701575396
O’Leary, A., Jemmott, J. B., Stevens, R., Rutledge, S. E., and Icard, L. D. (2014).
Optimism and education buer the eects of syndemic conditions on HIV
status among African American men who have sex with men. AIDS Behav.
18, 2080–2088. doi: 10.1007/s10461-014-0708-0
Oberjé, E., Dima, A., van Hulzen, A. W., Prins, J., and 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 Behav. 19,
1398–1407. doi: 10.1007/s10461-014-0880-2
Gruszczyńska and Rzeszutek Trajectories of Health-Related Quality of Life
Frontiers in Psychology | www.frontiersin.org 9 July 2019 | Volume 10 | Article 1664
Penniman, T. V., Taylor, S. L., Bird, C. E., Beckman, R., Collins, R. L., and
Cunningham, W. (2007). e associations of gender, sexual identity and
competing needs with healthcare utilization among people with HIV/AIDS.
J. Natl. Med. Assoc. 99, 419–427.
Psaros, C., O’Cleirigh, C., Bullis, J., Markowitz, S., and Safren, S. (2013). e
inuence of psychological variables on health related quality of life among
HIV positive individuals with a history of intravenous drug use. J. Psychoactive
Drugs 45, 304–312. doi: 10.1080/02791072.2013.825030
Qiao, S., Li, X., and Stanton, B. (2014). Social support and HIV-related risk
behaviors: a systematic review of the global literature. AIDS Behav. 18,
419–441. doi: 10.1007/s10461-013-0561-6
Rendina, H., Brett, M., and Parsons, J. (2018). e critical role of internalized
HIV-related stigma in the daily negative aective experiences of HIV-positive
gay and bisexual men. J. Aect. Disord. 227, 289–297. doi: 10.1016/j.
jad.2017.11.005
Ruiz-Perez, I., Olry, A., del Castilloc, L., Bano, J., Ruz, M., and Jimeneze,
A. (2009). No differences in quality of life between men and
womenundergoing HIV antiretroviral treatment. Impact of demographic,
clinical and psychosocial factors. AIDS Care 21, 943–952. doi:
10.1080/09540120802612840
Rzeszutek, M. (2017). Health-related quality of life and coping strategies among
people living with HIV: the moderating role of gender. Arch. Womens Ment.
Health 21, 247–257. doi: 10.1007/s00737-017-0801-2
Rzeszutek, M., and Gruszczyńska, E. (2018). Positive and negative aect change
among people living with HIV: a one-year prospective study. Int. J. Behav.
Med. 26, 28–37. doi: 10.1007/s12529-018-9741-0
Samji, H., Cescon, A., Hogg, R. S., Modur, S. P., Altho, K. N., Buchacz, K.,
et al. (2013). Closing the gap: Increases in life expectancy among treated
HIV-positive individuals in the United States and Canada. PLoS One 18,
144–156. doi: 10.1371/journal.pone.0081355
Schulz, U., and Schwarzer, R. (2003). Soziale Unterstützung bei der
Krankheitsbewältigung: Die Berliner Social Support Skalen (BSSS). Diagnostica
49, 73–82. doi: 10.1026//0012-1924.49.2.73
Solomon, S., Venkatesh, K., Brown, L., Verma, P., Cecelia, A., Daly, C., et al.
(2008). Gender-related dierences in quality of life domains of persons
living with HIV/AIDS in South India in the era prior to greater access to
antiretroviral therapy. AIDS Patient Care and STDs 22, 999–1005. doi: 10.1089/
apc.2008.0040
Song, B., Yan, C., Lin, Y., Fuxiang, W., and Wang, L. (2016). Health-related
quality of life in HIV-infected men who have sex with men in China: a
Cross-sectional study. Med. Sci. Monit. 22, 2859–2870. doi: 10.12659/
MSM.897017
Torres, T. S., Harrison, L. J., La Rosa, A. M., Cardoso, S. W., Zheng, L.,
Ngongondo, M., et al. (2018). Quality of life improvement in resource-
limited settings aer one year of second-line antiretroviral therapy use among
adult men and women. AIDS 32, 583–593. doi: 10.1097/QAD.0000000000001738
Turner-Cobb, J. M., Gore-Felton, C., Marouf, F., Koopman, C., Kim, P., et al.
(2002). Coping, social support, and attachment style as psychosocial correlates
of adjustment in men and women with HIV/AIDS. J. Behav. Med. 25,
337–353. doi: 10.1023/A:1015814431481
Vangelisti, A. L. (2009). Challenges in conceptualizing social support. J. Soc.
Pers. Relat. 26, 39–51. doi: 10.1177/0265407509105520
Vermunt, J. K., and Magidson, J. (2016). Technical guide for latent GOLD 5.1:
Basic, advanced, and syntax. Belmont, CA: Statistical Innovations.
Warner, L., Ziegelmann, J., Schüz, B., Wurm, S., Tesch-Roemer, C., and
Schwarzer, R. (2011). Maintaining autonomy despite multimorbidity: self-
ecacy and the two faces of social support. Eur. J. Ageing 8, 3–12. doi:
10.1007/s10433-011-0176-6
WHOQOL Group (1998). Development of the World Health Organization
WHOQOL-BREF quality of life assessment. Psychol. Med. 28, 551–558. doi:
10.1017/S0033291798006667
Conict of Interest Statement: e authors declare that the research was conducted
in the absence of any commercial or nancial relationships that could beconstrued
as a potential conict of interest.
Copyright © 2019 Gruszczyńska and Rzeszutek. is is an open-access article distributed
under the terms of the Creative Commons Attribution License (CC BY). e use,
distribution or reproduction in other forums is permitted, provided the original
author(s) and the copyright owner(s) are credited and that the original publication
in this journal is cited, in accordance with accepted academic practice. No use,
distribution or reproduction is permitted which does not comply with these terms.
Available via license: CC BY
Content may be subject to copyright.
