Trajectories of Posttraumatic Growth Following HIV Infection: Does One PTG Pattern Exist?

Abstract

The aim of this study was to examine the heterogeneity of change of posttraumatic growth (PTG) among people living with HIV (PLWH) in a 1-year prospective study. The goal was also to identify sociodemographic and clinical covariates and differences in baseline coping strategies. Particularly, time since diagnosis and positive reframing coping were of special interest. The sample consisted of 115 people with medically confirmed diagnosis of HIV infection. The participants filled out paper-and-pencil questionnaires three times with an interval of 6 months, including also sociodemographic and clinical data. Four trajectories of PTG were identified: curvilinear, low stable, high stable, and rapid change. Participants’ gender, education level, CD4 count and time since HIV diagnosis occurred to be significant covariates of class membership. Positive reframing and self-distraction differentiated only between the high stable and the rapid change trajectory, with lower values in the latter. The study results call for attention to the complexity of PTG patterns in a face of struggling with HIV infection. Specifically, interventions in clinical practice should take into account the fact that there is no single pattern of PTG that fits all PLWH and that these differences may be related to the sociodemographic and clinical characteristics as well as to coping strategies representing meaning-making mechanism.

Full text

Vol.:(0123456789)
Journal of Happiness Studies (2022) 23:1653–1668
https://doi.org/10.1007/s10902-021-00467-1
1 3
RESEARCH PAPER
Trajectories ofPosttraumatic Growth Following HIV
Infection: Does One PTG Pattern Exist?
MarcinRzeszutek1· EwaGruszczyńska2
Accepted: 12 October 2021 / Published online: 3 November 2021
© The Author(s) 2021
Abstract
The aim of this study was to examine the heterogeneity of change of posttraumatic growth
(PTG) among people living with HIV (PLWH) in a 1-year prospective study. The goal
was also to identify sociodemographic and clinical covariates and differences in baseline
coping strategies. Particularly, time since diagnosis and positive reframing coping were of
special interest. The sample consisted of 115 people with medically confirmed diagnosis
of HIV infection. The participants filled out paper-and-pencil questionnaires three times
with an interval of 6 months, including also sociodemographic and clinical data. Four tra-
jectories of PTG were identified: curvilinear, low stable, high stable, and rapid change.
Participants’ gender, education level, CD4 count and time since HIV diagnosis occurred to
be significant covariates of class membership. Positive reframing and self-distraction dif-
ferentiated only between the high stable and the rapid change trajectory, with lower values
in the latter. The study results call for attention to the complexity of PTG patterns in a face
of struggling with HIV infection. Specifically, interventions in clinical practice should take
into account the fact that there is no single pattern of PTG that fits all PLWH and that these
differences may be related to the sociodemographic and clinical characteristics as well as
to coping strategies representing meaning-making mechanism.
Keywords HIV/AIDS· Posttraumatic growth· Coping· Trajectory
1 Introduction
It has been more than two decades since Tedeschi and Calhoun (1996) coined the term
posttraumatic growth (PTG), which substantially altered the theory and practice of trau-
matic stress studies (Infurna & Jayawickreme, 2019; Tedeschi etal., 2018). Specifically,
the introduction of the PTG concept stimulated new lines of research on positive changes
* 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 SWPS University ofSocial Sciences andHumanities, Chodakowska 19/31, 03-815Warsaw,
Poland
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M.Rzeszutek, E.Gruszczyńska
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among survivors of traumatic life events, which became one of the leading research fields
in positive psychology, which was formed somewhat later (Seligman & Csikszentmiha-
lyi, 2000). It also resulted in the construction of several theoretical models of PTG, which
operationalize this phenomenon differently (e.g., Linley & Joseph, 2004; Maercker & Zoe-
llner, 2004; Pals & McAdams, 2004; Tedeschi & Calhoun, 1996, 2004). Unfortunately, the
multiplicity of theoretical approaches to PTG did not translate satisfactorily into a consen-
sus on the meaning of this term, and so the problem of what PTG actually is and how PTG
should be measured remains (Jayawickreme etal., 2021; Jayawickreme & Blackie, 2014).
On the one hand, Tedeschi and Calhoun (1996, 2004) treat PTG as an outcome of com-
plex, mainly cognitive, processing of traumatic experience. Conversely, other researchers
define PTG as a process for finding meaning after trauma (Pals & McAdams, 2004), an ele-
ment of psychological well-being (Linley & Jospeh, 2004), or even a manifestation of com-
pensatory illusion (Maercker & Zoellner, 2004). Although there is no general consensus on
how to operationalize PTG, the majority of authors record similar changes observed among
people exposed to highly stressful life events, which do not have to be traumatic by defini-
tion (Jayawickreme & Blackie, 2014). These changes include more satisfying interpersonal
relationships, finding new life possibilities, greater appreciation of life, openness to spir-
itual issues, and enhanced perception of personal strength (Tedeschi & Calhoun, 2004).
Aforementioned models of PTG have been studied intensely in recent years (Infurna &
Jayawickreme, 2019; Taku et al., 2020). Nevertheless, some alternative theoretical expla-
nations of PTG still have not been addressed adequately in the literature. One of these
explanations focuses on thorough coping process in the aftermath of experiencing highly
stressful events (Tennen & Affleck, 2009). It should be noted that Tedeschi and Calhoun
(1996) have already highlighted that not only experiencing the traumatic event itself but
a method to cope with it foster growth via facilitation of cognitive processing of trauma.
From that time, several studies have shown that different coping strategies may promote or
hinder PTG as a response to a life-threatening illness (see for review: Casellas-Grau etal.,
2017; Rzeszutek & Gruszczyńska, 2018). More specifically, meaning-focused coping
strategies, especially positive reappraisal, have been found to predict PTG among cancer
patients (e.g., Sears etal., 2003) and people living with HIV/AIDS (PLWH) (e.g., Siegel
etal., 2005). Conversely, avoidance coping strategies, e.g., denial or substance was found
to hinder the growth among patients struggling with terminal illness (Sears et al., 2003).
Nevertheless, a majority of studies in this area have been cross-sectional and longitudinal
studies on the PTG-coping association among PLWH showed no significant relationship
(Rzeszutek et al., 2017). These findings call for more advanced methodological designs
(Hamama-Raz etal., 2019).
Tennen and Affleck (2009) proposed the operationalization of PTG as a form of coping,
where aforementioned facets of PTG (e.g., enhanced perception of personal strength, open-
ness to spirituality) can be attributed with dealing with consequences of traumatic events.
In other words, PTG may serve for trauma survivors as a kind of active coping strategy
to find the meaning of trauma. Moreover, it was also reported that PTG can help enhance
more adaptive coping strategies to deal with current and future life stressors (Park etal.,
2005). However, some authors have observed that PTG can also represent defensive coping
with experiencing growth serving as a positive illusion (Cheng etal., 2018). Thus, it still
not known whether particular coping strategies promote or hinder PTG or whether PTG
facilitates adaptive or maladaptive coping, as the vast majority of those studies applied
cross-sectional frameworks (Hamama‐Raz etal., 2019). In fact, this problem can only be
addressed in longitudinal studies. First step in answering this question could be to examine
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if people representing different trajectories of PTG also differ in their baseline coping
strategies.
Diagnosis of and life with a potentially terminal somatic disease is a very strong stressor,
which has been classified as an event meeting the criterion of a traumatic stressor and which
can lead to posttraumatic stress disorder (PTSD; APA, 1994; Kangas etal. 2005). HIV/AIDS
has these consequences; roughly 30 percent to even 64 percent of PLWH meet the diagnos-
tic criteria of PTSD (Sherr et al., 2011). However, an increasing number of authors have
observed positive changes among this group of patients, comprising the phenomenon of PTG
(e.g., Cieślak etal., 2009; Milam, 2004, 2006; Siegel etal., 2005; Rzeszutek etal., 2017).
When exploring the issue of PTG accompanying the trauma of a life-threatening dis-
ease, one should also clarify the essence of this type of trauma, which is not easy and
provokes controversy, especially among PLWH (Neigh et al., 2016). Edmondson (2014)
proposed a PTSD model associated with enduring somatic threat. The posttraumatic symp-
toms accompanying patients have a complex etiology and variable dynamics. Although
they are usually initiated by the moment of diagnosis, they also result from a later struggle
with the disease, constant awareness of the real life threat, and, in some cases (see PLWH),
very strong social stressors (Sherr et al., 2011). Namely, trauma experienced by such
patients applies not only to the past (see diagnosis) but is also a continuous process induced
by internal (somatic) factors related to the present and/or future. This latter distinguishes it
from traditionally understood traumatic stressors as an event external to a person that has
happened in the past (see APA, 1994, 2013). Taking the above into consideration, there is
no agreement on when the critical moment that can potentially trigger a growth experience
will be, nor on how much time will have to elapse from that moment until the appearance
of PTG. Most researchers have observed that the critical moment is the moment of diagno-
sis (Casellas-Grau etal., 2017). However, other authors have argued that positive changes
may also appear many years after diagnosis, as was found often among PLWH (Sawyer
etal., 2010). Thus, it has been suggested to incorporate the trajectory models into studying
both resilience and psychopathology among individuals exposed to traumatic events to bet-
ter understand the unique patterns of adaptation (Galatzer-Levy etal., 2018).
Such approach is called person-centered as it allows identifying groups of people who
in an examined phenomenon represent the highest possible similarity inside a given group,
while at the same time representing a maximum diversity across these groups (e.g. Laursen &
Hoff, 2006). In this way, a data-driven heterogeneity regarding the magnitude and direction of
change can be detected in the sample. On the basis of previous studies, it is already known that
averaging individual processes will not provide an accurate explanation for the observable dif-
ferences in psychological functioning between PLWH, which still exist even after adjusting for
the course and progression of the disease itself. There is probably no single trajectory of PTG
among PLWH, and the conditions underlying these changes need an explanation.
Relatively little attention has been devoted to this topic so far, and existing studies,
largely maintained in a cross-sectional design, bring about a rather fragmentary and incon-
sistent picture of PTG among PLWH (Rzeszutek & Gruszczyńska, 2018; Sawyer et al.,
2010). In particular, most authors have mainly sought to document PTG prevalence among
PLWH and relate it to selected clinical variables, which has led to ambiguous conclu-
sions. For example, some studies have demonstrated a relationship between PTG and better
immune parameters (Milam, 2004, 2006), while other researchers have not observed this
relationship (Littlewood etal., 2008). Similar ambiguous results refer to PTG in relation
to other clinical variables, such as adherence to treatment (see Łuszczyńska etal., 2012
vs. Littlewood etal., 2008) or the status of HIV/AIDS (see Milam, 2004 vs. Littlewood
etal., 2008). The relatively strongest consensus prevails regarding the lack of association
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between PTG and the duration of HIV infection (e.g., Garrido-Hernansaiz et al., 2017;
Łuszczyńska etal., 2012; Milam, 2004, 2006), which provokes the question of what really
induces PTG in this group of patients. That is, should the aforementioned positive changes
be attributed to the ongoing adjustment to or coping with HIV/AIDS or do they represent a
real growth that goes beyond an adaptation to illness (Sawyer etal., 2010), usually defined
as a return to pre-illness well-being level (Lyubomirsky, 2010).
Even much less research has been devoted to the role of coping in explaining differences
in PTG among PLWH. Until now four such studies have been identified, result of which
point mainly to beneficial effects of a wide range of meaning-focused strategies (Kraaij
etal., 2008; Rzeszutek, etal., 2017; Siegel etal., 2005) as well as problem-focused strate-
gies (Kraaij etal., 2008, Ye et al., 2017). An enhancing potential of avoidant emotion-
focused strategies should also not be neglected (Kraaij etal., 2008). Nevertheless, all of
these studies applied a variable -center approach, which even if utilized longitudinal data
can only to some extent overcome the typical drawbacks observed in PTG studies (Pat-
Horenczyk etal., 2016). That is, at best their results illustrate general patterns and ignore a
clinically important heterogeneity of PTG trajectories among PLWH.
2 Current Study
Taking the aforementioned research gaps into a consideration, the aim of our study was to
examine patterns of PTG change among PLWH in a 1-year prospective study and to estab-
lish their sociodemographic and clinical correlates as well as related differences in coping
strategies. More specifically, we wanted to verify three hypotheses in the first study among
PLWH in a person-centered approach, which can be considered an added value to research
on PTG in general, where person-centered longitudinal analysis is still very scant (Cheng
etal., 2018; Hamama-Raz etal., 2019).
On the basis of the few longitudinal studies so far, we expected that changes of PTG
among PLWH will be heterogeneous, i.e., PLWH will differ in terms of staring point as
well as amount and direction of change of PTG throughout the study period (Hypothesis
1). However, as the study was conducted among PLWH at least 1 year after diagnosis,
which suggested that for the majority of them adaptation to the disease may already be
achieved (Sawyer etal., 2010), the most numerous trajectories should present stability of
PTG rather than changes. Still these trajectories may differ with regard to years after the
diagnosis as well as other clinical and sociodemographic variables (Hypothesis 2). Thus,
we examined a possibility of heterogeneous trajectories of PTG and wanted to identify
their specific covariates (Husson etal., 2017). Finally, there should be mechanisms under-
lying long-term changes in PTG (Infurna & Jayawickreme, 2019). The strategies of coping
with stress caused by chronic disease may not just undermine or facilitate development
of PTG but may also be important factors for its sustainability. Particularly, it refers to
meaning-focused coping, which essence is a cognitive reformulation of a stressful situation
as valuable and beneficial without changing its objective parameters. As such, it supports
a reconstruction of the positive meaning given to life, violated by an irreversible stressful
event, which is essential for an induction of a recovery process (Jayawickreme etal., 2021).
This role has been empirically verified in uncontrollable and long-lasting conditions (Folk-
man, 1997; Moskowitz etal., 2009; Prati & Pietrantoni, 2009). Therefore, while this part of
the study is more exploratory, we still expect that among tested coping strategies positive
reappraisal should be associated with more beneficial PTG trajectories (Hypothesis 3).
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3 Method
3.1 Participants andProcedure
The sample consisted of 115 adults with a medical diagnosis of HIV infection. Of 470
patients taking part in a larger cross-sectional project conducted in naturalistic setting
during standard medical care at out-patient clinic of the state hospital of infectious dis-
eases, 115 (25 percent) agreed to leave their personal contact data to participate in a
1-year longitudinal study. There were no significant differences in all sociodemographic
and clinical characteristics between the two groups, except for education level. Namely,
people with secondary and higher education were more likely to participate in the lon-
gitudinal study than people with secondary or vocational education (χ2 = 7.57, df = 1,
p = 0.006). The written informed consent was obtained from each person before the
study begun and there was no remuneration for participation. The participants com-
pleted three times at 6-month interval a paper-and-pencil set of the questionnaires,
which was distributed by the authors of this study and professional interviewers. The
eligibility criteria were being at least 18years old, having a confirmed medical diagno-
sis of HIV+, and undergoing antiretroviral treatment. Eligibility criteria also included
indicating in the Posttraumatic Growth Inventory (PTGI, see, Measures) a diagnosis of
HIV infection as a traumatic event. The exclusion criteria included HIV-related cogni-
tive disorders, which were screened by medical doctors. The study was approved by the
local ethics commission. Table1 summarizes the sociodemographic and clinical charac-
teristics of the sample.
Table 1 The sociodemographic
and clinical characteristics of the
sample (N = 115)
M, mean; SD, standard deviation
Variable N (%)
Gender
Male 97 (84%)
Female 18 (16%)
Age in years (M ± SD) 40.8 ± 10.9
Relationship status
Yes 68 (59%)
No 47 (41%)
Education
Elementary and vocational 8 (7%)
Secondary and university degree 107 (93%)
HIV/AIDS status
HIV + only 96 (84%)
HIV/AIDS 19 (16%)
HIV infection duration in years (M ± SD) 7.4 ± 6.1
Antiretroviral treatment (ART) duration in years
(M ± SD)
5.5 ± 4.7
CD4 count 621.8 ± 245.9
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3.2 Measures
To measure the intensity of posttraumatic growth, the Posttraumatic Growth Inventory was
used (PTGI; Tedeschi & Calhoun, 1996). As in the original version, it consists of 21 state-
ments that describe various changes resulting from traumatic or highly stressful events,
which are listed at the beginning of the questionnaire. In our study, the participants were
directly instructed to focus on their diagnosis of HIV infection as the event since which the
changes may have happened. They responded to each statement on a six-point scale, start-
ing from 0 = I did not experience this change as a result of the crisis to 5 = I experienced
this change to a very great degree as a result of the crisis. In the current study, only the
global PTG score was obtained (Park & Helgeson, 2006) by summarizing all the answers.
The Cronbach coefficient for this indicator was 0.96, 0.95, and 0.94 for the first, second,
and third measurement points, respectively.
Strategies of coping with stress were assessed by Brief-COPE Inventory (Carver, 1997).
This questionnaire comprises 28 items with a Likert-like response scale from 0 = I haven’t
been doing this at all to 3 = I’ve been doing this a lot. It results in 14 subscales, with two
items per each, illustrating a wide range of coping strategies, including self-distraction,
active coping, denial, substance use, use of emotional support, use of instrumental sup-
port, behavioral disengagement, venting, positive reframing, planning, humor, acceptance,
religion, and self-blame. In the instruction, participants were informed to focus on their
ways of coping with thedisease during last the four weeks when providing their answers.
Indicators were obtained by adding together the relevant answers and then averaging the
values. With only two items per scale, the Cronbach’s alpha ranged from 0.40 (humor) to
0.86 (substance use).
3.3 Data Analysis
To test homogeneity versus heterogeneity of change of PTG in the sample, latent class
growth modeling (LCGM; Nagin, 2005) was used. We began with estimating a single tra-
jectory model where homogeneity of change in the whole sample is presumed (1-class
solution). Next, additional models were estimated with an increasing number of trajecto-
ries (n + 1 class solution). Both linear and quadratic time trends were examined. A final
model was chosen based on comparisons of several indicators (Nylund etal., 2007). For
Bayesian Information Criterion (BIC), the Sample-size Adjusted BIC (SABIC), and Akai-
ke’s Information Criterion (AIC), the lowest values suggest the best solution. Entropy indi-
cates a quality of a class separation from 0 to 1, where 1 evidences a prefect classification
(Muthén & Muthén, 2000). Another evaluation of goodness of fit is provided by the results
of the BootstrapLikelihood Ratio Test (BLRT, McLachlan & Peel, 2000), which compares
neighboring models. Finally, a size of the smallest class is a practical criterion. In gen-
eral, classes smaller than 5 percent of the sample are considered spurious and unreplicable
(Hipp & Bauer, 2006), although in clinical samples they may still have a value.
After establishing the optimal number and shape of trajectories, we used the bias-
adjusted three-step approach (Bakk etal., 2013) to identify significant covariates of a tra-
jectory membership among sociodemographic and clinical variables presented in Table1.
In the final step, we tested, by means of one-way ANOVA, if members representing dif-
ferent trajectories used different coping strategies at baseline. Due to variance heterosce-
dasticity and unequal group sizes for multiple post hoc comparisons, we implemented
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bootstrapping with 500 samples to form a 95 percent confidence interval with the bias-
corrected and accelerated method (BCa 95% CI). Data analysis was performed using the
Latent GOLD 5.1.0.19007 and IBM SPSS Statistics version 25.
4 Results
4.1 Missing Data andDescriptive Statistics
We noted 29 percent dropout between the first and the last measurement of PTG, but miss-
ing data can be regarded as missing at random (Little’s MCAR test: χ2 = 19.64, df = 20,
p = 0.481). Also, there were no significant differences between initial and final sample for
any sociodemographic or clinical variable. Thus, all the data from the 115 participants
were included in the analysis, using the full information maximum likelihood algorithm.
The descriptive statistics of longitudinally assessed PTG and baseline coping for this sam-
ple are provided in Table2.
4.2 Trajectories ofPTG
Table3 presents a summary of model selection indices of latent class growth analysis.
For the model with one trajectory, both linear (s1 = 9.6, z = 1.72, ns) and quadratic (s2 =
− 4.2, z =− 1.53, ns) slope were insignificant, suggesting a lack of change of PTG at
the level of the sample means. However, models with more than one trajectory were
better fitted to the data. The lowest BIC was noted for the model with four trajectories.
Table 2 Descriptive statistics of
the studied variables (N = 115)
M, mean, SD, standard deviation; PTG, posttraumatic growth, num-
bers denote the relevant measurement points
Variable M SD Kurtosis Skewness
PTG_1 61.1 23.5 −0.39 −0.39
PTG_2 66.5 24.1 0.15 −0.84
PTG_3 63.5 22.3 −0.53 −0.33
Active coping 2.0 0.6 0.91 −0.59
Planning 2.0 0.7 1.20 −0.90
Positive reframing 1.9 0.7 0.31 −0.43
Acceptance 2.1 0.6 0.89 −0.40
Humor 1.4 0.7 −0.36 0.03
Religion 1.2 1.0 −1.14 0.32
Use of emotional support 1.3 0.8 −0.78 0.02
Use of instrumental support 1.8 0.6 0.18 −0.47
Self-distraction 1.6 0.7 −0.17 −0.38
Denial 1.0 0.8 −0.81 0.42
Venting 1.5 0.7 −0.26 −0.47
Substance use 1.0 1.0 −1.05 0.40
Behavioral disengagement 1.1 0.9 −0.53 0.46
Self-blame 1.4 0.9 −0.99 −0.23
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This model also provided the highest data separation among competing models. Finally,
the insignificant BLRT for the model with five trajectories indicated a better fit for the
model with one fewer class. Thus, in spite of a quite small frequency of the lowest class,
four trajectories were examined further. The obtained solution is plotted in Fig.1.
Trajectory 1, which has the highest number of participants (46 percent of the sample),
describes a curvilinear pattern of PTG change (s1 = 12.9, z = 2.33, p < 0.05; s2 =− 7.0,
z =− 2.55, p < 0.05). Trajectory 2 (25 percent of the sample), which has the highest
starting point, was stable during the time of the study (s1 = 14.4, z = 0.81, ns; s2 = −1.5,
z = −0.55, ns). For trajectory 3 (22 percent of the sample), although the pattern seems
curvilinear due to large standard errors, these changes are insignificant (s1 =− 22.0,
z = −1.82, ns; s2 = 9.6, z = 1.67, ns). Finally, trajectory 4, which has the lowest number
of participants (7 percent of the sample), although modelled as curvilinear, represents
rather piecewise growth with the turning point at the second measurement. A steep rise
from the lowest to the highest PTG values is followed by a small decrease between the
second and third measurement.
Table 3 Summary of model selection indices of latent class growth analysis
BIC, Bayesian Information Criterion; AIC, Akaike’s Information Criterion; SABIC, Sample-Size Adjusted
BIC; BLRT, Bootstrap Likelihood Ratio Test
Model BIC AIC SABIC Number of
parameters
Entropy BLRT Smallest profile
Value p% of N Frequency
1-Profile 2868 2857 2856 4
2-Profile 2823 2798 2795 9 0.68 69.14 < .001 39% 45
3-Profile 2823 2784 2778 14 0.70 24.27 .002 17% 19
4-Profile 2817 2766 2757 19 0.77 29.33 < .001 7% 8
5-Profile 2825 2758 2748 24 0.76 15.97 .05 7% 8
Fig. 1 Trajectories of posttraumatic growth in the studied sample
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4.3 Sociodemographic andClinical Covariates ofPTG Trajectories
Among covariates of class membership, the significant effects were noted for gen-
der (Wald = 17,413.26, p < 0.001), age (Wald = 14.11, p = 0.003), and education level
(Wald = 5977.39, p < 0.001), as well as for two HIV-related characteristics: CD4 count
(Wald = 26.47, p < 0.001) and, expectedly, time since diagnosis (Wald = 8.75, p = 0.033).
There were no differences for relationship status, years of antiretroviral therapy (ART), or
AIDS stage. The summary of results is presented in Table3. In general, trajectory 4 com-
prises a subgroup of men only, the oldest ones but with a relatively short time since diag-
nosis (although it differs significantly only from trajectory 2) and the lowest CD4 count.
For the highest and stable trajectory 2, the most distinguishing characteristics of members
are a relatively higher representation of women than in other groups, a lower education
level, and the longest time since HIV diagnosis. Members of trajectory 1 (curvilinear) and
3 (low stable) are similar in terms of covariates, with a difference only for gender ratio: a
higher prevalence of women is present in the curvilinear trajectory (see Table4).
4.4 Coping Strategies Across PTG Trajectories
Out of the 14 analyzed coping strategies, only two significant differences between the
groups were noted. Both of them are differences between trajectory 4, rapid change, and
trajectory 2, the highest and stable trajectory. These are positive reframing (mean differ-
ence =− 0.7; BCa 95% CI [− 1.4, −0.1]) and self-distraction (mean difference = − 0.8;
BCa 95% CI [−1.4, −0.3]), which were significantly lower in trajectory 4 (see Table5).
5 Discussion
The results of our study are consistent with our first hypothesis that PTG trajectories
among study participants would be heterogeneous. We managed to identify four PTG tra-
jectories (curvilinear, high/stable, low/stable, and rapid change), which can be considered
an added value to research on PTG in general, where person-centered longitudinal analysis
is still very scant (Cheng etal., 2018; Hamama-Raz etal., 2019).
In general, as expected, we noted only null to modest changes in PTG for most of
the sample, which was also observed in other studies (Garrido-Hernansaiz et al., 2017;
Rzeszutek etal., 2017). However, the obtained picture is more complex. Particularly, the
Table 4 The summary of analysis of covariates of class membership
Each subscript letter denotes categories that do not differ significantly from each other
Membership Covariate
Gender (%
of men)
Education (%
of higher)
Age (M ± SD) CD4 count (M ± SD) HIV infec-
tion duration
(M ± SD)
Trajectory 1 86a96a40.7 ± 10.8a653.6 ± 264.6a6.7 ± 5.8a
Trajectory 2 67b87b41.5 ± 10.7a618.1 ± 252.8a10.7 ± 7.8b
Trajectory 3 100a90.9a39.5 ± 11.1a597.2 ± 203.2a6.0 ± 2.9a
Trajectory 4 100a100a46.3 ± 11.2b484.0 ± 109.8b4.6 ± 2.6a
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curvilinear pattern of PTG change, which had the most participants and which showed a
small increase followed by a small decline of growth throughout the year, appears intrigu-
ing and may suggest that the growth process for those struggling with disease is not linear
as described by traditional PTG models (Tedeschi & Callhoun, 2004). Until now the cur-
vilinear nature of PTG has been studied mostly in case of the PTG- PTSD association, and
predominantly in studies applying the variable-centered approach (see review and meta-
analysis; Shakespeare-Finch & Lurie-Beck, 2014). Our result can also be an argument in
favor of PTG specificity in the context of serious chronic diseases, where experiencing
health threats is a dynamic process linked not only to the past (e.g., diagnosis), but also to
the present and future (Edmondson, 2014). Additionally, for PLWH, social reception of the
disease should be considered when looking for explanations of the dynamics of PTG, as
this construct, although assuming deep intrapersonal changes, can potentially be sensitive
to situational factors (trait-like vs. state-like PTG; Blackie etal., 2017).
However, to address this issue empirically, repeated measurement of PTG should be
accompanied by repeated measurement of disease status, disease-related stressors, and
other potentially significant psychosocial factors. This will enable researchers to look for
the correlated change (Schwarzer etal., 2006). In this context, the trajectory of rapid PTG
change is particularly interesting, especially as a very similar pattern of PTG was observed
among women with breast cancer (Danhauer etal., 2015). Further analyses showed the dis-
tinctive characteristics of this group in our study, which indicates the validity of its separa-
tion, which will be discussed later in more detail (Table5).
In line with the second hypothesis, selected sociodemographic and clinical vari-
ables were significant covariates of being a member of a given trajectory. Especially, as
expected, time since HIV diagnosis was the longest for PLWH in the highest and stable
Table 5 Differences in coping strategies between trajectories: Results of one-way ANOVA
M, mean; SD, standard deviation
*p < .05
Coping strategy Trajectory F (3, 11)
1 2 3 4
M SD M SD M SD M SD
Active coping 2.0 0.6 1.9 0.6 2.0 0.4 1.9 0.7 0.63
Planning 2.1 0.5 1.9 0.6 2.0 0.9 1.7 0.9 1.17
Positive reframing 1.9 0.6 2.1 0.7 1.8 0.6 1.4 0.9 2.60*
Acceptance 2.1 0.5 2.2 0.5 2.0 0.7 1.9 0.7 1.03
Humor 1.5 0.7 1.4 0.7 1.3 0.9 0.9 0.8 1.75
Religion 1.1 1.0 1.4 1.1 1.3 1.0 0.8 1.0 1.06
Use of emotional support 1.4 0.8 1.3 0.8 1.4 0.9 1.1 1.0 0.54
Use of instrumental support 1.9 0.6 1.8 0.7 1.7 0.6 1.3 0.9 2.20
Self-distraction 1.6 0.7 1.8 0.6 1.6 0.7 1.1 0.7 2.85*
Denial 0.9 0.8 1.1 0.7 1.0 1.0 1.1 1.0 0.24
Venting 1.6 0.7 1.4 0.7 1.6 0.7 1.4 0.6 0.67
Substance use 1.1 0.9 0.8 0.9 1.3 1.2 0.9 1.0 1.33
Behavioral disengagement 1.1 0.8 1.0 0.7 1.2 1.1 1.1 1.1 0.37
Self-blame 1.4 0.8 1.3 0.8 1.7 0.8 1.1 1.2 1.40
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Trajectories ofPosttraumatic Growth Following HIV Infection:…
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PTG trajectory. The fact that they had been infected with HIV from 4 to 6 years longer than
representatives of other trajectories may suggest a real growth, i.e., one that goes beyond
an adaptation to illness understood as regaining pre-diagnosis well-being (Hamama-Raz
etal., 2019). In cancer and HIV/AIDS, PTG shortly after the diagnosis may serve mainly
as a way to cope with illness-related distress (Sawyer etal., 2010). With time elapsed since
diagnosis, a transition between illusory and constructive growth (see Maercker & Zoellner,
2004) is more likely due to its processual nature, including for some people going through
a stage of struggling PTG (Pat-Horenczyk et al., 2016). In our study, this struggling is
observed in the members of the rapid change PTG trajectory. They also have the shortest
time since diagnosis and the lowest CD4 count. Together with older age, which also means
late onset of HIV infection, it creates a different psychological situation, in which the pro-
cess is not finished yet or PTG may be harder to achieve. PTG for this group may also be
triggered by participation in the study (Tedeschi & Kilmer, 2005), especially as this group
consists only of well-educated men.
The several studies conducted among both the general population (Vishnevsky etal.,
2010) and PLWH (Rzeszutek etal., 2016) point to higher PTG among women than men.
This was also the case in our study. The role of education is more equivocal, since it is
related directly or indirectly to other personally and socially valued resources (Hobfoll,
1989). In our study, lower education level was associated with the probability of being a
member of the high/stable PTG trajectory, which may support the thesis that PTG, even if
stabilized and not necessary illusory, may have a compensatory character in the face of lack
of other resources to gain control over one’s own life (Bellizzi & Blank, 2006). However,
since this cannot be regarded as a striking difference, this topic needs further research.
Our last hypothesis that baseline use of positive reframing would be beneficial to PTG
was supported up to a point. However, use of positive reframing differed significantly
between members of two trajectories. It was lowest among members of the rapid change
trajectory, whereas it was highest among members of the high and stable trajectory. This
finding refers directly to the aforementioned concept of constructive growth. Specifically,
according to Pat-Horenczyk etal. (2016), constructive or real PTG in case of an illness
should be based on a joint increase in PTG and salutary coping, whereas illusory growth is
an increase only in PTG.
Members of this rapid changetrajectory hadalso lower baseline use of self-distraction.
The subscale includes such items as I’ve been turning to work or other activities to take
my mind off things or I’ve been doing something to think about it less, such as going to
movies, reading etc. (Carver, 1997). Folkman’s (1997) research suggests that these strat-
egies can reduce negative emotions or enhance positive emotions, depending on person-
situation interaction. In the latter case, they may provide a detachment from chronic stress
and replenish personal resources necessary to sustain coping (Folkman & Moskowitz,
2000; Fredrickson, 1998). In this sense, cognitive and behavioral efforts described under
the term “self-distraction,” which has been viewed as a maladaptive strategy, may act far
beyond avoidance by creating positive events and activities and be a part of meaning-
focused coping (Folkman, 1997; Folkman & Moskowitz, 2000). This line of reasoning has
been recently directly confirmed in context of chronic stressors in the study by Waugh etal.
(2020). The overlap between avoidance and positive distraction was weak and only the lat-
ter was related to higher well-being and positive emotions and lower depressive symptoms.
In this manner, the picture becomes clearer. In our sample, there were no significant
differences in coping strategies between three trajectories of PTG. The only difference con-
cerns two trajectories, both with the highest PTG, but one is stable, whereas the other is
unstable, with a steep increase. Thus, it is not that a high baseline level of meaning-focused
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M.Rzeszutek, E.Gruszczyńska
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coping is associated with real PTG. Rather, the point is that a low baseline level of mean-
ing-focused coping is associated with illusory or struggling PTG. Still, the mechanism of
growth in this latter group remains unknown since it cannot be attributed in our sample to
strategies usually positively related to PTG (Prati & Pietrantoini, 2009).
5.1 Strengths andLimitations
Our study has several strengths, including a longitudinal person-centered approach with
three measurements of PTG in a special and difficult to access clinical sample, which is
PLWH. Nevertheless, a few limitations should be mentioned. Firstly, our study sample was
heterogonous with regard to time since HIV diagnosis. We examined a role of his variable
directly in our study, but still, this means that participants may have been assessed at very
different stages of the PTG process. Secondly, the final sample was relatively small, with
unequal gender ratio, which although representing adequately a percentage of women liv-
ing with HIV in a general population (UNAIDS, 2019), may lead to low power to detect
heterogeneity of trajectories among this group. Thirdly, the PTGI questionnaire was cre-
ated to operationalize positive changes after vast categories of traumatic events and, there-
fore, may not be sufficiently sensitive to capture a distinctiveness of chronic illness-related
threat (Casselas-Grau etal., 2017), especially with strong probability of social stigmatiza-
tion (Rueda etal., 2016). Similarly, low reliability of some coping subscales due to their
two-item length may indicate a lack of validity. It was the case for instance or the self-
distraction subscale, suggesting that its avoidant or meaning-making function could be not
only person- but item-related. Finally, our participants were relatively highly functional
PLWH, with high treatment compliance and CD4 counts similar to the general population
(UNAIDS, 2019). In samples of PLWH more diverse on clinical and sociodemographic
characteristic perhaps different pattern of results could be obtained. On the other hand, sev-
eral studies on clinical vs. psychosocial variables in PTG among PLWH showed a stronger
effect of the latter. Alternatively speaking, it was found that the self-reported PTG may be
related mostly to psychological and social factors, rather than to clinical characteristics of
HIV infection (Rzeszutek & Gruszczyńska, 2018). However, a lack of objective reports on
the clinical status in such studies is the major shortcoming, which is also the case in our
study. Thus, the strength of the effect of clinical variables may be underestimated due to a
weakness of measurement, but it does not change the fact that a enormous progress in HIV
treatment (UNAIDS, 2019) allows for successful medical control of HIV infection with a
reduced burden of compliance.
6 Conclusions
The results of our study show that PTG among PLWH should be treated as a process for
which both level and dynamics are related to time since diagnosis and basic sociodemo-
graphic characteristics of PLWH. Rapid growth, when co-occurring with a low level of
meaning-focused coping, may suggest struggling PTG, prone to further changes. On the
other hand, high use of this coping strategy is not a hallmark of high and stable PTG,
which makes a role meaning-making mechanism ambiguous and calls for searching for
potential modifiers of its effectiveness. From a clinical point of view, our research inform
about a complexity of growth process in the case of dealing with HIV infection (Cafaro
etal., 2016; Tedeschi etal., 2018), which on the general level corresponds with recently
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Trajectories ofPosttraumatic Growth Following HIV Infection:…
1 3
described by Bonnano (2021) challenges in predicting adjustment trajectories after expe-
riencing various types of trauma. This topic needs further investigation, especially as the
access to psychological care for the HIV/AIDS population is still very limited (National
AIDS Centre, 2019) and does not take into account cultural and contextual differences
(Rzeszutek etal., 2017). Specifically, a key finding for planning and implementing inter-
ventions at the individual, group, or community level is that there is no one pattern of PTG
that fits all PLWH.
Declarations
Conflict of interest The corresponding author declares that he has no conflict of interest. The second author
declares that she has no conflict of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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