Background: The aim of this longitudinal study was to examine the consistency of health-related quality of life (HRQoL) among people living with HIV (PLWH) by breaking down the variance of repeated HRQoL measures into trait, state, and method components and to test the stability of HRQoL over time. In addition, we wanted to examine whether HRQoL trait components are related to personality traits, while controlling for selected socio- medical variables. Methods: Three assessments were performed with a six-month lag on each assessment. Each participant filled out a World Health Organization (WHO) Quality of Life-BREF to assess HRQoL and a NEO-FFI to measure Big Five personality traits. Overall, 82 participants out of 141 (58.2% of the initial sample) participated in all the assessments. Results: The HRQoL among PLWH represented a stable trait to a somewhat greater extent than a situational variability, although the proportions were domain and time variant. More specifically, psychological domain appeared to be the most consistent, whereas social domain appeared to be the most prone to situational influences. The trait component of HRQoL was positively related to being in a relationship, being employed, and being extraverted, and negatively related to neuroticism, which altogether explained 26% of the trait variance. Conclusions: HRQoL among PLWH is rather distinct from personality and socio-medical data, which indicates its uniqueness in a clinical practise. Thus, there is a need for a more comprehensive assessment of HRQoL among this patient group to capture an additional source of variance in this important theoretical construct. Keywords: Health-related quality of life, Personality, Latent state-trait analysis, HIV/AIDS Background external factors. The first conceptualization is in line Although a massive body of literature exists on the con- with several “top-down” theories of well-being, indicat- cept of well-being, including psychological well-being ing a specific, mainly hereditary “set-point” level of well- (PWB) (e.g. [1–6]), many controversies still exist with re- being characteristic of individuals [13–15]. Conversely, spect to the definition, the dynamics, and the implica- “bottom-up” theories of well-being contend that it tions of PWB on various areas of individual and social changes over time and is tied to various life events (e.g. functioning . One of these unresolved research ques- [16–18]). Resolving the aforementioned controversy is of tions is whether PWB should be viewed as a relatively great importance not only for the theory, but also for stable trait throughout a person’s life, or a trait subject the practical application of research on PWB, specifically to situational variability [8–12]. In other words, it is not the implementation of interventions to enhance PWB clear how many variations in PWB are inherent in the . Nevertheless, until now, studies in this area mainly person, and how many are tied to occasion-specific, have been focused on one aspect of PWB, namely satis- faction with life (SWL), providing a rather coherent pic- ture of the relatively stable nature of this cognitive * Correspondence: firstname.lastname@example.org component of PWB over time [9, 11, 20, 21]. The tem- Faculty of Psychology, University of Warsaw, Stawki 5/7, 00-183 Warsaw, poral dynamics of other PWB components, including Poland Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Rzeszutek and Gruszczyńska Health and Quality of Life Outcomes (2018) 16:101 Page 2 of 10 quality of life (QoL), are greatly underscored, referring Current study especially to health-related quality of life (HRQoL) . Since most studies on PWB, and HRQoL in particular, So far, previous research findings have been in favor of among PLWH were conducted using a cross-sectional both a relatively consistent nature of HRQoL , as design , little is known about individual differences well as the notion that it may have some genetic basis in HRQoL dynamics in this patient group, particularly  underlying its situational variability [25, 26]. Ac- the proportion of state vs. trait variance in HRQoL. cording to Sprangers and Schwartz , these conflict- Therefore, we conducted a longitudinal study to decom- ing results may be attributed to the multidimensional pose variance of the repeated HRQoL measure into trait, character of HRQoL, which consists of both changeable state, and method components, and to verify the stability (e.g., emotional functioning) and relatively stable (e.g., of HRQoL over time among participants. In addition, we physical functioning) domains. Thus, investigating the wanted to examine whether the HRQoL trait component proportion of state vs. trait variance in HRQoL may be is related to personality traits, while controlling for se- crucial, not only from theoretical point of view, but also lected socio-medical factors. Since we did not have for implementing successful interventions tailored for a newly diagnosed patients, but instead had those who specific area of a patient’s functioning. had been under treatment for some time (see Table 1), The aforementioned problem seems to be of special im- we expected that the proportion of trait variance in portance among people living with HIV (PLWH). On one HRQoL would be higher compared with the proportion hand, due to great advances in HIV treatment, HIV infec- of state variance after separation from domain-specific tion is now a chronic, manageable health problem [27, 28]. method variance. Secondly, for the same reason, we ex- On the other hand, PLWH still struggle with intense HIV- pected that overall HRQoL among participants would be related distress originating from a wide variety of psycho- stable for 12 months. Finally, based on the top-down social stressors [29–35]. In addition, PLWH are reporting theory, we hypothesized that the HRQoL trait compo- significantly lower HRQoL, not only compared with the nent would be more strongly related to personality traits general population, but also in comparison with other (e.g., neuroticism, extraversion, openness to experience, chronic diseases . The literature on HRQoL and conscientiousness) than to socio-medical factors. among PLWH is huge, but very inconclusive [36–39], indicating the varying impact that clinical, and psy- Method chosocial factors may have on HRQoL. Importantly, Procedure whereas previous studies have spotlighted the major role Participants were recruited from patients at the out- of clinical variables in HRQoL among PLWH (e.g. ), patient clinic in the hospital of infectious diseases. After an increasing number of researchers recently have the informed consent was obtained, the participants highlighted psychosocial factors that may even outweigh completed a paper-and-pencil version of the inventories the significance of medical factors [37, 41, 42]. and participated in the study voluntary, as there was no Numerous authors have shown that PWB is influenced remuneration for the participation. The study’s eligibility greatly by personality traits [43–46]. More specifically, criteria were as follows: age 18 years or older, medically Steel et al. , in a meta-analytic review, found that the diagnosed as HIV-positive, and currently receiving med- variance in PWB explained by personality traits may ical care from the clinic where the study was performed. range from 39% to as much as 63%, which argument is The exclusion criteria included having HIV-related cog- used to support the hypothesis on the stability of PWB nitive disorders diagnosed by psychiatrists working at . As far as PLWH are concerned, it was revealed that the hospital. This study was approved by the ethics com- personality traits may be associated with some clinical mittee of the Faculty of Psychology, University of variables, e.g., medication adherence (neuroticism nega- Finance and Management in Warsaw. tively; ) and CD4 count (conscientiousness positively; ). However, the role of personality traits – e.g., Measures neuroticism, extraversion, openness to experience, and Health-related quality of life conscientiousness - is especially profound for HRQoL Health-related quality of life (HRQoL) was assessed in this patient group [49, 51–53]. Interestingly, using the WHO Quality of Life-BREF (WHOQOL- Burgeous et al.  found that neuroticism had a BREF), developed under a WHO initiative to assess this strong impact on HRQoL that outweighed the role of construct cross-culturally . WHOQOL-BREF con- health status, which is in line with a recent meta- sists of 26 items used to measure four domains: somatic analytic review conducted by Chan-Huang et al. , health, psychological health, social relationships, and en- indicating that personality significantly affects HRQoL vironment. Higher values indicate higher quality of life and that its effect is stronger than socio-demographic in each domain. Cronbach’s alpha coefficients for the and medical variables. current study ranged between .81 to .90 for somatic Rzeszutek and Gruszczyńska Health and Quality of Life Outcomes (2018) 16:101 Page 3 of 10 Table 1 Baseline Socio-Medical Characteristics of the Initial and Final Sample Variable Sample Initial Final N = 141 N =82 Gender Male 120 (85.1%) 70 (85.4%) Female 21 (14.9%) 12 (14.6%) Age in Years M ± SD (Range) 40.18 ± 10.24 (19–76) 40.50 ± 11.47 (21–76) Stable relationship status Yes 84 (59.6%) 49 (59.8%) No 57 (40.4%) 33 (40.2%) Education Elementary/Secondary 61 (43.3%) 31 (37.7%) University degree 80 (56.7%) 51 (62.3%) Employment Full employment 99 (70.2%) 53 (64.6%) Unemployment/Retirement 42 (29.2%) 29 (35.4%) HIV/AIDS status HIV+ only 120 (85.1%) 48 (80.5%) HIV/AIDS 21 (14.9%) 16 (19.5%) HIV Infection Duration in Years M ± SD (Range) 7.34 ± 6.20 (1–30) 7.39 ± 5.72 (1–30) Antiretroviral Treatment (ART) Duration in Years M ± SD (Range) 5.67 ± 5.10 (1–23) 5.76 ± 4.88 (1–21) CD4 Count M ± SD (Range) 609.57 ± 240.90 (200–2000) 645.73 ± 256.23 (200–2000) M Mean, SD Standard Deviation domains for T1, T2 and T3; ranged between .75 to .88 for have been used increasingly in analyses of longitudinal psychological domain for T1, T2 and T3; ranged between data to capture the within-time consistency vs. situation .69 to 83 for social domain for T1, T2 and T3; ranged be- variability of individual differences of a particular vari- tween .80 to .86 for environmental domain for T1, T2 and able over time. There are different versions of LTS T3. models, but we applied the one described by Schermelleh-Engel et al. , in which a single trait is a Personality dimensions second-order factor of state factors. Specifically, it was a Personality traits were measured with the NEO-FFI single-construct model (HRQoL) with four indicators questionnaire by Costa and McCrae . NEO-FFI con- (HRQoL domains) measuring a latent trait repeatedly sists of 60 items (12 per trait), to which participants within six-month lags by three latent state variables. responded on a five-point scale, from strongly disagree Also, as each domain was measured by a different part to strongly agree. Five indices were obtained: neuroti- of the questionnaire, we added four method factors to cism, extraversion, openness to experience, agreeable- capture a method-related variance. Thus, the LST model ness, and conscientiousness. The higher scores of each allowed for a breakdown of HRQoL variance into four indicate on higher level of each trait. The Cronbach’s parts: stable-trait variance, state-specific variance alpha for the current study ranged for all traits from .76 (expressed by latent state residuals), method variance, to .82 at T1, .72 to .76 at T2 and .71 to .75 at T3. and error variance. It may help to specify how many var- iances of HRQoL among PLWH were explained by pa- Data analysis tient characteristics and how many by situation-specific To verify the research hypotheses, a latent state-trait fluctuations over time. The LST models differ from la- (LST) analysis was performed [57, 58]. The LST models tent growth-curve (LGC) models, as these latter models Rzeszutek and Gruszczyńska Health and Quality of Life Outcomes (2018) 16:101 Page 4 of 10 may capture long-lasting and systematic changes within participants remaining. Table 1 presents the socio- a particular variable over a long period of time. However, medical characteristics for both initial (N = 141) and as our participants were not newly diagnosed patients, final (N = 82) sample. but had been infected and under treatment for some time already, we did not expect any significant system- Descriptive statistics and missing-data analysis atic changes within HRQoL. Therefore, LST models The studied variables are present in Table 2. Results were used instead of LGC models . within HRQoL domains are relatively stable within the The IBM SPSS Statistics and AMOS, both version 24 time frame. All the variables have a univariate skewness , were used for data analysis, which consisted of and kurtosis below values described by West et al.  three steps. The first step focused on testing measure- as potentially problematic for multivariate normal distri- ment invariance  and calculating consistency, occa- bution required for the maximum likelihood (ML) esti- sion specificity, and method specificity for each indicator mation. The Little’s Missing Completely at Random , which provided information on indicator reliability. (MCAR) Test (chi-square = 53.832, df = 50, p = .330) in- Next, latent mean changes were estimated to check dicated that the missing data were missing completely at whether HRQoL values changed over 12 months. Finally, random (MCAR, ), including socio-medical charac- individual values for trait factors were imputed and teristics. Thus, to avoid a reduction in the statistical regressed on sociodemographic, clinical, and personality power of the study, we used ML estimation, available in variables to answer the question of whether the HRQoL AMOS, to impute the missing data . Next, further trait variable is related to other personal characteristics. analyses were done for all the participants who took part in the study, i.e., N =141. Results Study sample Measurement invariance and variance decomposition The first assessment was conducted during June and July The goodness of fit of the model with configural invari- 2 2 2016. A total of 141 patients agreed to take part in the ance was satisfactory, χ (39) = 59.91, p =.02, χ /df = 1.54, study and provided their contact details (i.e., phone RMSEA = .06, 90% CI [.03, 0.9], CFI = .979, TLI = .957. number and/or e-mail address). The second assessment Therefore, we checked whether the more constrained was performed during January and February 2017. Out model with equal factor loadings of each domain variable of 141 participants from the first assessment, 113 partic- on latent state variables fit significantly worse (weak ipated in the second assessment. The last assessment factorial invariance). The comparison of models did not was performed during May and June 2017, with 82 reject the assumption of weak factorial assumption Table 2 Descriptive Statistics of The Studied Variables Variable M SD Kurtosis Skewness Minimum Maximum WHO_Somatic T1 25.21 4.93 .80 −.68 7 34 WHO_Somatic T2 25.10 5.16 .35 −.60 10 34 WHO_Somatic T3 24.06 5.51 .43 −.59 7 35 WHO_Psychological T1 22.70 3.93 1.54 −.88 6 30 WHO_Psychological T2 22.51 3.94 1.13 −.79 7 30 WHO_Psychological T3 21.43 4.64 .15 −.69 7 29 WHO_Social T1 11.33 2.28 .13 −.47 4 15 WHO_Social T2 11.06 2.58 .69 −.79 3 15 WHO_Social T3 10.43 2.63 .49 −.68 3 15 WHO_Enviromental T1 30.46 5.30 2.82 −1.29 9 40 WHO_Enviromental T2 30.57 4.86 1.01 −.75 13 40 WHO_Enviromental T3 29.73 5.62 1.91 −.92 8 39 Neuroticims 25.77 7.09 −.24 .02 7 44 Extraversion 24.84 5.24 1.93 −.92 2 35 Openness to experience 23.94 5.90 .16 .06 6 37 Agreeableness 28.81 6.19 .54 −.18 7 45 Conscientiousness 27.43 5.13 1.09 −0,67 7 37 T1 First Assessment (N = 141), T2 Second Assessment (B = 113), T3 Third Assessment (N = 82) Rzeszutek and Gruszczyńska Health and Quality of Life Outcomes (2018) 16:101 Page 5 of 10 (χ2 (6) = 8.58, p = .199). Thus, next in hierarchy model personal disposition (“trait”) explains only 42% of inter- with strong factorial invariance was tested with the individual differences in the overall HRQoL at T1, as intercepts of each domain equal within time and it many as 91% at T2, and 68% at T3. Thus, we observed did not fit the data significantly worse (χ2 (8) = 9.33, substantial variability in a variance structure decompos- p = .315; χ2 (53) = 77.81, p = .02, χ2/df = 1.47, RMSEA ition between domains, as well as within time. = .06, 90% CI [.03,0.8], CFI = .975, TLI = .962). It is presented in Fig. 1 as the final model, and variance Latent mean change components and reliability coefficients for its As the strong MI allows for mean comparison, for this standardized solutions are provided in Table 3. purpose, the mean of the first latent state factor was In general, reliability is satisfactory, albeit excluding fixed to be zero, whereas the means of the two social domain. A precision of measurement within this remaining latent state factors were freed . Conse- domain should be regarded as doubtful due to the low- quently, the intercepts of those factors can be then inter- est values and high variability between measurement preted as a difference relative to the first factor. For state points. The average consistency is the highest for psy- at T2 (see Fig. 1), it was equal, − 0.131, and insignificant chological domain (60%) and the lowest for social do- (p = .722), whereas for state at T3, it was equal − 0.993, main (45%). Thus, the domains seem to be differently and significant (p < .05). This indicates that although prone to occasion-specific influences. Also, we observed there was no change between T1 and T2, there was sig- that T1 and T3 values across domains are affected by nificant decrease in HRQoL level between T1 and T3. situation or person and situation interactions (as they are indistinguishable in the LTS models ), whereas Time-invariant correlates of HRQoL trait component T2 scores are more strongly linked to stable disposition A three-step hierarchical regression with stepwise (52–81%), as well as method (12–43%). Taken together, method of variables entry (probability of F; criteria: this may suggest problematic homogeneity of domain in- entry = .5 and removal = .10) was used to establish dicators. The occasion-specific variability is the most HRQoL trait (HRQoL-T) correlates. In the first step, pronounced for T1, as 47 to 54% of individual differ- sociodemographic variables were introduced (gender, ences in domain scores is due to the context of measure- age, education, employment, and relationship status). In ment. It is also clearly visible at the state level: Stable the second step, variables related to HIV infection (CD4 Fig. 1 The final latent state-trait-method model for three measurement points. Reported are standardized loading parameter estimates. Error terms of domain indicators are removed from the figure for sake of clarity. SR- latent state residuals; M – method factors; som – somatic domain of WHOQOL-BREF; psy - psychological domain of WHOQOL-BREF; soc. - social domain of WHOQOL-BREF; env - environmental domain of WHOQOL-BREF. Numbers 1, 2, 3 depict consecutive measurement points Rzeszutek and Gruszczyńska Health and Quality of Life Outcomes (2018) 16:101 Page 6 of 10 Table 3 Consistency, Occasion Specificity And Method Specificity For Each WHOQOL-BREF Domain Indicator Consistency Occasion specificity Method Specificity Reliability Error Somatic Domain WHO_Somatic T1 .34 .47 .19 .82 .18 WHO_Somatic T2 .62 .06 .32 .92 .08 WHO_Somatic T3 .54 .25 .20 .84 .16 Psychological Domain WHO_Psychological T1 .35 .49 .16 .91 .09 WHO_Psychological T2 .80 .07 .12 .82 .18 WHO_Psychological T3 .64 .30 .06 .73 .27 Social Domain WHO_Social T1 .39 .54 .07 .64 .36 WHO_Social T2 .52 .05 .43 .86 .14 WHO_Social T3 .44 .20 .36 .69 .31 Enviromental Domain WHO_Enviromental T1 .34 .47 .19 .81 .19 WHO_Enviromental T2 .81 .08 .12 .81 .19 WHO_Enviromental T3 .50 .24 .26 .88 .12 T1 First Assessment, T2 Second Assessment, T3 Third Assessment count, time since HIV diagnosis, duration of ARV treat- regression analysis. Their coefficients have the opposite ment, and HIV/AIDS status) were entered. Finally, Big sign, but have very similar strengths. Altogether, the cor- Five personality dimensions were included in the model. relates explain 26% of HRQoL-T variance. All the explanatory variables were measured at T1, and categorical variables were dummy-coded. Due to a num- Discussion ber of potentially inter-related variables, collinearity was The results of the study were partly in line with the first checked; VIF was below 1.2. The results are presented in research hypothesis, i.e., we showed that HRQoL among Table 4. Among sociodemographic variables, only being PLWH represents a stable trait to a somewhat greater in a stable relationship and being employed were sig- extent than a situational variability, although the propor- nificant, but weak-positive correlates of HRQoL-T. None tions were domain and time variant. More specifically, of the clinical variables was significantly related to we noticed differences between particular HRQoL do- HRQoL-T. After controlling for sociodemographic and mains, i.e., psychological domain appeared to be the clinical variables, neuroticism and extraversion signifi- most consistent, whereas social domain appeared to be cantly added to the model in the third step of the the most prone to situational influences. Although Table 4 Results Of Three-Step Hierarchical Regression Analysis With Stepwise Method of Variables Entry and WHOQOL-BREF Trait Component as Explained Variable 2 2 Model F df ΔFR Adjusted R Beta Full Employment 13.36*** 1; 139 – .29 .08 .29*** Full Employment 11.13*** 1; 138 8.20*** .37 .13 .24*** + Stable Relationship .23*** Full Employment 1.56*** 1; 137 .8.24*** .43 .18 .20** Stable Relationship .21*** + Extraversion .23*** Full Employment 13.22*** 1; 136 17.40*** .53 .26 .19** Stable Relationship .21** Extraversion .34*** + Neuroticism −.32*** *** p < .001; ** p < .01 Rzeszutek and Gruszczyńska Health and Quality of Life Outcomes (2018) 16:101 Page 7 of 10 several longitudinal studies have recently been con- Finally, personality traits, e.g., neuroticism and extra- ducted on HRQoL among PLWH (e.g. [37, 39]), none of version, appeared to be more strongly associated with them, to our best knowledge, has used the LST model. trait components of HRQoL when compared with socio- Thus, we do not have a direct benchmark to compare medical data, which corresponded with our third hy- our results within this specific study design. However, pothesis. Importantly, out of all the socio-medical data our findings may have important theoretical and prac- in our study, only being employed and being in a stable tical implications, as HRQoL is becoming a widely ac- relationship were significantly related to the consistency cepted patient-reported outcome in HIV research and of HRQoL, a finding that has been noted by other au- counselling, as it may provide information that’s often thors [69, 70]. In discussing this finding in the context difficult to obtain in clinical analysis . Therefore, it is of the trait-state conceptualization of well-being, it vital for HIV/AIDS health services to know which areas should be mentioned that these two personality traits of HRQoL among PLWH should be addressed upfront predicted the highest proportion of stability of well- in psychological interventions . being among different study samples, even over a long On one hand, consistency of psychological domain period of time [14, 21]. Additionally, this finding is in and occasion specificity of social aspects of HRQoL may line with the recent meta-analysis conducted by Chan- suggest that the former is more person-rooted and the Huang et al. , who observed that personality out- latter is a more situation-derived aspect of HRQoL. As weighs the significance of socio-medical data in predict- such, they should be addressed differently in psycho- ing HRQoL. Aforementioned results, therefore, are in logical interventions, first by person-oriented interven- line with HIV/AIDS literature on the role of personality tions (e.g., cognitive, behavioral interventions; ), and traits and HRQoL. Neuroticism, in particular, predicted second by more interpersonal-focused techniques . poorer HRQoL, mainly in psychological domains and in- But other explanations also must be considered in the dependent of health status [51, 71]. Extraversion was case of social domain of HRQoL. Namely, it was mea- positively related to HRQoL, especially within domains sured by the shortest three-item-only scale, which describing overall happiness and satisfaction from life, turned out to be unreliable enough among PLWH. Spe- HIV mastery, and sexual functioning . Nevertheless, cifically, it includes a potentially sensitive item (i.e., How existing studies are scarce and cross-sectional, so our re- satisfied are you with your sex life?), which, in such a search added to the literature by examining personality group, may measure different things during different as a correlate of HRQoL consistency among PLWH in a stages in HIV patients’ lives, so the scores may be longitudinal study design. Although these relationships blurred by time-variant heterogeneity of the item inter- are weak, which clearly implies a uniqueness of HRQoL pretation, not by a situation itself. Thus, WHOQQL- measurement over personality assessment, clinicians BREF reliability of measurement calls for improvement perhaps should consider the role of personality in imple- in social domain, at least as far as PLWH are menting successful psychosocial interventions to en- considered. hance HRQoL among PLWH. For instance, it was We also observed that HRQoL was relatively stable demonstrated that personality traits may impact HRQoL over time among participants, which was in line with among different patient groups . Specifically, person- our second hypothesis. Specifically, the aforemen- ality indirectly changes HRQoL by influencing the tioned results also should be seen within the context process of coping with illness  and illness appraisal of our sample, i.e., highly functional PLWH who have . Several studies conducted among PLWH showed been undergoing antiretroviral therapy (ART) for that coping and appraisal are crucial to the success of some time already, and due to it have a mean CD4 psychosocial interventions [65, 74, 75]. count similar to that of the healthy population . It also mayshedsomelight on whyclinical variables Strengths and limitations were unrelated to HRQoL among our participants, This study has a few strengths, namely longitudinal and which has been noted in recent studies . On a theory-driven study design, as well as examination of general level, this finding may be interpreted as a consistency of HRQoL among a high-risk sample, i.e., great advance in HIV/AIDS knowledge and treatment, PLWH. However, a few limitations should be mentioned. as new advances have reduced HIV infections from a First, the sample was relatively small, and there was terminal and fatal disease to a chronic and manage- comparatively high dropout. In addition, the sample able health condition . It seems that nowadays, consisted of highly functional PLWH with good medical HIV infection does not entail serious psychological control of HIV infection, predominantly men. Further- disturbances, as great progress in ART has provided more, due to organizational reasons, the sample was di- opportunities for PLWH to live a longer life and has verse in terms of HIV-infection duration. It is therefore enabled successful adaptation to this disease . likely that other results would be obtained in a sample Rzeszutek and Gruszczyńska Health and Quality of Life Outcomes (2018) 16:101 Page 8 of 10 with a different gender ratio or/and clinical characteristics, study design, analysed and interpreted the data and revised the final draft. Both authors read and approved the final manuscript. especially concerning the stability of HRQoL. On the other hand, as was mentioned before, our participants Ethics approval and consent to participate were rather homogenous with regard to socio-medical All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national variables, and dropout was a random factor that should research committee and with the 1964 Helsinki declaration and its later not be viewed as systematic selection bias. Furthermore, amendments or comparable ethical standards. This study was approved by we observed high variability in variance structure decom- the ethics committee of the Faculty of Psychology, University of Finance and Management in Warsaw. All participants sing informed consent. position of HRQoL, as well as the high percentage of remaining variance in its trait component - this should be Competing interests the subject of further studies. Finally, we used The authors declare that they have no competing interests. WHOQOL-BREF instead of WHOQOL-HIV-BREF, as at the time of conducting this study there was no Publisher’sNote Polish version of the WHOQOL-HIV-BREF. Never- Springer Nature remains neutral with regard to jurisdictional claims in theless, WHOQOL-BREF was also used extensively published maps and institutional affiliations. among HIV/AIDS population and proved to be a reli- Author details able and a valid instrument to assess HRQoL also in 1 Faculty of Psychology, University of Warsaw, Stawki 5/7, 00-183 Warsaw, this patient group [76–78]. Poland. Faculty of Psychology, University of Social Sciences and Humanities, Chodakowska 19/31, 03-815 Warsaw, Poland. Conclusions Received: 13 December 2017 Accepted: 8 May 2018 There is a consistency in HRQoL among PLWH, but also substantial occasion and method specificity that also References vary between domains and within time. Specifically, the 1. Constanza R, Fisher B, Ali S. Quality of life: an approach integrating social domain indicator in WHOQQL-BREF can be opportunities, human needs, and subjective wellbeing. Ecol Econ. 2007;61: 267–76. doi.org/10.1016/j.ecolecon.2006.02.023 regarded as unreliable, which should be considered 2. Diener E. Subjective well-being. In: Diener E, editor. The science of well- when using the tool. Conversely, psychological domain being. New York: Spring; 2009. p. 11–58. is the most consistent, therefore it represents mainly 3. Diener E, Oishi S, Ryan K. Universals and cultural differences in the causes and structure of happiness: a multilevel review. In: Keyes C, editor. Mental stable personal disposition. In addition, HRQoL among well-being. Dordrecht: Springer; 2010. p. 153–76. PLWH is rather distinct from personality and socio- 4. Ryff C. Happiness is everything, or is it? Explorations on the meaning of medical data, i.e., personality traits and socio-medical psychological well-being. J Pers Soc Psychol. 1989;57:1069–81. https://doi. org/10.1037/0022-3522.214.171.1249 data altogether explained 26% of variance in the HRQoL 5. Ryff C. Psychological well-being revisited: advances in the science and trait component, indicating a uniqueness of HRQoL as- practice of Eudaimonia. Psychother Psychosom. 2014;83:10–28. https://doi. sessment in clinical practice. Thus, there is a need for org/10.1159/000353263. 6. Steptoe A, Deaton A, Stone A. Subjective wellbeing, health, and ageing. more in-depth analyses of HRQoL evaluations to cover Lancet. 2015;14:640–8. these patients’ substantial individual differences that are 7. Diener E, Heintzelman S, Kushlev K, Tay L, Wirtz D, Lutes L, Oishi S. Findings not attributable to their personality, medical, or socio- All Psychologists Should Know From the New Science on Subjective Well- Being. Canad Psychol. 2016;58(2):87–104. https://doi.org/10.1037/ demographic characteristics. cap0000063 8. Brockman H, Delhey J. Introduction: the dynamics of happiness and the Abbreviations dynamics of happiness research. Soc Indic Res. 2010;97:1–5. https://doi.org/ HRQoL: Health-related quality of life; LGC: Latent growth-curve; LST: Latent 10.1007/s11205-009-9561-3. state-trait; MCAR: Missing Completely at Random; ML: Maximum likelihood; 9. Eid M, Diener E. Global judgments of subjective well-being: situational PLWH: People living with HIV; PWB: Psychological well-being; QoL: Quality of variability and long-term stability. Soc Indic Res. 2004;65:245–77. https://doi. life; WHO: World Health Organization; WHOQOL-BREF: WHO Quality of Life- org/10.1023/B:SOCI.0000003801.89195.bc. BREF 10. Kaczmarek Ł, Bujacz A, Eid M. Comparative latent state–trait analysis of satisfaction with life measures: the Steen happiness index and the Acknowledgements satisfaction with life scale. J Happ Stud. 2015;16:443–53. https://doi.org/10. The authors thank medical doctors from Warsaw’s Hospital for Infectious 1007/s10902-014-9517-4. Diseases for help in participants’ recruitment process. 11. Li Z, Yin X, Jiang S, Wang M, Cai T. Psychological mechanism of subjective well-being: a stable trait or situational variability. Soc Indic Res. 2014;118: Funding 523–34. https://doi.org/10.1007/s11205-013-0449-x. This work was supported by the University of Warsaw, Faculty ofPsychology 12. Lyubomirsky S, Lepper H. A measure of subjective happiness: preliminary under Grant BST181409/2017. reliability and construct validation. Soc Indic Res. 1999;46:137–55. https://doi. org/10.1023/A:1006824100041. Availability of data and materials 13. Brickman P, Campbell D. Hedonic relativism and planning the good society. The datasets used and/or analysed during the current study available from In: Appley MH, editor. Adaptation level theory. New York: Academic Press; the corresponding author on reasonable request. 14. Costa P, McCrae R. Influences of extraversion and neuroticism on subjective Authors’ contributions well-being. J Pers Soc Psychol. 1980;38:668–78. PMID: 7381680 MR was the main investigator, who designed the study, collected the data, 15. Cummins R. On the trail of the gold standard for life satisfaction. Soc Indic wrote the paper and participating in data analysis. EG participated in the Res. 1995;35:179–200. https://doi.org/10.1007/BF01079026. Rzeszutek and Gruszczyńska Health and Quality of Life Outcomes (2018) 16:101 Page 9 of 10 16. Veenhoven R. Is happiness a trait? Soc Indic Res. 1994;32:101–60. https://doi. 37. Lifson A, Grund B, Gardner E, et al. Improved quality of life with immediate org/10.1007/BF01078732. versus deferred initiation of antiretroviral therapy in early asymptomatic HIV 17. Lyubomirsky S, Sheldon K, Schkade D. Pursuing happiness: the architecture infection. AIDS. 2017;31:953–63. of sustainable change. Rev Gen Psychol. 2005;9:111–31. https://doi.org/10. 38. Cooper V, Clatworthy J, Harding R, Whetham J, Consortium E. Measuring 1037/1089-26126.96.36.199. quality of life among people living with HIV: a systematic review of reviews. Health Qual Life Out. 2017;15:220. 18. Odou N, Vella-Brodrick D. The efficacy of positive psychology interventions to increase well-being and the role of mental imagery ability. Soc Indic Res. 39. Torres T, Harrison L, La Rosa A, et al. Quality of life improvement in 2013;110:111–29. https://doi.org/10.1007/s11205-011-9919-1. resource-limited settings after one year of second-line antiretroviral therapy 19. Howell R, Kern M, Lyubomirsky S. Health benefits: meta-analytically use among adult men and women. AIDS. 2018;32:583–93. determining the impact of well-being on objective health outcomes. Health 40. Burgoyne R, Saunders D. Quality of life among urban Canadian HIV/AIDS Psychol Rev. 2007;1:83–136. https://doi.org/10.1080/17437190701492486 clinic outpatients. Int J STD AIDS. 2001;12:505–12. https://doi.org/10.1328/ 20. Fujita F, Diener E. Life satisfaction set point: stability and change. J Pers Soc a03564056. Psychol. 2005;88:158–64. https://doi.org/10.1037/0022-35188.8.131.52. 41. Ballester-Arnal R, Martínez S, Fumaz C, González-García G, Remor E, Fuste M. 21. Lucas R, Diener E. Subjective well-being. Handbook of emotions. In: Lewis A Spanish study on psychological predictors of quality of life in people with HIV. AIDS Behav. 2016;20:281–91. https://doi.org/10.1007/s10461-015-1208-6. M, Haviland-Jones J, editors. Handbook of emotions. 3rd ed. New York: Guilford; 2008. p. 471–84. 42. Oberjé E, Dima A, van Hulzen A, Prins J, de Bruin M. Looking beyond health-related quality of life: predictors of subjective well-being among 22. Sprangers M, Schwartz C. Reflections on changeability versus stability of people living with HIV in the Netherlands. AIDS Behav. 2015;19:1398–407. health-related quality of life: distinguishing between its environmental and genetic components. Health Qual Life Out. 2008;6:89. https://doi.org/10. https://doi.org/10.1007/s10461-014-0880-2. 1186/1477-7525-6-89 43. DeNeve K, Cooper H. The happy personality: a meta-analysis of 137 23. Learmonth Y, Alwick E, McAuley E, Mot R. Quality of life and health-related personality traits and subjective well-being. Psychol Bull. 1998;124:197–229. quality of life over 1 year in older women: monitoring stability and reliability https://doi.org/10.1037/0033-2909.124.2.19. of measurement. Soc Indic Res. 2015;123:267–79. https://doi.org/10.1007/ 44. Husson O, Vissers P, Denollet J, Mols F. The role of personality in the course s11205-014-0729-0. of health-related quality of life and disease-specific health status among 24. Sloan J, Xinghua Z. Genetics and quality of life. Curr Problem Cancer. 2006; colorectal cancer survivors: a prospective population-based study from the 30:255–60. https://doi.org/10.1016/j.currproblcancer.2006.09.001. PROFILES registry. Act Oncol. 2015;54:669–77. https://doi.org/10.3109/ 0284186X.2014.996663. 25. Beisland E, Aarstad A, Osthus A, Aarstad H. Stability of distress and health 45. Whitworth S, Loftus A, Skinner T, Gasson N, Barker R, et al. Personality affects related quality of life as well as relation to neuroticism, coping and TNM aspects of health-related quality of life in Parkinson’s disease via stage in head and neck cancer patients during follow-up. Act Oto-Laryng. psychological coping strategies. J Parkin Disease. 2013;3:45–53. https://doi. 2013;133:209–17. https://doi.org/10.3109/00016489.2012.720032. org/10.3233/JPD-120149. 26. Lorenz K, Cunningham W, Spritzer K, Hays R. Changes in symptoms and health-related quality of life in a nationally representative sample of adults 46. Weiss A, Bates T, Luciano M. Happiness is a personal(ity) thing: the genetics in treatment for HIV. Qual Life Res. 2006;15:951–8. https://doi.org/10.1007/ of personality and well-being in a representative sample. Psychol Sci. 2008; s11136-005-6010-x. 19:205–10. https://doi.org/10.1111/j.1467-9280.2008.02068.x. 27. Deeks S, Lewin S, Havlir D. The end of AIDS: HIV infection as a chronic 47. Steel P, Schmidt J, Shultz J. Refining the relationship between personality disease. Lancet. 2013;382:1525–33. https://doi.org/10.1016/S0140- and subjective well-being. Psychol Bull. 2008;134:138–61. https://doi.org/10. 6736(13)61809-7 1037/0033-2909.134.1.138. 48. Lucas R, Donnellan M. Estimating the reliability of single-item life 28. Samji H, Cescon A, Hogg R, Modur S, Althoff K. Closing the gap: increases in satisfaction measures: results from four national panel studies. Soc Indic Res. life expectancy among treated HIV-positive individuals in the United States 2012;105:323–31. https://doi.org/10.1007/s11205-011-9783-z. and Canada. PLoS One. 2013;18:144–56. 29. Adewuya A, Afolabi M, Ola B, Ogundele O, Ajibare A, Oladipo B, Fakande I. 49. Penedo F, Gonzalez J, Dahn J, Antoni M, Malow R, Costa P, Schneiderman Post-traumatic stress disorder (PTSD) after stigma related events in HIV N. Personality, quality of life and HAART adherence among men and infected individuals in Nigeria. Soc Psychiat Psychiat Epidem. 2009;44:761–6. women living with HIV/AIDS. J Psychosom Res. 2003;54:271–8. https://doi. https://doi.org/10.1007/s00127-009-0493-7. org/10.1016/S0022-3999(02)00482-8 30. Bogart L, Wagner G, Galvan F, Landrine H, Klein D. Perceived discrimination 50. O'Cleirigh C, Ironson G, Weiss A, Costa T. Conscientiousness predicts disease and mental health symptoms among black men with HIV. Cultural diversity progression (CD4 number and viral load) in people living with HIV. Health and ethnic minority. Psychol. 2011;17:295–302. https://doi.org/10.1037/ Psychol. 2007;26:473–80. doi.org/10.1037/0278-6184.108.40.2063 a0024056. 51. Burgess A, Carretero M, Elkington A, Pasqual-Marsettin E, Lobacaro C, Catalan J. The role of personality, coping style and social support in health 31. Emlet C. A comparison of HIV stigma and disclosure patterns between older related quality of life in HIV infection. Qual Life Res. 2000;9:423–37. https:// and younger adults living with HIV/AIDS. AIDS Patient Care STDs. 2006;20: 350–8. doi.org/10.1023/A:1008918719749. 52. Ironson G, Hayward H. Do positive psychosocial factors predict disease 32. Hansen N, Vaughan E, Cavanaugh C, Connell C, Sikkema K. Health-related progression in HIV-1? A review of the evidence. Psychosom Med. 2008;70: quality of life in bereaved HIV-positive adults: relationships between HIV 546–54. https://doi.org/10.1097/PSY.0b013e318177216c. symptoms, grief, social support, and Axis II indication. Health Psychol. 2009; 28:249–57. https://doi.org/10.1037/a0013168. 53. Lockenhoff C, Ironson G, O’Cleirigh C, Costa P. Five-factor model 33. Pantalone D, Hessler D, Simoni J. Mental health pathways from personality traits, spirituality, religiousness, and mental health among interpersonal violence to health-related outcomes in HIV-positive sexual people living with HIV. J Pers. 2009;77:1411–36. https://doi.org/10.1111/j. minority men. J Cons Clin Psychol. 2010;78:387–97. https://doi.org/10.1037/ 1467-6494.2009.00587.x. a0019307. 54. Chan Huang I, Lee J, Ketheeswaran P, Jones C, Revicki D, Wu A. Does 34. Rzeszutek M, Oniszczenko W, Firląg-Burkacka E. Temperament traits, coping personality affect health-related quality of life? A systematic review PLoS style and trauma symptoms in HIV+ men and women. AIDS Care. 2012;24: One. 2017;12:e0173806. https://doi.org/10.1371/journal.pone.0173806 1150–4. https://doi.org/10.1080/09540121.2012.687819. 55. WHOQOL Group. WHOQOL group, the World Health Organization quality of life assessment: position paper from the World Health Organization. Soc Sci 35. Rzeszutek M, Oniszczenko W, Firląg-Burkacka E. Gender differences in Med. 1995;41:1403–9. posttraumatic stress symptoms and the level of posttraumatic growth among a polish sample of HIV-positive individuals. AIDS Care. 2016;28: 56. Costa PT, McCrae RR. Revised NEO personality inventory (NEO-PI-R) and 1411–5. NEO five-factor inventory (NEO-FFI) professional manual. Odessa, FL: 36. Miners A, Phillips A, Kreif N, Rodger A, Speakman A, Fisher M, Anderson J, Psychological Assessment Resources; 1992. Collins S. Hart G, Sherr L, Lampe F. Health-related quality-of-life of people 57. Eid M. Latent class models for analysing variability and change. In: Ongand with HIV in the era of combination antiretroviral treatment: a cross-sectional A, van Dulmen M, editors. Handbook of methods in positive psychology. comparison with the general population. Lancet HIV 2014; e32–e40. doi: Oxford: Oxford University Press; 2007. p. 591–607. https://doi.org/10.1457// https://doi.org/10.1016/S2352-3018(14)70018-9. 056-6133.2045. Rzeszutek and Gruszczyńska Health and Quality of Life Outcomes (2018) 16:101 Page 10 of 10 58. Geiser C, Bishop J, Lockhart G, Shiffman S, Grenard J. Analyzing latent state- trait and multiple-indicator latent growth curve models as multilevel structural equation models. Front Psychol. 2013;4:975. https://doi.org/10. 3389/fpsyg.2013.00975. 59. Schermelleh-Engel K, Keith N, Moosbrugger H, Hodapp V. Decomposing person and occasion-specific effects: an extension of latent state-trait (LST) theory to hierarchical LST models. Psychol Meth. 2004;9:198–219. https://doi. org/10.1037/1082-989X.9.2.198. 60. IBM Corp. IBM SPSS statistics for windows. Version 24. Armonk, NY: IBM Corp; 2016. 61. Meredith W, Horn J. The role of factorial invariance in modeling growth and change. In: Collins LM, Sayer A, Collins L, Sayer A, editors. New methods for the analysis of change. Washington, DC, US: American Psychological Association; 2001. p. 203–40. https://doi.org/10.1037/10409-007. 62. West S, Finch J, Curran P. Structural equation models with nonnormal variables. Problems and remedies. In: Hoyle R, editor. Structural equation modeling: concepts, issues and applications. Newbury Park, CA: Sage; 1995. p. 56–75. 63. Graham J. Missing data analysis: making it work in the real world. Ann Rev Psychol. 2009;60:549–76. https://doi.org/10.1146/annurev.psych.58.110405. 64. Shin T, Davison M, Long J. Maximum likelihood versus multiple imputation for missing data in small longitudinal samples with nonnormality. Psychol Methods. 2016; https://doi.org/10.1037/met0000094. 65. Wu L, Li X. Community-based HIV/AIDS interventions to promote psychosocial well-being among people living with HIV/AIDS: a literature review. Health Psychol Behav Med. 2013;1:31–46. https://doi.org/10.1080/ 21642850.2013.822798. 66. Brown J, Vanable P. Cognitive–behavioral stress management interventions for persons living with HIV: a review and critique of the literature. Ann Behav Med. 2008;35:26–40. https://doi.org/10.1007/s12160-007-9010-y. 67. Roopnaraine T, Rawat R, Babirye F, Ochai R, Kadiyala S. “The group” in integrated HIV and livelihoods programming: opportunity or challenge? AIDS Care. 2012;24:649–57. https://doi.org/10.1080/09540121.2011.630349. 68. European AIDS Clinical Society (EACS) guidelines. Version 8.2 January 2017. http://www.eacsociety.org/guidelines/eacs-guidelines/eacs-guidelines.html. 69. Abramowitz S, Koenig L, Chandwani S, Orban L, Stein L. Characterizing social support: global and specific social support experiences of HIV- infected youth. AIDS Patient Care STDs. 2009;23:323–30. https://doi.org/10. 1089/apc.2008.0194. 70. Mavandadi S, Zanjani F, Have T, Oslin D. Psychological wellbeing among individuals aging with HIV: the value of social relationships. J Acq Imm Defic Synd. 2009;51:91–8. https://doi.org/10.1097/QAI.0b013e318199069b. 71. Talukdar A, Ghosal M, Sanyal D, Basu S. Determinants of quality of life in HIV-infected patients receiving highly active antiretroviral treatment at a medical college ART Center in Kolkata, India. J Inter Assoc Physic AIDS Care. 2012;12(4):284–90. https://doi.org/10.1177/1545109712445924. 72. Diener E, Oishi S, Lucas R. Personality, culture, and subjective well-being: emotional and cognitive evaluations of life. Ann Rev Psychol. 2003;54:403– 25. https://doi.org/10.1146/annurev.psych.54.101601.145056. 73. Wrosch C, Scheier M. Personality and quality of life: the importance of optimism and goal adjustment. Qual Life Res. 2003;12:59–72. https://doi.org/ 10.1023/A:1023529606137. 74. Shai N, Sikweyiya Y, van der Heijden I, Abrahams N, Jewkes R. “I was in the darkness but the group brought me light”: development, relevance and feasibility of the Sondela HIV adjustment and coping intervention. PLoS One. 2017;12(6):e0178135. https://doi.org/10.1371/journal.pone.0178135 75. Sikkema K, Kelly J. Behavioral medicine interventions can improve the quality-of-life and health of persons with HIV disease. Ann Behav Med. 1996; 18:40–8. https://doi.org/10.1007/BF02903938. 76. Fang C, Hsiung P, Yu C, Chen M, Wang J. Validation of the World Health Organization quality of life instrument in patients with HIV infection. Qual Life Res. 2002;11:753–62. https://doi.org/10.1023/A:1020870402019. 77. Hou W, Chen C, Liu H, Lai Y, Lee H, Lee N, Chang C, Chen P, Ko W, Shu B, Ko N. Mediating effects of social support on depression and quality of life among patients with HIV infection in Taiwan. AIDS Care. 2014;26:996–1003. https://doi.org/10.1080/09540121.2013.873764. 78. Ping-Chuan H, Chi-Tai F, Yu-Yin C, Mao-Yen C, Jung-Der W. Comparison of WHOQOL-BREF and SF-36 in patients with HIV infection. Qual Life Res. 2005; 14:141–50. https://doi.org/10.1007/s11136-004-6252.
Health and Quality of Life Outcomes
– Springer Journals
Published: May 24, 2018