Health Related Quality of Life and School Functioning in
Adolescents with a Chronic Disease
Merel Velu
Masterthesis Clinical Development Psychology
Studentnumber: 10351779
Supervisor Emma Children’s hospital: dr. L. Haverman
Supervisor UvA: dr. H. Larsen
Second assessor: dr. A.M.L. Collet D’Escury-Koenigs
University of Amsterdam
Abstract
Having a chronic disease could have an impact on the health-related quality of life (HRQOL) of adolescents. The first part of this study quantitatively examines HRQOL in adolescents with different chronic diseases aged 13 to 18 compared to a healthy norm group and examines if school related factors are associated with HRQOL in adolescents with a chronic disease. The second part of this study explores important themes concerning school in the daily life of adolescents with a chronic disease aged 12 to 18, based on qualitative data. The study sample of the first part of the study includes 811 adolescents with a chronic disease who receive care in the Emma Children’s hospital and who completed the Pediatric Quality of Life InventoryTM4.0 (PedsQLTM4.0) online at the KLIK website. Independent sample t-tests and multivariate regression analyses were used to analyze data. In the second part of this study, several focus groups, with a total of 15 adolescents with a chronic disease, were held and analyzed using thematic analysis methodology. Adolescents with different chronic diseases reported significantly lower HRQOL scores compared to healthy adolescents, except on the subscale emotional functioning. School functioning was found to be impaired in six of the nine disease groups examined compared to healthy adolescents. School absence and being a girl are negatively associated with HRQOL in adolescents with a chronic disease. Furthermore, school absence, falling behind in school, feeling different then classmates, misunderstanding of classmates and communication about illness to classmates were identified as important themes concerning school for adolescents with a chronic disease. Concluded is that HRQOL is affected in adolescents with a chronic disease. The study findings underline the
relevance to systematically monitor HRQOL in adolescents with a chronic disease in daily clinical practice.
Health Related Quality of Life and School Functioning in Adolescents with a Chronic Disease
In the Netherlands, at least 14% of the children and adolescents are currently growing up with a chronic disease (Mokkink et al., 2007), meaning a number of 500.000 children and adolescents. Due to improved medical care, the expectation for the future is that with greatly improved survival rates, the prevalence of chronic diseases will continue to increase in childhood (Halfon & Newacheck, 2010). A disease or condition is considered to be chronic if the diagnosis is based on medical scientific knowledge, it has no known cure and it has been present for at least three months or it has occurred three times or more during the past year and will probably reoccur (Mokkink, van der Lee, Grootenhuis, Offringa, & Heymans, 2008).
Adolescents with a chronic disease are at high risk for behavioral and emotional problems (Blackman & Conaway, 2013). It has been shown in previous research that having a chronic disease can cause adolescents to experience concern and stress about their health (Compas, Jaser, Dunn, & Rodriquez, 2012). Stress is also experienced from handling complicated medical procedures and treatment and feeling different from peers (Emerson et al., 2016). In addition, adolescents with a chronic disease are at higher risk for psychosocial adjustment problems (Moreira et al., 2013). In short, having a chronic disease could have an impact on the adolescents’ quality of life.
Quality of life is defined by the World Health Organization (1996) as the perception of individuals about their position in life, in relation to their standards, concerns, expectations and goals in the context of the culture and value system in which they live. When the subjective integration of functional status and physical symptoms is also incorporated, the definition used is Health Related Quality of Life (HRQOL; World Health Organization, 1996). When assessing the influence of a chronic disease on a given individual, physical measurements of the health condition alone are not sufficient. It is essential to measure HRQOL as well to assess the full impact of a chronic disease on the adolescent´s life (Haverman et al., 2012). Additionally, it is important to
measure HRQOL so that appropriate care can be offered when needed whereby adolescents with a chronic disease can function optimal in their daily life.
Previous studies have reported conflicting findings regarding the level of HRQOL in adolescents with a chronic disease compared to healthy adolescents. Engelen and colleagues (2009) found that adolescents with a chronic disease have a lower self-reported HRQOL in comparison with healthy adolescents on total HRQOL, psychosocial functioning, physical functioning, social functioning and school functioning. Other studies have found an impaired HRQOL, compared to healthy adolescents, in adolescents with chronic diseases such as inflammatory bowel disease (IBD; Loonen, Grootenhuis, Last, Koopman, & Derkx, 2002),juvenile idiopathic arthritis (JIA; Haverman et al., 2012), end-stage renal disease (ESRD; Schoenmaker et al., 2013), cancer (Husson et al., 2017), HIV (Banerjee, Pensi, & Banerjee, 2010) and sickle cell disease (Sehlo & Kamfar, 2015). However, several studies have found no difference in HRQOL comparing healthy adolescents with adolescents with chronic diseases such as HIV (Cohen et al., 2015), IBD (Jelenova et al., 2016), sickle cell disease (Kater et al., 1999) and hemophilia (Limperg et al., 2017). Lack of differences in HRQOL between adolescents with a chronic disease and healthy adolescents could possibly be explained by the discriminative validity of questionnaires (Grootenhuis, Koopman, Verrips, Vogels, & Last, 2007).
Moreover, differences in HRQOL between adolescents with different chronic diseases were shown in previous studies. Results from Varni and colleagues (2007) showed that adolescents with diabetes reported the highest HRQOL, while adolescents with cerebral palsy reported the lowest HRQOL. In addition, Ingerski and colleagues (2010) showed that adolescents with eosinophilic gastrointestinal disorder showed a significantly lower HRQOL compared to adolescents with cystic fibrosis (CF), IBD and epilepsy. These results suggest that specific chronic diseases possibly lead to a poorer HRQOL compared to other chronic diseases. Therefore, it is important to measure specific
consequences, as HRQOL, for each chronic disease separately. Furthermore, it is important to explore which HRQOL domains are the most impaired per chronic disease. Measuring HRQOL per chronic disease group is essential to indicate which adolescents are possibly at higher risk for an impaired HRQOL, whereby adequate care, education and opportunities for work and employment can be promoted (Mokkink et al., 2007).
Age and gender seem to be associated with HRQOL in healthy adolescents. A previous study found that children reported a higher HRQOL compared to adolescents, which shows that age is negatively related to HRQOL (Meade et al., 2015). Moreover, previous studies found that female adolescents have a poorer perception of their own health and report somatic symptoms and a lower HRQOL more often compared to male adolescents (Michel, Bisegger, Fuhr, & Abel, 2009; Meade, & Dowswell, 2015). This could possibly be explained by that females generally have more worries and are more concerned with their own health compared to male adolescents (Indregard, ihlebaek, & Eriksen, 2013). Therefore, it is important to assess if age and gender are associated with HRQOL in adolescents with a chronic disease.
In addition, previous literature shows some determining factors related to school which could possibly be associated with HRQOL in adolescents with a chronic disease. Firstly, school absence was found to be one of the main predictors of a low HRQOL in adolescents with JIA (Haverman et al., 2012). Adolescents with a chronic disease are missing a significant amount of days at school due to medical treatments and treatment side effects (Emerson et al., 2016), which can cause stress and negatively impacts social well-being (Shaw & McCabe, 2008). Furthermore, going to school is of utmost importance for adolescents with a chronic disease because it gives them structure and possibilities for social contacts (Mourik, 2005). However little research has been done so far on the influence of school absence on HRQOL in adolescents across different chronic diseases.
Secondly, higher rates of grade retention has been found among adolescents with cancer compared to healthy peers (Bonneau et al., 2011). Literature shows that grade retention has a negative influence on the self-esteem of adolescents (Peixoto et al., 2016) and on their social and emotional adjustment (Jimerson et al., 2006). In contrast, Wu, West and Hughes (2010) found a positive effect of grade retention compared to promoted peers on decreased sadness and withdrawal and increased behavioral engagement. Moreover, a short term positive effect of retention was found on peer liking and school belongingness, however this effect decreased in the longer term. The influence of grade retention on HRQOL by adolescents with a chronic disease has not been examined yet in the Netherlands.
Finally, the role of educational level in relation to HRQOL has not been clarified yet (Tchicaya, Lorentz, Demarest, Beissel, & Wagner, 2015). Only a few studies have examined the influence of educational level on HRQOL in adolescents with a chronic disease and solely included older participants. Results from these studies showed that educational level is positivelyassociated with HRQOL in patients with multiple sclerosis (Patti et al., 2007) and IBD (Casellas, López-Vivancos, Casado, & Malagelada, 2002). This could possibly be explained by the assumption that a higher educational level is associated with more awareness of the chronic disease and better abilities to cope with the difficulties of a chronic disease (Šabanagić-Hajrić & Alajbegović, 2015). It is crucial, to assess the association between school absence, grade retention and educational level with HRQOL in a larger cohort and with different disease groups to be able to generalize the results. Besides, it is important to gain more insight into school related factors associated with HRQOL, whereby parents and teachers can be informed about the consequences of these school related factors to the HRQOL of a adolescents with a chronic disease.
As stated above, going to school is of utmost importance for adolescents with a chronic disease and therefore the association between school related factors and HRQOL will be
quantitatively examined in this study. Previous literature describes the importance of the use of qualitative data next to the use of quantitative data, because it helps to gain deeper insights in the examined topics than would otherwise be possible via the use of quantitative data by itself (Halcomb & Hickman, 2015). Therefore, a qualitative approach is chosen in the second part of this study to capture the perspectives from adolescents with a chronic disease themselves about school related themes. These school related themes could provide additional insights into the school experiences of adolescents with a chronic disease. Moreover, these perspectives could contribute by improving educational facilities in the Emma Children’s Hospital, where this study is conducted. Educational facilities in the Emma Children’s Hospital consists of consultants who provide educational support or are teaching hospitalized children.
To determine HRQOL in adolescents with a chronic disease and which factors regarding school are associated with HRQOL, the following questions will be examined in part 1 of this study: (1a) What is the difference in self-reported HRQOL between adolescents aged 13 to 18 with a chronic disease who are receiving care in the Emma Children’s Hospital (EKZ chronic disease group) in comparison to a healthy norm group?; (1b) What is the difference in reported HRQOL between adolescents aged 13 to 18 within the following different chronic disease groups;
rheumatology, endocrinology, IBD, oncology, MDL, sickle cell disease, nephrology, coagulation diseases and HIV compared separately to the healthy norm group?; (2) Are the factors age, gender, school absence, grade retention and educational level associated with HRQOL in adolescents with a chronic disease? To identify important themes concerning school in the daily life of adolescents with a chronic disease, the following question will be explored in part 2 of this study: (3) What themes concerning school are relevant in the daily life of adolescents aged 12 to 18 with a chronic disease, based on qualitative data?
The hypothesis is that the EKZ chronic disease group will have a (1a) lower self-reported HRQOL in comparison with the healthy norm group. In addition, (1b) it is expected that adolescents within the following disease groups: rheumatology, endocrinology, IBD, oncology, MDL, sickle cell disease, nephrology, coagulation diseases and HIV possesses a lower self-reported HRQOL in comparison with the healthy norm group. Furthermore (2) it is expected that being a girl, age, school absence and grade retention are negatively associated, and that educational level is positively associated with HRQOL.
Method
Procedure
The data for the first part of this study has been collected online via the KLIK website (www.hetklikt.nu) in the period of September 2011 until April 2017. KLIK stands for ‘Kwaliteit Leven in Kaart’ which means quality of life in clinical practice. KLIK is an innovative web-based application which is developed by the psychosocial department of the Emma Children’s hospital (Haverman, Engelen, Rossum, Heymans, & Grootenhuis, 2011). It is mainly developed so that doctors can systematically discuss the Patient Reported Outcomes (PROs), for example HRQOL, to improve communication on psychosocial topics with patients during a consultation (Haverman et al., 2013). In this way, psychosocial problems can be identified earlier and extra psychosocial help could be offered if necessary. Adolescents within the following chronic disease groups:
rheumatology, nephrology, coagulation diseases, HIV, CF, sickle cell disease, oncology, spherocytosis, MDL, IBD, schisis/cranio, phenylketonuria, dermatology, spina bifida,
endocrinology, lysosomal storage disorders, muscular diseases, marfan and TPV home and who received care for their chronic disease in the Emma Children’s hospital were invited to participate in the KLIK program. They were first invited via a disease specific invitation letter in which is explained why using PROs is important and how they could register at the KLIK website. At the
beginning of the registration on the KLIK website the patients needed to actively confirm that they read the general terms and that they were aware of how their privacy would be managed by using KLIK. Besides, patients and, when younger than 16, their parents had to actively give consent for using their data for scientific research. Only the data of the adolescents and, when younger than 16, their parents who gave consent for using their data for scientific research were used for analyzing. Patients received an email after registration which contained an unchangeable password they could use to log in on the KLIK website and get access to their questionnaires.
For this study the social demographic questionnaire, school questionnaire and the PedsQL were used for analyses. Patients were asked to fill in the questionnaires approximately every three months depending on the date of their consultation. In order to select the completed questionnaires the most reliable way, it was chosen to analyze the data of the fill-in moments of the three
questionnaires that were completed on the same day or with the least time between them. Prior to a consult with their doctor an automatic reminder to fill in the questionnaires was send via email. A few days prior consultation it was checked if the patients filled in the questionnaires yet. When the patients didn’t fill in the questionnaires yet the patients or their parents were called with the
question if they could fill in the questionnaires before the consult. In addition, a few days prior a consult it was checked if patients who had an appointment were already registered on the KLIK website. When the patients were not registered yet, the patients or their parents were called to ask if they received the invitation letter about KLIK and if so if they wanted to register at KLIK. If
necessary, additional explanation was given about KLIK and the registration process. Furthermore, it was offered to send the invitation letter for KLIK and additional information about registration again via email. The completed questionnaires were systematically discussed during the consults by their doctor.
The data for the second part of this study was collected by organizing and analyzing focus groups. These focus groups were part of a larger study. Patients registered at KLIK received a pop up on the KLIK website asking whether they could be approached about participating in an
scientific study. Patients who stated yes were approached via email with more information about the project and an invitation to participate in this part of the study. Moreover, psychologists and
pediatricians working in Emma Children’s hospital were asked which patients of them could be approached with more information and an invitation for this part of the study. These patients were all approached via email and/or they were called with more information about the project and an invitation to participate. In total four focus groups were held with two to three participants per focus group. One of the focus groups was held with participants aged 12 to 13, two of the focus groups were held with participants aged 14-15 and one of the focus groups was held with participants 16 to 18. Additional individual conversations were held to reach data saturation. In all focus groups and individual conversations the elicitation technique ‘Klaag- en Jubelmuur’ in combination with metaplan was used (Temme & Bikker, 1999; Hampsink & Hagendoorn, 2006). All focus groups and individual conversations, started with a short introduction and explanation about the study. Hereafter, the participants were asked to write down words on post-it’s about things they did not like related to the hospital, including subjects as friends and school, and put them on a poster named ‘De Klaagmuur’, meaning the complaining wall. They were also asked to write done words on post-it’s about things they did like related to the hospital and put them on a poster named ‘De
Jubelmuur’, meaning the positive wall. These words were the starting point for the (group)
interview. Furthermore, these words were grouped into themes together with the participants which is called the metaplan method. For this study only themes related to school were analyzed.
The focus groups and individual conversations were held at the Emma’s Children’s Hospital and each took around 45 minutes to one hour. Next to the participants, a moderator who led the conversation and an observer was present during the focus groups and individual conversations.
Audio was recorded during the focus groups and was transcribed verbatim. The transcripts were analyzed using thematic analysis methodology (Braun, & Clarke, 2006), whereby the transcripts were read and open-coded independently by two psychologists and one psychology student using qualitative data analysis software MAXQDA version 12 (VERBI Software, 2015). Highlighted text was re-read and recoded and general initial codes were categorized into themes based on discussion in team meetings. The themes were formed by axial coding and the major themes (themes with the most codes) were eventually elected based on team consensus. The committee for medical ethics of the AMC approved the current study.
Participants
Adolescents who were eligible for the first part of this cross-sectional study were all diagnosed with a chronic disease from one of the following chronic disease groups: rheumatology, nephrology, coagulation diseases, HIV, CF, sickle cell disease, oncology, spherocytosis, MDL, IBD, schisis/cranio, phenylketonuria, dermatology, spina bifida, endocrinology, lysosomal storage disorders, muscular diseases, marfan and TPV home. These disease groups were chosen for this study because they were participating in KLIK. In addition, eligible adolescents were all receiving care for their chronic disease in the Emma Children’s Hospital, were all aged between 13 and 18 years, were all living in the Netherlands and were all registered on the KLIK website. Of the 1405 patients aged 13 to 18 who registered on the KLIK website, 1062 patients filled in the PedsQL (75.6%), and 341 patients did not fill in the PedsQL (24.3%) and were therefore excluded (see consort diagram, Figure 1). Of the 1062 patients who filled in the PedsQL, 811 patients and, when younger than 16, their parents (76.4%) gave their permission for participating in scientific research. These patients were labelled as participants (N=811). Patients and their parents who did not give permission for participating in scientific research were labelled as non-participants (N=251).
Figure 1. Consort diagram of patients and participants (part 1).
Adolescents who were eligible for the second part of this study were all diagnosed with a chronic disease, were receiving care for their chronic disease in the Emma Children’s Hospital, were aged between 12 and 18 and were all living in the Netherlands. Of the 43 approached patients, 15 participated in this part of this study (34.9%). 28 of the approached patients did not participate in this part of this study (65.1%) because they were not interested or it was not feasible to participate at the moment.
Materials
Sociodemographic background variables. A social demographic questionnaire was used to obtain information about the adolescent’s age, sex and disease group and about the parent’s age and their country of birth.
School questionnaire. A self-conducted school questionnaire was used to obtain information about the educational level of the participant, if they ever doubled a class and how many days they missed school the past three months.
HRQOL in adolescents aged 13 to 18 years. The self-report version of the Pediatric Quality of Life InventoryTM4.0 (PedsQL) for adolescents aged 13 to 18 years was used to assess
HRQOL (Varni, Burwinkle, Seid, & Skarr, 2003). The PedsQL is a generic questionnaire which contains 23 items divided over four subscales. The four subscales are: physical functioning (eight items), emotional functioning (five items), social functioning (five items), and school functioning (five items). On each item of the four domains the adolescents indicated the extent to which a specific problem was experienced in the last week. The adolescents could rate a problem by using a 5-point Likert scale: never a problem (0), almost never a problem (1), sometimes a problem (2),
often a problem (3) or almost always a problem (4). A total score of the PedsQL was computed
based on all the four subscales and also a psychosocial health score was computed based on the subscales emotional functioning, social functioning and school functioning. There was scored on a scale from a 0 to 100, where a higher score indicated a better HRQOL. The questionnaire was available in Dutch and English on the KLIK website. It can be stated that the PedsQL has a good feasibility, reliability (α=0.89) and validity to measure HRQOL in adolescents aged 13-18 years (Varni, Seid, & Kurtin, 2001;Varni et al., 2003). Also it has been shown that the Dutch version of the PedsQL has adequate psychometric properties to assess HRQOL (Engelen, Haentjens, Detmar, Koopman, & Grootenhuis, 2009). Normative data of healthy adolescents aged 13 to 17 in the Netherlands is available (Engelen et al., 2009). Furthermore normative data of healthy adolescents aged 18 is available (Limperg et al., 2014). The reference scores of the normative data are an adequate representation of the Dutch population (Engelen et al., 2009; Limperg et al., 2014). In the current study Cronbach’s α of the PedsQL ranged from .78 till .93 (Table 1).
Table 1. Chronbach’s α shown per PedsQL scale
PedsQL scales Cronbach’s α
Total score .93 Psychosocial functioning .89 Physical functioning .90 Emotional functioning .84 Social functioning .79 School functioning .78
Statistical analyses
Statistical Package for Social Sciences (SPSS) version 23 was used to manage and analyze the data for the first part in this study. Descriptive statistics were used to assess the sociodemographic characteristics of the adolescents and their parents. Baseline differences in the distribution of gender between participants and non-participants, the EKZ chronic disease group and the healthy norm group were measured by using Chi-Square tests. Baseline differences in age between participants and non-participants, the EKZ chronic disease group and the healthy norm group were measured by using Independent Sample T-Tests. Kolmogorov-Smirnov Tests were used in this study to check the assumption for normality. Furthermore Levene’s tests were used to check for homogeneity of variance.
Although the assumption of normality was not met in all of the disease groups, parametric tests were used because of the large sample size. Independent Sample T-tests were used to compare the HRQOL scores of the EKZ chronic disease group with the HRQOL scores of the healthy norm group and to compare the HRQOL scores of each chronic disease group separately with the HRQOL scores of the healthy norm group. Solely disease groups with 30 participants or more were used in the analyses to separately compare the HRQOL scores of each of the disease groups with the healthy norm group because it is difficult to link reliable conclusions to the results of smaller groups. The disease groups with 30 or more participants are: rheumatology, endocrinology, IBD, oncology, MDL, sickle cell disease, nephrology, coagulation diseases and HIV. To adjust for multiple testing, the significance level was calculated by using the Bonferroni correction. A significance level of .008 was applied to the HRQOL of adolescents by dividing .05 by the six PedsQL subscales. Cohen’s D was calculated by dividing the difference in mean scores between the EKZ chronic disease group and the healthy norm group by the pooled SD of both groups. These effect sizes were measured to assess the extent of differences between these two groups. According
to Cohen (1988), an effect size of .20 is considered small, an effect size of .50 is considered as medium and an effect size of .80 is considered large.
Originally MANOVA’s were chosen to compare the HRQOL scores of the EKZ chronic disease group with the HRQOL scores of the healthy norm group and to compare the HRQOL scores of each chronic disease group separately with the HRQOL scores of the healthy norm group. However the assumptions of homogeneity of variance-covariance matrices, equality of variance, normality and linearity were violated and therefore it would not be reliable to use MANOVA’s for these analyzes. An additional check has been performed by comparing the outcomes of the Independent Sample T-tests with the outcomes of the MANOVA’s. The outcomes of the Independent Sample T-tests did not show more significant differences in HRQOL scores between the measured groups compared to the outcomes of the MANOVA’s.
Linear Multivariate Regression Analyses were used to measure if age, gender, school absence, grade retention and educational level may be associated with HRQOL in adolescents with a chronic disease. The assumptions of homoscedasticity and multicollinearity were not violated. A Bonferroni corrected significance level of .008 was applied to adjust for multiple testing. To express the strength of the association between the predictors and the outcomes, standardized regression coefficients are reported (β). According to Cohen (1962), an β of 0.1 is considered small, an β of 0.3 is considered medium and an β of 0.5 is considered large for continuous predictors. In addition, an β of 0.2 is considered small, of 0.5 is considered medium and of 0.8 is considered large for dichotomous predictors. Furthermore, the explained variance (R2) is reported and tested with an ANOVA. Explained variances of 0.02 are considered small, of 0.13 are considered medium, and of 0.26 are considered large (Cohen, 1988).
Results
Baseline sample characteristics (part 1). The sociodemographic characteristics of the participants of the first part of this study are shown in Table 2. A significant difference in age was found between participants (M=15.9, SD=1.74) and non-participants (M=14.8, SD=1.28);
t(559.78)=10.51, p<.0001. No significant difference in the distribution of gender between
participants and non-participants was found.
Table 2. Sociodemographic characteristics of participants (part 1) Participants
Adolescent characteristics (N=811) N Mean SD
Age (years) 811 15.9 1.74
N %
Gender (female) 420 51.8
Different disease groups
Rheumatology 227 28.0
Endocrinology 105 12.9
IBD 99 12.2
Oncology 64 7.9
MDL 48 5.9
Sickle cell disease 47 5.8
Nephrology 46 5.7 Coagulation diseases 43 5.3 HIV 32 3.9 Other 100 12.3 Education child1 Primary school 7 1.0
Special primary school 5 .7
High school (vmbo) 197 29.4
High school (havo/vwo) 249 37.2
Special High school 28 4.2
Secondary education 137 20.5
No education/other 46 6.9
Parental characteristics (N=559) N Mean SD
Age (years) 2 533 46.64 5.15
N % Country of birth (The Netherlands) 490 87.7
Note.1142 missing: 142 adolescents did not complete the school questionnaire.
Baseline sample characteristics (part 2). The sociodemographic characteristics of the participants of the second part of this study are shown in Table 3.
Table 3. Sociodemographic characteristics of participants (part 2) Participants
Adolescent characteristics (N=15) N Mean SD
Age (years) 15 14.9 1.5
N %
Gender (female) 9 60
Different disease groups
Rheumatology 2 13.3
Oncology 3 20.0
MDL 1 6.7
Sickle cell disease 4 26.7
CF 2 13.3
Dermatology 1 6.7
Muscular diseases 1 6.7
Other 1 6.7
Education child1
High school (vmbo) 1 8.3
High school (havo/vwo) 8 66.7
Secondary education 2 16.7
No education/other 1 8.3
Parental characteristics (N=15) N Mean SD
Age (years) 2 14 49.1 8.4
N %
Country of birth (The Netherlands) 11 73.3
Note.13 missing: 3 parents did not fill in the question about education. 2. 1 missing: 1 parent
did not fill in their age.
HRQOL in adolescents aged 13 to 18. No significant baseline differences in age and the distribution of gender were found between the EKZ chronic disease group and the healthy norm group. With T-tests differences in HRQOL scores between the chronic disease group and the healthy norm group were examined (Table 4). Results demonstrated that adolescents with a chronic disease had significantly lower HRQOL scores on all HRQOL scales except emotional functioning. Effect sizes (d) were ranging from 0.25 to 0.54, and can be defined as small to medium (Cohen, 1988).
Table 4. HRQOL of adolescents with a chronic disease aged 13 to 18 compared to healthy peers
EKZ chronic disease group Healthy norm group Effect
size N Mean SD N Mean SD t p d Age (years) 811 15.9 1.74 185 15.7 1.81 -.97 .33 .20 N Mean SD N Mean SD t p d PedsQL scales Total score 811 77.54 16.44 185 83.78 9.57 6.86 .000 0.41 Psychosocial functioning 811 78.05 15.67 185 81.73 10.94 3.78 .000 0.25 Physical functioning 811 76.57 22.22 185 87.64 9.71 10.46 .000 0.54 Emotional functioning 811 78.63 19.55 185 77.76 15.66 -.65 .518 -0.05 Social functioning 811 85.37 16.36 185 89.70 12.33 4.04 .000 0.28 School functioning 811 70.16 19.60 185 77.73 13.05 6.41 .000 0.41
Note. a higher score (0-100) indicates a better HRQOL.
Bonferroni corrected significant p-values (p<.008) are shown in bold, p-values were measured by Independent Sample T-Tests.
HRQOL in adolescents per chronic disease. The results of comparing the HRQOL scores of adolescents within each of the following disease groups; rheumatology, endocrinology, IBD, oncology, MDL, sickle cell disease, nephrology, coagulation diseases and HIV separately with the healthy norm group are presented in Table 5. For a visual overview of the results per HRQOL domain see Figure 2 in appendices. HRQOL scores of adolescents in the rheumatology disease group were significantly lower compared to the healthy norm group on all HRQOL scales except emotional functioning. Effect sizes (d) were ranging from .34 to 1.03 and can be defined small to large (Cohen, 1988). HRQOL scores of adolescents in the endocrinology disease group were significantly lower than the healthy norm group on total HRQOL, social functioning and school functioning. Effect sizes (d) were ranging from .37 to .44 and can be defined small to medium (Cohen, 1988). HRQOL scores of adolescents in the IBD disease group were significantly lower than the healthy norm group on physical functioning and school functioning. Effect sizes (d) were .40 and .50 and can be defined small to medium (Cohen, 1988). HRQOL scores of adolescents in the oncology disease group were significantly lower than the healthy norm group on total HRQOL, physical functioning and school functioning. Effect sizes (d) were ranging from .64 to 1.23 and can be defined medium to large (Cohen, 1988). HRQOL scores of adolescents in the MDL disease group were significantly lower than the healthy norm group on total HRQOL, psychosocial
functioning, physical functioning and school functioning. Effect sizes (d) were ranging .54 to .84 and can be defined medium to large (Cohen, 1988). HRQOL scores of adolescents in the sickle cell disease group were significantly lower than the healthy norm group on physical functioning and school functioning. Effect sizes were .70 and .83 and can be defined medium to large (Cohen, 1988). HRQOL scores of adolescents in the nephrology disease group and in the coagulation disease group did not differ significantly from the HRQOL scores of the healthy norm group. HRQOL scores of adolescents in the HIV disease group were significantly higher than the healthy norm group on emotional functioning. Effect size (d) was -0.51 and can be defined as medium (Cohen, 1988).
groups size
PedsQL scale N Mean SD N Mean SD t p d
Disease group
Rheumatology Total scale 227 73.32 19.28 185 83.78 9.57 7.16 .000 0.67
Psychosocial functioning 227 76.62 17.96 185 81.73 10.94 3.56 .000 0.34
Physical functioning 227 67.15 25.27 185 87.64 9.71 11.24 .000 1.03
Emotional functioning 227 77.78 21.75 185 77.76 15.66 -.01 .992 0.00
Social functioning 227 82.86 17.08 185 89.70 12.33 4.71 .000 0.45
School functioning 227 69.21 21.82 185 77.73 13.05 4.91 .000 0.46
Endocrinology Total score 105 79.57 14.17 185 83.78 9.57 2.72 .007 0.37
Psychosocial functioning 105 77.48 14.53 185 81.73 10.94 2.61 .010 0.34
Physical functioning 105 83.48 16.79 185 87.64 9.71 2.32 .022 0.33
Emotional functioning 105 77.57 18.20 185 77.76 15.66 .09 .927 0.01
Social functioning 105 83.10 18.56 185 89.70 12.33 3.26 .001 0.44
School functioning 105 71.76 17.66 185 77.73 13.05 3.03 .003 0.40
IBD Total score 99 80.88 12.42 185 83.78 9.57 2.02 .045 0.27
Psychosocial functioning 99 79.73 13.32 185 81.73 10.94 1.36 .175 0.17
Physical functioning 99 83.05 14.01 185 87.64 9.71 2.90 .004 0.40
Emotional functioning 99 79.24 18.63 185 77.76 15.66 -.71 .477 -0.09
Social functioning 99 89.60 12.39 185 89.70 12.33 .07 .945 0.01
School functioning 99 70.35 17.66 185 77.73 13.05 3.66 .000 0.50
Oncology Total score 64 73.71 18.41 185 83.78 9.57 4.19 .000 0.81
Psychosocial functioning 64 76.74 16.80 185 81.73 10.94 2.22 .029 0.39 Physical functioning 64 68.02 26.86 185 87.64 9.71 5.71 .000 1.23 Emotional functioning 64 78.36 19.50 185 77.76 15.66 -.22 .824 -0.04 Social functioning 64 84.38 17.22 185 89.70 12.33 2.28 .025 0.39 School functioning 64 67.50 22.31 185 77.73 13.05 3.47 .001 0.64 MDL Total score 49 76.06 16.63 185 83.78 9.57 3.09 .003 0.68 Psychosocial functioning 49 75.35 15.11 185 81.73 10.94 2.75 .008 0.54 Physical functioning 49 77.41 21.88 185 87.64 9.71 3.16 .003 0.78 Emotional functioning 49 74.17 18.83 185 77.76 15.66 1.22 .228 0.22 Social functioning 49 86.35 16.14 185 89.70 12.33 1.34 .185 0.25 School functioning 49 65.52 19.05 185 77.73 13.05 4.20 .000 0.84
Sickle Cell disease Total score 47 78.21 14.18 185 83.78 9.57 2.55 .014 0.52
Psychosocial functioning 47 77.59 13.77 185 81.73 10.94 1.91 .060 0.36
Physical functioning 47 79.39 17.67 185 87.64 9.71 3.08 .003 0.70
Emotional functioning 47 78.83 18.80 185 77.76 15.66 -.40 .688 -0.07
Social functioning 47 87.66 12.29 185 89.70 12.33 1.02 .311 0.17
School functioning 47 66.28 16.10 185 77.73 13.05 5.11 .000 0.83
Nephrology Total score 46 82.80 13.03 185 83.78 9.57 0.48 .632 0.09
Psychosocial functioning 46 81.99 13.96 185 81.73 10.94 -.12 .906 -0.02
Physical functioning 46 84.31 15.64 185 87.64 9.71 1.38 .174 0.30
Emotional functioning 46 81.74 17.99 185 77.76 15.66 -1.38 .173 -0.25
Social functioning 46 89.35 12.81 185 89.70 12.33 .173 .862 0.03
School functioning 46 74.89 19.10 185 77.73 13.05 .95 .344 0.20
Coagulation diseases Total score 43 83.65 13.77 185 83.78 9.57 .06 .950 0.01
Psychosocial functioning 43 80.93 15.41 185 81.73 10.94 .32 .749 0.07
Physical functioning 43 88.74 14.55 185 87.64 9.71 -.47 .639 -0.10
Emotional functioning 43 82.79 20.88 185 77.76 15.66 -1.77 .077 -0.30
Social functioning 43 87.67 15.86 185 89.70 12.33 .92 .360 0.16
School functioning 43 72.33 19.34 185 77.73 13.05 1.74 .088 0.37
HIV Total score 32 85.77 12.28 185 83.78 9.57 -1.04 .302 -0.20
Psychosocial functioning 32 85.16 11.67 185 81.73 10.94 -1.62 .107 -0.31
Physical functioning 32 86.91 15.80 185 87.64 9.71 .25 .804 0.07
Emotional functioning 32 85.94 17.71 185 77.76 15.66 -2.68 .008 -0.51
Social functioning 32 92.97 10.61 185 89.70 12.33 -1.41 .160 -0.27
School functioning 32 76.56 14.22 185 77.73 13.05 .46 .645 0.09
Note. a higher score (0-100) indicates a better HRQOL.
Factors related to education associated with HRQOL in adolescents with a chronic disease. It was assessed if age, gender, school absence, grade retention and educational level are associated with HRQOL in adolescents with a chronic disease. Results of the Linear Multivariate Regression Analyses are shown in Table 6. Age was significantly negatively associated with the physical scale. With a standardized regression coefficient of -.09 which can be defined as small (Cohen, 1988). Being a girl was significantly negatively associated with all HRQOL scales except school functioning. Standardized regression coefficients ranged from .12 till .22 and can be defined as small (Cohen, 1988). School absence was significantly negatively associated with all the PedsQL scales. Standardized regression coefficients ranged from -.27 till -.39 and can be defined as medium (Cohen, 1988). No significant associations were found between educational level and grade retention and HRQOL. The explained variance ranged from .11 till .21 and is considered medium (Cohen,1988).
Table 6. Regression analyses of Health Related Quality of Life and education in adolescents with a chronic disease
Total score Psychosocial
functioning
Physical functioning Emotional
functioning
Social functioning School functioning
Predictors B SD β B SD β B SD β B SD β B SD β B SD β Age -.56 .36 -.06 -.14 .35 -.02 -1.34 .49 -.11* -.20 .46 -.02 -.01 .36 -.00 -.21 .44 -.02 Gender 6.77 1.19 .21** 4.80 1.16 .15** 10.47 1.60 .24** 7.90 1.50 .20** 4.52 1.19 .15** 1.99 1.45 .05 School absence -.59 .05 -.40** -.56 .05 -.39** -.66 .07 -.33** -.53 .07 -.29** -.41 .05 -.29** -.73 .07 -.40** Grade retention -.16 .79 -.01 -.33 .77 -.02 -.16 1.06 .01 -.16 .99 -.01 .34 .79 .02 -1.19 .96 -.05 Educational level .20 .41 .02 .32 .40 .03 -.04 .55 -.00 .56 .51 .04 .44 .41 .04 -.03 .50 -.00 R2 .21 .18 .19 .13 .11 .17 F Test 32.85** 26.79** 29.10** 18.72** 15.08** 25.86** *p <.008 (Bonferroni corrected) ** p <.001
Relevant themes concerning school for adolescents with a chronic disease. Major themes were: School absence, falling behind in school, feeling different then classmates, misunderstanding of classmates and communication about illness to classmates. In Table 7 the most important themes are shown in order of magnitude and corresponding examples of statements expressed by the adolescents in the focus groups.
Table 7. Themes emerging from focus groups
Themes Examples
1. School absence “because I had to go to the hospital all the time I missed a lot of school”
“recently it was during school and then I miss a lot of school” “ in the beginning I missed school three quarter of the year” 2. Falling behind in school “At one point I had so many tests that it was too much to catch up”
“ and when I go to the hospital I’ll probably like need to study a lot after so I can catch up because it is not easy”
“ and then you are at French class and then the teacher says we are going to repeat some things and then you really never heard about it”
3. Feeling different then classmates “I just want to be a normal person, who can participate in class and who is not tired and not sick”
“you become a little bit of an outsider”
“they see you as that really ill child who never comes to school” 4. Misunderstanding of classmates “ And then you only hear like ow, I’m really jealous that you only have
three hours of school, while jeah if they knew” “ it is really underestimated”
“ Some children on school don’t understand what cancer means and then they curse with it all day”
5. Communication about illness to classmates “ I think, maybe attention can be paid to how you tell it on school, because with me they act really seriously.”
“friends on school don’t even know I am sick”
“ I rather want nobody to know, only my teacher, then when something is wrong I can tell him”
Discussion
The main goals in this study were to determine (1a) HRQOL in adolescents with a chronic disease compared to the healthy norm group, (1b) HRQOL in adolescents within the following different chronic disease groups; rheumatology, endocrinology, IBD, oncology, MDL, sickle cell disease, nephrology, coagulation diseases and HIV compared separately to the healthy norm group, (2) factors associated with HRQOL in adolescents with a chronic
disease and to explore (3) important themes concerning school in the daily life of adolescents with a chronic disease, based on qualitative data.
The findings demonstrated that adolescents with different chronic diseases aged 13 to 18 have significant lower self-reported HRQOL scores compared to healthy adolescents, except on emotional functioning. Secondly, different results were found comparing each disease group with the healthy norm group. It was shown that school functioning was impaired in six of the nine disease groups compared to healthy adolescents. Third it was found that school absence and being a girl are negatively associated with HRQOL in adolescents with a chronic disease. Finally six themes: school absence, falling behind in school, feeling different than classmates, misunderstanding of classmates and communication about illness to classmates were identified as important themes concerning school for adolescents with a chronic disease.
It is remarkable that in each disease group investigated in this study adolescents with a chronic disease did not show a lower HRQOL score on emotional functioning compared to healthy adolescents. When adolescents go through puberty, they experience bodily and hormonal changes and are more vulnerable to stressors which could increase stress and anxiety (Patton, & Viner, 2007; Holder, & Blaustein, 2014). This may lead to poorer emotional functioning in healthy adolescents as well and thus could explain the lack of difference found between adolescents with a chronic disease compared to the healthy norm group.
In a few disease groups only a few or no differences were found in HRQOL scores compared to the healthy adolescents, which is not in line with the hypothesis. Firstly, in the oncology disease group not every HRQOL domain was found to be impaired, which is
contrary to previous research where adolescents with cancer showed impairment on an extensive list of issues (Sodergren et al., 2017). Perhaps this could be explained by that adolescents who are very sick are possibly (temporarily) not participating in KLIK. Furthermore, the oncology disease group consists of both newly diagnosed adolescents and cancer survivors. It is possible that survivors experience less stress and therefore have a higher HRQOL compared to adolescents that are in the middle of treatment. Future research should take this into account. Secondly, no differences in HRQOL were found between adolescents with coagulation diseases compared to healthy adolescents. This research finding is in line with previous research (Limperg, 2017). Presumably, adolescents with coagulation diseases don’t have an impaired HRQOL. Third, no differences were found in HRQOL scores between adolescents within the nephrology disease group compared to healthy adolescents. These findings are contrary to previous research (Schoenmaker et al., 2013). It could be the case that there is some bias regarding this specific disease group because pediatricians in the Emma Children’s hospital who treat adolescents with nephrology diseases choose which patients are participating in the KLIK program themselves. Whereas in other disease groups all new patients are invited to participate in the KLIK program. Fourth, only a few differences were found in HRQOL scores between adolescents within the sickle cell disease group and the healthy norm group, which is contradictory to previous research (Hijmans et al., 2010; Sehlo & Kamfar, 2015). From clinical experience it is known that the response rate in this disease group is low and that the patients that are participating in KLIK are the patients with a higher HRQOL. Therefore it is possible that the patients that participated in this study are not representative for other adolescents with sickle cell disease. Finally, according to adolescents with HIV, no impaired HRQOL was found compared to healthy adolescents and remarkably adolescents with HIV showed a higher HRQOL score on the subscale emotional functioning
compared to healthy adolescents. This could possibly be explained by social desirable answers of adolescents with HIV. HIV is seen as a highly stigmatized chronic disease (Mellins & Malee, 2013). Stigmatization might cause adolescents with HIV to feel the need to compensate by giving social desirable answers while reporting about their own functioning. It is important for future research to control for social desirability.
Furthermore, the lack of differences in HRQOL scores between adolescents within different disease groups compared to healthy adolescents could possibly be explained by the fact that receiving care in the Emma Children’s hospital has various benefits. Via the KLIK portal, adolescents with a chronic disease are monitored. In this way, psychosocial problems are identified earlier and extra psychosocial help is available in the Emma Children’s hospital when needed. Finally, because of the small sample size in the MDL chronic disease group (N=49), sickle cell disease group (N=47), nephrology disease group (N=46), coagulation disease group (N=43) and HIV disease group (N=32) the power to detect a significant difference was limited. Nevertheless, some differences were demonstrated with effect sizes ranging from medium to large which suggest that differences that were found between the healthy norm group and the different chronic disease groups with a small sample size are markedly different. Apart from that, there is only a small group of adolescents with a chronic disease present in the Netherlands. Therefore, it is always a challenge to get together a large sample of these specific disease groups. Nonetheless, it is very important to investigate the HRQOL of these groups so that the quality of care for adolescents with a chronic disease can increase further.
This study did not only focus on HRQOL outcome scores, but also tried to gain more insight into factors regarding school that might influence HRQOL in adolescents with a chronic disease. However, it is important to take into account that with the use of linear
multivariate regression analyses causality cannot be proven in this study. Girls showed lower HRQOL compared to boys, which is in line with the hypothesis. In this study, no difference in the distribution of gender was found between the chronic disease group and the healthy norm group. However, if differences in the distribution of gender are found between a chronic disease group and a healthy norm group in future HRQOL research it would be suggested to analyze girls and boys separately. Furthermore, age was only negatively associated with physical functioning by adolescents with a chronic disease in this study. However, it is shown in previous research that age is negatively associated with HRQOL (Meade et al. 2015). Presumably, no associations on the other domains between age and HRQOL were found in this study because only adolescents within a small age range participated in this study.
The association between three factors regarding school (school absence, grade retention and educational level) and HRQOL were assessed in this study. Especially school absence seems to be an important predictor of an impaired HRQOL in adolescents with a chronic disease not only for school functioning but also for the other domains of HRQOL. Secondly, educational level was not a determining factor for HRQOL in this study, which is in contrast with previous literature (Patti et al., 2007; Casellas, López-Vivancos, Casado, & Malagelada, 2002). However, in these studies solely adults were included. It could be the case that the association between educational level and HRQOL is not visible yet in the age 13 to 18 because there is only a minimum difference in educational level, whereas adults already had the change to complete a secondary and or a tertiary education or started working. Third, no effect of grade retention was found on HRQOL by adolescents with a chronic disease. However, this study did not take the time of retention into account. It is possible that grade retention has a stronger negative effect for adolescents who are older and more emotionally
mature when they are repeating a grade (Silberglitt, Jimerson, Burns, & Appleton, 2006). Future research should take the time of retention into account.
In the second part of this study, five important school related themes were identified based on qualitative data. The finding of school absence as the most important theme corresponds to the finding that school absence is negatively related to HRQOL in adolescents with a chronic disease. It is possible that the other four themes also have an influence on the HRQOL of adolescents with a chronic disease, because the adolescents in the focus groups voiced these themes as important school related aspects in their daily life. Therefore it is suggested that future research should quantitatively examine the relation between the other four school related themes and HRQOL in adolescents with a chronic disease. Furthermore, when psychosocial consequences of having a chronic disease are discussed and monitored in clinical practice, these school related themes should be included.
This study has some limitations. First, this is a cross-sectional study, whereby no statements about causality can be made and it is not possible to assess HRQOL in adolescents with a chronic disease over time. Secondly, while measuring HRQOL the time after diagnoses of the disease was not taken into account. It is known from previous literature that HRQOL can improve within the first year after the diagnosis (Otley et al, 2006). Future research could take the time after diagnoses into account. Furthermore, a significant difference in age was found between the non-participants and participants in the first part of this study. This is because a lot of the younger adolescents aged 13 to 16 were excluded in this study because their parents did not fill in the form in KLIK where they give permission for their child to participate in scientific research. Moreover, the response rates of the patients in the Emma Children’s hospital are not measured in this study, therefore it is not known if the participants
are representative for the whole chronic disease group. Finally, the response rate and the sample size for the focus groups were low, however data saturation was reached.
This study also has some strong points. Firstly, in the first part of this study online questionnaires were used, which is less time consuming and more efficient compared to data collection via pencil and paper and also a valid and reliable way to collect data (Young et al., 2009). Another strong point in this study is the use of self-report. Previous literature showed that adolescents with a chronic disease can reliably and validly self-report their HRQOL (Varni, Limbers, & Burwinkel, 2007). Besides, adolescents are the best judge about their own HRQOL, whereby with the use of self-report HRQOL the voice of the adolescent may be better expressed (Varni et al. 2007; Haverman, Limperg, Young, Grootenhuis, & Klaassen, 2016). Furthermore, in the second part of this study adolescents with a chronic disease were involved. Patient participating ensures that interventions, research and provided care are improving (Bovenkamp, Trappenburg, & Grit, 2010). It is also known that when children and adolescents are involved in research it will have a positive effect on their confidence and autonomy (Teunissen, Visse, Boer, & Abma, 2013). Finally, although some disease groups contained a small number of participants, the total of participants in the first part of this study was large.
Overall, in the first part of this study, from the data of 811 Dutch adolescents with a chronic disease aged 13 to 18 years receiving care in the Emma Children’s Hospital, it could be concluded that their HRQOL in general is impaired compared to healthy peers. It is very important to inform teachers and parents about the consequences of school absence and an impaired school functioning for adolescents with a chronic disease, whereby extra support on school can be offered if necessary. Furthermore, it is important to continue to systematically
monitor HRQOL in adolescents with a chronic disease so that adequate care can be offered when needed and adolescents with a chronic disease can function optimally in their daily life.
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