Husna A Ainuddin Faculty of Health Sciences, UiTM Puncak Alam Campus, Malaysia. Siew Yim Loh Department of Rehabilitation Medicine

Transcription

1 Article Psychometric properties of the self-report Malay version of the Pediatric Quality of Life (PedsQL TM ) 4.0 Generic Core Scales among multiethnic Malaysian adolescents Journal of Child Health Care 2015, Vol. 19(2) ª The Author(s) 2013 Reprints and permission: sagepub.co.uk/journalspermissions.nav DOI: / chc.sagepub.com Husna A Ainuddin Faculty of Health Sciences, UiTM Puncak Alam Campus, Malaysia Siew Yim Loh Department of Rehabilitation Medicine Karuthan Chinna Department of Social and Preventive Medicine Wah Yun Low Dean s office April Camilla Roslani Department of Surgery, Faculty of Medicine, University of Malaya, Malaysia Abstract Adolescence is the potential period for growth and optimal functioning, but developmental issues like time of transition from childhood to adulthood will create stress and affect the adolescent s quality of life (QOL). However, there is a lack of research tool for measuring adolescent s QOL in Malaysia. The aim of the study was to determine the validity and reliability of the self-report Malay version of the pediatric QOL (PedsQL ) 4.0 Generic Core Scales in assessing the QOL of Malaysian adolescents. A cross-sectional study design using the 23-item self-report Malay version of the PedsQL 4.0 Generic Core Scales was administered on a convenient cluster sampling (n ¼ 297 adolescent) from a secondary school. The internal consistency reliability had Cronbach s a values ranging from.70 to.89. Factor analysis reported a six-factor structure via principal axis factor analysis. In conclusion, the self-report Malay version of the pediatric QOL 4.0 Generic Core Scales is a reliable and valid tool to measure the QOL of multiethnic Malaysian adolescents. Corresponding author: Siew Yim Loh, Institute of Postgraduate Studies, University of Malaya, Kuala Lumpur, Malaysia. syloh@um.edu.my

2 230 Journal of Child Health Care 19(2) Keywords Adolescent, PedsQL, quality of life, reliability, validity Background Health-related quality of life (HRQOL) encompasses the physical, psychological, and social areas of health that are affected by a person s insights, main beliefs, outlooks, and understandings (Testa and Simonson, 1996). Buck (2012) detailed that the assessment of HRQOL has become an essential component in evaluating health outcomes evidenced by the collection of generic and condition-specific HRQOL instruments currently in use for both adults and children. Currently, there are a number of HRQOL measures being used in an effort to constantly improve the quality of health care for pediatric patients in clinical trials (Pilker, 1996), the population (Zahran et al., 2005), and clinical settings (Ganz, 1995). Today, most descriptions of HRQOL refer to them as multidimensional measures (Pal, 1996) that focus on individuals personal evaluation of their physical, psychological, and social health areas defined by the World Health Organization (World Health Organization, 1948). Although little is known about adolescents HRQOL, HRQOL is becoming increasingly acknowledged as an important health outcome instrument in clinical trials, health services research, and evaluations (Matza et al., 2004). Assessment of the QOL requires personal ratings by the adolescent on the multidimensional domains involving functional, physical, psychological, social, and school dimensions. There are currently a number of established generic and disease-specific HRQOL measures for children and adolescents (Varni et al., 2007) including Youth Quality of Life Instrument (Pal, 1996; WHO, 1948) and the Pediatric Quality of Life (PedsQOL ) 4.0GenericCoreScales(Varni et al., 1999, 2001, 2002, 2003). These assessments can have considerable importance for the comprehension of a child s psychosocial functioning and development such as their consciousness of their conditions and its outcome on their everyday life (Eiser and Morse, 2001; Noll et al., 1999). The PedsQL measurement model is a broadly used and internationally recognized generic approach to assess children s HRQOL using both generic core scales and disease-specific modules (Buck, 2012). The original English version of the PedsQL 4.0 Generic Core Scores has undergone extensive psychometric evaluation (Varnietal.,2003).Furthermore,thePedsQL 4.0 Generic Core Scales and disease-specific questionnaires have resulted from thorough studies and are appropriate for healthy populations in school settings (Varni et al., 2001) and community (Felder-Puig et al., 2008) as well as pediatric populations with acute (Varni et al., 2003) and chronic health conditions, such as cerebral palsy, cancer, end-stage renal disease, diabetes, and rheumatologic diseases (Goldstein et al., 2009; Varni et al., 2002, 2006, 2009a and 2009b). Additionally, the PedsQL 4.0 Generic Core Scales include both child self-report and parent proxy-report forms that can be easily administered (Felder-Puig et al., 2008). Furthermore, age-specific versions have been developed for parents of 2- to 4-year-old toddlers, for 5- to 7-year-old young children and their parents, 8- to 12-year-old children and their parents, and 13- to 18-year-old adolescents and their parents (Buck, 2012). The US English version of the PedsQL 4.0 Generic Core Scales has been linguistically validated in many countries where English is not the first language (Berkes et al., 2010; Chen et al., 2007; Gkoltsiou et al., 2008; Reinfjell et al., 2006). This paper describes the effort to determine the reliability and validity of the Malay version of the PedsQL 4.0 Generic Core Scores among multiethnic Malaysian adolescents. In addition, possible age and gender differences are also explored.

3 Ainuddin et al. 231 Methods The QOL study described in this paper was carried out using the self-report Malay version of the PedsQL 4.0 Generic Core Scales originally developed by Dr James W Varni. The translated questionnaire and consent for validation were obtained from the original author. It is a psychometric self-report and parent-report measure developed to assess the following domains of HRQOL: physical, emotional, social, and school functioning. Only the self-report for adolescents aged years was used in this study. It measures physical (eight items) and psychological functioning including emotional (five items), social (five items), and school (five items) functioning of the adolescents. A five-point response scale is utilized (0 ¼ never a problem and 4 ¼ always a problem). Items are reverse scored and linearly transformed to a0 100scale(0¼ 100, 1 ¼ 75, 2 ¼ 50, 3 ¼ 25, and 4 ¼ 0), so that higher scores indicate better functioning (Varni et al., 2001, 2003). The Physical Health Summary Score (eight items) is the same as the Physical Functioning Scale. To create the Psychosocial Health Summary Score (15 items), the mean is computed as the sum of the items divided by the number of items answered in the emotional, social, and school functioning scales. Scores for each domain range from 0 to 100 and higher scores indicate better QOL. The instrument had excellent internal consistency reliability for the Total Scale Score (a ¼.88 child), Physical Health Summary Score (a ¼.80 child), and Psychosocial Health Summary Score (a ¼.83 child) (Varni et al., 2001). Study design, study population, and data collection Approval for the conduct of this study was obtained from relevant authorities. In addition to this, only the self-report was utilized in this study. The sample size was calculated based on the recommended 10 participants per questionnaire item as suggested by Tabachnick and Fidell (2001) for factor analysis. The inclusion criteria include students who are able to understand, speak, and write Malay Language. In Malaysian secondary school, adolescents between 13 and 17 years of age are separated into five different grades as follows: 7th, 8th, 9th, 10th, and 11th grade. Using a convenient cluster sampling, a total of 297 students from 7th to 11th grade in 10 classes participated in the study. All students gave consent and filled up a personal information form and the questionnaire. Instructions on how to administer were given before the students fill up the forms and questionnaire. Approximately 10 minutes was taken for all the students to complete the questionnaire. Statistical analysis Data were analyzed using Statistical Package for the Social Sciences version The demographic profiles of the adolescents were described using mean, standard deviation, frequency, and percentage. The data were normally distributed and therefore, differences across gender, age, and race were computed using t test and one-way analysis of variance with Tukey s post hoc test. All results are presented with 95% confidence intervals. To determine the internal consistency reliability of the domains, the analysis for Cronbach s a value was performed. Explorative factor analysis was carried out using principal axis analysis with Promax rotation for factor loading analysis.

4 232 Journal of Child Health Care 19(2) Table 1. Demographic data of participants. Demographic n Percentage Gender Male Female Age (years) Race Malay Chinese Indian Others Religion Islam Buddhism Hinduism Others Table 2. Scale descriptive and internal consistency reliability. Domain Mean SD Cronbach s a value Total score Physical health Psychosocial health Emotional functioning Social functioning School functioning Results Sociodemographic profile of adolescents Table 1 presents the demographic data of the participants. The mean age of the participants was years, and 44.8% (n ¼ 133) of participants were male school-aged adolescents. A total of 297 adolescents participated in the study with a response rate of 90.3%. Of the participants, 51.5% were Chinese adolescents, followed by Malays (34.7%) and Indians (11.1%). As for age, 19.5% were 13 years old, 17.5% were 14 years old, 22.6% were 15 years old, 19.5% were 16 years old, and 20.9% were 17 years old adolescents. Mean and standard deviations The mean overall score of the PedsQL 4.0 Generic Core Scales was (SD ¼ 13.48; Table 2). The highest mean score was in physical functioning (mean ¼ 85.83; SD ¼ 13.34), while the lowest mean score was in emotional functioning (mean ¼ 69.43; SD ¼ 21.32).

5 Ainuddin et al. 233 Table 3. Construct validity of the Malay version of PedsQL TM 4.0 Generic Core Scores. Factor No item Physical functioning 1 Hard to walk more than one block.55 2 Hard to run.91 3 Hard to do sports or exercise.87 4 Hard to lift something heavy.54 5 Hard to take bath or shower.52 6 Hard to do house chores.55 7 Hurt or ache.31 8 Low energy.38 Emotional functioning 1 Feel afraid or scared.77 2 Feel sad or blue.80 3 Feel angry.69 4 Trouble sleeping.34 5 Worry about what will happen.84 Social functioning 1 Trouble getting along with peers.72 2 Other teens do want to be friends.90 3 Teased at.40 4 Doing things other peers do.50 5 Hard to keep up with peers.56 School functioning 1 Hard to concentrate.79 2 Forget things.55 3 Trouble keep up with school work.77 4 Missed school not well.75 5 Missed school see doctor.79 Internal consistency reliability The internal consistency reliability was based on the Cronbach s a of all the domains. George and Mallery (2003) stated that Cronbach s a values of.90 is considered excellent, while values of.70 are acceptable. The internal consistency of the Malay version of the PedsQL 4.0 Generic Core Scales was satisfactory with Cronbach s a values ranging from.70 to.89 for all domains. The highest Cronbach s a is in emotional functioning with a value of.82, while school functioning had the lowest Cronbach s a value of.70. Construct validity The Kaiser criterion was used to select those factors that have an eigenvalue of 1. Construct validity showed a six-factor structure with eigenvalues of more than 1 and a 62.5% variance response (Table 3). These factor loadings are emotional, social physical, and school functioning. However, physical and school functioning items loaded into two different factors each. All items

6 234 Journal of Child Health Care 19(2) Table 4. Factor correlations. Factor Table 5. Differences between quality of life and age, gender, and race variables (p values). Variable/ functioning Physical functioning Emotional functioning Social functioning School functioning Psychosocial health Overall QOL Age * Gender.06.02* Race *.002* QOL: quality of life. *p <.05. loaded into the factors with values of Hair et al. s (1998) guideline was used for practical significance, which specifies a factor loading of +.3 means the items are of minimal significance, +.4 indicates it is more important, and +.5 indicates the item is significant. Furthermore, the factor correlations were small to large. Table 4 reports the factor s correlation values that ranged from.19 to.57. Cohen (1988) stated that correlations are designated as small (.10.29), medium (.30.49), and large (¼.50) values. Difference in QOL among age, gender, and race of adolescents Results show that there were significant differences in QOL among age, gender, and race of adolescents (Table 5). In terms of age, there was a significant difference in the adolescents overall QOL (F(2, 291) ¼ 3.18, p ¼.01) in which post hoc test showed that 17-year-old adolescents (mean ¼ 82.49, SD ¼ 13.08) had significantly better overall QOL compared to 13-yearold adolescents (mean ¼ 74.36, SD ¼ 13.40). Meanwhile, there was a significant difference between genders in emotional functioning (t(291) ¼ 2.37, p ¼.02). Male adolescents reported better emotional functioning (mean ¼ 74.66, SD ¼ 20.55) than female adolescents (mean ¼ 69.02, SD ¼ 20.29). Furthermore, results also show a significant difference in terms of race in psychosocial health (F(3, 292) ¼ 4.26, p ¼.006) and overall QOL (F(3, 292) ¼ 5.15, p ¼.002) among adolescents. Post hoc test revealed that Indian adolescents had significantly better overall QOL (mean ¼ , SD ¼ 16.70) compared to Malay (mean ¼ 74.81, SD ¼ 13.33) and Chinese (mean ¼ 79.66, SD ¼ 12.30). In addition to this, Indian adolescents had significantly better psychosocial health (mean ¼ 80.10, SD ¼ 19.09) compared to Malay (mean ¼ 71.02, SD ¼ 14.54) and Chinese (mean ¼ 75.31, SD ¼ 14.55).

7 Ainuddin et al. 235 Discussion This study reveals the reliability and validity of the self-report Malay version of the PedsQL 4.0 Generic Core Scales in measuring HRQOL of 13- to 17-year-old multiethnic Malaysian adolescents. The internal consistency of the Malay version of the PedsQL 4.0 Generic Core Scale was good. The figures are similar to the study of Varni et al. (2001). Furthermore, a study by Abdul Rahman et al. (2011) also showed good internal consistency in children with disabilities, in Kelantan, Malaysia. Our findings show that the highest Cronbach s a was in emotional functioning, with a value of.82. Our findings also concurred with the UK English and Abdul Rahman et al. (2011) studies in which school functioning had the lowest internal consistency. Construct validity showed a six-factor structure. The results are in line with a study by Panepinto et al. (2008) in which the authors also reported a six-factor structure. However, the six factors were slightly different from what this study has concluded. In this study, the emotional and social functioning items loaded predominantly for two of the factors (first and third factor) in our factor analysis. Physical and school functioning loaded into two different factors each. The second factor consisted of questions 1 4 and questions 7 8 of physical functioning, while the remaining two questions (no 5 and 6) loaded in the sixth factor. These questions asked about bathing/shower and house chores. The participants may not think these questions as physical functioning questions but more as daily activities. Adolescents having problems in this aspect of QOL should further be evaluated by occupational therapist who deals with the activities of daily living. On the other hand, questions 1 3 of school functioning loaded into the fourth factor and questions 4 and 5 loaded into the fifth factor. The results were similar to the results of Varni et al. (2001). A natural explanation for this could be that the first three items that were related to school functioning mostly have a cognitive component, while the others are more related to physical aspects (Reinfjell et al., 2006). Furthermore, the items also emphasized on medical aspects such as not feeling well and going to the hospital. Factor correlations in Table 4 show low values among its factors, indicating discriminant validity of the instrument. Hair et al. (2006) stated that the variance-extracted estimates should be larger than the squared correlation estimate, and Fornell and Larcker (1981), furthermore, put forward that for two factors, A and B, the average variance extracted (AVE) for A and the AVE for B both need to be greater than the shared variance (i.e. square of the correlation) between A and B. Therefore, items should relate more strongly to their own factor than to another factor. Accordingly, the results show factor correlation values lower than the factor loading values of the items, indicating that the instrument measures distinct factors of QOL in adolescents. Difference in functioning and overall QOL was noted among the adolescents age, gender, and race. Regarding gender, female adolescents reported lower emotional functioning. This is similar to other studies in terms of gender differences in emotional health (Compas et al., 1993; Reinfjell et al., 2006; Rosenfield et al., 2000). In terms of age, it could be seen that 17-year-old adolescents had higher overall QOL than 13-yearold adolescents do. This result contradicted with Ng et al. s study (2005) in which they reported QOL of adolescents from Singapore, Japan, and Beijing. It was noted that the QOL of Singapore and Beijing adolescents declined when they were years of age but increased when they were 15 years old. Furthermore, in Japanese adolescents, all QOL scores clearly declined linearly with increasing age, from 12 to 15 years. With ethnicity, it could be seen that Indian adolescents had better overall QOL and psychosocial functioning when compared with Malay and Chinese adolescents. According to Ng et al. (2005), they had similar results in which Singaporean Indian adolescents also had the highest overall QOL in comparison with Singaporean Chinese and Malay adolescents. In particular, they also reported that Indian adolescents had a significantly higher

8 236 Journal of Child Health Care 19(2) psychosocial QOL scores than Chinese. Ng et al. (2005) further reported that socioeconomic and other factors partly explain these differences, but substantial ethnic differences remained after accounting for the influence of these factors. Implication for policy, practice, and future research The influence of ethnicity on health and HRQOL is complex and is mediated by a number of factors (Anderson and Armstead, 1995; Meyerowitz et al., 1998). Ethnic variances in HRQOL also reflect sociocultural influences particularly on the view of health and illness, the trait of symptoms, the theory of QOL and expectations of care as well as behavior patterns, emotional experiences, and cognitive evaluations (Schmidt and Bullinger, 2003). The knowledge we have gained from research on a specific pediatric QOL inventory that can be utilized across a variety of ethnic groups can be helpful in many ways. With this information, we can evaluate adolescents from different cultural backgrounds and examine its influencing factors on their QOL. Health policies can include a variety of approaches in health promotion and prevention according to the ethnicity of the population. For future research, it is suggested that the self-report Malay version of the PedsQL 4.0 Generic Core Scales be evaluated for test retest reliability using a larger multiethnic adolescent sample with comparison between both healthy and acute or chronic health adolescents. Limitations Concerning this study, the present findings have several potential limitations. First, the study sample is unlikely to be representative of the general adolescent population in Malaysia. Second, at the school level, we opt for convenience sampling in order to improve participation rate. As a result, there was an underrepresentation of the ethnic groups. Furthermore, information on the participants that dropped out was not available, which can restrict the generalizability of the study. Finally, this study did not differentiate HRQOL between healthy adolescents and adolescents with chronic health conditions as compared to the American validation study (Reinfjell et al., 2006). Conclusion The self-report Malay version of the PedsQL 4.0 Generic Core Scales is a valid and reliable tool that can be used in assessing QOL of multiethnic Malaysian adolescents. Acknowledgments We thank Dr James W Varni for the permission to use the self-report Malay version of PedsQL 4.0 Generic Core Scales in this study. We are also grateful to the Ministry of Education, Malaysia, for the approval of this study and a sincere gratitude to the students and staff of SMDU, a secondary school in Kuala Lumpur. Conflict of interest The authors declared no conflicts of interest. Funding The study was supported by UMRG371/11HTM, University of Malaya, Malaysia.

9 Ainuddin et al. 237 References Abdul Rahman A, Ariffin NH, Musa KI, Wan Ibrahim WP, Ibrahim MI, Othman A, et al. (2011) A preliminary study on the reliability of the Malay version of the quality of life inventory TM version 4.0 (PedsQL) Generic Core Scales among children with disabilities in Kelantan, Malaysia: Parent-proxy report. International Journal of Collaborative Research on Internal Medicine and Public Health 3(8): Anderson NB and Armstead CA (1995) Towards understanding the association of socioeconomic status and health: A new challenge for the biophysical approach. Psychosomatic Medicine 57: Berkes A, Pataki I, Kiss M, Kemeny C, Kardos L, Varni JW, et al. (2010) Measuring health-related quality of life in Hungarian children with heart disease: Psychometric properties of the Hungarian version of the Pediatric Quality of Life Inventory 4.0 Generic Core Scales and the Cardiac Module. Health and Quality of Life Outcomes 8: 14. Buck D (2012) The PedsQL as a measure of parent-rated quality of life in healthy UK toddlers: Psychometric properties and cross-cultural comparisons. Journal of Child Health 16: 331. Chen X, Origasa H, Ichida F, Kamibeppu K and Varni JW (2007) Reliability and validity of the pediatric quality of life inventory (PedsQL) short form 15 generic core scales in Japan. Quality Of Life Research 16(7): Cohen J (1988) Statistical Power Analysis for the Behavioural Sciences. New York, NY: Academic Press. Compas BE, Orosan PG and Grant KE (1993) Adolescent stress and coping-implication for psychopathology during adolescence. Journal of Adolescence 16: Eiser C and Morse R (2001) The measurement of quality of life in children: Past and future perspectives. Journal of Developmental Behavior Pediatrics 22: Felder-Puig R, Baumgartner M, Topf R, Gadner H and Formann AK (2008) Health-related quality of life in Austrian elementary school children. Medical Care 46(4): Fornell C and Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18(1): Ganz PA (1995) Impact of quality of life outcomes on clinical practice. Oncology 9(11): George D and Mallery P (2003) SPSS for Windows Step by Step: A Simple Guide and Reference. (4th ed.) Boston, MA: Allyn & Bacon. Gkoltsiou K, Dimitrakaki C, Tzavara C, Papaevangelou V, Varni JW and Tountas Y (2008) Measuring health-related quality of life in Greek children: Psychometric properties of the Greek version of the Pediatric Quality of Life Inventory 4.0 Generic Core Scales. Quality Of Life Research 17(2): Goldstein SL, Rosburg NM, Warady BA, Seikaly M, Mcdonald R, Limbers C, et al. (2009) Pediatric end stage renal disease health-related quality of life differs by modality: A PedsQL ESRD analysis. Pediatric Nephrology 24(8): Hair JF, Anderson RE, Tatham RL and Black WC (1998) Multivariate Data Analysis. 5th ed. Upper Saddle River, NJ: Prentice-Hall. Hair JF, Black WC, Babin BJ, Anderson RE and Tatham RL (2006) Multivariate Data Analysis (6th ed.). New Jersey, NJ: Pearson-Prentice Hall. Matza LS, Swensen AR, Flood EM, Secnik K and Leidy NK (2004) Assessment of health-related quality of life in children: A review of conceptual, methodological, and regulatory issue. Value in Health 7: Meyerowitz BE, Richardson J, Hudson S and Leedham B (1998) Ethnicity and cancer outcomes: Behavioural and psychological considerations. Psychology Bulletin 13(1): Ng TP, Lim LCC, Jin A and Shinfuku N (2005) Ethnic differences in quality of life in adolescents among Chinese, Malay and Indians in Singapore. Quality of Life Research 14:

10 238 Journal of Child Health Care 19(2) Noll RB, Garstein MA, Vannatta K, Corell J, Bukowski WM and David WH (1999) Social, emotional and behavioral functioning of children with cancer. Pediatrics 103: Pal DK (1996) Quality of life assessment in children: A review of conceptual and methodological issues in multidimensional health status measures. Journal of Epidemiology and Community Health 50(4): Panepinto JA, Pajewski NM, Foerster LM and Hoffmann RG (2008) The performance of the PedsQL Generic Core Scales in children with sickle cell disease. Journal of Pediatric Hematology and Oncology 30(9): Pilker B (1996) Quality of Life and Pharmacoeconomics in Clinical Trials. 2nd ed. New York, NY: Raven Press. Reinfjell T, Diseth TH, Veenstra M and Vikan A (2006) Measuring health-related quality of life in young adolescents: Reliability and validity in the Norwegian version of the Pediatric Quality of Life Inventory 4.0 (PedsQL) generic core scales. Health and Quality of Life Outcomes 4: 61. Rosenfield S, Vertefuille J and McAlpine DD (2000) Gender stratification and mental health: An exploratory of dimensions of the self. Social Psychology 63: Schmidt S and Bullinger M (2003) Current issues in cross-cultural quality of life instrument development. Archieve Physical Medicine Rehabilitation 84(4): Tabachnick BG and Fidell LS (2001) Using Multivariate Statistics. 4th ed. Boston, MA: Allyn & Bacon. Testa MA and Simonson DC (1996) Assessment of quality of life outcome. New England Journal of Medicine 334: Varni JW, Seid M and Rode C (1999) The PedsQL: Measurement model for the pediatric quality of life inventory. Medical Care 37(2): Varni JW, Seid M and Kurtin PS (2001). The pedsql 4.0 TM: reliability and validity of the pediatric quality of life inventorytm version 4.0 generic core scales in healthy and patient populations. Medical Care 39: Varni JW, Burwinkle TM, Katz ER, Meeske K and Dickinson P (2002) The PedsQL in pediatric cancer: Reliability and validity of the Pediatric Quality of Life Inventory Generic Core Scales, Multidimensional Fatigue Scale and Cancer Module. Cancer 94(7): Varni JW, Burwinkle TM, Seid M and Skarr D (2003) The PedsQL 4.0 as a pediatric population health measure: Feasibility, reliability and validity. Ambulatory Pediatrics 3(6): Varni JW, Burwinkle TM, Berrin SJ, Sherman SA, Artavia K, Malcarne VL, et al. (2006) The PedsQL in pediatric cerebral palsy: Reliability, validity and sensitivity of the Generic Core Scales and Cerebral Palsy Module. Developmental Medicine and Child Neurology 48(6): Varni JW, Burwinkle TM, Limbers CA and Szer IS (2007) The PedsQL as a patient-reported outcome in children and adolescents with fibromyalgia: An analysis of OMERACT domains. Health and Quality of Life Outcomes 5: 9. Varni JW, Limbers CA, Bryant WP and Wilson DP (2009a) The PedsQL multidimensional fatigue scale in type 1 diabetes: Feasibility, reliability, and validity. Pediatric Diabetes 10(5): Varni JW, Seid M, Smith Knight T, Burwinkle T, Brown J and Szer IS (2009b) The PedsQL in pediatric rheumatology: Reliability, validity and responsiveness of the Pediatric Quality of Life Inventory Generic Core Scales and Rheumatology Module. Arthritis and Rheumatism 46(3): World Health Organization (WHO) (1948) Constitution of the World Health Organization: Basic Document. Geneva, Switzerland: WHO. Zahran HS, Kobau R, Moriarty DG, Zack MM, Holt J and Donehoo R (2005) Health-related quality of life surveillance United States, Morbidity and Mortality Weekly Report Surveillance Summaries/CDC 54(4): 1 35.