Logo of springeropenLink to Publisher's site
Quality of Life Research
Qual Life Res. 2011 Sep; 20(7): 1103–1108.
Published online 2011 Jan 19. doi:  10.1007/s11136-010-9836-9
PMCID: PMC3161196

Fatigue in children: reliability and validity of the Dutch PedsQLTM Multidimensional Fatigue Scale



The aim of the study is to report on the feasibility, reliability, validity, and the norm-references of the Dutch version of the PedsQLTM Multidimensional Fatigue Scale.


The study participants are four hundred and ninety-seven parents of children aged 2–18 years and 366 children aged 5–18 years from various day care facilities, elementary schools, and a high school who completed the Dutch version of the PedsQLTM Multidimensional Fatigue Scale.


The number of missing items was minimal. All scales showed satisfactory internal consistency reliability, with Cronbach’s coefficient alpha exceeding 0.70. Test–retest reliability was good to excellent (ICCs 0.68–0.84) and inter-observer reliability varied from moderate to excellent (ICCs 0.56–0.93) for total scores. Parent/child concordance for total scores was poor to good (ICCs 0.25–0.68). The PedsQLTM Multidimensional Fatigue Scale was able to distinguish between healthy children and children with an impaired health condition.


The Dutch version of the PedsQLTM Multidimensional Fatigue Scale demonstrates an adequate feasibility, reliability, and validity in another sociocultural context. With the obtained norm-references, it can be utilized as a tool in the evaluation of fatigue in healthy and chronically ill children aged 2–18 years.

Keywords: Pediatrics, Fatigue, Health-related quality of life, Validation study, PedsQLTM


Fatigue is a common symptom in pediatric health conditions and is associated with poorer HRQOL [18]. The Pediatric Quality of Life Inventory (PedsQLTM) Multidimensional Fatigue Scale was designed by Varni and colleagues to measure fatigue in children. The original American version demonstrated adequate reliability and validity [14, 9]. Recently, this questionnaire has been translated into Dutch in accordance with internationally accepted methods [10].

Substantial cultural differences regarding sleep and fatigue in children have been reported, precluding generalization of instruments before assessment in other sociocultural contexts has been performed [1116]. Accordingly, our objectives were to obtain a norm-reference and to test the psychometric properties of the Dutch version of the PedsQLTM Multidimensional Fatigue Scale.

We hypothesized that the reliability and validity of the Dutch version is comparable to the original version. In addition, we expected adolescents to be more fatigued than younger children [17].


The PedsQLTM Multidimensional Fatigue Scale was distributed at day care facilities and schools in urban and suburban areas in the Netherlands, between October 2009 and May 2010. The questionnaire was self-administered, but children aged 5–7 years have been assisted by the researcher. One half of the participants were given the option to receive the questionnaire again after 2 weeks to assess test–retest reliability. The other half received two copies of the questionnaire to be completed independently by both parents to test inter-observer reliability.

The 18-item PedsQLTM Multidimensional Fatigue Scale reflects three subscales: general fatigue (GF), sleep/rest fatigue (SRF), and cognitive fatigue (CF). The questionnaire comprises parallel child self-reports for the ages 5–7 years (young child), 8–12 years (child), and 13–18 years (adolescent) and parent proxy-reports, the latter also including 2–4 years of age (toddler). The participants rated how often a particular problem occurred in the past month, using a 5-point Likert scale and for the young child self-report a 3-point scale. Each item is reverse-scored and rescaled to 0–100 scale, so that higher scores indicate fewer symptoms of fatigue.

Feasibility was evaluated from the percentage of missing answers [18]. Range of measurement was based on the percentage of scores at extremes of the scaling range. Scale internal consistency was assessed by calculating Cronbach’s coefficient alpha [19]. Test–retest and inter-observer reliability and the parent/child concordance were assessed by intraclass correlation coefficients (ICCs) [20]. ICCs were designated as ≤0.40 poor to fair agreement, 0.41–0.60 moderate agreement, 0.61–0.80 good agreement, and 0.81–0.100 excellent agreement [21, 22]. The ability of the questionnaire to distinguish between groups differing in health condition was computed using unpaired t-tests. Calculated effect sizes up to 0.20 were considered to be small, about 0.50 moderate and about 0.80 large [23].

The effect of sociodemographic variables was assessed using linear regression analysis. Within-group differences were assessed by analysis of variance with post hoc Bonferroni correction for age and education and t-tests for gender, country of birth, and family structure. Data were analyzed using SPSS 15.0.1. A P value of <0.05 was accepted as statistically significant.


In total, 1,257 parent reports and 1,000 child reports were distributed, of which 497 and 366 reports were returned, respectively (response rates 40 and 37%). Sociodemographic characteristics of the initial test are presented in Table 1. Most reported chronic health conditions were asthma, allergies, and attention-deficit hyperactivity disorder.

Table 1
Sociodemographic characteristics of sample

PedsQLTM Multidimensional Fatigue Scale scores are summarized in Table 2. Adolescents had more symptoms of GF than the other age ranges (mean difference = 6.20, P < 0.001 for child reports and 5.27, P < 0.001 for parent reports). Boys had more symptoms of CF than girls (mean difference = 3.42, P = 0.0034 for parent reports). Children of immigrants had more problems with SRF compared to children of parents born in the Netherlands (mean difference = 6.12, P = 0.017 for parent reports). Children in a single-parent family had more symptoms of CF (mean difference = 8.29, P = 0.037 for parent reports) and lower total fatigue scores (mean difference = 5.93, P = 0.039 for parent reports) than children living in a two-parent household. Children of low educated parents had more problems with CF (mean difference = 9.35, P = 0.03 for child reports) and lower total fatigue scores (mean difference = 7.17, P = 0.014 for child reports) than children of high educated parents.

Table 2
Scale descriptives for PedsQL Multidimensional Fatigue Scale

Missing responses for all items were rare: 0.2% in parent reports and 0.3% in child reports. No floor effects were detected. Ceiling effects ranged from 1.4% in child reports to 5.1% in parent reports. All child report and parent report scales approached or exceeded a Cronbach’s alpha of 0.70 (range 0.64–0.93) (Table 3). Forty-three children (12%) and 75 parents (15%) performed the retest. The retest response for the young child was too low for evaluation. Child report and parent report test–retest ICCs had moderate to excellent agreement (range 0.50 to 0.85) (Table 4). At group level, no significant differences emerged between the test- and retest, except for GF which was reported to be better after 2 weeks by the parents. Fifty-five participants (11%) returned two questionnaires, completed by both parents. Inter-observer reliability ICCs had poor to excellent agreement (range 0.27–0.93) (Table 5). At group level, there were no significant differences between fathers (n = 169) and mothers (n = 440), except for lower SRF scores reported by the mothers. Parent/child concordance ICCs had poor to good agreement (range 0.10–0.68) (Table 6). At group level, means of SRF, CF, and total fatigue of the parent report were significantly higher compared to the child report.

Table 3
Internal consistency reliability for PedsQL Multidimensional Fatigue Scale
Table 4
Test–retest reliability for PedsQL Multidimensional Fatigue Scale
Table 5
Inter-observer reliability for PedsQL Multidimensional Fatigue Scale
Table 6
Parent child concordance for PedsQL Multidimensional Fatigue Scale

Child report and the parent report total scores and most subscale scores demonstrated a significant difference between the healthy participants (89%) and the participants with an impaired health condition (11%) (Table 7). Effect sizes varied from small to medium, with children with an impaired health condition showing lower scores and thus more fatigue.

Table 7
Construct validity for PedsQL Multidimensional Fatigue Scale


Our cohort showed that adolescence was associated with more fatigue, which might reflect the decrease in sleep duration at that age [16, 17, 24]. Boys reported more fatigue than girls, indicating socially related gender differences [25]. Fatigue was more common in children living in a single-parent family, which might be explained by the higher prevalence of sleep problems in these children [16]. Being a child of an immigrant was associated with more fatigue, stressing the influence of the sociocultural background on fatigue. Children of low educated parents reported more fatigue, corresponding with lower reported HRQOL in populations with low socioeconomic status [26].

The questionnaire was found to be feasible because of the minimal missing item responses. All scales approached or exceeded a Cronbach’s alpha of 0.70, recommended for comparing groups. Parent report total scores approached or exceeded a Cronbach’s alpha of 0.90, recommended for analyzing individual patient scales [27, 28]. Test–retest reliability was low in the toddler version, suggesting that this version should be applied with caution. The low test–retest accordance in adolescents might be a real variation due to the fluctuations in nocturnal sleep duration. Inter-observer agreement was low in toddlers and adolescents. This observation is difficult to explain and requires further exploration. The father/child agreement and the mother/child agreement were low in the young child, the adolescent and the total sample. This may be due to differences in reasoning and response reactions between parent and child [29]. Children scored lower than their parents. The low parent/child concordance (including the weakest agreement for the young child and lower scores reported by the children) has consistently been observed in HRQOL measurement, particularly for internalizing problems [3033]. These findings support the need to measure the perspectives of the child and the parent, since both may influence healthcare utilization. The assumption was confirmed that the questionnaire was able to distinguish between healthy children and children with an impaired health condition. It is expected that a clinical sample with more severely ill children will demonstrate worse fatigue scores and higher effect sizes.

Regarding the current study several limitations need to be mentioned. First, we had low response rates, which might lead to non-response bias. Second, the ethnicity of the participants was rather homogenous; only 2% of the children and 6% of the parents were born outside the Netherlands, compared to 10 and 10%, respectively in the Dutch population [34]. A possible explanation is the language problems that immigrants experienced and therefore decided not to participate in this study. Third, the educational background of our parent respondents showed that 42% were highly educated, compared to 18% of the Dutch population [35]. Highly educated parents might have been more aware of the necessity of this study wherefore more willing to participate. Children from immigrants as well as from low educated parents may experience more fatigue; hence, the obtained norm-reference may underestimate fatigue in the general population. Information on nonparticipants was not available, thus generalization of the norm-results should be made with caution.

In conclusion, the Dutch version of the PedsQLTM Multidimensional Fatigue Scale demonstrates overall adequate psychometric properties in another sociocultural context. With the obtained norm-references, it can be utilized as a tool to evaluate fatigue in children.


We would like to thank all the children and parents who participated in this study. We are also grateful to the day care facilities and schools for making this project possible.

Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.



An erratum to this article can be found at http://dx.doi.org/10.1007/s11136-011-9869-8


1. Varni JW, Burwinkle TM, Katz ER, Meeske K, Dickinson P. 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. 2002;94(7):2090–2106. doi: 10.1002/cncr.10428. [PubMed] [Cross Ref]
2. Varni JW, Burwinkle TM, Limbers CA, Szer IS. The PedsQL as a patient-reported outcome in children and adolescents with fibromyalgia: An analysis of OMERACT domains. Health and Quality of Life Outcomes. 2007;5(9):9. doi: 10.1186/1477-7525-5-9. [PMC free article] [PubMed] [Cross Ref]
3. Varni JW, Burwinkle TM, Szer IS. The PedsQL Multidimensional Fatigue Scale in pediatric rheumatology: Reliability and validity. Journal of Rheumatology. 2004;31(12):2494–2500. [PubMed]
4. Varni JW, Limbers CA, Bryant WP, Wilson DP. The PedsQL Multidimensional Fatigue Scale in type 1 diabetes: Feasibility, reliability, and validity. Pediatric Diabetes. 2009;10(5):321–328. doi: 10.1111/j.1399-5448.2008.00482.x. [PubMed] [Cross Ref]
5. Meeske KA, Patel SK, Palmer SN, Nelson MB, Parow AM. Factors associated with health-related quality of life in pediatric cancer survivors. Pediatric Blood & Cancer. 2007;49(3):298–305. doi: 10.1002/pbc.20923. [PubMed] [Cross Ref]
6. Zebrack BJ, Chesler MA. Quality of life in childhood cancer survivors. Psychooncology. 2002;11(2):132–141. doi: 10.1002/pon.569. [PubMed] [Cross Ref]
7. Zeltzer LK, Recklitis C, Buchbinder D, Zebrack B, Casillas J, Tsao JC, et al. Psychological status in childhood cancer survivors: A report from the Childhood Cancer Survivor Study. Journal of Clinical Oncology. 2009;27(14):2396–2404. doi: 10.1200/JCO.2008.21.1433. [PMC free article] [PubMed] [Cross Ref]
8. Romberg-Camps MJ, Dagnelie PC, Hesselink-van de Kruijs MA, Kester AD, Engels LG, et al. Fatigue and health-related quality of life in inflammatory bowel disease: Results from a population-based study in the Netherlands: The IBD-South Limburg cohort. Inflammatory Bowel Diseases. 2010;16(12):2137–2147. doi: 10.1002/ibd.21285. [PubMed] [Cross Ref]
9. Varni JW, Limbers CA, Bryant WP, Wilson DP. The PedsQL Multidimensional Fatigue Scale in pediatric obesity: Feasibility, reliability and validity. International Journal of Pediatric Obesity. 2008;5(1):34–42. doi: 10.3109/17477160903111706. [PubMed] [Cross Ref]
10. Bullinger M, Alonso J, Apolone G, Leplège A, Sullivan M, Wood-Dauphinee S, et al. Translating health status questionnaires and evaluating their quality: The IQOLA Project approach. International Quality of Life Assessment. Journal of Clinical Epidemiology. 1998;51(11):913–923. [PubMed]
11. Latz S, Wolf AW, Lozoff B. Cosleeping in context: Sleep practices and problems in young children in Japan and the United States. Archives of Pediatrics and Adolescent Medicine. 1999;153(4):339–346. [PubMed]
12. Roberts RE, Roberts CR, Chen IG. Ethnocultural differences in sleep complaints among adolescents. Journal of Nervous and Mental Disease. 2000;188(4):222–229. doi: 10.1097/00005053-200004000-00005. [PubMed] [Cross Ref]
13. Jenni OG, O’Connor BB. Children’s sleep: An interplay between culture and biology. Pediatrics. 2005;115(1 Suppl):204–216. doi: 10.1542/peds.2004-0815B. [PubMed] [Cross Ref]
14. Liu X, Liu L, Owens JA, Kaplan DL. Sleep patterns and sleep problems among schoolchildren in the United States and China. Pediatrics. 2005;115(1 Suppl):241–249. doi: 10.1542/peds.2004-0815F. [PubMed] [Cross Ref]
15. LeBourgeois MK, Giannotti F, Cortesi F, Wolfson AR, Harsh J. The relationship between reported sleep quality and sleep hygiene in Italian and American adolescents. Pediatrics. 2005;115(1 Suppl):257–265. doi: 10.1542/peds.2004-0815H. [PMC free article] [PubMed] [Cross Ref]
16. Litsenburg RR, Waumans RC, Berg G, Gemke RJ. Sleep habits and sleep disturbances in Dutch children: A population-based study. European Journal of Pediatrics. 2010;169(8):1009–1015. doi: 10.1007/s00431-010-1169-8. [PMC free article] [PubMed] [Cross Ref]
17. Varni JW, Limbers CA. The PedsQL Multidimensional Fatigue Scale in young adults: Feasibility, reliability and validity in a University student population. Quality of Life Research. 2008;17(1):105–114. doi: 10.1007/s11136-007-9282-5. [PubMed] [Cross Ref]
18. McHorney CA, Ware JE, Jr, Raczek AE. The MOS 36-item short-form health survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs. Medical Care. 1993;31(3):247–263. doi: 10.1097/00005650-199303000-00006. [PubMed] [Cross Ref]
19. Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16:297–334. doi: 10.1007/BF02310555. [Cross Ref]
20. Deyo RA, Diehr P, Patrick DL. Reproducibility and responsiveness of health status measures. Statistics and strategies for evaluation. Control Clinical Trials. 1991;12(4 Suppl):142S–158S. doi: 10.1016/S0197-2456(05)80019-4. [PubMed] [Cross Ref]
21. Bartko JJ. The intraclass correlation coefficient as a measure of reliability. Psychological Reports. 1966;19(1):3–11. [PubMed]
22. Wilson KA, Dowling AJ, Abdolell M, Tannock IF. Perception of quality of life by patients, partners and treating physicians. Quality of Life Research. 2000;9(9):1041–1052. doi: 10.1023/A:1016647407161. [PubMed] [Cross Ref]
23. Cohen J. Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Erlbaum; 1988.
24. ter Wolbeek M, Doornen LJ, Kavelaars A, Heijnen CJ. Severe fatigue in adolescents: A common phenomenon? Pediatrics. 2006;117(6):e1078–e1086. doi: 10.1542/peds.2005-2575. [PubMed] [Cross Ref]
25. Upton P, Eiser C, Cheung I, Hutchings HA, Jenney M, Maddocks A. Measurement properties of the UK-English version of the Pediatric Quality of Life Inventory 4.0 (PedsQL) generic core scales. Health and Quality of Life Outcomes. 2005;3(22):22. doi: 10.1186/1477-7525-3-22. [PMC free article] [PubMed] [Cross Ref]
26. Varni JW, Burwinkle TM, Seid M. The PedsQL 4.0 as a school population health measure: Feasibility, reliability, and validity. Quality of Life Research. 2006;15(2):203–215. doi: 10.1007/s11136-005-1388-z. [PubMed] [Cross Ref]
27. Nunnally JC, Bernstein IR. Psychometric theory. New York: McGraw-Hill; 1994.
28. Streiner DL, Norman GR. Health measurement scales. A practical guide to their development and use. New York: Oxford University Press; 2003.
29. Davis E, Nicolas C, Waters E, Cook K, Gibbs L, Gosch A, et al. Parent-proxy and child self-reported health-related quality of life: Using qualitative methods to explain the discordance. Quality of Life Research. 2007;16(5):863–871. doi: 10.1007/s11136-007-9187-3. [PubMed] [Cross Ref]
30. Eiser C, Morse R. Can parents rate their child’s health-related quality of life? Results of a systematic review. Quality of Life Research. 2001;10(4):347–357. doi: 10.1023/A:1012253723272. [PubMed] [Cross Ref]
31. Theunissen NC, Vogels TG, Koopman HM, Verrips GH, Zwinderman KA, Verloove-Vanhorick SP, et al. The proxy problem: Child report versus parent report in health-related quality of life research. Quality of Life Research. 1998;7(5):387–397. doi: 10.1023/A:1008801802877. [PubMed] [Cross Ref]
32. Upton P, Lawford J, Eiser C. Parent-child agreement across child health-related quality of life instruments: A review of the literature. Quality of Life Research. 2008;17(6):895–913. doi: 10.1007/s11136-008-9350-5. [PubMed] [Cross Ref]
33. Felder-Puig R, Frey E, Proksch K, Varni JW, Gadner H, Topf R, et al. Validation of the German version of the Pediatric Quality of Life Inventory (PedsQL) in childhood cancer patients off treatment and children with epilepsy. Quality of Life Research. 2004;13(1):223–234. doi: 10.1023/B:QURE.0000015305.44181.e3. [PubMed] [Cross Ref]
34. Bevolking; geslacht, leeftijd, herkomstgroepering en generatie, 1 januari. http://www.stateline.cbs.nl. 2010 [cited July 24, 2010].
35. Beroepsbevolking; behaalde onderwijs naar herkomst geslacht en leeftijd. http://www.stateline.cbs.nl. 2010 [cited August 7, 2010].
PubReader format: click here to try


Save items

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...


  • MedGen
    Related information in MedGen
  • PubMed
    PubMed citations for these articles

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...