Skip to main content
Mijn bibliotheek
Shop
Nieuws Boeken Tijdschriften E-learning Web-tv Video Audio
Tips voor Mijn BSL
UITGEBREID ZOEKEN
Inloggen
Top
Quality of Life Research
Gepubliceerd in: Quality of Life Research 9/2013

01-11-2013

Factorial invariance of pediatric patient self-reported fatigue across age and gender: a multigroup confirmatory factor analysis approach utilizing the PedsQL™ Multidimensional Fatigue Scale

Auteurs: James W. Varni, A. Alexander Beaujean, Christine A. Limbers

Gepubliceerd in: Quality of Life Research | Uitgave 9/2013

Log in om toegang te krijgen
share
DELEN

Deel dit onderdeel of sectie (kopieer de link)

  • Optie A:
    Klik op de rechtermuisknop op de link en selecteer de optie “linkadres kopiëren”
  • Optie B:
    Deel de link per e-mail
    Link delen
publish
Publiceer nu

Abstract

Objectives

In order to compare multidimensional fatigue research findings across age and gender subpopulations, it is important to demonstrate measurement invariance, that is, that the items from an instrument have equivalent meaning across the groups studied. This study examined the factorial invariance of the 18-item PedsQL™ Multidimensional Fatigue Scale items across age and gender and tested a bifactor model.

Methods

Multigroup confirmatory factor analysis (MG-CFA) was performed specifying a three-factor model across three age groups (5–7, 8–12, and 13–18 years) and gender. MG-CFA models were proposed in order to compare the factor structure, metric, scalar, and error variance across age groups and gender. The analyses were based on 837 children and adolescents recruited from general pediatric clinics, subspecialty clinics, and hospitals in which children were being seen for well-child checks, mild acute illness, or chronic illness care.

Results

A bifactor model of the items with one general factor influencing all the items and three domain-specific factors representing the General, Sleep/Rest, and Cognitive Fatigue domains fit the data better than oblique factor models. Based on the multiple measures of model fit, configural, metric, and scalar invariance were found for almost all items across the age and gender groups, as was invariance in the factor covariances. The PedsQL™ Multidimensional Fatigue Scale demonstrated strict factorial invariance for child and adolescent self-report across gender and strong factorial invariance across age subpopulations.

Conclusions

The findings support an equivalent three-factor structure across the age and gender groups studied. Based on these data, it can be concluded that pediatric patients across the groups interpreted the items in a similar manner regardless of their age or gender, supporting the multidimensional factor structure interpretation of the PedsQL™ Multidimensional Fatigue Scale.
vorige artikel Development and validation of the positive affect and well-being scale for the neurology quality of life (Neuro-QOL) measurement system
volgende artikel Strengthening the assessment of factorial invariance across population subgroups: a commentary on Varni et al. (2013)
Literatuur
1.
Varni, J. W., & Limbers, C. A. (2009). The Pediatric Quality of Life Inventory™: Measuring pediatric health-related quality of life from the perspective of children and their parents. Pediatric Clinics of North America, 56, 843–863.PubMedCrossRef
2.
Eiser, C. (2004). Use of quality of life measures in clinical trials. Ambulatory Pediatrics, 4, 395–399.PubMedCrossRef
3.
Varni, J. W., Handen, B. L., Corey-Lisle, P. K., Guo, Z., Manos, G., Ammerman, D. K., et al. (2012). Effect of Aripiprazole 2 to 15 mg/d on health-related quality of life in the treatment of irritability associated with Autistic Disorder in children: A post hoc analysis of two controlled trials. Clinical Therapeutics, 34, 980–992.PubMedCrossRef
4.
FDA. (2009). Guidance for industry: Patient-reported outcome measures: Use in medical product development to support labeling claims: Food and Drug Administration, U.S. Department of Health and Human Services, Rockville, MD.
5.
Varni, J. W., Seid, M., & Rode, C. A. (1999). The PedsQL™: Measurement model for the Pediatric Quality of Life Inventory. Medical Care, 37, 126–139.PubMedCrossRef
6.
Varni, J. W., Seid, M., & Kurtin, P. S. (2001). PedsQL™ 4.0: Reliability and validity of the Pediatric Quality of Life Inventory™ version 4.0 Generic Core Scales in healthy and patient populations. Medical Care, 39, 800–812.PubMedCrossRef
7.
Varni, J. W., Limbers, C. A., & Burwinkle, T. M. (2007). How young can children reliably and validly self-report their health-related quality of life?: An analysis of 8,591 children across age subgroups with the PedsQL™ 4.0 Generic Core Scales. Health and Quality of Life Outcomes, 5(1), 1–13.PubMedCrossRef
8.
Sweeting, H. (1995). Reversals of fortune?: Sex differences in health in childhood and adolescence. Social Science and Medicine, 40, 77–90.PubMedCrossRef
9.
Arrington-Sanders, R., Yi, M. S., Tsevat, J., Wilmott, R. W., Mrus, J. M., & Britto, M. T. (2006). Gender differences in health-related quality of life of adolescents with cystic fibrosis. Health and Quality of Life Outcomes, 4(5), 1–8.
10.
Jorngarden, A., Wettergen, L., & von Essen, L. (2006). Measuring health-related quality of life in adolescents and young adults: Swedish normative data for the SF-36 and the HADS, and the influence of age, gender, and method of administration. Health and Quality of Life Outcomes, 4(91), 1–10.
11.
Reinfjell, T., Diseth, T. H., Veenstra, M., & 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), 1–9.
12.
Upton, P., Eiser, C., Cheung, I., Hutchings, H. A., Jenney, M., Maddocks, A., et al. (2005). 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, 3(22), 1–7.
13.
Vaez, M., & Laflamme, L. (2003). Health behaviors, self-rated health, and quality of life: A study among first-year Swedish university students. Journal of American College Health, 51, 156–162.PubMedCrossRef
14.
Meredith, W., & Teresi, J. A. (2006). An essay on measurement and factorial invariance. Medical Care, 44, S69–S77.PubMedCrossRef
15.
Gregorich, S. E. (2006). Do self-report instruments allow meaningful comparisons across diverse population groups?: Testing measurement invariance using the confirmatory factor analysis framework. Medical Care, 44, S78–S94.PubMedCrossRef
16.
Varni, J. W., Stucky, B. D., Thissen, D., DeWitt, E. M., Irwin, D. E., Lai, J. S., et al. (2010). PROMIS Pediatric Pain Interference Scale: An item response theory analysis of the pediatric pain item bank. Journal of Pain, 11, 1109–1119.PubMedCrossRef
17.
French, B., & Finch, W. (2006). Confirmatory factor analytic procedures for the determination of measurement invariance. Structural Equation Modeling, 13, 378–402.CrossRef
18.
Teresi, J. A. (2006). Overview of quantitative measurement methods: Equivalence, invariance, and differential item functioning in health applications. Medical Care, 44, S39–S49.PubMedCrossRef
19.
Vandenberg, R., & Lance, C. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4–70.CrossRef
20.
Little, T. D. (1997). Mean and covariance structures (MACS) analyses of cross-cultural data: Practical and theoretical issues. Multivariate Behavioral Research, 32, 53–76.CrossRef
21.
Limbers, C. A., Newman, D. A., & Varni, J. W. (2008). Factorial invariance of child self-report across healthy and chronic health condition groups: A confirmatory factor analysis utilizing the PedsQL™ 4.0 Generic Core Scales. Journal of Pediatric Psychology, 33, 630–639.PubMedCrossRef
22.
Limbers, C. A., Newman, D. A., & Varni, J. W. (2008). Factorial invariance of child self-report across socioeconomic status groups: A multigroup confirmatory factor analysis utilizing the PedsQL™ 4.0 Generic Core Scales. Journal of Behavioral Medicine, 31, 401–411.PubMedCrossRef
23.
Limbers, C. A., Newman, D. A., & Varni, J. W. (2009). Factorial invariance of child self-report across race/ethnicity groups: A multigroup confirmatory factor analysis approach utilizing the PedsQL™ 4.0 Generic Core Scales. Annals of Epidemiology, 19, 575–581.PubMedCrossRef
24.
Varni, J. W., Limbers, C. A., & Newman, D. A. (2009). Using factor analysis to confirm the validity of children’s self-reported health-related quality of life across different modes of administration. Clinical Trials, 6, 185–195.PubMedCrossRef
25.
Varni, J. W., Limbers, C. A., Newman, D. A., & Seid, M. (2008). Longitudinal factorial invariance of the PedsQL™ 4.0 Generic Core Scales child self-report version: One year prospective evidence from the California State Children’s Health Insurance Program (SCHIP). Quality of Life Research, 17, 1153–1162.PubMedCrossRef
26.
Newman, D. A., Limbers, C. A., & Varni, J. W. (2010). Factorial invariance of child self-report across English and Spanish language groups in a Hispanic population utilizing the PedsQL™ 4.0 Generic Core Scales. European Journal of Psychological Assessment, 26, 194–202.CrossRef
27.
Limbers, C. A., Newman, D. A., & Varni, J. W. (2008). Factorial invariance of child self-report across age subgroups: A confirmatory factor analysis of ages 5 to 16 years utilizing the PedsQL™ 4.0 Generic Core Scales. Value in Health, 11, 659–668.PubMedCrossRef
28.
Varni, J. W., Limbers, C. A., & Newman, D. A. (2008). Factorial invariance of the PedsQL™ 4.0 Generic Core Scales child self-report across gender: A multigroup confirmatory factor analysis with 11,356 children ages 5 to 18. Applied Research in Quality of Life, 3, 137–148.CrossRef
29.
Varni, J. W., Burwinkle, T. M., Katz, E. R., Meeske, K., & 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, 2090–2106.PubMedCrossRef
30.
Varni, J. W., Burwinkle, T. M., & Szer, I. S. (2004). The PedsQL™ Multidimensional Fatigue Scale in pediatric rheumatology: Reliability and validity. Journal of Rheumatology, 31, 2494–2500.PubMed
31.
Varni, J. W., Burwinkle, T. M., Limbers, C. A., & Szer, I. S. (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), 1–12.PubMedCrossRef
32.
Palmer, S. N., Meeske, K. A., Katz, E. R., Burwinkle, T. M., & Varni, J. W. (2007). The PedsQL™ Brain Tumor Module: Initial reliability and validity. Pediatric Blood & Cancer, 49, 287–293.CrossRef
33.
Marcus, S. B., Strople, J. A., Neighbors, K., Weissberg-Benchell, J., Nelson, S. P., Limbers, C. A., et al. (2009). Fatigue and health-related quality of life in pediatric inflammatory bowel disease. Clinical Gastroenterology and Hepatology, 7, 554–561.PubMedCrossRef
34.
Gold, J. I., Mahrer, N. E., Yee, J., & Palermo, T. M. (2009). Pain, fatigue, and health-related quality of life in children and adolescents with chronic pain. Clinical Journal of Pain, 25, 407–412.PubMedCrossRef
35.
Varni, J. W., Limbers, C. A., Bryant, W. P., & Wilson, D. P. (2009). The PedsQL™ Multidimensional Fatigue Scale in type 1 diabetes: Feasibility, reliability, and validity. Pediatric Diabetes, 10, 321–328.PubMedCrossRef
36.
Varni, J. W., Limbers, C. A., Bryant, W. P., & Wilson, D. P. (2010). The PedsQL™ Multidimensional Fatigue Scale in pediatric obesity: Feasibility, reliability, and validity. International Journal of Pediatric Obesity, 5, 34–42.PubMedCrossRef
37.
Varni, J. W., Limbers, C. A., Bryant, W. P., & Wilson, D. P. (2012). Assessment of fatigue in pediatric patients with short stature utilizing the PedsQL™ Multidimensional Fatigue Scale. Children’s Health Care, 41, 162–181.CrossRef
38.
Pohl, J. F., Limbers, C. A., Kay, M. T., Harman, A., Rollins, M., & Varni, J. W. (2012). Health-related quality of life in pediatric patients with long-standing pancreatitis. Journal of Pediatric Gastroenterology and Nutrition, 54, 657–663.PubMedCrossRef
39.
MacAllister, W. S., Christodoulou, C., Troxell, R., Milazzo, M., Block, P., Preston, T. E., et al. (2009). Fatigue and quality of life in pediatric multiple sclerosis. Multiple Sclerosis, 15, 1502–1508.PubMedCrossRef
40.
Varni, J. W., & Limbers, C. A. (2008). The PedsQL™ Multidimensional Fatigue Scale in young adults: Feasibility, reliability and validity in a university student population. Quality of Life Research, 17, 105–114.PubMedCrossRef
41.
Robert, R. S., Paxton, R. J., Palla, S. L., Yang, G., Askins, M. A., Joy, S. E., et al. (2012). Feasibility, reliability, and validity of the Pediatric Quality of Life Inventory™ Generic Core Scales, Cancer Module, and Multidimensional Fatigue Scale in long-term adult survivors of pediatric cancer. Pediatric Blood & Cancer, 59, 703–707.CrossRef
42.
Gordijn, M. S., Cremers, E. M., Kaspers, G. J., & Gemke, R. J. (2011). Fatigue in children: Reliability and validity of the Dutch PedsQL™ Multidimensional Fatigue Scale. Quality of Life Research, 20, 1103–1108.PubMedCrossRef
43.
Kobayashi, K., Okano, Y., & Hohashi, N. (2011). Reliability and validity of the PedsQL™ Multidimensional Fatigue Scale in Japan. Quality of Life Research, 20, 1091–1102.PubMedCrossRef
44.
Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8, 430–457.CrossRef
45.
Aday, L. A. (1996). Designing and conducting health surveys: A comprehensive guide (2nd ed.). San Francisco: Jossey-Bass.
46.
Fowler, F. J. (1995). Improving survey questions: Design and evaluation. Thousand Oaks, CA: Sage.
47.
Schwarz, N., & Sudman, N. (Eds.). (1996). Answering questions: Methodology for determining cognitive and communicative processes in survey research. San Francisco: Jossey-Bass.
48.
Ringold, S., Wallace, C. A., & Rivara, F. P. (2009). Health-related quality of life, physical function, fatigue, and disease activity in children with established polyarticular juvenile idiopathic arthritis. Journal of Rheumatology, 36, 1330–1336.PubMedCrossRef
49.
Dampier, C., Lieff, S., Lebeau, P., Rhee, S., McMurray, M., Rogers, Z., et al. (2010). Health-related quality of life in children with sickle cell disease: A report from the Comprehensive Sickle Cell Centers Clinical Trial Consortium. Pediatric Blood & Cancer, 55, 485–494.CrossRef
50.
Fairclough, D. L. (2002). Design and analysis of quality of life studies in clinical trials: Interdisciplinary statistics. New York: Chapman & Hall/CRC.
51.
Millsap, R. E. (2011). Statistical approaches to measurement invariance. New York: Routledge.
52.
Chen, F. F., West, S. G., & Sousa, K. H. (2006). A comparison of bifactor and second-order models of quality of life. Multivariate Behavioral Research, 41, 189–225.CrossRef
53.
Reise, S., Morizot, J., & Hays, R. (2007). The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of Life Research, 16, 19–31.PubMedCrossRef
54.
Reise, S. P., Moore, T. M., & Haviland, M. G. (2010). Bifactor models and rotations: Exploring the extent to which multidimensional data yield univocal scale scores. Journal of Personality Assessment, 92, 544–559.PubMedCrossRef
55.
Byrne, B. M., & Stewart, S. M. (2006). The MACS approach to testing for multigroup invariance of a second-order structure: A walk through the process. Structural Equation Modeling, 13(2), 287–321.CrossRef
56.
Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255.CrossRef
57.
Meade, A. W., Johnson, E. C., & Braddy, P. W. (2008). Power and sensitivity of alternative fit indices in tests of measurement invariance. Journal of Applied Psychology, 93(3), 568–592.PubMedCrossRef
58.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246.PubMedCrossRef
59.
McDonald, R. P. (1989). An index of goodness-of-fit based on noncentrality. Journal of Classification, 6(1), 97–103.CrossRef
60.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.CrossRef
61.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.
62.
Bentler, P. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.PubMedCrossRef
63.
McDonald, R. P. (1989). An index of goodness-of-fit based on noncentrality. Journal of Classification, 6, 97–103.CrossRef
64.
Bentler, P. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software.
65.
Marsh, H. W., Hau, K.-T., & Grayson, D. (2005). Goodness of fit in structural equation models. In A. Maydeu-Olivares & J. J. McArdle (Eds.), Contemporary psychometrics: A festschrift for Roderick P. McDonald (pp. 275–340). Mahwah, NJ: Lawrence Erlbaum.
66.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.CrossRef
67.
Sivo, S. A., Xitao, F., Witta, E. L., & Willse, J. T. (2006). The search for “optimal” cutoff properties: Fit index criteria in structural equation modeling. Journal of Experimental Education, 74(3), 267–288.CrossRef
68.
Yu, C.-Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. Unpublished Doctoral Dissertation, University of California, Los Angeles. Available from ProQuest Dissertations and Theses (AAT 3066425).
69.
Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling, 11(3), 320–341.CrossRef
70.
Team, R. D. C. (2011). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
71.
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36.
72.
Revelle, W. (2012). Psych: Procedures for psychological, psychometric, and personality research (Version 1.2.4) [Computer software]. Evanston, IL: Northwestern University.
73.
Green, S. B., Akey, T. M., Fleming, K. K., Hershberger, S. L., & Marquis, J. G. (1997). Effect of the number of scale points on Chi square fit indices in confirmatory factor analysis. Structural Equation Modeling, 4, 108–120.CrossRef
74.
West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with non-normal variables: Problems and remedies. In R. A. Hoyle (Ed.), Structural equation modeling: Concepts, issues and applications (pp. 56–75). Newbury Park, CA: Sage.
75.
Bentler, P. M., & Chou, C. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16, 78–117.CrossRef
76.
Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57, 519–530.CrossRef
77.
Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185.PubMedCrossRef
78.
Velicer, W. F., Eaton, C. A., & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In R. D. Goffin & E. Helmes (Eds.), Problems and solutions in human assessment: Honoring Douglas Jackson at seventy (pp. 41–71). Boston: Kluwer.
79.
Osborne, J. W., Costello, A. B., & Kellow, T. J. (2008). Best practices in exploratory factor analysis. In J. W. Osborne (Ed.), Best practices in quantitative methods (pp. 86–99). Los Angeles: Sage.CrossRef
80.
Garrido, L. E., Abad, F. J., & Ponsoda, V. (2011). Performance of Velicer’s minimum average partial factor retention method with categorical variables. Educational and Psychological Measurement, 71, 551–570.CrossRef
81.
Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Thousands Oaks, CA: Sage.
82.
Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16–29.CrossRef
83.
Enders, C. K. (2001). The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods, 6, 352–370.PubMedCrossRef
84.
Zinbarg, R. E., Revelle, W., Yovel, I., & Li, W. (2005). Cronbach’s α, Revelle’s β, and Mcdonald’s ωh: Their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70, 123–133.CrossRef
85.
Raju, N. S., Laffitte, L. J., & Byrne, B. M. (2002). Measurement equivalence: A comparison of methods based on confirmatory factor analysis and item response theory. Journal of Applied Psychology, 87, 517–529.PubMedCrossRef
86.
American Educational Research Association, A. P. A., & National Council on Measurement in Education. (1999). American Educational Research Association, American Psychological Association, & National Council on Measurement in Education Standards for educational and psychological testing. Washington, DC.
87.
ter Wolbeek, M., van Doornen, L. J. P., Kavelaars, A., & Heijnen, C. J. (2006). Severe fatigue in adolescents: A common phenomenon? Pediatrics, 117, e1078–e1086.PubMedCrossRef
88.
Viner, R. M., Clark, C., Taylor, S. J. C., Bhui, K., Klineberg, E., Head, J., et al. (2008). Longitudinal risk factors for persistent fatigue in adolescents. Archives of Pediatrics and Adolescent Medicine, 162, 469–475.PubMedCrossRef
89.
ter Wolbeek, M., van Doornen, L. J. P., Kavelaars, A., & Heijnen, C. J. (2008). Predictors of persistent and new-onset fatigue in adolescent girls. Pediatrics, 121, e449–e457.PubMedCrossRef
90.
Reise, S. P., Bonifay, W. E., & Haviland, M. G. (2013). Scoring and modeling psychological measures in the presence of multidimensionality. Journal of Personality Assessment, 95, 129–140.
91.
Satorra, A., & Bentler, P. M. (2001). A scaled difference Chi square test statistic for moment structure analysis. Psychometrika, 66, 507–514.CrossRef
Metagegevens
Titel
Factorial invariance of pediatric patient self-reported fatigue across age and gender: a multigroup confirmatory factor analysis approach utilizing the PedsQL™ Multidimensional Fatigue Scale
Auteurs
James W. Varni
A. Alexander Beaujean
Christine A. Limbers
Publicatiedatum
01-11-2013
Uitgeverij
Springer Netherlands
Gepubliceerd in
Quality of Life Research / Uitgave 9/2013
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-013-0370-4

Andere artikelen Uitgave 9/2013

Quality of Life Research 9/2013 Naar de uitgave

OriginalPaper

Quality-of-life loss of people admitted to burn centers, United States

OriginalPaper

Can pain and function be distinguished in the Oxford Knee Score in a meaningful way? An exploratory and confirmatory factor analysis

Review

Measurement properties of the QuickDASH (Disabilities of the Arm, Shoulder and Hand) outcome measure and cross-cultural adaptations of the QuickDASH: a systematic review

Review

Does providing feedback on patient-reported outcomes to healthcare professionals result in better outcomes for patients? A systematic review

OriginalPaper

Psychometric evaluation of the CASP-19 quality of life scale in an older Irish cohort

OriginalPaper

Validation of a Dutch version of the Neurological Fatigue Index (NFI-MS) for patients with multiple sclerosis in the Netherlands