Development and psychometric properties of the PROMIS® pediatric fatigue item banks
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This paper reports on the development and psychometric properties of self-reported pediatric fatigue item banks as part of the Patient-Reported Outcomes Measurement Information System (PROMIS).
Candidate items were developed by using PROMIS qualitative methodology. The resulting 39 items (25 tiredness related and 14 energy related) were field tested in a sample that included 3,048 participants aged 8–17 years. We used confirmatory factor analysis (CFA) to evaluate dimensionality and differential item functioning (DIF) analysis to evaluate parameter stability between genders and by age; we examined residual correlations to evaluate local dependence (LD) among items and estimated the parameters of item response theory (IRT) models.
Of 3,048 participants, 48 % were males, 60 % were white, and 23 % had at least one chronic condition. CFA results suggest two moderately correlated factors. Two items were removed due to high LD, and three due to gender-based DIF. Two item banks were calibrated separately using IRT: Tired and (Lack of) Energy, which consisted of 23 and 11 items, respectively; 10- and 8-item short-forms were created.
The PROMIS assessment of self-reported fatigue in pediatrics includes two item banks: Tired and (Lack of) Energy. Both demonstrated satisfactory psychometric properties and can be used for research settings.
KeywordsPROMIS Fatigue Children Item response theory Health-related quality of life Patient-reported outcomes
This work was funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant 1U01AR052181-01. Information on the Patient-Reported Outcomes Measurement Information System (PROMIS) can be found at http://nihroadmap.nih.gov.
- 1.North American Nursing Diagnosis Association. (1996). Nursing diagnoses: Definition and classification, 1997–1998. Philadelphia, PA: McGraw-Hill.Google Scholar
- 2.Butbul-Aviel, Y., Stremler, R., Benseler, S. M., Cameron, B., Laxer, R. M., Ota, S., et al. (2011). Sleep and fatigue and the relationship to pain, disease activity and quality of life in juvenile idiopathic arthritis and juvenile dermatomyositis. Rheumatology, 50(11), 2051–2060.PubMedCrossRefGoogle Scholar
- 13.Currie, C., Hurrelmann, K., Setterbulte, W., Smith, R., Todd, J., & World Health Organization. (2000). Health and health behaviour among young people: Health behaviour in school-aged children. Copenhagen: World Health Organization Regional Office for Europe.Google Scholar
- 14.Ghandour, R. M., Overpeck, M. D., Huang, Z. J., Kogan, M. D., & Scheidt, P. C. (2004). Headache, stomachache, backache, and morning fatigue among adolescent girls in the United States: Associations with behavioral, sociodemographic, and environmental factors. Archives of Pediatrics and Adolescent Medicine, 158(8), 797.PubMedCrossRefGoogle Scholar
- 17.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(7), 2090–2106.PubMedCrossRefGoogle Scholar
- 22.Lai, J. S., Cella, D., Kupst, M. J., Holm, S., Kelly, M. E., Bode, R. K., et al. (2007). Measuring fatigue for children with cancer: Development and validation of the pediatric functional assessment of chronic illness therapy-fatigue (pedsFACIT-F). Journal of Pediatric Hematology/oncology, 29(7), 471–479.PubMedCrossRefGoogle Scholar
- 23.Wright, B. D., & Masters, G. N. (1985). Rating scale analysis: Rasch measurement. Chicago: MESA Press.Google Scholar
- 24.Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., et al. (2010). The patient reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179–1194.PubMedCrossRefGoogle Scholar
- 28.DeWalt, D. A., Rothrock, N., Yount, S., Stone, A. A., & PROMIS Cooperative Group. (2007). Evaluation of item candidates: The PROMIS qualitative item review. Medical Care, 45(5 suppl 1), S12–S21.Google Scholar
- 30.Irwin, D. E., Varni, J. W., Yeatts, K., & DeWalt, D. A. (2009). Cognitive interviewing methodology in the development of a pediatric item bank: A patient reported outcomes measurement information system (PROMIS) study. Health and Quality of Life Outcomes, 7(3), 1–10.Google Scholar
- 32.Reeve, B. B., Hays, R. D., Bjorner, J. B., Cook, K. F., Crane, P. K., Teresi, J. A., et al. (2007). Psychometric evaluation and calibration of health-related quality of life item banks: Plans for the patient-reported outcomes measurement information system (PROMIS). Medical Care, 45(5 suppl 1), S22–S31.PubMedCrossRefGoogle Scholar
- 34.Joreskog, K., & Sorbom, D. (2003). LISREL 8.5. Lincolnwood, IL: Scientific Software International, Inc.Google Scholar
- 35.Hill, C. D., Edwards, M. C., Thissen, D., Langer, M. M., Wirth, R. J., & Burwinkle, T. M. (2007). Practical issues in the application of item response theory: A demonstration using items from the pediatric quality of life inventory (PedsQL) 4.0 generic core scales. Medical Care, 45(5 suppl 1), S39–S47.Google Scholar
- 36.Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores, Psychometrika Monograph Supplement, No. 17.Google Scholar
- 38.Du Toit, M. (2003). IRT from SSI: BILOG-MG, MULTILOG, PARSCALE, TESTFACT. Lincolnwood, IL: Scientific Software International.Google Scholar
- 40.Bjorner, J. B., Smith, K. J., Edelen, M. O., Stone, C., & Thissen, D. (2007). IRTFIT: A macro for item fit and local dependence tests under IRT models. Lincoln, RI: QualityMetric Incorporated.Google Scholar
- 41.Thissen, D. (2003). IRTLRDIF -Software for the computation of the statistics involved in item response theory likelihood-ratio test for differential item functioning (Version 2.0b).Google Scholar
- 42.Thissen, D., Steinberg, L., & Wainer, H. (1993). Detection of differential item functioning using the parameters of item response models. In P.W. Holland & H. Wainer (Eds.), Differential item functioning (pp. 67–113). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
- 43.Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300.Google Scholar
- 45.Lai, J. S., Cella, D., Choi, S. W., Junghaenel, D. U., Christodolou, C., Gershon, R., et al. (2011). How item banks and their application can influence measurement practice in rehabilitation medicine: A PROMIS fatigue item bank example. Archives of Physical Medicine and Rehabilitation, 92(10 suppl), S20–S27.PubMedCrossRefGoogle Scholar
- 46.Lai, J.-S., Butt, Z., Zelko, F., Cella, D., Krull, K., Kieran, M., et al. (2011). Development of a parent-report cognitive function item bank using item response theory and exploration of its clinical utility in computerized adaptive testing. Journal of Pediatric Psychology, 36(7), 766–779.PubMedCrossRefGoogle Scholar
- 49.Cella, D., Eton, D. T., Lai, J. S., Peterman, A. H., & Merkel, D. E. (2002). Combining anchor and distribution-based methods to derive minimal clinically important differences on the functional assessment of cancer therapy (FACT) anemia and fatigue scales. Journal of Pain and Symptom Management, 24(6), 547–561.PubMedCrossRefGoogle Scholar