Quality of Life Research

, Volume 22, Issue 9, pp 2417–2427 | Cite as

Development and psychometric properties of the PROMIS® pediatric fatigue item banks

  • Jin-Shei Lai
  • Brian D. Stucky
  • David Thissen
  • James W. Varni
  • Esi Morgan DeWitt
  • Debra E. Irwin
  • Karin B. Yeatts
  • Darren A. DeWalt



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.


PROMIS 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


  1. 1.
    North American Nursing Diagnosis Association. (1996). Nursing diagnoses: Definition and classification, 1997–1998. Philadelphia, PA: McGraw-Hill.Google Scholar
  2. 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
  3. 3.
    Levy-Marchal, C., Papoz, L., de Beaufort, C., Doutreix, J., Froment, V., Voirin, J., et al. (1992). Clinical and laboratory features of type 1 diabetic children at the time of diagnosis. Diabetic Medicine, 9(3), 279–284.PubMedCrossRefGoogle Scholar
  4. 4.
    Marcus, S. B., Strople, J. A., Neighbors, K., Weissberg–Benchell, J., Nelson, S. P., Limbers, C., et al. (2009). Fatigue and health-related quality of life in pediatric inflammatory bowel disease. Clinical Gastroenterology and Hepatology, 7(5), 554–561.PubMedCrossRefGoogle Scholar
  5. 5.
    Amato, M. P., Goretti, B., Ghezzi, A., Lori, S., Zipoli, V., Moiola, L., et al. (2010). Cognitive and psychosocial features in childhood and juvenile MS. Neurology, 75(13), 1134–1140.PubMedCrossRefGoogle Scholar
  6. 6.
    Buskila, D. (2009). Pediatric fibromyalgia. Rheumatic Diseases Clinics of North America, 35(2), 253–261.PubMedCrossRefGoogle Scholar
  7. 7.
    Elliott, I. M., Lach, L., & Smith, M. L. (2005). I just want to be normal: A qualitative study exploring how children and adolescents view the impact of intractable epilepsy on their quality of life. Epilepsy & Behavior, 7(4), 664–678.CrossRefGoogle Scholar
  8. 8.
    Wolfe, J., Grier, H. E., Klar, N., Levin, S. B., Ellenbogen, J. M., Salem-Schatz, S., et al. (2000). Symptoms and suffering at the end of life in children with cancer. The New England Journal of Medicine, 342(5), 326–333.PubMedCrossRefGoogle Scholar
  9. 9.
    Jalmsell, L., Kreicbergs, U., Onelöv, E., Steineck, G., & Henter, J.-I. (2006). Symptoms affecting children with malignancies during the last month of life: A nationwide follow-up. Pediatrics, 117(4), 1314.PubMedCrossRefGoogle Scholar
  10. 10.
    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(12), 1502–1508.PubMedCrossRefGoogle Scholar
  11. 11.
    Schanberg, L. E., Gil, K. M., Anthony, K. K., Yow, E., & Rochon, J. (2005). Pain, stiffness, and fatigue in juvenile polyarticular arthritis: Contemporaneous stressful events and mood as predictors. Arthritis and Rheumatism, 52(4), 1196–1204.PubMedCrossRefGoogle Scholar
  12. 12.
    Meeske, K., Katz, E. R., Palmer, S. N., Burwinkle, T., & Varni, J. W. (2004). Parent proxy-reported health-related quality of life and fatigue in pediatric patients diagnosed with brain tumors and acute lymphoblastic leukemia. Cancer, 101(9), 2116–2125.PubMedCrossRefGoogle Scholar
  13. 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. 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
  15. 15.
    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(5), 469–475.PubMedCrossRefGoogle Scholar
  16. 16.
    Eddy, L., & Cruz, M. (2007). The relationship between fatigue and quality of life in children with chronic health problems: A systematic review. Journal for Specialists in Pediatric Nursing, 12(2), 105–114.PubMedCrossRefGoogle Scholar
  17. 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
  18. 18.
    Hinds, P. S., Hockenberry, M., Tong, X., Rai, S. N., Gattuso, J. S., McCarthy, K., et al. (2007). Validity and reliability of a new instrument to measure cancer-related fatigue in adolescents. Journal of Pain and Symptom Management, 34(6), 607–618.PubMedCrossRefGoogle Scholar
  19. 19.
    Collins, J. J., Byrnes, M. E., Dunkel, I. J., Lapin, J., Nadel, T., Thaler, H. T., et al. (2000). The measurement of symptoms in children with cancer. Journal of Pain and Symptom Management, 19(5), 363–377.PubMedCrossRefGoogle Scholar
  20. 20.
    Chalder, T., Berelowitz, G., Pawlikowska, T., Watts, L., Wessely, S., Wright, D., et al. (1993). Development of a fatigue scale. Journal of Psychosomatic Research, 37(2), 147–153.PubMedCrossRefGoogle Scholar
  21. 21.
    Goligher, E. C., Pouchot, J., Brant, R., Kherani, R. B., Avina-Zubieta, J. A., Lacaille, D., et al. (2008). Minimal clinically important difference for 7 measures of fatigue in patients with systemic lupus erythematosus. Journal of Rheumatology, 35(4), 635–642.PubMedGoogle Scholar
  22. 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. 23.
    Wright, B. D., & Masters, G. N. (1985). Rating scale analysis: Rasch measurement. Chicago: MESA Press.Google Scholar
  24. 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
  25. 25.
    Cella, D., Yount, S., Rothrock, N., Gershon, R., Cook, K., Reeve, B., et al. (2007). The patient-reported outcomes measurement information system (PROMIS): Progress of an NIH roadmap cooperative group during its first two years. Medical Care, 45(5 suppl 1), S3–S11.PubMedCrossRefGoogle Scholar
  26. 26.
    Cella, D., Gershon, R., Lai, J. S., & Choi, S. (2007). The future of outcomes measurement: Item banking, tailored short-forms, and computerized adaptive assessment. Quality of Life Research, 16(Suppl 1), 133–141.PubMedCrossRefGoogle Scholar
  27. 27.
    Irwin, D. E., Stucky, B. D., Thissen, D., Dewitt, E. M., Lai, J. S., Yeatts, K., et al. (2010). Sampling plan and patient characteristics of the PROMIS pediatrics large-scale survey. Quality of Life Research, 19(4), 585–594.PubMedCrossRefGoogle Scholar
  28. 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
  29. 29.
    Walsh, T., Irwin, D., Meier, A., Varni, J., & DeWalt, D. (2008). The use of focus groups in the development of the PROMIS pediatrics item bank. Quality of Life Research, 17(5), 725–735.PubMedCrossRefGoogle Scholar
  30. 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
  31. 31.
    Kolen, M. J., & Brennan, R. L. (2004). Test equating, scaling, and linking: Methods and practices. New York: Springer.CrossRefGoogle Scholar
  32. 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
  33. 33.
    Yeatts, K. B., Stucky, B., Thissen, D., Irwin, D., Varni, J. W., DeWitt, E. M., et al. (2010). Construction of the pediatric asthma impact scale (PAIS) for the patient-reported outcomes measurement information system (PROMIS). Journal of Asthma, 47(3), 295–302.PubMedCrossRefGoogle Scholar
  34. 34.
    Joreskog, K., & Sorbom, D. (2003). LISREL 8.5. Lincolnwood, IL: Scientific Software International, Inc.Google Scholar
  35. 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. 36.
    Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores, Psychometrika Monograph Supplement, No. 17.Google Scholar
  37. 37.
    Samejima, F. (1997). The graded response model. In W. J. van der Linden & R. Hambleton (Eds.), Handbook of modern item response theory (pp. 85–100). New York: Springer.CrossRefGoogle Scholar
  38. 38.
    Du Toit, M. (2003). IRT from SSI: BILOG-MG, MULTILOG, PARSCALE, TESTFACT. Lincolnwood, IL: Scientific Software International.Google Scholar
  39. 39.
    Orlando, M., & Thissen, D. (2003). Further examination of the performance of S-X2, an item fit index for dichotomous item response theory models. Applied Psychological Measurement, 27, 289–298.CrossRefGoogle Scholar
  40. 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. 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. 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. 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
  44. 44.
    Steinberg, L., & Thissen, D. (2006). Using effect sizes for research reporting: Examples using item response theory to analyze differential item functioning. Psychological Methods, 11(4), 402–415.PubMedCrossRefGoogle Scholar
  45. 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. 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
  47. 47.
    Lai, J. S., Crane, P. K., & Cella, D. (2006). Factor analysis techniques for assessing sufficient unidimensionality of cancer related fatigue. Quality of Life Research, 15(7), 1179–1190.PubMedCrossRefGoogle Scholar
  48. 48.
    Cella, D., Lai, J. S., & Stone, A. (2010). Self-reported fatigue: One dimension or more? Lessons from the functional assessment of chronic illness therapy-fatigue (FACIT-F) questionnaire. Supportive Care in Cancer, 19(9), 1441–1450.PubMedCrossRefGoogle Scholar
  49. 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
  50. 50.
    Revicki, D., Hays, R., Cella, D., & Sloan, J. (2008). Recommended methods for determining responsiveness and minimally important differences for patient-reported outcomes. Journal of Clinical Epidemiology, 61(2), 102–109.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Jin-Shei Lai
    • 1
  • Brian D. Stucky
    • 2
  • David Thissen
    • 3
  • James W. Varni
    • 4
    • 5
  • Esi Morgan DeWitt
    • 6
  • Debra E. Irwin
    • 7
  • Karin B. Yeatts
    • 7
  • Darren A. DeWalt
    • 8
  1. 1.Department of Medical Social Sciences and PediatricsNorthwestern University Feinberg School of MedicineChicagoUSA
  2. 2.RAND CorporationSanta MonicaUSA
  3. 3.Department of PsychologyUniversity of North Carolina at Chapel HillChapel HillUSA
  4. 4.Department of Pediatrics, College of MedicineTexas A&M UniversityCollege StationUSA
  5. 5.Department of Landscape Architecture and Urban Planning, College of ArchitectureTexas A&M UniversityCollege StationUSA
  6. 6.Division of Rheumatology, Department of PediatricsCincinnati Children’s Hospital Medical CenterCincinnatiUSA
  7. 7.Department of EpidemiologyUniversity of North Carolina at Chapel HillChapel HillUSA
  8. 8.Division of General Medicine and Clinical Epidemiology, Cecil G. Sheps Center for Health Services ResearchUniversity of North Carolina at Chapel HillChapel HillUSA

Personalised recommendations