Advertisement

Quality of Life Research

, Volume 25, Issue 7, pp 1803–1813 | Cite as

The PROMIS FatigueFM Profile: a self-report measure of fatigue for use in fibromyalgia

  • Anna L. Kratz
  • Stephen Schilling
  • Jenna Goesling
  • David A. Williams
Article

Abstract

Purpose

Fibromyalgia (FM) is characterized by myriad symptoms and problems. Fatigue is one of the most common, distressing, and disabling symptoms in FM. The purpose of this study was to use fatigue item banks that were developed as part of the Patient-Reported Outcomes Measurement Information System (PROMIS) to devise a self-report measure of fatigue for use in individuals with FM.

Methods

A sample of 532 adults with FM (age range = 18–77, 96.1 % female) completed the PROMIS fatigue item bank. Factor analyses and item response theory analyses were used to identify dimensionality and optimally performing items. These data were used in combination with clinical input to select items for a fatigue self-report measure for use in FM.

Results

Factor analyses revealed four distinct factors in the PROMIS fatigue item bank; items for each univariate subscale were identified by selecting four items with high item information values. A 16-item measure, the PROMIS FatigueFM Profile, consisting of four 4-item short forms reflecting fatigue experience (“intensity”) and fatigue impact in three subdomains—social, cognitive, and motivation—was created. The new PROMIS FatigueFM Profile short forms showed excellent internal reliability, low ceiling and floor effects, and equivalent or higher test information compared to the standard 4- and 7-item PROMIS fatigue short forms.

Conclusions

The newly developed PROMIS FatigueFM Profile, a 16-item measure consisting of four 4-item short forms of self-reported fatigue severity, shows early evidence of good psychometric characteristics, provides the ability to use short forms that assess distinct aspects of fatigue experience and fatigue impact, and demonstrates equivalent or higher levels of test information compared to standard PROMIS fatigue short forms with similar number of items. The PROMIS FatigueFM Profile indicated fatigue experience and impact levels approximately 1.5 standard deviations above the normative sample mean across all short forms. Future work to evaluate the validity and reliability of this new measure in individuals with FM is needed.

Keywords

Fibromyalgia Fatigue Self-report measure Short form PROMIS 

Notes

Acknowledgments

Research reported in this publication was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases under award No. U01AR55069-01 (PI: Williams) and award No. 1K01AR064275 (PI: Kratz). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Compliance with ethical standards

Conflict of interest

The authors have no conflicts of interest to report.

Ethical approval

This study was approved by the relevant research ethics committee.

Informed consent

Informed consent was obtained from all participants in the study.

Supplementary material

11136_2016_1230_MOESM1_ESM.docx (35 kb)
Supplementary material 1 (DOCX 34 kb)

References

  1. 1.
    Mease, P. J., Arnold, L. M., Crofford, L. J., Williams, D. A., Russell, I. J., Humphrey, L., et al. (2008). Identifying the clinical domains of fibromyalgia: Contributions from clinician and patient Delphi exercises. Arthritis and Rheumatism, 59(7), 952–960.CrossRefPubMedGoogle Scholar
  2. 2.
    Bennett, R. M., Jones, J., Turk, D. C., Russell, I. J., & Matallana, L. (2007). An internet survey of 2,596 people with fibromyalgia. BMC Musculoskeletal Disorders, 8, 27.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Wolfe, F., Ross, K., Anderson, J., Russell, I. J., & Hebert, L. (1995). The prevalence and characteristics of fibromyalgia in the general population. Arthritis and Rheumatism, 38(1), 19–28.CrossRefPubMedGoogle Scholar
  4. 4.
    Arnold, L. M., Bradley, L. A., Clauw, D. J., Glass, J. M., & Goldenberg, D. L. (2008). Evaluating and diagnosing fibromyalgia and comorbid psychiatric disorders. Journal of Clinical Psychiatry, 69(10), e28.CrossRefPubMedGoogle Scholar
  5. 5.
    Humphrey, L., Arbuckle, R., Mease, P., Williams, D. A., Samsoe, B. D., & Gilbert, C. (2010). Fatigue in fibromyalgia: a conceptual model informed by patient interviews. BMC Musculoskeletal Disorders, 11, 216.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Lai, J. S., Cella, D., Choi, S., Junghaenel, D. U., Christodoulou, C., Gershon, R., & Stone, A. (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.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Amtmann, D., Cook, K. F., Jensen, M. P., Chen, W. H., Choi, S., Revicki, D., et al. (2010). Development of a PROMIS item bank to measure pain interference. Pain, 150(1), 173–182.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Williams, D. A., Schilling, S., Shibata, K. N., Zwinck, L., Matallana, L., & Arnold, L. (2009). Relevance of PROMIS item banks to individuals with fibromyalgia. Arthritis and Rheumatism, 60(10), S1404.Google Scholar
  10. 10.
    Cook, K. F., Bamer, A. M., Roddey, T. S., Kraft, G. H., Kim, J., & Amtmann, D. (2012). A PROMIS fatigue short form for use by individuals who have multiple sclerosis. Quality of Life Research, 21(6), 1021–1030.CrossRefPubMedGoogle Scholar
  11. 11.
    Schilling, S. G., & Bock, R. D. (2005). High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature. Psychometrika, 70(3), 1–23.Google Scholar
  12. 12.
    Schilling, S. G. (2005). ORDFAC: A computer program for ordinal factor analysis. Ann Arbor: University of Michigan.Google Scholar
  13. 13.
    Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.CrossRefGoogle Scholar
  14. 14.
    Schwartz, G. E. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464.CrossRefGoogle Scholar
  15. 15.
    Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph, 17, 1–100 .Google Scholar
  16. 16.
    Thissen, D., Chen, W. H., & Bock, R. D. (2003). MULTILOG7: Multiple category item analysis and test scoring using item response theory. Chicago, IL: Scientific Software International Inc.Google Scholar
  17. 17.
    Ihaka, R. (1988). R: Past and future history. Auckland, New Zealand: The University of Auckland.Google Scholar
  18. 18.
    Lai, J. S., Butt, Z., Wagner, L., Sweet, J. J., Beaumont, J. L., Vardy, J., et al. (2009). Evaluating the dimensionality of perceived cognitive function. Journal of Pain and Symptom Management, 37(6), 982–995.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Smets, E. M., Garssen, B., Bonke, B., & De Haes, J. C. (1995). The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. Journal of Psychosomatic Research, 39(3), 315–325.CrossRefPubMedGoogle Scholar
  20. 20.
    Arnold, L. M., Williams, D. A., Hudson, J. I., Martin, S. A., Clauw, D. J., & Crofford, L. J., et al. (2011). Development of responder definitions for fibromyalgia clinical trials. Arthritis and Rheumatism, 64(3), 885–894.CrossRefGoogle Scholar
  21. 21.
    Cook, K., Kallen, M., Cella, D., Crane, P., Eldadah, B., & Hays, R., et al. (2014). The patient reported outcomes measurement information system (PROMIS) perspective on: Universally-relevant vs. disease-attributed scales. http://www.nihpromis.org/Documents/Universally-Relevant_vs_Disease-Attributed_2014-2-12_final508.pdf?AspxAutoDetectCookieSupport=1.
  22. 22.
    Magasi, S., Ryan, G., Revicki, D., Lenderking, W., Hays, R. D., Brod, M., et al. (2012). Content validity of patient-reported outcome measures: Perspectives from a PROMIS meeting. Quality of Life Research, 21(5), 739–746.CrossRefPubMedGoogle Scholar
  23. 23.
    Kratz, A. L., Schilling, S. G., Goesling, J., & Williams, D. A. (2015). Development and initial validation of a brief self-report measure of cognitive dysfunction in fibromyalgia. The Journal of Pain, 16(6), 527–536.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Anna L. Kratz
    • 1
  • Stephen Schilling
    • 2
  • Jenna Goesling
    • 3
  • David A. Williams
    • 4
  1. 1.Department of Physical Medicine and RehabilitationUniversity of MichiganAnn ArborUSA
  2. 2.Institute for Social ResearchUniversity of MichiganAnn ArborUSA
  3. 3.Department of AnesthesiologyUniversity of MichiganAnn ArborUSA
  4. 4.Departments of Anesthesiology, Medicine, Psychiatry, and PsychologyUniversity of MichiganAnn ArborUSA

Personalised recommendations