Skip to main content
Log in

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

  • Published:
Quality of Life Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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. 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.

    Article  PubMed  Google Scholar 

  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. Schilling, S. G. (2005). ORDFAC: A computer program for ordinal factor analysis. Ann Arbor: University of Michigan.

    Google Scholar 

  13. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.

    Article  Google Scholar 

  14. Schwartz, G. E. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464.

    Article  Google Scholar 

  15. Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph, 17, 1–100 .

    Google Scholar 

  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. Ihaka, R. (1988). R: Past and future history. Auckland, New Zealand: The University of Auckland.

    Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anna L. Kratz.

Ethics declarations

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.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 34 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kratz, A.L., Schilling, S., Goesling, J. et al. The PROMIS FatigueFM Profile: a self-report measure of fatigue for use in fibromyalgia. Qual Life Res 25, 1803–1813 (2016). https://doi.org/10.1007/s11136-016-1230-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-016-1230-9

Keywords

Navigation