Quality of Life Research

, Volume 23, Issue 4, pp 1245–1253 | Cite as

Measuring daily fatigue using a brief scale adapted from the Patient-Reported Outcomes Measurement Information System (PROMIS®)

  • Christopher ChristodoulouEmail author
  • Stefan Schneider
  • Doerte U. Junghaenel
  • Joan E. Broderick
  • Arthur A. Stone



Daily assessments can provide insight into the temporal characteristics of fatigue. They can demonstrate consistency or reveal variability, as when fatigue changes with the underlying medical condition, improves with therapy, or worsens as a medication side effect. We adapted a fatigue measure from the Patient-Reported Outcomes Measurement Information System (PROMIS®) for daily assessment and examined its psychometric properties in a month-long prospective study.


Three groups of 100 participants each were drawn from two fatigue-related clinical disorders [osteoarthritis (OA) and premenstrual syndrome/premenstrual dysphoric disorder (PMS/PMDD)], and a general population sample (GP). They completed brief daily web-based fatigue measures at home on 28 consecutive evenings.


Compliance was high for all samples, based on the percent of participants who remained in the study (98 % for GP and OA, 95 % for PMS/PMDD). The new scale performed consistently across the groups, sensitively measuring fatigue with high reliability (>0.90) especially in the average to high fatigue level range. Supporting known-groups validity, fatigue scores were elevated in the clinical groups as compared to the GP. The scale was sensitive to change, with the PMS/PMDD sample showing a linear increase in fatigue prior to menses onset, and a sharp drop off afterward.


The scale was psychometrically sound across diverse clinical and general population samples, though less reliable when assessing lower levels of fatigue.


Fatigue Daily diary Patient-reported outcome 



This research was supported by a grant from the National Institutes of Health Roadmap for Medical Research, Grant (1U01-AR057948-01). The authors thank Gim Yen Toh, Laura Wolff, and Lauren Cody for their assistance with data collection. A.A.S. is a Senior Scientist with the Gallup Organization and a Senior Consultant with ERT, Inc.


  1. 1.
    Ranjith, G. (2005). Epidemiology of chronic fatigue syndrome. Occupational Medicine, 55(1), 13–19.PubMedCrossRefGoogle Scholar
  2. 2.
    Ricci, J. A., Chee, E., Lorandeau, A. L., & Berger, J. (2007). Fatigue in the U.S. workforce: prevalence and implications for lost productive work time. Journal of Occupational and Environmental Medicine, 49(1), 1–10.PubMedCrossRefGoogle Scholar
  3. 3.
    DeLuca, J. (Ed.). (2005). Fatigue as a window to the brain. Cambridge, MA: MIT Press.Google Scholar
  4. 4.
    Wessely, S., Hotopf, M., & Sharpe, D. (1998). Chronic fatigue and its syndromes. New York: Oxford University Press.Google Scholar
  5. 5.
    Christodoulou, C. (2012). Approaches to the measurement of fatigue. In G. Matthews, P. A. Desmond, C. Neubauer, & P. A. Hancock (Eds.), The handbook of operator fatigue. Burlington, VT: Ashgate Publishing.Google Scholar
  6. 6.
    Christodoulou, C. (2005). The assessment and measurement of fatigue. In J. DeLuca (Ed.), Fatigue as a window to the brain (pp. 19–35). New York: MIT Press.Google Scholar
  7. 7.
    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
  8. 8.
    Lai, J. S., Cella, D., Choi, S., Junghaenel, D. U., Christodoulou, 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.PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    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.PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Broderick, J. E., Schneider, S., Schwartz, J. E., & Stone, A. A. (2010). Interference with activities due to pain and fatigue: Accuracy of ratings across different reporting periods. Quality of Life Research, 19(8), 1163–1170. Google Scholar
  11. 11.
    Broderick, J. E., Schwartz, J. E., Vikingstad, G., Pribbernow, M., Grossman, S., & Stone, A. A. (2008). The accuracy of pain and fatigue items across different reporting periods. Pain, 139(1), 146–157.PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Friedberg, F., & Sohl, S. J. (2008). Memory for fatigue in chronic fatigue syndrome: the relation between weekly recall and momentary ratings. International Society of Behavioral Medicine, 15(1), 29–33.CrossRefGoogle Scholar
  13. 13.
    Schneider, S., Choi, S. W., Junghaenel, D. U., Schwartz, J. E., & Stone, A. A. (2013). Psychometric characteristics of daily diaries for the Patient-Reported Outcomes Measurement Information System (PROMIS(R)): a preliminary investigation. Quality of Life Research, 22(7), 1859–1869.Google Scholar
  14. 14.
    Schneider, S., Junghaenel, D. U., Keefe, F. J., Schwartz, J. E., Stone, A. A., & Broderick, J. E. (2012). Individual differences in the day-to-day variability of pain, fatigue, and well-being in patients with rheumatic disease: associations with psychological variables. Pain, 153(4), 813–822.PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Watson, T., & Mock, V. (2004). Exercise as an intervention for cancer-related fatigue. Physical Therapy, 84(8), 736–743.PubMedGoogle Scholar
  16. 16.
    Wolfe, F., Hawley, D. J., & Wilson, K. (1996). The prevalence and meaning of fatigue in rheumatic disease. Journal of Rheumatology, 23(8), 1407–1417.PubMedGoogle Scholar
  17. 17.
    Zautra, A. J., Fasman, R., Parish, B. P., & Davis, M. C. (2007). Daily fatigue in women with osteoarthritis, rheumatoid arthritis, and fibromyalgia. Pain, 128(1–2), 128–135.PubMedCrossRefGoogle Scholar
  18. 18.
    Power, J. D., Badley, E. M., French, M. R., Wall, A. J., & Hawker, G. A. (2008). Fatigue in osteoarthritis: a qualitative study. BMC Musculoskeletal Disorders, 9, 63. PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Mortola, J. F. (1992). Issues in the diagnosis and research of premenstrual syndrome. Clinical Obstetrics and Gynecology, 35(3), 587–598.PubMedCrossRefGoogle Scholar
  20. 20.
    Dickerson, L. M., Mazyck, P. J., & Hunter, M. H. (2003). Premenstrual syndrome. American Family Physician, 67(8), 1743–1752.PubMedGoogle Scholar
  21. 21.
    Tschudin, S., Bertea, P. C., & Zemp, E. (2010). Prevalence and predictors of premenstrual syndrome and premenstrual dysphoric disorder in a population-based sample. Archives of Women’s Mental Health, 13(6), 485–494.PubMedCrossRefGoogle Scholar
  22. 22.
  23. 23.
    Bolen, J., Schieb, L., Hootman, J. M., Helmick, C. G., Theis, K., Murphy, L. B., et al. (2010). Differences in the prevalence and severity of arthritis among racial/ethnic groups in the United States, National Health Interview Survey, 2002, 2003, and 2006. Preventing Chronic Disease, 7(3), A64.PubMedCentralPubMedGoogle Scholar
  24. 24.
    Christodoulou, C., Junghaenel, D. U., DeWalt, D. A., Rothrock, N., & Stone, A. A. (2008). Cognitive interviewing in the evaluation of fatigue items: Results from the Patient-Reported Outcomes Measurement Information System (PROMIS). Quality of Life Research, 17(10), 1239–1246.PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Riley, W. T., Rothrock, N., Bruce, B., Christodoulou, C., Cook, K., Hahn, E. A., et al. (2010). Patient-Reported Outcomes Measurement Information System (PROMIS) domain names and definitions revisions: further evaluation of content validity in IRT-derived item banks. Quality of Life Research, 19(9), 1311–1321. PubMedCentralPubMedCrossRefGoogle Scholar
  26. 26.
    DeWalt, D. A., Rothrock, N., Yount, S., & Stone, A. A. (2007). Evaluation of item candidates: the PROMIS qualitative item review. Medical Care, 45(5 Suppl 1), S12–S21.PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    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
  28. 28.
    Junghaenel, D. U., Christodoulou, C., Lai, J. S., & Stone, A. A. (2011). Demographic correlates of fatigue in the US general population: results from the Patient-Reported Outcomes Measurement Information System (PROMIS) initiative. Journal of Psychosomatic Research, 71(3), 117–123.PubMedCentralPubMedCrossRefGoogle Scholar
  29. 29.
    Rothrock, N. E., Hays, R. D., Spritzer, K., Yount, S. E., Riley, W., & Cella, D. (2010). Relative to the general US population, chronic diseases are associated with poorer health-related quality of life as measured by the Patient-Reported Outcomes Measurement Information System (PROMIS). Journal of Clinical Epidemiology, 63(11), 1195–1204.PubMedCentralPubMedCrossRefGoogle Scholar
  30. 30.
    Cook, K. F., Bamer, A. M., Amtmann, D., Molton, I. R., & Jensen, M. P. (2012). Six patient-reported outcome measurement information system short form measures have negligible age- or diagnosis-related differential item functioning in individuals with disabilities. Archives of Physical Medicine and Rehabilitation, 93(7), 1289–1291.Google Scholar
  31. 31.
    Samejima, F. (1970). Estimation of latent ability using a response pattern of graded scores. Psychometrika, 35, 139.Google 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), S22–S31.PubMedCrossRefGoogle Scholar
  33. 33.
    Muthén, L. K., & Muthén, B. O. (2010). Mplus user’s guide (6th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
  34. 34.
    Crane, P. K., Gibbons, L. E., Ocepek-Welikson, K., Cook, K., Cella, D., Narasimhalu, K., et al. (2007). A comparison of three sets of criteria for determining the presence of differential item functioning using ordinal logistic regression. Quality of Life Research, 16, 69–84.PubMedCrossRefGoogle Scholar
  35. 35.
    Choi, S. W., Gibbons, L. E., & Crane, P. K. (2011). lordif: An R package for detecting differential item functioning using iterative hybrid ordinal logistic regression/item response theory and Monte Carlo simulations. Journal of Statistical Software, 39(8), 1–30.PubMedCentralPubMedGoogle Scholar
  36. 36.
    Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  37. 37.
    Baker, F. B. (2001). The basics of item response theory. College Park: ERIC Clearinghouse on Assessment and Evaluation.Google Scholar
  38. 38.
    Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.CrossRefGoogle Scholar
  39. 39.
    Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070.PubMedCrossRefGoogle Scholar
  40. 40.
    Piasecki, T. M., Hufford, M. R., Solhan, M., & Trull, T. J. (2007). Assessing clients in their natural environments with electronic diaries: rationale, benefits, limitations, and barriers. Psychological Assessment, 19(1), 25–43.PubMedCrossRefGoogle Scholar
  41. 41.
    Gilbert, D. G., McClernon, F. J., Rabinovich, N. E., Plath, L. C., Jensen, R. A., & Meliska, C. J. (1998). Effects of smoking abstinence on mood and craving in men: Influences of negative-affect-related personality traits, habitual nicotine intake and repeated measurements. Personality and Individual Differences, 25(3), 399–423.CrossRefGoogle Scholar
  42. 42.
    Sharpe, J. P., & Gilbert, D. G. (1998). Effects of repeated administration of the Beck Depression Inventory and other measures of negative mood states. Personality and Individual Differences, 24(4), 457–463.CrossRefGoogle Scholar
  43. 43.
    Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.PubMedCrossRefGoogle Scholar
  44. 44.
    Harris, K. M., Schonlau, M., & Lurie, N. (2009). Surveying a nationally representative internet-based panel to obtain timely estimates of influenza vaccination rates. Vaccine, 27(6), 815–818.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Christopher Christodoulou
    • 1
    Email author
  • Stefan Schneider
    • 2
  • Doerte U. Junghaenel
    • 2
  • Joan E. Broderick
    • 2
  • Arthur A. Stone
    • 2
  1. 1.Department of NeurologyStony Brook UniversityStony BrookUSA
  2. 2.Department of PsychiatryStony Brook UniversityStony BrookUSA

Personalised recommendations