Advertisement

Quality of Life Research

, Volume 21, Issue 2, pp 257–268 | Cite as

Single-item screens identified patients with elevated levels of depressive and somatization symptoms in outpatient physical therapy

  • Dennis L. Hart
  • Mark W. Werneke
  • Steven Z. George
  • Daniel Deutscher
Article

Abstract

Objective

Develop efficient and accurate screening tools to identify elevated levels of depressive or somatization symptoms, which can adversely affect functional status outcomes.

Methods

We conducted a secondary analysis of prospectively collected depressive and somatization symptoms (Symptom Checklist 90-Revised) data from 10,920 patients receiving outpatient physical therapy for a variety of neuromusculoskeletal diagnoses. Item response theory methods were used to analyze data, with particular emphasis on differential item functioning among groups of patients, and to identify potential screening items. Screening item accuracy for identifying patients with elevated symptoms was assessed with receiver-operating characteristic analyses.

Results

Seven items for depressive and 10 items for somatization symptoms represented unidimensional scales. Differential item functioning was negligible for demographic and clinical variables known to affect functional status outcomes. Items providing maximum information at the 88th percentile for depressive and 77th percentile for somatization scales accurately dichotomized patients into elevated versus not elevated symptom levels.

Conclusions

Lack of differential item functioning suggested depressive and somatization screening could be useful in routine clinical practice and allowed the development of single-item screens that accurately identified patients with elevated depressive or somatization symptoms. Item response theory-based single-item screens may facilitate evaluation and management of heterogeneous populations receiving outpatient physical therapy.

Keywords

Item response theory Depression Somatization Rehabilitation Screening 

References

  1. 1.
    Dionne, C. E., Le Sage, N., Franche, R. L., Dorval, M., Bombardier, C., & Deyo, R. A. (2011). Five questions predicted long-term, severe, back-related functional limitations: Evidence from three large prospective studies. Journal of Clinical Epidemiology, 64(1), 54–66.PubMedCrossRefGoogle Scholar
  2. 2.
    Pincus, T., Burton, A. K., Vogel, S., & Field, A. P. (2002). A systematic review of psychological factors as predictors of chronicity/disability in prospective cohorts of low back pain. Spine, 27(5), E109–E120.PubMedCrossRefGoogle Scholar
  3. 3.
    Deutscher, D., Horn, S. D., Dickstein, R., Hart, D. L., Smout, R. J., Gutvirtz, M., et al. (2009). Associations between treatment processes, patient characteristics, and outcomes in outpatient physical therapy practice. Archives of Physical Medicine and Rehabilitation, 90, 1349–1363.PubMedCrossRefGoogle Scholar
  4. 4.
    Haggman, S., Maher, C. G., & Refshauge, K. M. (2004). Screening for symptoms of depression by physical therapists managing low back pain. Physical Therapy, 84(12), 1157–1166.PubMedGoogle Scholar
  5. 5.
    Mannion AF, Junge A, Taimela S, Muntener M, Lorenzo K, Dvorak J. Active therapy for chronic low back pain: part 3. Factors influencing self-rated disability and its change following therapy. Spine (Phila Pa 1976). 2001 Apr 15;26(8):920–929.Google Scholar
  6. 6.
    Hart, D. L., Werneke, M. W., George, S. Z., Matheson, J. W., Wang, Y. C., Cook, K. F., et al. (2009). Screening for elevated levels of fear-avoidance beliefs regarding work or physical activities in people receiving outpatient therapy. Physical Therapy, 89(8), 770–785.PubMedCrossRefGoogle Scholar
  7. 7.
    Bair, M. J., Robinson, R. L., Katon, W., & Kroenke, K. (2003). Depression and pain comorbidity: A literature review. Archives of Internal Medicine, 163(20), 2433–2445.PubMedCrossRefGoogle Scholar
  8. 8.
    McWilliams, L. A., Cox, B. J., & Enns, M. W. (2003). Mood and anxiety disorders associated with chronic pain: An examination in a nationally representative sample. Pain, 106(1–2), 127–133.PubMedCrossRefGoogle Scholar
  9. 9.
    Hilty, D. M., Bourgeois, J. A., Chang, C. H., & Servis, M. E. (2001). Somatization disorder. Current Treatment Options in Neurology, 3(4), 305–320.PubMedCrossRefGoogle Scholar
  10. 10.
    Allen, L. A., Woolfolk, R. L., Escobar, J. I., Gara, M. A., & Hamer, R. M. (2006). Cognitive-behavioral therapy for somatization disorder: A randomized controlled trial. Archives of Internal Medicine, 166(14), 1512–1518.PubMedCrossRefGoogle Scholar
  11. 11.
    Janca, A., Isaac, M., & Ventouras, J. (2006). Towards better understanding and management of somatoform disorders. International Review of Psychiatry, 18(1), 5–12.PubMedCrossRefGoogle Scholar
  12. 12.
    Dionne, C. E. (2005). Psychological distress confirmed as predictor of long-term back-related functional limitations in primary care settings. Journal of Clinical Epidemiology, 58(7), 714–718.PubMedCrossRefGoogle Scholar
  13. 13.
    Dionne, C. E., Koepsell, T. D., Von Korff, M., Deyo, R. A., Barlow, W. E., & Checkoway, H. (1997). Predicting long-term functional limitations among back pain patients in primary care settings. Journal of Clinical Epidemiology, 50(1), 31–43.PubMedCrossRefGoogle Scholar
  14. 14.
    Derogatis, L. R., & Melisaratos, N. (1983). The Brief Symptom Inventory: An introductory report. Psychological Medicine, 13(3), 595–605.PubMedCrossRefGoogle Scholar
  15. 15.
    Derogatis, L. R., Lipman, R. S., & Covi, L. (1973). SCL-90: An outpatient psychiatric rating scale–preliminary report. Psychopharmacology Bulletin, 9(1), 13–28.PubMedGoogle Scholar
  16. 16.
    Deutscher, D., Hart, D. L., Dickstein, R., Horn, S. D., & Gutvirtz, M. (2008). Implementing an integrated electronic outcomes and electronic health record process to create a foundation for clinical practice improvement. Physical Therapy, 88(2), 270–285.PubMedCrossRefGoogle Scholar
  17. 17.
    van der Linden, W., & Hambleton, R. K. (Eds.). (1997). Handbook of modern item response theory. New York, NY: Springer.Google Scholar
  18. 18.
    DeSalvo, K. B., Fisher, W. P., Tran, K., Bloser, N., Merrill, W., & Peabody, J. (2006). Assessing measurement properties of two single-item general health measures. Quality of Life Research, 15(2), 191–201.PubMedCrossRefGoogle Scholar
  19. 19.
    Millsap, R. E., & Everson, H. T. (1993). Methodology review: Statistical approaches for assessing measurement bias. Applied Psychological Measurement, 17, 287–334.Google Scholar
  20. 20.
    Lord, F. M. (1980). Applications of item response theory to practical testing problems. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  21. 21.
    Swinkels, I. C., Hart, D. L., Deutscher, D., van den Bosch, W. J., Dekker, J., de Bakker, D. H., et al. (2008). Comparing patient characteristics and treatment processes in patients receiving physical therapy in the United States, Israel and the Netherlands. Cross sectional analyses of data from three clinical databases. BMC Health Services Research, 8(1), 163.PubMedCrossRefGoogle Scholar
  22. 22.
    Hart, D. L., Wang, Y. C., Stratford, P. W., & Mioduski, J. E. (2008). Computerized adaptive test for patients with knee impairments produced valid and responsive measures of function. Journal of Clinical Epidemiology, 61(11), 1113–1124.PubMedCrossRefGoogle Scholar
  23. 23.
    Hart, D. L., Wang, Y. C., Stratford, P. W., & Mioduski, J. E. (2008). Computerized adaptive test for patients with foot or ankle impairments produced valid and responsive measures of function. Quality of Life Research, 17(8), 1081–1091.PubMedCrossRefGoogle Scholar
  24. 24.
    Hart, D. L., Werneke, M. W., Wang, Y. C., Stratford, P. W., & Mioduski, J. E. (2010). Computerized adaptive test for patients with lumbar spine impairments produced valid and responsive measures of function. Spine, 35(24), 2157–2164.PubMedGoogle Scholar
  25. 25.
    Rossignol, M., Arsenault, B., Dionne, C. E., Poitras, S., Tousignant, M., & Truchon, M., et al. (2006). Clinic on Low-Back Pain in Interdisciplinary Practice (CLIP) Guidelines. In MPH Do (Ed.), Montreal, Canada: Agence de la sante et de services sociaux de Montreal.Google Scholar
  26. 26.
    Derogatis, L. R., Rickels, K., & Rock, A. F. (1976). The SCL-90 and the MMPI: A step in the validation of a new self-report scale. British Journal of Psychiatry, 128, 280–289.PubMedCrossRefGoogle Scholar
  27. 27.
    Hyphantis, T., Tomenson, B., Paika, V., Almyroudi, A., Pappa, C., Tsifetaki, N., et al. (2009). Somatization is associated with physical health-related quality of life independent of anxiety and depression in cancer, glaucoma and rheumatological disorders. Quality of Life Research, 18(8), 1029–1042.PubMedCrossRefGoogle Scholar
  28. 28.
    Von Korff, M., Le Resche, L., & Dworkin, S. F. (1993). First onset of common pain symptoms: A prospective study of depression as a risk factor. Pain, 55(2), 251–258.CrossRefGoogle Scholar
  29. 29.
    Bernstein, I. H., Jaremko, M. E., & Hinkley, B. S. (1994). On the utility of the SCL-90-R with low-back pain patients. Spine (Phila Pa 1976), 19(1), 42–48.CrossRefGoogle Scholar
  30. 30.
    Cassisi, J. E., Sypert, G. W., Lagana, L., Friedman, E. M., & Robinson, M. E. (1993). Pain, disability, and psychological functioning in chronic low back pain subgroups: Myofascial versus herniated disc syndrome. Neurosurgery, 33(3), 379–385. (discussion 85–86).PubMedCrossRefGoogle Scholar
  31. 31.
    Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Newbury Park, CA: Sage.Google Scholar
  32. 32.
    Hays, R. D., Morales, L. S., & Reise, S. P. (2000). Item response theory and health outcomes measurement in the 21st century. Medical Care, 38(9 Suppl), II28–II42.PubMedGoogle Scholar
  33. 33.
    Muthén, L. K., & Muthén, B. O. (2006). Mplus user’s guide (4th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
  34. 34.
    Bjorner, J. B., Kosinski, M., & Ware, J. E., Jr. (2003). The feasibility of applying item response theory to measures of migraine impact: A re-analysis of three clinical studies. Quality of Life Research, 12(8), 887–902.PubMedCrossRefGoogle Scholar
  35. 35.
    Fliege, H., Becker, J., Walter, O. B., Bjorner, J. B., Klapp, B. F., & Rose, M. (2005). Development of a computer-adaptive test for depression (D-CAT). Quality of Life Research, 14(10), 2277–2291.PubMedCrossRefGoogle Scholar
  36. 36.
    Hu, L. T., & Bentler, P. (1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.CrossRefGoogle Scholar
  37. 37.
    Tucker, L., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10.CrossRefGoogle Scholar
  38. 38.
    Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. A. Long (Eds.), Testing structural equation models. Newbury Park, CA: Sage Publications.Google Scholar
  39. 39.
    Cook, K. F., Kallen, M. A., & Amtmann, D. (2009). Having a fit: Impact of number of items and distribution of data on traditional criteria for assessing IRT’s unidimensionality assumption. Quality of Life Research, 18(4), 447–460.PubMedCrossRefGoogle Scholar
  40. 40.
    Samejima, F. (1969). Estimation of ability using a response pattern of graded responses. Psycometrika. Monograph 17.Google Scholar
  41. 41.
    PARSCALE for Windows. v 4.1. Lincolnwood, IL: Scientific Software International.; 2003.Google Scholar
  42. 42.
    Dodd, B. G., Koch, W. R., & De Ayala, R. J. (1989). Operational characteristics of adaptive testing procedures using the Graded Response Model. Applied Psychological Measurement, 13(2), 129–143.CrossRefGoogle Scholar
  43. 43.
    Thissen, D. (2000). Reliability and measurement precision. In H. Wainer (Ed.), Computerized adaptive testing a primer (2nd ed., pp. 159–184). Wahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  44. 44.
    Rose, M., Bjorner, J. B., Becker, J., Fries, J. F., & Ware, J. E. (2008). Evaluation of a preliminary physical function item bank supported the expected advantages of the Patient-Reported Outcomes Measurement Information System (PROMIS). Journal of Clinical Epidemiology, 61(1), 17–33.PubMedCrossRefGoogle Scholar
  45. 45.
    Crane, P. K., Gibbons, L. E., Jolley, L., & van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar. Medical Care, 44(11 Suppl 3), S115–S123.PubMedCrossRefGoogle Scholar
  46. 46.
    Deutscher, D., Hart, D. L., Crane, P. K., & Dickstein, R. (2010). Cross cultural differences in knee functional status outcomes in a polyglot society represented true disparities not biased by differential item functioning. Physical Therapy, 11(3), 288–303.Google Scholar
  47. 47.
    Hart, D. L., Deutscher, D., Crane, P. K., & Wang, Y. C. (2009). Differential item functioning was negligible in an adaptive test of functional status for patients with knee impairments who spoke English or Hebrew. Quality of Life Research, 18(8), 1067–1083.PubMedCrossRefGoogle Scholar
  48. 48.
    Delitto, A., Erhard, R. E., & Bowling, R. W. (1995). A treatment-based classification approach to low back syndrome: Identifying and staging patients for conservative treatment. Physical Therapy, 75(6), 470–489.PubMedGoogle Scholar
  49. 49.
    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 Suppl 1, 69–84.PubMedCrossRefGoogle Scholar
  50. 50.
    Stata Statistical Software: Release 9.2. College Station, TX2007.Google Scholar
  51. 51.
    Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36.PubMedGoogle Scholar
  52. 52.
    Choi, B. C. (1998). Slopes of a receiver operating characteristic curve and likelihood ratios for a diagnostic test. American Journal of Epidemiology, 148(11), 1127–1132.PubMedGoogle Scholar
  53. 53.
    Sackett, D. L., Straus, S. E., Richardson, W. S., Rosenberg, W., & Haynes, R. B. (2000). Evidence-based medicine. How to practice and teach EBM (2nd ed.). New York, NY: Churchill Livingstone.Google Scholar
  54. 54.
    Dujardin, B., Van den Ende, J., Van Gompel, A., Unger, J. P., & Van der Stuyft, P. (1994). Likelihood ratios: A real improvement for clinical decision making? European Journal of Epidemiology, 10(1), 29–36.PubMedCrossRefGoogle Scholar
  55. 55.
    Jaeschke, R., Guyatt, G., & Sackett, D. L. (1994). Users’ guides to the medical literature. III. How to use an article about a diagnostic test. A. Are the results of the study valid? Evidence-based medicine working group. JAMA, 271(5), 389–391.PubMedCrossRefGoogle Scholar
  56. 56.
    George, S. Z., & Zeppieri, G. (2009). Physical therapy utilization of graded exposure for patients with low back pain. Journal of Orthopaedic and Sports Physical Therapy, 39(7), 496–505.PubMedGoogle Scholar
  57. 57.
    Werneke, M. W., & Hart, D. L. (2003). Discriminant validity and relative precision for classifying patients with nonspecific neck and back pain by anatomic pain patterns. Spine, 28(2), 161–166.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Dennis L. Hart
    • 1
  • Mark W. Werneke
    • 2
  • Steven Z. George
    • 3
  • Daniel Deutscher
    • 4
  1. 1.Department of Consulting and Research, Focus On Therapeutic Outcomes, Inc.White StoneUSA
  2. 2.Spine Rehabilitation at CentraState Medical CenterFreeholdUSA
  3. 3.Department of Physical Therapy, Center for Pain Research and Behavioral Health, Brooks Center for Rehabilitation StudiesUniversity of FloridaGainesvilleUSA
  4. 4.Director of Research & Development, Physical Therapy ServiceMaccabi Healthcare ServicesTel AvivIsrael

Personalised recommendations