Advertisement
Original research| Volume 101, ISSUE 1, P11-19, January 2020

Download started.

Ok

Measuring Pain in TBI: Development of the TBI-QOL Pain Interference Item Bank and Short Form

Published:September 25, 2019DOI:https://doi.org/10.1016/j.apmr.2019.07.019

      Abstract

      Objective

      To develop a pain interference item bank, computer adaptive test (CAT), and short form for use by individuals with traumatic brain injury (TBI).

      Design

      Cross-sectional survey study.

      Setting

      Five TBI Model Systems rehabilitation hospitals.

      Participants

      Individuals with TBI (N=590).

      Interventions

      Not applicable.

      Outcome Measures

      Traumatic Brain Injury–Quality of Life (TBI-QOL) Pain Interference item bank.

      Results

      Confirmatory factor analysis provided evidence of a single underlying trait (χ2 [740]=3254.030; P<.001; Comparative Fix Index=0.988; Tucker-Lewis Index=0.980; Root Mean Square Error of Approximation=0.076) and a graded response model (GRM) supported item fit of 40 Pain Interference items. Items did not exhibit differential item functioning or local item dependence. GRM calibration data were used to inform the selection of a 10-item static short form and to program a TBI-QOL Pain Interference CAT. Comparative analyses indicated excellent comparability and reliability across test administration formats.

      Conclusion

      The 40-item TBI-QOL Pain Interference item bank demonstrated strong psychometric properties. End users can administer this measure as either a 10-item short form or CAT.

      Keywords

      List of abbreviations:

      CAT (computer adaptive test), CFA (confirmatory factor analysis), CFI (comparative fit index), DIF (differential item functioning), GRM (graded response model), IRT (item response theory), PRO (patient-reported outcome), PROMIS (Patient-Reported Outcomes Measurement Information System), RMSEA (Root Mean Square Error of Approximation), TBI (traumatic brain injury), TBI-QOL (Traumatic Brain Injury–Quality of Life measurement system), TLI (Tucker-Lewis index)
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Archives of Physical Medicine and Rehabilitation
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Faul M.
        • Xu L.
        • Wald M.M.
        • Coronado V.G.
        Traumatic Brain Injury in the United States: Emergency Department Visits, Hospitalizations and Deaths 2002–2006.
        Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Atlanta (GA)2010 (Available at: https://www.cdc.gov/traumaticbraininjury/pdf/blue_book.pdf. Accessed October 24, 2019)
        • Nampiaparampil D.E.
        Prevalence of chronic pain after traumatic brain injury: a systematic review.
        JAMA. 2008; 300: 711-719
        • Sullivan M.D.
        • Ballantyne J.C.
        Must we reduce pain intensity to treat chronic pain?.
        Pain. 2016; 157: 65-69
        • Kerns R.D.
        • Turk D.C.
        • Rudy T.E.
        The West Haven-Yale Multidimensional Pain Inventory (WHYMPI).
        Pain. 1985; 23: 345-356
        • Cleeland C.S.
        • Ryan K.M.
        Pain assessment: global use of the Brief Pain Inventory.
        Ann Acad Med Singapore. 1994; 23: 129-138
        • Becker J.
        • Schwartz C.
        • Saris-Baglama R.N.
        • Kosinski M.
        • Bjorner J.B.
        Using item response theory (IRT) for developing and evaluating the pain impact questionnaire (PIQ-6 (TM)).
        Pain Med. 2007; 8: 129-144
        • Pollard C.A.
        Preliminary validity study of the pain disability index.
        Percept Mot Skills. 1984; 59: 974
        • Dworkin R.H.
        • Turk D.C.
        • Farrar J.T.
        • et al.
        Core outcome measures for chronic pain clinical trials: IMMPACT recommendations.
        Pain. 2005; 113: 9-19
        • Bellamy N.
        • Buchanan W.W.
        • Goldsmith C.H.
        • Campbell J.
        • Stitt L.W.
        Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee.
        J Rheumatol. 1988; 15: 1833-1840
        • Roland M.
        • Morris R.
        A study of the natural history of low-back pain. Part II: development of guidelines for trials of treatment in primary care.
        Spine (Phila Pa 1976). 1983; 8: 145-150
        • Roland M.
        • Morris R.
        A study of the natural history of back pain. Part I: development of a reliable and sensitive measure of disability in low-back pain.
        Spine (Phila Pa 1976). 1983; 8: 141-144
        • Cella D.
        • Yount S.
        • Rothrock N.
        • et al.
        The Patient-Reported Outcomes Measurement Information System (PROMIS): progress of an NIH Roadmap cooperative group during its first two years.
        Med Care. 2007; 45: 3-11
        • Reeve B.B.
        • Hays R.D.
        • Bjorner J.B.
        • et al.
        Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS).
        Med Care. 2007; 45: 22-31
        • Carlozzi N.
        • Lai J.S.
        • Schilling S.
        • et al.
        Extending PROMIS and neuro-QOL to Huntington disease: new measures of chorea, speech and swallowing difficulties, and end of life concerns.
        Qual Life Res. 2015; 24: 66
        • Cella D.
        • Lai J.S.
        • Nowinski C.J.
        • et al.
        Neuro-QOL: brief measures of health-related quality of life for clinical research in neurology.
        Neurology. 2012; 78: 1860-1867
        • Cella D.F.
        • Nowinski C.
        • Peterman A.
        • et al.
        The Neurology Quality of Life Measurement (Neuro-QOL) Initiative.
        Arch Phys Med Rehabil. 2011; 92: 28-36
        • Bode R.K.
        • Lai J.S.
        • Cella D.
        • Heinemann A.W.
        Issues in the development of an item bank.
        Arch Phys Med Rehabil. 2003; 84: 52-60
        • Ware Jr., J.E.
        Conceptualization and measurement of health-related quality of life: comments on an evolving field.
        Arch Phys Med Rehabil. 2003; 84: 43-51
        • Amtmann D.
        • Cook K.F.
        • Jensen M.P.
        • et al.
        Development of a PROMIS item bank to measure pain interference.
        Pain. 2010; 150: 173-182
        • Tulsky D.S.
        • Kisala P.A.
        • Victorson D.
        • et al.
        TBI-QOL: development and calibration of item banks to measure patient reported outcomes following traumatic brain injury.
        J Head Trauma Rehabil. 2016; 31: 40-51
        • PROMIS
        PROMIS Instrument Development and Psychometric Evaluation Scientific Standards.
        (Available at:) (Accessed October 24, 2019)
        • Carlozzi N.E.
        • Tulsky D.S.
        • Kisala P.A.
        Traumatic brain injury patient-reported outcome measure: identification of health-related quality-of-life issues relevant to individuals with traumatic brain injury.
        Arch Phys Med Rehabil. 2011; 92: 52-60
        • Tulsky D.S.
        • Tyner C.E.
        • Boulton A.J.
        • et al.
        Development of the TBI-QOL Headache Pain and Short Form.
        J Head Trauma Rehabil. 2019; 34: 298-307
        • Tourangeau R.
        Cognitive sciences and survey methods.
        in: Jabine T. Straf M. Tanur J. Tourangeau R. Cognitive aspects of survey methodology: building a bridge between disciplines. National Academy Press, Washington (DC)1984: 73-100
      1. Willis GB. Cognitive interviewing: a “how to” guide. Presented at: Meeting of the American Statistical Association. August 8-12, 1999; Alexandria, VA.

        • Eremenco S.L.
        • Cella D.
        • Arnold B.J.
        A comprehensive method for the translation and cross-cultural validation of health status questionnaires.
        Eval Health Prof. 2005; 28: 212-232
        • MetaMetrics
        The LEXILE framework for reading.
        MetaMetrics Inc, Durham1995
        • Traumatic Brain Injury Model Systems National Data Center
        Traumatic brain injury model systems national data base inclusion criteria.
        (Available at:) (Accessed October 24, 2019)
        • Kisala P.A.
        • Boulton A.J.
        • Cohen M.L.
        • et al.
        Interviewer-versus self-administration of PROMIS® measures for adults with traumatic injury.
        Health Psychol. 2019; 38: 435
        • Rutherford C.
        • Costa D.
        • Mercieca-Bebber R.
        • Rice H.
        • Gabb L.
        • King M.
        Mode of administration does not cause bias in patient-reported outcome results: a meta-analysis.
        Qual Life Res. 2016; 25: 559-574
        • Muthén B.
        • Muthén L.
        Mplus user's guide.
        6th ed. Muthén & Muthén, Los Angeles1998-2010
      2. Steiger JH, Lind JC. Statistically based tests for the number of common factors. Paper presented at: Annual meeting of the Psychometric Society. May 28, 1980; Iowa City, IA.

        • Bentler P.M.
        Comparative fit indexes in structural models.
        Psychol Bull. 1990; 107: 238
        • Tucker L.R.
        • Lewis C.
        A reliability coefficient for maximum likelihood factor analysis.
        Psychometrika. 1973; 38: 1-10
        • Bentler P.M.
        • Bonett D.G.
        Significance tests and goodness of fit in the analysis of covariance structures.
        Psychol Bull. 1980; 88: 588
        • Browne M.W.
        • Cudeck R.
        Alternative ways of assessing model fit.
        in: Bollen K.A. Long J.S. Testing structural equation models. Sage, Newbury Park, CA1993: 136-162
        • Hu Lt
        • Bentler P.M.
        Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives.
        Struct Equ Modeling. 1999; 6: 1-55
        • Samejima F.
        • van der Liden W.
        • Hambleton R.
        The graded response model.
        in: Handbook of modern item response theory. Springer, New York1996: 85-100
        • Orlando M.
        • Thissen D.
        Further investigation of the performance of S-X2: an item fit index for use with dichotomous item response theory models.
        Appl Psychol Meas. 2003; 27: 289-298
        • Choi S.W.
        • Gibbons L.E.
        • Crane P.K.
        Lordif: an R package for detecting differential item functioning using iterative hybrid ordinal logistic regression/item response theory and Monte Carlo simulations.
        J Stat Softw. 2011; 39: 1
        • Stocking M.L.
        • Lord F.M.
        Developing a common metric in item response theory.
        Appl Psychol Meas. 1983; 7: 201-210
        • Thissen D.
        • Pommerich M.
        • Billeaud K.
        • Williams V.S.L.
        Item response theory for scores on tests including polytomous items with ordered responses.
        Appl Psychol Meas. 1995; 19: 39-49
        • Choi S.W.
        Firestar: computerized adaptive testing simulation program for polytomous item response theory models.
        Appl Psychol Meas. 2009; 33: 644-645
        • Gershon R.
        • Rothrock N.E.
        • Hanrahan R.T.
        • Jansky L.J.
        • Harniss M.
        • Riley W.
        The development of a clinical outcomes survey research application: assessment center.
        Qual Life Res. 2010; 19: 677-685
        • Northwestern University
        NIH Toolbox® and PROMIS® iPad Apps.
        (Available at: http://www.healthmeasures.net/resource-center/data-collection-tools/nih-toolbox-ipad-app)
        Date accessed: January 11, 2019
        • Harris P.A.
        • Taylor R.
        • Thielke R.
        • Payne J.
        • Gonzalez N.
        • Conde J.G.
        Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.
        J Biomed Inform. 2009; 42: 377-381
        • Lai J.S.
        • Cella D.
        • Choi S.
        • et al.
        How item banks and its applications can influence measurement practice in rehabilitation medicine: a PROMIS fatigue item bank example.
        Arch Phys Med Rehabil. 2011; 92: 20-27