Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-23T05:03:12.508Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling the Structure of Acute Sport-Related Concussion Symptoms: A Bifactor Approach

Published online by Cambridge University Press:  06 August 2018

Lindsay D. Nelson ,
Mark D. Kramer ,
Christopher J. Patrick  and
Michael A. McCrea
Lindsay D. Nelson*
Affiliation:
Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin
Mark D. Kramer
Affiliation:
Steve Hicks School of Social Work, University of Texas at Austin, Austin, Texas
Christopher J. Patrick
Affiliation:
Department of Psychology, Florida State University, Tallahassee, Florida
Michael A. McCrea
Affiliation:
Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin
*
Correspondence and reprint requests to: Lindsay Nelson, 8701 West Watertown Plank Road, Milwaukee, WI. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: Concussions cause diverse symptoms that are often measured through a single symptom severity score. Researchers have postulated distinct dimensions of concussion symptoms, raising the possibility that total scores may not accurately represent their multidimensional nature. This study examined to what degree concussion symptoms, assessed by the Sport Concussion Assessment Tool 3 (SCAT3), reflect a unidimensional versus multidimensional construct to inform how the SCAT3 should be scored and advance efforts to identify distinct phenotypes of concussion. Methods: Data were aggregated across two prospective studies of sport-related concussion, yielding 219 high school and college athletes in the acute (<48 hr) post-injury period. Item-level ratings on the SCAT3 checklist were analyzed through exploratory and confirmatory factor analyses. We specified higher-order and bifactor models and compared their fit, interpretability, and external correlates. Results: The best-fitting model was a five-factor bifactor model that included a general factor on which all items loaded and four specific factors reflecting emotional symptoms, torpor, sensory sensitivities, and headache symptoms. The bifactor model demonstrated better discriminant validity than the counterpart higher-order model, in which the factors were highly correlated (r=.55–.91). Conclusions: The SCAT3 contains items that appear unidimensional, suggesting that it is appropriate to quantify concussion symptoms with total scores. However, evidence of multidimensionality was revealed using bifactor modeling. Additional work is needed to clarify the nature of factors identified by this model, explicate their clinical and research utility, and determine to what degree the model applies to other stages of injury recovery and patient subgroups. (JINS, 2018, 24, 793–804)

Keywords

Head injuryConcussionSportsExploratory factor analysisConfirmatory factor analysisBifactor modelHierarchical modelHigher-order factor modelPsychometric propertiesClinical phenotypes
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 24 , Issue 8 , September 2018 , pp. 793 - 804
Copyright
Copyright © The International Neuropsychological Society 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Archer, R.P., Handel, R.W., Ben-Porath, Y.S., & Tellegen, A. (2014). MMPI-A-RF Manual for administration, scoring, and interpretation. Minneapolis, MN: University of Minnesota Press.Google Scholar
Ayr, L.K., Yeates, K.O., Taylor, H.G., & Browne, M. (2009). Dimensions of postconcussive symptoms in children with mild traumatic brain injuries. Journal of the International Neuropsychological Society, 15(1), 19–30. doi: 10.1017/S1355617708090188 Google Scholar
Ben-Porath, Y.S., & Tellegen, A. (2008). Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF). Minneapolis, MN: University of Minnesota Press.Google Scholar
Broglio, S.P., McCrea, M., McAllister, T., Harezlak, J., Katz, B., & Hack, D., . . . Investigators, Care Consortium. (2017). A national study on the effects of concussion in collegiate athletes and US military service academy members: The NCAA-DoD Concussion Assessment, Research and Education (CARE) Consortium Structure and Methods. Sports Medicine, 47(7), 1437–1451. doi: 10.1007/s40279-017-0707-1 Google Scholar
Broshek, D.K., De Marco, A.P., & Freeman, J.R. (2015). A review of post-concussion syndrome and psychological factors associated with concussion. Brain Injury, 29(2), 228–237. doi: 10.3109/02699052.2014.974674 Google Scholar
Campbell, D.T., & Fiske, D.W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.Google Scholar
Chen, F.F., Hayes, A., Carver, C.S., Laurenceau, J.P., & Zhang, Z. (2012). Modeling general and specific variance in multifaceted constructs: A comparison of the bifactor model to other approaches. Journal of Personality, 80(1), 219–251. doi: 10.1111/j.1467-6494.2011.00739.x Google Scholar
Collins, M.W., Kontos, A.P., Reynolds, E., Murawski, C.D., & Fu, F.H. (2014). A comprehensive, targeted approach to the clinical care of athletes following sport-related concussion. Knee Surgery, Sports Traumatology, Arthroscopy, 22(2), 235–246. doi: 10.1007/s00167-013-2791-6 Google Scholar
Cucina, J., & Byle, K. (2017). The bifactor model fits better than the higher-order model in more than 90% of comparisons of mental abilities test batteries. Journal of Intelligence, 5, 27.Google Scholar
Derogatis, L.R. (1993). BSI, Brief Symptom Inventory: Administration, scoring, and procedures manual (4th ed.). Minneapolis, MN: National Computer Systems.Google Scholar
Derogatis, L.R. (2001). Brief Symptom Inventory 18 (BSI-18): Administration, scoring, and procedures manual. Bloomington, MN: Pearson.Google Scholar
Dick, R.W. (2009). Is there a gender difference in concussion incidence and outcomes? British Journal of Sports Medicine, 43(Suppl. 1), i46–i50. doi: 10.1136/bjsm.2009.058172 Google Scholar
Ebesutani, C., Smith, A., Bernstein, A., Chorpita, B.F., Higa-McMillan, C., & Nakamura, B. (2011). A bifactor model of negative affectivity: Fear and distress components among younger and older youth. Psychological Assessment, 23(3), 679–691. doi: 10.1037/a0023234 Google Scholar
Field, M., Collins, M.W., Lovell, M.R., & Maroon, J. (2003). Does age play a role in recovery from sports-related concussion? A comparison of high school and collegiate athletes. Journal of Pediatrics, 142(5), 546–553. doi: 10.1067/mpd.2003.190 Google Scholar
Franke, L.M., Czarnota, J.N., Ketchum, J.M., & Walker, W.C. (2015). Factor analysis of persistent postconcussive symptoms within a military sample with blast exposure. Journal of Head Trauma Rehabilitation, 30(1), E34–E46. doi: 10.1097/HTR.0000000000000042 Google Scholar
Galetta, K.M., Brandes, L.E., Maki, K., Dziemianowicz, M.S., Laudano, E., Allen, M., & Balcer, L.J. (2011). The King-Devick test and sports-related concussion: Study of a rapid visual screening tool in a collegiate cohort. Journal of the Neurological Sciences, 309(1-2), 34–39. doi: 10.1016/j.jns.2011.07.039 Google Scholar
Gignac, G.E., & Watkins, M.W. (2013). Bifactor modeling and the estimation of model-based reliability in the WAIS-IV. Multivariate Behavioral Research, 48(5), 639–662. doi: 10.1080/00273171.2013.804398 Google Scholar
Guskiewicz, K.M., Ross, S.E., & Marshall, S.W. (2001). Postural stability and neuropsychological deficits after concussion in collegiate athletes. Journal of Athletic Training, 36(3), 263–273.Google Scholar
Harris, P.A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J.G. (2009). Research electronic data capture (REDCap)--A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42(2), 377–381. doi: 10.1016/j.jbi.2008.08.010 Google Scholar
Helmick, K., Guskiewicz, K., Barth, J., Cantu, R., Kelly, J., McDonald, E., & Warden, D. (2006). Defense and Veterans Brain Injury Center Working Group on the Acute Management of Mild Traumatic Brain Injury in Military Operational Settings: Clinical practice guideline and recommendations. Washington, DC: Defense and Veteran Brain Injury Center. Retrieved from http://www.pdhealth.mil/downloads/clinical_practice_guideline_recommendations.pdf.Google Scholar
Joyce, A.S., Labella, C.R., Carl, R.L., Lai, J.S., & Zelko, F.A. (2015). The Postconcussion Symptom Scale: Utility of a three-factor structure. Medicine and Science in Sports and Exercise, 47(6), 1119–1123. doi: 10.1249/MSS.0000000000000534 Google Scholar
Kontos, A.P., Elbin, R.J., Schatz, P., Covassin, T., Henry, L., Pardini, J., & Collins, M.W. (2012). A revised factor structure for the post-concussion symptom scale: Baseline and postconcussion factors. American Journal of Sports Medicine, 40(10), 2375–2384. doi: 10.1177/0363546512455400 Google Scholar
Mansolf, M., & Reise, S.P. (2017). When and why the second-order and bifactor models are distinguishable. Intelligence, 61, 120–129.Google Scholar
McCrea, M., Guskiewicz, K.M., Marshall, S.W., Barr, W., Randolph, C., Cantu, R.C., & Kelly, J.P. (2003). Acute effects and recovery time following concussion in collegiate football players: The NCAA Concussion Study. Journal of the American Medical Association, 290(19), 2556–2563. doi: 10.1001/jama.290.19.2556 Google Scholar
McCrea, M., Kelly, J.P., Randolph, C., Kluge, J., Bartolic, E., Finn, G., & Baxter, B. (1998). Standardized assessment of concussion (SAC): On-site mental status evaluation of the athlete. Journal of Head Trauma Rehabilitation, 13(2), 27–35.Google Scholar
McCrory, P., Meeuwisse, W., Dvorak, J., Aubry, M., Bailes, J., Broglio, S., & Vos, P.E. (2017). Consensus statement on concussion in sport-the 5th international conference on concussion in sport held in Berlin, October 2016. British Journal of Sports Medicine, 51, 838–847. doi: 10.1136/bjsports-2017-097699 Google Scholar
McCrory, P., Meeuwisse, W.H., Aubry, M., Cantu, B., Dvorak, J., Echemendia, R.J., & Turner, M. (2013). Consensus statement on concussion in sport: The 4th International Conference on Concussion in Sport held in Zurich, November 2012. British Journal of Sports Medicine, 47(5), 250–258. doi: 10.1136/bjsports-2013-092313 Google Scholar
McMahon, P., Hricik, A., Yue, J.K., Puccio, A.M., Inoue, T., & Lingsma, H.F., . . . TRACK-TBI Investigators. (2014). Symptomatology and functional outcome in mild traumatic brain injury: Results from the prospective TRACK-TBI study. Journal of Neurotrauma, 31(1), 26–33. doi: 10.1089/neu.2013.2984 Google Scholar
Merritt, V.C., & Arnett, P.A. (2014). Premorbid predictors of postconcussion symptoms in collegiate athletes. Journal of Clinical and Experimental Neuropsychology, 36(10), 1098–1111. doi: 10.1080/13803395.2014.983463 Google Scholar
Meterko, M., Baker, E., Stolzmann, K.L., Hendricks, A.M., Cicerone, K.D., & Lew, H.L. (2012). Psychometric assessment of the Neurobehavioral Symptom Inventory-22: The structure of persistent postconcussive symptoms following deployment-related mild traumatic brain injury among veterans. Journal of Head Trauma Rehabilitation, 27(1), 55–62. doi: 10.1097/HTR.0b013e318230fb17 Google Scholar
Muthen, B., & Asparouhov, T. (2006). Item response mixture modeling: Application to tobacco dependence criteria. Addictive Behaviors, 31(6), 1050–1066. doi: 10.1016/j.addbeh.2006.03.026 Google Scholar
Muthén, L.K., & Muthén, B.O. (1998-2015). Mplus User’s Guide (7th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
Nelson, L.D., & Foell, J. (2018). Externalizing proneness and psychopathy. In C.J. Patrick (Ed.), Handbook of psychopathy (2nd ed.). New York, NY: Guilford Press.Google Scholar
Nelson, L.D., LaRoche, A.A., Pfaller, A.Y., Lerner, E.B., Hammeke, T.A., Randolph, C., & McCrea, M.A. (2016). Prospective, head-to-head study of three computerized neurocognitive assessment tools (CNTs): Reliability and validity for the assessment of sport-related concussion. Journal of the International Neuropsychological Society, 22(1), 24–37. doi: 10.1017/S1355617715001101 Google Scholar
Nelson, L.D., Tarima, S., LaRoche, A.A., Hammeke, T.A., Barr, W.B., Guskiewicz, K., &McCrea, M.A. (2016). Preinjury somatization symptoms contribute to clinical recovery after sport-related concussion. Neurology, 86, 1856–1863.Google Scholar
Patrick, C.J., Curtin, J.J., & Tellegen, A. (2002). Development and validation of a brief form of the Multidimensional Personality Questionnaire. Psychological Assessment, 14(2), 150–163.Google Scholar
Patrick, C.J., Fowles, D.C., & Krueger, R.F. (2009). Triarchic conceptualization of psychopathy: Developmental origins of disinhibition, boldness, and meanness. Development and Psychopathology, 21(3), 913–938.Google Scholar
Patrick, C.J., Hicks, B.M., Nichol, P.E., & Krueger, R.F. (2007). A bifactor approach to modeling the structure of the psychopathy checklist-revised. Journal of Personality Disorders, 21(2), 118–141. doi: 10.1521/pedi.2007.21.2.118 Google Scholar
Piland, S.G., Motl, R.W., Ferrara, M.S., & Peterson, C.L. (2003). Evidence for the Factorial and Construct Validity of a Self-Report Concussion Symptoms Scale. Journal of Athletic Training, 38(2), 104–112.Google Scholar
Potter, S., Leigh, E., Wade, D., & Fleminger, S. (2006). The Rivermead Post Concussion Symptoms Questionnaire: A confirmatory factor analysis. Journal of Neurology, 253(12), 1603–1614. doi: 10.1007/s00415-006-0275-z Google Scholar
Reise, S.P. (2012). Invited paper: The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696. doi: 10.1080/00273171.2012.715555 Google Scholar
Reise, S.P., Morizot, J., & Hays, R.D. (2007). The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of Life Research, 16(Suppl. 1), 19–31. doi: 10.1007/s11136-007-9183-7 Google Scholar
Rodriguez, A., Reise, S.P., & Haviland, M.G. (2016). Evaluating bifactor models: Calculating and interpreting statistical indices. Psychological Methods, 21(2), 137–150. doi: 10.1037/met0000045 Google Scholar
Root, J.M., Zuckerbraun, N.S., Wang, L., Winger, D.G., Brent, D., Kontos, A., & Hickey, R.W. (2016). History of somatization Is associated with prolonged recovery from concussion. Journal of Pediatrics, 174, 39–44 e31. doi: 10.1016/j.jpeds.2016.03.020 Google Scholar
Sady, M.D., Vaughan, C.G., & Gioia, G.A. (2014). Psychometric characteristics of the postconcussion symptom inventory in children and adolescents. Archives of Clinical Neuropsychology, 29(4), 348–363. doi: 10.1093/arclin/acu014 Google Scholar
Thomas, M.L. (2012). Rewards of bridging the divide between measurement and clinical theory: Demonstration of a bifactor model for the Brief Symptom Inventory. Psychological Assessment, 24(1), 101–113. doi: 10.1037/a0024712 Google Scholar
Vanderploeg, R.D., Silva, M.A., Soble, J.R., Curtiss, G., Belanger, H.G., Donnell, A.J., & Scott, S.G. (2015). The structure of postconcussion symptoms on the Neurobehavioral Symptom Inventory: A comparison of alternative models. Journal of Head Trauma Rehabilitation, 30(1), 1–11. doi: 10.1097/HTR.0000000000000009 Google Scholar
Waljas, M., Iverson, G.L., Hartikainen, K.M., Liimatainen, S., Dastidar, P., Soimakallio, S., & Ohman, J. (2012). Reliability, validity and clinical usefulness of the BNI fatigue scale in mild traumatic brain injury. Brain Injury, 26(7-8), 972–978. doi: 10.3109/02699052.2012.660511 Google Scholar
Wechsler, D. (2001). Wechsler test of adult reading: WTAR. San Antonio, TX: The Psychological Corporation.Google Scholar
Supplementary material: File

Nelson et al. supplementary material

Nelson et al. supplementary material 1

Download Nelson et al. supplementary material(File)
File 23.2 KB