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Influence of Special Education, ADHD, Autism, and Learning Disorders on ImPACT Validity Scores in High School Athletes

Published online by Cambridge University Press:  09 December 2020

Julia E. Maietta
Affiliation:
Department of Psychology, University of Nevada, Las Vegas, NV, USA
Kimberly A. Barchard
Affiliation:
Department of Psychology, University of Nevada, Las Vegas, NV, USA
Hana C. Kuwabara
Affiliation:
Department of Psychology, University of Nevada, Las Vegas, NV, USA
Bradley D. Donohue
Affiliation:
Department of Psychology, University of Nevada, Las Vegas, NV, USA
Staci R. Ross
Affiliation:
Center for Applied Neuroscience, Las Vegas, NV, USA
Thomas F. Kinsora
Affiliation:
Center for Applied Neuroscience, Las Vegas, NV, USA
Daniel N. Allen*
Affiliation:
Department of Psychology, University of Nevada, Las Vegas, NV, USA
*
*Correspondence and reprint requests to: Daniel N. Allen, Ph.D., Department of Psychology, University of Nevada Las Vegas, Box 455030, 4505 Maryland Parkway, Las Vegas, NV 89154-5030, USA. Tel.: +1 702 895 0121; Fax: +1 702 895 0195. Email: daniel.allen@unlv.edu

Abstract

Objective:

The Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) is commonly used to assist with post-concussion return-to-play decisions for athletes. Additional investigation is needed to determine whether embedded indicators used to determine the validity of scores are influenced by the presence of neurodevelopmental disorders (NDs).

Method:

This study examined standard and novel ImPACT validity indicators in a large sample of high school athletes (n = 33,772) with or without self-reported ND.

Results:

Overall, 7.1% of athletes’ baselines were judged invalid based on standard ImPACT validity criteria. When analyzed by group (healthy, ND), there were significantly more invalid ImPACT baselines for athletes with an ND diagnosis or special education history (between 9.7% and 54.3% for standard and novel embedded validity criteria) when compared to athletes without NDs. ND history was a significant predictor of invalid baseline performance above and beyond other demographic characteristics (i.e., age, sex, and sport), although it accounted for only a small percentage of variance. Multivariate base rates are presented stratified for age, sex, and ND.

Conclusions:

These data provide evidence of higher than normal rates of invalid baselines in athletes who report ND (based on both the standard and novel embedded validity indicators). Although ND accounted for a small percentage of variance in the prediction of invalid performance, negative consequences (e.g., extended time out of sports) of incorrect decision-making should be considered for those with neurodevelopmental conditions. Also, reasons for the overall increase noted here, such as decreased motivation, “sandbagging”, or disability-related cognitive deficit, require additional investigation.

Type
Regular Research
Copyright
Copyright © INS. Published by Cambridge University Press, 2020

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References

REFERENCES

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Association. Retrieved from https://doi.org/10.1176/appi.books.9780890425596 Google Scholar
Abeare, C.A., Messa, I., Zuccato, B.S., Merker, B., & Erdodi, L. (2018). Prevalence of invalid performance on baseline testing for sport-related concussion by age and validity indicator. JAMA Neurology, 75(6), 697703. https://doi.org/10.1001/jamaneurol.2018.0031 CrossRefGoogle ScholarPubMed
Alsalaheen, B., Stockdale, K., Pechumer, D., & Broglio, S.P. (2016). Validity of the immediate post concussion assessment and cognitive testing (ImPACT). Sports Medicine, 46(10), 14871501. https://doi.org/10.1007/s40279-016-0532-y CrossRefGoogle Scholar
Benjamini, Y. & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, 57(1), 289300.Google Scholar
Binder, L.M., Iverson, G.L., & Brooks, B.L. (2009). To err is human: “Abnormal” neuropsychological scores and variability are common in healthy adults. Archives of Clinical Neuropsychology, 24(1), 3146. https://doi.org/10.1093/arclin/acn001 CrossRefGoogle Scholar
Brett, B.L. & Solomon, G.S. (2017). Comparison of neurocognitive performance in contact and noncontact nonconcussed high school athletes across a two-year interval. Developmental Neuropsychology, 42(2), 7082. https://doi.org/10.1080/87565641.2016.1243114 CrossRefGoogle ScholarPubMed
Brooks, B.L., Iverson, G.L., & Holdnack, J.A. (2013). Understanding and using multivariate base rates with the WAIS-IV/WMS-IV. In Holdnack, J.A., Drozdick, L.W., Weiss, L.G., & Iverson, G.L. (Eds.), WAIS-IV/WMS-IV/ACS: Advanced clinical interpretations (pp. 75102). Berlin: Elsevier Science.Google Scholar
Castile, L., Collins, C.L., McIlvain, N.M., & Comstock, R.D. (2012). The epidemiology of new versus recurrent sports concussions among high school athletes, 2005–2010. British Journal of Sports Medicine, 46(8), 603610. https://doi.org/10.1136/bjsports-2011-090115 CrossRefGoogle ScholarPubMed
Chafetz, M. (2011). Reducing the probability of false positives in malingering detection of social security disability claimants. The Clinical Neuropsychologist, 25(7), 12391252. https://doi.org/10.1080/13854046.2011.586785 CrossRefGoogle ScholarPubMed
Chrisman, S.P., Rivara, F.P., Schiff, M.A., Zhou, C., & Comstock, R.D. (2013). Risk factors for concussive symptoms 1 week or longer in high school athletes. Brain Injury, 27(1), 19. https://doi.org/10.3109/02699052.2012.722251 CrossRefGoogle ScholarPubMed
Cook, N.E., Karr, J.E., Brooks, B.L., Garcia-Barrera, M.A., Holdnack, J.A., & Iverson, G.L. (2019). Multivariate base rates for the assessment of executive functioning among children and adolescents. Child Neuropsychology, 25(6), 836858. https://doi.org/10.1080/09297049.2018.1543389 CrossRefGoogle ScholarPubMed
Covassin, T., Stearne, D., Elbin, R. (2008). Concussion history and postconcussion neurocognitive performance and symptoms in collegiate athletes. Journal of Athletic Training, 43(2), 119124. https://doi.org/10.4085/1062-6050-43.2.119 CrossRefGoogle ScholarPubMed
Elbin, R.J., Kontos, A.P., Kegel, E., Johnson, E., Burkhart, S., & Schatz, P. (2013). Individual and combined effects of LD and ADHD on computerized neurocognitive concussion test performance: Evidence for separate norms. Archives of Clinical Neuropsychology, 28(5), 476484. https://doi.org/10.1093/arclin/act024 CrossRefGoogle ScholarPubMed
Elbin, R.J., Schatz, P., & Covassin, T. (2011). One-year test-retest reliability of the online version of ImPACT in high school athletes. American Journal of Sports and Medicine, 39(11), 23192324. https://doi.org/10.1177/0363546511417173 CrossRefGoogle ScholarPubMed
Erdal, K. (2012). Neuropsychological testing for sports-related concussion: How athletes can sandbag their baseline testing without detection. Archives of Clinical Neuropsychology, 27(5), 473479. https://doi.org/10.1093/arclin/acs050 CrossRefGoogle ScholarPubMed
Gardner, R.M., Yengo-Kahn, A., Bonfield, C.M., & Solomon, G.S. (2017). Comparison of baseline and post-concussion ImPACT test scores in young athletes with stimulant-treated and untreated ADHD. The Physician and Sportsmedicine, 45(1), 110. https://doi.org/10.1080/00913847.2017.1248221 CrossRefGoogle ScholarPubMed
Gaudet, C.E. & Weyandt, L.L. (2017). Immediate post-concussion and cognitive testing (ImPACT): A systematic review of the prevalence and assessment of invalid performance. The Clinical Neuropsychologist, 31(1), 4358. https://doi.org/10.1080/13854046.2016.1220622 CrossRefGoogle ScholarPubMed
Higgins, K.L., Denney, R.L., Maerlender, A. (2017). Sandbagging on the immediate post-concussion assessment and cognitive testing (ImPACT) in a high school athlete population. Archives of Clinical Neuropsychology, 32(3), 259266. https://doi.org/10.1093/arclin/acw108 Google Scholar
Holdnack, J.A. (2019). The development, expansion, and future of the WAIS-IV as a cornerstone in comprehensive cognitive assessments. In Goldstein, G., Allen, D.N., & DeLuca, J. (Eds.), Handbook of psychological assessment (pp. 103139). Berlin: Elsevier Academic Press. https://doi.org/10.1016/B978-0-12-802203-0.00004-3 CrossRefGoogle Scholar
Inman, T.H. & Berry, D.T. (2002). Cross-validation of indicators of malingering: A comparison of nine neuropsychological tests, four tests of malingering, and behavioral observations. Archives of Clinical Neuropsychology, 17(1), 123.CrossRefGoogle ScholarPubMed
Iverson, G.L. & Franzen, M.D. (1996). Using multiple objective memory procedures to detect simulated malingering. Journal of Clinical and Experimental Neuropsychology, 18(1), 3851. https://doi.org/10.1080/01688639608408260 CrossRefGoogle ScholarPubMed
Larrabee, G.J. (2014). False-positive rates associated with the use of multiple performance and symptom validity tests. Archives of Clinical Neuropsychology, 29(4), 364373. https://doi.org/10.1093/arclin/acu019 CrossRefGoogle ScholarPubMed
Lippa, S.M. (2018). Performance validity testing in neuropsychology: A clinical guide, critical review, and update on a rapidly evolving literature. The Clinical Neuropsychologist, 32(3), 391421. https://doi.org/10.1080/13854046.2017.1406146 CrossRefGoogle ScholarPubMed
Lovell, M.R. (2018). ImPACT Administration and Interpretation Manual [Measurement Instrument]. Retrieved from https://impacttest.com/ Google Scholar
Kaye, S., Sundman, M.H., Hall, E.E., Williams, E., Patel, K., & Ketcham, C.J. (2019). Baseline neurocognitive performance and symptoms in those with attention deficit hyperactivity disorders and history of concussion with previous loss of consciousness. Frontiers in Neurology, 10, 396. https://doi.org/10.3389/fneur.2019.00396 CrossRefGoogle ScholarPubMed
Kerr, Z.Y., Snook, E.M., Lynall, R.C., Dompier, T.P., Sales, L., Parsons, J.T., & Hainline, B. (2015). Concussion-related protocols and preparticipation assessments used for incoming student-athletes in National Collegiate Athletic Association member institutions. Journal of Athletic Training, 50(11), 11741181.CrossRefGoogle ScholarPubMed
Manderino, L.M. & Gunstad, J. (2018a). Collegiate student athletes with history of ADHD or academic difficulties are more likely to produce an invalid protocol on baseline ImPACT testing. Clinical Journal or Sports Medicine, 28(2), 111116. https://doi.org/10.1097/JSM.0000000000000433 CrossRefGoogle ScholarPubMed
Manderino, L.M. & Gunstad, J. (2018b). Performance of the immediate post-concussion assessment and cognitive testing protocol validity indices. Archives of Clinical Neuropsychology, 33(5), 596605. https://doi.org/10.1093/arclin/acx102 CrossRefGoogle ScholarPubMed
Manderino, L.M., Zachman, A.M., & Gunstad, J. (2019). Novel ImPACT validity indices in collegiate student-athletes with and without histories of ADHD or academic difficulties. The Clinical Neuropsychologist, 33(8), 14551466. https://doi.org/10.1080/13854046.2018.1539191 CrossRefGoogle ScholarPubMed
Mayes, S.D. & Calhoun, S.L. (2006). Frequency of reading, math, and writing disabilities in children with clinical disorders. Learning and Individual Differences, 16, 145157.CrossRefGoogle Scholar
McClure, D.J., Zuckerman, S.L., Kutscher, S.J., Gregory, A.J., Solomon, G.S. (2013). Baseline neurocognitive testing in sports-related concussions: The importance of a prior night’s sleep. The American Journal of Sports Medicine, 42(2), 472478. https://doi.org/10.1177/0363546513510389 CrossRefGoogle ScholarPubMed
Parke, E.M., Thaler, N.S., Etcoff, L.M., & Allen, D.N. (2015). Intellectual profiles in children with ADHD and comorbid learning and motor disorders. Journal of Attention Disorders, 110. https://doi.org/10.1177/1087054715576343 Google ScholarPubMed
Peltonen, K., Vartiainen, M., Laitala-Leinonen, T., Koskinen, S., Luoto, T., Pertab, J., & Hokkanen, L. (2019). Adolescent athletes with learning disability display atypical maturational trajectories on concussion baseline testing: Implications based on a Finnish sample. Child Neuropsychology, 25(3), 336351. https://doi.org/10.1080/09297049.2018.1474865 CrossRefGoogle ScholarPubMed
Poysophon, P. & Rao, A.L. (2018). Neurocognitive deficits associated with ADHD in athletes: A systematic review. Sports Health – A Multidisciplinary Approach, 10(4), 317326. https://doi.org/10.1177/1941738117751387 CrossRefGoogle ScholarPubMed
Raab, C.A., Peak, A.S., & Knoderer, C. (2020). Half of purposeful baseline sandbaggers undetected by impact’s embedded invalidity indicators. Archives of Clinical Neuropsychology, 35(3), 283290. https://doi.org/10.1093/arclin/acz001 CrossRefGoogle ScholarPubMed
Rabinowtitz, A.R., Merritt, V.C., & Arnett, P.A. (2015). The return-to-play incentive and the effect of motivation on neuropsychological test-performance: Implications for baseline concussion testing. Developmental Neuropsychology, 40(1), 2933. https://doi.org/10.1080/87565641.2014.1001066 CrossRefGoogle Scholar
Rice, S.G. (2008). Medical conditions affecting sports participation. Pediatrics, 121(4), 841848. https://doi.org/10.1542/peds.2008-0080 CrossRefGoogle ScholarPubMed
Salinas, C.M., Dean, P., LoGalbo, A., Dougherty, M., Field, M., & Webbe, F.M. (2016). Attention-deficit hyperactivity disorder status and baseline neurocognitive performance in high school athletes. Applied Neuropsychology: Child, 5(4), 264272. https://doi.org/10.1080/21622965.2015.1052814 CrossRefGoogle ScholarPubMed
Schatz, P., Elbin, R.J., Anderson, M.N., Savage, J., & Covassin, T. (2017). Exploring sandbagging behaviors, effort, and perceived utility of the ImPACT baseline assessment in college athletes. Sport, Exercise, and Performance Psychology, 6(3), 243251. https://doi.org/10.1037/spy0000100 CrossRefGoogle Scholar
Schatz, P. & Glatts, C. (2013). “Sandbagging” baseline test performance on ImPACT, without detection, is more difficult than it appears. Archives of Clinical Neuropsychology, 28(3), 236244. https://doi.org/10.1093/arclin/act009s CrossRefGoogle ScholarPubMed
Schatz, P., Kelley, T., Ott, S.D., Solomon, G.S., Elbin, R.J., Higgins, K., & Scolaro Moser, R. (2014). Utility of repeated assessment after invalid baseline neurocognitive test performance. Journal of Athletic Training, 49(5), 659664. https://doi.org/10.4085/1062-6050-49.3.37 CrossRefGoogle ScholarPubMed
Schatz, P., Scolaro Moser, R., Solomon, G.S., Ott, S.D., & Karpf, R. (2012). Prevalence of invalid computerized baseline neurocognitive test results in high school and collegiate athletes. Journal of Athletic Training, 47(3), 289296. https://doi.org/10.4085/1062-6050-47.3.14 CrossRefGoogle ScholarPubMed
Vickery, C.D., Berry, D.T., Dearth, C.S., Vagnini, V.L., Baser, R.E., Cragar, D.E., & Orey, S.A. (2004). Head injury and the ability to feign neuropsychological deficits. Archives of Clinical Neuropsychology, 19(1), 3748.CrossRefGoogle ScholarPubMed
Walton, S.R., Broshek, D.K., Freeman, J.R., Cullum, C.M., & Resch, J.E. (2018). Valid but invalid: Suboptimal ImPACT baseline performance in university athletes. Medicine & Science in Sports & Exercise, 50(7), 13771384. https://doi.org/10.1249/MSS.0000000000001592 CrossRefGoogle ScholarPubMed
Zuckerman, S.L., Lee, Y.M., Odom, M.J., Solomon, G.S., Sills, A.K. (2013). Baseline neurocognitive scores in athletes with attention deficit-spectrum disorders and/or learning disability. Journal of Neurosurgery Pediatrics, 12(2), 103109. https://doi.org/10.3171/2013.5.PEDS12524 CrossRefGoogle ScholarPubMed