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Psychometric Properties of the NIH Toolbox Cognition Battery in Healthy Older Adults: Reliability, Validity, and Agreement with Standard Neuropsychological Tests

Published online by Cambridge University Press:  01 July 2019

Emmi P. Scott*
Affiliation:
Department of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA
Anne Sorrell
Affiliation:
Appalachian State University, Boone, NC, USA
Andreana Benitez
Affiliation:
Department of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA
*
*Correspondence and reprint requests to: Emmi P. Scott, Department of Neurology, Medical University of South Carolina, 96 Jonathan Lucas Street MSC 323, Charleston, SC 29425, USA. E-mail: scottemm@musc.edu

Abstract

Objective:

Few independent studies have examined the psychometric properties of the NIH Toolbox Cognition Battery (NIHTB-CB) in older adults, despite growing interest in its use for clinical purposes. In this paper we report the test–retest reliability and construct validity of the NIHTB-CB, as well as its agreement or concordance with traditional neuropsychological tests of the same construct to determine whether tests could be used interchangeably.

Methods:

Sixty-one cognitively healthy adults ages 60–80 completed “gold standard” (GS) neuropsychological tests, NIHTB-CB, and brain MRI. Test–retest reliability, convergent/discriminant validity, and agreement statistics were calculated using Pearson’s correlations, concordance correlation coefficients (CCC), and root mean square deviations.

Results:

Test–retest reliability was acceptable (CCC = .73 Fluid; CCC = .85 Crystallized). The NIHTB-CB Fluid Composite correlated significantly with cerebral volumes (r’s = |.35−.41|), and both composites correlated highly with their respective GS composites (r’s = .58−.84), although this was more variable for individual tests. Absolute agreement was generally lower (CCC = .55 Fluid; CCC = .70 Crystallized) due to lower precision in fluid scores and systematic overestimation of crystallized composite scores on the NIHTB-CB.

Conclusions:

These results support the reliability and validity of the NIHTB-CB in healthy older adults and suggest that the fluid composite tests are at least as sensitive as standard neuropsychological tests to medial temporal atrophy and ventricular expansion. However, the NIHTB-CB may generate different estimates of performance and should not be treated as interchangeable with established neuropsychological tests.

Type
Regular Research
Copyright
Copyright © INS. Published by Cambridge University Press, 2019. 

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References

REFERENCES

Altman, D.G. (1991). Practical Statistics for Medical Research. London: Chapman and Hall.Google Scholar
Altman, D.G. & Bland, J.M. (1983). Measurement in medicine: The analysis of method comparison studies. Journal of the Royal Statistical Society. Series D (The Statistician), 32(3), 307317. doi: 10.2307/2987937Google Scholar
Azab, M., Carone, M., Ying, S.H., & Yousem, D.M. (2015). Mesial temporal sclerosis: Accuracy of NeuroQuant versus neuroradiologist. AJNR. American Journal of Neuroradiology, 36(8), 14001406. doi: 10.3174/ajnr.A4313CrossRefGoogle ScholarPubMed
Barchard, K.A. (2012). Examining the reliability of interval level data using root mean square differences and concordance correlation coefficients. Psychological Methods, 17(2), 294308. doi: 10.1037/a0023351CrossRefGoogle ScholarPubMed
Barnhart, H.X., Haber, M.J., & Lin, L.I. (2007). An overview on assessing agreement with continuous measurements. Journal of Biopharmaceutical Statistics, 17(4), 529569. doi: 10.1080/10543400701376480CrossRefGoogle ScholarPubMed
Berg, J.-L., Durant, J., Banks, S.J., & Miller, J.B. (2016). Estimates of premorbid ability in a neurodegenerative disease clinic population: comparing the Test of Premorbid Functioning and the Wide Range Achievement Test, 4th Edition. The Clinical Neuropsychologist, 30(4), 547557. doi: 10.1080/13854046.2016.1186224CrossRefGoogle Scholar
Bondi, M.W., Edmonds, E.C., Jak, A.J., Clark, L.R., Delano-Wood, L., McDonald, C.R., … Salmon, D.P. (2014). Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. Journal of Alzheimer’s Disease: JAD, 42(1), 275289. doi: 10.3233/JAD-140276CrossRefGoogle ScholarPubMed
Buckley, R.F., Sparks, K.P., Papp, K.V., Dekhtyar, M., Martin, C., Burnham, S., … Rentz, D.M. (2017). Computerized cognitive testing for use in clinical trials: A comparison of the NIH Toolbox and cogstate C3 batteries. The Journal of Prevention of Alzheimer’s Disease, 4(1), 311. doi: 10.14283/jpad.2017.1Google ScholarPubMed
Carlozzi, N.E., Tulsky, D.S., Wolf, T.J., Goodnight, S., Heaton, R.K., Casaletto, K.B., … Heinemann, A.W. (2017). Construct validity of the NIH Toolbox Cognition Battery in individuals with stroke. Rehabilitation Psychology, 62(4), 443454. doi: 10.1037/rep0000195CrossRefGoogle ScholarPubMed
Carrasco, J.L. & Jover, L. (2003). Estimating the generalized concordance correlation coefficient through variance components. Biometrics, 59(4), 849858.CrossRefGoogle ScholarPubMed
Casaletto, K.B., Umlauf, A., Beaumont, J., Gershon, R., Slotkin, J., Akshoomoff, N. & Heaton, R.K. (2015). Demographically corrected normative standards for the English version of the NIH toolbox cognition battery. Journal of the International Neuropsychological Society: JINS, 21(5), 378391. doi: 10.1017/S1355617715000351CrossRefGoogle ScholarPubMed
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155159.CrossRefGoogle ScholarPubMed
Dodge, H.H., Zhu, J., Harvey, D., Saito, N., Silbert, L.C., Kaye, J.A., … Albin, R.L. (2014). Biomarker progressions explain higher variability in stage-specific cognitive decline than baseline values in Alzheimer disease. Alzheimer’s & Dementia, 10(6), 690703. doi: 10.1016/j.jalz.2014.04.513CrossRefGoogle ScholarPubMed
Gershon, R.C., Cook, K.F., Mungas, D., Manly, J.J., Slotkin, J., Beaumont, J.L., & Weintraub, S. (2014). Language measures of the NIH Toolbox cognition battery. Journal of the International Neuropsychological Society: JINS, 20(6), 642651. doi: 10.1017/S1355617714000411CrossRefGoogle ScholarPubMed
Gershon, R.C., Wagster, M.V., Hendrie, H.C., Fox, N.A., Cook, K.F., & Nowinski, C.J. (2013). NIH Toolbox for assessment of neurological and behavioral function. Neurology, 80(11, Suppl. 3), S2S6. doi: 10.1212/WNL.0b013e3182872e5fCrossRefGoogle ScholarPubMed
Golden, C.J. (1978). Stroop color and word test. A manual for clinical and experimental uses. Chicago: Stoelting.Google Scholar
Hackett, K., Krikorian, R., Giovannetti, T., Melendez-Cabrero, J., Rahman, A., Caesar, E.E., … Isaacson, R.S. (2018). Utility of the NIH Toolbox for assessment of prodromal Alzheimer’s disease and dementia. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 10, 764772. doi: 10.1016/j.dadm.2018.10.002Google ScholarPubMed
Heaton, R.K., Akshoomoff, N., Tulsky, D., Mungas, D., Weintraub, S., Dikmen, S., … Gershon, R. (2014). Reliability and validity of composite scores from the NIH Toolbox Cognition Battery in adults. Journal of the International Neuropsychological Society: JINS, 20(6), 588598. doi: 10.1017/S1355617714000241CrossRefGoogle ScholarPubMed
Heister, D., Brewer, J.B., Magda, S., Blennow, K., & McEvoy, L.K. (2011). Predicting MCI outcome with clinically available MRI and CSF biomarkers. Neurology, 77(17), 16191628. doi: 10.1212/WNL.0b013e3182343314CrossRefGoogle ScholarPubMed
Holdnack, J.A., Tulsky, D.S., Slotkin, J., Tyner, C.E., Gershon, R., Iverson, G.L., & Heinemann, A.W. (2017). NIH Toolbox premorbid ability adjustments: Application in a traumatic brain injury sample. Rehabilitation Psychology, 62(4), 496508. doi: 10.1037/rep0000198CrossRefGoogle Scholar
Ivnik, R.J., Malec, J.F., Smith, G.E., Tangalos, E.G., & Petersen, R.C. (1996). Neuropsychological tests’ norms above age 55: COWAT, BNT, MAE token, WRAT-R reading, AMNART, STROOP, TMT, and JLO. The Clinical Neuropsychologist, 10(3), 262278. doi: 10.1080/13854049608406689CrossRefGoogle Scholar
Lang, S., Cadeaux, M., Opoku-Darko, M., Gaxiola-Valdez, I., Partlo, L.A., Goodyear, B.G., … Kelly, J. (2017). Assessment of cognitive, emotional, and motor domains in patients with diffuse gliomas using the national institutes of health toolbox battery. World Neurosurgery, 99, 448456. doi: 10.1016/j.wneu.2016.12.061CrossRefGoogle ScholarPubMed
Lin, L.I. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45(1), 255. doi:10.2307/2532051CrossRefGoogle ScholarPubMed
Loring, D.W., Bowden, S.C., Staikova, E., Bishop, J.A., Drane, D.L., & Goldstein, F.C. (2019). NIH toolbox picture sequence memory test for assessing clinical memory function: Diagnostic relationship to the rey auditory verbal learning test. Archives of Clinical Neuropsychology, 34(2), 268276. doi: 10.1093/arclin/acy028CrossRefGoogle ScholarPubMed
Lucas, J.A., Ivnik, R.J., Willis, F.B., Ferman, T.J., Smith, G.E., Parfitt, F.C., … Graff-Radford, N.R. (2005). Mayo’s Older African Americans Normative Studies: Normative data for commonly used clinical neuropsychological measures. The Clinical Neuropsychologist, 19(2), 162183. doi: 10.1080/13854040590945265CrossRefGoogle ScholarPubMed
Mathews, M., Abner, E., Kryscio, R., Jicha, G., Cooper, G., Smith, C., … Schmitt, F. A. (2014). Diagnostic accuracy and practice effects in the National Alzheimer’s Coordinating Center Uniform Data Set neuropsychological battery. Alzheimer’s & Dementia, 10(6), 675683. doi: 10.1016/j.jalz.2013.11.007CrossRefGoogle ScholarPubMed
McGraw, K.O. & Wong, S.P. (1996). Forming inferences about some intraclass correlation coefficients. Psychological Methods, 1(1), 3046. doi: 10.1037/1082-989X.1.1.30CrossRefGoogle Scholar
Mungas, D., Heaton, R., Tulsky, D., Zelazo, P.D., Slotkin, J., Blitz, D., … Gershon, R. (2014). Factor structure, convergent validity, and discriminant validity of the NIH Toolbox Cognitive Health Battery (NIHTB-CHB) in adults. Journal of the International Neuropsychological Society, 20(06), 579587. doi: 10.1017/S1355617714000307CrossRefGoogle ScholarPubMed
Nestor, S.M., Rupsingh, R., Borrie, M., Smith, M., Accomazzi, V., Wells, J.L., … the Alzheimer’s Disease Neuroimaging Initiative. (2008). Ventricular enlargement as a possible measure of Alzheimer’s disease progression validated using the Alzheimer’s disease neuroimaging initiative database. Brain, 131(9), 24432454. doi: 10.1093/brain/awn146CrossRefGoogle ScholarPubMed
O’Shea, A., Cohen, R., Porges, E.C., Nissim, N.R., & Woods, A.J. (2016). Cognitive aging and the hippocampus in older adults. Frontiers in Aging Neuroscience, 8. doi: 10.3389/fnagi.2016.00298CrossRefGoogle Scholar
Passing, H. & Bablok, N. (1983). A new biometrical procedure for testing the equality of measurements from two different analytical methods. Application of linear regression procedures for method comparison studies in clinical chemistry, Part I. Journal of Clinical Chemistry and Clinical Biochemistry. Zeitschrift Fur Klinische Chemie Und Klinische Biochemie, 21(11), 709720.Google ScholarPubMed
Raz, N., Lindenberger, U., Rodrigue, K.M., Kennedy, K.M., Head, D., Williamson, A., … Acker, J.D. (2005). Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cerebral Cortex (New York, N.Y.: 1991), 15(11), 16761689. doi: 10.1093/cercor/bhi044CrossRefGoogle ScholarPubMed
Reitan, R.M. & Wolfson, D. (1985). The Halstead-Reitan neuropsychological test battery: theory and clinical interpretation. Tucson, Ariz.: Neuropsychology Press.Google Scholar
Rey, A. (1964). L’examen clinique en psychologie. In Le Psychologue (2e éd.). Paris: Presses universitaires de France. WorldCat.org.Google Scholar
Schmidt, M. (1996). Rey Auditory and Verbal Learning Test: A handbook. Los Angeles: Western Psychological Services.Google Scholar
Shirk, S.D., Mitchell, M.B., Shaughnessy, L.W., Sherman, J.C., Locascio, J.J., Weintraub, S., & Atri, A. (2011). A web-based normative calculator for the uniform data set (UDS) neuropsychological test battery. Alzheimer’s Research and Therapy, 3(6), 32. doi: 10.1186/alzrt94CrossRefGoogle ScholarPubMed
Sinha, P., Wong, A.W.K., Kallogjeri, D., & Piccirillo, J.F. (2018). Baseline cognition assessment among patients with oropharyngeal cancer using PROMIS and NIH Toolbox. JAMA Otolaryngology–Head & Neck Surgery, 144(11), 978. doi: 10.1001/jamaoto.2018.0283CrossRefGoogle ScholarPubMed
Steiger, J.H. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87(2), 245251. doi: 10.1037/0033-2909.87.2.245CrossRefGoogle Scholar
Tulsky, D.S., Carlozzi, N.E., Holdnack, J., Heaton, R. K., Wong, A., Goldsmith, A., & Heinemann, A.W. (2017). Using the NIH Toolbox cognition battery (NIHTB-CB) in individuals with traumatic brain injury. Rehabilitation Psychology, 62(4), 413424. doi: 10.1037/rep0000174CrossRefGoogle ScholarPubMed
Wechsler, D. (1987). Manual for the Wechsler memory scale-revised. San Antonio, TX: Psychological Corporation.Google Scholar
Wechsler, D. (1997). Wechsler memory scale: WMS-III (3rd ed.). San Antonio: Psychological Corp., Harcourt Brace. WorldCat.org.Google Scholar
Weintraub, S., Dikmen, S.S., Heaton, R.K., Tulsky, D.S., Zelazo, P.D., Bauer, P.J., … Gershon, R.C. (2013). Cognition assessment using the NIH Toolbox. Neurology, 80(Issue 11, Suppl. 3), S54S64. doi: 10.1212/WNL.0b013e3182872dedCrossRefGoogle ScholarPubMed
Weintraub, S., Dikmen, S.S., Heaton, R.K., Tulsky, D.S., Zelazo, P.D., Slotkin, J., … Gershon, R. (2014). The cognition battery of the NIH toolbox for assessment of neurological and behavioral function: validation in an adult sample. Journal of the International Neuropsychological Society: JINS, 20(6), 567578. doi: 10.1017/S1355617714000320CrossRefGoogle Scholar
Weintraub, S., Salmon, D., Mercaldo, N., Ferris, S., Graff-Radford, N.R., Chui, H., … Morris, J.C. (2009). The Alzheimer’s Disease Centers’ Uniform Data Set (UDS): The neuropsychologic test battery. Alzheimer Disease & Associated Disorders, 23(2), 91. doi: 10.1097/WAD.0b013e318191c7ddCrossRefGoogle ScholarPubMed
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