A much needed metric: Defining reliable and statistically meaningful change of the oral version Symbol Digit Modalities Test (SDMT)

L. B. Strober, J. M. Bruce, P. A. Arnett, K. N. Alschuler, J. DeLuca, N. Chiaravalloti, A. Lebkuecher, M. Di Benedetto, J. Cozart, J. Thelen, M. Cadden, E. Guty, C. A.F. Román

Research output: Contribution to journalArticlepeer-review


Background:: The Symbol Digit Modalities Test (SDMT) has been recommended for use in clinical trials and outcome studies to monitor cognitive change. However, defining what is a meaningful change has been elusive for several years. Objective:: The present investigation aimed to develop methods for assessing individual-level statistically significant change on the SDMT - reliable change indices (RCIs) and standardized regression-based (SRB) equations. Methods:: A total of 219 healthy individuals completed the oral version SDMT at baseline, 6-month and 1-year follow-up. Results:: The SDMT demonstrated high reliability across all time points (r's = 0.83 to 0.86). Reliable change scores of 7, 8, and 10 points for the 6-month intervals represented statistically meaningful change at the 0.70, 0.80, and 0.90 confidence intervals, respectively. Over 1-year, a difference of 8, 10, and 12 was statistically meaningful at the 0.70, 0.80, and 0.90 confidence intervals, respectively. SRB equations are also provided taking into account additional factors found to be predictive of SDMT scores over time. Conclusion:: Clinicians frequently denote a decline of 4 points on the SDMT as meaningful. Results in this large normative sample show that higher cut-points are needed to demonstrate statistically significant decline at the individual level. RCIs are provided for 6 month and one year assessment, which is typical in clinical practice and trials. SRB equations are also provided for use when applicable and may provide a more precise assessment of meaningful change.

Original languageEnglish (US)
Article number103405
JournalMultiple Sclerosis and Related Disorders
StatePublished - Jan 2022

All Science Journal Classification (ASJC) codes

  • Neurology
  • Clinical Neurology

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