Learning and cognitive fatigue trajectories in multiple sclerosis defined using a burst measurement design

Roee Holtzer, Frederick Foley, Vanessa D'Orio, Jessica Spat, Melissa Shuman, Cuiling Wang

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Background: Compromised learning and cognitive fatigue are critical clinical features in multiple sclerosis. Objectives: This study was designed to determine the effect of repeated exposures within and across study visits on performance measures of learning and cognitive fatigue in relapsing-remitting multiple sclerosis (RRMS). Methods: Thirty patients with RRMS and 30 controls were recruited. Using a burst measurement design (i.e. repeated assessments within and across study visits) the oral version of the Symbol Digit Modalities Test (SDMT) was administered three times during the baseline and two consecutive monthly follow-up visits for a total of nine test administrations. Learning was assessed within and across study visits whereas cognitive fatigue was assessed during the course of each test administration that was divided into three 30-second intervals. Results: Linear mixed-effect models revealed compromised learning within (95% CI: 2.6355 to 3.9867) and across (95% CI: 1.3250 to 3.1861) visits and worse cognitive fatigue (95% CI: -2.1761 to -0.1720) in patients with RRMS compared with controls. Among patients with RRMS, worse self-rated cognitive dysfunction predicted poor learning within (95% CI: -0.1112 to -0.0020) and across (95% CI: -0.0724 to -0.0106) visits. Conclusions: Burst design is optimal to study learning and cognitive fatigue. This methodology, using the SDMT or other time-efficient tests as outcome measures, can be successfully implemented in longitudinal studies and clinical trials.

Original languageEnglish (US)
Pages (from-to)1518-1525
Number of pages8
JournalMultiple Sclerosis Journal
Issue number11
StatePublished - Oct 2013


  • burst measurement design
  • cognitive fatigue
  • learning

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology


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