ArticleJournal of neurology2025
Learning as a digital hallmark of cognition in multiple sclerosis: lessons from a digital symbol digit modalities test.
Article in Journal of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundMultiple Sclerosis (MS) often causes cognitive impairment that significantly impacts functional independence. The Symbol Digit Modalities Test (SDMT) is the gold standard for screening and monitoring cognitive functioning in people with MS (pwMS). Electronic SDMT (eSDMT) adaptations offer potential for remote monitoring and capturing more detailed performance metrics, compared to conventional pen-and-paper administration.
objectivesThis study aimed to evaluate novel metrics derived from a validated eSDMT to enhance cognitive assessment and detect cognitive impairment in pwMS.
methodsWe included 93 pwMS who performed an eSDMT which enables automatic collection of single-stimulus reaction times (RTs) and accuracy of responses. We investigated Performance Index-a scoring metric based on speed and accuracy-and the slope of individual RTs during the eSDMT task, which reflects response speed changes. We assessed the concurrent validity and test-retest reliability of Performance Index, as well as the impact of sociodemographic variables. Correlations with gold-standard cognitive tests of learning (California Verbal Learning Test, Brief Visuospatial Memory Test) were also explored. Hierarchical linear and logistic regression analyses were used to predict oral SDMT scores and broader cognitive impairment, respectively.
resultsParticipants exhibited improving RTs as the eSDMT progressed, reflecting generalized intra-trial learning. eSDMT Performance Index showed excellent concurrent validity with oral SDMT score, good-to-excellent test-retest reliability for remote administration, and excellent discriminant validity in detecting cognitive impairment. Including response speed trends further improved oral SDMT score prediction and detection of cognitive impairment. Response speed improvements correlated with higher scores at gold-standard neuropsychological tests of learning.
conclusionsIntra-trial learning metrics can enhance the validity of digital cognitive testing in pwMS. Future research should explore the predictive value of these metrics, particularly in remote and autonomous scenarios. The ability to collect reliable information on multiple cognitive processes in a cost- and time-effective manner via digital testing could significantly improve both clinical care and research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.