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Towards individualized monitoring of cognition in multiple sclerosis in the digital era: A one-year cohort study

Ka‐Hoo Lam, Ioan Gabriel Bucur, Pim van Oirschot, Frank de Graaf, Hans Weda, Eva Strijbis, Bernard M.J. Uitdehaag, Tom Heskes, Joep Killestein, Vincent de Groot

2022Multiple Sclerosis and Related Disorders20 citationsDOIOpen Access PDF

Abstract

BACKGROUND: Cognitive impairment is frequent in multiple sclerosis (MS), but reliable, sensitive and individualized monitoring in clinical practice is still limited. Smartphone-adapted tests may enhance the assessment of function as tests can be performed more frequently and within the daily living environment. The objectives were to prove reproducibility of a smartphone-based Symbol Digit Modalities Test (sSDMT), its responsiveness to relevant change in clinical cognitive outcomes, and develop an individual-based monitoring method for cognition. METHODS: In a one-year cohort study with 102 patients with MS, weekly sSDMTs were performed and analyzed on reproducibility parameters: the standard error of measurement (SEM) and smallest detectable change (SDC). Responsiveness of the sSDMT to relevant change in the 3-monthly clinically assessed SDMT (i.e. 4-point change) was quantified with the area under the receiver operating characteristic curve (AUC). Curve fitting of the weekly sSDMT scores of individual patients was performed with a local linear trend model to estimate and visualize the de-noised cognitive state and 95% confidence interval (CI). The optimal assessment frequency was determined by analyzing the CI bandwidth as a function of sSDMT assessment frequency. RESULTS: Weekly sSDMT showed improved reproducibility estimates (SEM=2.94, SDC=8.15) compared to the clinical SDMT. AUC-values did not exceed 0.70 in classifying relevant change in cSDMT. However, utilizing weekly sSDMT measurements, estimated state curves and the 95% CI were plotted showing detailed changes within individuals over time. With a test frequency of once per 12 days, 4-point changes in sSDMT can be detected. CONCLUSION: A local linear trend model applied on sSDMT scores of individual patients increases the signal-to-noise ratio substantially, which improves the detection of statistically reliable changes. Therefore, this fine-grained individual-based monitoring approach can be used to complement current clinical assessment to enhance clinical care in MS. TRIAL REGISTRATION: Netherlands Trial Register NL7070; https://www.trialregister.nl/trial/7070.

Topics & Concepts

ReproducibilityMedicineReceiver operating characteristicConfidence intervalCognitionCohortArea under the curveClinical PracticeAudiologyPhysical therapyPhysical medicine and rehabilitationInternal medicineStatisticsMathematicsPsychiatryMultiple Sclerosis Research StudiesCancer survivorship and careHearing Loss and Rehabilitation
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