Longitudinal trajectories of digital upper limb biomarkers for multiple sclerosis

Background: Upper limb dysfunction is a common debilitating feature of relapsing-remitting multiple sclerosis (RRMS). We aimed to examine the longitudinal trajectory of the iPad (R)-based Manual Dexterity Test (MDT) and predictors of change over time. Methods: We prospectively enrolled RRMS patients (limited to Expanded Disability Status Scale (EDSS) < 4). Longitudinal data was analysed using mixed-effect modelling and latent class mixed models. We then examined whether group membership in latent classes predicted confirmed slowing in MDT. Results: Seven hundred and twenty-one participants had complete data for analysis. At a population level, MDT remained stable over time. No practice effect was seen. Baseline disability and T2 lesion volume were the strongest predictors of longitudinal MDT performance. We identified two latent class trajectories of MDT. The slower latent class was typified by greater variability and a weak association with confirmed worsening of MDT and EDSS. When compared to trajectory analysis stratified by baseline MDT, latent class analysis (LCA) was able to identify those at greater risk of confirmed slowing, signifying the importance of latent processes in upper limb function in pwMS. Conclusion: In this cohort of mild to moderate RRMS, MDT scores remained stable over time with no evidence of a practice effect at a population level. Trajectory analysis based on LCA identified a cohort with greater variability and risk of disability progression and domain specific worsening. Our findings demonstrate the importance of latent processes in determining upper limb function in pwMS.