Evidence-Based Cognitive Rehabilitation for Persons With Multiple Sclerosis: An Updated Systematic Review 2016 to 2024.

OBJECTIVE: To systematically review and synthesize the evidence on cognitive rehabilitation (CR) interventions for persons with Multiple Sclerosis (PwMS) published between 2016 and 2024, extending prior reviews to identify effective treatments and future research directions. DATA SOURCE: Searches of PubMed, PsycINFO, CINAHL, Embase, and Global Health databases used MeSH terms and keywords for "multiple sclerosis" and cognitive domains (e.g., attention, memory, executive function). STUDY SELECTION: From 10,460 unique articles, 214 articles met initial inclusion. Following a detailed review, 130 were excluded, yielding 84 studies; 17 were secondary analyses of previously published research, leaving 67 with original data. DATA EXTRACTION: Data on study design, participant characteristics, intervention type, targeted cognitive domains, and outcomes were extracted from eligible studies by two independent reviewers, with discrepancies resolved by a third reviewer. Articles were abstracted, and levels of evidence were determined using American Academy of Neurology criteria. DATA SYNTHESIS: The current review yielded seven class I studies, 30 class II studies, 18 class III studies, and 12 class IV. Five interventions received support for a practice standard, five received support as practice guidelines, and several studies provided support for 17 practice options. CONCLUSION: This review shows a continued increase in CR research in MS. Much work remains to be done to optimize rehabilitation potential by applying the most methodologically rigorous research designs to provide class I evidence in support of specific treatment strategies. Longitudinal studies are needed for understanding the long-term effectiveness and durability of CR interventions. Furthermore, there is a need to establish stronger connections between treatment outcomes and everyday life functional activities, ensuring that CR strategies demonstrate meaningful improvements in real-world tasks.