Comparing exercise with virtual reality gaming on gait and cognition in relapsing-remitting multiple sclerosis: a randomized controlled trial

Background: Exercise and virtual reality gaming may mitigate gait and cognitive deficits in relapsing-remitting multiple sclerosis (RRMS). The main aim was to compare the efficacy of both interventions on gait and cognition and gait in RRMS. Secondary aims were to explore the efficacy of both interventions on serum biomarkers and to explore the predictors of treatment response.
Methods: Forty-eight participants with RRMS were randomized to exercise (n=19), VR (n=19), or wait-list control (n=10) for eight weeks. Primary outcomes were the 10-meter walk test (10MWT) and the Symbol Digit Modalities Test (SDMT). Secondary outcomes included serum levels of neurofilament light chain (NfL), brain-derived neurotrophic factor (BDNF), and insulin-like growth factor-1 (IGF-1). Extreme Gradient Boosting (XGBoost), Random Forest, and logistic regression models were trained to predict treatment response.
Results: The exercise group improved 10MWT performance by 2.41 seconds and increased IGF-1 levels ..., , # Comparing exercise with virtual reality gaming on gait and cognition in relapsing-remitting multiple sclerosis: a randomized controlled trial

Dataset DOI: [10.5061/dryad.x69p8czzx](https://doi.org/10.5061/dryad.x69p8czzx)

## Description of the data and file structure

1\. Study Description

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This dataset was generated from a single-blinded randomized controlled trial investigating the effects of exercise training and immersive virtual reality (VR) gaming compared with usual care on gait performance, cognitive function, and circulating neurobiological biomarkers in individuals with relapsing-remitting multiple sclerosis (RRMS).

Participants were randomly assigned to one of three groups: exercise intervention, VR gaming intervention, or wait-list control. All outcomes were assessed at baseline (pretest) and immediately after an 8-week intervention period (post).

The data were used for conventional statistical analyses and for supervised machine..., All data included in this dataset were collected from human participants following approval by the relevant institutional ethics committee.

Written informed consent was obtained from all participants, including explicit consent for the use and publication of de-identified research data in the public domain.

The dataset has been fully anonymized prior to deposition. All direct identifiers (such as names, contact information, national identification numbers, and exact dates of birth) were removed. Participants are represented only by randomly assigned study IDs.

The dataset contains only non-identifiable demographic variables (e.g., age in years, sex), clinical measures, biochemical markers, and functional test outcomes.

No personally identifiable information (PII) is included in this dataset, and the risk of re-identification is minimal.,