Evidence map›Paper›PMID 41514436›Full record

Trial reportJournal of neuroengineering and rehabilitation2026

Improving dynamic balance in relapsing-remitting MS: insights from robotic-assisted rehabilitation therapy.

Jessica Podda, Ludovico Pedullà, Giorgia Marchesi, Valentina Squeri, Alice De Luca, Alice Bellosta, Giulia Rogina, Andrea Vitiello, Laura Isolabella, Margherita Monti Bragadin and 3 more

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of neuroengineering and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Jessica PoddaNeuroBRITE Research Center, Italian Multiple Sclerosis Foundation, Genoa, Italy. jessica.podda@aism.it.
Ludovico PedullàNeuroBRITE Research Center, Italian Multiple Sclerosis Foundation, Genoa, Italy.
Giorgia MarchesiMovendo Technology s.r.l, Genoa, Italy.
Valentina SqueriItalian Institute of Technology, Genoa, Italy.
Alice De LucaItalian Institute of Technology, Genoa, Italy.
Alice BellostaNeuroBRITE Research Center, Italian Multiple Sclerosis Foundation, Genoa, Italy.
Giulia RoginaItalian Multiple Sclerosis Society, Genoa, Italy.
Andrea VitielloItalian Multiple Sclerosis Society, Genoa, Italy.
Laura IsolabellaItalian Multiple Sclerosis Society, Genoa, Italy.
Margherita Monti BragadinNeuroBRITE Research Center, Italian Multiple Sclerosis Foundation, Genoa, Italy.
Mario Alberto BattagliaDepartment of Life Science, University of Siena, Siena, Italy.
Andrea TacchinoNeuroBRITE Research Center, Italian Multiple Sclerosis Foundation, Genoa, Italy.
Giampaolo BrichettoNeuroBRITE Research Center, Italian Multiple Sclerosis Foundation, Genoa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBalance deficits affect over 75% of people with multiple sclerosis (PwMS), significantly limiting mobility and daily activities. To address these symptoms, technology-assisted rehabilitation has recently shown promise for restoring balance. This study aimed to test the non-inferiority of a rehabilitative protocol that combines robotic-assisted training with conventional physiotherapy (ROB) compared to a protocol consisting solely of traditional balance training (TRAD) for PwMS.

methodsForty-two PwMS with relapsing-remitting course were randomly assigned to either ROB or TRAD group. Participants had to undergo 20 sessions (2/week for 10 weeks, 45 min each), focusing on balance exercises targeting static and dynamic control. In the ROB group, traditional exercises were progressively integrated with robotic-based tasks using the hunova® system (Movendo Technology s.r.l., Genoa, Italy). The Berg Balance Scale (BBS) was the primary outcome measure, while the Two-Minute Walking Test (2MWT) and the Composite Score (COMP) from the Sensory Organization Test were selected as secondary outcomes. Additional robotic metrics from hunova® were analyzed to assess stability under static and dynamic conditions.

resultsBoth groups showed significant overall improvements in BBS (p = 0.003), 2MWT (p = 0.031), and COMP (p < 0.001), supporting the non-inferiority of the robotic-assisted protocol. However, participants in the ROB group demonstrated additional improvements in unstable conditions compared to TRAD group. Specifically, elastic balance tasks resulted in significant reductions in path length (p = 0.047) and trunk variability (p = 0.017). Additionally, reactive balance metrics showed significant decreases in the first oscillation time for both left and right directions (p = 0.027 and p = 0.029), as well as in the average oscillation time for both directions (p = 0.006 and p = 0.012).

conclusionsRobotic-assisted rehabilitation combined with conventional physiotherapy is at least as effective as traditional therapy for improving balance in PwMS, demonstrating non-inferiority for the primary outcome. Additionally, the greater dynamic balance improvements observed in the ROB group suggest that robotic technology may provide added benefits by enhancing specific balance mechanisms. Since these improvements persisted for at least two months post-treatment, robotic-assisted training may serve as a complementary or alternative approach to conventional rehabilitation strategies for balance disorders in MS.

Indexed as

Exercise TherapyMultiple Sclerosis, Relapsing-RemittingPostural BalanceRoboticsAdultFemaleHumansMaleMiddle AgedPhysical Therapy ModalitiesTreatment Outcome

Identifiers

PMID41514436
PMCPMC12882523

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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.