Evidence map›Paper›PMID 42846581›Full record

ArticleFrontiers in rehabilitation sciences2026

Long-term electromyography-based virtual reality training for chronic upper-limb motor paralysis after stroke: an exploratory single-case experimental design.

Yohei Nagaoka, Shinya Suzuki, Tsuyoshi Nakajima, Hiroyuki Ohtsuka, Akimasa Hayashi, Max Ortiz-Catalan, Yukari Ohki

Abstract read
In one paragraph

Article in Frontiers in rehabilitation sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yohei Nagaoka *Department of Integrative Physiology, Kyorin University School of Medicine, Tokyo, Japan.
Shinya Suzuki *Department of Integrative Physiology, Kyorin University School of Medicine, Tokyo, Japan.
Tsuyoshi NakajimaDepartment of Integrative Physiology, Kyorin University School of Medicine, Tokyo, Japan.
Hiroyuki OhtsukaDepartment of Integrative Physiology, Kyorin University School of Medicine, Tokyo, Japan.
Akimasa HayashiDepartment of Integrative Physiology, Kyorin University School of Medicine, Tokyo, Japan.
Max Ortiz-CatalanPrometei Pain Rehabilitation Center, Vinnytsia, Ukraine.
Yukari OhkiDepartment of Integrative Physiology, Kyorin University School of Medicine, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Stroke commonly causes various symptoms, such as motor paralysis. Limited motor recovery during the chronic stage of stroke is a challenge in rehabilitation. Motor training using myoelectric pattern recognition (MPR) and virtual reality (VR) may improve maladaptive sensorimotor integration, thereby promoting motor recovery after stroke (MVR training). We evaluated the feasibility of adding upper-limb MVR training to conventional rehabilitation therapy for >3 months in individuals with chronic motor paralysis after stroke. Methods: Two individuals with chronic stroke participated in this preliminary, single-case study. An open-label, repeated-measures design was used for Participant A, and an ABA-type single-case experimental design was used for Participant B. During the intervention period (Participant A: 40 sessions over 8 months; Participant B: 20 sessions over 3 months), participants underwent MVR training in addition to conventional rehabilitation care. During MVR training, the participants performed the posture-matching test (30 min, twice a week), requiring them to voluntarily move their paretic hand with real-time visual feedback of the virtual hand that was controlled by the MPR of the paretic upper-limb muscles. Results: Both participants completed the planned training sessions without serious adverse events. The Fugl-Meyer assessment score of the upper extremities was used to assess upper-limb motor function. Following VR training, the improvement from baseline exceeded the estimated minimal clinically important difference in Participant A (+7 points) but not in Participant B (+5 points). Discussion: In conclusion, these findings suggest that long-term MVR training is feasible and can be implemented without serious adverse events in individuals with chronic motor paralysis after stroke. Although improvement in upper-limb motor function was observed in one participant, further studies with larger samples and controlled designs are warranted to assess the efficacy of MVR training.

Indexed as

clinical feasibilityeffectivenessmyoelectric pattern recognitionrehabilitationsafetyvisual feedback

Identifiers

PMID42846581
PMCPMC13642837

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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.