Evidence map›Paper›PMID 41956492›Full record

ArticlePhysical activity and nutrition2026

Effects of a 3D motion recognition-based virtual training system on balance and gait performance in older adults.

Dae-Joong Yang, Hyeong-Seok Lee

Abstract read
In one paragraph

Article in Physical activity and nutrition, 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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0citing papers in PubMed
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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Dae-Joong YangDepartment of Physical Therapy, Sehan University, Yeongam, Republic of Korea.
Hyeong-Seok LeeDepartment of Physical Therapy, Sehan University, Yeongam, Republic of Korea.

Funding

Sehan University
6 · The paper itself

Abstract

purposeThis study examined the effects of a 3D motion-recognition-based virtual training system on balance and gait in older adults.

methodsThirty older adults ( ≥ 60 years) were randomly assigned to the experimental group (n = 15) and the control group (n = 15). The experimental group underwent 3D motion-recognition-based virtual training for 8 weeks, while the control group performed a conventional exercise program without 3D motion-recognition-based virtual training. Balance measures (surface area, weight distribution, and limit of stability) and gait measures (cadence, gait speed, and stride length) were assessed before and after the intervention. Data were analyzed using paired t-tests and analysis of covariance (ANCOVA).

resultsAfter the intervention, the experimental group showed significant improvements in all balance and gait parameters (p < 0.05), whereas the control group demonstrated significant improvements in balance and gait parameters except for surface area (p < 0.05). Notably, weight distribution, limit of stability, cadence, and gait velocity showed significantly greater improvements in the experimental group compared with the control group (p < 0.05). However, no significant between-group differences were observed for surface area and stride length.

conclusionThe 3D motion-recognition-based virtual training system is an effective intervention for improving balance and gait function in older adults, contributing to enhanced mobility and balance ability. Future studies should further investigate the long-term effects of this intervention and work toward optimizing the training protocols.

Indexed as

3D motion recognitionbalanceexergamegaitolder adultsvirtual training

Identifiers

PMID41956492
PMCPMC13065386

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LicenceCC BY-NC
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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.