Evidence map›Paper›PMID 41937820›Full record

ArticleFrontiers in psychology2026

Advanced utilization of motion decomposition and visualization technology in gymnastics education.

Hechong Yang, Guangxin Cheng, Shuaichao Cheng

Abstract read
In one paragraph

Article in Frontiers in psychology, 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

3 authors.

Hechong YangSchool of Physical Education, Shandong University of Technology, Zibo, China.
Guangxin ChengSchool of Sports, Southwest University, Chongqing, China.
Shuaichao ChengSchool of Physical Education, Shaoyang University, Shaoyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the ongoing advancements in higher education physical education reforms, gymnastics courses-a crucial element of sports education-necessitate the use of enhanced and new pedagogical approaches. Conventional gymnastics education encounters restrictions stemming from intricate motions, significant learning difficulties, and clear demonstration constraints, frequently resulting in students struggling to comprehend and master technical skills. To address these limitations, the present study integrates movement decomposition methodology with multiple visualization technologies to construct a structured, feedback-oriented instructional framework. Recent advancements in information technology within physical education have facilitated the development of visualization techniques in educational environments, providing innovative methods and tools for gymnastics training. This research focuses on the action decomposition teaching technique, incorporating visualization technologies including slow-motion replays, motion trajectory overlays, 3D motion models, and synchronous comparative analysis. It delineates a four-stage pedagogical model-"decomposition-visualization-practice-correction"-which differs from prior single-method approaches by systematically embedding real-time visual feedback at every stage of skill acquisition, and substantiates its efficacy through instructional trials and case analyses. Research findings demonstrate that visualization technology markedly enhances students' cognitive comprehension of movement structures, elevates the quality of movement execution, boosts classroom engagement and participation, and cultivates students' self-correction skills, thus realizing a significant innovation in gymnastics teaching methodologies. This study offers a pragmatic application demonstration and research foundation for the transformation of gymnastics instruction in higher education institutions.

Indexed as

decompositiondigital educationgymnasticsinstructionmovementpedagogical reformtechnologyvisualization

Identifiers

PMID41937820
PMCPMC13047740

What OpenQuestion holds

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Registered trials

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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.