Evidence map›Paper›PMID 41938018›Full record

ReviewFrontiers in sports and active living2026

Research status and evolutionary trends of digital sports: a perspective of co-word analysis.

An Mengbin, Li Mengchen, Ru Xueyan, Jia Junjie

Abstract readReview
In one paragraph

Review in Frontiers in sports and active living, 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

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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

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

4 authors.

An MengbinDepartment of Combat Sports and Special Training, Belarusian State University of Physical Culture, Minsk, Belarus.
Li MengchenCollege of Physical Education, Henan Normal University, Xinxiang, China.
Ru XueyanGraduate Student Department, Jilin Sport University, Changchun, China.
Jia JunjieCollege of Physical Education, Henan Normal University, Xinxiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Despite the extensive penetration of digital technologies into the field of sports, a comprehensive review of the knowledge structure and developmental trends in this domain remains lacking. This study aims to employ co-word analysis to systematically map, for the first time, the knowledge landscape of international digital sports research from 2005 to 2025, identifying its core themes and evolutionary trends. Methods: Drawing on a sample of 1,318 core English-language papers from the Web of Science database, this study utilized BICOMB 2.0, SPSS 26.0, and UCINET 6.0 to conduct high-frequency word analysis, social network analysis, and cluster analysis. Result: A total of 39 high-frequency keywords were extracted, and four main research clusters were identified: Digital Innovation Promoting the Development of Sports Media; Implementation of Digital Devices in Sports Training; Integration of Digital Technology in Physical Education; Impact of Digital Transformation on Sports Health. The study found that "exercise," "gamification," and "physical activity" were the most closely interconnected terms, while keywords such as "kinesiology" and "biomechanics" remained at the periphery of the research network. Conclusion: This study reveals, for the first time, the four major clusters within digital sports research and their underlying socio-technical logics-platformization, the datafication of the body, technological mediation, and the health paradox. It also highlights the disconnect between traditional sports science and digital research. This macro-level framework provides a foundation for subsequent theoretical integration and interdisciplinary dialogue.

Indexed as

bibliometric analysisco-word analysisdigital sportsresearch trendsocial network analysisword frequency analysis

Identifiers

PMID41938018
PMCPMC13043642

What OpenQuestion holds

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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.