Evidence map›Paper›PMID 40325108›Full record

ArticleScientific reports2025

A study on factors influencing digital sports participation among Chinese secondary school students based on explainable machine learning.

XiaoTao Cai, Yi Xian, TongYi Liu, YuXin Zhou, Qing Chen, HaoNan Cui

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

6 authors.

XiaoTao CaiInstitute of Physical Education, Sichuan University, Chengdu, 610065, China.
Yi XianInstitute of Physical Education, Sichuan University, Chengdu, 610065, China.
TongYi LiuInstitute of Physical Education, Sichuan University, Chengdu, 610065, China. liuty@scu.edu.cn.
YuXin ZhouCollege of Biomedical Engineering, Sichuan University, Chengdu, 610065, China.
Qing ChenInstitute of Physical Education, Sichuan University, Chengdu, 610065, China.
HaoNan CuiInstitute of Physical Education, Southwest Jiaotong University, Chengdu, 611756, China.

Funding

Annual Project of the Sichuan University Student Sports Association 23CDTXQ002Fundamental Research Funds for the Central Universities of Sichuan University 2024PE012
6 · The paper itself

Abstract

This study utilized data from 4,925 Hong Kong students in the 2018 Programme for International Student Assessment (PISA) to investigate factors influencing secondary school students' use of digital devices for sports participation and their threshold effects. Univariate analysis revealed significant differences in 16 variables including gender. Multilevel logistic regression identified five significant influencing factors (p < 0.05): academic performance, weekly physical education class days, household ICT resources, school ICT resources, and ICT social perception, which were incorporated as features in machine learning models. Through grid search with 5-fold cross-validation, we constructed and optimized four basic machine learning models (GNB, GBDT, KNN, and LR), then developed an ensemble stacking model using base models with AUC values exceeding 0.70. The best-performing stacking model was selected for interpretability analysis. These findings were supported by swarm plots consistent with the multilevel logistic regression results, confirming the reliability of the conclusions. Feature dependence plots further demonstrated threshold effects for specific variables. The study concludes that the entrenched cultural preference for academics over physical activity, weekly physical education class frequency (with two days as the threshold), household and school ICT resources (using high requirements as the threshold), and differences in ICT social perception significantly contribute to digital sports participation disparities among secondary students. Therefore, academically high-achieving students require particular attention in digital sports promotion. Schools should strive to increase the number of weekly physical education classes to at least two days or more, and actively configure equipment and environment to promote the digitalization of physical education. Furthermore, relevant departments can explore the social attributes of digital sports to enhance the possibility of secondary school students' participation in digital sports.

Indexed as

Machine LearningSportsStudentsAdolescentEast Asian PeopleExerciseFemaleHong KongHumansLogistic ModelsMaleSchoolsDigital sportsMachine learningSecondary school studentsSHAPSports

Identifiers

PMID40325108
PMCPMC12052776

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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 Socratic graph.