Evidence map›Paper›PMID 41358216›Full record

ArticleFrontiers in public health2025

Random forest-based identification and ranking of predictive factors for physical activity in Chinese college students.

Ding-You Zhang, Hu Lou, Jun Liu, Bo Li

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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

3 citing papers in PubMed.

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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

4 authors.

Ding-You ZhangInstitute of Sports Science, Nantong University, Nantong, China.
Hu LouInstitute of Sports Science, Nantong University, Nantong, China.
Jun LiuInstitute of Sports Science, Nantong University, Nantong, China.
Bo LiInstitute of Sports Science, Nantong University, Nantong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To explore the key predictors of physical activity (PA) levels of Chinese university students, and to analyse the predictive roles of different variables and their relative importance by means of the Random Forest (RF) algorithm. Methods: A cross-sectional study was conducted using a stratified whole-group sampling method, covering 17 provinces of the country and collecting 10,182 valid questionnaires. Assessment of PA levels using the Physical Activity Rating Scale-3 (PARS-3) divides participants into attainment and non-attainment groups. The independent variables encompass the individual and interpersonal organisational levels of the socio-ecological model (SEM), comprising a total of 39 variables. These variables include demographic characteristics, psycho-behavioural factors, and social support, which were measured using several standardised scales. Feature importance analysis was performed using the Random Forest algorithm, and the model parameters were optimised with a grid search and 5-fold cross-validation to identify the most significant factors predicting PA. Results: The RF model had an accuracy of 0.704 and an AUC value of 0.762. Characteristic importance analysis revealed that exercise adherence (exercise behaviour), sex, exercise adherence (effort investment), mastery of sports skills, exercise motivation (ability), alcohol consumption level, exercise adherence [emotional experience, exercise motivation (social), and exercise motivation (fun) ranked as the top nine predictive factors]. Specifically, all sub-dimensions of exercise adherence (exercise behaviour) positively predict PA (SHAP values > 0); sex, males are more likely than females to meet the standard group criteria (OR > 1, Conclusion: Exercise adherence, sex, mastery of sports skills, and alcohol consumption level are significant factors predicting PA levels among Chinese university students. Recommendations for promoting PA include enhancing the "emotional value" and social attributes of exercise, addressing female students' willingness to participate, and improving physical capabilities through skills training to effectively elevate activity levels.

Indexed as

ExerciseStudentsAdolescentAdultAlgorithmsChinaCross-Sectional StudiesFemaleHumansMaleMotivationRandom ForestSocial SupportSurveys and QuestionnairesUniversitiesYoung Adultmachine learningphysical activityrandom forestsocio-ecological modeluniversity students

Identifiers

PMID41358216
PMCPMC12675355

What OpenQuestion holds

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LicenceCC BY
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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.