Identifying Key Factors in University Students' Physical and Mental Health: An Integrated Regression and Machine Learning Approach.

Qin Jiang, Sirui Wu, Nengzhong Xie, Yanxue Zhao, Yan Li, Xiaoyu Wu, Xuebing Wang

Abstract read

In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 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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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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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

7 authors.

Qin JiangSchool of Marxism, Guangxi University, Nanning 530004, China.

Sirui WuSchool of Marxism, Guangxi University, Nanning 530004, China.

Nengzhong XieStudent Affairs Office, Guangxi University, Nanning 530004, China.

Yanxue ZhaoSchool of Marxism, Guangxi University, Nanning 530004, China.

Yan LiSchool of Marxism, Guangxi University, Nanning 530004, China.

Xiaoyu WuSchool of Physical Education, Guangxi University, Nanning 530004, China.

Xuebing WangSchool of Physical Education, Guangxi University, Nanning 530004, China.ORCID 0009-0002-0506-5092

Funding

The Training Program for 1000 Young and Middle-aged Outstanding Teachers of Higher Education Institutions in Guangxi Province 2023QGRW001

6 · The paper itself

Abstract

This study sought to identify key factors in university students' physical and mental health by using a combination of methods, including classical statistical analysis and machine learning techniques. Physical and mental health test data were collected from undergraduates of the 2020 to 2023 cohorts at a university. A self-designed questionnaire on factors associated with physical and mental health was also sent to randomly selected undergraduate students from the same university. The study data were analyzed by one-way analysis of variance, Pearson correlation analysis, hierarchical regression analysis, and a machine learning model. The results revealed that participation in school sports clubs (

Indexed as

correlative factorsmachine learningmental healthphysical health

Identifiers

PMID42073849

DOI10.3390/bs16040486

PMCPMC13114069

What OpenQuestion holds

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LicenceCC BY

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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.