Evidence map›Paper›PMID 40473702›Full record

ArticleScientific reports2025

The relationship between resting EEG features and physical exercise among university students with depressive symptoms.

Qun Zhao, Yanran Si, Lijuan Mao, Xing Wang, Peng Wang, Shufan Li, Zhaohui Guo, Jianghua Zhu

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

8 authors.

Qun Zhao *Donghua University, Shanghai, 201620, China.
Yanran Si *Shanghai International Studies University, Shanghai, 201620, China.
Lijuan MaoShanghai University of Sport, Shanghai, 200438, China.
Xing WangShanghai University of Sport, Shanghai, 200438, China.
Peng WangShanghai University of Sport, Shanghai, 200438, China.
Shufan LiShanghai University of Sport, Shanghai, 200438, China.
Zhaohui GuoShanghai University of Sport, Shanghai, 200438, China.
Jianghua ZhuDonghua University, Shanghai, 201620, China. zhujh@dhu.edu.cn.

Funding

Shanghai Key Lab of Human Performance (Shanghai University of sport) 11DZ2261100
6 · The paper itself

Abstract

To explore the relationship between resting EEG features and physical exercise among university students with depressive symptoms. A total of 1221 university students were recruited to complete a basic information form, the Patient Health Questionnaire-9 (PHQ-9), and the Physical Activity Rating Scale-3 (PARS-3). Resting EEG signals were collected from a subsample of 132 students. Latent Class Analysis was used to classify depressive symptoms, and the relationship between different resting EEG features and physical exercise was analyzed. Depressive symptoms among university students were classified into three latent classes, with Class 3 exhibiting the most severe symptoms. Significant differences were observed among the three groups in delta lateralization in the central region, alpha1 and alpha2 power in the parietal region, theta and alpha1 lateralization in the lateral frontal region, and alpha1 lateralization in the posterior temporal region (all p < 0.05). Alpha1 lateralization in the dorsolateral frontal cortex (F3, F4) was positively correlated with PARS-3 scores (r = 0.183, p = 0.036) and physical exercise intensity (r = 0.225, p = 0.010). Theta and alpha1 lateralization in the lateral frontal region (F7, F8) were positively correlated with exercise frequency (r = 0.229, p = 0.008; r = 0.214, p = 0.014, respectively). EEG lateralization features in the dorsolateral prefrontal and lateral frontal regions may serve as shared neurophysiological markers reflecting both depressive symptom severity and engagement in physical exercise among university students.

Indexed as

DepressionElectroencephalographyExerciseRestStudentsAdolescentAdultFemaleHumansMaleUniversitiesYoung AdultDepressive symptomsLatent class analysisPhysical exerciseResting EEGUniversity students

Identifiers

PMID40473702
PMCPMC12141633

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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.