This paper is in the following e-collection/theme issue:

Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2111) Diagnostic Tools in Mental Health (326) Mental Health Issues in Adolescence (637) Problematic Internet Use, Online Gambling and Game Addiction (190)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72260, first published .
Mental Health Issues and 24-Hour Movement Guidelines–Based Intervention Strategies for University Students With High-Risk Social Network Addiction: Cross-Sectional Study Using a Machine Learning Approach

Mental Health Issues and 24-Hour Movement Guidelines–Based Intervention Strategies for University Students With High-Risk Social Network Addiction: Cross-Sectional Study Using a Machine Learning Approach

Mental Health Issues and 24-Hour Movement Guidelines–Based Intervention Strategies for University Students With High-Risk Social Network Addiction: Cross-Sectional Study Using a Machine Learning Approach

Authors of this article:

Lin Luo1, 2 Author Orcid Image ;   Junfeng Yuan1 Author Orcid Image ;   Chen Xu1 Author Orcid Image ;   Huilin Xu1 Author Orcid Image ;   Haojie Tan1 Author Orcid Image ;   Yinhao Shi1 Author Orcid Image ;   Haiping Zhang1 Author Orcid Image ;   Haijun Xi1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Lin Luo   1, 2 , PhD ;   Junfeng Yuan   1 , MA ;   Chen Xu   1 , MA ;   Huilin Xu   1 , MA ;   Haojie Tan   1 , MA ;   Yinhao Shi   1 , MA ;   Haiping Zhang   1 , MA ;   Haijun Xi   1 , MA

1 School of Physical Education, Guizhou Normal University, Guiyang, China

2 Key Laboratory of Brain Function and Brain Disease Prevention and Treatment of Guizhou Province, Guiyang, China

Corresponding Author:

  • Lin Luo, PhD
  • School of Physical Education
  • Guizhou Normal University
  • University Town, Siya Road, Huaxi District
  • Guiyang 550025
  • China
  • Phone: 86 86751983
  • Email: 460022831@gznu.edu.cn