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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

Journals

  1. Zhang Z, Li R, Xie Y, Chen Z, Bailey R, Khoo S. Prevalence of meeting 24-hour movement guidelines in China: a systematic review and meta-analysis. BMC Public Health 2025;26(1) View
  2. Tam W, Hou C, Eisingerich A. Super Mario Bros. and Yoshi Games’ Affordance of Childlike Wonder and Reduced Burnout Risk in Young Adults: In-Depth Mixed Methods Cross-Sectional Study. JMIR Serious Games 2025;13:e84219 View
  3. Estrella T, Capdevila L, Alfonso C, Losilla J. Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines. JMIR Human Factors 2026;13:e78648 View
  4. NGADAN D, MAZLAN N, JAMEL A, ISMAIL S, MAHENDRAN D, KAPITONOVA M. PREVALENCE OF SOCIAL MEDIA ADDICTION AND ITS ASSOCIATED FACTORS AMONG UNDERGRADUATE STUDENTS AT UNIVERSITI MALAYSIA SARAWAK (UNIMAS), SARAWAK. AVICENNA BULLETIN 2026;28(1):67 View