Exploring the Association of Fear, Mental Health, and Lifestyle with Insomnia: A Machine Learning Approach among University Students
Year: 2026
Type: article
Study design: Observational Study
Abstract: Objective: The aim of this study was to identify key predictors of insomnia among university students, focusing on psychological, behavioral, and lifestyle factors. The study also aimed to explore the role of machine learning in identifying insomnia-related patterns and informing personalized intervention strategies. Methods: A cross-sectional study was conducted with 576 students from universities in Birjand during the 2023-2024 academic year. Machine learning methods, including Random Forest, Decision Tree, and SHAP analysis, were used for feature importance analysis and knowledge extraction. Results: Self-reported questionnaires revealed that fear of not falling asleep particularly in the late hours and stress were the most significant predictors of insomnia. Anxiety, poor sleep hygiene, and depression were also strongly associated with insomnia. Other associated factors included financial and family concerns, bedtime, wake-up time, sleep duration, and social media usage. Machine learning models further highlighted these factors, with those showing poor sleep hygiene and high anxiety levels being more likely to experience insomnia. Discussion: The findings underscore the complex relationship between mental health issues and sleep disorders, particularly insomnia, in university students. The results also suggest a bidirectional relationship between insomnia, anxiety, depression, and stress. The use of machine learning showed potential for identifying insomnia-related patterns and informing personalized intervention strategies. These results highlight the need for effective strategies to address insomnia in students, particularly in light of the growing stressors faced in academic and personal life. However, due to the cross-sectional design, causal inferences cannot be drawn.
Source: Sleep Science
Institutions: Birjand University of Medical Sciences, University of Birjand
Language: English
Topic: Sleep and related disorders
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7225383610