Investigating the Relationship between Depression and Sleep Cycle: A Data-Driven Analytical Study

Authors:

Dhruvi Karamsiddhe, Neha Gunjal

Abstract

Depression among university students and young professionals has become an increasingly pressing public health concern, with sleep disturbances playing a pivotal role in both the onset and progression of depressive symptoms. This study presents a data-driven investigation of the relationship between sleep patterns, academic and work-related stress, and depression, analyzing demographic, academic, lifestyle, and psychosocial factors. Using statistical techniques and machine learning models, the research examines how deviations from optimal sleep duration interact with stressors such as academic workload, financial pressure, and work intensity to influence depression severity, as measured by standardized assessment scales. The findings indicate that irregular sleep duration—particularly sleeping fewer than six hours or more than nine hours per day—is strongly associated with elevated depression scores, while academic and occupational pressures act as significant mediating variables. Adequate sleep emerges as a protective factor, mitigating the adverse effects of stress on mental health. The analytical framework combines correlation analysis, regression modeling, and predictive classification to ensure robustness and interpretability of results. The study also proposes future directions, including longitudinal cohort analysis, integration of wearable sleep-monitoring technologies, and AI-based digital mental health interventions. Overall, this research advances understanding of the multifaceted sleep–depression relationship and supports sleep-focused preventive strategies for mental health improvement.

Keywords:

Depression, Sleep Cycle, Mental Health Analytics, Machine Learning, Student Stress, Predictive Modeling, Sleep Hygiene