Investigating the Relationship Between Depression and Sleep Cycle: A Data-Driven Analysis

Abstract

Depression and sleep disturbances share a bidirectional relationship that significantly impacts mental health and daily functioning. This research project aims to investigate the correlation between depression severity and sleep cycle patterns using data analysis techniques. Through a combination of psychological assessment tools and sleep tracking data, this study will identify key patterns, such as changes in REM sleep, sleep duration, and sleep onset latency among individuals experiencing depressive symptoms. The goal is to use statistical and machine learning approaches to uncover insights that may aid early detection and treatment strategies for depression.

Introduction

Mental health disorders like depression are closely linked with sleep disturbances such as insomnia and hypersomnia. These are not only symptoms but may also serve as early indicators or predictors of depressive episodes. With the rise of digital health tools, continuous sleep monitoring opens up new opportunities for early intervention and personalized mental health care.

???? Objectives

1. Analyze sleep cycle variables (REM duration, sleep latency, total sleep time) across different depression levels.

2. Examine statistical correlations between sleep metrics and depression scores.

3. Develop predictive models to forecast depressive episodes using sleep data.

4. Propose interventions or early warning systems based on sleep indicators.

? Problem Statement

Existing research often isolates sleep and depression from real-life influences like academic stress, work demands, and socioeconomic pressures—especially in young adults. This research addresses the lack of integrated, real-world models explaining how these factors interact to influence depression risk.

???? Methodology

A. Data Collection & Preprocessing

• Dataset includes 17 variables covering demographics, lifestyle, academic/professional stress, and mental health indicators.

• Data cleaning and multiple imputation used to handle missing data.

B. Analytical Techniques

• Correlation Analysis: Pearson and Spearman correlations used to identify relationships among variables.

• Regression Models: Multiple linear regressions to quantify associations.

• Machine Learning Models:

  • Random Forest – for handling non-linear relationships

  • Support Vector Machines (SVM) – for robust classification

  • Gradient Boosting – for optimized predictions

  • Neural Networks – for recognizing complex patterns

C. Model Evaluation

• Classification Metrics: Accuracy, Precision, Recall, F1-Score, AUC-ROC

• Regression Metrics: Mean Squared Error (MSE), R²

• Cross-validation to ensure model generalizability

Conclusion

1) Depression is closely linked to sleep, stress, and socioeconomic factors. 2) Sleep duration is the most modifiable risk factor identified. 3) Machine learning improves accuracy in identifying at-risk individuals. 4) Comprehensive multi-level interventions are needed for prevention. 5) Collaboration among healthcare, education, and policymakers is critical.

References

[1] Nutt, D., Wilson, S., & Paterson, L. (2008). Sleep disorders as core symptoms of depression. Dialogues in Clinical Neuroscience. [2] Ford, D.E., & Kamerow, D.B. (1989). Epidemiologic study of sleep disturbances and psychiatric disorders. JAMA. [3] Baglioni, C., et al. (2016). Sleep and mental disorders: A meta-analysis. Lancet Psychiatry. [4] Lee, M., Kim, S., & Lee, Y. (2020). Detecting depression using smartwatches and machine learning. JMIR mHealth and uHealth.

Copyright

Copyright © 2025 Dhruvi Karamsiddhe, Neha Gunjal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.