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Updated: Jun 6, 2025

Author Spotlight: Unveiling the Connection Between Sleep Disorders and Cognitive Symptoms in Depression
Published on: April 26, 2024
Using the Complex Network Model to Associate Nutritional, Psychological, and Physical Parameters and Aspects of Sleep
Pedro Paulo Menezes Scariot1, Ana Luiza Paula Garbuio1, Andrea Corazzi Pelosi1
1Research Group on Technology Applied to Exercise Physiology (GTAFE), Laboratory of Multidisciplinary Research, São Francisco University, Bragança Paulista 12916-900, SP, Brazil.
Abstract:
Background/Objectives: Major depressive disorder is a significant public health concern linked to factors such as a low-quality diet, a sedentary lifestyle, and poor sleep quality, all of which contribute to its development; nevertheless, the existing literature lacks a comprehensive framework to effectively integrate these interrelated influences. Methods: To address this gap, we conducted a questionnaire-based study involving 411 individuals aged 18 to 74 and employed a weighted complex network model to clarify the associations among nutritional factors, physical activity levels, psychological parameters, and sleep profiles and depression. In addition to constructing networks that encompass distinct subgroups based on general context, sex differences (female vs. male), and four age categories, our network was designed with a clearly defined target: the score from the Beck Depression Inventory. Results: In all networks studied, psychological parameters (e.g., tension, depression, hostility, fatigue, confusion, and total mood disturbance) emerged as the most influential nodes in relation to the targeted node (Eigenvector centrality of approximately 0.30). Additionally, sleep quality was identified as the next most relevant parameter for the general network (Eigenvector = 0.25), while nutritional factors-particularly carbohydrates-demonstrated greater prestige within the male network (Eigenvector = 0.06). Nutritional parameters had a stronger influence on depressive symptoms among individuals aged 29-39 years (Eigenvectors = 0.09, 0.09, and 0.14 for energy, carbohydrates, and fat, respectively). Conclusions: This novel approach allowed for a clearer visualization of how the studied parameters impact depressive symptoms, revealing significant differences when certain aspects are examined independently across distinct groups.
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