What question did this study set out to answer?

This study aims to investigate the complex relationships between lifestyle behaviors and depressive symptoms using network analysis.

April 13, 2026Open Access

Unraveling links between lifestyle behaviors and depressive symptoms: a network analysis using NHANES 2007–2018 data

Key Points

This study aims to investigate the complex relationships between lifestyle behaviors and depressive symptoms using network analysis.
Utilized NHANES data from 2007–2018 with 4,040 participants aged 20-80.
Assessed depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9).
Collected data on five lifestyle behaviors: healthy diet, alcohol consumption, screen time, smoking, and physical activity.
Estimated network models using R to calculate strength and bridging influences.
Smoking, alcohol use, and screen time emerged as key bridging nodes between lifestyle behaviors and depressive symptoms.
Healthy diet negatively correlated with appetite problems and psychomotor agitation.
Screen time positively associated with trouble sleeping and appetite problems.
Physical activity was related to reduced fatigue.
Global network strength declined with age, with younger individuals displaying stronger connectivity.

Abstract

Implementing effective interventions targeting specific depressive symptoms is essential for reducing the overall burden of depression. Our study employed network analysis to explore the complex interrelationships between multiple lifestyle behaviors and depressive symptoms, and to compare network differences across different age groups. Using data from the National Health and Nutrition Examination Survey (NHANES), we analyzed 4,040 participants between the ages of 20 and 80 from the 2007–2018 survey cycles. Depressive symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ-9), and data on five lifestyle behaviors—healthy diet, alcohol consumption, screen time, smoking, and physical activity—were collected. Network models were estimated using R version 4.4.3 to calculate strength, bridge strength, and age-related differences. The network analysis revealed that smoking (bridge expected influence; BEI = 2.59), alcohol use (BEI = 2.19), and screen time (BEI = 2.17) were the most prominent bridging nodes linking lifestyle behaviors and depressive symptoms. In addition, healthy diet (LB1) was negatively associated with appetite problems (PHQ5; edge weight = -0.02), and psychomotor agitation or retardation (PHQ8; edge weight = -0.02); screen time (LB3) was positively linked to trouble sleeping (PHQ3; edge weight = 0.04), and appetite problems (PHQ5; edge weight = 0.03); smoking (LB4) was associated with appetite problems (PHQ5; edge weight = 0.04) and depressed mood (PHQ2; edge weight = 0.03); physical activity (LB5) was negatively related to fatigue (PHQ4; edge weight = -0.05). Finally, global network strength differed significantly across age groups, with a clear age-related decline in overall connectivity: the Youth group exhibited the highest global strength (4.56), followed by the Middle-aged group (4.07) and the Older group (3.45). These findings contribute to the development of more nuanced and effective public health strategies for the prevention and treatment of depression.

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