@article{ART003259711},
author={Jeong In Lee},
title={Analysis of Health Behaviors and Sociodemographic Factors Influencing Depression among Adults},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2025},
volume={11},
number={5},
pages={19}
TY - JOUR
AU - Jeong In Lee
TI - Analysis of Health Behaviors and Sociodemographic Factors Influencing Depression among Adults
JO - Journal of Internet of Things and Convergence
PY - 2025
VL - 11
IS - 5
PB - The Korea Internet of Things Society
SP - 19
EP -
SN - 2466-0078
AB - This study aimed to identify health behaviors and sociodemographic factors associated with depression among adults. Using data from the Korea Health Panel, depression was found to be significantly associated with age(χ²=74.31, p<.001), educational(χ²=53.64, p<.001), family living together (χ²=78.93, p<.001), employment income(χ²=21.60, p<.001), regular exercise(χ²=5.66, p=0.017), chronic disease status(χ²=122.97, p<0.001), subjective health status(χ²=254.99, p<.001), disability(χ²=96.40, p<.001), stress level(χ²=411.46, p<.001), professional counseling(χ²=619.22, p<.001), and medication use(χ ²=860.50, p<.001). The results of the multivariate logistic regression analysis showed that females were significantly more likely to experience depression compared to males (Adjusted OR = 1.60, 95% CI = 1.04–2.44, p = 0.029). Compared with those aged 20–39 years, individuals aged 40–64 years (OR = 0.14, 95% CI = 0.03–0.60, p = 0.009) and those aged 65 years or older (OR = 0.07, 95% CI = 0.01–0.32, p < 0.001) had a significantly lower likelihood of depression. Participants who were not living with family members were more likely to experience depression (OR = 1.58, 95% CI = 1.07–2.33, p = 0.019). Those who perceived “a little” stress had a lower likelihood of depression than those who perceived “a lot” of stress (OR = 0.48, 95% CI = 0.26–0.90, p = 0.023). Participants who were not taking medication also showed a lower likelihood of depression (OR = 0.05, 95% CI = 0.01–0.20, p = 0.001). These findings highlight the importance of considering both health-related behaviors and sociodemographic characteristics in predicting and understanding adult depression. However, the interpretation of the results should be made with caution, as the study was based on cross-sectional data, limiting causal inference. In addition, the analysis relied on self-reported responses regarding depression status rather than clinically diagnosed classifications, which may restrict the generalizability of the findings.
KW - depression;Health Behaviors;Sociodemographic Factors;Adults;Mental Health
DO -
UR -
ER -
Jeong In Lee. (2025). Analysis of Health Behaviors and Sociodemographic Factors Influencing Depression among Adults. Journal of Internet of Things and Convergence, 11(5), 19.
Jeong In Lee. 2025, "Analysis of Health Behaviors and Sociodemographic Factors Influencing Depression among Adults", Journal of Internet of Things and Convergence, vol.11, no.5 19.
Jeong In Lee "Analysis of Health Behaviors and Sociodemographic Factors Influencing Depression among Adults" Journal of Internet of Things and Convergence 11.5 19 (2025) : 19.
Jeong In Lee. Analysis of Health Behaviors and Sociodemographic Factors Influencing Depression among Adults. 2025; 11(5), 19.
Jeong In Lee. "Analysis of Health Behaviors and Sociodemographic Factors Influencing Depression among Adults" Journal of Internet of Things and Convergence 11, no.5 (2025) : 19.
Jeong In Lee. Analysis of Health Behaviors and Sociodemographic Factors Influencing Depression among Adults. Journal of Internet of Things and Convergence, 11(5), 19.
Jeong In Lee. Analysis of Health Behaviors and Sociodemographic Factors Influencing Depression among Adults. Journal of Internet of Things and Convergence. 2025; 11(5) 19.
Jeong In Lee. Analysis of Health Behaviors and Sociodemographic Factors Influencing Depression among Adults. 2025; 11(5), 19.
Jeong In Lee. "Analysis of Health Behaviors and Sociodemographic Factors Influencing Depression among Adults" Journal of Internet of Things and Convergence 11, no.5 (2025) : 19.
