A Neural Network Model of Risk and Protective Factors for Poor Sleep Quality in Healthcare Providers: Role of Aggression and Self-regulation





Aggression, Adolescents, Self-regulation, Sleep quality


Background: Although the literature discusses the benefits of good sleep on physiological, psychological, and physical health, poor sleep quality is common among healthcare providers. The present study aimed to predict poor sleep quality among healthcare providers based on multiple risk factors (aggression, gender, and age) and the protective factor of self-regulation using a neural network model.

Methods: Using multistage cluster sampling, a group of 400 healthcare workers (70% female, with an age average of 32 years) from Kermanshah city in western Iran were selected for the cross-sectional study. Data were collected using the Pittsburgh Sleep Quality Index (PSQI), the Buss-Perry Aggression Questionnaire (BPAQ), and the Self-Regulation Questionnaire (SRQ). A neural network model and receiver operating characteristic (ROC) curve were used for data analysis.

Results: Four hidden units were found in a single hidden layer extracted using the current model. More than 84% of the training and testing models accurately predicted good and poor sleepers. The neural network model's good predictive value was indicated by the Area under the ROC Curve (AUC=0.863). The results imply that self-regulation (0.30), anger (0.20), physical aggression (0.19), verbal aggression (0.11), hostility (0.10), age (0.06), and sex (0.05) have normalized importance values ranging from 18% to 100%, making them significant predictors of both the good and poor sleep subgroups.

Conclusions: The present neural network-based algorithm, which considers the risk and protective factors of poor sleep quality, could be effectively used by healthcare providers.


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How to Cite

A Neural Network Model of Risk and Protective Factors for Poor Sleep Quality in Healthcare Providers: Role of Aggression and Self-regulation. (2024). International Journal of Health Studies, 10(2), 1-7. https://doi.org/10.22100/ijhs.v10i2.1063