Analysis of Factors Influencing Insomnia and Construction of a Prediction Model: A Cross-sectional Survey on Rescuers.
- Author:
Xiao Yong SAI
1
;
Qiao CHEN
2
;
Ting Gang LUO
3
;
Yuan Yuan SUN
3
;
Yu Jian SONG
3
;
Juan CHEN
3
Author Information
- Publication Type:Journal Article
- Keywords: Cross sectional survey; Influencing factors; Insomnia; Prediction model; Rescuers
- MeSH: Adolescent; Adult; China; epidemiology; Cross-Sectional Studies; Humans; Incidence; Income; statistics & numerical data; Male; Models, Theoretical; Occupational Diseases; epidemiology; etiology; Rescue Work; statistics & numerical data; Resilience, Psychological; Risk Factors; Sleep Initiation and Maintenance Disorders; epidemiology; etiology; Smoking; epidemiology; Socioeconomic Factors; Young Adult
- From: Biomedical and Environmental Sciences 2020;33(7):502-509
- CountryChina
- Language:English
-
Abstract:
Objective:To determine the factors influencing insomnia and construct early insomnia warning tools for rescuers to informbest practices for early screening and intervention.
Methods:Cluster sampling was used to conduct a cross-sectional survey of 1,133 rescuers from one unit in Beijing, China. Logistic regression modeling and R software were used to analyze insomnia-related factors and construct a PRISM model, respectively.
Results:The positive rate of insomnia among rescuers was 2.74%. Accounting for participants' age, education, systolic pressure, smoking, per capita family monthly income, psychological resilience, and cognitive emotion regulation, logistic regression analysis revealed that, compared with families with an average monthly income less than 3,000 yuan, the odds ratio ( ) values and the [95% confidence interval ( )] for participants of the following categories were as follows: average monthly family income greater than 5,000 yuan: 2.998 (1.307-6.879), smoking: 4.124 (1.954-8.706), and psychological resilience: 0.960 (0.933-0.988). The ROC curve area of the PRISM model (AUC) = 0.7650, specificity = 0.7169, and sensitivity = 0.7419.
Conclusion:Insomnia was related to the participants' per capita family monthly income, smoking habits, and psychological resilience on rescue workers. The PRISM model's good diagnostic value advises its use to screen rescuer early sleep quality. Further, advisable interventions to optimize sleep quality and battle effectiveness include psychological resilience training and smoking cessation.
