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A total of 1,133 male rescuers aged 17–36 years old (20.49 ± 1.855) with systolic blood pressure between 100–170 mmHg (118.16 ± 8.287) participated in this study. Family per capita monthly incomes were < 3,000, 3,000–4,999, and ≥ 5,000 CNY for 46%, 32%, and 22% of participants, respectively. Further, 3.6%, 43.4%, and 53.0% of participants reported having an education level of middle school and below, high school, and university and above, respectively. In addition, 19.6% and 80.4% of participants were smokers and nonsmokers, respectively. Cognitive emotion regulation was set at 18–90 points (47.04 ± 14.33) and psychological resilience at 10–50 points (41.50 ± 10.43) (Table 1). Finally, 2.74% of patients voluntarily reported that they were either diagnosed with insomnia or had taken anti-insomnia drugs in the hospital above a grade II ranking.
Table 1. Comparison of basic data between two groups
Item Cases (n = 1,133) Insomnia cases (n = 31) Non-insomnia cases (n = 1,102) Age ($\bar {\rm{x} }$ ± s, years old) 1,133 21 ± 2 20 ± 2 Educational background [n (%)] Middle school and below 41 1 (3.2) 40 (3.6) High school 492 11 (35.5) 481 (43.7) University and above 600 19 (61.3) 581 (52.8) Systolic blood pressure ($\bar {\rm{x} }$ ± s, mmHg) 1,133 122 ± 11 118 ± 8 Per capita family monthly income (CNY) < 3,000 521 10 (32.3) 511 (46.4) 3,000–4,999 363 5 (16.1) 358 (32.5) ≥ 5,000 249 16 (51.6) 233 (21.1) Smoking [n (%)] No 911 16 (51.6) 895 (81.2) Yes 222 15 (48.4) 207 (18.8) Cognitive emotional regulation ($\bar {\rm{x} }$ ± s) 1,113 51 ± 17 47 ± 14 Psychological resilience ($\bar {\rm{x} }$ ± s) 1,113 37 ± 15 42 ± 10 -
Univariate analysis was conducted with insomnia as the dependent variable and factors such as age, educational background, systolic blood pressure, family per capita monthly income, smoking, cognitive emotional regulation, and psychological resilience as independent variables. Results showed that systolic blood pressure and per capita family monthly income significantly (P < 0.05) affected insomnia; notably, the Cochran-Armit age trend test revealed a linear trend between family per capita monthly income and insomnia (P = 0.003). While no significant relationship between insomnia and education level was observed, it is notable that insomnia tended to increase in step with education level: participants with university degrees or higher had the highest rates of insomnia (61.3%); meanwhile, participants holding only a middle school qualification had the lowest rates (3.2%).
After adjusting for the effects of age, educational background, systolic blood pressure, and cognitive emotion regulation, binary logistic regression analysis was used for multivariate analysis. Family per capita monthly income, smoking behavior, and psychological resilience all proved statistically significant. Table 2 presents the relevant OR and 95% CI values. Ultimately, smokers were 4.124 times more likely to suffer from insomnia than nonsmokers; meanwhile, results revealed that increases in psychological resilience lower the probability of insomnia.
Table 2. Analysis of influencing factors of insomnia
Item OR 95% CI P value Age (years) ≤ 22 1.000 > 22 1.336 0.598–2.985 0.480 Education background Middle school and below 1.000 0.760 High school 1.128 0.136–9.338 0.911 University and above 1.495 0.186–12.010 0.705 Systolic blood pressure (mmHg) < 140 1.000 ≥ 140 1.662 0.198–13.973 0.640 Per capita family monthly income (CNY) < 3,000 1.000 3,000–4,999 0.622 0.208–1.865 0.397 ≥ 5,000 2.998 1.307–6.879 0.010 Smoking No Yes 4.124 1.954–8.706 0.000 Cognitive emotional regulation 1.019 0.993–1.046 0.162 Psychological resilience 0.960 0.933–0.988 0.005 Note. Age, educational background, systolic blood pressure, per capita family monthly income, smoking, cognitive emotional regulation and psychological resilience were taken into account. P < 0.05 is considered to have significant difference. -
The ROC area (AUC) of the predictive model of ROC curve analysis nomogram = 0.7650, specificity = 0.7169, and sensitivity = 0.7419 (Figure 2). Ultimately, the PRISM model had good diagnostic value.
doi: 10.3967/bes2020.067
Analysis of Factors Influencing Insomnia and Construction of a Prediction Model: A Cross-sectional Survey on Rescuers
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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 (OR) values and the [95% confidence interval (CI)] 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. -
Key words:
- Rescuers /
- Insomnia /
- Influencing factors /
- Cross sectional survey /
- Prediction model
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Table 1. Comparison of basic data between two groups
Item Cases (n = 1,133) Insomnia cases (n = 31) Non-insomnia cases (n = 1,102) Age ( $\bar {\rm{x} }$ ± s, years old)1,133 21 ± 2 20 ± 2 Educational background [n (%)] Middle school and below 41 1 (3.2) 40 (3.6) High school 492 11 (35.5) 481 (43.7) University and above 600 19 (61.3) 581 (52.8) Systolic blood pressure ( $\bar {\rm{x} }$ ± s, mmHg)1,133 122 ± 11 118 ± 8 Per capita family monthly income (CNY) < 3,000 521 10 (32.3) 511 (46.4) 3,000–4,999 363 5 (16.1) 358 (32.5) ≥ 5,000 249 16 (51.6) 233 (21.1) Smoking [n (%)] No 911 16 (51.6) 895 (81.2) Yes 222 15 (48.4) 207 (18.8) Cognitive emotional regulation ( $\bar {\rm{x} }$ ± s)1,113 51 ± 17 47 ± 14 Psychological resilience ( $\bar {\rm{x} }$ ± s)1,113 37 ± 15 42 ± 10 Table 2. Analysis of influencing factors of insomnia
Item OR 95% CI P value Age (years) ≤ 22 1.000 > 22 1.336 0.598–2.985 0.480 Education background Middle school and below 1.000 0.760 High school 1.128 0.136–9.338 0.911 University and above 1.495 0.186–12.010 0.705 Systolic blood pressure (mmHg) < 140 1.000 ≥ 140 1.662 0.198–13.973 0.640 Per capita family monthly income (CNY) < 3,000 1.000 3,000–4,999 0.622 0.208–1.865 0.397 ≥ 5,000 2.998 1.307–6.879 0.010 Smoking No Yes 4.124 1.954–8.706 0.000 Cognitive emotional regulation 1.019 0.993–1.046 0.162 Psychological resilience 0.960 0.933–0.988 0.005 Note. Age, educational background, systolic blood pressure, per capita family monthly income, smoking, cognitive emotional regulation and psychological resilience were taken into account. P < 0.05 is considered to have significant difference. -
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