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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.
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 1. Comparison of basic data between two groups
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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.
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. Table 2. Analysis of influencing factors of insomnia
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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.
Analysis of Factors Influencing Insomnia and Construction of a Prediction Model: A Cross-sectional Survey on Rescuers
doi: 10.3967/bes2020.067
- Received Date: 2020-03-16
- Accepted Date: 2020-06-28
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Key words:
- Rescuers /
- Insomnia /
- Influencing factors /
- Cross sectional survey /
- Prediction model
Abstract:
Citation: | SAI Xiao Yong, CHEN Qiao, LUO Ting Gang, SUN Yuan Yuan, SONG Yu Jian, CHEN Juan. Analysis of Factors Influencing Insomnia and Construction of a Prediction Model: A Cross-sectional Survey on Rescuers[J]. Biomedical and Environmental Sciences, 2020, 33(7): 502-509. doi: 10.3967/bes2020.067 |