Comparison of sociodemographic characteristics

The 1103 questionnaires were distributed uniformly, and 858 valid questionnaires (70.61% females) were obtained after excluding those with incomplete scale tests and questionnaires with missing general data. Table 1 presents the sociodemographic characteristics of the participants. Results show that the participants were mostly females (n = 661, 77%) and that the mean age and mean body mass index were 41.97 ± 12.9 years and 26 ± 5.9 kg/m2, respectively. Most of the participants were married (n = 486, 56.6%), living in the city (n = 646, 75.3%), employed (n = 692, 80.7%), and white (n = 442, 51.5%). Among the participants, 15 (1.7%) were diagnosed with COVID-19, 123 participants (14.3%) suffered from “somewhat economic impact,” and 37 (4.3%) suffered from “severe economic impact”; 86 participants (10.0%) were smokers and 301 participants (35.1%) suffered from stress.

Table 1 Characteristics of the study population (N = 858)

According to their ISI scores, participants were assigned to the non-insomnia group (691 cases) or the insomnia group (167 cases). Table 2 shows the demographic data and scale information of the insomnia and non-insomnia groups in Germany. Compared to females of the insomnia group, males of the same group were linked to a greater prevalence of insomnia (36.6 versus 14.5%), and there are significant differences in gender (χ2 = 46.95, P < 0.05) compared with the non-insomnia group. In terms of marital status, the prevalence of insomnia was 18.1% in the single participants, 17.3% in the married participants, and 56.1% in those with other marital statuses. Similarly, there are significant differences in marital status (χ2 = 36.95, P < 0.05) compared with the non-insomnia group. There were also significant differences in education level (χ2 = 33.14, P < 0.05), ethnicity (χ2= 11.57, P < 0.05), employment status (χ2 = 82.31, P < 0.05), financial suffering (χ2 = 45.75, P < 0.05), smoking status (χ2 = 10.46, P < 0.05), and stress levels (χ2 = 113.44, P < 0.05) between the two groups, but the results showed no significant correlations between insomnia and living areas (χ2 = 0.02, P > 0.05) or COVID-19 groups (χ2 = 3.99, P > 0.05).

Table 2 Comparison of sociodemographic characteristics between non-insomnia and insomnia groupsComparison of sleep- and emotion-related scales

Among the participants, 19.5% had symptoms of insomnia according to the ISI score (> 7), 6.6% had symptoms of anxiety according to the GAD‑2 score (> 3), and 4.8% had symptoms of depression according to the PHQ‑2 score (> 3) (Table 3).

Table 3 Demographics of the scores

Comparing the scores of sleep- and emotion-related scales between the two groups, the ISI score, PHQ‑4 score, PHQ‑2 score, and GAD‑2 score of the non-insomnia group had significantly lower mean scores (P < 0.001). On the other hand, the non-insomnia group had significantly higher mean scores in quality of life and quality of health compared with the insomnia group (P < 0.001, Table 4).

Table 4 Comparison of ISI, PHQ‑4, PHQ‑2, GAD‑2, quality of life, and quality of health scores between the insomnia group and non-insomnia groupsCorrelations between sleep- and emotion-related scales

The correlations between insomnia (ISI scale), anxiety (GAD‑2 scale), and depression (PHQ‑2 scale) were further analyzed. Pearson correlation analysis showed that there is a positive correlation between the ISI score and the PHQ‑2 score (r = 0.521, P < 0.01), GAD‑2 score (r = 0.483, P < 0.01), and PHQ‑4 score (r = 0.562, P < 0.01). Conversely, the ISI score was negatively correlated with the quality of life score (r = −0.490, P < 0.01) and the quality of health score (r = −0.437, P < 0.01). The detailed results are shown in Table 5.

Table 5 Correlation analysis of ISI scores with PHQ‑2, GAD‑2, stress, quality of life and quality of healthLogistic regression analysis of insomnia, anxiety, and depression

The binary logistic regression method was used for multivariate analysis. The group of participants with ISI score > 7, PHQ‑2 score > 3, and GDA‑2 score > 3 were divided into normal and symptomatic groups as dependent variables, and three logistic regression models were established for each. On the other hand, gender, marital status, living areas, educational level, ethnicity, employment status, financial suffering, smoking, and stress were considered as independent variables, with the last category used as the reference. Table 6 describes the results of the multivariate logistic regression analysis. Findings showed that compared with females, males (OR = 2.59, 95% CI 1.789–3.759, P < 0.001) had a higher risk for developing clinical insomnia. Compared with living in the country, living in the city (OR = 2.40, 95% CI 1.064–5.413, P = 0.035) was observed to be a potential risk factor for depression. Lastly, Asian ethnicity (OR = 3.32, 95% CI 1.056–10.435, P = 0.04) and no smoking (OR = 5.99, 95% CI 1.947–18.377, P = 0.002) were potential risk factors for anxiety.

Table 6 Logistic regression analysis of insomnia, anxiety, and depression in German persons