Latent profile analysis and influencing factors of sleep quality in community perimenopausal women: a cross-sectional study

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However, there are few studies on the potential profile of sleep quality in perimenopausal women in the community. Therefore, this study aims to explore different potential trajectories of sleep quality in perimenopausal women in the community and analyze the influencing factors of different trajectories. Methods A cross-sectional study was conducted from July 2024 to December 2024, and a total of 281 perimenopausal women in the community were recruited from 4 communities in Bengbu. The participants completed the Pittsburgh Sleep Quality Index (PSQI), and self-rating anxiety scale (SAS), self-rating depression scale (SDS) and simplified coping style questionnaire (SCSQ). Latent profile analysis(LPA) was employed to identify latent profiles of sleep quality of perimenopausal women in the community. The predictors of sleep quality in different latent profiles were assessed via multinomial logistic regression analysis. One-way ANOVA, chi-square test or Fisher exact test, and the Kruskal-Walis test were used to compare the PSQI scores of perimenopausal women in the community under different latent profile characteristics. Results In this study, 88 out of 281 perimenopausal women community in the had PSQI scores more than 7 points, and the incidence of sleep disorders was 33.3%. The sleep quality of perimenopausal women in the community could be divided into three different potential trajectories, including 193 cases (68.7%) in the good sleep quality group, 68 cases (24.2%) in the general sleep quality group, and 20 cases (7.1%) in the poor sleep quality group. Taking the good sleep quality group as the reference group, drinking history ( OR = 2.087), chronic disease history ( OR = 2.221), spouse health status ( OR = 1.880) and anxiety ( OR = 4.358) were risk factors for predicting the general sleep quality of perimenopausal women in the community ( P < 0.05). Spouse health status ( OR = 2.130) and anxiety ( OR = 19.512) were risk factors for poor sleep quality of perimenopausal women in the community ( P < 0.05). Conclusions There are three qualitatively different potential trajectory categories of sleep quality in perimenopausal women in the community, and drinking history, chronic disease, poor spouse health and anxiety have predictive effects on their trajectory categories. In the future, community nursing staff can take targeted interventions according to different categories of sleep quality in perimenopausal women to improve sleep quality and level of health promotion. Figures Figure 1 Introduction Perimenopause is an inevitable stage for women. At present, there are about 167 million perimenopausal women in China, accounting for about one quarter of the world [ 1 ]. Due to the decline of female reproductive function in this special period, a series of changes in physical and psychological care are triggered, and sleep disorders are one of the more serious complications [ 2 ]. According to surveys, the incidence of sleep disorders in premenopausal women is 16%-42%, and that in perimenopausal women can reach 39%∼47% [ 3 ]. It is even as high as 42%∼60%, mainly manifested as difficulty in falling asleep, waking up early from multiple dreams, poor sleep quality, easy to wake up in the middle, and other symptoms [ 4 ].Studies by Chinese scholars have shown that perimenopausal related sleep disorders are common in women aged 40 to 55, with a prevalence of 13.2%∼65.1% [ 5 – 6 ]. Good sleep can eliminate fatigue, repair body damage, start new nerve excitatory activities, and also help to improve the body's immunity and enhance the body's ability to resist diseases [ 7 ]. Poor sleep quality is a risk factor for cardiovascular disease, diabetes and obesity[ 8 ]. Perimenopausal women undergo various physiological changes in various organs and endocrine glands of the body, and still bear the double pressure of daily life and career, which makes perimenopausal sleep disorders become a major risk factor affecting women's physical and mental health.Studies have shown that insomnia disorder in perimenopausal period can not only cause inattention, mood disorders and other problems, but also increase the risk of anxiety and depression in this population. Both of them have a high degree of comorbidity, which seriously affects their physical and mental health and reduces their quality of life [ 9 , 10 ]. In addition, the sleep quality of perimenopausal women is also affected by many factors, such as physiological, psychological and social aspects [ 11 , 12 ]. Individual sleep characteristics are often complex, diverse and highly heterogeneous [ 13 ], and most of the existing studies only classify the presence or absence of sleep disorders by the critical value of the scale, which makes it difficult to identify the characteristic differences of groups. Identification of the class of their change trajectories and the establishment of predictors are conducive to the identification of people with moderate and high risk of sleep disorders, and the implementation of dynamic management. Different from traditional variance-centered statistical analysis methods, latent profile analysis (LPA) emphasizes human-centered, and accurately and objectively identifies subgroups with heterogeneous characteristics by constructing latent class models [ 14 ]. At present, researches using potential profile to analyze phenomena related to nursing field have gradually emerged [ 15 ]. Through the potential profile analysis of the study group, its needs and risks can be more accurately identified and personalized nursing services can be provided for it [ 16 , 17 ]. In a previous study that examined the relationship between sleep trajectory and the incidence of cardiovascular events in middle-aged women, more than 30% of women had insomnia symptoms; The results showed that nearly a quarter of women had persistent insomnia symptoms during midlife, and 14% had persistent sleep deprivation during midlife. About 7% of women develop a pattern of persistent insomnia symptoms and short sleep duration during midlife [ 18 ]. Chinese scholars explored three potential trajectory categories of sleep disorders in 120 perimenopausal women visiting the gynecological clinic of Maternal and Child Health Hospital, namely, mild sleep disorder group (21.67%), moderate sleep disorder group (37.50%), and severe sleep disorder group (65.83%). The predictive effects of social interaction, hormone level and psychological state on the trajectory category were also explored [ 19 ]. However, to the best of our knowledge, there are no domestic studies involving potential profile analysis of sleep quality in community perimenopausal women in China. Therefore, the latent profile analysis method was used to analyze the sleep quality of perimenopausal women in the community, and the predictors of different sleep quality latent profile class were constructed to understand the influencing factors of different sleep quality latent profile class. Methods Participants The convenience sampling method was used to continuously select perimenopausal women from four communities in Bangshan District, Yuhui District, Huaishang District and Longzihu District of Bengbu City, Anhui Province, China, and a questionnaire survey was conducted from July to December 2024. Inclusion criteria: (1) Perimenopausal women aged 40∼60 years old, according to the diagnostic criteria of perimenopausal women in the tenth edition of Chinese Obstetrics and Gynecology: a period of transition from reproductive period to menopause, from the beginning of ovarian function decline to 1 year after the last menstrual period [ 20 ]; (2) clear consciousness, normal communication and understanding ability, able to write; (3) Informed consent. Exclusion criteria: (1) severe heart, lung, liver and kidney function diseases; (2) Estrogen replacement therapy or sedatives in the past 3 months; (3) patients with a history of mental illness. Sample For descriptive cross-sectional studies of quantitative variables, the sample size was calculated as follows [ 21 ] : . At the 95% confidence interval, represents the absolute error or precision, which was 0.05 in this study, and p is the incidence rate of sleep disorders in perimenopausal women that can be based on the data from previous research [ 3 , 6 ], which was 13.2% ∼65.1%.According to the formula, the the oretical sample size was 176∼349. Considering an invalid response rate of 15% during the study, it was concluded that 202∼401 perimenopausal women need to be investigated. Data collection Investigators used uniform instructions and conducted the survey by distributing paper questionnaires offline. The researchers had obtained the ethical review permission from the school ethics committee before the investigation began. The investigators of this study included nursing teachers and undergraduate students. All investigators received unified training and were responsible for recruiting participants who met the inclusion criteria, while explaining the purpose, significance and content of the study to the participants. The research process remains anonymous. During the collection process, after obtaining the informed consent of the participants, the researchers introduced the requirements for filling out the questionnaires to them. Each questionnaire took approximately 15 to 20 minutes to complete and was filled out by the research subjects themselves. The questionnaires were distributed on the spot and collected immediately after completion. Two researchers checked the data and eliminated the questionnaires with illogical and irregular answers. Measures Participants’ general characteristics The self-designed general information questionnaire was adopted, and the contents included: age, marital status, occupation, educational level, monthly income, current menstrual status, smoking history, drinking history, physical exercise situation and other life behaviors, chronic disease history, entertainment consumption activities (referring to outdoor entertainment consumption activities with relatives or friends, etc.), and the health status of the spouse. Psychological condition The anxiety status of perimenopausal women in the community can be evaluated through the Self-rating Anxiety Scale (SAS). This scale was developed by Zung and is used to assess the subjective feelings of anxiety of the research subjects [ 22 ]. The scale consists of three dimensions: physical response, cognition and behavior, with a total of 20 items. A score of <50 indicates no anxiety, ≥ 50 indicates mild anxiety, ≥ 60 indicates moderate anxiety, and ≥ 70 indicates severe anxiety. The higher the score, the more severe the anxiety of the person being evaluated.In this study, a SAS score of ≥ 50 indicates anxiety.The Cronbach′s α coefficient of this scale is 0.77. The depression status of perimenopausal women in the community can be evaluated through the Self-rating Depression Scale (SDS). This scale was developed by Zung and can be used to assess the subjective feelings of depression in the research subjects [ 23 ]. The scale consists of four dimensions: psychoemotional symptoms, somatic disorders, psychomotor disorders, and psychological disorders of depression, with a total of 20 items. The classification and scoring criteria are the same as those of the Self-Rating Anxiety Scale. The higher the score, the more severe the depression of the person being evaluated. In this study, SDS ≥ 50 points indicates depression. The Cronbach′s α coefficient of this scale is 0.78. Coping style The coping styles of perimenopausal women in the community were evaluated through the simplified coping style questionnaire (SCSQ) [ 24 ]. This scale is a simplification and modification of the ways of coping questionaire (WCQ) developed by Folkman and Lararus [ 25 ] by Chinese scholars in combination with the characteristics of Chinese culture. This questionnaire consists of two dimensions, namely the positive coping (PC) dimension and the negative coping (NC) dimension, with a total of 20 items. The score is calculated by a multi-level scoring method. If it is not adopted, it is scored as 0 points. If it is adopted occasionally, it is scored as 1 point. If it is adopted sometimes, it is scored as 2 points. If it is adopted frequently, it is scored as 3 points. The positive coping dimension contains 12 items, and the calculation result is the average score of the positive coping items. The negative coping dimension contains 8 items, and the calculation result is the average score of the negative coping items. When the score of negative coping is high, the score of psychological problems or symptoms is also high. When the positive coping score is high, the psychological problem or symptom score is low [ 24 ]. The Cronbach′s α coefficient of the entire scale in this study was 0.90. The Cronbach′s α coefficient of the Positive Coping Scale was 0.89. The Cronbach′s α coefficient of the Negative Coping Scale was 0.78. Quality of sleep The sleep quality of perimenopausal women in the community was evaluated by the pittsburgh sleep quality index (PSQI) scale, which reflected the subjective sleep quality of the subjects in the last month [ 26 ]. The PSQI scale included 7 domains: subjective sleep quality, sleep onset duration, sleep time, sleep efficiency, sleep disturbance, hypnotic medication, and daytime dysfunction. Each domain was scored from 0 to 3.In this study, the Chinese version of PSQI score greater than 7 points was defined as having sleep disorders [ 27 ]. The Cronbach's α coefficient of the scale was 0.84. Ethical considerations In accordance with the Declaration of Helsinki, this research was approved by the Ethics Committee of Bengbu Medical University, Anhui Province, China(Approval No. 2025 − 262). All participants were informed of the purpose of the study before recruitment, and all participants were asked to voluntarily sign a written consent form. To protect the participants’ privacy, all collected data were preserved anonymously and confidentially. Statistical analysis Mplus version 7.4 was used to explore the latent profiles of sleep status in perimenopausal women in the community. Data for each item in the seven dimensions were entered into the LPA. In this study, one to five potential profile models were explored sequentially from the initial model (1 profile) to the determination of the most appropriate model with a log-likelihood test. The LPA model fit test indices included the Akaike information criterion (AIC), the Bayesian information criterion (BIC), and the adjusted Bayesian information criterion (aBIC), with a lower value indicating a better-fitting model [ 28 ]. The classification accuracy was evaluated with entropy values (from 0 to 1, with better values close to 1) [ 29 ]. The Lo-Mendel-Rubin Test (LMR) and bootstrap likelihood ratio test (BLRT) were used to assess the P values in the comparisons among models with different numbers of classes. A low P value indicated that the k-class model fit better than the k-1-class model [ 30 ]. To explore the differences in data such as the characteristics of the participants for the subtypes based on LPA, IBM SPSS Statistics version 25.0 was used (IBM Corp., Armonk, NY, USA).Continuous data that conformed to a normal distribution were expressed as( ± S ) (mean ± standard deviation) and analyzed using analysis of variance for intergroup comparisons. Categorical data were described by frequency (n) and percentage (%) and analyzed using the Chi -square test or Fisher's exact probability test for intergroup comparisons. Continuous data that did not conform to a normal distribution were expressed as M (P 25 ∼P 75 ) and analyzed using the non-parametric Kruskal-Wallis H test. Multivariate logistic regression analysis was used to analyze the influencing factors of potential profile of sleep quality, and a P value < 0.05 was considered statistically significant. Results Characteristics of participants A total of 338 perimenopausal women in the community participated in the study. Among 57 unqualified questionnaires (7 questionnaires had the same answer to all questions, and 50 questionnaires had more than 10% of the items not completed), a total of 281 valid questionnaires were analyzed, and the recovery rate was 83.1%. The age of the participants was between 40 and 60 years old, 90.0% of the participants were married, 49.5% of the participants had junior high school education or below, 45.6% of the participants were employed, and most of the participants had a monthly income less than 4000 RMB.More details can be found in Table 1 . Table 1 Univariate analysis of general characteristics and their potential profiles of sleep quality perimenopausal women in the community [ n = 281, ( % / x̅ ± S )] Variables N = 281 Class 1 Class 2 Class 3 χ 2 / F P Age (years) 40∼49 139(49.5) 97(50.3) 37(54.4) 5(25.0) 5.503 0.064 50∼60 142(50.5) 96(49.7) 31(45.6) 15(75.0) Marital status Married 253(90.0) 173(89.6) 61(89.7) 19(95.0) 0.592 0.744 Divorced/widowed 28(10.0) 20(10.4) 7(10.3) 1(5.0) Level of education Junior high school and below 139(49.5) 94(48.7) 30(44.1) 15(75.0) 6.347 0.175 Technical secondary school/High school 61(21.7) 44(22.8) 15(22.1) 2(10.0) Bachelor's degree or above 81(28.8) 55(28.5) 23(33.8) 3(15.0) Occupation Employed 128(45.6) 92(47.67) 24(35.29) 12(60.0) 4.917 0.086 Others 153(54.4) 101(52.33) 44(64.71) 8(40.0) Income(RMB) ≤ 4000 163(58.0) 109(56.48) 41(60.29) 13(65.0) 0.733 0.693 >4000 118(42.0) 84(43.52) 27(39.71) 7(35.0) Current menstrual status Rule 113(40.2) 90(46.6) 19(27.9) 4(20.0) 18.811 P < 0.01 Irregular 79(28.1) 43(22.3) 24(35.3) 12(60.0) Already menopausal 89(31.7) 60(31.1) 25(36.8) 4(20.0) Smoking No 267(95.0) 185(95.8) 64(94.1) 18(90.0) 1.466 0.481 Yes 14(5.0) 8(4.2) 4(5.9) 2(10.0) Drinking history No 191(68.0) 138(71.5) 35(51.5) 18(90.0) 14.068 P < 0.001 Yes 90(32.0) 55(28.5) 33(48.5) 2(10.0) Physical exercise No 164(58.4) 124(64.2) 33(48.5) 7(35.0) 9.950 P < 0.01 Yes 117(41.6) 69(35.8) 35(51.5) 13(65.0) Chronic disease history No 225(80.1) 167(74.2) 45(20.0) 13(5.8) 16.117 P < 0.001 Yes 56(19.9) 26(46.4) 23(41.1) 7(12.5) Entertainment consumption No 99(35.2) 69(35.7) 22(32.3) 8(40.0) 0.469 0.791 Yes 182(64.3) 124(64.3) 46(67.7) 12(60.0) Spouse's health status Very good 145(51.6) 118(61.1) 25(36.8) 2(10.0) 36.339 P < 0.001 good 95(33.8) 59(30.6) 23(33.8) 13(65.0) General/Poor 41(14.6) 16(8.3) 20(29.4) 5(25.0) Depression No 169(60.1) 132(68.4) 31(45.6) 6(30.0) 19.071 P < 0.001 Yes 112(39.9) 61(31.6) 37(54.4) 14(70.0) Anxiety No 213(75.8) 170(88.1) 38(55.9) 5(25.0) 58.718 P < 0.001 Yes 68(24.2) 23(11.9) 30(44.1) 15(75.0) Positive coping - 1.69 ± 0.68 1.52 ± 0.63 1.86 ± 0.49 2.783 0.064 Negative coping - 0.96 ± 0.53 1.05 ± 0.53 0.87 ± 0.42 1.126 0.326 Note: RMB, Renminbi; χ 2 , Chi -square test; F , Analysis of variance Results of latent profile analysis of sleep quality perimenopausal women in the community The scores of 7 dimensions of PSQI were used as the observation indicators to fit 1 to 5 profiles models. With the increase of the number of profiles, the values of AIC, BIC and aBIC decreased, and the Entropy values were all > 0.8, and the Entropy reached the highest when the latent profiles was 2, but the LMR test value did not reach the significant level ( P > 0.05). LMR and BLRT were significant in profiles 3 and 4 ( P < 0.05). Considering the model division and the practical significance of the research results, profiles 3 was selected as the best fitting model, as detailed in Table 2 . The average attribution probability of sleep status of perimenopausal women in community to the three potential profiles was 96.5%, 92.2% and 100%, respectively, indicating that the results of the three potential profiles were reliable. More details can be found in Table 3 . Table 2 The fitting results of the potential profile model for sleep quality of perimenopausal women in the community(n = 281) Profiles AIC BIC aBIC LMR (P) BLRT (P) Entropy Proportions(%) 1-Profiles 4854.820 4905.757 4861.363 - - - 1 2-Profiles 4195.232 4195.232 4205.514 0.0186 0.0000 0.999 0.929/0.071 3-Profiles 3942.079 4051.230 3956.101 0.0002 0.0000 0.903 0.687/0.242/0.071 4-Profiles 3613.603 3751.860 3631.364 0.0083 0.0000 0.932 0.683/0.214/0.032/0.071 5-Profiles 3583.739 3751.103 3605.239 0.2520 0.0000 0.898 0.107/0.032/0.641/0.149/0.071 Note. AIC, Akaike information criterion; BIC, Bayesian information criterion; aBIC, adjusted Bayesian information criterion; LMR, Lo-Mendell-Rubin test; BLRT, Bootstrapped likelihood ratio test. Table 3 Average probability of belonging to the three potential categories of sleep quality perimenopausal women in the community Class 1 2 3 1 0.965 0.035 0.000 2 0.078 0.922 0.000 3 0.000 0.000 1.000 PSQI scores and nomenclature of three potential profile of sleep quality in perimenopausal women in the community According to Table 4 and Fig. 1, the differences in the scores of the seven dimensions of PSQI among the three latent class were statistically significant ( P < 0.01). There were 193 cases (68.7%) in Class 1, and the scores of 7 dimensions were generally low, and the PSQI total score of this group was 5(3∼7) low, so the Class 1 was named "good sleep quality group". There were 68 cases (24.2%) in Class 2, and the scores of 7 dimensions were at a medium level. The total PSQI score of this group was 10(8∼12), so the Class 2 was named as "general sleep quality group". There were 20 patients in Class 3 (7.1%), and the scores of 7 dimensions were higher than those in Class 1 and Class 2. The total PSQI score of this group was 15(15∼17), so Class 3 was named as "poor sleep quality group". Table 4 PSQI scores and three potential profiles of sleep quality in perimenopausal women in the community [n = 281, (P 25 ∼P 75 )] Group N Subjective sleep quality Sleep Latency Sleep Duration Sleep efficiency Sleep Disturbances Use of Sleep Medication Daytime dysfunction Total score of the PSQI Good sleep quality group 193 0(0∼1) 0(0∼1) 2(1∼3) 1(0∼2) 1(0∼1) 0(0∼0) 0(0∼1) 5(3∼7) General sleep quality group 68 1(1∼2) 2(1∼2) 3(2∼3) 2(1∼3) 1(1∼2) 0(0∼0) 2(1∼2) 10(8∼12) Poor sleep quality group 20 2(2∼2) 2(2∼2) 3(3∼3) 3(2.3∼3) 2(1∼2) 2(2∼2) 3(2∼3) 15(15∼17) H 149.657 115.352 25.210 25.458 81.261 201.855 134.421 155.817 P P < 0.01 P < 0.01 P < 0.01 P < 0.01 P < 0.01 P < 0.01 P < 0.01 P 0.05) in age, marital status, education level, occupation, monthly income, smoking history, entertainment consumption, positive and negative coping styles among perimenopausal women with different sleep quality class. There were significant differences ( P < 0.05) in drinking history, current menstrual status, physical exercise, chronic medical history, spouse's health status, depression and anxiety among community perimenopausal women with different sleep quality class. More details can be found in Table 1 . Multivariate analysis of potential profile of sleep quality perimenopausal women in the community Taking the sleep quality of perimenopausal women in the community as the dependent variable, and the good sleep group as the reference group, the statistically significant indicators in the above Table 1 were included as independent variables, including drinking history, current menstrual status, physical exercise, chronic medical history, spouse's health status, depression and anxiety status, for multinomial Logistic regression analysis. General sleep quality group = 2, poor sleep quality group = 3, set reference group: good sleep quality group = 1; The specific assignment can be found in Table 5 . Table 5 Variable assignment Variables Categories Variable assignment BMI X1 0 =<18.5, 1=18.5∼<24, 2=24∼<28, 3 = ≥ 28 Drinking alcohol X3 0=No, 1=Yes Menstruation X4 0=Rule, 1=Irregular, 2=Already menopausal Chronic disease X5 0=No, 1=Yes Physical exercise X6 0=No, 1=Yes spouse health X7 0=Very good, 1 = Good, 2=General/Poor Depression X8 0=No, 1=Yes Anxiety X9 0=No, 1=Yes Quality of sleep Y Y1=Good Sleep quality Group, Y2=General sleep quality group, Y3=Poor sleep quality group The results of multinomial Logistic regression analysis showed that: Taking the good sleep quality group as the reference group, drinking history ( OR = 2.087), chronic disease history ( OR = 2.221), spouse health status ( OR = 1.880) and anxiety ( OR = 4.358) were risk factors for predicting the general sleep quality of perimenopausal women in community ( P < 0.05). Taking the good sleep quality group as the reference group, spouse health status ( OR = 2.130) and anxiety ( OR = 19.512) were risk factors for poor sleep quality in community perimenopausal women ( P < 0.05). More details can be found in Table 6 . Table 6 Multinomial Logistic regression analysis of potential profile of sleep quality in community perimenopausal women (n = 281) Group Variables B S.E. Wald χ 2 P OR 95%CI Class 2 a Current menstrual status 0.309 0.196 2.477 0.116 1.362 0.927 ~ 2.000 Drinking alcohol 0.736 0.325 5.138 0.023 2.087 1.105 ~ 3.944 Physical exercise 0.487 0.343 2.010 0.156 1.627 0.830 ~ 3.188 Chronic disease history 0.798 0.374 4.541 0.033 2.221 1.066 ~ 4.627 Spouse's health status 0.631 0.222 8.093 0.004 1.880 1.217 ~ 2.903 Depression -0.086 0.392 0.048 0.827 0.918 0.425 ~ 1.979 Anxiety 1.472 0.414 12.622 0.000 4.358 1.935 ~ 9.818 Class 3 a Current menstrual status 0.222 0.347 0.410 0.522 1.248 0.633 ~ 2.463 Drinking history -1.458 0.807 3.264 0.071 0.233 0.048 ~ 1.132 Physical exercise 0.536 0.629 0.727 0.394 1.710 0.498 ~ 5.866 Chronic disease history 0.846 0.624 1.836 0.175 2.330 0.686 ~ 7.915 Spouse's health status 0.756 0.378 4.001 0.045 2.130 1.015 ~ 4.469 Depression -0.323 0.741 0.190 0.663 0.724 0.169 ~ 3.094 Anxiety 2.971 0.747 15.835 0.000 19.512 4.516 ~ 84.300 Note: a refers to the good sleep quality group and serves as the reference group. Discussion To the best of our knowledge, this study represents the first investigation characterizing latent sleep quality profiles and their influencing factors in community-dwelling perimenopausal women. By integrating latent profile analysis and factor analysis, the study aims to elucidate the distinct patterns of sleep quality associated with each latent profile in this group.This study has some novelty. Epidemiological data show that the incidence of sleep problems in perimenopausal women increases from 16%∼42–39%∼47% with the change of life cycle, the extreme changes of hormones and the increase of age [ 3 ]. At the same time, related studies in China have shown that perimenopausal related sleep disorders are common in women aged 40∼55 years, with a prevalence of 13.2%∼65.1% [ 5 , 6 ]. Perimenopausal women generally face sleep problems such as difficulty falling asleep, frequent awakenings at night, and shortened sleep duration. This study found that there were 88 cases of perimenopausal women in the community with PSQI scores > 7 points, that is the probability of sleep disorders was 33.3%, which was consistent with the results of another study [ 31 ]. This is also similar to the results of a community-based survey in the United States, which showed that 38% of women in the menopausal transition suffer from sleep disorders [ 32 ]. A Russian study showed that the incidence of sleep disorders in perimenopausal women was 61.2% [ 33 ]. The results of a cross-sectional survey of a nationally representative random sample in Korea showed that 26% of the participants had poor sleep quality according to PSQI [ 12 ]. Although the prevalence of sleep disorders in perimenopausal women varies in different races, regions and populations, the overall prevalence is high and shows an upward trend. The prevalence of sleep disorders in perimenopausal women is high, which may be affected by regional rhythm of life, social and cultural environment and other factors. Perimenopausal women face the double burden of work and family, and their nerves are in a tense state for a long time, which is more likely to cause sleep problems and affect their quality of daily life. Perimenopause is a critical transitional period in the life cycle of women, during which women's sleep status changes significantly. This study identified three potential trajectories of sleep quality of perimenopausal women in the community through the latent class growth model, namely, good sleep quality group, general sleep quality group, and poor sleep quality group. The study results showed that there was heterogeneity in sleep quality of perimenopausal women in the community, which was similar to the findings of an existing study [ 19 ]. This classification reflects the heterogeneity of sleep quality among perimenopausal women in the community within each latent trait. It complements previous studies that viewed community perimenopausal women as a homogenous whole and, to a certain extent, can provide guidance for the development of targeted interventions to improve sleep quality in community perimenopausal women. For the community perimenopausal women in the good sleep quality group, there is a certain degree of sleep time disorder, but the total score of PSQI is 5(3∼7), which does not exceed the range of sleep disorders defined by the Chinese version of PSQI (> 7). They may be in a more comfortable sleep environment or have higher positive coping ability, and show better sleep quality. For this class of women, we should advocate and encourage them to maintain a good sleep status. The general trend of sleep quality in the general sleep quality group was similar to that in the good sleep quality group, but the poor subjective sleep quality, sleep onset time and daytime dysfunction of perimenopausal women in the community were more obvious, which may be related to short sleep time, low sleep efficiency and serious sleep disorders. The sleep quality of perimenopausal women in the community has not been further deteriorated, and targeted non-drug intervention can be used. Improve the overall quality of sleep. In the Chinese Expert Consensus on the Clinical Management of Postmenopausal Insomnia, the Chinese Preventive Medicine Association proposed a series of non-drug therapies such as appropriate exercise, diet and nutrition management, physical and psychological therapy. It recommended aerobic and resistance exercise with transition from low intensity to moderate intensity, and paid attention to balanced nutrition in diet with adequate intake of protein, probiotics and vitamins. Caffeinated substances should be avoided, and transcranial magnetic stimulation, light therapy, or hyperbaric oxygen therapy should be used to improve sleep if necessary [ 6 ]. The proportion of the poor sleep quality group was the lowest, but the scores of the poor sleep quality group were higher than those of the other two groups, especially in the sleep duration dimension. Poor sleep quality and short sleep duration increase the risk of cardiovascular disease, all-cause mortality, and cancer-related mortality [ 34 ]. Therefore, to improve the attention of community perimenopausal women to sleep problems, community nursing staff should focus on strengthening education, populating the core concept of sleep quality, encouraging women to accurately identify sleep problems through professional means, providing targeted sleep hygiene guidance, behavior therapy and medical intervention when necessary, so as to effectively improve sleep quality and effective sleep duration and reduce related health risks. This study found that the factors influencing the sleep quality trajectory of perimenopausal women in the community included drinking history, chronic diseases, spouse's health status, and anxiety. This study found that compared with the community perimenopausal women in the group with good sleep quality, the probability of drinking history in the group with general sleep quality was 2.087 times higher than that in the group with good sleep quality. A national cohort study in Korea showed that alcohol risk ( OR = 1.53) was a risk factor for poor sleep quality in menopausal women [ 12 ]. Studies have shown that alcohol intake is a trigger for sleep fragmentation [ 35 ]. Relying on alcohol to promote sleep can also disrupt the structure of the sleep cycle, forming a vicious cycle. Community nursing staff can take cognitive behavioral therapy to correct the wrong idea of alcohol as a sleep aid in perimenopausal women, establish a positive feedback regulation system and gradually improve their sleep quality. Perimenopausal women face not only gynecological problems, but also chronic diseases such as cardiovascular and cerebrovascular diseases and endocrine diseases. The results of this study showed that compared with the community perimenopausal women with good sleep quality, the probability of chronic diseases in the group with general sleep quality was 2.221 times higher than that in the group with good sleep quality. The results of a systematic review and meta-analysis on the influencing factors of sleep disorders in perimenopausal women showed that women with chronic diseases had a 1.39-fold increased risk of sleep disorders [ 11 ]. Similarly, a national survey study in Korea also showed that chronic diseases were risk factors for poor sleep quality in menopausal women [ 12 ]. Perimenopausal women with a history of diabetes have a higher incidence of sleep disorders [ 36 ]. Studies at home and abroad have shown that there is a correlation between the prevalence of diabetes and poor sleep quality of patients. The high blood glucose state of the body is unfavorable to the central nervous system, which may cause abnormal neurobehavior, neurotransmitter and autonomic nervous dysfunction, thus causing sleep disorders [ 37 , 38 ]. Persistent chronic diseases may have adverse effects on the physical and mental health of perimenopausal women [ 39 ]. Therefore, multidisciplinary team collaboration and multi-level intervention may be the key strategy to implement comprehensive perimenopausal health management [ 40 ], so as to alleviate the negative impact of chronic diseases on perimenopausal women. Our study also found that the spouses of community perimenopausal women in the general and poor sleep quality groups had worse health status than those in the good sleep group, with 1.880 times worse in the general sleep quality group and 2.130 times worse in the poor sleep quality group. Studies have shown that there is a certain correlation between poor husband's health and abnormal sleep in perimenopausal women [ 41 ]. When the health status of their spouse deteriorated, the sleep quality of women also decreased synchronously, indicating a bidirectional association. When a spouse has chronic diseases (such as cardiovascular and cerebrovascular diseases), women need to provide frequent nighttime care, which leads to sleep fragmentation. In addition, spouse disease may increase family economic pressure and further induce anxiety insomnia. Couples who live together for a long time develop similar circadian rhythms. A UK Biobank study of 47,420 couples found significant sleep-wake synchrony, which reduced nighttime interference and improved sleep efficiency [ 35 ]. Therefore, community nursing staff need to encourage perimenopausal women and their spouses to actively participate in health management, seek nursing support from family, society or professional institutions, share the care pressure and reduce the psychological burden of both sides. Family members other than lovers should be considered in perimenopausal women's health education to encourage them to understand the experience of perimenopausal women, and to establish a higher quality social support system for perimenopausal women, so that they can be freed from the heavy burden of physical and mental pressure. Improve the family care of perimenopausal women by family members (especially their spouses), fully understand the various physical and mental changes they face, take targeted measures to improve the sleep quality of perimenopausal women, promote the physical and mental health of perimenopausal women [ 42 ]. With the continuous decline of estrogen levels and the disorder of autonomic nervous function, perimenopausal women are often prone to mood fluctuations, such as irritability, suspicion, anxiety and depression. These psychological problems have gradually become the focus of public attention.Studies have pointed out that there is a close relationship between mental state and physical health, and long-term anxiety may lead to physical health problems [ 43 ]. Anxiety is one of the common psychological problems in perimenopausal women, which is mainly manifested as nervousness, fear, worry and irritability, etc. It is related to women's personal characteristics, life pressure, family relationship and social support [ 44 ]. The results of this study showed that compared with the good sleep quality group, the anxiety of the perimenopausal women in the general sleep quality group was 4.358 times higher than that of the good sleep quality group, and the anxiety of the poor sleep quality group was 19.512 times higher than that of the good sleep quality group. This finding is similar to that in a previous study [ 19 ]. In a sampling study conducted by Simbar et al. [ 45 ], the proportion of women with mild to severe anxiety in perimenopausal women was as high as 83.70%. In this study, the incidence of anxiety in community perimenopausal women was 24.20%, which was higher than another study in China [ 46 ]. Anxiety may lead to sleep disorders in perimenopausal women, such as difficulty falling asleep, lack of sleep and poor sleep quality, and affect their daytime function [ 47 ]. As a result, perimenopausal women have cognitive dysfunction such as distraction, memory decline and slow thinking, which affects their work efficiency. Body discomfort such as palpitation, shortness of breath, dizziness and stomachache may affect their health. In addition, anxiety not only aggravates the physical discomfort of perimenopausal women, affects their sleep quality and social function, but also reduces their self-esteem and self-confidence, and even leads to more serious psychological disorders such as depression [ 46 ]. A number of previous studies have shown that depression is an influencing factor for sleep disorders in perimenopausal women [ 11 , 12 , 19 , 48 ]. However, in this study, it was not concluded that depression is a risk factor for poor sleep quality in community perimenopausal women. Due to the heterogeneity of the study population, some previous studies were based on outpatients or hospital patients [ 19 , 48 ], who usually seek medical treatment actively due to prominent symptoms, have more severe depression, and are more likely to be associated with sleep disorders. However, the community sample included more patients with mild symptoms or who did not seek medical treatment, and the degree of depression may be generally mild, leading to the dilution of the association. Future studies can refine the stratification and design of research subjects, include depression patients in the hospital to compare the differences in the community population, and dynamically monitor the association between depression and sleep quality. Depression and anxiety are common mood disorders in perimenopausal women. At the same time, sleep disorders themselves are also typical symptoms of patients with depression and anxiety. A multi-center study showed that there was a causal relationship between sleep quality and depression and anxiety in perimenopausal women, which influenced each other and formed a vicious circle [ 49 ]. Long-term poor sleep quality increases the risk of diabetes, cardiovascular disease, depression, anxiety, heart attack, obesity, and stroke [ 50 ]. Perimenopausal women are in an important stage of social role transformation, family and work pressure, and multiple objective negative factors are easy to lead to anxiety and depression. Therefore, during the perimenopausal period, women need to pay attention to their psychological status and actively seek social support, so as to better cope with the challenges brought by physiological changes and improve their sleep quality. At the same time, family members, friends and social organizations should also give more care and support to perimenopausal women in the community to help them go through this special physiological stage. Limitations However, this study still has some shortcomings. This study is mainly cross-sectional, with small sample size and limited coverage. In addition, the Pittsburgh Sleep Quality Index (PSQI) is self-reported by the respondents, and some subjective items and options may cause bias. To longitudinally analyze the potential characteristics of sleep disorders in perimenopausal women in the community at different time points, comprehensively analyze the various factors affecting the sleep quality of perimenopausal women in the community, and verify the scientific validity and feasibility of this study. Conclusions In summary, there is obvious population heterogeneity in the sleep quality of perimenopausal women in the community, and drinking history, chronic diseases, spouse health status and anxiety have predictive effects on their potential profile class. In the future, community nursing staff can take targeted interventions to improve sleep quality and health promotion according to the sleep quality class and risk factors of perimenopausal women. Abbreviations PSQI pittsburgh sleep quality index RMB renminbi SAS self-ating anxietyscale SDS self-ating depressionscale SCSQ simplified coping style questionnaire PC positive coping NC negative coping AIC Akaike information criterion BIC Bayesian information criterion aBIC adjusted Bayesian information criterion LMR Lo-Mendel-Rubin Test BLRT bootstrap likelihood ratio test Declarations Ethics approval and consent to participate In accordance with the Declaration of Helsinki, this research was approved by the Ethics Committee of Bengbu Medical University, Anhui Province, China(approval no. 2025-262). All participants were informed of the purpose of the study before recruitment, and all participants were asked to voluntarily sign a written consent form. To protect the participants’ privacy, all collected data were preserved anonymously and confidentially. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Competing interests The authors declare no competing interests. Funding This study was supported by the Anhui Province College Students Innovation and Entrepreneurship Training Program (NO. S202410367045) and Bengbu Medical University humanities and social science Research Youth Fund project (NO. 2024byzd165sk). The funding organization had no role in the study design, data collection, management, analysis, interpretation, manuscript writing, or the decision to submit the report for publication. Author contributions S.D.H. —conception, design, drafting the article. Z.H.S. , J.J.L , Z.Y.W , G.J.M. —data collection. S.D.H. , X.T.S , S.T.T. , J.Z.L —conception, design, data analysis and interpretation, drafting the article. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7100582","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514653054,"identity":"009d50a4-0109-4bb9-95c9-6a5a2cd82114","order_by":0,"name":"Shoudi Hu","email":"","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shoudi","middleName":"","lastName":"Hu","suffix":""},{"id":514653055,"identity":"d48ab6c5-09ff-4e94-839b-17b593edf4f2","order_by":1,"name":"Zihan Shan","email":"","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zihan","middleName":"","lastName":"Shan","suffix":""},{"id":514653056,"identity":"5df637b8-e7c8-4b0e-ad1d-8d1f6a3cd75b","order_by":2,"name":"Xintong Shen","email":"","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xintong","middleName":"","lastName":"Shen","suffix":""},{"id":514653057,"identity":"95b475e9-3794-4736-aa05-60da6e7763cc","order_by":3,"name":"Shuting Tang","email":"","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuting","middleName":"","lastName":"Tang","suffix":""},{"id":514653058,"identity":"77fceb4b-59b4-4453-acfd-923e0ce88c46","order_by":4,"name":"JiaJia Lu","email":"","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"JiaJia","middleName":"","lastName":"Lu","suffix":""},{"id":514653059,"identity":"8f9af3e7-baf5-4354-b783-74bf62dfe68a","order_by":5,"name":"Zhiyuan Wang","email":"","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyuan","middleName":"","lastName":"Wang","suffix":""},{"id":514653060,"identity":"77564769-eb2b-4236-b3c2-6b612a939f02","order_by":6,"name":"Guangjiao Meng","email":"","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guangjiao","middleName":"","lastName":"Meng","suffix":""},{"id":514653061,"identity":"341f2454-bd4e-4dc9-aa75-6fa33bb09c90","order_by":7,"name":"Jinzhi Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACNvmHDQcSDP7JGUiA+cyEtfAzJB988KHggDHxWiQb0pINZ3w4kLiBaC0GB86YSfMY3EnfLt2dJsFQYZ3YwH72AH4tB3tAWp7l7pxzdpsEw5n0xAaevAT8Wg7zgLQw5264kbtNgrHtcGKDBI8Bfi3HIFrSDcBa/hGhRbKHDeh9g8MJEC0NRGjhl2AGBrJBmiHQL5stEo6lG7fx5ODXwgY0+UDCHxt5c+nejTc+1FjL9rOfwa8FFSSADCFB/SgYBaNgFIwCHAAAd3RIziuTZfMAAAAASUVORK5CYII=","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jinzhi","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-11 10:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7100582/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7100582/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12905-025-04217-w","type":"published","date":"2025-12-30T15:58:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91505718,"identity":"1e83ff30-2b8a-4ac1-8278-2aab43706574","added_by":"auto","created_at":"2025-09-17 08:21:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70123,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrajectory diagram of the potential profile model of sleep quality perimenopausal women in the community\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7100582/v1/399c581ad84c38d7a09b4a27.jpg"},{"id":99546124,"identity":"37e30916-e6d7-4bd1-9529-a045d09ab6b3","added_by":"auto","created_at":"2026-01-05 16:10:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1542136,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7100582/v1/eebda870-57ef-4297-800e-c90fe42c2d3f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Latent profile analysis and influencing factors of sleep quality in community perimenopausal women: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePerimenopause is an inevitable stage for women. At present, there are about 167\u0026nbsp;million perimenopausal women in China, accounting for about one quarter of the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Due to the decline of female reproductive function in this special period, a series of changes in physical and psychological care are triggered, and sleep disorders are one of the more serious complications [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to surveys, the incidence of sleep disorders in premenopausal women is 16%-42%, and that in perimenopausal women can reach 39%∼47% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is even as high as 42%∼60%, mainly manifested as difficulty in falling asleep, waking up early from multiple dreams, poor sleep quality, easy to wake up in the middle, and other symptoms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].Studies by Chinese scholars have shown that perimenopausal related sleep disorders are common in women aged 40 to 55, with a prevalence of 13.2%∼65.1% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Good sleep can eliminate fatigue, repair body damage, start new nerve excitatory activities, and also help to improve the body's immunity and enhance the body's ability to resist diseases [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Poor sleep quality is a risk factor for cardiovascular disease, diabetes and obesity[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Perimenopausal women undergo various physiological changes in various organs and endocrine glands of the body, and still bear the double pressure of daily life and career, which makes perimenopausal sleep disorders become a major risk factor affecting women's physical and mental health.Studies have shown that insomnia disorder in perimenopausal period can not only cause inattention, mood disorders and other problems, but also increase the risk of anxiety and depression in this population. Both of them have a high degree of comorbidity, which seriously affects their physical and mental health and reduces their quality of life [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition, the sleep quality of perimenopausal women is also affected by many factors, such as physiological, psychological and social aspects [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Individual sleep characteristics are often complex, diverse and highly heterogeneous [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and most of the existing studies only classify the presence or absence of sleep disorders by the critical value of the scale, which makes it difficult to identify the characteristic differences of groups. Identification of the class of their change trajectories and the establishment of predictors are conducive to the identification of people with moderate and high risk of sleep disorders, and the implementation of dynamic management. Different from traditional variance-centered statistical analysis methods, latent profile analysis (LPA) emphasizes human-centered, and accurately and objectively identifies subgroups with heterogeneous characteristics by constructing latent class models [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. At present, researches using potential profile to analyze phenomena related to nursing field have gradually emerged [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Through the potential profile analysis of the study group, its needs and risks can be more accurately identified and personalized nursing services can be provided for it [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn a previous study that examined the relationship between sleep trajectory and the incidence of cardiovascular events in middle-aged women, more than 30% of women had insomnia symptoms; The results showed that nearly a quarter of women had persistent insomnia symptoms during midlife, and 14% had persistent sleep deprivation during midlife. About 7% of women develop a pattern of persistent insomnia symptoms and short sleep duration during midlife [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Chinese scholars explored three potential trajectory categories of sleep disorders in 120 perimenopausal women visiting the gynecological clinic of Maternal and Child Health Hospital, namely, mild sleep disorder group (21.67%), moderate sleep disorder group (37.50%), and severe sleep disorder group (65.83%). The predictive effects of social interaction, hormone level and psychological state on the trajectory category were also explored [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, to the best of our knowledge, there are no domestic studies involving potential profile analysis of sleep quality in community perimenopausal women in China. Therefore, the latent profile analysis method was used to analyze the sleep quality of perimenopausal women in the community, and the predictors of different sleep quality latent profile class were constructed to understand the influencing factors of different sleep quality latent profile class.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eThe convenience sampling method was used to continuously select perimenopausal women from four communities in Bangshan District, Yuhui District, Huaishang District and Longzihu District of Bengbu City, Anhui Province, China, and a questionnaire survey was conducted from July to December 2024.\u003c/p\u003e\u003cp\u003eInclusion criteria: (1) Perimenopausal women aged 40∼60 years old, according to the diagnostic criteria of perimenopausal women in the tenth edition of Chinese Obstetrics and Gynecology: a period of transition from reproductive period to menopause, from the beginning of ovarian function decline to 1 year after the last menstrual period [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; (2) clear consciousness, normal communication and understanding ability, able to write; (3) Informed consent. Exclusion criteria: (1) severe heart, lung, liver and kidney function diseases; (2) Estrogen replacement therapy or sedatives in the past 3 months; (3) patients with a history of mental illness.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSample\u003c/h3\u003e\n\u003cp\u003eFor descriptive cross-sectional studies of quantitative variables, the sample size was calculated as follows [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] :\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e. At the 95% confidence interval, \u003cimg src=\"data:image/png;base64,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\" style=\"width: 97px; height: 37.2583px;\" width=\"97\" height=\"37.2583\"\u003e represents the absolute error or precision, which was 0.05 in this study, and \u003cem\u003ep\u003c/em\u003e is the incidence rate of sleep disorders in perimenopausal women that can be based on the data from previous research [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which was 13.2% ∼65.1%.According to the formula, the the oretical sample size was 176∼349. Considering an invalid response rate of 15% during the study, it was concluded that 202∼401 perimenopausal women need to be investigated.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eInvestigators used uniform instructions and conducted the survey by distributing paper questionnaires offline. The researchers had obtained the ethical review permission from the school ethics committee before the investigation began. The investigators of this study included nursing teachers and undergraduate students. All investigators received unified training and were responsible for recruiting participants who met the inclusion criteria, while explaining the purpose, significance and content of the study to the participants. The research process remains anonymous. During the collection process, after obtaining the informed consent of the participants, the researchers introduced the requirements for filling out the questionnaires to them. Each questionnaire took approximately 15 to 20 minutes to complete and was filled out by the research subjects themselves. The questionnaires were distributed on the spot and collected immediately after completion. Two researchers checked the data and eliminated the questionnaires with illogical and irregular answers.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u0026rsquo; general characteristics\u003c/h2\u003e\u003cp\u003eThe self-designed general information questionnaire was adopted, and the contents included: age, marital status, occupation, educational level, monthly income, current menstrual status, smoking history, drinking history, physical exercise situation and other life behaviors, chronic disease history, entertainment consumption activities (referring to outdoor entertainment consumption activities with relatives or friends, etc.), and the health status of the spouse.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePsychological condition\u003c/h2\u003e\u003cp\u003eThe anxiety status of perimenopausal women in the community can be evaluated through the Self-rating Anxiety Scale (SAS). This scale was developed by Zung and is used to assess the subjective feelings of anxiety of the research subjects [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The scale consists of three dimensions: physical response, cognition and behavior, with a total of 20 items. A score of \u0026lt;50 indicates no anxiety, \u0026ge;\u0026thinsp;50 indicates mild anxiety, \u0026ge;\u0026thinsp;60 indicates moderate anxiety, and \u0026ge;\u0026thinsp;70 indicates severe anxiety. The higher the score, the more severe the anxiety of the person being evaluated.In this study, a SAS score of \u0026ge;\u0026thinsp;50 indicates anxiety.The Cronbach\u0026prime;s α coefficient of this scale is 0.77.\u003c/p\u003e\u003cp\u003eThe depression status of perimenopausal women in the community can be evaluated through the Self-rating Depression Scale (SDS). This scale was developed by Zung and can be used to assess the subjective feelings of depression in the research subjects [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The scale consists of four dimensions: psychoemotional symptoms, somatic disorders, psychomotor disorders, and psychological disorders of depression, with a total of 20 items. The classification and scoring criteria are the same as those of the Self-Rating Anxiety Scale. The higher the score, the more severe the depression of the person being evaluated. In this study, SDS\u0026thinsp;\u0026ge;\u0026thinsp;50 points indicates depression. The Cronbach\u0026prime;s α coefficient of this scale is 0.78.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCoping style\u003c/h3\u003e\n\u003cp\u003eThe coping styles of perimenopausal women in the community were evaluated through the simplified coping style questionnaire (SCSQ) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This scale is a simplification and modification of the ways of coping questionaire (WCQ) developed by Folkman and Lararus [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] by Chinese scholars in combination with the characteristics of Chinese culture. This questionnaire consists of two dimensions, namely the positive coping (PC) dimension and the negative coping (NC) dimension, with a total of 20 items. The score is calculated by a multi-level scoring method. If it is not adopted, it is scored as 0 points. If it is adopted occasionally, it is scored as 1 point. If it is adopted sometimes, it is scored as 2 points. If it is adopted frequently, it is scored as 3 points. The positive coping dimension contains 12 items, and the calculation result is the average score of the positive coping items. The negative coping dimension contains 8 items, and the calculation result is the average score of the negative coping items. When the score of negative coping is high, the score of psychological problems or symptoms is also high. When the positive coping score is high, the psychological problem or symptom score is low [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The Cronbach\u0026prime;s α coefficient of the entire scale in this study was 0.90. The Cronbach\u0026prime;s α coefficient of the Positive Coping Scale was 0.89. The Cronbach\u0026prime;s α coefficient of the Negative Coping Scale was 0.78.\u003c/p\u003e\n\u003ch3\u003eQuality of sleep\u003c/h3\u003e\n\u003cp\u003eThe sleep quality of perimenopausal women in the community was evaluated by the pittsburgh sleep quality index (PSQI) scale, which reflected the subjective sleep quality of the subjects in the last month [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The PSQI scale included 7 domains: subjective sleep quality, sleep onset duration, sleep time, sleep efficiency, sleep disturbance, hypnotic medication, and daytime dysfunction. Each domain was scored from 0 to 3.In this study, the Chinese version of PSQI score greater than 7 points was defined as having sleep disorders [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The Cronbach's α coefficient of the scale was 0.84.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eEthical considerations\u003c/h2\u003e\u003cp\u003e In accordance with the Declaration of Helsinki, this research was approved by the Ethics Committee of Bengbu Medical University, Anhui Province, China(Approval No. 2025\u0026thinsp;\u0026minus;\u0026thinsp;262). All participants were informed of the purpose of the study before recruitment, and all participants were asked to voluntarily sign a written consent form. To protect the participants\u0026rsquo; privacy, all collected data were preserved anonymously and confidentially.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eMplus version 7.4 was used to explore the latent profiles of sleep status in perimenopausal women in the community. Data for each item in the seven dimensions were entered into the LPA. In this study, one to five potential profile models were explored sequentially from the initial model (1 profile) to the determination of the most appropriate model with a log-likelihood test.\u003c/p\u003e\u003cp\u003eThe LPA model fit test indices included the Akaike information criterion (AIC), the Bayesian information criterion (BIC), and the adjusted Bayesian information criterion (aBIC), with a lower value indicating a better-fitting model [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The classification accuracy was evaluated with entropy values (from 0 to 1, with better values close to 1) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The Lo-Mendel-Rubin Test (LMR) and bootstrap likelihood ratio test (BLRT) were used to assess the \u003cem\u003eP\u003c/em\u003e values in the comparisons among models with different numbers of classes. A low \u003cem\u003eP\u003c/em\u003e value indicated that the k-class model fit better than the k-1-class model [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo explore the differences in data such as the characteristics of the participants for the subtypes based on LPA, IBM SPSS Statistics version 25.0 was used (IBM Corp., Armonk, NY, USA).Continuous data that conformed to a normal distribution were expressed as(\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e\u0026plusmn;\u003cem\u003eS\u003c/em\u003e) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) and analyzed using analysis of variance for intergroup comparisons. Categorical data were described by frequency (n) and percentage (%) and analyzed using the \u003cem\u003eChi\u003c/em\u003e-square test or Fisher's exact probability test for intergroup comparisons. Continuous data that did not conform to a normal distribution were expressed as \u003cem\u003eM\u003c/em\u003e (P\u003csub\u003e25\u003c/sub\u003e∼P\u003csub\u003e75\u003c/sub\u003e) and analyzed using the non-parametric Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e test. Multivariate logistic regression analysis was used to analyze the influencing factors of potential profile of sleep quality, and a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of participants\u003c/h2\u003e\u003cp\u003eA total of 338 perimenopausal women in the community participated in the study. Among 57 unqualified questionnaires (7 questionnaires had the same answer to all questions, and 50 questionnaires had more than 10% of the items not completed), a total of 281 valid questionnaires were analyzed, and the recovery rate was 83.1%. The age of the participants was between 40 and 60 years old, 90.0% of the participants were married, 49.5% of the participants had junior high school education or below, 45.6% of the participants were employed, and most of the participants had a monthly income less than 4000 RMB.More details can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eUnivariate analysis of general characteristics and their potential profiles of sleep quality perimenopausal women in the community\u003c/b\u003e [ n\u0026thinsp;=\u0026thinsp;281, ( % / x̅\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e\u0026plusmn;\u003cem\u003eS\u003c/em\u003e )]\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;281\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClass 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClass 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eClass 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e/\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40∼49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139(49.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97(50.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37(54.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5(25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50∼60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142(50.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96(49.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31(45.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15(75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e253(90.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e173(89.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61(89.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19(95.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.744\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced/widowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28(10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20(10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7(10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1(5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior high school and below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139(49.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94(48.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30(44.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15(75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical secondary school/High school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61(21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44(22.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15(22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2(10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor's degree or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81(28.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55(28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23(33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3(15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128(45.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92(47.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24(35.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12(60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153(54.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101(52.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44(64.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8(40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome(RMB)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;4000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e163(58.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109(56.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41(60.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13(65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;4000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118(42.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84(43.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27(39.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7(35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent menstrual status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRule\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113(40.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90(46.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19(27.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4(20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIrregular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79(28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43(22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24(35.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12(60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlready menopausal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89(31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60(31.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25(36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4(20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e267(95.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e185(95.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64(94.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18(90.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.481\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14(5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8(4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4(5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2(10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e191(68.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e138(71.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35(51.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18(90.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90(32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55(28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33(48.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2(10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical exercise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164(58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e124(64.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33(48.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7(35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117(41.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69(35.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35(51.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13(65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic disease history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e225(80.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e167(74.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45(20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13(5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56(19.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26(46.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23(41.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7(12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEntertainment consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99(35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69(35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22(32.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8(40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.791\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e182(64.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e124(64.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46(67.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12(60.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpouse's health status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery good\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e145(51.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e118(61.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25(36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2(10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e36.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95(33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59(30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23(33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13(65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral/Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41(14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16(8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20(29.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5(25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e169(60.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e132(68.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31(45.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6(30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112(39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61(31.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37(54.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14(70.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e213(75.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170(88.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38(55.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5(25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e58.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68(24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23(11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30(44.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15(75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive coping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative coping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: RMB, Renminbi; \u003cb\u003eχ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e, \u003cem\u003eChi\u003c/em\u003e-square test; \u003cem\u003eF\u003c/em\u003e, Analysis of variance\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eResults of latent profile analysis of sleep quality perimenopausal women in the community\u003c/h2\u003e\u003cp\u003eThe scores of 7 dimensions of PSQI were used as the observation indicators to fit 1 to 5 profiles models. With the increase of the number of profiles, the values of AIC, BIC and aBIC decreased, and the Entropy values were all \u0026gt;\u0026thinsp;0.8, and the Entropy reached the highest when the latent profiles was 2, but the LMR test value did not reach the significant level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). LMR and BLRT were significant in profiles 3 and 4 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Considering the model division and the practical significance of the research results, profiles 3 was selected as the best fitting model, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The average attribution probability of sleep status of perimenopausal women in community to the three potential profiles was 96.5%, 92.2% and 100%, respectively, indicating that the results of the three potential profiles were reliable. More details can be found in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe fitting results of the potential profile model for sleep quality of perimenopausal women in the community(n\u0026thinsp;=\u0026thinsp;281)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfiles\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eaBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLMR\u003c/p\u003e\u003cp\u003e(P)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBLRT\u003c/p\u003e\u003cp\u003e(P)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEntropy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eProportions(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1-Profiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4854.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4905.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4861.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2-Profiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4195.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4195.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4205.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.929/0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3-Profiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3942.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4051.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3956.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.687/0.242/0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4-Profiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3613.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3751.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3631.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.683/0.214/0.032/0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5-Profiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3583.739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3751.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3605.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.107/0.032/0.641/0.149/0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote. AIC, Akaike information criterion; BIC, Bayesian information criterion; aBIC, adjusted Bayesian information criterion; LMR, Lo-Mendell-Rubin test; BLRT,\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eBootstrapped likelihood ratio test.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAverage probability of belonging to the three potential categories of sleep quality perimenopausal women in the community\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePSQI scores and nomenclature of three potential profile of sleep quality in perimenopausal women in the community\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;1, the differences in the scores of the seven dimensions of PSQI among the three latent class were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). There were 193 cases (68.7%) in Class 1, and the scores of 7 dimensions were generally low, and the PSQI total score of this group was 5(3∼7) low, so the Class 1 was named \"good sleep quality group\". There were 68 cases (24.2%) in Class 2, and the scores of 7 dimensions were at a medium level. The total PSQI score of this group was 10(8∼12), so the Class 2 was named as \"general sleep quality group\". There were 20 patients in Class 3 (7.1%), and the scores of 7 dimensions were higher than those in Class 1 and Class 2. The total PSQI score of this group was 15(15∼17), so Class 3 was named as \"poor sleep quality group\".\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePSQI scores and three potential profiles of sleep quality in perimenopausal women in the community [n\u0026thinsp;=\u0026thinsp;281, (P\u003csub\u003e25\u003c/sub\u003e∼P\u003csub\u003e75\u003c/sub\u003e)]\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSubjective sleep quality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSleep Latency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSleep Duration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSleep efficiency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSleep Disturbances\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUse of Sleep Medication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDaytime dysfunction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eTotal score of the PSQI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGood sleep quality group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0∼1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0(0∼1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2(1∼3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(0∼2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1(0∼1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0(0∼0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0(0∼1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5(3∼7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral sleep quality group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(1∼2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2(1∼2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(2∼3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2(1∼3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1(1∼2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0(0∼0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2(1∼2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e10(8∼12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor sleep quality group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(2∼2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2(2∼2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(3∼3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3(2.3∼3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2(1∼2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2(2∼2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3(2∼3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15(15∼17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e81.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e201.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e134.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e155.817\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eUnivariate analysis of potential profile of sleep quality perimenopausal women in the community\u003c/h2\u003e\u003cp\u003eUnivariate analysis showed that there was no significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in age, marital status, education level, occupation, monthly income, smoking history, entertainment consumption, positive and negative coping styles among perimenopausal women with different sleep quality class. There were significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in drinking history, current menstrual status, physical exercise, chronic medical history, spouse's health status, depression and anxiety among community perimenopausal women with different sleep quality class. More details can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eMultivariate analysis of potential profile of sleep quality perimenopausal women in the community\u003c/h2\u003e\u003cp\u003eTaking the sleep quality of perimenopausal women in the community as the dependent variable, and the good sleep group as the reference group, the statistically significant indicators in the above Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were included as independent variables, including drinking history, current menstrual status, physical exercise, chronic medical history, spouse's health status, depression and anxiety status, for multinomial Logistic regression analysis. General sleep quality group\u0026thinsp;=\u0026thinsp;2, poor sleep quality group\u0026thinsp;=\u0026thinsp;3, set reference group: good sleep quality group\u0026thinsp;=\u0026thinsp;1; The specific assignment can be found in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable assignment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariable assignment\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026thinsp;=\u0026lt;18.5, 1=18.5∼\u0026lt;24, 2=24∼\u0026lt;28, 3\u0026thinsp;=\u0026thinsp;\u0026ge;\u0026thinsp;28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking alcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0=No, 1=Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenstruation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0=Rule, 1=Irregular, 2=Already menopausal\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0=No, 1=Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical exercise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0=No, 1=Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003espouse health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0=Very good, 1\u0026thinsp;=\u0026thinsp;Good, 2=General/Poor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0=No, 1=Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0=No, 1=Yes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuality of sleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY1=Good Sleep quality Group, Y2=General sleep quality group, Y3=Poor sleep quality group\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results of multinomial Logistic regression analysis showed that: Taking the good sleep quality group as the reference group, drinking history (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.087), chronic disease history (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.221), spouse health status (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.880) and anxiety (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.358) were risk factors for predicting the general sleep quality of perimenopausal women in community (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Taking the good sleep quality group as the reference group, spouse health status (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.130) and anxiety (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19.512) were risk factors for poor sleep quality in community perimenopausal women (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). More details can be found in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultinomial Logistic regression analysis of potential profile of sleep quality in community perimenopausal women (n\u0026thinsp;=\u0026thinsp;281)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eS.E.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eWald χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 2\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrent menstrual status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.927\u0026thinsp;~\u0026thinsp;2.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDrinking alcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.105\u0026thinsp;~\u0026thinsp;3.944\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysical exercise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.830\u0026thinsp;~\u0026thinsp;3.188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChronic disease history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.066\u0026thinsp;~\u0026thinsp;4.627\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpouse's health status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.217\u0026thinsp;~\u0026thinsp;2.903\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.425\u0026thinsp;~\u0026thinsp;1.979\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.935\u0026thinsp;~\u0026thinsp;9.818\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 3\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrent menstrual status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.633\u0026thinsp;~\u0026thinsp;2.463\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDrinking history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.048\u0026thinsp;~\u0026thinsp;1.132\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysical exercise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.498\u0026thinsp;~\u0026thinsp;5.866\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChronic disease history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.686\u0026thinsp;~\u0026thinsp;7.915\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpouse's health status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.015\u0026thinsp;~\u0026thinsp;4.469\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.169\u0026thinsp;~\u0026thinsp;3.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.516\u0026thinsp;~\u0026thinsp;84.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: a refers to the good sleep quality group and serves as the reference group.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this study represents the first investigation characterizing latent sleep quality profiles and their influencing factors in community-dwelling perimenopausal women. By integrating latent profile analysis and factor analysis, the study aims to elucidate the distinct patterns of sleep quality associated with each latent profile in this group.This study has some novelty.\u003c/p\u003e\u003cp\u003eEpidemiological data show that the incidence of sleep problems in perimenopausal women increases from 16%∼42\u0026ndash;39%∼47% with the change of life cycle, the extreme changes of hormones and the increase of age [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. At the same time, related studies in China have shown that perimenopausal related sleep disorders are common in women aged 40∼55 years, with a prevalence of 13.2%∼65.1% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Perimenopausal women generally face sleep problems such as difficulty falling asleep, frequent awakenings at night, and shortened sleep duration. This study found that there were 88 cases of perimenopausal women in the community with PSQI scores\u0026thinsp;\u0026gt;\u0026thinsp;7 points, that is the probability of sleep disorders was 33.3%, which was consistent with the results of another study [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This is also similar to the results of a community-based survey in the United States, which showed that 38% of women in the menopausal transition suffer from sleep disorders [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A Russian study showed that the incidence of sleep disorders in perimenopausal women was 61.2% [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The results of a cross-sectional survey of a nationally representative random sample in Korea showed that 26% of the participants had poor sleep quality according to PSQI [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although the prevalence of sleep disorders in perimenopausal women varies in different races, regions and populations, the overall prevalence is high and shows an upward trend. The prevalence of sleep disorders in perimenopausal women is high, which may be affected by regional rhythm of life, social and cultural environment and other factors. Perimenopausal women face the double burden of work and family, and their nerves are in a tense state for a long time, which is more likely to cause sleep problems and affect their quality of daily life.\u003c/p\u003e\u003cp\u003ePerimenopause is a critical transitional period in the life cycle of women, during which women's sleep status changes significantly. This study identified three potential trajectories of sleep quality of perimenopausal women in the community through the latent class growth model, namely, good sleep quality group, general sleep quality group, and poor sleep quality group. The study results showed that there was heterogeneity in sleep quality of perimenopausal women in the community, which was similar to the findings of an existing study [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This classification reflects the heterogeneity of sleep quality among perimenopausal women in the community within each latent trait. It complements previous studies that viewed community perimenopausal women as a homogenous whole and, to a certain extent, can provide guidance for the development of targeted interventions to improve sleep quality in community perimenopausal women.\u003c/p\u003e\u003cp\u003eFor the community perimenopausal women in the good sleep quality group, there is a certain degree of sleep time disorder, but the total score of PSQI is 5(3∼7), which does not exceed the range of sleep disorders defined by the Chinese version of PSQI (\u0026gt;\u0026thinsp;7). They may be in a more comfortable sleep environment or have higher positive coping ability, and show better sleep quality. For this class of women, we should advocate and encourage them to maintain a good sleep status.\u003c/p\u003e\u003cp\u003eThe general trend of sleep quality in the general sleep quality group was similar to that in the good sleep quality group, but the poor subjective sleep quality, sleep onset time and daytime dysfunction of perimenopausal women in the community were more obvious, which may be related to short sleep time, low sleep efficiency and serious sleep disorders. The sleep quality of perimenopausal women in the community has not been further deteriorated, and targeted non-drug intervention can be used. Improve the overall quality of sleep. In the Chinese Expert Consensus on the Clinical Management of Postmenopausal Insomnia, the Chinese Preventive Medicine Association proposed a series of non-drug therapies such as appropriate exercise, diet and nutrition management, physical and psychological therapy. It recommended aerobic and resistance exercise with transition from low intensity to moderate intensity, and paid attention to balanced nutrition in diet with adequate intake of protein, probiotics and vitamins. Caffeinated substances should be avoided, and transcranial magnetic stimulation, light therapy, or hyperbaric oxygen therapy should be used to improve sleep if necessary [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe proportion of the poor sleep quality group was the lowest, but the scores of the poor sleep quality group were higher than those of the other two groups, especially in the sleep duration dimension. Poor sleep quality and short sleep duration increase the risk of cardiovascular disease, all-cause mortality, and cancer-related mortality [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Therefore, to improve the attention of community perimenopausal women to sleep problems, community nursing staff should focus on strengthening education, populating the core concept of sleep quality, encouraging women to accurately identify sleep problems through professional means, providing targeted sleep hygiene guidance, behavior therapy and medical intervention when necessary, so as to effectively improve sleep quality and effective sleep duration and reduce related health risks.\u003c/p\u003e\u003cp\u003eThis study found that the factors influencing the sleep quality trajectory of perimenopausal women in the community included drinking history, chronic diseases, spouse's health status, and anxiety. This study found that compared with the community perimenopausal women in the group with good sleep quality, the probability of drinking history in the group with general sleep quality was 2.087 times higher than that in the group with good sleep quality. A national cohort study in Korea showed that alcohol risk (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.53) was a risk factor for poor sleep quality in menopausal women [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies have shown that alcohol intake is a trigger for sleep fragmentation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Relying on alcohol to promote sleep can also disrupt the structure of the sleep cycle, forming a vicious cycle. Community nursing staff can take cognitive behavioral therapy to correct the wrong idea of alcohol as a sleep aid in perimenopausal women, establish a positive feedback regulation system and gradually improve their sleep quality.\u003c/p\u003e\u003cp\u003ePerimenopausal women face not only gynecological problems, but also chronic diseases such as cardiovascular and cerebrovascular diseases and endocrine diseases. The results of this study showed that compared with the community perimenopausal women with good sleep quality, the probability of chronic diseases in the group with general sleep quality was 2.221 times higher than that in the group with good sleep quality. The results of a systematic review and meta-analysis on the influencing factors of sleep disorders in perimenopausal women showed that women with chronic diseases had a 1.39-fold increased risk of sleep disorders [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similarly, a national survey study in Korea also showed that chronic diseases were risk factors for poor sleep quality in menopausal women [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Perimenopausal women with a history of diabetes have a higher incidence of sleep disorders [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Studies at home and abroad have shown that there is a correlation between the prevalence of diabetes and poor sleep quality of patients. The high blood glucose state of the body is unfavorable to the central nervous system, which may cause abnormal neurobehavior, neurotransmitter and autonomic nervous dysfunction, thus causing sleep disorders [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Persistent chronic diseases may have adverse effects on the physical and mental health of perimenopausal women [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, multidisciplinary team collaboration and multi-level intervention may be the key strategy to implement comprehensive perimenopausal health management [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], so as to alleviate the negative impact of chronic diseases on perimenopausal women.\u003c/p\u003e\u003cp\u003eOur study also found that the spouses of community perimenopausal women in the general and poor sleep quality groups had worse health status than those in the good sleep group, with 1.880 times worse in the general sleep quality group and 2.130 times worse in the poor sleep quality group. Studies have shown that there is a certain correlation between poor husband's health and abnormal sleep in perimenopausal women [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. When the health status of their spouse deteriorated, the sleep quality of women also decreased synchronously, indicating a bidirectional association. When a spouse has chronic diseases (such as cardiovascular and cerebrovascular diseases), women need to provide frequent nighttime care, which leads to sleep fragmentation. In addition, spouse disease may increase family economic pressure and further induce anxiety insomnia. Couples who live together for a long time develop similar circadian rhythms. A UK Biobank study of 47,420 couples found significant sleep-wake synchrony, which reduced nighttime interference and improved sleep efficiency [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Therefore, community nursing staff need to encourage perimenopausal women and their spouses to actively participate in health management, seek nursing support from family, society or professional institutions, share the care pressure and reduce the psychological burden of both sides. Family members other than lovers should be considered in perimenopausal women's health education to encourage them to understand the experience of perimenopausal women, and to establish a higher quality social support system for perimenopausal women, so that they can be freed from the heavy burden of physical and mental pressure. Improve the family care of perimenopausal women by family members (especially their spouses), fully understand the various physical and mental changes they face, take targeted measures to improve the sleep quality of perimenopausal women, promote the physical and mental health of perimenopausal women [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWith the continuous decline of estrogen levels and the disorder of autonomic nervous function, perimenopausal women are often prone to mood fluctuations, such as irritability, suspicion, anxiety and depression. These psychological problems have gradually become the focus of public attention.Studies have pointed out that there is a close relationship between mental state and physical health, and long-term anxiety may lead to physical health problems [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Anxiety is one of the common psychological problems in perimenopausal women, which is mainly manifested as nervousness, fear, worry and irritability, etc. It is related to women's personal characteristics, life pressure, family relationship and social support [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The results of this study showed that compared with the good sleep quality group, the anxiety of the perimenopausal women in the general sleep quality group was 4.358 times higher than that of the good sleep quality group, and the anxiety of the poor sleep quality group was 19.512 times higher than that of the good sleep quality group. This finding is similar to that in a previous study [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In a sampling study conducted by Simbar et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], the proportion of women with mild to severe anxiety in perimenopausal women was as high as 83.70%. In this study, the incidence of anxiety in community perimenopausal women was 24.20%, which was higher than another study in China [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Anxiety may lead to sleep disorders in perimenopausal women, such as difficulty falling asleep, lack of sleep and poor sleep quality, and affect their daytime function [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. As a result, perimenopausal women have cognitive dysfunction such as distraction, memory decline and slow thinking, which affects their work efficiency. Body discomfort such as palpitation, shortness of breath, dizziness and stomachache may affect their health.\u003c/p\u003e\u003cp\u003eIn addition, anxiety not only aggravates the physical discomfort of perimenopausal women, affects their sleep quality and social function, but also reduces their self-esteem and self-confidence, and even leads to more serious psychological disorders such as depression [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. A number of previous studies have shown that depression is an influencing factor for sleep disorders in perimenopausal women [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. However, in this study, it was not concluded that depression is a risk factor for poor sleep quality in community perimenopausal women. Due to the heterogeneity of the study population, some previous studies were based on outpatients or hospital patients [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], who usually seek medical treatment actively due to prominent symptoms, have more severe depression, and are more likely to be associated with sleep disorders. However, the community sample included more patients with mild symptoms or who did not seek medical treatment, and the degree of depression may be generally mild, leading to the dilution of the association. Future studies can refine the stratification and design of research subjects, include depression patients in the hospital to compare the differences in the community population, and dynamically monitor the association between depression and sleep quality.\u003c/p\u003e\u003cp\u003eDepression and anxiety are common mood disorders in perimenopausal women. At the same time, sleep disorders themselves are also typical symptoms of patients with depression and anxiety. A multi-center study showed that there was a causal relationship between sleep quality and depression and anxiety in perimenopausal women, which influenced each other and formed a vicious circle [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Long-term poor sleep quality increases the risk of diabetes, cardiovascular disease, depression, anxiety, heart attack, obesity, and stroke [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePerimenopausal women are in an important stage of social role transformation, family and work pressure, and multiple objective negative factors are easy to lead to anxiety and depression. Therefore, during the perimenopausal period, women need to pay attention to their psychological status and actively seek social support, so as to better cope with the challenges brought by physiological changes and improve their sleep quality. At the same time, family members, friends and social organizations should also give more care and support to perimenopausal women in the community to help them go through this special physiological stage.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eHowever, this study still has some shortcomings. This study is mainly cross-sectional, with small sample size and limited coverage. In addition, the Pittsburgh Sleep Quality Index (PSQI) is self-reported by the respondents, and some subjective items and options may cause bias. To longitudinally analyze the potential characteristics of sleep disorders in perimenopausal women in the community at different time points, comprehensively analyze the various factors affecting the sleep quality of perimenopausal women in the community, and verify the scientific validity and feasibility of this study.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, there is obvious population heterogeneity in the sleep quality of perimenopausal women in the community, and drinking history, chronic diseases, spouse health status and anxiety have predictive effects on their potential profile class. In the future, community nursing staff can take targeted interventions to improve sleep quality and health promotion according to the sleep quality class and risk factors of perimenopausal women.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePSQI pittsburgh sleep quality index\u003c/p\u003e\n\u003cp\u003eRMB renminbi\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSAS self-ating anxietyscale\u003c/p\u003e\n\u003cp\u003eSDS self-ating depressionscale\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSCSQ simplified coping style questionnaire\u003c/p\u003e\n\u003cp\u003ePC positive coping\u003c/p\u003e\n\u003cp\u003eNC negative coping\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAIC Akaike information criterion\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBIC Bayesian information criterion \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eaBIC adjusted Bayesian information criterion\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLMR Lo-Mendel-Rubin Test \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBLRT bootstrap likelihood ratio test\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with the Declaration of Helsinki, this research was approved by the Ethics Committee of Bengbu Medical University, Anhui Province, China(approval no. 2025-262). All participants were informed of the purpose of the study before recruitment, and all participants were asked to voluntarily sign a written consent form. To protect the participants\u0026rsquo; privacy, all collected data were preserved anonymously and confidentially.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Anhui Province College Students Innovation and Entrepreneurship Training Program (NO. S202410367045) and Bengbu Medical University humanities and social science Research Youth Fund project (NO. 2024byzd165sk). The funding organization had no role in the study design, data collection, management, analysis, interpretation, manuscript writing, or the decision to submit the report for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS.D.H.\u003c/strong\u003e\u0026mdash;conception, design, drafting the article. \u003cstrong\u003eZ.H.S.\u003c/strong\u003e, \u003cstrong\u003eJ.J.L\u003c/strong\u003e, \u003cstrong\u003eZ.Y.W\u003c/strong\u003e, \u003cstrong\u003eG.J.M.\u003c/strong\u003e\u0026mdash;data collection. \u003cstrong\u003eS.D.H.\u003c/strong\u003e, \u003cstrong\u003eX.T.S\u003c/strong\u003e, \u003cstrong\u003eS.T.T.\u003c/strong\u003e, \u003cstrong\u003eJ.Z.L\u003c/strong\u003e \u0026mdash;conception, design, data analysis and interpretation, drafting the article. \u003cstrong\u003eS.D.H.\u003c/strong\u003e, \u003cstrong\u003eS.T.T.\u003c/strong\u003e, \u003cstrong\u003eJ.Z.L\u0026nbsp;\u003c/strong\u003e\u0026mdash;conception, design, interpretation of data, critical revision of the draft. \u003cstrong\u003eS.D.H.\u003c/strong\u003e, \u003cstrong\u003eJ.Z.L\u003c/strong\u003e\u0026mdash;design, data analysis and interpretation, drafting the article. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to all participants who agreed to participate voluntarily in this study\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang Xixi, Li Jinzhi. Research progress on bio-psycho-social factors affecting the occurrence of perimenopausal syndrome. J Bengbu Med Coll, 2016;41(03):419-420. doi: 10. 13898/j.cnki.Issn.1000-2200.2016.03.046\u003c/li\u003e\n\u003cli\u003eHarrington YA, Parisi JM, Duan D, Rojo-Wissar DM, Holingue C, Spira AP. Sex Hormones, Sleep, and Memory: Interrelationships Across the Adult Female Lifespan. Front Aging Neurosci. 2022;14:800278. doi: 10.3389/fnagi.2022.800278.\u003c/li\u003e\n\u003cli\u003eZhao M, Sun M, Zhao R, Chen P, Li S. Effects of exercise on sleep in perimenopausal women: A meta-analysis of randomized controlled trials. 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BMC Psychiatry. 2020;20(1):202. doi: 10.1186/s12888-020-02617-w. \u003c/li\u003e\n\u003cli\u003ePan Weiying, Chi Ying. To explore the effects of psychological counseling combined with physical exercise on the quality of life and negative emotions of perimenopausal women. Maternal and Child Health Care of China. 2022;37(11):2104-2107. doi:10.19829/j.zgfybj.issn.1001-4411.2022.11.049.\u003c/li\u003e\n\u003cli\u003eChi Ying, Pan Weiying. To analyze the incidence and influencing factors of anxiety in perimenopausal women. Maternal and Child Health Care of China. 2022;37(13):2341-2344. doi:10.19829/j.zgfybj.issn.1001-4411.2022.13.005.\u003c/li\u003e\n\u003cli\u003eLiu Jing, Han Lu, Zhang Xiuhua, Cai Zhengmao, Tang wei. Perimenopausal women anxiety depression mood sleep quality Epidemiological investigation and influencing factors analysis. 2023;38(08):1524-1528. doi:10.19829/j.zgfybj.issn.1001-4411.2023.08.043.\u003c/li\u003e\n\u003cli\u003eWang M, Kartsonaki C, Guo Y, Lv J, Gan W, Chen ZM, et al. Factors related to age at natural menopause in China: results from the China Kadoorie Biobank. Menopause. 2021;28(10):1130-1142. doi: 10.1097/GME.0000000000001829.\u003c/li\u003e\n\u003cli\u003eBaron KG, Reid KJ. Circadian misalignment and health. Int Rev Psychiatry. 2014;26(2):139-54. doi: 10.3109/09540261.2014.911149.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7100582/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7100582/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSleep disorders in perimenopausal women are serious problems, which have a negative impact on women's physical and mental health. However, there are few studies on the potential profile of sleep quality in perimenopausal women in the community. Therefore, this study aims to explore different potential trajectories of sleep quality in perimenopausal women in the community and analyze the influencing factors of different trajectories.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted from July 2024 to December 2024, and a total of 281 perimenopausal women in the community were recruited from 4 communities in Bengbu. The participants completed the Pittsburgh Sleep Quality Index (PSQI), and self-rating anxiety scale (SAS), self-rating depression scale (SDS) and simplified coping style questionnaire (SCSQ). Latent profile analysis(LPA) was employed to identify latent profiles of sleep quality of perimenopausal women in the community. The predictors of sleep quality in different latent profiles were assessed via multinomial logistic regression analysis. One-way ANOVA, chi-square test or Fisher exact test, and the Kruskal-Walis test were used to compare the PSQI scores of perimenopausal women in the community under different latent profile characteristics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn this study, 88 out of 281 perimenopausal women community in the had PSQI scores more than 7 points, and the incidence of sleep disorders was 33.3%. The sleep quality of perimenopausal women in the community could be divided into three different potential trajectories, including 193 cases (68.7%) in the good sleep quality group, 68 cases (24.2%) in the general sleep quality group, and 20 cases (7.1%) in the poor sleep quality group. Taking the good sleep quality group as the reference group, drinking history (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.087), chronic disease history (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.221), spouse health status (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.880) and anxiety (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.358) were risk factors for predicting the general sleep quality of perimenopausal women in the community (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Spouse health status (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.130) and anxiety (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19.512) were risk factors for poor sleep quality of perimenopausal women in the community (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThere are three qualitatively different potential trajectory categories of sleep quality in perimenopausal women in the community, and drinking history, chronic disease, poor spouse health and anxiety have predictive effects on their trajectory categories. In the future, community nursing staff can take targeted interventions according to different categories of sleep quality in perimenopausal women to improve sleep quality and level of health promotion.\u003c/p\u003e","manuscriptTitle":"Latent profile analysis and influencing factors of sleep quality in community perimenopausal women: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 08:20:56","doi":"10.21203/rs.3.rs-7100582/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-22T21:34:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-22T19:48:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-12T09:32:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-11T09:40:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-10T09:08:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43065218601970493221646619629357036793","date":"2025-09-09T20:58:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116181990139240531868531599450791325033","date":"2025-09-09T15:21:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205943004436166340084298819960718143353","date":"2025-09-09T07:28:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239567919367716128407367049296811163828","date":"2025-09-09T05:03:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294777538548741548980344000527595311503","date":"2025-09-09T01:59:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244647064049083108254694979548822236520","date":"2025-09-09T01:28:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98977695816556035713747217047519519887","date":"2025-09-09T01:27:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-09T00:00:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-20T16:57:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-08T08:51:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-06T16:21:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2025-08-06T16:16:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9c99698-f40f-4b1a-addc-4ef7400c445d","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:08:26+00:00","versionOfRecord":{"articleIdentity":"rs-7100582","link":"https://doi.org/10.1186/s12905-025-04217-w","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2025-12-30 15:58:14","publishedOnDateReadable":"December 30th, 2025"},"versionCreatedAt":"2025-09-17 08:20:56","video":"","vorDoi":"10.1186/s12905-025-04217-w","vorDoiUrl":"https://doi.org/10.1186/s12905-025-04217-w","workflowStages":[]},"version":"v1","identity":"rs-7100582","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7100582","identity":"rs-7100582","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}