Heart rate variability, a potential assessment tool for identifying anxiety, depression, and sleep disorders in elderly individuals

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Methods We examined 355 patients (163 men, 192 women) at Xijing Hospital from July 2021 to December 2022 during health check-ups. Demographics were recorded, and emotional status was assessed using the Hamilton Anxiety Scale (HAMA) and the Hamilton Depression Scale (HAMD). The Pittsburgh Sleep Quality Scale (PSQI) evaluated sleep quality. Patients were categorized into groups A-G based on the presence of emotional states and sleep disorders. HRV indices—SDNN, SDANN, RMSSD, PNN50, LF/HF, LF, and HF—were analyzed using ANOVA and multivariate logistic regression. Results No statistically significant differences were observed in demographic, clinical, and lifestyle factors across the eight groups. Variables assessed included age, sex, body mass index (BMI), fasting blood glucose, glycated hemoglobin (HbA1c), blood lipids, blood pressure, heart rate, and histories of smoking and alcohol consumption. Additionally, the presence of hypertension, diabetes, coronary heart disease, marital status, income, and education level were evaluated, with all showing equivalence ( P > 0.05). Significant differences in HRV indices were observed across groups, particularly in group G (patients with anxiety, depression and sleep disorders), which showed decreased HRV parameters except LF/HF, and group H (control group), which showed increased parameters, also except LF/HF ( P < 0.01). Anxiety was an independent risk factor for reduced SDNN, SDANN, and LF ( P ≤ 0.01), and increased LF/HF ratio ( P < 0.01). Depression was linked to decreased SDNN, RMSSD, PNN50, and HF ( P < 0.05). Sleep disorders independently predicted reduced PNN50 and SDANN ( P < 0.01). Conclusion HRV indices of individuals with varying emotional states and sleep disorders exhibited varying degrees of decrease. Anxiety, depression, and sleep disorders presented a superimposed effect on HRV. Among these factors, sleep disorders have the least impact on HRV. Anxiety Depression Sleep disorders Heart rate variability Elderly individuals Introduction The aging population has emerged as a pressing global social issue, as evidenced by the data from China's seventh population census. By 2020, it is projected that there will be 260 million individuals aged 60 or older in China, comprising 18.70% of the total population[1]. This represents a notable increase of 5.44% from the sixth national population census conducted in 2010[2]. The size and proportion of the elderly demographic in China have been experiencing rapid growth. A meta-analysis has revealed that the prevalence of depression among the elderly in our country has reached levels as high as 25.55% [3], with anxiety symptoms affecting approximately 22.11% of the older population [4]. Additionally, sleep disorders have been found to affect 46.0% of elderly individuals [5]. In efforts to enhance the well-being of the elderly, medical treatment should not only focus on physical ailments but also on the maintenance of their mental health. Heart rate variability (HRV) is defined as the variance in time intervals between consecutive heartbeats. It is a collection of parameter values utilized to quantify variations in the R-R interval through various algorithms, serving as a precise indicator of alterations in the autonomic nervous system[6]. This tool can be employed to assess the sympathetic and vagal tone of the heart in a non-invasive manner, determining the equilibrium between the two [7]. Long-term anxiety and depression, along with associated negative emotions and sleep disorders, may result in varying levels of autonomic nervous dysfunction, thereby impacting disease progression [8]. The consensus within the academic community is that HRV serves as a valuable indicator for assessing the severity and prognosis of clinical anxiety and depression [9]. Nevertheless, there remains a dearth of quantitative metrics and corresponding clinical investigations regarding alterations in autonomic nervous function among elderly individuals with anxiety, depression, and sleep disorders in comparison to their healthy counterparts. Furthermore, the utility of anxiety, depression, and sleep disorder scales for evaluating these conditions in older adults with cognitive impairment is constrained. Therefore, this study aims to examine alterations in autonomic nervous function among elderly individuals with anxiety, depression, and sleep disorders through the utilization of the HRV index in a clinical setting. It is anticipated to offer clinical utility with objective, accurate and simple evaluation indicators for elderly patients in the future. 1. Methods 1.1 Subjects of Study This case-control study involved the continuous collection of information from inpatients and outpatients who underwent mental status and sleep quality evaluations at Xijing Hospital between July 2021 and December 2022. The sample size was determined based on the prevalence of anxiety, depression, and sleep disorders among elderly individuals, with exclusion criteria applied to cases that dropped out during the study or failed to meet data requirements. Ultimately, 313 patients were selected for the study group. Control group samples were primarily drawn from the inpatient and outpatient populations undergoing physical examinations at Xijing Hospital, resulting in a total of 42 participants. The participants were randomly assessed using the HAMA, HAMD, and PSQI scales by researchers with professional training in psychosomatic medicine to ensure alignment between the participants' actual psychological and sleep conditions and the scale scores. All subjects were grouped as follows. Group A: Anxiety group, 45 cases; Group B: Depression group, 42 cases; Group C: Sleep disorder group, 43 cases; Group D: Anxiety combined with depression group, 44 cases; Group E: Anxiety combined with sleep disorder group, 46 cases; Group F: Depression combined with sleep disorder group, 47 cases; Group G: Anxiety, depression and sleep disorder group, 46 cases; Group H: Control group, 42 cases. Participants with psychological or sleep disorders must meet the following criteria: 1) Age ≥ 60 years; 2) Meeting the diagnostic criteria of the Chinese Classification of Mental Disorders-3 (CCMD-3) [ 10 ]; 3) A Hamilton Depression Scale (HAMD) score of ≥ 17 [ 11 ], a Hamilton Anxiety Scale (HAMA) score of ≥ 14 [ 12 ], or a Pittsburgh Sleep Quality Index (PSQI) score of ≥ 10 [ 13 ]; 4) Participants must agree to accept a 24-hour ECG monitoring; 5) Participants must have complete medical records and provide signed informed consent. Exclusion criteria for the patients include: 1) Patients with a history of atrioventricular block, atrial fibrillation, premature atrial or ventricular arrhythmias; 2) Patients diagnosed with mental retardation, delirium, schizophrenia, or other mental illnesses according to the CCMD-3; 3) A range of factors that may impact HRV, including hyperthyroidism, anemia, infection, trauma, and other physical ailments; 4) Patients with severe organic diseases such as acute cerebral infarction, malignant tumors, and cachexia; 5) Those who have recently undergone major catastrophic events or experienced significant stress; 6) Individuals experiencing insomnia due to physical discomfort or fatigue; 7) Patients with Parkinson's syndrome, sleep apnea syndrome; 8) Recent use of anti-anxiety and antidepressant medications; 9) Participants who have recently used medications that impact heart rate, such as beta-blockers, thyroid hormones, and digitalis within the preceding two weeks. The inclusion criteria for the control group were as follows: 1) Individuals aged 60 years or older; 2) Scoring below 17 on the HAMD and below 17 on the HAMA, as well as below 14 points on the PSQI; 3) Willingness to participate in a 24-hour ECG monitoring; and 4) Possession of complete medical records and signed informed consent. The exclusion criteria for the control group include: 1) Patients with a history of atrioventricular block, atrial fibrillation, premature atrial or ventricular arrhythmias; 2) Individuals meeting the diagnostic criteria for anxiety and depression according to the CCMD-3; 3) Patients diagnosed with sleep disorders based on the PSQI and subjective reports of frequent and persistent difficulty falling asleep and/or maintaining sleep, as well as dissatisfaction with sleep as determined by clinicians; 4) Individuals with conditions known to affect HRV, such as hyperthyroidism, anemia, and other physical illnesses; 5) Patients with severe organic diseases such as acute cerebral infarction, severe infection and cachexia; 6) Recent major catastrophic events or high stress levels; 7) Parkinson's syndrome, sleep apnea syndrome, and recent use of anti-anxiety and antidepressant medications. 1.2 Demographic data Demographic data was collected using a standardized questionnaire to ensure the scientific rigor and accuracy of the study, allowing for a comprehensive review of participants' background information and health status, including age, sex, body mass index, fasting blood glucose, glycated hemoglobin, blood lipids, blood pressure, heart rate, smoking history, drinking history, hypertension history, diabetes history, coronary heart disease history, marital status, income, and education. 1.3 Research methods All the participants were evaluated their psychological state using the HAMD and the HAMA. Additionally, the PSQI was utilized to assess the sleep quality of the patients. Two trained evaluators from the psychosomatic department conducted joint examinations of the patients and independently scored them, with the average scores being recorded. The participants underwent 24-hour monitoring using a V12 dynamic ECG recorder. ECG interference signals were eliminated through computer processing, and manual filtering was used to remove artifacts and ectopic beats. The HRV index was computed using an algorithmic system. The recorded data included four time-domain analysis indicators: standard deviation of normal to normal intervals (SDNN), the standard deviation of the averages of 5-minute RR intervals (SDANN), root mean square of successive RR interval differences (RMSSD), percentage of successive RR intervals that differ by more than 50 ms (PNN50), and three frequency domain analysis indicators: low frequency (LF), high frequency, (HF), LF/HF [ 14 , 15 ]. Prior to the examination, participants were advised to abstain from alcohol, strong tea, coffee, and strenuous exercise. All participants underwent assessment using the HAMA consisting of 14 items and the HAMD consisting of 17 items. A diagnosis of anxiety disorder was made if HAMA scores were equal to or greater than 14, while a diagnosis of depression disorder was made if HAMD scores were equal to or greater than 17, based on the symptoms reported by the participants over the past three months. Sleep quality was assessed using the PSQI, with scores equal to or greater than 10 indicating a sleep disorder. Higher PSQI scores were indicative of poorer sleep quality. 1.4 statistical analysis Normality of the variables was assessed using the Shapiro-Wilk test, and variables conforming to a normal distribution were presented as mean ± standard deviation \(\:（\stackrel{-}{\text{X}}\pm\:\text{S}）\) . The variables exhibiting skewed distribution were analyzed using the interquartile method. Subsequently, one-way ANOVA was employed to evaluate differences in means among multiple samples that met the criteria of normal distribution and homogeneity of variance. In cases where the assumption of homogeneity of variance was violated, the Welch test was utilized. Post-hoc analysis was conducted using the LSD-t test to identify significant differences between specific pairs of groups. For data that deviated from normal distribution, pairwise comparisons between groups were performed using Tamhane's T2 test. Finally, a multivariate logistic regression analysis was conducted to examine the relationship between mental health conditions (including anxiety, depression, and sleep disorders) as independent variables and the decrease in relevant indicators of HRV as dependent variables. The analysis was performed using SPSS 26.0 software, and a two-sided test with a significance level of P <0.05 indicated significant differences. 2. Results 2.1 comparison of clinical baseline data There were no significant differences in age, sex, body mass index, fasting blood glucose, glycated hemoglobin, blood lipid, blood pressure, heart rate, smoking history, drinking history, hypertension history, diabetes history, coronary heart disease history, marital status, income and education among the eight groups ( P > 0.05). See Table 1 for details. Additionally, due to the inevitable use of sedative and hypnotic drugs among people with sleep disorders, the population using the drugs cannot be completely excluded, but this study compared the number of people using sedative and hypnotic drugs between groups with sleep disorders. It can be seen from Table 2 that the proportion of people using the drugs in groups C, E, F and G is similar, and there is no statistical difference between groups ( P > 0.05). Table 1 Comparison of Clinical baseline data across seven patients groups and control group Group A Group B Group C Group D Group E Group F Group G Group H P Value n 45 42 43 44 46 47 46 42 Famale [n(%)] 25(55.6) 22(47.6) 24(55.8) 27(61.4) 20(43.5) 29(61.7) 26(56.5) 19(45.2) 0.581 Age (year) 69.7 ± 6.2 69.3 ± 8.4 70.8 ± 13.0 70.7 ± 8.3 68.9 ± 8.7 72.3 ± 9.6 72.7 ± 8.7 72.0 ± 8.0 0.271 BMI (kg/m 2 ) 25.2 ± 2.8 25.6 ± 3.8 24.9 ± 4.3 24.3 ± 3.2 24.7 ± 3.4 24.0 ± 2.9 23.9 ± 2.9 24.1 ± 3.0 0.147 HbA1c (%) 6.3 ± 1.6 5.9 ± 0.9 5.6 ± 1.1 6.1 ± 1.5 6.2 ± 2.2 6.5 ± 2.3 6.2 ± 1.2 6.0 ± 1.1 0.262 FBG (mmol/L) 6.3 ± 2.3 5.7 ± 1.3 6.0 ± 2.3 5.9 ± 1.9 5.4 ± 1.4 6.2 ± 2.3 6.2 ± 2.0 5.5 ± 1.3 0.095 TC (mmol/L) 4.2 ± 1.1 4.3 ± 1.1 4.0 ± 1.3 4.3 ± 1.1 4.2 ± 1.0 3.9 ± 1.0 4.1 ± 1.0 3.6 ± 1.3 0.059 TG (mmol/L) 1.7 ± 1.0 1.7 ± 1.0 1.4 ± 1.1 1.4 ± 0.9 1.6 ± 1.0 1.4 ± 0.7 1.3 ± 0.6 1.2 ± 0.6 0.053 HDL (mmol/L) 1.1 ± 0.3 1.1 ± 0.2 1.2 ± 0.5 1.2 ± 0.4 1.2 ± 0.3 1.1 ± 0.4 1.3 ± 0.4 1.2 ± 0.4 0.125 LDL (mmol/L) 2.6 ± 0.9 2.6 ± 0.9 2.3 ± 1.1 2.5 ± 0.9 2.5 ± 0.9 2.3 ± 0.8 2.3 ± 0.9 2.0 ± 1.1 0.054 SBP (mmHg) 130 ± 16 128 ± 15 130 ± 20 132 ± 19 128 ± 15 130 ± 18 135 ± 21 127 ± 16 0.425 DBP (mmHg) 77 ± 10 76 ± 11 79 ± 13 74 ± 12 78 ± 11 74 ± 14 76 ± 12 73 ± 11 0.128 HR 65.9 ± 9.6 70.3 ± 10.3 69.7 ± 8.7 67.3 ± 9.2 65.9 ± 9.1 66.2 ± 8.8 66.7 ± 7.8 65.8 ± 9.0 0.111 Smoking [n(%)] 14(31.1) 16(38.1) 13(30.2) 15(34.1) 18(39.1) 16(34.0) 17(37.0) 15(35.7) 0.973 Drinking [n(%)] 13(28.9) 7(16.7) 8(18.6) 9(20.5) 14(30.4) 14(29.8) 12(26.1) 8(19.0) 0.719 Hypertension[n(%)] 29(64.4) 25(59.5) 26(60.5) 31(70.5) 35(76.1) 38(80.9) 36(78.3) 25(59.5) 0.074 Diabetes [n(%)] 9(20.0) 10(23.8) 8(18.6) 11(25.0) 7(15.2) 15(31.9) 13(28.3) 9(21.4) 0.623 CHD[n(%)] 18(40.0) 19(45.2) 20(46.5) 22(50.0) 17(37.0) 25(53.2) 21(45.7) 19(45.2) 0.797 Average annual income (ten thousand yuan) 6.1 ± 2.2 6.5 ± 2.7 5.2 ± 2.4 5.5 ± 2.5 5.3 ± 2.5 5.4 ± 2.3 5.2 ± 2.5 6.0 ± 2.5 0.114 Married [n(%)] 43(95.6) 41(97.6) 43(100) 42(95.5) 43(93.5) 45(95.7) 45(97.8) 39(92.9) 0.733 Education years 3.7 ± 2.2 3.5 ± 2.3 3.3 ± 2.7 4.3 ± 2.8 4.2 ± 3.1 4.5 ± 2.8 4.6 ± 2.7 4.5 ± 2.8 0.135 Note: The measurement data in the table is expressed as ( \(\:\stackrel{-}{\text{x}}\) ±s), and the number of counting data is expressed as [n (%)]. HbA1c: glycosylated hemoglobin, FBG: fasting blood glucose, TC: serum total cholesterol, TG: triglyceride, HDL: high density lipoprotein, LDL: low density lipoprotein, SBP: systolic blood pressure, DBP: diastolic blood pressure, HR: heart rate, CHD: coronary heart disease. Table 2 Comparison of the number of patients using sedative-hypnotic in group C, E, F and G Group C (n = 43) Group E (n = 46) Group F (n = 47) Group G (n = 46) P Value Number of individuals using sleep medication [n(%)] 32(74.4) 35(76.1) 37(78.7) 39(84.8) 0.509 2.2 HRV analysis HRV serves as a valuable metric for assessing autonomic nervous system function, particularly in its capacity to reflect the equilibrium between sympathetic and vagal nerve activity. It has been widely used in different areas of research, from basic research to clinical applications to assess health and disease states. Therefore, in this study, we compared various HRV indicators among the eight groups, and the results showed that there were significant differences in HRV parameters such as SDNN, SDANN, RMSSD, PNN50, LF/HF, LF, HF among different groups. In comparison to Group A and Group B, the HRV parameters of Group D exhibited a decrease; in comparison to Group A and Group C, the HRV parameters of Group E, with the exception of LF/HF and HF, also showed a decrease; in comparison to Group B and Group C, all HRV parameters of Group F displayed a decrease. Furthermore, in comparison to Group C, the HRV parameters of Groups E and F experienced a decrease, excluding LF/HF. Group G demonstrated a significant decrease in HRV parameters, excluding LF/HF, when compared to the other groups, while Group H showed an increase in HRV parameters, excluding LF/HF, with a statistically significant difference ( P < 0.01). See Table 3 for details. Table 3 Comparison of HRV among seven patients groups and control group Groups n SDNN SDANN RMSSD PNN50(%) LF/HF LF HF Group A 45 113.0 ± 29.2 97.7 ± 22.9 55.5 ± 46.2 23.3 ± 23.4 2.7 ± 1.2 395.2 ± 261.2 165.7 ± 109.8 Group B 42 117.8 ± 31.5 109.2 ± 25.5 47.6 ± 51.3 14.7 ± 17.0 1.1 ± 0.7 353.4 ± 158.8 459.0 ± 418.6 Group C 43 127.2 ± 25.3 114.2 ± 34.5 59.2 ± 45.7 33.1 ± 21.8 1.1 ± 0.5 746.8 ± 262.3 779.5 ± 346.1 Group D 44 92.8 ± 17.5 81.7 ± 24.3 22.3 ± 8.5 5.0 ± 9.7 1.1 ± 0.7 131.8 ± 90.3 134.5 ± 76.8 Group E 46 107.0 ± 19.1 97.5 ± 27.2 28.1 ± 13.2 5.1 ± 4.2 2.2 ± 3.1 331.3 ± 193.5 232.4 ± 142.0 Group F 47 100.1 ± 28.2 92.6 ± 40.0 41.4 ± 24.7 9.6 ± 7.7 0.7 ± 0.7 126.0 ± 62.4 284.9 ± 157.4 Group G 46 82.9 ± 29.7 71.4 ± 26.7 17.3 ± 6.6 1.4 ± 2.0 1.1 ± 0.6 73.6 ± 75.5 81.0 ± 76.5 Group H 42 183.4 ± 55.0 135.0 ± 47.8 198.0 ± 92.5 53.7 ± 26.7 0.6 ± 0.5 7502.1 ± 7246.6 16562.9 ± 16328.0 F值 42.3 16.5 73.9 51.2 13.6 44.3 44.4 P值 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 Note: SDNN: standard deviation of normal to normal intervals, SDANN: the standard deviation of the averages of 5-minute RR intervals, RMSSD: root mean square of successive RR interval differences, PNN50: percentage of successive RR intervals that differ by more than 50 ms, LF: low frequency, HF: high frequency. 2.3 The effect of anxiety on HRV This study focused on a cohort of patients diagnosed with anxiety, depression, and sleep disorders to investigate the potential influence of these prevalent mood disorders on HRV. Multivariate logistic regression analysis was employed to examine the association between anxiety, depression, and sleep disorders with various indicators of HRV, including SDNN, SDANN, RMSSD, PNN50, LF, HF, and LF/HF, as dependent variables. The presence of anxiety, depression, and sleep disorders were considered as independent variables in the model. Various confounding factors, including BMI, smoking history, drinking history, hypertension, diabetes, and coronary heart disease, were controlled for in the study of patients with anxiety, depression, and sleep disorders. Regression analysis revealed a significant association between anxiety and multiple HRV indicators. Specifically, anxiety was identified as an independent risk factor for reduced SDNN, SDANN, and LF ( P ≤ 0.01). Furthermore, the analysis indicated that anxiety was also an independent risk factor for an increase in the LF/HF ratio ( P < 0.01). Please refer to Table 4 for further information. Table 4 Logistic regression analysis was used to analyze the effect of anxiety patients on HRV HRV parameters β Wald χ 2 P OR 95%CI SDNN(ms) 1.253 6.443 0.01 3.50 1.33ཞ9.03 SDANN(ms) 1.255 6.836 0.01 3.51 1.37ཞ8.99 LF/HF -3.717 34.922 <0.01 0.24 0.01ཞ0.08 LF(Hz) 2.779 17.136 <0.01 16.10 4.32ཞ60.01 2.4 The effect of depression on HRV The study investigated the impact of depression on HRV by examining indicators such as SDNN, RMSSD, PNN50, and HF as dependent variables, with anxiety, depression, and sleep disorders as independent variables. Multivariate logistic regression analysis revealed that depression was identified as an independent risk factor for the decrease in SDNN, RMSSD, PNN50, and HF ( P < 0.05). Further details can be found in Table 5 . Table 5 Logistic regression analysis was used to analyze the effect of depression patients on HRV HRV parameters β Wald χ 2 P OR 95%CI SDNN(ms) 1.19 5.63 0.02 3.28 1.23ཞ8.76 RMMSD(ms) 2.06 17.60 <0.01 7.81 2.99ཞ20.39 PNN50(ms) 3.64 31.87 <0.01 38.00 10.75ཞ134.36 HF(Hz) 2.33 11.99 <0.01 10.92 2.75ཞ38.51 2.5 The effect of sleep disorders on HRV In a manner akin to the research methodologies employed in studies on anxiety and depression, the present study designated the decrease in HRV index as the dependent variable, with the presence of anxiety, depression, and sleep disorder serving as the independent variables. Following logistic regression analysis, it was determined that sleep disorder emerged as a significant independent risk factor for the reduction of PNN50 and SDANN ( P < 0.01). Further information can be found in Table 6 . Table 6 Logistic regression analysis was used to analyze the effect of patients with sleep disorders on HRV HRV parameters β Wald χ 2 P OR 95%CI PNN50(ms) 1.25 6.69 <0.01 3.48 1.35ཞ8.95 SDANN(ms) 1.72 12.45 <0.01 5.61 2.15ཞ14.61 3. Discussion HRV serves as a comprehensive measure of the regulation of the cardiac autonomic nervous system, encompassing frequency domain, time domain, and nonlinear indices. Frequency domain indicators such as LF、HF、LF/HF, time domain indicators such as SDNN、SDANN、RMSSD、PNN50, and non-linear indicators such as Poincare map and sample entropy are utilized to assess the complexity and non-linear characteristics of HRV. It is widely accepted among scholars that SDNN primarily signifies the overall activity of the autonomic nerve system. SDANN and LF are indicative of sympathetic nerve activity, with their values decreasing as sympathetic nerve tension increases. RMSSD、PNN50 and HF, on the other hand, reflects vagal nerve activity, with its value decreasing as vagal nerve tension reduces. The LF/HF ratio serves as a quantitative measure for assessing the functional equilibrium of the sympathetic and vagus nerves. The interpretation of HRV indices remains inconclusive. A study suggests that while LF is modulated by sympathetic nerve function, it is not advisable to rely solely on LF to gauge sympathetic nervous system activity, as it is also impacted significantly by vagus nerve activity and other factors. LF/HF can offer insights into the regulatory function of the autonomic nervous system, however, its accuracy is compromised by nonlinear relationships and various influencing factors [ 16 ]. In this research, the elderly population was categorized into groups based on varying emotional states and sleep patterns to analyze discrepancies in HRV indicators among the eight groups. Findings revealed that individuals combined with anxiety, depression, and sleep disorders exhibited a decrease in all HRV metrics, with the exception of LF/HF, compared to the normal control group, with the most pronounced decrease observed in this cohort. Specifically, individuals with comorbid mood and sleep disturbances experienced a more substantial decline in HRV compared to those with singular anxiety, depression, or sleep disorders. This suggests that anxiety, depression, and sleep disorders may contribute to a reduction in HRV and exhibit a synergistic effect, a phenomenon not previously explored in existing literature. Consequently, our findings propose a novel hypothesis: a pronounced decrease in HRV may correlate with heightened emotional instability in patients and poorer sleep quality [ 17 , 18 ]. Recent research has increasingly demonstrated the impact of mood disorders, such as anxiety and depression, on HRV. In particular, depressed mood has been associated with a heightened susceptibility to various cardiac ailments. While existing research has examined the correlation between depression and HRV, there remains a dearth of studies focusing on this relationship within the elderly demographic. This investigation utilized multivariate logistic regression analysis to investigate the distinct influence of anxiety and depression on HRV among elderly individuals. The findings revealed that anxiety independently posed a risk for the diminishment of SDNN, SDANN, and LF ( P <0.05). This suggests that individuals with anxiety may experience a decrease in HRV measures, and that anxiety is an independent risk factor for an increase in the LF/HF ratio. The LF/HF ratio is commonly used as a marker of cardiovascular health, with an elevated ratio potentially indicating an autonomic nervous system imbalance. Similarly, depressive mood was found to be an independent risk factor for a reduction in SDNN, RMSSD, PNN50 and HF, suggesting that individuals with depression may experience decreased levels of these indicators due to the impact of their mood on balance of autonomic nervous system. Among them, RMSSD, PNN50, and HF have been identified as closely associated with parasympathetic nerve activity, suggesting that individuals with depression may exhibit diminished parasympathetic nerve activity. This finding supports the notion that depressive symptoms may exert an inhibitory influence on parasympathetic nerve activity, resulting in a reduction in HRV. To sum up, the assessment of emotional disorders in the elderly population is significantly influenced by subjective factors inherent in the scales used, and various limitations, such as sensory impairments and cognitive dysfunction, may hinder the accurate measurement of these scales. Therefore, the utilization of HRV as an objective and easily accessible clinical monitoring indicator becomes imperative. By monitoring changes in SDNN, LF, and HF, healthcare professionals can effectively identify the presence and type of mood disorder in elderly patients. Previous research has predominantly focused on examining the impact of various physiological and psychological factors on HRV. However, the prevalence of sleep disorders among the elderly poses a significant challenge to their overall physical and mental well-being, yet the potential influence of sleep disorders on HRV in this population remains largely unexplored. It is important to note that not only physical ailments, but also mental health conditions such as anxiety and depression, can contribute to the development of sleep disorders [ 19 ]. Hence, this study employed multivariate Logistic regression analysis to examine the potential influence of sleep disorders on HRV among elderly individuals. The results of the analysis indicated that sleep disorder emerged as a significant independent risk factor for the decline in PNN50 and SDANN. These findings align with some of the prior research, such as the study conducted by Trinder et al., which demonstrated a reduction in certain HRV measures, including RMSSD and PNN50, during nighttime in individuals with sleep disorders [ 20 ]. Moreover, Tobaldini et al. discovered a negative correlation between sleep disturbances and HRV in a cohort of young adults. Their findings indicated that individuals with insomnia exhibited notably decreased nighttime HRV, particularly in relation to parasympathetic parameters [ 21 ]. Another study utilized Actigraphy, a tool for evaluating sleep patterns, to investigate sleep quality and determined that diminished sleep efficiency was linked to reduced HRV [ 22 ]. This indicates that sleep disorders may result in dysfunction of specific autonomic nervous system functions, particularly those pertaining to cardiovascular well-being. The limitation of this study is the lack of a clearly defined normal threshold for various HRV parameters in healthy individuals, despite the observed decrease in these parameters in individuals with anxiety, depression, and sleep disorders, particularly when all three conditions are present simultaneously. Future extensive clinical investigations are anticipated to establish a threshold level for HRV in the elderly demographic, thereby positioning HRV as a primary objective measure for assessing emotions. Overall, HRV serves as a non-invasive and readily available tool for physicians to detect patients potentially impacted by negative mood and sleep disorders [ 23 ]. Investigating the correlation between anxiety, depression, sleep disorders, and HRV among older individuals will enhance comprehension of the psychological mechanisms underpinning associated illnesses and facilitate the formulation of potentially efficacious treatment strategies. Declarations Ethics approval and consent to participate This study protocol was conducted in compliance with the Declaration of Helsinki and approved by the Xijing Hospital Ethics Committee (approval number: KY20222043-C-1), and all participants provided informed consent. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the following grants: National Natural Science Foundation of China (No. 82070263) and Air Force Medical University Clinical Research Program (2022LC2252). Authors' contributions The data on the clinical characteristics and heart rate variability metrics of elderly participants with and without psychological or sleep disorders were analyzed and interpreted by WL and SW. HG assisted in participant recruitment, while RL developed the study protocols and made significant contributions to the manuscript. All authors reviewed and approved the final version of the manuscript. Acknowledgements Not applicable References Bulletin of the 7th National Population Census (. 5) -- Age Composition of the population [J]. Stat China, 2021(05):10–1. Tong YF. The latest developments and trends of China's population: Combined with the analysis of the 7th National Population Census data[J]. J China Inst Labor Relations. 2021;35(04):15–25. 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Hartmann R, Schmidt FM, Sander C, et al. Heart Rate Variability as Indicator of Clinical State in Depression[J]. Front Psychiatry. 2019;9:735. Branch of Psychiatry, Chinese Medical Association. The 3rd edition of Chinese Classification and Diagnosis Criteria for Mental Disorders [M]. Jinan: Shandong Science and Technology; 2001. Boessen R, Groenwold RHH, Knol MJ, et al. Comparing HAMD17 and HAMD subscales on their ability to differentiate active treatment from placebo in randomized controlled trials[J]. J Affect Disord. 2013;145(3):363–9. Rabinowitz J, Williams JBW, Hefting N, et al. Consistency checks to improve measurement with the Hamilton Rating Scale for Anxiety (HAM-A)[J]. J Affect Disord. 2023;325:429–36. Buysse DJ, Reynolds CR, Monk TH, et al. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research[J]. Psychiatry Res. 1989;28(2):193–213. Catai AM, Pastre CM, Godoy MFD, et al. Heart rate variability: are you using it properly? Standardisation checklist of procedures[J]. Braz J Phys Ther. 2020;24(2):91–102. Ji XC, Guan L, Li WY, et al. Research progress of clinical application of heart rate variability [J]. J Cardio-Cerebrovascular Dis Integr Chin Western Med. 2020;18(17):2809–11. Dong X, Xu XD, Tan JY et al. Application and controversy of LF, HF and LF/HF in heart rate variability analysis [J]. Advances in physiological science,2023:1–13. Cattaneo LA, Franquillo AC, Grecucci A, et al. Is Low Heart Rate Variability Associated with Emotional Dysregulation, Psychopathological Dimensions, and Prefrontal Dysfunctions? An Integrative View[J]. J personalized Med. 2021;11(9):872. Yang H, Haack M, Dang R, et al. Heart rate variability rebound following exposure to persistent and repetitive sleep restriction[J]. Volume 42. Sleep; 2019. (New York, N.Y.). 2. Baglioni C, Battagliese G, Feige B, et al. Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies.[J]. J Affect Disord. 2011;135(1–3):10–9. Trinder J, Kleiman J, Carrington M, et al. Autonomic activity during human sleep as a function of time and sleep stage[J]. J Sleep Res. 2001;10(4):253–64. Tobaldini E, Cogliati C, Fiorelli EM, et al. One night on-call: Sleep deprivation affects cardiac autonomic control and inflammation in physicians[J]. Eur J Intern Med. 2013;24(7):664–70. Boudreau P, Dumont G, Boivin D. Circadian variation of heart rate variability during different sleep stages[J]. Sleep Med. 2013;14(suppS1):e76. Shaffer F, Mccraty R, Zerr CL. A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability[J]. Front Psychol, 2014,5(1040). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4765795","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332404098,"identity":"7bc58b15-23ee-4d86-a113-22cfcad83af5","order_by":0,"name":"Wenna Liu","email":"","orcid":"","institution":"Xijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenna","middleName":"","lastName":"Liu","suffix":""},{"id":332404100,"identity":"2fc5d9f7-fa7d-4d3c-acc2-b785ee5113d1","order_by":1,"name":"Shutong Wang","email":"","orcid":"","institution":"Xijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shutong","middleName":"","lastName":"Wang","suffix":""},{"id":332404102,"identity":"f7c5c8f5-3d6c-4650-840b-0e6bd63ec304","order_by":2,"name":"Hanyang Gu","email":"","orcid":"","institution":"Xijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hanyang","middleName":"","lastName":"Gu","suffix":""},{"id":332404104,"identity":"b8d69a30-4652-4b15-95d8-daabfdd8fb55","order_by":3,"name":"Rong Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYHACxgNAQo69AcxhJk4PSIsxzwFStST2EK3F4EbyhsOVe+rSe8ROp0kwVFgnNrCfPUBAS1rBwTPPDuf2SOduk2A4k57YwJOXgFeL2e0cg4MNBw7k7gdpYWw7nNggwWNAjJa6dB6wln/Ea2FOgGhpIEKL/f1nBUAthw2BftlskXAs3biNJwe/FsmewxsfAh0mD7Rl440PNday/exn8GsBAiQFCUDMRkg9qpZRMApGwSgYBdgAAJRwSaWaGwYoAAAAAElFTkSuQmCC","orcid":"","institution":"Xijing Hospital","correspondingAuthor":true,"prefix":"","firstName":"Rong","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-07-19 02:59:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4765795/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4765795/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63048655,"identity":"68d7bfe7-3a18-448b-b65c-f28c98b417bd","added_by":"auto","created_at":"2024-08-22 13:26:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":670510,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4765795/v1/69db12d9-92b2-41b5-a9ff-c0022b378f53.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Heart rate variability, a potential assessment tool for identifying anxiety, depression, and sleep disorders in elderly individuals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe aging population has emerged as a pressing global social issue, as evidenced by the data from China\u0026apos;s seventh population census. By 2020, it is projected that there will be 260 million individuals aged 60 or older in China, comprising 18.70% of the total population[1]. This represents a notable increase of 5.44% from the sixth national population census conducted in 2010[2]. The size and proportion of the elderly demographic in China have been experiencing rapid growth. A meta-analysis has revealed that the prevalence of depression among the elderly in our country has reached levels as high as 25.55% [3], with anxiety symptoms affecting approximately 22.11% of the older population [4]. Additionally, sleep disorders have been found to affect 46.0% of elderly individuals [5]. In efforts to enhance the well-being of the elderly, medical treatment should not only focus on physical ailments but also on the maintenance of their mental health.\u003c/p\u003e\n\u003cp\u003eHeart rate variability (HRV) is defined as the variance in time intervals between consecutive heartbeats. It is a collection of parameter values utilized to quantify variations in the R-R interval through various algorithms, serving as a precise indicator of alterations in the autonomic nervous system[6]. This tool can be employed to assess the sympathetic and vagal tone of the heart in a non-invasive manner, determining the equilibrium between the two [7]. Long-term anxiety and depression, along with associated negative emotions and sleep disorders, may result in varying levels of autonomic nervous dysfunction, thereby impacting disease progression [8]. The consensus within the academic community is that HRV serves as a valuable indicator for assessing the severity and prognosis of clinical anxiety and depression [9]. Nevertheless, there remains a dearth of quantitative metrics and corresponding clinical investigations regarding alterations in autonomic nervous function among elderly individuals with anxiety, depression, and sleep disorders in comparison to their healthy counterparts. Furthermore, the utility of anxiety, depression, and sleep disorder scales for evaluating these conditions in older adults with cognitive impairment is constrained. Therefore, this study aims to examine alterations in autonomic nervous function among elderly individuals with anxiety, depression, and sleep disorders through the utilization of the HRV index in a clinical setting. It is anticipated to offer clinical utility with objective, accurate and simple evaluation indicators for elderly patients in the future.\u003c/p\u003e"},{"header":"1. Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Subjects of Study\u003c/h2\u003e \u003cp\u003eThis case-control study involved the continuous collection of information from inpatients and outpatients who underwent mental status and sleep quality evaluations at Xijing Hospital between July 2021 and December 2022. The sample size was determined based on the prevalence of anxiety, depression, and sleep disorders among elderly individuals, with exclusion criteria applied to cases that dropped out during the study or failed to meet data requirements. Ultimately, 313 patients were selected for the study group. Control group samples were primarily drawn from the inpatient and outpatient populations undergoing physical examinations at Xijing Hospital, resulting in a total of 42 participants.\u003c/p\u003e \u003cp\u003eThe participants were randomly assessed using the HAMA, HAMD, and PSQI scales by researchers with professional training in psychosomatic medicine to ensure alignment between the participants' actual psychological and sleep conditions and the scale scores.\u003c/p\u003e \u003cp\u003eAll subjects were grouped as follows. Group A: Anxiety group, 45 cases; Group B: Depression group, 42 cases; Group C: Sleep disorder group, 43 cases; Group D: Anxiety combined with depression group, 44 cases; Group E: Anxiety combined with sleep disorder group, 46 cases; Group F: Depression combined with sleep disorder group, 47 cases; Group G: Anxiety, depression and sleep disorder group, 46 cases; Group H: Control group, 42 cases.\u003c/p\u003e \u003cp\u003eParticipants with psychological or sleep disorders must meet the following criteria: 1) Age\u0026thinsp;\u0026ge;\u0026thinsp;60 years; 2) Meeting the diagnostic criteria of the Chinese Classification of Mental Disorders-3 (CCMD-3) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]; 3) A Hamilton Depression Scale (HAMD) score of \u0026ge;\u0026thinsp;17 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], a Hamilton Anxiety Scale (HAMA) score of \u0026ge;\u0026thinsp;14 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], or a Pittsburgh Sleep Quality Index (PSQI) score of \u0026ge;\u0026thinsp;10 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; 4) Participants must agree to accept a 24-hour ECG monitoring; 5) Participants must have complete medical records and provide signed informed consent.\u003c/p\u003e \u003cp\u003eExclusion criteria for the patients include: 1) Patients with a history of atrioventricular block, atrial fibrillation, premature atrial or ventricular arrhythmias; 2) Patients diagnosed with mental retardation, delirium, schizophrenia, or other mental illnesses according to the CCMD-3; 3) A range of factors that may impact HRV, including hyperthyroidism, anemia, infection, trauma, and other physical ailments; 4) Patients with severe organic diseases such as acute cerebral infarction, malignant tumors, and cachexia; 5) Those who have recently undergone major catastrophic events or experienced significant stress; 6) Individuals experiencing insomnia due to physical discomfort or fatigue; 7) Patients with Parkinson's syndrome, sleep apnea syndrome; 8) Recent use of anti-anxiety and antidepressant medications; 9) Participants who have recently used medications that impact heart rate, such as beta-blockers, thyroid hormones, and digitalis within the preceding two weeks.\u003c/p\u003e \u003cp\u003eThe inclusion criteria for the control group were as follows: 1) Individuals aged 60 years or older; 2) Scoring below 17 on the HAMD and below 17 on the HAMA, as well as below 14 points on the PSQI; 3) Willingness to participate in a 24-hour ECG monitoring; and 4) Possession of complete medical records and signed informed consent.\u003c/p\u003e \u003cp\u003eThe exclusion criteria for the control group include: 1) Patients with a history of atrioventricular block, atrial fibrillation, premature atrial or ventricular arrhythmias; 2) Individuals meeting the diagnostic criteria for anxiety and depression according to the CCMD-3; 3) Patients diagnosed with sleep disorders based on the PSQI and subjective reports of frequent and persistent difficulty falling asleep and/or maintaining sleep, as well as dissatisfaction with sleep as determined by clinicians; 4) Individuals with conditions known to affect HRV, such as hyperthyroidism, anemia, and other physical illnesses; 5) Patients with severe organic diseases such as acute cerebral infarction, severe infection and cachexia; 6) Recent major catastrophic events or high stress levels; 7) Parkinson's syndrome, sleep apnea syndrome, and recent use of anti-anxiety and antidepressant medications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Demographic data\u003c/h2\u003e \u003cp\u003eDemographic data was collected using a standardized questionnaire to ensure the scientific rigor and accuracy of the study, allowing for a comprehensive review of participants' background information and health status, including age, sex, body mass index, fasting blood glucose, glycated hemoglobin, blood lipids, blood pressure, heart rate, smoking history, drinking history, hypertension history, diabetes history, coronary heart disease history, marital status, income, and education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Research methods\u003c/h2\u003e \u003cp\u003eAll the participants were evaluated their psychological state using the HAMD and the HAMA. Additionally, the PSQI was utilized to assess the sleep quality of the patients. Two trained evaluators from the psychosomatic department conducted joint examinations of the patients and independently scored them, with the average scores being recorded.\u003c/p\u003e \u003cp\u003eThe participants underwent 24-hour monitoring using a V12 dynamic ECG recorder. ECG interference signals were eliminated through computer processing, and manual filtering was used to remove artifacts and ectopic beats. The HRV index was computed using an algorithmic system. The recorded data included four time-domain analysis indicators: standard deviation of normal to normal intervals (SDNN), the standard deviation of the averages of 5-minute RR intervals (SDANN), root mean square of successive RR interval differences (RMSSD), percentage of successive RR intervals that differ by more than 50 ms (PNN50), and three frequency domain analysis indicators: low frequency (LF), high frequency, (HF), LF/HF [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Prior to the examination, participants were advised to abstain from alcohol, strong tea, coffee, and strenuous exercise.\u003c/p\u003e \u003cp\u003eAll participants underwent assessment using the HAMA consisting of 14 items and the HAMD consisting of 17 items. A diagnosis of anxiety disorder was made if HAMA scores were equal to or greater than 14, while a diagnosis of depression disorder was made if HAMD scores were equal to or greater than 17, based on the symptoms reported by the participants over the past three months. Sleep quality was assessed using the PSQI, with scores equal to or greater than 10 indicating a sleep disorder. Higher PSQI scores were indicative of poorer sleep quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4 statistical analysis\u003c/h2\u003e \u003cp\u003eNormality of the variables was assessed using the Shapiro-Wilk test, and variables conforming to a normal distribution were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:（\\stackrel{-}{\\text{X}}\\pm\\:\\text{S}）\\)\u003c/span\u003e\u003c/span\u003e. The variables exhibiting skewed distribution were analyzed using the interquartile method. Subsequently, one-way ANOVA was employed to evaluate differences in means among multiple samples that met the criteria of normal distribution and homogeneity of variance. In cases where the assumption of homogeneity of variance was violated, the Welch test was utilized. Post-hoc analysis was conducted using the LSD-t test to identify significant differences between specific pairs of groups. For data that deviated from normal distribution, pairwise comparisons between groups were performed using Tamhane's T2 test. Finally, a multivariate logistic regression analysis was conducted to examine the relationship between mental health conditions (including anxiety, depression, and sleep disorders) as independent variables and the decrease in relevant indicators of HRV as dependent variables. The analysis was performed using SPSS 26.0 software, and a two-sided test with a significance level of \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 indicated significant differences.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.1 comparison of clinical baseline data\u003c/h2\u003e \u003cp\u003eThere were no significant differences in age, sex, body mass index, fasting blood glucose, glycated hemoglobin, blood lipid, blood pressure, heart rate, smoking history, drinking history, hypertension history, diabetes history, coronary heart disease history, marital status, income and education among the eight groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details. Additionally, due to the inevitable use of sedative and hypnotic drugs among people with sleep disorders, the population using the drugs cannot be completely excluded, but this study compared the number of people using sedative and hypnotic drugs between groups with sleep disorders. It can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e that the proportion of people using the drugs in groups C, E, F and G is similar, and there is no statistical difference between groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eComparison of Clinical baseline data across seven patients groups and control group\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=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup D\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGroup F\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGroup G\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGroup H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamale [n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20(43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29(61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26(56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19(45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e135\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e127\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e65.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking [n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15(34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18(39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16(34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17(37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15(35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking [n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12(26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8(19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension[n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35(76.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38(80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36(78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25(59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes [n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15(31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13(28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9(21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD[n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17(37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25(53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21(45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19(45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage annual income (ten thousand yuan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried [n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(97.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42(95.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43(93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45(95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45(97.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39(92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: The measurement data in the table is expressed as (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s), and the number of counting data is expressed as [n (%)]. HbA1c: glycosylated hemoglobin, FBG: fasting blood glucose, TC: serum total cholesterol, TG: triglyceride, HDL: high density lipoprotein, LDL: low density lipoprotein, SBP: systolic blood pressure, DBP: diastolic blood pressure, HR: heart rate, CHD: coronary heart disease.\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=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the number of patients using sedative-hypnotic in group C, E, F and G\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup C (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup E (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup F (n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup G (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of individuals using sleep medication [n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(74.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(76.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39(84.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.509\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=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2 HRV analysis\u003c/h2\u003e \u003cp\u003eHRV serves as a valuable metric for assessing autonomic nervous system function, particularly in its capacity to reflect the equilibrium between sympathetic and vagal nerve activity. It has been widely used in different areas of research, from basic research to clinical applications to assess health and disease states. Therefore, in this study, we compared various HRV indicators among the eight groups, and the results showed that there were significant differences in HRV parameters such as SDNN, SDANN, RMSSD, PNN50, LF/HF, LF, HF among different groups. In comparison to Group A and Group B, the HRV parameters of Group D exhibited a decrease; in comparison to Group A and Group C, the HRV parameters of Group E, with the exception of LF/HF and HF, also showed a decrease; in comparison to Group B and Group C, all HRV parameters of Group F displayed a decrease. Furthermore, in comparison to Group C, the HRV parameters of Groups E and F experienced a decrease, excluding LF/HF. Group G demonstrated a significant decrease in HRV parameters, excluding LF/HF, when compared to the other groups, while Group H showed an increase in HRV parameters, excluding LF/HF, with a statistically significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for details.\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\u003eComparison of HRV among seven patients groups and control group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\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\u003eSDNN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSDANN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePNN50(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLF/HF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113.0\u0026thinsp;\u0026plusmn;\u0026thinsp;29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.7\u0026thinsp;\u0026plusmn;\u0026thinsp;22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.5\u0026thinsp;\u0026plusmn;\u0026thinsp;46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;23.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e395.2\u0026thinsp;\u0026plusmn;\u0026thinsp;261.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e165.7\u0026thinsp;\u0026plusmn;\u0026thinsp;109.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117.8\u0026thinsp;\u0026plusmn;\u0026thinsp;31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109.2\u0026thinsp;\u0026plusmn;\u0026thinsp;25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.6\u0026thinsp;\u0026plusmn;\u0026thinsp;51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e353.4\u0026thinsp;\u0026plusmn;\u0026thinsp;158.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e459.0\u0026thinsp;\u0026plusmn;\u0026thinsp;418.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.2\u0026thinsp;\u0026plusmn;\u0026thinsp;25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.2\u0026thinsp;\u0026plusmn;\u0026thinsp;34.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.2\u0026thinsp;\u0026plusmn;\u0026thinsp;45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e746.8\u0026thinsp;\u0026plusmn;\u0026thinsp;262.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e779.5\u0026thinsp;\u0026plusmn;\u0026thinsp;346.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.7\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e131.8\u0026thinsp;\u0026plusmn;\u0026thinsp;90.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e134.5\u0026thinsp;\u0026plusmn;\u0026thinsp;76.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.0\u0026thinsp;\u0026plusmn;\u0026thinsp;19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.5\u0026thinsp;\u0026plusmn;\u0026thinsp;27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e331.3\u0026thinsp;\u0026plusmn;\u0026thinsp;193.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e232.4\u0026thinsp;\u0026plusmn;\u0026thinsp;142.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.1\u0026thinsp;\u0026plusmn;\u0026thinsp;28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.6\u0026thinsp;\u0026plusmn;\u0026thinsp;40.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.4\u0026thinsp;\u0026plusmn;\u0026thinsp;24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e126.0\u0026thinsp;\u0026plusmn;\u0026thinsp;62.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e284.9\u0026thinsp;\u0026plusmn;\u0026thinsp;157.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.9\u0026thinsp;\u0026plusmn;\u0026thinsp;29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.4\u0026thinsp;\u0026plusmn;\u0026thinsp;26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.6\u0026thinsp;\u0026plusmn;\u0026thinsp;75.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e81.0\u0026thinsp;\u0026plusmn;\u0026thinsp;76.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183.4\u0026thinsp;\u0026plusmn;\u0026thinsp;55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.0\u0026thinsp;\u0026plusmn;\u0026thinsp;47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198.0\u0026thinsp;\u0026plusmn;\u0026thinsp;92.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.7\u0026thinsp;\u0026plusmn;\u0026thinsp;26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7502.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7246.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16562.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16328.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF值\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP值\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: SDNN: standard deviation of normal to normal intervals, SDANN: the standard deviation of the averages of 5-minute RR intervals, RMSSD: root mean square of successive RR interval differences, PNN50: percentage of successive RR intervals that differ by more than 50 ms, LF: low frequency, HF: high frequency.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The effect of anxiety on HRV\u003c/h2\u003e \u003cp\u003eThis study focused on a cohort of patients diagnosed with anxiety, depression, and sleep disorders to investigate the potential influence of these prevalent mood disorders on HRV. Multivariate logistic regression analysis was employed to examine the association between anxiety, depression, and sleep disorders with various indicators of HRV, including SDNN, SDANN, RMSSD, PNN50, LF, HF, and LF/HF, as dependent variables. The presence of anxiety, depression, and sleep disorders were considered as independent variables in the model. Various confounding factors, including BMI, smoking history, drinking history, hypertension, diabetes, and coronary heart disease, were controlled for in the study of patients with anxiety, depression, and sleep disorders. Regression analysis revealed a significant association between anxiety and multiple HRV indicators. Specifically, anxiety was identified as an independent risk factor for reduced SDNN, SDANN, and LF (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01). Furthermore, the analysis indicated that anxiety was also an independent risk factor for an increase in the LF/HF ratio (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Please refer to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for further information.\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\u003eLogistic regression analysis was used to analyze the effect of anxiety patients on HRV\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRV parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWald χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eSDNN(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.33ཞ9.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDANN(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.37ཞ8.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLF/HF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01ཞ0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLF(Hz)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.32ཞ60.01\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=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4 The effect of depression on HRV\u003c/h2\u003e \u003cp\u003eThe study investigated the impact of depression on HRV by examining indicators such as SDNN, RMSSD, PNN50, and HF as dependent variables, with anxiety, depression, and sleep disorders as independent variables. Multivariate logistic regression analysis revealed that depression was identified as an independent risk factor for the decrease in SDNN, RMSSD, PNN50, and HF (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Further details 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\u003eLogistic regression analysis was used to analyze the effect of depression patients on HRV\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRV parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWald χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eSDNN(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.23ཞ8.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMMSD(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.99ཞ20.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNN50(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.75ཞ134.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF(Hz)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.75ཞ38.51\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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5 The effect of sleep disorders on HRV\u003c/h2\u003e \u003cp\u003eIn a manner akin to the research methodologies employed in studies on anxiety and depression, the present study designated the decrease in HRV index as the dependent variable, with the presence of anxiety, depression, and sleep disorder serving as the independent variables. Following logistic regression analysis, it was determined that sleep disorder emerged as a significant independent risk factor for the reduction of PNN50 and SDANN (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Further information 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\u003eLogistic regression analysis was used to analyze the effect of patients with sleep disorders on HRV\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRV parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWald χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003ePNN50(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.35ཞ8.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDANN(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.15ཞ14.61\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"},{"header":"3. Discussion","content":"\u003cp\u003eHRV serves as a comprehensive measure of the regulation of the cardiac autonomic nervous system, encompassing frequency domain, time domain, and nonlinear indices. Frequency domain indicators such as LF、HF、LF/HF, time domain indicators such as SDNN、SDANN、RMSSD、PNN50, and non-linear indicators such as Poincare map and sample entropy are utilized to assess the complexity and non-linear characteristics of HRV. It is widely accepted among scholars that SDNN primarily signifies the overall activity of the autonomic nerve system. SDANN and LF are indicative of sympathetic nerve activity, with their values decreasing as sympathetic nerve tension increases. RMSSD、PNN50 and HF, on the other hand, reflects vagal nerve activity, with its value decreasing as vagal nerve tension reduces. The LF/HF ratio serves as a quantitative measure for assessing the functional equilibrium of the sympathetic and vagus nerves. The interpretation of HRV indices remains inconclusive. A study suggests that while LF is modulated by sympathetic nerve function, it is not advisable to rely solely on LF to gauge sympathetic nervous system activity, as it is also impacted significantly by vagus nerve activity and other factors. LF/HF can offer insights into the regulatory function of the autonomic nervous system, however, its accuracy is compromised by nonlinear relationships and various influencing factors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this research, the elderly population was categorized into groups based on varying emotional states and sleep patterns to analyze discrepancies in HRV indicators among the eight groups. Findings revealed that individuals combined with anxiety, depression, and sleep disorders exhibited a decrease in all HRV metrics, with the exception of LF/HF, compared to the normal control group, with the most pronounced decrease observed in this cohort. Specifically, individuals with comorbid mood and sleep disturbances experienced a more substantial decline in HRV compared to those with singular anxiety, depression, or sleep disorders. This suggests that anxiety, depression, and sleep disorders may contribute to a reduction in HRV and exhibit a synergistic effect, a phenomenon not previously explored in existing literature. Consequently, our findings propose a novel hypothesis: a pronounced decrease in HRV may correlate with heightened emotional instability in patients and poorer sleep quality [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent research has increasingly demonstrated the impact of mood disorders, such as anxiety and depression, on HRV. In particular, depressed mood has been associated with a heightened susceptibility to various cardiac ailments. While existing research has examined the correlation between depression and HRV, there remains a dearth of studies focusing on this relationship within the elderly demographic. This investigation utilized multivariate logistic regression analysis to investigate the distinct influence of anxiety and depression on HRV among elderly individuals. The findings revealed that anxiety independently posed a risk for the diminishment of SDNN, SDANN, and LF (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). This suggests that individuals with anxiety may experience a decrease in HRV measures, and that anxiety is an independent risk factor for an increase in the LF/HF ratio. The LF/HF ratio is commonly used as a marker of cardiovascular health, with an elevated ratio potentially indicating an autonomic nervous system imbalance. Similarly, depressive mood was found to be an independent risk factor for a reduction in SDNN, RMSSD, PNN50 and HF, suggesting that individuals with depression may experience decreased levels of these indicators due to the impact of their mood on balance of autonomic nervous system. Among them, RMSSD, PNN50, and HF have been identified as closely associated with parasympathetic nerve activity, suggesting that individuals with depression may exhibit diminished parasympathetic nerve activity. This finding supports the notion that depressive symptoms may exert an inhibitory influence on parasympathetic nerve activity, resulting in a reduction in HRV.\u003c/p\u003e \u003cp\u003eTo sum up, the assessment of emotional disorders in the elderly population is significantly influenced by subjective factors inherent in the scales used, and various limitations, such as sensory impairments and cognitive dysfunction, may hinder the accurate measurement of these scales. Therefore, the utilization of HRV as an objective and easily accessible clinical monitoring indicator becomes imperative. By monitoring changes in SDNN, LF, and HF, healthcare professionals can effectively identify the presence and type of mood disorder in elderly patients.\u003c/p\u003e \u003cp\u003ePrevious research has predominantly focused on examining the impact of various physiological and psychological factors on HRV. However, the prevalence of sleep disorders among the elderly poses a significant challenge to their overall physical and mental well-being, yet the potential influence of sleep disorders on HRV in this population remains largely unexplored. It is important to note that not only physical ailments, but also mental health conditions such as anxiety and depression, can contribute to the development of sleep disorders [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Hence, this study employed multivariate Logistic regression analysis to examine the potential influence of sleep disorders on HRV among elderly individuals. The results of the analysis indicated that sleep disorder emerged as a significant independent risk factor for the decline in PNN50 and SDANN. These findings align with some of the prior research, such as the study conducted by Trinder et al., which demonstrated a reduction in certain HRV measures, including RMSSD and PNN50, during nighttime in individuals with sleep disorders [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, Tobaldini et al. discovered a negative correlation between sleep disturbances and HRV in a cohort of young adults. Their findings indicated that individuals with insomnia exhibited notably decreased nighttime HRV, particularly in relation to parasympathetic parameters [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Another study utilized Actigraphy, a tool for evaluating sleep patterns, to investigate sleep quality and determined that diminished sleep efficiency was linked to reduced HRV [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This indicates that sleep disorders may result in dysfunction of specific autonomic nervous system functions, particularly those pertaining to cardiovascular well-being.\u003c/p\u003e \u003cp\u003eThe limitation of this study is the lack of a clearly defined normal threshold for various HRV parameters in healthy individuals, despite the observed decrease in these parameters in individuals with anxiety, depression, and sleep disorders, particularly when all three conditions are present simultaneously. Future extensive clinical investigations are anticipated to establish a threshold level for HRV in the elderly demographic, thereby positioning HRV as a primary objective measure for assessing emotions.\u003c/p\u003e \u003cp\u003eOverall, HRV serves as a non-invasive and readily available tool for physicians to detect patients potentially impacted by negative mood and sleep disorders [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Investigating the correlation between anxiety, depression, sleep disorders, and HRV among older individuals will enhance comprehension of the psychological mechanisms underpinning associated illnesses and facilitate the formulation of potentially efficacious treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was conducted in compliance with the Declaration of Helsinki and approved by the Xijing Hospital Ethics Committee (approval number: KY20222043-C-1), and all participants provided informed consent.\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 datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the following grants: National Natural Science Foundation of China (No. 82070263) and Air Force Medical University Clinical Research Program (2022LC2252).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data on the clinical characteristics and heart rate variability metrics of elderly participants with and without psychological or sleep disorders were analyzed and interpreted by WL and SW. HG assisted in participant recruitment, while RL developed the study protocols and made significant contributions to the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBulletin of the 7th National Population Census (. 5) -- Age Composition of the population [J]. Stat China, 2021(05):10\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTong YF. The latest developments and trends of China's population: Combined with the analysis of the 7th National Population Census data[J]. J China Inst Labor Relations. 2021;35(04):15\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRong J, Ge YH, Meng NN, et al. A meta-analysis of the prevalence of depression among the elderly in China from 2010 to 2019 [J]. Chin J evidence-based Med. 2020;20(01):26\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu L, CAI YY, Shi SX, et al. Meta-analysis of the prevalence of anxiety disorder in elderly people in China [J]. J Clin Psychiatry. 2011;21(2):87\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang ZJ, Zhao M, Chen TW, et al. Meta-analysis of the prevalence of sleep disorders in the elderly in China [J]. Chin J Gen Med. 2022;25(16):2036\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarandina A, Rodrigues GD, Di Francesco P, et al. Effects of transcutaneous auricular vagus nerve stimulation on cardiovascular autonomic control in health and disease[J]. Auton Neurosci. 2021;236:102893.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao WJ, Zhang JP, Ma JX, et al. Advances in the clinical applications of heart rate variability [J]. J Practical Electrocardiol. 2022;31(2):137\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu ZP, WU HL. Study on heart rate variability of generalized anxiety disorder and its comorbidities depressive disorder [J]. Chin J Gen Med. 2019;22(33):4069\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartmann R, Schmidt FM, Sander C, et al. Heart Rate Variability as Indicator of Clinical State in Depression[J]. Front Psychiatry. 2019;9:735.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBranch of Psychiatry, Chinese Medical Association. The 3rd edition of Chinese Classification and Diagnosis Criteria for Mental Disorders [M]. Jinan: Shandong Science and Technology; 2001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoessen R, Groenwold RHH, Knol MJ, et al. Comparing HAMD17 and HAMD subscales on their ability to differentiate active treatment from placebo in randomized controlled trials[J]. J Affect Disord. 2013;145(3):363\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabinowitz J, Williams JBW, Hefting N, et al. Consistency checks to improve measurement with the Hamilton Rating Scale for Anxiety (HAM-A)[J]. J Affect Disord. 2023;325:429\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuysse DJ, Reynolds CR, Monk TH, et al. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research[J]. Psychiatry Res. 1989;28(2):193\u0026ndash;213.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatai AM, Pastre CM, Godoy MFD, et al. Heart rate variability: are you using it properly? Standardisation checklist of procedures[J]. Braz J Phys Ther. 2020;24(2):91\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi XC, Guan L, Li WY, et al. Research progress of clinical application of heart rate variability [J]. J Cardio-Cerebrovascular Dis Integr Chin Western Med. 2020;18(17):2809\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong X, Xu XD, Tan JY et al. Application and controversy of LF, HF and LF/HF in heart rate variability analysis [J]. Advances in physiological science,2023:1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCattaneo LA, Franquillo AC, Grecucci A, et al. Is Low Heart Rate Variability Associated with Emotional Dysregulation, Psychopathological Dimensions, and Prefrontal Dysfunctions? An Integrative View[J]. J personalized Med. 2021;11(9):872.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang H, Haack M, Dang R, et al. Heart rate variability rebound following exposure to persistent and repetitive sleep restriction[J]. Volume 42. Sleep; 2019. (New York, N.Y.). 2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaglioni C, Battagliese G, Feige B, et al. Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies.[J]. J Affect Disord. 2011;135(1\u0026ndash;3):10\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrinder J, Kleiman J, Carrington M, et al. Autonomic activity during human sleep as a function of time and sleep stage[J]. J Sleep Res. 2001;10(4):253\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTobaldini E, Cogliati C, Fiorelli EM, et al. One night on-call: Sleep deprivation affects cardiac autonomic control and inflammation in physicians[J]. Eur J Intern Med. 2013;24(7):664\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoudreau P, Dumont G, Boivin D. Circadian variation of heart rate variability during different sleep stages[J]. Sleep Med. 2013;14(suppS1):e76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaffer F, Mccraty R, Zerr CL. A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability[J]. Front Psychol, 2014,5(1040).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anxiety, Depression, Sleep disorders, Heart rate variability, Elderly individuals","lastPublishedDoi":"10.21203/rs.3.rs-4765795/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4765795/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study investigates how anxiety, depression, and sleep disorders impact heart rate variability (HRV) in the elderly, exploring the clinical implications of HRV changes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe examined 355 patients (163 men, 192 women) at Xijing Hospital from July 2021 to December 2022 during health check-ups. Demographics were recorded, and emotional status was assessed using the Hamilton Anxiety Scale (HAMA) and the Hamilton Depression Scale (HAMD). The Pittsburgh Sleep Quality Scale (PSQI) evaluated sleep quality. Patients were categorized into groups A-G based on the presence of emotional states and sleep disorders. HRV indices\u0026mdash;SDNN, SDANN, RMSSD, PNN50, LF/HF, LF, and HF\u0026mdash;were analyzed using ANOVA and multivariate logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNo statistically significant differences were observed in demographic, clinical, and lifestyle factors across the eight groups. Variables assessed included age, sex, body mass index (BMI), fasting blood glucose, glycated hemoglobin (HbA1c), blood lipids, blood pressure, heart rate, and histories of smoking and alcohol consumption. Additionally, the presence of hypertension, diabetes, coronary heart disease, marital status, income, and education level were evaluated, with all showing equivalence (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Significant differences in HRV indices were observed across groups, particularly in group G (patients with anxiety, depression and sleep disorders), which showed decreased HRV parameters except LF/HF, and group H (control group), which showed increased parameters, also except LF/HF (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Anxiety was an independent risk factor for reduced SDNN, SDANN, and LF (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01), and increased LF/HF ratio (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Depression was linked to decreased SDNN, RMSSD, PNN50, and HF (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Sleep disorders independently predicted reduced PNN50 and SDANN (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHRV indices of individuals with varying emotional states and sleep disorders exhibited varying degrees of decrease. Anxiety, depression, and sleep disorders presented a superimposed effect on HRV. Among these factors, sleep disorders have the least impact on HRV.\u003c/p\u003e","manuscriptTitle":"Heart rate variability, a potential assessment tool for identifying anxiety, depression, and sleep disorders in elderly individuals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-20 12:01:46","doi":"10.21203/rs.3.rs-4765795/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f8bab2c2-0261-4a68-8722-59bca2a92ffd","owner":[],"postedDate":"August 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-30T13:14:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-20 12:01:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4765795","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4765795","identity":"rs-4765795","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}