Effectiveness of cognitive behavior therapy for sleep disturbance and glycemic control in persons with type 2 diabetes mellitus: A community-based randomized controlled trial in China.

BACKGROUND: Poor sleep quality is a common clinical feature in patients with type 2 diabetes mellitus (T2DM), and often negatively related with glycemic control. Cognitive behavioral therapy (CBT) may improve sleep quality and reduce blood sugar levels in patients with T2DM. However, it is not entirely clear whether CBT delivered by general practitioners is effective for poor sleep quality in T2DM patients in community settings. AIM: To test the effect of CBT delivered by general practitioners in improving sleep quality and reducing glycemic levels in patients with T2DM in community. METHODS: A cluster randomized controlled trial was conducted from September 2018 to October 2019 in communities of China. Overall 1033 persons with T2DM and poor sleep quality received CBT plus usual care or usual care. Glycosylated hemoglobin A1c (HbAlc) and sleep quality [Pittsburgh Sleep Quality Index (PSQI)] were assessed. Repeated measures analysis of variance and generalized linear mixed effects models were used to estimate the intervention effects on hemoglobin A1c and sleep quality. RESULTS: The CBT group had 0.64, 0.50, and 0.9 lower PSQI scores than the control group at 2 mo, 6 mo, and 12 mo, respectively. The CBT group showed 0.17 and 0.43 lower HbAlc values than the control group at 6 mo and 12 mo. The intervention on mean ΔHbAlc values was significant at 12 mo (t = 3.68, P < 0.01) and that mean ΔPSQI scores were closely related to ΔHbAlc values (t = 7.02, P < 0.01). Intention-to-treat analysis for primary and secondary outcomes showed identical results with completed samples. No adverse events were reported. CONCLUSION: CBT delivered by general practitioners, as an effective and practical method, could reduce glycemic levels and improve sleep quality for patients with T2DM in community.

Effect of group cognitive behavioural therapy on psychological stress and blood glucose in people with type 2 diabetes mellitus: A community-based cluster randomized controlled trial in China.

OBJECTIVE: To assess whether group cognitive behavioural therapy (GCBT) delivered by general practitioners reduces anxiety and depression and improves glycaemic levels in adults with type 2 diabetes mellitus. METHODS: We conducted a community-based cluster randomized controlled trial in adults with type 2 diabetes mellitus from 48 communities in China. Participants received either GCBT plus usual care (UC) or UC only. General practitioners were trained in GCBT before intervention in the intervention group. The primary outcome was glycated haemoglobin (HbA1c ) concentration. Outcome data were collected from all participants at baseline, 2 months, 6 months and 1 year. The secondary outcomes were depression (Patient Health Questionnaire-9; PHQ-9) and anxiety (General Anxiety Disorder questionnaire; GAD-7). RESULTS: The GCBT group showed greater improvement in GAD-7 and PHQ-9 scores, respectively, than the UC group after 2 months post-baseline (T = -6.46, p < 0.0001; T = -5.29, p < 0.001), 6 months (T = -4.58, p < 0.001; T = -4.37, p < 0.001) and 1 year post-intervention (T = -3.91, p < 0.001; T = -3.57, p < 0.001). There was no difference in HbA1c values between the GCBT and UC groups at 2 months while the values were lower in the GCBT group at 6 months and 1 year (T = -6.83, p < 0.001; T = -4.93, p < 0.001, respectively). Subgroup analysis indicated a long-term effect of GCBT only for mild and moderate anxiety and mild depression groups. Similarly, HbA1c values reduced only in the mild and moderate anxiety and the mild depression groups. CONCLUSIONS: General practitioners can deliver GCBT interventions. GCBT plus UC is superior to UC for reducing mild/moderate anxiety and depression, and improving glycaemic levels. TRIAL REGISTRATION: Chinese clinical trials registration (ChiCTR-IOP-16008045).

Effects of cognitive behavioral therapy on sleep disturbances and quality of life among adults with type 2 diabetes mellitus: A randomized controlled trial.

BACKGROUND AND AIMS: Cognitive behavioral therapy (CBT) is recommended as the first-line nonpharmacotherapy for sleep complaints. However, there are no studies that tested CBT for improving sleep quality and increasing quality of life (QOL) in patients with type 2 diabetes mellitus (T2DM). Therefore, this study aims to test the effect of CBT on sleep disturbances and QOL in patients with T2DM. METHODS AND RESULTS: In total, 187 participants with T2DM and comorbid poor sleep quality were included in the analysis with the control group of 93 receiving usual care (UC) only and the intervention group of 94 receiving CBT with aerobic exercise plus UC, The Pittsburgh Sleep Quality Index (PSQI), the Diabetes-Specific Quality of Life Scale (DSQLS) and the glycated hemoglobin (HbA1C) values were collected at baseline, after the 2-month intervention, and 6 months of follow-up. The CBT group had 3.03 points lower PSQI scores (95% confidence interval [CI]: 2.07-4.00, P < 0.001) and 7.92 points lower total DSQLS scores (95% CI: 4.98-10.87, P < 0.001) than the control group after 6-month follow-up. No difference was found in HbAlc between the two groups (t = -0.47, P = 0.64) after 2-month intervention, while the CBT group had 0.89 units lower HbAlc (95% CI: 0.49-1.28, P < 0.001) than the control group after 6-month follow-up. CONCLUSION: CBT is effective for sleep disturbances and can also improve sleep quality, increase QOL, and decrease glycemic levels in participants with T2DM. TRIAL REGISTRATION: Chinese Clinical Trials Registration (Practical study of the appropriate technique for improvement of quality of life of the patients with type 2 diabetes in communities: ChiCTR-IOP-16008045).

Interaction of sleep quality and anxiety on quality of life in individuals with type 2 diabetes mellitus.

PURPOSE: Sleep disturbances and anxious symptoms are very common in people with type 2 diabetes mellitus(T2DM). This study aimed to assess the interactive effects of poor sleep quality and anxious symptoms on the quality of life of people with T2DM. METHODS: Nine hundred and forty-four participants with T2DM were enrolled in a cross-sectional study. Demographic and physiological characteristics were recorded. Each participant completed a Chinese version of the Pittsburgh Sleep Quality Index, the Patient Health Questionnaire-9 and General Anxiety Disorder questionnaire, and the Diabetes Specificity Quality of Life scale. The products of poor sleep quality and anxiety were added to a logistic regression model to evaluate the multiplicative interactions, expressed as the relative excess risk of interaction, the attributable proportion of interaction, and the synergy index. RESULTS: Poor sleep quality and anxiety symptoms were associated with reduced quality of life. There was a significant interaction between poor sleep quality and anxiety symptoms; this combined effect significantly reduced quality of life scores by 6.09-fold. The relative excess risk of interactions was 1.36. CONCLUSIONS: The combined effect of poor sleep quality and anxiety symptoms reduces quality of life in people with T2DM. TRIAL REGISTRATION: ChiCTR-IOP-16008045. Registered 3 March 2016. A clinical study to investigate gum infection in patients undergoing kidney dialysis.

Relationship between subjective sleep disturbances and glycaemia in Chinese adults with type 2 diabetes: findings from a 1.5-year follow-up study.

We wanted to determine whether subjective sleep disturbance was associated with serum glycated hemoglobin (HbA1c) in people with type 2 diabetes mellitus. In total, 944 randomly-selected people with diabetes completed the Chinese version of the Pittsburgh Sleep Quality Index (PSQI). Participants' glycaemia was assessed using HbA1c in March 2016 and September 2017. The PSQI score and the change in score(△PSQI), and the HbA1c and its change (△HbAlc) were analysed by sex and age (30-45, 46-60, 61-75, and 76-89 years). Associations between time point PSQI and △PSQI with static HbA1c and △HbA1c were analysed using multiple linear regression. The results showed subjective sleep disturbance among people with diabetes was not correlated with serum HbAlc (ß coefficient = 0.032, P = 0.103). However, cross-sectional multiple linear regression showed the relationship was present in women (ß coefficient = 0.163, P < 0.01). In multiple linear regression, △PSQI score was correlated with △HbAlc value (ß coefficient = 0.142, P < 0.01). The regression coefficient (ß) for the relationship between △PSQI score and △HbA1c in men was greater than that in women, and for age was ß61-75years < ß46-60years < ß30-45years. The strongest relationship between △PSQI and △HbA1c was in men aged 30-45 years (ß = 0.452, P < 0.01). Subjective sleep disturbance among people with diabetes was not related to glycaemic status in the whole sample, but there was a correlation in women. The change in subjective sleep disturbance correlated with the change in glycaemia, most strongly in younger participants, especially men aged 30-45 years.

Relationship between family behavior factors and overweight/obesity in primary and junior school students / 中国学校卫生

Objective@#To analyze the relationship between family behaviors and overweight/obesity in primary and junior school students aged 6-14 years in Xuzhou, and to provide a reference for a targeted measure to prevent and control overweight and obesity.@*Methods@#Using multistage stratified cluster random sampling, a total of 6 220 students aged 6-14 years old from 10 primary schools and 10 junior schools were investigated by a self-designed questionnaire. Chi-square and multivariate Logistic regression models were used to explore the relationship between family behaviors and overweight/obesity in primary and junior school students.@*Results@#The rate of overweight/obesity in primary and junior boys was higher than that in primary and junior girls. The rate of overweight/obesity in urban students was higher than that of rural students(P<0.05). The Chi-square analysis showed that overweight of parents, irregular breakfast, eating fast food, eating sweets, drinking sweetened beverage, long screen time and short sleep duration were risk family behavior factors of overweight/obesity in primary and junior boy students(P<0.05). The risk family behavior factors of overweight/obesity in primary and junior girl students were overweight of parents, irregular breakfast, eating fast food and eating sweets(P<0.05). The risk family behavior factors of overweight/obesity, such as drinking sweetened beverage and short sleep duration, were also related to primary girls(P<0.05), and long screen time was related to junior girls(P<0.05). The multivariate Logistic regression showed that such family behavior factors as irregular breakfast(OR-boy=1.58, OR-girl=1.74), eating fast food(OR-boy=1.37, OR-girl=1.11), eating sweets(OR-boy=1.85, OR-girl=1.52), drinking sweetened beverage(OR-boy=1.64, OR-girl=1.33) and short sleep duration(OR-boy=1.56, OR-girl=1.69) were positively correlated with the prevalence of overweight/obesity in primary students. Long screen time was also correlated to overweight/obesity primary boy students(OR=1.18). Family behavior factors for child overweight and obesity induded overweight of parents(OR-boy=1.29, OR-girl=1.23) and eating sweets(OR-boy=1.44, OR-girl=1.51). Irregular breakfast(OR=1.51), eating fast food(OR=1.22), drinking sweetened beverage (OR=1.75) and long visual screen time (OR=1.15) were also positively correlated with the prevalence of overweight/obesity in junior boy students.@*Conclusion@#Family behavior factors were positively correlated with the prevalence of overweight/obesity in primary and junior students. The influence of family behavior factors were different between primary and junior students. Behavioral interventions based on family should be adopted to prevent and control the overweight/obesity of children.

The prevalence of alcohol dependence and its association with hypertension: a population-based cross-sectional study4 in Xuzhou city, China.

BACKGROUND: To describe the prevalence of alcohol dependence and to explore the relationship between alcohol dependence and newly detected hypertension in China. METHODS: A multistage stratified cluster sampling method was used to obtain samples from February to June 2013. The Michigan Alcoholism Screening Test was used to estimate alcohol dependence level. A standard questionnaire measured other independent variables. Enumeration data were analyzed using chi-square; quantitative data were analyzed using t-tests. Spearman correlation analysis and multivariate logistic regression analysis were performed to identify the relationship between alcohol dependence and hypertension. RESULTS: The alcohol dependence rate was 11.56%; 22.02% of males (3854/17501) and 1.74% of females (324/18656) were classified as alcohol dependent. The newly detected hypertension rate was 9.46% (3422/36157). Significant associations were found between alcohol dependence levels and blood pressure (P < 0.01). Alcohol dependence was positively correlated with systolic blood pressure (r = 0.071, P < 0.01) and diastolic blood pressure (r = 0.077, P < 0.01) and was an independent risk factor for hypertension after adjusting for confounders (low alcohol dependence: odds ratio [OR] = 1.44, 95% confidence intervals [CI] = 1.14-1.81, P < 0.01; light alcohol dependence: OR = 1.35, 95% CI = 1.11-1.64, P < 0.01; medium alcohol dependence: OR = 1.83, 95% CI = 1.40-2.41, P < 0.01). CONCLUSION: Alcohol dependence was high and associated with hypertension. Health education and precautions against alcoholism should be implemented in Xuzhou city.

Prevalence and determinants of depressive and anxiety symptoms in adults with type 2 diabetes in China: a cross-sectional study.

OBJECTIVES: To evaluate the prevalence and determinants of anxiety and depression and to assess their impact on glycaemic control in participants with type 2 diabetes mellitus. DESIGN: A cross-sectional study. SETTING: Community-based investigation in Xuzhou, China. PARTICIPANTS: 893 Chinese men and women aged 18-84 years who fulfilled the inclusion criteria. METHODS: People with type 2 diabetes completed the Pittsburgh Sleep Quality Index and the Zung Self-Rating Anxiety and Depression Scales. Demographic and physiological characteristics were recorded. Multiple logistic regression was used to evaluate the combined effect of factors associated with anxiety and depression and to assess the effects of anxiety and depression on glycaemic control. RESULTS: The prevalence of depressive symptoms and anxiety symptoms was 56.1% and 43.6%, respectively. Multivariate logistic regression analysis indicated that anxiety symptoms were associated with being woman, low income, chronic disease, depressive symptoms and poor sleep quality. Depressive symptoms were associated with being woman, older age, low education level, being single, diabetes complications, anxiety symptoms and poor sleep quality. Glycaemic control was not related to anxiety symptoms (OR=1.31, 95% CIs 0.94 to 1.67) or depressive symptoms (OR=1.23, 95% CI 0.85 to 1.63). A combination of depressive symptoms and anxiety symptoms was associated with poor glycaemic control (relative excess risk due to interaction: 4.93, 95% CI 2.09 to 7.87; attributable proportion due to interaction: 0.27, 95% CI 0.12 to 0.45). CONCLUSIONS: There was a high prevalence of depressive and anxiety symptoms in this Chinese sample of participants, although depression and anxiety were not singly associated with glycaemic control. However, a combination of depressive and anxiety symptoms was negatively correlated with glycaemic control in participants with type 2 diabetes.

Combined effects of sleep quality and depression on quality of life in patients with type 2 diabetes.

BACKGROUND: Poor sleep quality and depression negatively impact the health-related quality of life of patients with type 2 diabetes, but the combined effect of the two factors is unknown. This study aimed to assess the interactive effects of poor sleep quality and depression on the quality of life in patients with type 2 diabetes. METHODS: Patients with type 2 diabetes (n = 944) completed the Diabetes Specificity Quality of Life scale (DSQL) and questionnaires on sleep quality and depression. The products of poor sleep quality and depression were added to the logistic regression model to evaluate their multiplicative interactions, which were expressed as the relative excess risk of interaction (RERI), the attributable proportion (AP) of interaction, and the synergy index (S). RESULTS: Poor sleep quality and depressive symptoms both increased DSQL scores. The co-presence of poor sleep quality and depressive symptoms significantly reduced DSQL scores by a factor of 3.96 on biological interaction measures. The relative excess risk of interaction was 1.08. The combined effect of poor sleep quality and depressive symptoms was observed only in women. CONCLUSIONS: Patients with both depressive symptoms and poor sleep quality are at an increased risk of reduction in diabetes-related quality of life, and this risk is particularly high for women due to the interaction effect. Clinicians should screen for and treat sleep difficulties and depressive symptoms in patients with type 2 diabetes.

Effects of sleep duration and sleep quality on prevalence of type 2 diabetes mellitus: A 5-year follow-up study in China.

OBJECTIVE: To explore the interactions of sleep quality and sleep duration on the development of type 2 diabetes mellitus (DM2) in Chinese adults. RESEARCH DESIGN AND METHODS: We randomly selected 11,842 Chinese subjects from the Xuzhou community of China and obtained self-reported quality and duration of sleep by questionnaire. DM2 was assessed by fasting blood glucose. Sleep quality was categorized as good, common, or poor. Sleep duration was measured by average hours of sleep per night. We evaluated interaction, relative excess risk of interaction (RERI), the attributable proportion (AP), and the synergy index (S) using a logistic regression model. RESULTS: The relative risk for the development of DM2 was higher in subjects with short sleep duration (1.67 [1.34-2.16]) or poor sleep quality (1.91 [1.31-2.74]) or long sleep duration (1.45 [1.02-1.77]). DM2 occurred more frequently with poor sleep quality combined with short sleep duration (odds ratio: 6.21; 95% confidence interval (CI): 2.78-11.81). RERI, AP, and S values (and their 95% CI) were 3.99 (1.41-7.76), 0.64 (0.45-0.76), and 5.15 (3.74-7.89) for the interaction between poor sleep quality and short sleep duration. In subjects with poor sleep quality combined with long sleep duration, the RERI, AP, and S values (and 95% CI) were 0.13 (-0.19 to 0.66), 0.07 (-0.35 to 0.18), and 1.19 (0.85-2.11). CONCLUSIONS: Interactions between poor sleep quality and short sleep duration were additive. Preventive measures should focus on short sleep duration and poor sleep quality.

Association of sleep quality and quality of life in type 2 diabetes mellitus: a cross-sectional study in China.

OBJECTIVE: The aim of this study is to investigate sleep quality and quality of life, and to assess the relationship between sleep quality and quality of life in Chinese patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS: 944 patients with T2DM were enrolled in our study. General characteristics and laboratory testing such as glycosylated hemoglobin A1c (HbA1c) were measured. Each patient completed a Chinese version of the Pittsburgh sleep quality index (PSQI) and the diabetes specificity quality of life scale (DSQL) questionnaires. A PSQI global score >7 was defined as poor sleep quality. A global DSQL score <40 was defined as a good quality of life. Multiple logistic regression analysis was used to examine the relationships between PSQI and DSQL. RESULTS: Poor quality of life in participants was associated with a longer duration of diabetes, a greater number of diabetes complications, no alcohol drinking, poor glycemic control and having depression and anxiety (all P<0.001). Of the participants, 33.6% of them were poor sleepers according to their PSQI. Poor sleepers had significantly lower DSQL (P<001). After adjustment for confounders, poor sleep quality was positively associated with a lower health-related quality of life (OR: 3.67, 95% CI: 1.30-10.33, P<0.001). CONCLUSIONS: Our results suggest that poor sleep is prevalent in T2DM and inversely associated with quality of life. It is necessary for primary health-care workers to include sleep related knowledge in diabetes self-management programs to improve sleep quality in diabetes patients.

[Interaction between quality and duration of sleep on the prevalence of type 2 diabetes].

OBJECTIVE: To explore the effects related to quality and duration of sleep and their interactions on the prevalence of type-2 diabetes (T2DM). METHODS: 9 622 people aged 18 years and over were recruited for our cross-sectional study during March 2013 to May 2013. Unconditional logistic regression was used to analyze the relationship between quality and duration of sleep on T2DM. Bootstrap was used to calculate the relative excess risk of interaction (RERI), the attributable proportion (AP) of interaction and the synergy index (SI). 95% confidence intervals (CI) of RERI, AP and SI were estimated. RESULTS: Concerning the comparison between cases and controls on both individual and total scores, other scores were all significantly different (P < 0.01), except for two items (time for falling asleep and drugs for hypnosis). The prevalence of T2DM in volunteers with poor sleeping quality was higher than that in volunteers with good sleeping quality (P < 0.01). Individuals with sleep duration <6 hours had a higher prevalence of T2DM, when compared with individuals with sleep duration of 6-8 hours (P < 0.01). After adjusting for age, gender, level of education, occupation, family history of diabetes, status on cigarette smoking, alcohol intake, physical activities and body mass index (BMI), the prevalence of T2DM appeared the highest in those with poor sleeping quality and short duration (OR = 4.78, 95% CI:3.32-6.99; P < 0.01), when compared with those who had good sleep quality and 6-8 h sleep duration. The risk of T2DM still increased in people who had poor sleep or long duration (OR = 1.92, 95% CI:1.18-3.31; P < 0.01). Values of RERI, AP and SI (with 95% CI) were 2.33 (1.23-8.79), 0.67(0.21-0.83) and 6.87 (2.33-10.75), respectively, for the interaction between poor sleep quality and short sleep duration, while 0.33 (-0.12-1.13), 0.17 (-0.03-0.51), 1.56 (0.76-2.74) for the interaction between good sleep quality and long sleep duration. CONCLUSION: Our results suggested that there were additive interactions between poor quality and shorter duration of sleep.

Salt intake, knowledge of salt intake, and blood pressure control in Chinese hypertensive patients.

A cross-sectional study involving 2502 subjects was conducted to evaluate salt intake, knowledge of salt intake, and blood pressure control in hypertensive patients. The blood pressure control rate was 33.5% among the hypertensive patients. Of the patients, 69.9% had salt intake higher than 6 g/d. Overall 35.0% knew the recommended salt intake, and 94.9% knew that "excess salt intake can result in hypertension." Altogether, 85.8% of patients had received health education related to a low-salt diet at some time. Patients who consumed less than 6 g/d of salt had a higher control rate than those who consumed more than 6 g/d (48.7% vs. 27.0%; χ(2) = 111.0; P < .001). Patients with knowledge of the recommended salt intake had a higher control rate than those without (45.8% vs. 26.9%; χ(2) = 91.3; P < .001). Our findings suggest a high salt intake and low blood pressure control rate among Chinese hypertensive patients. Knowledge of recommended salt intake is inappropriate for patients with education of a low-salt diet.

Interaction of sleep quality and sleep duration on impaired fasting glucose: a population-based cross-sectional survey in China.

OBJECTIVES: To explore the interactions of sleep quality and sleep duration and their effects on impaired fasting glucose (IFG) in Chinese adults. DESIGN: Cross-sectional survey. SETTING: Community-based investigation in Xuzhou, China. PARTICIPANTS: 15 145 Chinese men and women aged 18-75 years old who fulfilled the inclusion criteria. PRIMARY AND SECONDARY OUTCOME MEASURES: The Pittsburgh Sleep Quality Index was used to produce sleep quality categories of good, common and poor. Fasting blood glucose levels were assessed for IFG. Sleep duration was measured by average hours of sleep per night, with categories of <6, 6-8 and >8 h. The products of sleep and family history of diabetes, obesity and age were added to the logistic regression model to evaluate the addictive interaction and relative excess risk of interaction (RERI) on IFG. The attributable proportion (AP) of the interaction and the synergy index (S) were applied to evaluate the additive interaction of two factors. Bootstrap measures were used to calculate 95% CI of RERI, AP and S. RESULTS: The prevalence of IFG was greatest in those with poor sleep quality and short sleep duration (OR 6.37, 95% CI 4.66 to 8.67; p<0.001) compared with those who had good sleep quality and 6-8 h sleep duration, after adjusting for confounders. After adjusting for potential confounders RERI, AP and S values (and their 95% CI) were 1.69 (0.31 to 3.76), 0.42 (0.15 to 0.61) and 2.85 (2.14 to 3.92), respectively, for the interaction between poor sleep quality and short sleep duration, and 0.78 (0.12 to 1.43), 0.61 (0.26 to 0.87) and -65 (-0.94 to -0.27) for the interaction between good sleep quality and long sleep duration. CONCLUSIONS: The results suggest that there are additive interactions between poor sleep quality and short sleep duration.

[Time-series analysis on the relationship between air pollution and daily mortality in Beijing].

To quantitively evaluate the associations between ambient air pollutant and daily mortality of Beijing and to supply the scientific bases for formulating control measures. Air pollutants including CO, SO2, NOx, TSP, PM10. time series analysis Poisson regression was used to evaluate the relationship between cause-specific deaths and air pollutant, considering the potential confounding factors such as seasonal and long-term patterns, meteorological factors (air temperature, air humidity), as well as adjusting the influence of flu epidemics in winter of 1998. The results showed that in single-factor Poisson regression analysis, there is a significant positive correlation between the four pollutants and daily mortality except for the relationship between TSP and coronary heart disease deaths. In multi-factor Poisson regression analysis, when SO2 increase in 100 micrograms/m3, respiratory deaths, cardiovascular and cerebro-vascular deaths, coronary heart disease deaths and chronic obstructive pulmonary deaths increased by 4.21%, 3.97%, 10.68%, 19.22% respectively. Meanwhile, each 100 micrograms/m3 increase in TSP associated with 3.19% increase in the respiratory deaths and 0.62% increase in the cardiovascular and cerebrovascular deaths. It is suggested that air pollution is a risk factor for health and an increase of air pollution level might lead to a raise in daily mortality.