ObjectiveTo systematically review the correlation between sleep quality and social support of the elderly.MethodsDatabases including PubMed, MEDLINE, The Cochrane Library, Springerlink, ProQuest, CMB, CNKI, VIP, and WanFang Data were searched to collect studies on the correlation between sleep quality and social support of the elderly from January 1996 to January 2020. Two reviewers independently screened literature, extracted data and evaluated risk of bias of included studies. Meta-analysis was then performed using RevMan 5.3 software.ResultsA total of 9 studies involving 2 427 elderly people were included. The meta-analysis showed that the combined correlation coefficient between sleep quality and social support was -0.40 (95%CI ?0.54 to ?0.26). The correlation between sleep quality and social support of the elderly varied with the year of publication and sample size, however without regular change. The correlation coefficient of the elderly from institutions (hospital or pension institutions) was higher than that of the community (?0.33 vs. ?0.26); the correlation coefficient of the elderly with health problems was higher than those without health problems (?0.32 vs. ?0.25); the results measured by non-random sampling method were higher than those measured by random sampling (?0.37 vs. ?0.23); and the results measured by Pittsburgh sleep quality index (PSQI) and social support rating scale (SSRS) were higher than those measured by PSQI and perceived social support scale (PSSS) (?0.30 vs. ?0.13).ConclusionsThe higher the level of social support of the elderly in China, the lower the score of PSQI, and the better the sleep quality, in which there are differences in different sample sources and physical conditions.
Objective To understand the incidence of frailty in maintenance hemodialysis (MHD) patients, and to explore the correlation and influencing factors of frailty in MHD patients, so as to provide some basis for the intervention of frailty in MHD patients. Methods Patients who underwent MHD in the Department of Nephrology of West China Hospital of Sichuan University from January to March 2021 were selected. Frail scale and Pittsburgh Sleep Quality Index (PSQI) were used for evaluation, and the influencing factors of frail in patients with MHD and its correlation with frail were analyzed. Results A total of 141 patients with MHD were included, including 57 cases without frailty (40.43%), 71 cases in early frailty (50.35%), and 13 cases in frailty (9.22%). 54 cases (38.30%) had very good sleep quality, 56 cases (39.72%) had good sleep quality, 24 cases (17.02%) had average sleep quality, and 7 cases (4.96%) had very poor sleep quality. The frailty of MHD patients was positively correlated with age (rs=0.265, P=0.002), PSQI (rs=0.235, P=0.005) and magnesium (rs=0.280, P=0.001). Logistic regression analysis showed that the influencing factors of MHD patients’ frailty were gender [odds ratio (OR) =4.321, 95%confidence interval (CI) (1.525, 12.243), P=0.006], PSQI [OR=1.110, 95%CI (1.009, 1.222), P=0.032], magnesium [OR=122.072, 95%CI (4.752, 3 135.528), P=0.004], hypertension [OR=0.112, 95%CI (0.023, 0.545), P=0.007] and other diseases [OR=0.102, 95%CI (0.019, 0.552), P=0.008]. Conclusions The incidence of frailty in MHD patients is high. Gender, PSQI, magnesium, hypertension and other diseases are the influencing factors of frailty in MHD patients, and there is a correlation between frailty and sleep. It is suggested that renal medical staff should pay more attention to the assessment of MHD frailty and sleep, and carry out multi-disciplinary personalized intervention to improve the quality of life of MHD patients.
Objective To analyze the causes of missed diagnosis of sleep apnea hypopnea syndrome ( SAHS) . Methods 42 missed diagnosed cases with SAHS from May 2009 to May 2011 were retrospectively analyzed and related literatures were reviewed. Results The SAHS patients often visited the doctors for complications of SAHS such as hypertension, diabetes mellitus, metabolic syndrome, etc. Clinical misdiagnosis rate was very high. Lack of specific symptoms during the day, complicated morbidities, and insufficient knowledge of SAHS led to the high misdiagnosis rate and the poor treatment effect of patients with SAHS. Conclusion Strengthening the educational propaganda of SAHS, detail medical history collection, and polysomnography monitoring ( PSG) as early as possible can help diagnose SAHS more accurately and reduce missed diagnosis.
由于高血壓的高患病率與高致殘致死率, 已經成為我國重點防治的心血管疾病和社會普遍關注的重大公共衛生問題之一。大量流行病學、臨床和基礎研究已證實睡眠呼吸暫停低通氣綜合征( sleep apnea-hypopnea syndrome, SAHS) 與高血壓發病和療效關系密切[ 1-8 ] , 是高血壓發生的主要病因之一, 由此“睡眠呼吸暫停相關性高血壓”一詞便應運而生[ 9-1 0] , 它是指由SAHS 引發和加重的高血壓。本期刊載的“阻塞性睡眠呼吸暫停相關性高血壓臨床診斷和治療專家共識”( 以下簡稱共識) , 為睡眠呼吸暫停相關性高血壓的診治提供了規范性的指導意見, 對推動我國該領域的防治水平有重要作用。我們期望“共識”能為讀者認識和防治睡眠呼吸暫停相關性高血壓提供必要的指導和幫助, 使我國為數眾多的睡眠呼吸暫停相關性高血壓患者得到規范的診治。
Objective To conduct a scoping review on the influencing factors of comorbid sleep disorders in adolescents with epilepsy, so as to provide a reference basis for clinical prevention and early intervention. MethodsFollowing the methodological framework of scoping review, relevant studies were systematically searched in PubMed, Web of Science, Cochrane Library, Embase, Chinese Biomedical Literature Database, CNKI, Wanfang, and VIP Database. The retrieval time limit was from the establishment of each database to April 30, 2025. The included literatures were summarized and analyzed. ResultsA total of 17 literatures were included, and 17 influencing factors related to comorbid sleep disorders in adolescents with epilepsy were identified, which were mainly divided into six categories: demographic factors, characteristics and severity of epilepsy, treatment-related factors, psychiatric comorbidities, organic neurological damage and neurocognitive development and psychosocial and environmental factors. ConclusionSleep disorders are relatively common in adolescents with epilepsy, and their influencing factors are multifaceted.There is a lack of large-sample research on this population in our country, especially the lack of discussion of the causal relationship between influencing factors and sleep disorders, resulting in a lack of basis for early intervention. Future research urgently needs to systematically identify key influencing factors and explore their internal mechanisms through large-sample surveys, so as to lay a solid scientific foundation for the construction of evidence-based intervention programs.
The method of using deep learning technology to realize automatic sleep staging needs a lot of data support, and its computational complexity is also high. In this paper, an automatic sleep staging method based on power spectral density (PSD) and random forest is proposed. Firstly, the PSDs of six characteristic waves (K complex wave, δ wave, θ wave, α wave, spindle wave, β wave) in electroencephalogram (EEG) signals were extracted as the classification features, and then five sleep states (W, N1, N2, N3, REM) were automatically classified by random forest classifier. The whole night sleep EEG data of healthy subjects in the Sleep-EDF database were used as experimental data. The effects of using different EEG signals (Fpz-Cz single channel, Pz-Oz single channel, Fpz-Cz + Pz-Oz dual channel), different classifiers (random forest, adaptive boost, gradient boost, Gaussian na?ve Bayes, decision tree, K-nearest neighbor), and different training and test set divisions (2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, single subject) on the classification effect were compared. The experimental results showed that the effect was the best when the input was Pz-Oz single-channel EEG signal and the random forest classifier was used, no matter how the training set and test set were transformed, the classification accuracy was above 90.79%. The overall classification accuracy, macro average F1 value, and Kappa coefficient could reach 91.94%, 73.2% and 0.845 respectively at the highest, which proved that this method was effective and not susceptible to data volume, and had good stability. Compared with the existing research, our method is more accurate and simpler, and is suitable for automation.
ObjectiveTo explore the influence of 3D printing assisting educational intervention on the anxiety and sleep outcomes in the patients with trauma. MethodA total of 40 patients were selected between October 2014 and June 2015. The patients were randomly divided into the intervention group and control group with 20 patients in each. The outcomes from admitted to the 7th day after the surgery were evaluated, including visual analogue scale (VAS) scores, state-trait anxiety inventory (STAI) score, Likert score, and the condition of anxiety, pain, and sleep outcomes. ResultsThe differences in VAS scores, STAI scores, and Likert scores between the two groups were significant (P<0.05). Conclusions3D printing assisting educational intervention is a useful intervention that can improve post-operative outcomes for the patients with trauma.
Objective To summarize and analyze the clinical and video-EEG (VEEG) characteristics of adult sleep-related epilepsy, so as to provide evidence for clinical diagnosis, differential diagnosis and treatment. Methods The clinical data, routine EEG and long-term VEEG of 187 adult patients with sleep-related epilepsy treated in Department of Neurology, Xiangya Hospital, Central South University from January 2017 to December 2017 were retrospectively analyzed by χ2 test. Results Clinical manifestations: The duration of sleep-related epilepsy in 187 adults was concentrated in 1～10 years (101 cases, 54.01%); the frequency of seizures was mainly from several to dozens of times a year (99 cases, 52.94%); 119 cases (63.64%) had two or more types of seizures. Among the patients, 121 cases (39.29%) had focal origin, 152 cases (49.35%) had bilateral tonic clonus and 110 cases (58.82%) were treated with two or more drugs. EEG results: ① The detection rate of epileptiform discharges in routine EEG was 22.78%, and that in long-term video EEG was 80.43%. There was significant difference between the two methods (P< 0.01); ② Eighteen epileptiform discharges were monitored by routine EEG during interparoxysmal period and 111 epileptiform discharges were monitored by video EEG; and ③ Fifty-six epileptic events were monitored and all occurred in the process of long-term VEEG monitoring, 50 of them occurred in sleep (89.29%) and 6 in awake (10.71%); 45 cases (80.36%) were diagnosed as epileptic seizures, 9 cases (16.07%) were diagnosed as non-epileptic seizures, and 2 cases (3.57%) could not be determined. ④ The detection rate of epileptic discharges during sleep was higher than that during awake period in long-term VEEG monitoring (P< 0.01). The detection rate of epileptiform discharges in NREM stage I–II was the highest in sleep stage. Conclusion Sleep-related epilepsy in adults has certain clinical features and EEG manifestations. Compared with conventional EEG, long-term video-EEG can improve the detection rate of epileptiform discharges, provide diagnostic basis for the qualitative analysis of sleep-related seizures, and reflect the relationship between epileptiform discharges and sleep, and provide basis for the clinical diagnosis and treatment of sleep-related epilepsy in adults.
ObjectiveTo observe the correlation between obstructive sleep apnea syndrome (OSAS) and central serous chorioretinopathy (CSC).MethodsFrom October 2016 to December 2018, 50 cases of CSC patients (CSC group) and 50 healthy people (control group) matched by age and sex who were diagnosed in the ophthalmological examination of Xi’an No.3 Hospital were included in the study. According to the course of the disease, CSC was divided into acute phase and chronic phase, with 20 and 30 cases respectively. The average age (Z=1.125) and body mass index (BMI) (Z=0.937) of the two groups were compared, and the difference was not statistically significant (P＞0.05); the age of patients with different courses of CSC (Z=1.525) and gender composition ratio (χ2=0.397) and BMI (Z=1.781) were compared, the difference was not statistically significant (P＞0.05). The Berlin questionnaire was used to assess the OSAS risk of subjects in the CSC group and the control group; polysomnography was used to monitor the apnea-hypopnea index (AHI) and minimum blood oxygen saturation (MOS) during night sleep. OSAS diagnostic criteria: typical sleep snoring, daytime sleepiness, AHI (times/h) value ≥ 5. The severity of OSAS was classified as mild OSAS: 5≤AHI＜15; moderate OSAS: 15≤AHI＜30; severe OSAS: AHI≥30. Non-normally distributed measurement data were compared by rank sum test; count data were compared by χ2 test. Spearman correlation analysis was performed on the correlation between OSAS and CSC.ResultsThe AHI data in the CSC group and the control group were 17.46±3.18 and 15.72±4.48 times/h, respectively; the MOS were (83.48±4.68)% and (87.40±3.82)%, respectively; those diagnosed with OSAS were respectively 36 (72.00%, 36/50) and 13 (26.00%, 13/50) cases. AHI (Z=0.312), MOS (Z=0.145), and OSAS incidence (χ2=21.17) were compared between the two groups of subjects, and the differences were statistically significant (P=0.028, 0.001,＜0.001). The AHI of acute and chronic CSC patients were 15.95±3.02 and 18.47±2.92 times/h; the MOS were (86.10±11.07)% and (81.73±4.58)%, respectively. There were statistically significant differences in AHI (Z=0.134) and MOS (Z=0.112) in patients with different course of disease (P=0.005, 0.001). The results of Spearman correlation analysis showed that OSAS and CSC were positively correlated (r=0.312, P=0.031).ConclusionOSAS may be a risk factor for the onset of CSC.
Objectives To study the characteristics and influencing factors of sleep disorder in patients with epilepsy. Methods One hundred and eighty-four patients with epilepsy who were admitted to the outpatient department and the epilepsy center in the Second Affiliated Hospital of Zhejiang University from October 2016 to October 2017 were enrolled. Their clinical data were collected in detail and their sleep related scales were evaluated. Sleep related assessment tools: Chinese version of the Pittsburgh sleep quality index scale (PSQI), the Epworth sleepiness scale (ESS), Berlin Questionnaire (BQ), Quality Of Life In People With Epilepsy-31 (QOLIE-31), Beck Anxiety Inventory (BAI) and Beck Depression Inventory(BDI). Results Among the 184 cases of patients with epilepsy, 100 cases were male (54.3%), 84 cases were female (45.7%), 35 cases (19.0%) had sleep disorders, 89 cases (48.4%) with poor quality of life, 23 cases (12.5%) with anxiety, 47 cases (25.5%) with depression, 59 cases (32.1%) had daytime sleepiness, and 30 cases (16.3%) with OSAS. there were statistically significant differences in age, history of hypertension, seizure frequency, quality of life , anxiety and depression in epilepsy patients with sleep disorder compared those without sleep disorder (P<0.05). The seizure frequency, quality of life, anxiety and depression were analyzed by logistic regression analysis, suggesting that seizure frequency (P=0.011) and depression (P<0.001) are independent risk factors of sleep disorders. Conclusions Epileptic patients with sleep disorder have higher frequency of seizures, poorer quality of life, and are more likely to be associated with anxiety and depression, and the frequency and depression are independent risk factors of sleep disorder in patients with epilepsy.