Objective:To compare the effects of standard cognitive behavioral therapy for insomnia (CBT-I) and enhanced cognitive behavioral therapy for insomnia(CBT-I Plus) in patients with chronic insomnia disorder comorbid anxiety or depressive symptoms.Methods:This prospective study included 148 patients with chronic insomnia disorder and anxiety/depression symptoms who were treated at the Sleep Disorder clinic of Shanghai Mental Health Center between July 2020 and August 2023. Participants (56 males, 92 females; aged 18-65 years, mean age 35.08±10.30 years) were randomly assigned in a 1∶2 ratio to the CBT-I group ( n=54) or CBT-I Plus group ( n=94). The CBT-I Plus group received additional treatments targeting anxiety and depressive symptoms. Treatment lasted 8 weeks, with assessment conducted at baseline, weeks 2, 4, and 8. Depression severity was measured using the 17-item Hamilton Depression Rating Scale (HAMD 17), anxiety severity with the Hamilton Anxiety Scale (HAMA), and sleep quality with the Pittsburgh Sleep Quality Index (PSQI). Paired sample t-tests were used to evaluate within-group changes, repeated-measures ANOVA compared treatment effects between groups, and ANCOVA was employed to adjust for confounding variables. Results:Significant reductions in PSQI, HAMD 17, and HAMA scores were observed in both groups after treatment: CBT-I group: PSQI ((14.15±2.54) vs. (7.50±3.35), t=13.25), HAMD 17 ((14.70±4.09) vs. (7.40±4.61), t=9.33), and HAMA ((14.94±4.11) vs. (5.56±3.67), t=12.38) (all P<0.001).CBT-I Plus group: PSQI ((14.87±3.01) vs. (7.19±3.86), t=18.75), HAMD 17 ((16.84±3.91) vs. (6.84±4.79), t=17.42), and HAMA ((15.57±3.93) vs. (6.10±4.57), t=18.39) (all P<0.001). After adjusting for HAMD 17 scores and medication use, no statistically significant between-group differences were observed in changes in PSQI, HAMD 17, and HAMA scores ( P>0.05). A significant time-by-group interaction was found for the PSQI daytime dysfunction subscale ( F=4.87, P<0.01). Conclusion:Both CBT-I and CBT-I Plus improve sleep and emotional symptoms in patients with chronic insomnia disorder and comorbid anxiety/depression symptoms. However, CBT-I Plus has no significant advantages over standard CBT-I. Further studies are needed to refine the timing and content of interventions.

Objective:To compare the effects of standard cognitive behavioral therapy for insomnia (CBT-I) and enhanced cognitive behavioral therapy for insomnia(CBT-I Plus) in patients with chronic insomnia disorder comorbid anxiety or depressive symptoms.Methods:This prospective study included 148 patients with chronic insomnia disorder and anxiety/depression symptoms who were treated at the Sleep Disorder clinic of Shanghai Mental Health Center between July 2020 and August 2023. Participants (56 males, 92 females; aged 18-65 years, mean age 35.08±10.30 years) were randomly assigned in a 1∶2 ratio to the CBT-I group ( n=54) or CBT-I Plus group ( n=94). The CBT-I Plus group received additional treatments targeting anxiety and depressive symptoms. Treatment lasted 8 weeks, with assessment conducted at baseline, weeks 2, 4, and 8. Depression severity was measured using the 17-item Hamilton Depression Rating Scale (HAMD 17), anxiety severity with the Hamilton Anxiety Scale (HAMA), and sleep quality with the Pittsburgh Sleep Quality Index (PSQI). Paired sample t-tests were used to evaluate within-group changes, repeated-measures ANOVA compared treatment effects between groups, and ANCOVA was employed to adjust for confounding variables. Results:Significant reductions in PSQI, HAMD 17, and HAMA scores were observed in both groups after treatment: CBT-I group: PSQI ((14.15±2.54) vs. (7.50±3.35), t=13.25), HAMD 17 ((14.70±4.09) vs. (7.40±4.61), t=9.33), and HAMA ((14.94±4.11) vs. (5.56±3.67), t=12.38) (all P<0.001).CBT-I Plus group: PSQI ((14.87±3.01) vs. (7.19±3.86), t=18.75), HAMD 17 ((16.84±3.91) vs. (6.84±4.79), t=17.42), and HAMA ((15.57±3.93) vs. (6.10±4.57), t=18.39) (all P<0.001). After adjusting for HAMD 17 scores and medication use, no statistically significant between-group differences were observed in changes in PSQI, HAMD 17, and HAMA scores ( P>0.05). A significant time-by-group interaction was found for the PSQI daytime dysfunction subscale ( F=4.87, P<0.01). Conclusion:Both CBT-I and CBT-I Plus improve sleep and emotional symptoms in patients with chronic insomnia disorder and comorbid anxiety/depression symptoms. However, CBT-I Plus has no significant advantages over standard CBT-I. Further studies are needed to refine the timing and content of interventions.

Paradoxical insomnia(Para-I),also known as pseudoinsomnia or sleep state misperception,is a condition in which the patient complains of severe insomnia but has no objective evidence of sleep disorder,and daytime functioning may be disrupted disproportionately to the degree of patient-reported sleep loss.Para-I is characterized by overestimation of sleep latency(SL)and underestimation of total sleep time(TST).Incorrect assessment of sleep quality hinders the diagnosis,evaluation of severity,and assessment of clinical efficacy of sleep disorders.The pathogenesis of Para-I remains unclear,but may be related to factors such as depression,anxiety,personality traits,social relationships and specific changes in brain structure and function.Studies on the polysomnography(PSG)of the patients with insomnia have found that changes in non-rapid eye movement(NREM)and rapid eye movement(REM)sleep may be related to the degree of subjective-objective sleep discrepancy.PSG is a valuable diagnostic tool for sleep disorders.It allows for the analysis of sleep structure and related physiological and behavioral changes by monitoring various parameters,including electroencephalogram(EEG),electromyogram(EMG),electrooculogram(EOG),oro-nasal airflow,thoracic and abdominal respiratory motions,oxygen saturation,electrocardiogram(ECG)and snoring.In recent years,studies have increasingly explored the sleep EEG and treatment of Para-I with PSG,resulting in significant progress.This article reviews the latest advances in the electroencephalographic characteristics and treatment of Para-I,providing new ideas for precise treatment.

Insomnia disorder is a common clinical mental disorder.Currently, clinical subtyping of insomnia disorder relies primarily on symptomatic descriptions, lacking objective measures and subtyping-based treatment approaches. In recent years, increasing attention has been drawn to sleep electroencephalography (EEG) as a valuable tool for observing abnormal sleep architecture and continuity of insomnia disorder. Sleep EEG analysis holds the potential to elucidate the underlying biological mechanisms of insomnia disorder, facilitating data-driven subtyping and enhancing personalized therapeutic strategies.Five types of sleep EEG subtypes of insomnia disorder were systematically searched and summarized: classifications derived from objective sleep duration, power spectral characteristics, cyclic alternating pattern, spindle and microarousal.EEG characteristics of each subtype and clinical outcomes are discussed.This review aims to provide evidence-based insights for clinical subtyping and personalized treatment of insomnia disorder.

Objective:The current study aims to explore the factors related to the efficacy of cognitive behavior therapy for insomnia (CBT-I) from the perspective of patients and to provide references for more effective implementation of CBT-I.Methods:Using qualitative research methods, 21 insomnia patients with depression/anxiety were treated with CBT-I for 8 consecutive times. Pittsburgh Sleep Quality Index (PSQI), Hamilton Depression Scale (HAMD 17), and Hamilton Anxiety Scale (HAMA) were assessed at baseline and the end of the 8th week of treatment. The paired sample t-test was conducted. Semi-structured interviews were performed at week 2, week 4, and week 8 respectively and thematic analysis was used to code and analyze the interview data. Results:Compared with baseline data, the symptoms of insomnia (13.6±2.0 vs. 6.9±2.4), depression (14.6±5.5 vs. 5.0±3.6), and anxiety (17.2±3.4 vs. 5.3±3.9) were significantly improved after 8 weeks of CBT-I treatment ( t=-3.31, -3.19, -2.94, all P<0.01). The patient factors influencing the efficacy of CBT-I were treatment expectation and approval, motivation, compliance, and internalization of treatment content. The therapist factors were professionalism, well-directed, treatment style, supervision, and giving hope. Conclusion:Compliance and high levels of participation of the patients can benefit the treatment efficacy of CBT-I. Therapists should have sufficient experience, stimulate patients′ motivation, improve patients′ compliance, and carry out adequate psychological education in the early stage to increase the efficacy of CBT-I.

Objective:To investigate the physical and mental experience, treatment compliance and use barriers of patients with insomnia in using digital cognitive behavioral therapy for insomnia (dCBT-I) in order to provide qualitative evidence for the development and application optimization of the dCBT-I technology paradigm.Methods:From July to November 2021, a semi-structured interview outline was used to conduct in-depth interviews with the dCBT-I users ( n=10) to record their original feelings about the use of dCBT-I. Interpretative phenomenology's text analysis was used to explore the participants' experience and cognition of dCBT-I. Results:Text analysis and key information calibration were carried out on the verbatim transcripts of semi-structured interview recordings, and three core themes were extracted, namely stickiness factor, use barrier and optimization direction, as well as eight sub-themes, namely professionalism, accessibility, benefit experience, difficulty in task execution, instruction generalization, difficulty in software operation, enrich treatment content and personalized guidance.Conclusion:The present study showed that participants were receptive to the dCBT-I intervention and would be benefited from it.However, dCBT-I still needs to be optimized and improved to reduce the operating difficulty and explore more appropriate timing of manual intervention.

Sleep staging is the basis for solving sleep problems. There's an upper limit for the classification accuracy of sleep staging models based on single-channel electroencephalogram (EEG) data and features. To address this problem, this paper proposed an automatic sleep staging model that mixes deep convolutional neural network (DCNN) and bi-directional long short-term memory network (BiLSTM). The model used DCNN to automatically learn the time-frequency domain features of EEG signals, and used BiLSTM to extract the temporal features between the data, fully exploiting the feature information contained in the data to improve the accuracy of automatic sleep staging. At the same time, noise reduction techniques and adaptive synthetic sampling were used to reduce the impact of signal noise and unbalanced data sets on model performance. In this paper, experiments were conducted using the Sleep-European Data Format Database Expanded and the Shanghai Mental Health Center Sleep Database, and achieved an overall accuracy rate of 86.9% and 88.9% respectively. When compared with the basic network model, all the experimental results outperformed the basic network, further demonstrating the validity of this paper's model, which can provide a reference for the construction of a home sleep monitoring system based on single-channel EEG signals.
China ; Sleep Stages ; Sleep ; Electroencephalography ; Databases, Factual

Insomnia disorder is the most common sleep-wake disorder,and long-term insomnia has a serious negative impact on the physical and mental health of individuals.It is crucial for researchers and clinicians to select appropriate measurement tools as evaluative indicators for insomnia.There are some commonly used insomnia assessment scales in the world,including Pittsburgh Sleep Quality Index(PSQ1),Insomnia Severity Index(ISI),etc.These scales are widely used to assess insomnia symptoms and sleep quality,providing researchers and clinicians with reliable quantitative tools.In addition to conventional insomnia assessment scales,some scales evaluate sleep cognition,sleep hygiene,and sleep conditions of different groups of people.Domestic scholars are actively developing sleep assessment tools suitable for the Chinese population,which also include sleep assessment for special groups.In addition,some sleep assessment with traditional Chinese medicine characteristics have also been developed to meet the needs of integrated traditional Chinese and Western medicine treatment.During the process of scale development,researchers should clarify the purpose of scale,select appropriate psychometric methods,and emphasize the reliability and validity of the scale.Furthermore,it is important to develop scales that can differentiate subtypes of insomnia and enhance the diversity of insomnia-related measures.This article summarizes the current situation of development of Chinese versions of insomnia-related scales,and provides evaluation and future prospects for existing scales.

Polysomnography (PSG) monitoring is an important method for clinical diagnosis of diseases such as insomnia, apnea and so on. In order to solve the problem of time-consuming and energy-consuming sleep stage staging of sleep disorder patients using manual frame-by-frame visual judgment PSG, this study proposed a deep learning algorithm model combining convolutional neural networks (CNN) and bidirectional gate recurrent neural networks (Bi GRU). A dynamic sparse self-attention mechanism was designed to solve the problem that gated recurrent neural networks (GRU) is difficult to obtain accurate vector representation of long-distance information. This study collected 143 overnight PSG data of patients from Shanghai Mental Health Center with sleep disorders, which were combined with 153 overnight PSG data of patients from the open-source dataset, and selected 9 electrophysiological channel signals including 6 electroencephalogram (EEG) signal channels, 2 electrooculogram (EOG) signal channels and a single mandibular electromyogram (EMG) signal channel. These data were used for model training, testing and evaluation. After cross validation, the accuracy was (84.0±2.0)%, and Cohen's kappa value was 0.77±0.50. It showed better performance than the Cohen's kappa value of physician score of 0.75±0.11. The experimental results show that the algorithm model in this paper has a high staging effect in different populations and is widely applicable. It is of great significance to assist clinicians in rapid and large-scale PSG sleep automatic staging.
Humans ; Polysomnography ; China ; Sleep Stages ; Sleep ; Algorithms

Objective:The current study aims to explore the factors related to the efficacy of cognitive behavior therapy for insomnia (CBT-I) from the perspective of patients and to provide references for more effective implementation of CBT-I.Methods:Using qualitative research methods, 21 insomnia patients with depression/anxiety were treated with CBT-I for 8 consecutive times. Pittsburgh Sleep Quality Index (PSQI), Hamilton Depression Scale (HAMD 17), and Hamilton Anxiety Scale (HAMA) were assessed at baseline and the end of the 8th week of treatment. The paired sample t-test was conducted. Semi-structured interviews were performed at week 2, week 4, and week 8 respectively and thematic analysis was used to code and analyze the interview data. Results:Compared with baseline data, the symptoms of insomnia (13.6±2.0 vs. 6.9±2.4), depression (14.6±5.5 vs. 5.0±3.6), and anxiety (17.2±3.4 vs. 5.3±3.9) were significantly improved after 8 weeks of CBT-I treatment ( t=-3.31, -3.19, -2.94, all P<0.01). The patient factors influencing the efficacy of CBT-I were treatment expectation and approval, motivation, compliance, and internalization of treatment content. The therapist factors were professionalism, well-directed, treatment style, supervision, and giving hope. Conclusion:Compliance and high levels of participation of the patients can benefit the treatment efficacy of CBT-I. Therapists should have sufficient experience, stimulate patients′ motivation, improve patients′ compliance, and carry out adequate psychological education in the early stage to increase the efficacy of CBT-I.