Disappearance and dissemination of sleep symptoms: the importance of sleep medicine expertise for psychiatry. A comment on Forbes et al.

Commentary of 'Elemental psychopathology: distilling constituent symptoms and patterns of repetition in the diagnostic criteria of the DSM-5' Vincent P. Martin 1, Régis Lopez 2,3, Jean-Arthur Micoulaud-Franchi 4,5, Christophe Gauld 4,6,.

Revised European guidelines for the accreditation of sleep medicine centres.

The accreditation of sleep centres aims to ensure high-quality diagnosis and management of sleep centres. European accreditation standards were introduced in 2006, and were aimed at centres offering inpatient polysomnography and vigilance tests (Mean Sleep Latency Test and Maintenance of Wakefulness Test). Since then, the practice of sleep medicine has evolved, with greater use of ambulatory polysomnography and polygraphy. As a result, in many sleep centres, actual clinical practice, although of a high standard, is no longer in accordance with the published guidelines. The current criteria have been revised with the introduction of level-based criteria. Level 1 and 2 centres offer full diagnostic testing in a laboratory-based setting. Level 1 practices will usually be university affiliated, and have a full teaching and active research role. Level 3 and 4 practices may offer both inpatient and ambulatory testing. Level 3 practices perform polysomnography, while level 4 practices (usually monodisciplinary and focussed on sleep apnea) perform polygraphy only. The role of the medical and paramedical team, training, appropriate equipment, patient care pathways and patient management according to national/European recommendations is underlined for accreditation at each level. It is anticipated that the guidelines will be reviewed and if necessary revised after 4 years.

Predictability of sleep in insomnia: sleep patterns of patients from a sleep psychology clinic.

The present study aims at identifying sleep patterns in insomnia in a clinical sample using three strategies to define poor nights. Sleep diaries and self-reported questionnaires were collected from 77 clinical patients with insomnia. The conditional probabilities of observing a poor night after 1, 2, or 3 consecutive poor nights were computed according to three strategies with same criteria for sleep onset latency, wake after sleep onset, and sleep efficiency, but varying criterion for total sleep time. Latent profile analyses were conducted to derive sleep patterns. Uni- and multivariate analyses were conducted to characterise the sleep patterns identified. A total of 1586 nights were analysed. The strategy used significantly influenced the average percentage of reported poor nights. Two to three sleep patterns were derived per strategy. Within each strategy, sleep patterns differed from each other on sleep variables and night-to-night variability. Results suggest the existence of sleep patterns in insomnia among individuals consulting in psychological clinics. Adding a total sleep time of 6-h cut-off as a criterion to define poor nights increases the accuracy of the strategy to define poor night and allows to identify sleep patterns of poor nights in insomnia.

Insights from the 2nd China intelligent sleep staging competition.

STUDY OBJECTIVES: Artificial intelligence (AI) is quickly advancing in the field of sleep medicine, which bodes well for the potential of actual clinical use. In this study, an analysis of the 2nd China Intelligent Sleep Staging Competition was conducted to gain insights into the general level and constraints of AI-assisted sleep staging in China. METHODS: The outcomes of 10 teams from the children's track and 13 teams from the adult track were investigated in this study. The analysis included overall performance, differences between five different sleep stages, variations across subjects, and performance during stage transitions. RESULTS: The adult track's accuracy peaked at 80.46%, while the children's track's accuracy peaked at 88.96%. On average, accuracy rates stood at 71.43% for children and 68.40% for adults. All results were produced within a mere 5-min timeframe. The N1 stage was prone to misclassification as W, N2, and R stages. In the adult track, significant differences were apparent among subjects (p < 0.05), whereas in the children's track, such differences were not observed. Nonetheless, both tracks experienced a performance decline during stage transitions. CONCLUSIONS: The computational speed of AI is remarkably fast, simultaneously holding the potential to surpass the accuracy of physicians. Improving the machine learning model's classification of the N1 stage and transitional periods between stages, along with bolstering its robustness to individual subject variations, is imperative for maximizing its ability in assisting clinical scoring.

Performance of artificial intelligence chatbots in sleep medicine certification board exams: ChatGPT versus Google Bard.

PURPOSE: To conduct a comparative performance evaluation of GPT-3.5, GPT-4 and Google Bard in self-assessment questions at the level of the American Sleep Medicine Certification Board Exam. METHODS: A total of 301 text-based single-best-answer multiple choice questions with four answer options each, across 10 categories, were included in the study and transcribed as inputs for GPT-3.5, GPT-4 and Google Bard. The first output responses generated were selected and matched for answer accuracy against the gold-standard answer provided by the American Academy of Sleep Medicine for each question. A global score of 80% and above is required by human sleep medicine specialists to pass each exam category. RESULTS: GPT-4 successfully achieved the pass mark of 80% or above in five of the 10 exam categories, including the Normal Sleep and Variants Self-Assessment Exam (2021), Circadian Rhythm Sleep-Wake Disorders Self-Assessment Exam (2021), Insomnia Self-Assessment Exam (2022), Parasomnias Self-Assessment Exam (2022) and the Sleep-Related Movements Self-Assessment Exam (2023). GPT-4 demonstrated superior performance in all exam categories and achieved a higher overall score of 68.1% when compared against both GPT-3.5 (46.8%) and Google Bard (45.5%), which was statistically significant (p value < 0.001). There was no significant difference in the overall score performance between GPT-3.5 and Google Bard. CONCLUSIONS: Otolaryngologists and sleep medicine physicians have a crucial role through agile and robust research to ensure the next generation AI chatbots are built safely and responsibly.

Undergraduate dental sleep medicine teaching at German university dental schools - a questionnaire-based survey.

PURPOSE: Diagnosing and treating obstructive sleep apnea (OSA) requires fundamental understanding of sleep medicine, including training and clinical experience. So far, dental sleep medicine (DSM) has not yet become a mandatory part of dental education in Germany. This questionnaire-based survey for both lecturers and students aimed to evaluate DSM education among undergraduate students. METHODS: A structured questionnaire was sent to the managing directors and student councils of all 30 German university dental schools. The questionnaire contained 13 questions on teaching quantity and content, lecturers' knowledge, and future interest in DSM. For each university dental school, only one questionnaire should be completed by the student council and the managing director. A scoring system assessed lecturers' knowledge based on clinical experience and qualifications. Descriptive data and correlation coefficients were calculated (P < 0.05). RESULTS: The responses of 24 lecturers (80%) and 28 students (93.3%) could be evaluated. DSM was reported to be included in the curriculum by 14 lecturers (58.3%) and 4 students (14.3%). Mean teaching hours per semester were 1.4 ± 1.4 h (lecturers) and 0.2 ± 0.6 h (students) accordingly. Greater knowledge of lecturers in DSM was positively correlated with the inclusion of DSM in the curriculum (P = 0.022) and with the number of teaching hours per semester (P = 0.001). CONCLUSION: Postgraduate education and incorporating DSM knowledge into undergraduate education ("Teach the Teacher") seems to play a key role in fundamentally training future dentists in this field.

Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM).

Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual's sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars-three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors.

Investigating the Link between Circadian Clock Gene Expressions, Chronotype, Insomnia, and Daytime Sleepiness in Patients with Obstructive Sleep Apnea.

INTRODUCTION: This study aimed to investigate the relationship between obstructive sleep apnea (OSA), circadian rhythms, and individual sleep-wake preferences, as measured by chronotype, and to assess the association between circadian clock gene expression and subjective sleep-related variables. METHODS: A total of 184 individuals were recruited, underwent polysomnography (PSG), and completed questionnaires including a chronotype questionnaire (CQ), insomnia severity index (ISI), and Epworth sleepiness scale (ESS). Blood samples were collected in the evening before and morning after PSG. Gene expression analysis included BMAL1, CLOCK, PER1, CRY1, NPAS2, and NR1D1. RESULTS: In the OSA group, the subjective amplitude (AM score of CQ) positively correlated with all circadian clock genes in the morning (R ≥ 0.230 and p < 0.05 for each one), while the morningness-eveningness (ME score of CQ) was only associated with the evening BMAL1 level (R = 0.192; p = 0.044). In healthy controls, insomnia severity correlated with evening expression of BMAL1, PER1, and CRY1. CONCLUSIONS: The findings highlight the complex interplay between OSA, circadian rhythms, and sleep-related variables, suggesting potential determinants of morning chronotype in OSA and implicating disrupted circadian clock function in subjective feelings of energy throughout the day. Further research is warranted to elucidate underlying mechanisms and guide personalized management strategies.

Construction of an index system of core competence assessment for sleep medicine nurse specialists in China: a Delphi study.

AIM: To construct an evaluation index system for the core competence of nurses in sleep medicine specialties. BACKGROUND: Specialized nurses in sleep medicine must handle treatment, nursing, and management well, which requires nurses to have excellent competence in knowledge, skills, and ability. However, a competency evaluation system for sleep medicine nurses has not been established in China. METHODS: We used a literature review and an expert meeting to establish a draft indicator system. Subsequently, two rounds of correspondence were conducted with 27 experts from 5 provinces using the Delphi method to solicit their opinions on the core competency evaluation indicators for sleep medicine nurse specialists and to qualitatively evaluate the experts' scores. The consensus was defined in advance as a mean score of 4.0 or above, with at least 75% agreement among participants. In this way, the final indicator framework was determined. RESULTS: The final evaluation indexes of the core competencies for sleep medicine Nurse Specialists included 6 first-level indexes (Practical ability, Theoretical Knowledge, Critical thinking, Communication and coordination, Nursing Management, and Professional Development), 16 s-level indexes, and 64 third-level indexes. The effective response rates of the two expert consultation rounds were 100%. The expert authority coefficients were 0.878 and 0.865 in the first and second rounds of consultation, respectively. In the second round of consultation, the first, second, and third indexes of Kendall's coefficient of concordance were 0.373, 0.351, and 0.286, respectively (P < 0.05). CONCLUSION: The core competence evaluation index system for sleep medicine nurses established in this study is scientific and reliable and can provide a theoretical reference for the training, assessment, and evaluation of sleep medicine nurses in the future. IMPLICATIONS FOR NURSING MANAGEMENT: The evaluation index system of sleep medicine nurses' core competence will provide an effective practical framework for nursing management to evaluate, train, and examine the core competence of sleep medicine nurses.

Dental sleep medicine education among undergraduate dental students in France.

INTRODUCTION: Dental sleep medicine (DSM) is an emerging discipline that studies the oral and maxillofacial causes and consequences of sleep-related problems. DSM is of upmost importance given the major medical challenge it represents. Therefore, to verify whether the future generation of dentists would be ready to face this challenge, the main objective of this study was to assess the degree of involvement of the French dental schools in teaching DSM at the undergraduate level. MATERIALS AND METHODS: All 16 dental schools in France were asked to participate in the study by answering to an online survey concerning the DSM curriculum during the 2018-2019 academic year. The survey was addressed to the head of the dental school and/or to relevant course coordinators and contained 10 questions related to the type, content and amount of DSM teaching to undergraduate dental students. RESULTS: Nine of the 16 (56.2%) French dental schools responded to the questionnaire. All these nine reported the inclusion of DSM in their undergraduate curriculum. The total average hours dedicated to teaching DSM was 5.6 h (SD 4.2; range 1-15 h). Seven of the 9 dental schools spent most of their DSM curriculum teaching time in the fifth year. All of them reviewed obstructive sleep apnoea and sleep-related bruxism and covered some topics related to therapies for sleep-related breathing disorders, such as the use of oral appliance. CONCLUSION: The results of this survey showed that, although the average hourly volume is relatively high, the DSM teaching in French dental schools appeared to be non-standardised, heterogeneous and often lacunar. It is therefore essential to develop a common curriculum and implement it in all dental schools to provide undergraduate students a comprehensive and updated teaching in DSM.