Nature and Science of Sleep最新文献

Background: Carotid atherosclerosis (CAS) is a critical cardiovascular complication in elderly patients with obstructive sleep apnea (OSA). Current risk assessment tools inadequately capture OSA-specific pathophysiological mechanisms for CAS prediction in this high-risk population.

Methods: This multicenter retrospective study included 1196 elderly patients (≥60 years) with polysomnography-confirmed OSA from six tertiary hospitals in China. CAS was diagnosed by carotid ultrasound. LASSO (Least Absolute Shrinkage and Selection Operator) regression identified optimal predictive features from 18 candidate variables. Four machine learning algorithms were developed and validated using 5-fold cross-validation. Model interpretability was achieved through SHapley Additive exPlanations (SHAP) analysis.

Results: Among participants, 273 (22.8%) had CAS. LASSO regression selected eight optimal features. XGBoost achieved the best performance with test AUC of 0.854, accuracy of 79.8%, sensitivity of 81.2%, and specificity of 78.5%. SHAP analysis revealed systolic blood pressure (importance: 0.3148) and percentage of sleep time with oxygen saturation <90% (T90, importance: 0.2660) as the most influential predictors, surpassing traditional apnea-hypopnea index. Other significant predictors included alcohol consumption, mean oxygen saturation, body mass index, age, platelet count, and smoking status.

Conclusion: This study developed the first machine learning-based CAS prediction model for elderly OSA patients, achieving clinically relevant performance (AUC=0.854). The prominence of T90 over conventional apnea-hypopnea index suggests nocturnal hypoxemic burden is more important than respiratory event frequency for cardiovascular risk stratification in this population.

Purpose: Given rising numbers in sleep disorders like insomnia and insufficient availability of treatment options, the need for well-validated digital interventions rises. This pilot study aims at assessing the feasibility of an app-program which combines sleep-training based on core elements of Cognitive Behavioural Therapy for Insomnia (CBT-I) with reliable sleep-monitoring based on heart rate variability via an ECG-sensor.

Patients and methods: About 48 participants (26 females) aged 30-73 (M = 50.33 ± 11.88) were included in the study. At the beginning of the baseline (T0), at start (T1) and end (T2) of the 6-week training phase as well as 4 weeks after the end of the program (T3; follow-up) several questionnaires assessing sleep quality, insomnia severity, general psychological symptom severity, depression, anxiety as well as quality of life were completed. Furthermore, ambulatory polysomnography (PSG) was conducted three times at T0, T1 and T2. General feasibility was assessed by conducting interviews.

Results: Overall, the app-program as well as the study protocol was deemed as feasible according to the participants, besides some difficulties regarding app-instructions and certain technical issues, as well as some expected complaints about worse sleep quality during PSG-recordings. For statistical results, insomnia severity (p < 0.001, r = 0.67), sleep quality (p < 0.001, r = 0.56), general psychological symptom severity (p < 0.001, r = 0.68), depression (p = 0.002, r = 0.50) and anxiety (p < 0.001, r = 0.60) improved significantly during the training phase, while quality of life [physical (p = 0.014, r = 0.41) and psychological health (p = 0.049, r = 0.35)] improved significantly during the follow-up-period. PSG data revealed a significant decrease in Wake After Sleep Onset over the course of the study (p = 0.025, r = 0.36), yet no significant changes were found for other sleep parameters.

Conclusion: The app-program was largely feasible and potentially effective in improving sleep and well-being. PSG-derived WASO changes highlight the value of objective sleep measures. Future studies should refine protocols and include control conditions for greater generalizability.

Background: Postoperative sleep disorders (PSD) are common complications that may impair recovery and increase the risk of adverse events after surgery. Transcutaneous electrical acupoint stimulation (TEAS), a non-invasive therapy based on traditional Chinese medicine, has shown potential in improving sleep quality. This study aims to investigate whether TEAS can alleviate PSD in older patients undergoing urologic laparoscopic surgery.

Methods: This is a prospective, randomized, double-blind, controlled clinical trial. A total of 124 patients scheduled for urologic laparoscopic surgery will be randomly assigned to either the TEAS group (receiving active stimulation) or the sham group (receiving placebo stimulation). Randomization will also be stratified according to the use of dexmedetomidine. The primary outcome is the Athens Insomnia Scale (AIS) score on the postoperative night (D2), compared between groups using ANCOVA adjusted for preoperative (baseline) AIS and the dexmedetomidine stratification factor. Secondary outcomes include the night-to-night trajectory of AIS across D0-D2, objective sleep parameters measured by a portable monitor on D0-D2, postoperative VAS pain scores, perioperative changes in intestinal microbiota and serum brain-gut peptides, intraoperative hemodynamics and drug consumption, durations of surgery/anesthesia/extubation, postoperative complications, and healthcare-related costs.

Discussion: This trial aims to explore whether TEAS can improve postoperative sleep quality in older patients by regulating intestinal microbiota. The findings are expected to provide evidence supporting the clinical application of TEAS for managing PSD in this patient population.

Clinical trial registration: ChiCTR2400086911 (Registered on 15 July 2024) https://www.chictr.org.cn/showproj.html?proj=220900.

Background: Assessing an individual's acceptance of sleep medications is valuable for clinicians, as it may be the starting point for discussing the treatment of insomnia, whether pharmacologically or nonpharmacologically. This study developed a rating scale for measuring psychological receptivity to hypnotics and validated it in a general population.

Methods: We conducted an anonymous survey in a general Korean population. First, we developed a rating scale for psychological receptivity to hypnotic use using exploratory factor analysis (EFA) in Sample I (N = 300), followed by confirmatory factor analysis (CFA) in Sample II (N = 300). Convergent validity was assessed using the Insomnia Severity Index (ISI), Glasgow Sleep Effort Scale (GSES), Adaptive Cognition and Behaviors about Sleep-6 (ACBS-6), and Acceptance and Action Questionnaire-II (AAQ-II).

Results: From the EFA, six survey items were selected from the collected items. The CFA among Sample II showed a good fit for the two-factor model (Factor 1: Involuntary nature of insomnia; Factor 2: Acceptance of sleep medication use) of our Sleeping Pills Receptivity and Involuntariness Scale-6 (SPRIS-6) (comparative fit index = 0.99, Tucker-Lewis index = 0.99, root-mean-square-error of approximation = 0.02, and standardized root-mean-square residual = 0.02). According to the multi-group CFA, the two-factor structure of the SPRIS-6 measures psychological receptivity to sleep medications in the same way, regardless of whether or not participants reported insomnia. Using McDonald's coefficient of 0.80, the two-factor structure of the SPRIS-6 demonstrated good internal consistency. Linear regression analysis showed that the ISI score was positively influenced by the SPRIS-6 (β = 0.16, p = 0.002), GSES (β = 0.49, p = 0.001), and AAQ-II (β = 0.18, p = 0.001), whereas it was inversely influenced by the ACBS-6 (β = -0.10, p = 0.037).

Conclusion: The SPRIS-6 is a reliable and valid rating scale that measures psychological receptivity to sleep medication use.

Purpose: Obstructive sleep apnea (OSA) is increasingly recognized as a systemic disorder in which inflammation plays a pivotal role in its pathophysiology and comorbidities. This study aimed to conduct the first comprehensive bibliometric analysis of global research on inflammation in OSA, in order to map knowledge structures, identify influential contributors, and highlight emerging research trends.

Patients and methods: Publications related to OSA and inflammation were retrieved from the Web of Science Core Collection (WoSCC) (1996-2025). After exclusions, 2,075 articles and reviews were analyzed using bibliometric tools including CiteSpace, VOSviewer, and the bibliometrix R package. Citation frequencies, co-citation networks, and collaboration patterns among countries, institutions, authors, and journals were examined.

Results: The number of publications has shown robust growth (annual rate 12.11%). China and the United States were the leading contributors, with Harvard University, the University of Chicago, and Institut National De La Sante Et De La Recherche Medicale (INSERM) among the most productive institutions. Influential authors included Gozal D, Kheirandish-Gozal L, and Khalyfa A. Sleep and Breathing was the most prolific journal, while Circulation and American Journal of Respiratory and Critical Care Medicine were highly co-cited sources. Highly cited works established the link between intermittent hypoxia, systemic inflammation, and cardiovascular/metabolic consequences. Thematic clusters revealed major research focuses on cardiovascular disease, metabolic syndrome, oxidative stress, neuroinflammation (including microglial activation), and therapeutic strategies such as continuous positive airway pressure (CPAP), adenotonsillectomy, and bariatric surgery. Emerging topics included gut microbiota, vitamin D, and neurodegenerative diseases.

Conclusion: Research on inflammation is evolving from mechanistic insights toward clinical management and interdisciplinary exploration. Looking ahead, priority should be given to biomarker discovery and the development of anti-inflammatory therapeutic strategies. These findings provide a roadmap for targeted research and clinical translation in the field of OSA-related inflammation, ultimately improving patient outcomes.

Objective: This study validated the accuracy of an artificial‑intelligence (AI) smartwatch algorithm that directly estimates the apnea-hypopnea index (AHI) by comparing its performance with AI-scored Level 1 polysomnography (PSG) in Korean adults. The model was trained in South‑American cohorts, allowing inter‑ethnic validation.

Methods: A total of 90 adults underwent simultaneous Level 1 PSG and smartwatch recording. Fifty‑three datasets with ≥ 3 hours of valid watch data were analyzed. AHI values were obtained as follows: expert‑scored PSG (pAHI), AI‑scored PSG (aiAHI), and smartwatch output (eAHI). Agreement was assessed with Spearman correlation, intraclass correlation coefficients, and receiver‑operating‑characteristic curves.

Results: eAHI correlated strongly with aiAHI (ρ = 0.88, ICC = 0.87) and pAHI (ρ = 0.85, ICC = 0.82). For detecting moderate‑to‑severe OSA (aiAHI ≥ 15 events/h), the smartwatch algorithm yielded 92.3% sensitivity, 92.6% specificity, and 92.5% overall accuracy. Bland-Altman analysis revealed systematic underestimation of actual AHI by the smartwatch, particularly in mild OSA.

Conclusion: This study demonstrates that the evaluated smartwatch-based AHI estimation algorithm shows high concordance with PSG-derived values, particularly for the detection and classification of moderate to severe OSA. However, it should be noted that this smartwatch algorithm tends to underestimate the AHI of OSA due to limitations in scoring unit and recording duration calculation. These findings support the clinical utility of wearable technology as a practical and scalable tool for early identification and longitudinal monitoring of OSA in real-world environments, while highlighting the need for further optimization to accurately detect mild cases.

Chronic subjective tinnitus (CST) is frequently accompanied by anxiety and sleep disturbances, together forming a self-reinforcing cycle that significantly impairs patients' quality of life and complicates clinical management. This narrative review aims to elucidate the shared pathophysiological mechanisms underlying these interrelated conditions and to evaluate current multidisciplinary therapeutic strategies. Relevant literature was identified through a targeted search of PubMed, Scopus, and Web of Science databases, focusing on recent clinical and translational studies addressing the neurobiology and treatment of CST, anxiety, and sleep disorders. Emerging evidence highlights the involvement of maladaptive auditory-limbic network connectivity, neurotransmitter imbalances (eg, GABA, glutamate, serotonin), dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, autonomic dysfunction, and disruption of circadian and sleep-wake regulatory pathways in the persistence and mutual reinforcement of these symptoms. Multimodal interventions-including cognitive behavioral therapy, sound therapy, pharmacotherapy, neuromodulation techniques, and targeted sleep interventions-have demonstrated synergistic benefits, particularly when tailored to individual neurobiological profiles. From a clinical perspective, recognizing the bidirectional interactions among tinnitus, affective dysregulation, and sleep impairment is essential for developing effective, integrative treatment frameworks. This review underscores the necessity of personalized, mechanism-informed therapeutic approaches and outlines key directions for future research.

Purpose: To investigate the association between objectively monitored sleep patterns and overestimation of myopia among university students.

Methods: A cross-sectional study enrolled 144 university students aged 17-23 years. Sleep parameters (total duration, light sleep, deep sleep, bedtime/woke time) were objectively monitored for 7 consecutive days using smart wearable devices. Ocular biometric parameters were measured using an optical biometer. Spherical equivalent refraction (SE) was obtained via auto-refraction both before and after cycloplegia. Based on the difference in SE before and after cycloplegia (DIFF = SE_post-cycloplegia - SE_pre-cycloplegia), participants with DIFF ≥ +0.50 D were defined as the overestimated myopia group. Linear mixed-effects models (LMM) were used to analyze the association between sleep parameters and overestimated myopia, adjusting for confounders including age, sex, parental myopia history, and daily near work activities.

Results: A total of 126 participants completed data analysis. The prevalence of overestimated myopia was 26.19%. Compared to the non-overestimated myopia group, the overestimated myopia group exhibited the following significant characteristics: shorter total sleep duration by approximately 0.6 h (6.82 ± 0.84 h vs 7.43 ± 0.74 h, P < 0.001); shorter light sleep duration by 0.46 h (4.73±0.93 h vs 5.19±1.05 h, P = 0.006); and delayed bedtime by 0.80 h (1:42±1:04 vs 00:53 ± 00:52, P < 0.001). No statistically significant differences were observed between the groups in deep sleep duration or woke time.

Conclusion: Among university students, a sleep pattern characterized by shorter sleep duration and delayed bedtime is significantly associated with overestimation of myopia. This finding suggests that sleep behavior may be a potential factor influencing measurement error in non-cycloplegic refraction. Attention should be paid to the sleep status of examinees in clinical practice to enhance the accuracy of refractive measurements.

Purpose: This study aimed to clarify the value of upper airway dynamic magnetic resonance (UADMR) in assessing the level and degree of obstruction in the upper airway during sleep in patients with obstructive sleep apnea hypopnea syndrome (OSAHS).

Patients and methods: Patients with OSAHS diagnosed by polysomnography (PSG) from February 2025 to March 2025 were included in this study, and UADMR was performed to detect the obstructive levels (palatopharyngeal level, root of tongue level, and epiglottic level) and assess the degree of obstruction at the root of the tongue and epiglottic levels. Subgroup analyses were performed according to different obstructive characteristics and the results of UADMR were assessed for correlation analysis with the results of PSG or manual titration of positive airway pressure. This study used Müller maneuver to simulate the airway collapse during sleep.

Results: A total of 34 OSAHS patients were included in this study. It was observed that the hypopnea index, apnea hypopnea index, and tongue axial change ratio were significantly higher in OSAHS patients with multiple layers of airway obstruction than in those with palatopharyngeal obstruction only. The anteroposterior diameter change ratio at the level of the root of the tongue and the area change ratio at the level of the epiglottis in patients with OSAHS were positively correlated with the results of the PSG, and the tongue axial change ratio was positively associated with the results of the manual titration of positive airway pressure.

Conclusion: UADMR is an effective method for assessing the level and degree of upper airway obstruction during sleep in patients with OSAHS.

Objective: Nonrestorative sleep (NRS) is common among college students; however, its temporal changes and predictors are poorly understood. This study aimed to identify NRS trajectories among first-year Chinese college students and to examine how childhood emotional abuse (EA) and resilience predict these trajectories.

Methods: Six hundred and fourteen first-year Chinese college students were enrolled in a 12-month longitudinal tracking study, assessed by the Childhood Trauma Questionnaire-Short Form, 10-item Connor-Davidson Resilience Scale, Morning and Evening Questionnaire-5, and NRS Scale (NRSS). Data were analyzed employing latent growth curve modeling, latent class growth modeling, and multivariable logistic regression.

Results: The global score of NRSS (higher scores indicate fewer symptoms of NRS) showed a linear increase overall. EA predicted poorer restorative sleep at baseline (β = -0.255, p < 0.001). Resilience predicted better restorative sleep over time (β_T0 = 0.271, β_T1 = 0.327, β_T2 = 0.292, all p < 0.001). Latent class analysis identified two NRSS trajectories: (a) high-increasing class (41.5% of the sample; declining NRS) and (b) low-stable class (58.5%; persistent high NRS). Higher levels of EA corresponded to greater likelihood of belonging to the low-stable class relative to the high-increasing class (OR=1.177, 95% CI [1.106, 1.252]). In contrast, higher resilience corresponded to lower likelihood of being in the low-stable class (OR=0.915, 95% CI [0.890, 0.941]).

Conclusion: EA is a predisposing factor for NRS among college students, while resilience is a protective factor for restorative sleep. It is essential to consider EA and resilience in any intervention efforts to reduce NRS.