Journal of Clinical Sleep Medicine最新文献

Study objectives: To evaluate whether telemedicine-delivered myofunctional therapy (MT) produces measurable structural changes in the upper airway (UA) of patients with obstructive sleep apnea (OSA).

Methods: This prospective, nonrandomized, blinded study included 60 adults with moderate-to-severe OSA, assigned to three groups: moderate OSA + MT (n = 19), severe OSA + MT + CPAP (n = 20), and OSA + CPAP without MT (control, n = 18). MT consisted of daily oropharyngeal exercises for 3 months, monitored through telemedicine. Pre- and postintervention assessments included sleep studies, submental ultrasound (interarterial distance, tongue thickness, and tongue volume), Iowa Oral Performance Instrument (IOPI) scores, and drug-induced sleep endoscopy (DISE; VOTE classification). Ultrasound measurements were performed by a single blinded examiner.

Results: After 3 months, the moderate OSA + MT group showed significant reductions in tongue volume (-8 cm³; P = .002), tongue thickness (-4 mm; P <.001), and interarterial distance (-5 mm; P < .001). In the severe OSA + MT + CPAP group, tongue volume (-12 cm³; P < .001) and interarterial distance (-7 mm; P < .001) decreased, while tongue thickness remained unchanged. No significant changes occurred in controls. Absence of tongue collapse during DISE increased from 15% to 80% in moderate OSA + MT (P = .039) and from 15% to 55% in severe OSA + MT + CPAP (P = .109).

Conclusions: Telemedicine-based MT promotes measurable UA remodeling. Submental ultrasound provides a practical, noninvasive monitoring tool, supporting MT as a scalable, patient-centered strategy to enhance structural and functional outcomes in OSA management. This prospective study explored whether myofunctional therapy (MT), delivered through a telemedicine platform, could lead to measurable changes in the upper airway of patients with obstructive sleep apnea (OSA). Sixty adults were followed for three months with submental ultrasound and drug-induced sleep endoscopy. Patients with moderate OSA who performed MT showed clear reductions in tongue size and airway width, while no significant changes occurred in CPAP-only controls. These results suggest that telemedicine-based MT can expand upper airway structures, particularly in moderate OSA, and may help guide individualized treatment strategies.

Current knowledge/study rationale: Myofunctional therapy has been reported to improve symptoms of OSA, but its structural effects on the upper airway and the role of telemedicine-based delivery remain poorly documented.

Study impact: This study provides evidence that MT can induce measurable airway remodeling, especially in moderate OSA. Submental ultrasound proved useful for monitoring these changes, supporting the use of telemedicine MT as a scalable, patient-centered approach to individualized OSA management.

Purpose: Co-morbid insomnia and obstructive sleep apnea (COMISA) poses greater cardiovascular risks than either condition alone. We investigated whether COMISA associated with insomnia with short sleep duration (ISSD) phenotype is associated with an increased risk of incident hypertension in a large random general population sample.

Methods: From the 1741 participants of the Penn State Adult Cohort, 1395 were followed-up after 7.5 years and 786 did not have hypertension at baseline. Hypertension was determined by a self-report of receiving treatment for high blood pressure. Insomnia symptoms were defined as either a complaint of chronic insomnia lasting ≥ 1 year or a complaint of difficulty falling asleep, staying asleep, nonrestorative sleep, or early morning awakening. All subjects underwent 8-h in-laboratory polysomnography. Obstructive sleep apnea was defined as an obstructive apnea/hypopnea index ≥ 5 event/h. Objective short sleep duration was defined as < 6 h sleep.

Results: The mean age of the study population was 47.5 ± 12.7 years and 51.3% were women. Compared to good sleepers, the highest risk of incident hypertension was in the COMISA with ISSD phenotype (OR = 4.25, 95%CI = 1.52-11.90), followed by OSA-alone (OR = 3.31, 95%CI = 1.85-5.92) and ISSD (OR = 2.27, 95%CI = 1.29-4). The insomnia with normal sleep duration phenotype alone or with OSA (COMISA) was not significantly associated with incident hypertension.

Conclusions: The additive effect of COMISA on hypertension risk is associated with the ISSD phenotype, the most severe biological phenotype of insomnia. Obtaining objective sleep duration in addition to apnea/hypopnea and/or oxygen saturation indices may lead to a more accurate diagnosis and treatment of COMISA.

Study objectives: Evidence of dairy's association with sleep is limited. Therefore, we evaluated the association between dairy product consumption and self-reported and objective sleep outcomes in U.S. adults.

Methods: Participants included 23,480 men and women (mean age 46.7 ± 17.3 years) from the 2005-2020 National Health and Nutrition Examination Survey. Dairy intake (total and sub-types) was estimated from two 24-h dietary recalls. We categorized average daily dairy intake as low, moderate, and high based on sex-specific tertiles. Sleep duration (< 7, 7-9 (ref.), ≥ 9 h/day) and trouble sleeping (yes/no) were obtained from questionnaires. Objective sleep measures were derived from accelerometer data in a subsample (n = 4,972). Survey-weighted multivariate logistic and linear regression were used to evaluate dairy intake in relation to self-reported and objective sleep outcomes, respectively.

Results: Mean (SD) total dairy intake was 1.76 (1.34) cup-eq/day. High total dairy intake was associated with 16% and 23% lower odds of short and long sleep duration, respectively (all P-trend ≤ 0.01), and lower odds of trouble sleeping (OR_{high vs. low}=0.86; 95% CI 0.75-1.00; P-trend < 0.05). Similar associations between milk and sleep duration, and high-fat dairy and trouble sleeping were observed (all P-trend < 0.03). Moreover, high vs. low total dairy intake was associated with a higher sleep regularity index, and fermented dairy intake was associated with less variable sleep midpoint, and lower odds of poor sleep (all P-trend < 0.03).

Conclusions: Dairy consumption is associated with improved odds of adequate sleep, no trouble sleeping, and less variable sleep in U.S. adults. However, additional studies are warranted to assess causality. Current Knowledge/Study Rationale: Emerging research suggests that diet influences sleep health; however, evidence on the specific role of dairy products remains limited. We examined the association between dairy product consumption (total and subtypes) and both self-reported and objective sleep metrics in a nationally representative sample of U.S. adults. Study Impact: Dairy intake, especially of fermented dairy products, is associated with better sleep patterns, via improved odds of having adequate sleep, no trouble sleeping, and less sleep variability. Longitudinal studies and clinical interventions are needed to investigate causal associations and to clarify underlying mechanisms.

Background: Adherence to positive airway pressure (PAP) therapy in obstructive sleep apnoea (OSA) remains suboptimal. Previous cognitive behavioural therapy (CBT) has relied on passive education approaches and has yielded mixed results.

Objective: To evaluate whether personalised video footage showing one's own sleep-disordered breathing events (Self-Viewing of Sleep Apnoea Videos, SVSV) improves PAP adherence.

Methods: In this randomised controlled trial, 228 treatment-naïve patients with moderate to severe OSA were assigned to either the SVSV or standard CBT-only (control) group. All participants received standard CBT before PAP initiation. The SVSV group additionally viewed video footage of apnoeas from their own diagnostic polysomnography. The primary outcome was good adherence, defined as ≥ 4 h of sleep per night on ≥ 70% of nights for 90 days. Secondary outcomes included the number of days with adequate use, mean daily usage time, and apnoea-hypopnoea index (AHI) during PAP.

Results: Good adherence was achieved in 89.5% of the SVSV and 78.1% of the control group (absolute difference, 11.4%; 95% confidence interval (CI), 1.94% to 20.86%). Compared with the control group, the SVSV group had more days with adequate usage (mean difference of 9.05 days; CI, 2.29 to 15.81) and longer daily usage (mean difference of 0.55 h; CI, 0.13 to 0.98). AHI during PAP therapy was similar between the two groups.

Conclusions: This study supports the clinical utility of patient-specific visual feedback to improve PAP adherence. SVSV-a simple, low-cost, and scalable strategy-resulted in a higher proportion of patients achieving good adherence and greater usage. 1. Current Knowledge/Study Rationale: Adherence to positive airway pressure therapy in obstructive sleep apnoea is often inadequate, limiting its therapeutic benefit. Previous educational approaches, including video-based cognitive behavioural therapy, have yielded inconsistent effects on adherence. 2. Study Impact: This prospective, randomised controlled trial shows that allowing patients to view video segments of their own sleep-disordered breathing events, extracted from diagnostic polysomnography, significantly improves positive airway pressure adherence. This personalised self-viewing intervention is simple, cost-effective, and easily integrated into existing clinical workflows, with particular value for underserved populations and individuals with limited health literacy.

Study objectives: To evaluate the emerging role of artificial intelligence (AI) in diagnosis, risk stratification, and adult surgical planning for patients with obstructive sleep apnea (OSA), and to assess its potential clinical value and limitations.

Methods: A narrative literature review was conducted A targeted systematic search elements to synthesize recent developments in AI applications across the OSA care continuum. Studies were selected based on relevance to diagnostic accuracy, wearable and home-based sleep monitoring, outcome prediction, and integration into surgical workflows. Special attention was given to evidence involving drug-induced sleep endoscopy, predictive modeling for surgical response, and AI-driven tools validated in real-world or telehealth settings.

Results: AI-powered models demonstrated high concordance with manual scoring of in-laboratory sleep studies, improved accuracy in event detection using wearable data, and effective classification of OSA severity from reduced physiological signals. Predictive algorithms integrating clinical and imaging data enhanced risk stratification and surgical candidate selection. In particular, deep learning models outperformed traditional clinical predictors in forecasting responses to hypoglossal nerve stimulation. However, variability in data quality, lack of pediatric-specific validation, and concerns regarding algorithm bias and transparency remain significant barriers. We emphasize drug-induced sleep endoscopy (DISE) analytics and hypoglossal nerve stimulation (HNS)/MMA outcome prediction, presenting diagnostic AI only insofar as it feeds pre-operative decision support.

Conclusions: Artificial intelligence offers powerful tools to support individualized, efficient, and scalable OSA management. Its integration into clinical pathways could optimize diagnosis and treatment decision-making, especially in surgical contexts. Clinical translation will depend on external/temporal validation, calibrated probability outputs, decision-curve/net-benefit, and prospective decision-impact (target selection change, OR time, postoperative outcomes), with equity audits across subgroups. Future efforts should prioritize robust validation, interpretability, and equitable deployment to ensure safe and effective implementation.

Idiopathic hypersomnia is a heterogeneous disorder with daytime and nighttime symptoms that have a profound 24-hour effect on individuals' well-being. Daytime signs and symptoms include excessive daytime sleepiness, unrefreshing naps, autonomic dysfunction, fatigue, and brain fog; nighttime signs and symptoms include long sleep, and sleep inertia. Symptom overlap between idiopathic hypersomnia and narcolepsy type 2 and the varied disease course of idiopathic hypersomnia present challenges in diagnosis. Furthermore, the diagnostic criteria for idiopathic hypersomnia are inadequate in their current form, and their use can lead to misdiagnosis and suboptimal treatment. The unclear pathophysiology of idiopathic hypersomnia and the absence of reliable biomarkers necessitate careful use of a variety of tests to make an accurate diagnosis. The Idiopathic Hypersomnia Severity Scale captures symptoms that distinguish idiopathic hypersomnia from narcolepsy, but this scale requires further evaluation in real-world populations and should be used with objective measures of sleep parameters and other validated instruments to accurately capture the full range of symptoms over a 24-hour period. This review describes a holistic view of the symptoms and impact of idiopathic hypersomnia, and frames diagnostic challenges and treatment considerations within the context of this view. Expert clinical opinion that aims to improve the diagnostic accuracy and treatment of idiopathic hypersomnia is provided.

Study objectives: Obstructive sleep apnea (OSA) is a prevalent but underdiagnosed condition linked to serious health risks. Due to the limited accessibility of polysomnography (PSG), AI (Artificial Intelligence)-based speech analysis has gained attention as a non-invasive screening tool. This Bayesian meta-analysis evaluates the diagnostic accuracy of AI models trained on awake speech and examines factors affecting performance.

Methods: We systematically searched Medline/PubMed, Embase, Scopus, Web of Science, and IEEE Xplore databases. Eligible studies included adults with OSA diagnosis via in-lab polysomnography or home sleep apnea tests and evaluated AI models using speech recordings. Models evaluated using random-split test sets or k-fold cross-validation were included in a Bayesian bivariate meta-analysis and meta-regression. Publication bias was examined using a selection model approach, while risk of bias and evidence quality were assessed with QUADAS-2 and GRADE.

Results: From 6,254 screened articles, 8 studies comprising 24 AI models, trained and tested on 1,060 and 825 participants were included. All studies used professional microphone recordings in the controlled hospital settings. AI models analysing awake speech recordings demonstrated pooled sensitivity and specificity of 82.9% (95% CrI: 80.0-86.4%) and 83.3% (95% CrI: 80.7-86.1%), respectively. The diagnostic odds ratio was 24.3 (95% CrI: 18.2-35.0). Higher mean age improved sensitivity. No significant effects were seen for OSA severity, model type, OSA prevalence, or male percentage. Publication bias was not evident.

Conclusion: AI models trained on awake speech recordings demonstrate good diagnostic accuracy for OSA and hold potential as a practical, scalable screening tool in both clinical and community-based settings.