PurposeIn-laboratory polysomnography (PSG) is the gold standard test for diagnosing certain paediatric sleep conditions. Children with neurodevelopmental disorders (NDD) often have difficulty tolerating PSG, but parent and patient experiences of PSG for children with NDD have not been thoroughly explored. The study aim was to evaluate the parent-reported experience of in-laboratory PSG undertaken in children with NDD and to identify factors predictive of poorer experience.MethodsIn this cross-sectional multicentre study, parents of 143 children with NDD who underwent in-laboratory PSG completed a customised survey to provide feedback on parent and child worry levels, subjective tolerance and overall experience of PSG, and hypothetical preference between in-laboratory PSG versus an in-home mat-based sleep test. ANOVA, Chi-squared and Kruskal–Wallis tests were used to determine participant factors associated with these outcomes.ResultsOn average, parents rated their child’s worry level with respect to undergoing PSG as ‘moderate,’ but their own worry levels lower. Autism spectrum / neuromuscular disorder diagnoses were risk factors for both higher worry score and reporting that sleep during PSG was non-representative of usual sleep at home. Parental preference was for in-home (mat-based) testing, with 57% indicating a preference for this if it wereavailable vs. 7% preferring in-laboratory testing.ConclusionParent/carer reports regarding in-laboratory PSG experiences for their children with NDD suggest the test is associated with child worry and concerns that the sleep is not-representative of usual sleep at home. Consumer preference favours in-home sleep study testing over current in-laboratory diagnostic testing.Clinical trial registrationThis study is part of a larger trial ACTRN12622001544763.

IntroductionSleep architectural disruption is associated with atrial fibrillation (AF); however, associated autonomic influences remain unclear and it is unknown if this detriment persists during wakefulness. We hypothesize sleep disruption and autonomic dysfunction have diurnal patterning in patients with paroxysmal AF.MethodsWe analyzed data from the Sleep Apnea and Atrial Fibrillation Biomarkers and Electrophysiologic Atrial Triggers (SAFEBEAT) study designed to examine paroxysmal AF and sleep apnea, including simultaneous collection of continuous electrocardiogram monitoring (Heartrak Telemetry®) and actigraphy (Actiwatch GTX) for 7–21 days. Heart rate variability (HRV) measures in time-domain (standard deviation of normal-to-normal (NN) intervals (SDNN), coefficient of variation (CV)) and frequency-domain (low frequency power (LFP), high frequency power (HFP)) were used as surrogates of autonomic function and averaged per sleep/wake per day. A linear mixed-effects model assuming compound symmetry correlation structure was used to assess the relationship of HRV with actigraphy-derived sleep data.ResultsThe analytic sample (age 60.1 ± 12.0 years, body mass index 32.6 ± 6.7 kg/m2, 36% female, 75% White) included 100 participants with paroxysmal AF. Longer sleep latency was associated with lower HFP during wakefulness (coefficient − 0.0501, p = 0.031). Higher sleep efficiency was associated with increased SDNN (coefficient 0.0007, p = 0.014) and CV (coefficient 0.0167, p = 0.047). Higher arousal index was associated with increased CV (coefficient 0.0166, p = 0.007) and LFP (coefficient 0.0232, p = 0.003). During sleep, longer average awakenings duration was associated with increased LFP/HFP ratio (coefficient 0.1977, p < 0.001) and reduced HFP (coefficient − 0.1338, p < 0.001). Significant sleep-wake interactions were observed for sleep latency with HFP (p = 0.024), sleep efficiency with SDNN and CV (both p < 0.01), WASO with SDNN, CV, and LFP (all p < 0.05), and frequency of awakenings with CV and LFP (both p < 0.05).ConclusionsActigraphy-based measures of sleep disruption were associated with autonomic function alterations exhibiting diurnal variability in paroxysmal AF. Greater overall HRV and parasympathetic modulation were related to better sleep quality. Increased sympathetic activation was associated with sleep fragmentation. Results provide insights into differential autonomic dysfunction related to sleep disruption that may contribute to atrial arrhythmogenesis.

PurposeDecreased delta and increased alpha wave activity during sleep may be specific pathophysiological features of major depressive disorder; however, their usefulness as biomarkers remains unclear. We examined the use of mean alpha and delta wave power value indices during sleep to identify major depressive disorder using a portable electroencephalography device.MethodsWe compared the mean alpha and delta wave power value indices of six unmedicated patients with major depressive disorder and seven age- and sex-matched healthy controls using a portable electroencephalography device in this case-controlled study.ResultsThe ratio of the mean alpha power values for the non-rapid and rapid eye movement periods was significantly lower in the major depressive disorder group (1.3 ± 0.2) than in the healthy group (2.3 ± 0.6; P = 0.004). The ratio of the mean delta power values for the non-rapid eye movement and rapid eye movement periods did not differ between groups but negatively correlated significantly with the Hamilton Rating Scale for Depression score (r = -0.784, P = 0.002). The area under the receiver operating characteristic curve (95% confidence interval) of the mean alpha power ratio for non-rapid eye movement and rapid eye movement periods for distinguishing the two groups was 0.93 (0.78–1.00), and both sensitivity and specificity exceeded 85% at a cut-off value ≤ 1.71.ConclusionThe alpha- and delta-related power value indices may capture different aspects of major depressive disorder pathology.

PurposeVitamin D (Vit D) deficiency has been associated with both obstructive sleep apnea (OSA) and chronic obstructive pulmonary disease (COPD) as well as with their combination, known as overlap syndrome (OS). There is evidence that continuous positive airway pressure (CPAP) may lead to an increase of Vit D levels in OSA patients. However, the effect of CPAP treatment on Vit D levels in OS patients has yet to be determined. The aim of the present study was to investigate the effect of one year of CPAP therapy on Vit D levels in patients with OS.MethodsVit D serum levels were measured in consecutive OS patients at baseline and after one year of CPAP therapy. Compliance with CPAP therapy was assessed by the data retrieved from the CPAP device.ResultsVit D serum levels were measured in 46 OS patients (43 males). Among participants, 27 had good and 19 poor compliance with CPAP therapy. Results showed that serum Vit D levels increased after 12 months of CPAP therapy from 21.3 ± 8.4 to 23.8 ± 8.7 ng/ml (p = 0.001). Moreover, patients with good CPAP compliance demonstrated higher serum 25(OH)D levels compared to those with poor compliance (25.8 ± 7.6 versus 20.4 ± 9.6 ng/ml, respectively; p = 0.038).ConclusionsIn conclusion, 12 months of CPAP therapy improved Vit D serum levels in OS patients, more so in compliant patients.

PurposeTo determine the prevalence of high-risk OSA among people diagnosed with psoriasis and to investigate relationships between the risk of OSA and the characteristics of psoriasis.MethodsThis cross-sectional study was conducted after the approval of the review board during February 2023 and February 2024. Inclusion criteria were psoriasis patients aged ≥ 18 years who visited the dermatologic clinic, Siriraj Hospital, Thailand. Demographic data, anthropometric measurements, underlying conditions, types of psoriasis, Psoriasis Area and Severity Index scores, disease duration, percentage of body surface area involvement, Epworth Sleepiness Scale (ESS), and STOP-Bang questionnaire were collected. Patients who were unable to answer these questionnaires were excluded.ResultsOf the 200 participants (106 men, 94 women), 108 patients (54%) were identified as high-risk for OSA; of them, 70 were men (64.8%) and 38 were women (35.2%). Within this group, statistically significant differences were observed in male (p < 0.001), age (p = 0.02), and the presence of hypertension, diabetes, and dyslipidemia. Both BMI and an ESS score > 10 were also significantly elevated in the high-risk group (p < 0.05). However, no significant correlations were detected between various characteristics of psoriasis and the risk of OSA. Only male sex [adjusted odd ratios (OR) = 3.51], HT (OR = 2.87), and an ESS score > 10 (OR = 4.13), showed statistically significant associations with an increased risk of OSA (p < 0.05).ConclusionPsoriasis patients had a higher prevalence of high-risk OSA compared to the general Thai population. This underscores the importance of screening individuals with psoriasis, particularly those exhibiting concurrent HT, male sex, and EDS, for OSA.

PurposeSeveral clinically available variables have been identified as predictors of non-response to oral appliance (OA) treatment, including endotypical traits such as severe upper airway collapsibility, unstable ventilatory control, and low arousal threshold. This study aimed to identify potential predictors of non-response to OA treatment in patients with OSA non-adherent to treatment with positive airway pressure.MethodsPatients in this study were initially treated with OAs with and without elastic bands in a crossover design. Subsequently, each patient selected their preferred treatment modality for continued therapy based on subjective preferences. The chosen OA treatment. The chosen OA treatment modality was titrated optimally based on reduction of REI. Patients not reaching > 50% reduction of REI from baseline were classified as non-responders. Statistical analyses were conducted using Student’s t-test and Pearson’s chi-squared test to assess differences in baseline variables between responders and non-responders, and logistic regression analyses were performed to investigate variables associated with not responding to OA treatment.ResultsOverall, 63.2% (n = 36) of the patients were responders to OA treatment following titration. Smaller distance from habitual bite position to maximal retruded position (Odds ratio: 0.28, p = 0.016), positional OSA (Odds ratio: 0.94, p = 0.024) and a higher number of the endotypical OSA traits severe collapsibility, high loop gain and low arousal threshold (Odds ratio: 7.41, p = 0.038), were found to predict non-response to OA treatment.ConclusionThese novel findings suggest that severe upper airway collapsibility, high loop gain and low arousal threshold, identified through clinically available variables, appear to be important predictors of non-response to OA treatment, along with short distance from habitual bite position to maximal retruded position and positional OSA.Trial registration numberNCT05987618 (clinicaltrials.gov).

ObjectiveTo evaluate the reliability of the GeneActiv actigraphy device in measuring sleep parameters and compare its performance with polysomnography (PSG) in older adults with self-reported sleep disturbances.MethodsThis sub-study was part of a pilot double-blinded randomized controlled crossover trial (CleverLights Study, ANZCTR ID 12619000138189). Participants (n = 12, mean age 67.7 years) underwent two nights of sleep studies with simultaneous GeneActiv actigraphy and PSG, separated by a 2-week interval. Sleep parameters including time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), sleep efficiency (SE), and number of awakenings were assessed. Intraclass Correlation Coefficients (ICCs) and Bland-Altman plots were used to determine reliability and agreement between methods.ResultsGeneActiv actigraphy demonstrated strong correlations with PSG for TST (ICC = 0.79, p = 0.001) and SE (ICC = 0.85, p < 0.001), but tended to overestimate these parameters. Actigraphy also significantly underestimated the number of awakenings (ICC = 0.45, p = 0.021). Correlations with observed TIB (ICC = 0.30, p = 0.433), WASO (ICC = 0.33, p = 0.386), and SOL (ICC = 0.32, p = 0.056) were non-significant. Bland-Altman plots revealed proportional bias, especially in SOL and the number of awakenings.ConclusionCompared to PSG, the GeneActiv actigraphy device provides reliable measurements for total sleep time and sleep efficiency, but agreement was weaker for wake after sleep onset, sleep onset latency, and the number of awakenings. The device showed consistent performance across multiple nights, suggesting good reproducibility. However, it systematically overestimated total sleep time and underestimates wake-related parameters, hence it may not fully replace PSG for detailed sleep assessments.

IntroductionMost pharmacologic treatments with clinical data for insomnia symptoms are controlled substances. An exception is doxepin, an antihistamine that has shown efficacy for the treatment of difficulties with sleep maintenance. This analysis assessed the utility of doxepin 3 mg in the treatment of difficulties with sleep onset.Patients and methodsA pooled analysis of two phase 3, randomized, controlled trials was conducted. Patients with primary insomnia received doxepin 3 mg or placebo 30 min before bedtime. Latency to persistent sleep (LPS) was assessed by polysomnography at screening (baseline), night 1 following a single dose (the main outcome of interest), and nights 15 and 29. Patient-reported latency to sleep onset (LSO) was recorded in a diary the following morning.ResultsA total of 310 patients, 153 randomized to placebo and 157 randomized to doxepin 3 mg, were included. Doxepin 3 mg resulted in a small but statistically significant 22% improvement in LPS compared with placebo on night 1 following a single dose (risk ratio: 0.78; 95% CI: 0.64, 0.94). A similar improvement was seen in a subgroup of patients with baseline LPS > 35 min. This subgroup had an 11-min reduction in LPS, compared with a 6.4-min reduction for the overall population. There was a nonsignificant 12% reduction in LSO in the overall population (risk ratio: 0.88; 95% CI: 0.73, 1.05).ConclusionsDoxepin 3 mg has a statistically significant effect on sleep latency on the first night of treatment in adults with insomnia that did not reach the clinical significance threshold.

PurposeTo establish a simple and noninvasive screening test for sleep apnea (SA) that imposes less burden on potential patients. The specific objective of this study was to verify the effectiveness of past and future single-lead electrocardiogram (ECG) data from SA occurrence sites in improving the estimation accuracy of SA and sleep apnea syndrome (SAS) using machine learning.MethodsThe Apnea-ECG dataset comprising 70 ECG recordings was used to construct various machine-learning models. The time window size was adjusted based on the accuracy of SA detection, and the performance of SA detection and SAS diagnosis (apnea‒hypopnea index ≥ 5 was considered SAS) was compared.ResultsUsing ECG data from a few minutes before and after the occurrence of SAs improved the estimation accuracy of SA and SAS in all machine learning models. The optimal range of the time window and achieved accuracy for SAS varied by model; however, the sensitivity ranged from 95.7 to 100%, and the specificity ranged from 91.7 to 100%.ConclusionsECG data from a few minutes before and after SA occurrence were effective in SA detection and SAS diagnosis, confirming that SA is a continuous phenomenon and that SA affects heart function over a few minutes before and after SA occurrence. Screening tests for SAS, using data obtained from single-lead ECGs with appropriate past and future time windows, should be performed with clinical-level accuracy.

PurposeAccurately identifying sleep states (REM, NREM, and Wake) and brief awakenings (arousals) is essential for diagnosing sleep disorders. Polysomnography (PSG) is the gold standard for such assessments but is costly and requires overnight monitoring in a lab. Home sleep testing (HST) offers a more accessible alternative, relying primarily on breathing measurements but lacks electroencephalography, limiting its ability to evaluate sleep and arousals directly. This study evaluates a deep learning algorithm which determines sleep states and arousals from breathing signals.MethodsA novel deep learning algorithm was developed to classify sleep states and detect arousals from respiratory inductance plethysmography signals. Sleep states were predicted for 30-s intervals (one sleep epoch), while arousal probabilities were calculated at 1-s resolution. Validation was conducted on a clinical dataset of 1,299 adults with suspected sleep disorders. Performance was assessed at the epoch level for sensitivity and specificity, with agreement analyses for arousal index (ArI) and total sleep time (TST).ResultsThe algorithm achieved sensitivity and specificity of 77.9% and 96.2% for Wake, 93.9% and 80.4% for NREM, 80.5% and 98.2% for REM, and 66.1% and 86.7% for arousals. Bland–Altman analysis showed ArI limits of agreement ranging from - 32 to 24 events/hour (bias: - 4.4) and TST limits from - 47 to 64 min (bias: 8.0). Intraclass correlation was 0.74 for ArI and 0.91 for TST.ConclusionThe algorithm identifies sleep states and arousals from breathing signals with agreement comparable to established variability in manual scoring. These results highlight its potential to advance HST capabilities, enabling more accessible, cost-effective and reliable sleep diagnostics.