To characterize and evaluate the estimation of oxygen saturation measured by a wrist-worn reflectance pulse oximeter during sleep. Ninety-seven adults with sleep disturbances were enrolled. Oxygen saturation was simultaneously measured using a reflectance pulse oximeter (Galaxy Watch 4 [GW4], Samsung, South Korea) and a transmittance pulse oximeter (polysomnography) as a reference. The performance of the device was evaluated using the root mean squared error (RMSE) and coverage rate. Additionally, GW4-derived oxygen desaturation index (ODI) was compared with the apnea-hypopnea index (AHI) derived from polysomnography. The GW4 had an overall RMSE of 2.3% and negligible bias of -0.2%. A Bland-Altman density plot showed good agreement between the GW4 and the reference pulse oximeter. RMSEs were 1.65 ± 0.57%, 1.76 ± 0.65%, 1.93 ± 0.54%, and 2.93 ± 1.71% for normal (n = 18), mild (n = 21), moderate (n = 23), and severe obstructive sleep apnea (n = 35), respectively. The data rejection rate was 26.5%, which was caused by fluctuations in contact pressure and the discarding of data less than 70% of saturation. A GW4-ODI ≥5/h had the highest ability to predict AHI ≥15/h with sensitivity, specificity, accuracy, and area under the curve of 89.7%, 64.1%, 79.4%, and 0.908, respectively. This study evaluated the estimation of oxygen saturation by the GW4 during sleep. This device complies with both Food and Drug Administration and International Organization for Standardization standards. Further improvements in the algorithms of wearable devices are required to obtain more accurate and reliable information about oxygen saturation measurements.

Purpose: To compare the characteristics of obstructive sleep apnea (OSA) between patients with epilepsy and patients without epilepsy and to investigate CPAP (Continuous Positive Airway Pressure) effect on seizures. Methods: Medical and polysomnography (PSG) data from 235 adult OSA patients with epilepsy (OE; 183 males; mean age, 49.8 years) and 268 age- and sex-matched OSA patients without epilepsy (OSE; 216 males; mean age, 51.3 years), obtained between March 2014 and May 2020 and housed in a database in a university-affiliated hospital, were retrospectively reviewed. All subjects completed surveys addressing comorbidities and medications, and sleep-related questionnaires including the Insomnia Severity Index, Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, and Beck Depression Inventory-II. Results: Compared with the OSE group, the OE group reported fewer sleep-related complaints and less severe OSA-related PSG parameters, with a lower apnea-hypopnea index (24.9 vs. 33.4 events/h; p < 0.003), arousal index (23.3 vs. 30.8 events/h; p < 0.001), and oxygen desaturation index (19.6 vs. 28.8; p < 0.002). The OE group had fewer smokers and lower alcohol consumption but a higher body mass index (27.0 vs. 25.9 kg/m²; p < 0.001). No correlations were observed between OSA-related PSG parameters and epilepsy-related factors, such as age at seizure onset, seizure type, frequency of seizures, presence of nocturnal seizures, and number of antiseizure medications, in the OE group. Patients with OE who demonstrated good compliance with CPAP therapy exhibited a decrease in seizure frequency. Conclusions: The OE group exhibited less severe disease characteristics than their age- and sex-matched OSE counterparts. Nevertheless, because the coexistence of OSA and epilepsy is high, CPAP therapy can reduce the frequency of seizures. Therefore, it is important to evaluate the presence of OSA in patients with epilepsy and to treat the conditions concurrently.

Objective: We aimed to investigate relationships between sleep disturbances and phenoconversion to neurodegenerative diseases in patients with REM sleep behavior disorder (RBD). Method: Using a comprehensive sleep database in a university-affiliated hospital between December 2014 and March 2021, we reviewed the data of 226 patients with RBD (182 patients with idiopathic RBD (iRBD) and 44 patients with symptomatic RBD (sRBD) with a neurodegenerative disease). Results: Among 226 patients with RBD (male, 61.5%), the mean age at RBD onset and mean disease duration were 59.4 ± 10.5 and 5.9 ± 5.6 years, respectively. Further, 111 (49.1%) patients had periodic limb movements during sleep (PLMS, PLM index ≥ 15/h), while 110 patients (48.7%) had comorbid obstructive sleep apnea (OSA, respiratory disturbance index ≥ 15/h). There was a positive correlation between age at RBD onset and the apnea-hypopnea index and Pittsburgh Sleep Quality Index. Compared to patients with iRBD, patients with sRBD showed a lower N3 sleep (3.3 ± 5.0 vs. 1.6 ± 3.1%, p = 0.004) and higher periodic limb movement index (36.3 ± 31.8 vs. 56.9 ± 47.5/h, p = 0.021) at the baseline. Among the 186 patients with iRBD, 18 (8.0%) developed neurodegenerative diseases (converters, mean follow-up duration: 2.5 ± 1.6 years) and 164 did not (non-converters, mean follow-up 2.4 ± 2.2 years). There was no significant between-group difference in the demographics and baseline clinical features. Continuous positive airway pressure (CPAP) therapy was prescribed in 101 patients with OSA; among them, 71 (70%) patients agreed to use it. CPAP improved dream enactment behaviors. Conclusion: In our study, 8.0% of patients with iRBD showed phenoconversion within a mean follow-up duration of 2.5 years. Polysomnographic parameters could not predict phenoconversion to neurodegenerative disease. However, approximately half of the patients with RBD presented with significant sleep disorders, including OSA or PLMS. CPAP therapy may alleviate RBD symptoms in patients with RBD-OSA.

Objectives: Galaxy Watch 3 (GW3) is a commercially available smartwatch equipped with a sleep-tracking function capable of collecting longitudinal sleep data in a real-world environment. We aimed to investigate the validity of GW3 for estimating sleep stages compared with reference data from polysomnography (PSG).Methods: Thirty-two healthy adults (mean age 37.8, male 87.5%) were recruited to wear a GW3 concurrently with in-laboratory overnight PSG recording. Sleep parameters, including total sleep time (TST) and the duration of each sleep stage (light, deep, and rapid eye movement [REM] sleep), were calculated for both GW3 and PSG. Sleep parameters were compared using intraclass correlation coefficients (ICCs) and Bland–Altman plots. The epoch-by-epoch classification performance was evaluated to determine the sensitivity, specificity, accuracy, kappa values, and confusion matrices.Results: Bland–Altman plots showed moderate agreement between GW3 and PSG for TST (ICC=0.640), light sleep (ICC=0.518), and deep sleep (ICC=0.639), whereas REM sleep duration was not reliably estimated using the GW3. The GW3 overestimated TST by a mean of 9.5 min. The sensitivity of epoch-by-epoch sleep detection was 0.954; however, the specificity was 0.524. The sensitivity of each sleep stage estimation was 0.695 for light sleep, 0.612 for deep sleep, and 0.598 for REM sleep. The overall accuracy of GW3 in distinguishing the four-stage sleep epochs was 0.651.Conclusions: GW3 demonstrated high performance in sleep detection but moderate performance in wake determination and sleep stage estimation compared with PSG results, which were comparable to previously reported results for other consumer wearable devices.

Kim JS, et al. J Clin Neurol. 2021 Jul;17(3):385-392. https://doi.org/10.3988/jcn.2021.17.3.385

Several risk factors for osteoporotic fractures have been identified but reports of the association of lipid parameters with the occurrence of osteoporotic fractures have been limited. We aimed to examine whether serum total cholesterol (TC) variability is associated with osteoporotic fractures. The study included 3,00,326 subjects who had undergone three or more health examinations between 2003 and 2008. The primary endpoint was the incidence of osteoporotic fractures, including vertebral, hip, distal radius, and humerus fractures. TC variability was evaluated based on the following three parameters: coefficient of variation (CV), standard deviation (SD), and variability independent of the mean (VIM). A total of 29,044 osteoporotic fracture events (9.67%) were identified during a median of 11.6 years of follow-up. The risk of osteoporotic fractures in the highest quartile was significantly higher compared with the lowest quartile according to the three indices of TC variability with adjusted hazard ratios (HR) and 95% confidence intervals (CI) as follows: CV (HR 1.11, 95% CI [1.08–1.15]), SD (HR 1.07, 95% CI [1.04–1.11]) and VIM (HR 1.07, 95% CI [1.04–1.11]). The Kaplan–Meier curves showed a significantly positive relationship between the higher quartile of TC variability and overall osteoporotic fractures. The association remained significant in subgroup analyses of vertebral and hip fractures, regardless of the indices of TC variability. Our study showed that visit-to-visit TC variability was found to be associated with osteoporotic fracture risk. Maintaining TC levels stable may help attenuate the osteoporotic fracture risk in the future.