Diagnosis of Glaucoma Based on Few-Shot Learning with Wide-Field Optical Coherence Tomography Angiography.

This study evaluated the utility of incorporating deep learning into the relatively novel imaging technique of wide-field optical coherence tomography angiography (WF-OCTA) for glaucoma diagnosis. To overcome the challenge of limited data associated with this emerging imaging, the application of few-shot learning (FSL) was explored, and the advantages observed during its implementation were examined. A total of 195 eyes, comprising 82 normal controls and 113 patients with glaucoma, were examined in this study. The system was trained using FSL instead of traditional supervised learning. Model training can be presented in two distinct ways. Glaucoma feature detection was performed using ResNet18 as a feature extractor. To implement FSL, the ProtoNet algorithm was utilized to perform task-independent classification. Using this trained model, the performance of WF-OCTA through the FSL technique was evaluated. We trained the WF-OCTA validation method with 10 normal and 10 glaucoma images and subsequently examined the glaucoma detection effectiveness. FSL using the WF-OCTA image achieved an area under the receiver operating characteristic curve (AUC) of 0.93 (95% confidence interval (CI): 0.912-0.954) and an accuracy of 81%. In contrast, supervised learning using WF-OCTA images produced worse results than FSL, with an AUC of 0.80 (95% CI: 0.778-0.823) and an accuracy of 50% (p-values < 0.05). Furthermore, the FSL method using WF-OCTA images demonstrated improvement over the conventional OCT parameter-based results (all p-values < 0.05). This study demonstrated the effectiveness of applying deep learning to WF-OCTA for glaucoma diagnosis, highlighting the potential of WF-OCTA images in glaucoma diagnostics. Additionally, it showed that FSL could overcome the limitations associated with a small dataset and is expected to be applicable in various clinical settings.

Minimal effect of long-term clonazepam on cognitive function in patients with isolated RBD.

STUDY OBJECTIVES: Despite its widespread use in patients with isolated rapid eye movement sleep behavior disorder (iRBD), the cognitive effect of clonazepam is uncertain. This study aimed to investigate effect of cumulative clonazepam on cognitive function in patients with iRBD. METHODS: Demographic characteristics, baseline cognitive test, and most recent cognitive test information were collected retrospectively. Based on cumulative clonazepam doses, patients were classified into four subgroups: group 1 < 365 mg (1 mg * 1 year); 365 mg ≤ group 2 < 1,095 mg (1 mg * 3 years); 1,095 mg ≤ group 3 < 2,190 mg (1 mg * 6 years); and group 4 ≥ 2,190 mg. Cognitive test scores were calculated as z-scores adjusted for age, education, and sex. RESULTS: This study included 101 patients with iRBD (63 males). Groups 1, 2, 3, and 4 had 14, 20, 32, and 35 patients, respectively. In within-group comparisons, follow-up Digit Span Backward test and the Trail Making Test A (TMT-A) scores decreased in group 3, and follow-up TMT-A and the Trail Making Test B scores decreased significantly in group 4. In the multiple regression analysis to determine influential factors on cognitive decline, cumulative clonazepam dose did not show a significant correlation with any cognitive domain. Follow-up cognitive function showed significant correlation only with baseline cognitive function. CONCLUSIONS: Memory and executive functions tended to decline in patients with iRBD. However, there was no significant effect of cumulative clonazepam. There was no evidence that long-term use of clonazepam was related to cognitive decline in patients with iRBD.

Modest Effects of Neurofeedback-Assisted Meditation Using a Wearable Device on Stress Reduction: A Randomized, Double-Blind, and Controlled Study.

BACKGROUND: To evaluate the therapeutic effectiveness and safety of a neurofeedback wearable device for stress reduction. METHODS: A randomized, double-blind, controlled study was designed. Participants had psychological stress with depression or sleep disturbances. They practiced either neurofeedback-assisted meditation (n = 20; female, 15 [75.0%]; age, 49.40 ± 11.76 years) or neurofeedback non-assisted meditation (n = 18; female, 11 [61.1%]; age, 48.67 ± 12.90 years) for 12 minutes twice a day for two weeks. Outcome variables were self-reported questionnaires, including the Korean version of the Perceived Stress Scale, Beck Depression Inventory-II, Insomnia Severity Index, Pittsburgh Sleep Quality Index, and State Trait Anxiety Index, quantitative electroencephalography (qEEG), and blood tests. Satisfaction with device use was measured at the final visit. RESULTS: The experimental group had a significant change in PSS score after two weeks of intervention compared with the control group (6.45 ± 0.95 vs. 3.00 ± 5.54, P = 0.037). State anxiety tended to have a greater effect in the experimental group than in the control group (P = 0.078). Depressive mood and sleep also improved in each group, with no significant difference between the two groups. There were no significant differences in stress-related physiological parameters, such as stress hormones or qEEG, between the two groups. Subjective device satisfaction was significantly higher in the experimental group than in the control group (P = 0.008). CONCLUSION: Neurofeedback-assisted meditation using a wearable device can help improve subjective stress reduction compared with non-assisted meditation. These results support neurofeedback as an effective adjunct to meditation for relieving stress. TRIAL REGISTRATION: Clinical Research Information Service Identifier: KCT0007413.

Efficacy of cranial electrotherapy stimulation on mood and sense of well-being in people with subclinical insomnia.

Cranial electrotherapy stimulation is a non-invasive brain stimulation method characterised by using a microcurrent. The objective of the study was to investigate whether a novel device with a stable supplement of electronic stimulation would improve sleep and the accompanying mood symptoms in people with subclinical insomnia. People who had insomnia symptoms without meeting the criteria for chronic insomnia disorder were recruited and randomly assigned to an active or a sham device group. They were required to use the provided device for 30 min each time, twice a day for 2 weeks. Outcome measures included questionnaires for sleep, depression, anxiety, and quality of life, 4 day actigraphy, and 64-channel electroencephalography. Fifty-nine participants (male 35.6%) with a mean age of 41.1 ± 12.0 years were randomised. Improvement of depression (p = 0.032) and physical well-being (p = 0.041) were significant in the active device group compared with the sham device group. Anxiety was also improved in the active device group, although the improvement was not statistically significant (p = 0.090). Regarding sleep, both groups showed a significant improvement in subjective rating, showing no significant group difference. The change in electroencephalography after the 2 week intervention was significantly different between the two groups, especially for occipital delta (p = 0.008) and beta power (p = 0.012), and temporo-parieto-occipital theta (p = 0.022). In conclusion, cranial electrotherapy stimulation can serve as an adjunctive therapy to ameliorate psychological symptoms and to alter brain activity. The effects of the device in a clinical population and an optimal set of parameters of stimulation should be further investigated.

In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography.

Importance: Consumer-level sleep analysis technologies have the potential to revolutionize the screening for obstructive sleep apnea (OSA). However, assessment of OSA prediction models based on in-home recording data is usually performed concurrently with level 1 in-laboratory polysomnography (PSG). Establishing the predictability of OSA using sound data recorded from smartphones based on level 2 PSG at home is important. Objective: To validate the performance of a prediction model for OSA using breathing sound recorded from smartphones in conjunction with level 2 PSG at home. Design, Setting, and Participants: This diagnostic study followed a prospective design, involving participants who underwent unattended level 2 home PSG. Breathing sounds were recorded during sleep using 2 smartphones, one with an iOS operating system and the other with an Android operating system, simultaneously with home PSG in participants' own home environment. Participants were 19 years and older, slept alone, and had either been diagnosed with OSA or had no previous diagnosis. The study was performed between February 2022 and February 2023. Main Outcomes and Measures: Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds. Results: Of the 101 participants included during the study duration, the mean (SD) age was 48.3 (14.9) years, and 51 (50.5%) were female. For the iOS smartphone, the sensitivity values at apnea-hypopnea index (AHI) levels of 5, 15, and 30 per hour were 92.6%, 90.9%, and 93.3%, respectively, with specificities of 84.3%, 94.4%, and 94.4%, respectively. Similarly, for the Android smartphone, the sensitivity values at AHI levels of 5, 15, and 30 per hour were 92.2%, 90.0%, and 92.9%, respectively, with specificities of 84.0%, 94.4%, and 94.3%, respectively. The accuracy for the iOS smartphone was 88.6%, 93.3%, and 94.3%, respectively, and for the Android smartphone was 88.1%, 93.1%, and 94.1% at AHI levels of 5, 15, and 30 per hour, respectively. Conclusions and Relevance: This diagnostic study demonstrated the feasibility of predicting OSA with a reasonable level of accuracy using breathing sounds obtained by smartphones during sleep at home.

Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study.

BACKGROUND: Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography. OBJECTIVE: This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment. METHODS: The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography. RESULTS: The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal. CONCLUSIONS: Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep.

Effects of cranial electrotherapy stimulation on improving depressive symptoms in people with stress: A randomized, double-blind controlled study.

BACKGROUND: Cranial electrotherapy stimulation (CES) is a form of neurostimulation that delivers alternating microcurrent via electrodes on the head. We investigated the effectiveness of CES in reducing stress. METHODS: Participants who experienced subjective stress combined with subclinical depression or insomnia were recruited based on interviews and questionnaires. The subjects were randomly assigned to the active CES or sham groups and asked to use the device for 30 min twice a day for three weeks. Psychological rating scales, quantitative electroencephalography (QEEG), and serial salivary cortisol levels were measured before and after the intervention. RESULTS: Sixty-two participants (58 females, mean age = 47.3 ± 8.2 years) completed the trial. After intervention, the depression scores improved significantly to a nearly normal level (Beck depression inventory-II, 31.3 ± 11.6 to 10.8 ± 7.2, p < 0.001) in the CES group, which were greater improvement compared to the sham group (p = 0.020). There were significant group-by-visit interactions in absolute delta power in the temporal area (p = 0.033), and theta (p = 0.038), beta (p = 0.048), and high beta power (p = 0.048) in the parietal area. CES led to a flattening of the cortisol slope (p = 0.011) and an increase in bedtime cortisol (p = 0.036) compared to the sham group. LIMITATIONS: Bias may have been introduced during the process because device use and sample collection were self-conducted by participants at home. CONCLUSIONS: CES can alleviate depressive symptoms and stress response, showing a potential as an adjunctive therapy for stress.

Etomidate versus Propofol for Electroconvulsive Therapy in Patients with Major Depressive Disorders in Terms of Clinical Responses to Treatment: A Retrospective Analysis.

General anesthetic agents may be associated with the clinical efficacy of electroconvulsive therapy (ECT), as they may influence seizure quality and duration. Hence, a retrospective study was conducted to compare the clinical effects and seizure variables of etomidate and propofol during ECT. Patients treated with ECT under anesthesia with etomidate (n = 43) or propofol (n = 12) were retrospectively analyzed. Seizure variables (seizure duration, intensity, and threshold) and hemodynamic changes during ECT were assessed and recorded. Clinical responses to treatment were evaluated using the Clinical Global Impression scale and mood at discharge after the course of ECT. Adverse effects were also recorded. The demographic characteristics were similar between the two groups. There were no significant differences in the Clinical Global Impression scale scores, mood at discharge, and adverse effects between the two groups (p > 0.05); however, etomidate was associated with a significantly longer motor (42.0 vs. 23.65 s, p < 0.001) and electroencephalogram (51.8 vs. 33.5 s, p < 0.001) seizure duration than propofol. In conclusion, etomidate showed more favorable seizure profiles than propofol during ECT; however, both agents (etomidate and propofol) were associated with similar clinical efficacy profiles at discharge.

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.

BACKGROUND: The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via deep learning is emerging as a decent alternative for their convenience and potential accuracy. However, sound-based sleep staging has only been studied using in-laboratory sound data. In real-world sleep environments (homes), there is abundant background noise, in contrast to quiet, controlled environments such as laboratories. The use of sound-based sleep staging at homes has not been investigated while it is essential for practical use on a daily basis. Challenges are the lack of and the expected huge expense of acquiring a sufficient size of home data annotated with sleep stages to train a large-scale neural network. OBJECTIVE: This study aims to develop and validate a deep learning method to perform sound-based sleep staging using audio recordings achieved from various uncontrolled home environments. METHODS: To overcome the limitation of lacking home data with known sleep stages, we adopted advanced training techniques and combined home data with hospital data. The training of the model consisted of 3 components: (1) the original supervised learning using 812 pairs of hospital polysomnography (PSG) and audio recordings, and the 2 newly adopted components; (2) transfer learning from hospital to home sounds by adding 829 smartphone audio recordings at home; and (3) consistency training using augmented hospital sound data. Augmented data were created by adding 8255 home noise data to hospital audio recordings. Besides, an independent test set was built by collecting 45 pairs of overnight PSG and smartphone audio recording at homes to examine the performance of the trained model. RESULTS: The accuracy of the model was 76.2% (63.4% for wake, 64.9% for rapid-eye movement [REM], and 83.6% for non-REM) for our test set. The macro F1-score and mean per-class sensitivity were 0.714 and 0.706, respectively. The performance was robust across demographic groups such as age, gender, BMI, or sleep apnea severity (accuracy 73.4%-79.4%). In the ablation study, we evaluated the contribution of each component. While the supervised learning alone achieved accuracy of 69.2% on home sound data, adding consistency training to the supervised learning helped increase the accuracy to a larger degree (+4.3%) than adding transfer learning (+0.1%). The best performance was shown when both transfer learning and consistency training were adopted (+7.0%). CONCLUSIONS: This study shows that sound-based sleep staging is feasible for home use. By adopting 2 advanced techniques (transfer learning and consistency training) the deep learning model robustly predicts sleep stages using sounds recorded at various uncontrolled home environments, without using any special equipment but smartphones only.

Altered Brain Glymphatic Flow at Diffusion-Tensor MRI in Rapid Eye Movement Sleep Behavior Disorder.

Background Brain glymphatic dysfunction may contribute to the development of α-synucleinopathies. Yet, noninvasive imaging and quantification remain lacking. Purpose To examine glymphatic function of the brain in isolated rapid eye movement sleep behavior disorder (RBD) and its relevance to phenoconversion with use of diffusion-tensor imaging (DTI) analysis along the perivascular space (ALPS). Materials and Methods This prospective study included consecutive participants diagnosed with RBD, age- and sex-matched control participants, and participants with Parkinson disease (PD) who were enrolled and examined between May 2017 and April 2020. All study participants underwent 3.0-T brain MRI including DTI, susceptibility-weighted and susceptibility map-weighted imaging, and/or dopamine transporter imaging using iodine 123-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane SPECT at the time of participation. Phenoconversion status to α-synucleinopathies was unknown at the time of MRI. Participants were regularly followed up and monitored for any signs of α-synucleinopathies. The ALPS index reflecting glymphatic activity was calculated by a ratio of the diffusivities along the x-axis in the projection and association neural fibers to the diffusivities perpendicular to them and compared according to the groups with use of the Kruskal-Wallis and Mann-Whitney U tests. The phenoconversion risk in participants with RBD was evaluated according to the ALPS index with use of a Cox proportional hazards model. Results Twenty participants diagnosed with RBD (12 men; median age, 73 years [IQR, 66-76 years]), 20 control participants, and 20 participants with PD were included. The median ALPS index was lower in the group with RBD versus controls (1.53 vs 1.72; P = .001) but showed no evidence of a difference compared with the group with PD (1.49; P = .68). The conversion risk decreased with an increasing ALPS index (hazard ratio, 0.57 per 0.1 increase in the ALPS index [95% CI: 0.35, 0.93]; P = .03). Conclusion DTI-ALPS in RBD demonstrated a more severe reduction of glymphatic activity in individuals with phenoconversion to α-synucleinopathies. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Filippi and Balestrino in this issue.

Ultrasound-guided intra-articular corticosteroid injection in a patient with manubriosternal joint involvement of ankylosing spondylitis: A case report.

BACKGROUND: Manubriosternal joint (MSJ) disease is a rare cause of anterior chest pain but can be a major sign of systemic arthritic involvement. In patients with ankylosing spondylitis (AS), a type of systemic arthritis, chest pain can be due to MSJ involvement and can be improved by ultrasound-guided corticosteroid injection into the joint. CASE SUMMARY: A 64-year-old man visited our pain clinic complaining of anterior chest pain. There were no abnormal findings on lateral sternum X-ray, but arthritic changes in the MSJ were observed on single-photon emission computed tomography-computed tomography. We performed additional laboratory tests, and he was finally diagnosed with AS. For pain relief, we performed ultrasound-guided intra-articular (IA) corticosteroid injections into the MSJ. After the injections, his pain nearly resolved. CONCLUSION: For patients complaining of anterior chest pain, AS should be considered, and single-photon emission computed tomography-computed tomography can be helpful in diagnosis. In addition, ultrasound-guided IA corticosteroid injections may be effective for pain relief.

Predictability of Radiologically Measured Psoas Muscle Area for Intraoperative Hypotension in Older Adult Patients Undergoing Femur Fracture Surgery.

This retrospective study aimed to determine the predictive value of radiologically measured psoas muscle area (PMA) for intraoperative hypotension (IOH) using receiver operating characteristic (ROC) curves in older adult patients with hip fractures. The cross-sectional axial area of the psoas muscle was measured by CT at the level of the 4th lumbar vertebrae and normalized by body surface area (BSA). The modified frailty index (mFI) was used to assess frailty. IOH was defined as an absolute threshold of mean arterial blood pressure (MAP) < 65 mmHg or a relative decrease in MAP > 30% from baseline MAP. Among the 403 patients, 286 (71.7%) had developed IOH. PMA normalized by BSA in male patients was 6.90 ± 0.73 in the no-IOH group and 4.95 ± 1.20 in the IOH group (p < 0.001). PMA normalized by BSA in female patients was 5.18 ± 0.81 in the no-IOH group and 3.78 ± 0.75 in the IOH group (p < 0.001). The ROC curves showed that the area under the curve for PMA normalized by BSA and modified frailty index (mFI) were 0.94 for male patients, 0.91 for female patients, and 0.81 for mFI (p < 0.001). In multivariate logistic regression, low PMA normalized by BSA, high baseline systolic blood pressure, and old age were significant independent predictors of IOH (adjusted odds ratio: 3.86, 1.03, and 1.06, respectively). PMA measured by computed tomography showed an excellent predictive value for IOH. Low PMA was associated with developing IOH in older adult patients with hip fractures.

Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation.

BACKGROUND: Multinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home. OBJECTIVE: The purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home environment where various noises exist. METHODS: This study included 1018 polysomnography (PSG) audio data sets, 297 smartphone audio data sets synced with PSG, and a home noise data set containing 22,500 noises to train the model to predict breathing events, such as apneas and hypopneas, based on breathing sounds that occur during sleep. The whole breathing sound of each night was divided into 30-second epochs and labeled as "apnea," "hypopnea," or "no-event," and the home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed using epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI). RESULTS: Epoch-by-epoch OSA event detection showed an accuracy of 86% and a macro F1-score of 0.75 for the 3-class OSA event detection task. The model had an accuracy of 92% for "no-event," 84% for "apnea," and 51% for "hypopnea." Most misclassifications were made for "hypopnea," with 15% and 34% of "hypopnea" being wrongly predicted as "apnea" and "no-event," respectively. The sensitivity and specificity of the OSA severity classification (AHI≥15) were 0.85 and 0.84, respectively. CONCLUSIONS: Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multinight monitoring and real-time diagnostic technologies in the home environment.

Network structure of insomnia symptoms in shift workers compared to non-shift workers.

Insomnia is a commonly occurring sleep problem in shift workers. So far, no studies have investigated how insomnia symptoms present differently in shift workers and non-shift workers. The purpose of this study was to compare the network structures and centrality indices of shift and non-shift workers using network analysis and network comparison test. Participants included 1339 hospital employees, where 542 were shift workers and 797 were non-shift workers. Overall, a significant difference between network structures were observed. In particular, daytime dysfunction emerged as a strongly connected symptom in shift workers, as evidenced by strength centrality. Increased use of sleeping medication and decreased habitual sleep efficiency were more strongly associated with increased daytime dysfunction in shift workers. Sleep latency and sleep quality were also more strongly linked in shift workers. These results are in part attributable to differing causes of insomnia in shift and non-shift workers. Furthermore, the results indicate that shift workers are more vulnerable and susceptible to changes in sleep-related indices, such as sleep efficiency and latency. The findings suggest that certain insomnia symptoms are more consequential in shift workers, emphasizing the need for a differentiated approach in treating insomnia according to shift work.

The effect of proto-type wearable light-emitting devices on serum 25-hydroxyvitamin D levels in healthy adults: a 4-week randomized controlled trial.

BACKGROUND: Many people in modern society have insufficient exposure to ultraviolet B (UVB) sunlight, which may lead to vitamin D deficiency. We aimed to investigate the effect of a proto-type wearable light-emitting diode (LED) device emitting UVB light on serum 25-hydroxyvitamin D levels. METHODS: A total of 136 healthy adults were randomly assigned to receive either an active device emitting UVB light with a peak wavelength of 285 nm (n = 64) or a sham device emitting visible light (n = 72). All participants wore the device for a total of two minutes, one minute on each forearm, every day for 4 weeks. Serum 25-hydroxyvitamin D levels were assessed at baseline, 2, and 4 weeks of intervention, and 2 weeks after the end of the intervention. RESULTS: A significant difference was found between the experimental and control groups in changes in serum 25-hydroxyvitamin D levels from baseline after two (0.25 ± 3.10 ng/mL vs. -1.07 ± 2.68 ng/mL, p = 0.009) and 4 weeks of intervention (0.75 ± 3.98 ng/mL vs. -1.75 ± 3.04 ng/mL, p < 0.001). In the experimental group, the dropout rate due to mild, self-limiting adverse skin reactions was 11.8% (9/76). The mean total 25-hydroxyvitamin D production after UVB exposure was estimated at 0.031 ng/mL per 1 cm2 of skin area. CONCLUSIONS: A prototype wearable LED UVB device was effective for improving 25-hydroxyvitamin D status. The development of a safer wearable LED device for phototherapy may provide a novel daily, at-home option for vitamin D supplementation.

Comparison of autonomous sensory meridian response and binaural auditory beats effects on stress reduction: a pilot study.

This study aimed to compare the effects of Autonomous sensory meridian response (ASMR) and binaural beat (BB) on stress reduction, and to determine whether ASMR and BB can induce changes in quantitative electroencephalography (QEEG). A double-blind randomized trial was conducted. Subjects with stress were recruited considering their perceived stress scale (PSS), Beck depression inventory-II (BDI-II), insomnia severity index (ISI), and state-trait anxiety inventory-state anxiety (STAI-S) scores. Subjects listened to ASMR or BB with music (8 Hz for daytime, 5 Hz for nighttime) for 15 min in daytime and 30 min before going to sleep for 3 weeks. QEEG was measured before and after the intervention. Seventy-six participants (57 female, mean age = 46.12 ± 12.01) finished the trial. After the intervention, PSS, BDI-II, ISI, STAI-S, and PSQI scores improved significantly in both groups. BDI-II and ISI mean scores were normalized in both groups after the intervention. Changes of absolute beta and high beta power in the ASMR group were larger than those in the BB group (p = 0.026, p = 0.040, respectively). Both ASMR and BB are equally effective in reducing stress levels. Unlike BB, ASMR can lead to an increase in beta and high beta waves associated with cortical arousal.

Lower Serum Calcium Levels Associated with Disrupted Sleep and Rest-Activity Rhythm in Shift Workers.

Vitamin D deficiency is prevalent in many developed countries, and several studies suggest that vitamin D plays an essential role in brain function. A recent study showed that vitamin D deficiency was closely associated with daytime sleepiness and shorter sleep time. The relationshipbetween vitamin D levels and calcium levels is well established, and calcium level regulates slow-wave sleep generation. It is conceivable that the sleep disturbance in vitamin D deficiency may be due to an altered calcium level. Nonetheless, calcium levels, sleep disturbances, and activity rhythms have not been investigated directly. Therefore, we hypothesized that calcium and vitamin D levels might be important in regulating sleep and activity rhythm, and we analyzed the correlation with calcium levels by actigraphy analysis. Interestingly, a negative correlation was found between calcium level and sleep latency, total sleep time, use of sleep medicine, and daytime dysfunction among shift workers. In contrast, non-shift workers showed a negative correlation between the calcium level and the circadian phase. These findings suggest that low serum calcium levels may disrupt sleep-wake control and rest-activity rhythm, even if they are within the normal range.

End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices.

Purpose: Nocturnal sounds contain numerous information and are easily obtainable by a non-contact manner. Sleep staging using nocturnal sounds recorded from common mobile devices may allow daily at-home sleep tracking. The objective of this study is to introduce an end-to-end (sound-to-sleep stages) deep learning model for sound-based sleep staging designed to work with audio from microphone chips, which are essential in mobile devices such as modern smartphones. Patients and Methods: Two different audio datasets were used: audio data routinely recorded by a solitary microphone chip during polysomnography (PSG dataset, N=1154) and audio data recorded by a smartphone (smartphone dataset, N=327). The audio was converted into Mel spectrogram to detect latent temporal frequency patterns of breathing and body movement from ambient noise. The proposed neural network model learns to first extract features from each 30-second epoch and then analyze inter-epoch relationships of extracted features to finally classify the epochs into sleep stages. Results: Our model achieved 70% epoch-by-epoch agreement for 4-class (wake, light, deep, REM) sleep stage classification and robust performance across various signal-to-noise conditions. The model performance was not considerably affected by sleep apnea or periodic limb movement. External validation with smartphone dataset also showed 68% epoch-by-epoch agreement. Conclusion: The proposed end-to-end deep learning model shows potential of low-quality sounds recorded from microphone chips to be utilized for sleep staging. Future study using nocturnal sounds recorded from mobile devices at home environment may further confirm the use of mobile device recording as an at-home sleep tracker.

A Longitudinal Study on Attenuated Structural Covariance in Patients With Somatic Symptom Disorder.

Objective: This study was performed to investigate altered regional gray matter volume (rGMV) and structural covariance related to somatic symptom disorder (SSD) and longitudinal changes after treatment. Additionally, this study examined the relationships of structural alteration with its phenotypic subtypes. Methods: Forty-three unmedicated patients with SSD and thirty normal controls completed psychological questionnaires and neurocognitive tests, as well as brain magnetic resonance imaging. Voxel-based morphometry and structural covariances were compared between groups and between subgroups within the SSD group. After 6 months of treatment, SSD patients were followed up for assessments. Results: Patients with SSD exhibited attenuated structural covariances in the pallidal-cerebellar circuit (FDR < 0.05-0.1), as well as regions in the default mode and sensorimotor network (FDR < 0.2), compared to normal controls. The cerebellar rGMVs were negatively correlated with the severity of somatic symptoms. In subgroup analyses, patients with somatic pain showed denser structural covariances between the bilateral superior temporal pole and left angular gyrus, the left middle temporal pole and left angular gyrus, and the left amygdala and right inferior orbitofrontal gyrus, while patients with headache and dizziness had greater structural covariance between the right inferior temporal gyrus and right cerebellum (FDR < 0.1-0.2). After 6 months of treatment, patients showed improved symptoms, however there was no significant structural alteration. Conclusion: The findings suggest that attenuated structural covariance may link to dysfunctional brain network and vulnerability to SSD; they also suggested that specific brain regions and networks may contribute to different subtypes of SSD.

Age-Limited Effects of Low-Frequency Transcutaneous Electric Nerve Stimulation on Insomnia: A 4-Week Multi-Center, Randomized Controlled Study.

OBJECTIVE: Insomnia disorder is a common condition with considerable harmful effects on health. We investigated the therapeutic efficacy and safety of low-frequency transcutaneous electric nerve stimulation (LF-TENS) as an alternative treatment option for insomnia disorder. METHODS: A 4-week, multi-center, randomized controlled study was conducted. A total of 160 individuals aged 40 to 80 years with insomnia disorder were included and randomized to the experimental group receiving active device (n=81) or control group receiving sham device (n=79). Both groups used the device for four weeks, more than five days a week. The participants also completed pre- and post-intervention assessment with questionnaires, sleep diaries, wrist actigraphy, and blood tests. RESULTS: There was no significant between-group difference in the changes of mood and sleep parameters and blood test results among the two study groups. Meanwhile, in the exploratory sub-group analysis of patients aged over 60 years, the experimental group showed better improvement after intervention in the change of Pittsburgh Sleep Quality Index (PSQI) score (-2.63±3.25 vs. -1.20±2.28, p=0.039; Cohen's d=0.99 vs. 0.45) and blood cortisol level (-1.65±3.37 µg/dL vs. -0.16±3.49 µg/dL, p=0.007; Cohen's d=0.56 vs. 0.05). In addition, no serious adverse reaction occurred during the study period in both groups. CONCLUSION: The effect of LF-TENS was limited to older patients aged over 60 years, which might be related to the modulation of hypothalamic-pituitary-adrenal axis activity.