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.

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.

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.

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.

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.

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.

Clinical manifestation of patients with isolated rapid eye movement sleep behavior disorder after modest-to-long disease duration.

STUDY OBJECTIVES: Patients with isolated rapid eye movement (REM) sleep behavior disorder (iRBD) are known to be at risk of neurodegenerative diseases but the time process of neurodegeneration remains unclear and its ethnic difference is rarely discussed. The objective of this study was to investigate the clinical manifestation of iRBD according to disease duration in a Korean cohort. METHODS: Patients who had been diagnosed with iRBD for at least 5 years were tracked and those currently remaining as iRBD were invited for motor, autonomic, olfactory, color discrimination, and cognitive tests. Cross-sectional comparison between patients with iRBD with modest disease duration (5-9.9 years) and long disease duration (≥10 years) was conducted. RESULTS: There were total of 198 patients with iRBD (135 males, 68.2%) tracked, whose average age at diagnosis was 65.9 years. Thirty-three had developed parkinsonism and 17 had developed dementia, resulting in a phenoconversion risk of 35.7% at 10 years. Hazard rate showed a gradually increasing trend over time, beginning from 2.1 conversions per year to 8.5 at 10 years. Patients with iRBD with a long disease duration (n = 19) had slightly higher motor scores than those with a modest duration (n = 82). However, they showed no difference in the frequency of motor abnormality or other clinical markers. CONCLUSIONS: There is a possible racial or geographical difference of phenoconversion risk. The progression of neurodegeneration might be very subtle in that patients with iRBD with longer disease duration do not necessarily show higher frequency of neurodegeneration markers.

Longitudinal qEEG changes correlate with clinical outcomes in patients with somatic symptom disorder.

OBJECTIVE: The quantitative electroencephalography (qEEG) of patients with somatic symptom disorder (SSD) was not yet thoroughly studied. This study aimed to investigate qEEG of SSD patients compared with those of normal controls (NCs), and changes therein after treatment. METHODS: SSD patients currently without treatment and age- and sex-matched NCs were recruited. Spectral analysis of 64-channel EEG recording was performed and somatization, anxiety, and depression were evaluated via self-rating scales at baseline. After six months of treatment as usual, SSD patients were longitudinally followed up for assessments. RESULTS: At baseline, the SSD group (n = 44) had higher alpha (p = 0.047) and lower beta 2 (p = 0.027) and gamma power (p = 0.001) compared with NCs (n = 29). After 6-month treatment, SSD patients showed improvement in symptoms, as well as increased beta 1 (p = 0.032), beta 2 (p = 0.012), and gamma power (p = 0.009) compared with baseline. A significant correlation was observed between the change in somatization score and temporal gamma power (r = -0.424, p = 0.031), and between the change in anxiety score and beta 2 power in the frontal (r = -0.420, p = 0.033) and central (r = -0.484, p = 0.012) regions. CONCLUSIONS: EEG findings in this study may provide neurophysiological features of SSD. The alpha enhancement and reduced fast wave activity may reflect attentional dysfunction in patients with SSD. Decreased fast wave activity is reversible and may serve as a state marker of SSD.

Effects of Probiotic NVP-1704 on Mental Health and Sleep in Healthy Adults: An 8-Week Randomized, Double-Blind, Placebo-Controlled Trial.

The human gut microbiome is closely linked to mental health and sleep. We aimed to verify the efficacy and safety of probiotic NVP-1704, a mixture of Lactobacillus reuteri NK33 and Bifidobacterium adolescentis NK98, in improving stress, depression, anxiety, and sleep disturbances, along with the measurement of some blood biomarkers. A total of 156 healthy adults with subclinical symptoms of depression, anxiety, and insomnia were retrospectively registered and randomly assigned to receive either NVP-1704 (n = 78) or a placebo (n = 78) for eight weeks. Participants completed the Stress Response Inventory, Beck's Depression and Anxiety Inventory, Pittsburg Sleep Quality Index, and Insomnia Severity Index at baseline, at four and eight weeks of treatment. Pre- and post-treatment blood tests for biomarkers were conducted. After intervention, gut microbiota composition was quantified by pyrosequencing the bacterial 16S rRNA gene. The NVP-1704 group had a more significant reduction in depressive symptoms at four and eight weeks of treatment, and anxiety symptoms at four weeks compared to the placebo group. Those receiving NVP-1704 also experienced an improvement in sleep quality. NVP-1704 treatment led to a decrease in serum interleukin-6 levels. Furthermore, NVP-1704 increased Bifidobacteriaceae and Lactobacillacea, whereas it decreased Enterobacteriaceae in the gut microbiota composition. Our findings suggest that probiotic NVP-1704 could be beneficial for mental health and sleep.

Differences in sleep measures and waking electroencephalography of patients with insomnia according to age and sex.

STUDY OBJECTIVES: Sleep characteristics are known to be different according to age and sex. The objective of this study was to investigate differences in sleep parameters and quantitative electroencephalography of patients with insomnia according to age and sex. METHODS: Patients with insomnia disorder ages 40-79 years were recruited. Each participant was assessed with the Pittsburgh Sleep Quality Index, 4-day wrist actigraphy, and quantitative electroencephalography derived from a 64-channel electroencephalogram system. These variables were compared between age groups (40-64 years vs 65-79 years) and sexes. RESULTS: Among 173 participants, 61 (35%) were ages 65-79 years and 64 (35%) were males. The older group reported shorter (P = .009) total sleep time than the middle-aged group based on the Pittsburgh Sleep Quality Index, while women slept longer than men based on actigraphy (P = .040). Regarding electroencephalography, women had higher relative beta power than men (P = .006). Older patients showed slower dominant occipital frequency than younger patients (P = .008). The age effect was more noticeable on both clinical variables and quantitative electroencephalography for women. Compared with younger women, older women reported shorter total sleep time in the Pittsburgh Sleep Quality Index (P = .025), underestimated their sleep time (Pittsburgh Sleep Quality Index total sleep time/actigraphic total sleep time, P = .034), and showed reduced alpha power in the frontal area (P = .009). CONCLUSIONS: Clinicians should be aware of the age and sex difference on manifestation of insomnia, which may further impact an individual's behaviors, such as staying in bed for a longer time or seeking sleep aids.

Confidence-Based Framework Using Deep Learning for Automated Sleep Stage Scoring.

STUDY OBJECTIVES: Automated sleep stage scoring is not yet vigorously used in practice because of the black-box nature and the risk of wrong predictions. The objective of this study was to introduce a confidence-based framework to detect the possibly wrong predictions that would inform clinicians about which epochs would require a manual review and investigate the potential to improve accuracy for automated sleep stage scoring. METHODS: We used 702 polysomnography studies from a local clinical dataset (SNUBH dataset) and 2804 from an open dataset (SHHS dataset) for experiments. We adapted the state-of-the-art TinySleepNet architecture to train the classifier and modified the ConfidNet architecture to train an auxiliary confidence model. For the confidence model, we developed a novel method, Dropout Correct Rate (DCR), and the performance of it was compared with other existing methods. RESULTS: Confidence estimates (0.754) reflected accuracy (0.758) well in general. The best performance for differentiating correct and wrong predictions was shown when using the DCR method (AUROC: 0.812) compared to the existing approaches which largely failed to detect wrong predictions. By reviewing only 20% of epochs that received the lowest confidence values, the overall accuracy of sleep stage scoring was improved from 76% to 87%. For patients with reduced accuracy (ie, individuals with obesity or severe sleep apnea), the possible improvement range after applying confidence estimation was even greater. CONCLUSION: To the best of our knowledge, this is the first study applying confidence estimation on automated sleep stage scoring. Reliable confidence estimates by the DCR method help screen out most of the wrong predictions, which would increase the reliability and interpretability of automated sleep stage scoring.

Age as a Moderator of Social Skills Intervention Response Among Korean Adolescents with Autism Spectrum Disorder.

Few studies have investigated the effect of age on treatment effectiveness of evidence-based social skills training in autism spectrum disorder (ASD) adolescents. This study evaluated the effectiveness of Program for the Education and Enrichment of Relational Skills (PEERS®) in different ages of ASD adolescents. A total of 110 ASD adolescents aged 11-19 years were divided into early, middle, and late adolescence groups, and the changes in outcome measures following intervention were compared among the three groups. We found that the improvements in social competence after PEERS® were significant, but the treatment effects were not significantly different among the three age groups. The findings suggest that PEERS® is beneficial for ASD adolescents regardless of patient age and adolescence stage.

The Effects of Continuation-Maintenance Electroconvulsive Therapy on Reducing Hospital Re-Admissions in Patients with Treatment-Resistant Schizophrenia.

OBJECTIVE: Continuation-maintenance electroconvulsive therapy (C/M-ECT) is used to prevent relapse or recurrence in patients with severe mental illnesses. We aimed to investigate the effect of C/M-ECT on reducing hospital re-admissions in patients with treatment-resistant schizophrenia. METHODS: We applied a mirror-image design by retrospectively examining re-hospitalization rates of 18 patients with schizophrenia spectrum disorders. We compared the numbers of psychiatric admissions during the actual period over which C/M-ECT was administered with the same period prior to the beginning of C/M-ECT. RESULTS: The number of psychiatric admissions was reduced significantly during C/M-ECT (0.33±0.77) compared with that of the same period prior to C/M-ECT (2.67±1.33) (Wilcoxon signed rank Z=-3.663; p＜0.001). CONCLUSION: This finding shows that C/M-ECT augmentation could successfully reduce the re-hospitalization rates in patients with treatment-resistant schizophrenia.