Assessment of Stress and Well-Being of Japanese Employees Using Wearable Devices for Sleep Monitoring Combined With Ecological Momentary Assessment: Pilot Observational Study.

BACKGROUND: Poor sleep quality can elevate stress levels and diminish overall well-being. Japanese individuals often experience sleep deprivation, and workers have high levels of stress. Nevertheless, research examining the connection between objective sleep assessments and stress levels, as well as overall well-being, among Japanese workers is lacking. OBJECTIVE: This study aims to investigate the correlation between physiological data, including sleep duration and heart rate variability (HRV), objectively measured through wearable devices, and 3 states (sleepiness, mood, and energy) assessed through ecological momentary assessment (EMA) and use of rating scales for stress and well-being. METHODS: A total of 40 office workers (female, 20/40, 50%; mean age 40.4 years, SD 11.8 years) participated in the study. Participants were asked to wear a wearable wristband device for 8 consecutive weeks. EMA regarding sleepiness, mood, and energy levels was conducted via email messages sent by participants 4 times daily, with each session spaced 3 hours apart. This assessment occurred on 8 designated days within the 8-week timeframe. Participants' stress levels and perception of well-being were assessed using respective self-rating questionnaires. Subsequently, participants were categorized into quartiles based on their stress and well-being scores, and the sleep patterns and HRV indices recorded by the Fitbit Inspire 2 were compared among these groups. The Mann-Whitney U test was used to assess differences between the quartiles, with adjustments made for multiple comparisons using the Bonferroni correction. Furthermore, EMA results and the sleep and HRV indices were subjected to multilevel analysis for a comprehensive evaluation. RESULTS: The EMA achieved a total response rate of 87.3%, while the Fitbit Inspire 2 wear rate reached 88.0%. When participants were grouped based on quartiles of well-being and stress-related scores, significant differences emerged. Specifically, individuals in the lowest stress quartile or highest subjective satisfaction quartile retired to bed earlier (P<.001 and P=.01, respectively), whereas those in the highest stress quartile exhibited greater variation in the midpoint of sleep (P<.001). A multilevel analysis unveiled notable relationships: intraindividual variability analysis indicated that higher energy levels were associated with lower deviation of heart rate during sleep on the preceding day (ß=-.12, P<.001), and decreased sleepiness was observed on days following longer sleep durations (ß=-.10, P<.001). Furthermore, interindividual variability analysis revealed that individuals with earlier midpoints of sleep tended to exhibit higher energy levels (ß=-.26, P=.04). CONCLUSIONS: Increased sleep variabilities, characterized by unstable bedtime or midpoint of sleep, were correlated with elevated stress levels and diminished well-being. Conversely, improved sleep indices (eg, lower heart rate during sleep and earlier average bedtime) were associated with heightened daytime energy levels. Further research with a larger sample size using these methodologies, particularly focusing on specific phenomena such as social jet lag, has the potential to yield valuable insights. TRIAL REGISTRATION: UMIN-CTR UMIN000046858; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000053392.

Home-Based Cognitive Intervention for Healthy Older Adults Through Asking Robots Questions: Randomized Controlled Trial.

Background: Asking questions is common in conversations, and while asking questions, we need to listen carefully to what others say and consider the perspective our questions adopt. However, difficulties persist in verifying the effect of asking questions on older adults' cognitive function due to the lack of a standardized system for conducting experiments at participants' homes. Objective: This study examined the intervention effect of cognitive training moderated by robots on healthy older adults. A focus on the feasibility of the intervention at participants' homes was also maintained. Feasibility was evaluated by considering both the dropout rate during the intervention and the number of questions posed to each participant during the experiment. Methods: We conducted a randomized controlled trial with 81 adults older than 65 years. Participants were recruited through postal invitations and then randomized into 2 groups. The intervention group (n=40) received sessions where participants listened to photo-integrated stories and posed questions to the robots. The control group (n=41) received sessions where participants listened to photo-integrated stories and only thanked the robots for confirming participation. The participants participated in 12 dialogue sessions for 2-3 weeks. Scores of global cognitive functioning tests, recall tests, and verbal fluency tasks measured before and after the intervention were compared between the 2 groups. Results: There was no significant intervention effect on the Telephone Interview for Cognitive Status-Japanese scores, recall tests, and verbal fluency tasks. Additionally, our study successfully concluded with no participant dropouts at follow-up, confirming the feasibility of our approach. Conclusions: There was no statistically significant evidence indicating intervention benefits for cognitive functioning. Although the feasibility of home-based interventions was demonstrated, we identified areas for improvement in the future, such as setting up more efficient session themes. Further research is required to identify the effectiveness of an improved cognitive intervention involving the act of asking questions.

Amyloid-ß prediction machine learning model using source-based morphometry across neurocognitive disorders.

Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aß) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aß-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aß-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.

A comparative study on dietary diversity and gut microbial diversity in children with autism spectrum disorder, attention-deficit hyperactivity disorder, their neurotypical siblings, and non-related neurotypical volunteers: a cross-sectional study.

BACKGROUND: Previous research has shown a significant link between gut microbiota in children with autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). However, much remains unknown because of the heterogeneity of disorders and the potential confounders such as dietary patterns and control group variations. METHODS: Children aged 6-12 years who had been clinically diagnosed with ASD and/or ADHD, their unaffected neurotypical siblings, and non-related neurotypical volunteers were recruited cross-sectionally. The ASD diagnosis was confirmed using the Autism Diagnostic Observation Schedule-2 (ADOS-2) in all patients, including those with ADHD. Standardized DNA extraction and sequencing methods were used to compare gut microbial alpha-diversity among the groups. Dietary diversity was calculated from a standardized dietary questionnaire form. We compared the difference in gut microbiome between patients with ASD and/or ADHD with neurotypical siblings and non-related neurotypical controls. RESULTS: Ninety-eight subjects were included in the study (18 with ASD, 19 with ADHD, 20 with both ASD and ADHD, 13 neurotypical siblings, and 28 non-related neurotypical controls). The alpha-diversity indices, such as Chao 1 and Shannon index, showed a significant difference between the groups in a Linear mixed-effect model (F(4, 93) = 4.539, p = .02), (F(4, 93) = 3.185, p = .017), respectively. In a post-hoc pairwise comparison, patients with ASD had lower alpha-diversity compared with non-related controls after Bonferroni correction. Dietary diversity shown in Shannon index did not differ among the groups (F(4, 84) = 1.494, p = .211). CONCLUSIONS: Our study indicates disorder-specific microbiome differences in patients with ASD. In future research on gut microbiota in neurodevelopmental disorders, it is necessary to consider the impact of ASD and ADHD co-occurrence, and strictly control for background information such as diet, to elucidate the gut-microbiota interaction in ASD and ADHD for exploring the potential of therapeutic interventions.

Intestinal metabolites predict treatment resistance of patients with depression and anxiety.

BACKGROUND: The impact of the gut microbiota on neuropsychiatric disorders has gained much attention in recent years; however, comprehensive data on the relationship between the gut microbiome and its metabolites and resistance to treatment for depression and anxiety is lacking. Here, we investigated intestinal metabolites in patients with depression and anxiety disorders, and their possible roles in treatment resistance. RESULTS: We analyzed fecal metabolites and microbiomes in 34 participants with depression and anxiety disorders. Fecal samples were obtained three times for each participant during the treatment. Propensity score matching led us to analyze data from nine treatment responders and nine non-responders, and the results were validated in the residual sample sets. Using elastic net regression analysis, we identified several metabolites, including N-ε-acetyllysine; baseline levels of the former were low in responders (AUC = 0.86; 95% confidence interval, 0.69-1). In addition, fecal levels of N-ε-acetyllysine were negatively associated with the abundance of Odoribacter. N-ε-acetyllysine levels increased as symptoms improved with treatment. CONCLUSION: Fecal N-ε-acetyllysine levels before treatment may be a predictive biomarker of treatment-refractory depression and anxiety. Odoribacter may play a role in the homeostasis of intestinal L-lysine levels. More attention should be paid to the importance of L-lysine metabolism in those with depression and anxiety.

Reliability of Telepsychiatry Assessments Using the Attention-Deficit/Hyperactivity Disorder Rating Scale-IV for Children With Neurodevelopmental Disorders and Their Caregivers: Randomized Feasibility Study.

BACKGROUND: Given the global shortage of child psychiatrists and barriers to specialized care, remote assessment is a promising alternative for diagnosing and managing attention-deficit/hyperactivity disorder (ADHD). However, only a few studies have validated the accuracy and acceptability of these remote methods. OBJECTIVE: This study aimed to test the agreement between remote and face-to-face assessments. METHODS: Patients aged between 6 and 17 years with confirmed Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition diagnoses of ADHD or autism spectrum disorder (ASD) were recruited from multiple institutions. In a randomized order, participants underwent 2 evaluations, face-to-face and remotely, with distinct evaluators administering the ADHD Rating Scale-IV (ADHD-RS-IV). Intraclass correlation coefficient (ICC) was used to assess the reliability of face-to-face and remote assessments. RESULTS: The participants included 74 Japanese children aged between 6 and 16 years who were primarily diagnosed with ADHD (43/74, 58%) or ASD (31/74, 42%). A total of 22 (30%) children were diagnosed with both conditions. The ADHD-RS-IV ICCs between face-to-face and remote assessments showed "substantial" agreement in the total ADHD-RS-IV score (ICC=0.769, 95% CI 0.654-0.849; P<.001) according to the Landis and Koch criteria. The ICC in patients with ADHD showed "almost perfect" agreement (ICC=0.816, 95% CI 0.683-0.897; P<.001), whereas in patients with ASD, it showed "substantial" agreement (ICC=0.674, 95% CI 0.420-0.831; P<.001), indicating the high reliability of both methods across both conditions. CONCLUSIONS: Our study validated the feasibility and reliability of remote ADHD testing, which has potential benefits such as reduced hospital visits and time-saving effects. Our results highlight the potential of telemedicine in resource-limited areas, clinical trials, and treatment evaluations, necessitating further studies to explore its broader application. TRIAL REGISTRATION: UMIN Clinical Trials Registry UMIN000039860; http://tinyurl.com/yp34x6kh.

Feasibility of remote interviews in assessing disease severity in patients with major depressive disorder: A pilot study.

AIM: Interview quality is an important factor in the success of clinical trials for major depressive disorder (MDD). There is a substantial need to establish a reliable, remote clinical assessment interview system that can replace traditional in-person interviews. METHODS: We conducted a multicenter, randomized, unblinded, prospective, cross-sectional study to assess the reliability of remote interviews in patients with MDD (UMIN000041839). Eligible patients with MDD underwent remote and in-person sessions of the Montgomery-Åsberg Depression Rating Scale (MADRS) assessment performed by different raters within 28 days of providing consent. Patients were randomized to a group first assessed using in-person interviews and secondarily using remote interviews (in-person-first group) or a group first assessed by remote interviews and secondarily using in-person interviews (remote-first group). Nineteen trained people (15 clinical psychologists, 3 nurses, and 1 clinical laboratory technologist) performed interviews. RESULTS: Of 59 patients (in-person-first group, n = 32; remote-first group, n = 27) who completed both remote and in-person interviews, 51% (n = 30) were women; the mean age was 41.6 years (range, 21-64 years). There was a strong association between remote and in-person MADRS scores (r = 0.891, kappa = 0.901). An overall intraclass correlation coefficient (ICC) of 0.886 (95% confidence interval, 0.877-0.952) indicated good consistency between MADRS scores in remote and in-person interviews. The ICC decreased as the severity of depression increased. CONCLUSION: Our results suggest remote interviews are a feasible alternative option to in-person interviews in assessing symptom severity in MDD patients and could promote clinical trials in Japan.

Live two-way video versus face-to-face treatment for depression, anxiety, and obsessive-compulsive disorder: A 24-week randomized controlled trial.

AIM: Live two-way video, easily accessible from home via smartphones and other devices, is becoming a new way of providing psychiatric treatment. However, lack of evidence for real-world clinical setting effectiveness hampers its approval by medical insurance in some countries. Here, we conducted the first large-scale pragmatic, randomized controlled trial to determine the effectiveness of long-term treatment for multiple psychiatric disorders via two-way video using smartphones and other devices, which are currently the primary means of telecommunication. METHODS: This randomized controlled trial compared two-way video versus face-to-face treatment for depressive disorder, anxiety disorder, and obsessive-compulsive disorder in the subacute/maintenance phase during a 24-week period. Adult patients with the above-mentioned disorders were allocated to either a two-way video group (≥50% video sessions) or a face-to-face group (100% in-person sessions) and received standard treatment covered by public medical insurance. The primary outcome was the 36-Item Short-Form Health Survey Mental Component Summary (SF-36 MCS) score. Secondary outcomes included all-cause discontinuation, working alliance, adverse events, and the severity rating scales for each disorder. RESULTS: A total of 199 patients participated in this study. After 24 weeks of treatment, two-way video treatment was found to be noninferior to face-to-face treatment regarding SF-36 MCS score (48.50 vs 46.68, respectively; p < 0.001). There were no significant differences between the groups regarding most secondary end points, including all-cause discontinuation, treatment efficacy, and satisfaction. CONCLUSION: Two-way video treatment using smartphones and other devices, was noninferior to face-to-face treatment in real-world clinical settings. Modern telemedicine, easily accessible from home, can be used as a form of health care.

Associations between glycemic variability, sleep quality, and daily steps in subjects without diabetes using wearable devices.

Background: Since there are limited studies on the associations between glycemic variability (GV) and sleep quality or physical activity in subjects without diabetes, we evaluated the associations between GV, as assessed by continuous glucose monitoring (CGM), and both sleep quality and daily steps using wearable devices in healthy individuals. Methods: Forty participants without diabetes were monitored by both an intermittently scanned CGM and a smartwatch-type activity tracker for 2 weeks. The standard deviation (SD) and coefficient of variation (CV) of glucose were evaluated as indices of GV. The activity tracker was used to calculate each participant's average step count per day. We also calculated sleep duration, sleep efficiency, and sleep latency based on data from the activity tracker. Spearman's correlation coefficient was used to assess the association between GV and sleep indices or daily steps. For each participant, periods were divided into quartiles according to step counts throughout the day. We compared mean parameter differences between the periods of lowest quartile and highest quartile (lower 25% and upper 25%). Results: SD glucose was significantly positively correlated with sleep latency (R = 0.23, P < 0.05). There were no significant correlations among other indices in GV and sleep quality (P > 0.05). SD glucose and CV glucose levels in the upper 25% period of daily steps were lower than those in the lower 25% period in each participant (both, P < 0.01). Conclusion: In subjects without diabetes, GV evaluated by intermittently scanned CGM was positively associated with the time to fall asleep. Furthermore, GV in the days of larger daily steps was decreased compared to the days of smaller daily steps in each participant.

The relationship between sleep, gut microbiota, and metabolome in patients with depression and anxiety: A secondary analysis of the observational study.

BACKGROUND: Growing attention is paid to the association between alterations in the gut microbiota and their metabolites in patients with psychiatric disorders. Our study aimed to determine how gut microbiota and metabolomes are related to the sleep quality among patients with depression and anxiety disorders by analyzing the datasets of our previous study. METHODS: Samples were collected from 40 patients (depression: 32 patients [80.0%]); anxiety disorders: 8 patients [20.0%]) in this study. Gut microbiomes were analyzed using 16S rRNA gene sequencing and gut metabolomes were analyzed by a mass spectrometry approach. Based on the Pittsburgh Sleep Quality Index (PSQI), patients were categorized into two groups: the insomnia group (PSQI score ≥ 9, n = 20) and the non-insomnia group (PSQI score < 9, n = 20). RESULTS: The insomnia group showed a lower alpha diversity in the Chao1 and Shannon indices than the non-insomnia group after the false discovery rate (FDR) correction. The relative abundance of genus Bacteroides showed a positive correlation with PSQI scores in the non-insomnia group. The concentrations of glucosamine and N-methylglutamate were significantly higher in the insomnia group than in the non-insomnia group. CONCLUSIONS: Our findings suggest that specific taxa could affect the sleep quality among patients with depression and anxiety disorders. Further studies are needed to elucidate the impact of sleep on specific gut microbiota and metabolomes in depression and anxiety disorders.

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression.

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this causal depression network (CDN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis Principal Component Analysis (PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CDN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CDN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes (t = -2.35, p = 0.019). This evidence further supports that treatment interventions converge on a CDN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

Assessment of glycemic variability and lifestyle behaviors in healthy nondiabetic individuals according to the categories of body mass index.

BACKGROUND: There are limited data about the association between body mass index (BMI), glycemic variability (GV), and life-related factors in healthy nondiabetic adults. METHODS: This cross-sectional study was carried out within our ethics committee-approved study called "Exploring the impact of nutrition advice on blood sugar and psychological status using continuous glucose monitoring (CGM) and wearable devices". Prediabetes was defined by the HbA1c level of 5.7-6.4% and /or fasting glucose level of 100-125 mg/dL. Glucose levels and daily steps were measured for 40 participants using Free Style Libre and Fitbit Inspire 2 under normal conditions for 14 days. Dietary intakes and eating behaviors were assessed using a brief-type self-administered dietary history questionnaire and a modified questionnaire from the Obesity Guidelines. RESULTS: All indices of GV were higher in the prediabetes group than in the healthy group, but a significant difference was observed only in mean amplitude of glycemic excursions (MAGE). In the multivariate analysis, only the presence of prediabetes showed a significant association with the risk of higher than median MAGE (Odds, 6.786; 95% CI, 1.596-28.858; P = 0.010). Additionally, the underweight (BMI < 18.5) group had significantly higher value in standard deviation (23.7 ± 3.5 vs 19.8 ± 3.7 mg/dL, P = 0.038) and coefficient variability (22.6 ± 4.6 vs 18.4 ± 3.2%, P = 0.015), compared to the normal group. This GV can be partially attributed to irregularity of eating habits. On the contrary, the overweight (BMI ≥ 25) group had the longest time above the 140 or 180 mg/dL range, which may be due to eating style and taking fewer steps (6394 ± 2337 vs 9749 ± 2408 steps, P = 0.013). CONCLUSIONS: Concurrent CGM with diet and activity monitoring could reduce postprandial hyperglycemia through assessment of diet and daily activity, especially in non- normal weight individuals.

Effect of education regarding treatment guidelines for schizophrenia and depression on the treatment behavior of psychiatrists: A multicenter study.

AIM: This study aims to examine the real-world effectiveness of education regarding clinical guidelines for psychiatric disorders using 'the Effectiveness of guidelines for dissemination and education in psychiatric treatment (EGUIDE)' project. METHODS: The EGUIDE project is a nationwide prospective implementation study of two clinical practice guidelines, i.e., the Guideline for Pharmacological Therapy of Schizophrenia and the Treatment Guidelines for Major Depressive Disorders, in Japan. Between 2016 and 2019, 782 psychiatrists belonging to 176 hospitals with psychiatric wards participated in the project and attended lectures on clinical practice guidelines. The proportions of guideline-recommended treatments in 7405 patients with schizophrenia and 3794 patients with major depressive disorder at participating hospitals were compared between patients under the care of psychiatrists participating in the project and those not participating in the project. Clinical and prescribing data on the patients discharged from April to September each year from participating hospitals of the project were also analyzed. RESULTS: The proportions of three quality indicators (antipsychotic monotherapy regardless of whether other psychotropics medication, antipsychotic monotherapy without other psychotropics and no prescription of anxiolytics or hypnotics) for schizophrenia were higher among participating psychiatrists than among nonparticipating psychiatrists. As similar results were obtained in major depressive disorder, the effectiveness of the project for the dissemination of guideline-recommended treatment has been replicated. CONCLUSION: This strategy of providing education regarding the clinical guidelines for psychiatric disorders was effective in improving the treatment-related behavior of psychiatrists. The use of this education-based strategy might contribute to resolving the mental health treatment gap.