Very short sleep duration reveals a proteomic fingerprint that is selectively associated with incident diabetes mellitus but not with incident coronary heart disease: a cohort study.

BACKGROUND: The molecular pathways linking short and long sleep duration with incident diabetes mellitus (iDM) and incident coronary heart disease (iCHD) are not known. We aimed to identify circulating protein patterns associated with sleep duration and test their impact on incident cardiometabolic disease. METHODS: We assessed sleep duration and measured 78 plasma proteins among 3336 participants aged 46-68 years, free from DM and CHD at baseline, and identified cases of iDM and iCHD using national registers. Incident events occurring in the first 3 years of follow-up were excluded from analyses. Tenfold cross-fit partialing-out lasso logistic regression adjusted for age and sex was used to identify proteins that significantly predicted sleep duration quintiles when compared with the referent quintile 3 (Q3). Predictive proteins were weighted and combined into proteomic scores (PS) for sleep duration Q1, Q2, Q4, and Q5. Combinations of PS were included in a linear regression model to identify the best predictors of habitual sleep duration. Cox proportional hazards regression models with sleep duration quintiles and sleep-predictive PS as the main exposures were related to iDM and iCHD after adjustment for known covariates. RESULTS: Sixteen unique proteomic markers, predominantly reflecting inflammation and apoptosis, predicted sleep duration quintiles. The combination of PSQ1 and PSQ5 best predicted sleep duration. Mean follow-up times for iDM (n = 522) and iCHD (n = 411) were 21.8 and 22.4 years, respectively. Compared with sleep duration Q3, all sleep duration quintiles were positively and significantly associated with iDM. Only sleep duration Q1 was positively and significantly associated with iCHD. Inclusion of PSQ1 and PSQ5 abrogated the association between sleep duration Q1 and iDM. Moreover, PSQ1 was significantly associated with iDM (HR = 1.27, 95% CI: 1.06-1.53). PSQ1 and PSQ5 were not associated with iCHD and did not markedly attenuate the association between sleep duration Q1 with iCHD. CONCLUSIONS: We here identify plasma proteomic fingerprints of sleep duration and suggest that PSQ1 could explain the association between very short sleep duration and incident DM.

A first-in-human study of the anti-inflammatory profibrinolytic TMS-007, an SMTP family triprenyl phenol.

AIMS: TMS-007, an SMTP family member, modulates plasminogen conformation and enhances plasminogen-fibrin binding, leading to promotion of endogenous fibrinolysis. Its anti-inflammatory action, mediated by soluble epoxide hydrolase inhibition, may contribute to its efficacy. Evidence suggests that TMS-007 can effectively treat experimental thrombotic and embolic strokes with a wide time window, while reducing haemorrhagic transformation. We aim to evaluate the safety, pharmacokinetics and pharmacodynamics of TMS-007 in healthy volunteers. METHODS: This was a randomized, placebo-controlled, double blind, dose-escalation study, administered as a single intravenous infusion of TMS-007 in cohorts of healthy male Japanese subjects. Six cohorts were planned, but only five were completed. In each cohort (n = 8), individuals were randomized to receive one of five doses of TMS-007 (3, 15, 60, 180 or 360 mg; n = 6) or placebo (n = 2). RESULTS: TMS-007 was generally well tolerated, and no serious adverse events were attributed to the drug. A linear dose-dependency was observed for plasma TMS-007 levels. No symptoms of bleeding were observed on brain MRI analysis, and no bleeding-related responses were found on laboratory testing. The plasma levels of the coagulation factor fibrinogen and the anti-fibrinolysis factor α2 -antiplasmin levels were unchanged after TMS-007 dosing. A slight increase in the plasma level of plasmin-α2 -antiplasmin complex, an index of plasmin formation, was observed in the TMS-007 group in cohort 2. CONCLUSIONS: TMS-007 is generally well tolerated and exhibits favourable pharmacokinetic profiles that warrant further clinical development.

Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning.

Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as mood disorders. Given the importance of accurate modeling and reliable predictions of heart rate fluctuations for the prevention and control of certain diseases, it is paramount to identify models with the best performance in such tasks. The objectives of this study were to compare the results of three different forecasting models (Autoregressive Model, Long Short-Term Memory Network, and Convolutional Long Short-Term Memory Network) trained and tested on heart rate beats per minute data obtained from twelve heterogeneous participants and to identify the architecture with the best performance in terms of modeling and forecasting heart rate behavior. Heart rate beats per minute data were collected using a wearable device over a period of 10 days from twelve different participants who were heterogeneous in age, sex, medical history, and lifestyle behaviors. The goodness of the results produced by the models was measured using both the mean absolute error and the root mean square error as error metrics. Despite the three models showing similar performance, the Autoregressive Model gave the best results in all settings examined. For example, considering one of the participants, the Autoregressive Model gave a mean absolute error of 2.069 (compared to 2.173 of the Long Short-Term Memory Network and 2.138 of the Convolutional Long Short-Term Memory Network), achieving an improvement of 5.027% and 3.335%, respectively. Similar results can be observed for the other participants. The findings of the study suggest that regardless of an individual's age, sex, and lifestyle behaviors, their heart rate largely depends on the pattern observed in the previous few minutes, suggesting that heart rate can be reasonably regarded as an autoregressive process. The findings also suggest that minute-by-minute heart rate prediction can be accurately performed using a linear model, at least in individuals without pathologies that cause heartbeat irregularities. The findings also suggest many possible applications for the Autoregressive Model, in principle in any context where minute-by-minute heart rate prediction is required (arrhythmia detection and analysis of the response to training, among others).

Incident diabetes mellitus may explain the association between sleep duration and incident coronary heart disease.

AIMS/HYPOTHESIS: Sleep duration is a risk factor for incident diabetes mellitus and CHD. The primary aim of the present study was to investigate, in sex-specific analyses, the role of incident diabetes as the possible biological mechanism for the reported association between short/long sleep duration and incident CHD. Considering that diabetes is a major risk factor for CHD, we hypothesised that any association with sleep duration would not hold for cases of incident CHD occurring before incident diabetes ('non-diabetes CHD') but would hold true for cases of incident CHD following incident diabetes ('diabetes-CHD'). METHODS: A total of 6966 men and 9378 women aged 45-73 years from the Malmö Diet Cancer Study, a population-based, prospective cohort, who had answered questions on habitual sleep duration and did not have a history of prevalent diabetes or CHD were included in the analyses. Incident cases of diabetes and CHD were identified using national registers. Sex-specific Cox proportional hazards regression models were stratified by BMI and adjusted for known covariates of diabetes and CHD. RESULTS: Mean follow-up times for incident diabetes (n = 1137/1016 [men/women]), incident CHD (n = 1170/578), non-diabetes CHD (n = 1016/501) and diabetes-CHD (n = 154/77) were 14.2-15.2 years for men, and 15.8-16.5 years for women. In men, short sleep duration (< 6 h) was associated with incident diabetes (HR 1.35, 95% CI 1.01, 1.80), CHD (HR 1.41, 95% CI 1.06, 1.89) and diabetes-CHD (HR 2.34, 95% CI 1.20, 4.55). Short sleep duration was not associated with incident non-diabetes CHD (HR 1.35, 95% CI 0.98, 1.87). Long sleep duration (≥ 9 h) was associated with incident diabetes (HR 1.37, 95% CI 1.03, 1.83), CHD (HR 1.33, 95% CI 1.01, 1.75) and diabetes-CHD (HR 2.10, 95% CI 1.11, 4.00). Long sleep duration was not associated with incident non-diabetes CHD (HR 1.33, 95% CI 0.98, 1.80). In women, short sleep duration was associated with incident diabetes (HR 1.53, 95% CI 1.16, 2.01), CHD (HR 1.46, 95% CI 1.03, 2.07) and diabetes-CHD (HR 2.88, 95% CI 1.37, 6.08). Short sleep duration was not associated with incident non-diabetes CHD (HR 1.29, 95% CI 0.86, 1.93). CONCLUSIONS/INTERPRETATION: The associations between sleep duration and incident CHD directly reflect the associations between sleep duration and incident diabetes. Incident diabetes may thus be the explanatory mechanism for the association between short and long sleep duration and incident CHD.

Validity and reliability of the Oura Ring Generation 3 (Gen3) with Oura sleep staging algorithm 2.0 (OSSA 2.0) when compared to multi-night ambulatory polysomnography: A validation study of 96 participants and 421,045 epochs.

PURPOSE: To evaluate the validity and the reliability of the Oura Ring Generation 3 (Gen3) with Oura Sleep Staging Algorithm 2.0 (OSSA 2.0) through multi-night polysomnography (PSG). PARTICIPANTS AND METHODS: Participants were 96 generally healthy Japanese men and women aged between 20 and 70 years contributing with 421,045 30-s epochs. Sleep scoring was performed according to American Academy of Sleep Medicine criteria. Each participant could contribute with a maximum of three polysomnography (PSG) nights. Within-participant means were created for each sleep measure and paired t-tests were used to compare equivalent measures obtained from the PSG and Oura Rings (non-dominant and dominant hand). Agreement between sleep measures were assessed using Bland-Altman plots. Interrater reliability for epoch accuracy was determined by prevalence-adjusted and bias-adjusted kappa (PABAK). RESULTS: The Oura Ring did not significantly differ from PSG for the measures time in bed, total sleep time, sleep onset latency, sleep period time, wake after sleep onset, time spent in light sleep, and time spent in deep sleep. Oura Rings worn on the non-dominant- and dominant-hand underestimated sleep efficiency by 1.1 %-1.5 % and time spent in REM sleep by 4.1-5.6 min. The Oura Ring had a sensitivity of 94.4 %-94.5 %, specificity of 73.0 %-74.6 %, a predictive value for sleep of 95.9 %-96.1 %, a predictive value for wake of 66.6 %-67.0 %, and accuracy of 91.7 %-91.8 %. PABAK was 0.83-0.84 and reliability was 94.8 %. Sleep staging accuracy ranged between 75.5 % (light sleep) and 90.6 % (REM sleep). CONCLUSIONS: The Oura Ring Gen3 with OSSA 2.0 shows good agreement with PSG for global sleep measures and time spent in light and deep sleep.

Associations of Subjective Sleep Quality with Wearable Device-Derived Resting Heart Rate During REM Sleep and Non-REM Sleep in a Cohort of Japanese Office Workers.

Background: Associations between subjective sleep quality and stage-specific heart rate (HR) may have important clinical relevance when aiming to optimize sleep and overall health. The majority of previously studies have been performed during short periods under laboratory-based conditions. The aim of this study was to investigate the associations of subjective sleep quality with heart rate during REM sleep (HR REMS) and non-REM sleep (HR NREMS) using a wearable device (Fitbit Versa). Methods: This is a secondary analysis of data from the intervention group of a randomized controlled trial (RCT) performed between December 3, 2018, and March 2, 2019, in Tokyo, Japan. The intervention group consisted of 179 Japanese office workers with metabolic syndrome (MetS), Pre-MetS or a high risk of developing MetS. HR was collected with a wearable device and sleep quality was assessed with a mobile application where participants answered The St. Mary's Hospital Sleep Questionnaire. Both HR and sleep quality was collected daily for a period of 90 days. Associations of between-individual and within-individual sleep quality with HR REMS and HR NREMS were analyzed with multi-level model regression in 3 multivariate models. Results: The cohort consisted of 92.6% men (n=151) with a mean age (± standard deviation) of 44.1 (±7.5) years. A non-significant inverse between-individual association was observed for sleep quality with HR REMS (HR REMS -0.18; 95% CI -0.61, 0.24) and HR NREMS (HR NREMS -0.23; 95% CI -0.66, 0.21), in the final multivariable adjusted models; a statistically significant inverse within-individual association was observed for sleep quality with HR REMS (HR REMS -0.21 95% CI -0.27, -0.15) and HR NREMS (HR NREMS -0.21 95% CI -0.27, -0.14) after final adjustments for covariates. Conclusion: The present study shows a statistically significant within-individual association of subjective sleep quality with HR REMS and HR NREMS. These findings emphasize the importance of considering sleep quality on the individual level. The results may contribute to early detection and prevention of diseases associated with sleep quality which may have important implications on public health given the high prevalence of sleep disturbances in the population.

Association between Metabolic Syndrome Status and Daily Physical Activity Measured by a Wearable Device in Japanese Office Workers.

(1) Background: This study examined the cross-sectional association between metabolic syndrome (MetS) status classified into three groups and daily physical activity (PA; step count and active minutes) using a wearable device in Japanese office workers. (2) Methods: This secondary analysis used data from 179 participants in the intervention group of a randomized controlled trial for 3 months. Individuals who had received an annual health check-up and had MetS or were at a high risk of MetS based on Japanese guidelines were asked to use a wearable device and answer questionnaires regarding their daily life for the entire study period. Multilevel mixed-effects logistic regression models adjusted for covariates associated with MetS and PA were used to estimate associations. A sensitivity analysis investigated the associations between MetS status and PA level according to the day of the week. (3) Results: Compared to those with no MetS, those with MetS were not significantly associated with PA, while those with pre-MetS were inversely associated with PA [step count Model 3: OR = 0.60; 95% CI: 0.36, 0.99; active minutes Model 3: OR = 0.62; 95% CI: 0.40, 0.96]. In the sensitivity analysis, day of the week was an effect modifier for both PA (p < 0.001). (4) Conclusions: Compared to those with no MetS, those with pre-MetS, but not MetS, showed significantly lower odds of reaching their daily recommended PA level. Our findings suggest that the day of the week could be a modifier for the association between MetS and PA. Further research with longer study periods and larger sample sizes are needed to confirm our results.

A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection.

Cardiovascular diseases (CVDs) remain a leading cause of death globally. According to the American Heart Association, approximately 19.1 million deaths were attributed to CVDs in 2020, in particular, ischemic heart disease and stroke. Several known risk factors for CVDs include smoking, alcohol consumption, lack of regular physical activity, and diabetes. The last decade has been characterized by widespread diffusion in the use of wristband-style wearable devices which can monitor and collect heart rate data, among other information. Wearable devices allow the analysis and interpretation of physiological and activity data obtained from the wearer and can therefore be used to monitor and prevent potential CVDs. However, these data are often provided in a manner that does not allow the general user to immediately comprehend possible health risks, and often require further analytics to draw meaningful conclusions. In this paper, we propose a disentangled variational autoencoder (ß-VAE) with a bidirectional long short-term memory network (BiLSTM) backend to detect in an unsupervised manner anomalies in heart rate data collected during sleep time with a wearable device from eight heterogeneous participants. Testing was performed on the mean heart rate sampled both at 30 s and 1 min intervals. We compared the performance of our model with other well-known anomaly detection algorithms, and we found that our model outperformed them in almost all considered scenarios and for all considered participants. We also suggest that wearable devices may benefit from the integration of anomaly detection algorithms, in an effort to provide users more processed and straightforward information.

Sleep Satisfaction May Modify the Association between Metabolic Syndrome and BMI, Respectively, and Occupational Stress in Japanese Office Workers.

The association between obesity and psychological stress is ambiguous. The aim is to investigate the association between metabolic syndrome (MetS) and body mass index (BMI), respectively, with occupational stress among Japanese office workers. The study is a secondary analysis of the intervention group from a randomized controlled trial. There are 167 participants included in the analysis. Occupational stress is self-reported using the Brief Job Stress Questionnaire (BJSQ). BMI and the classification of MetS/pre-MetS was based on the participants' annual health check-up data. The primary exposure is divided into three groups: no MetS, pre-MetS, and MetS in accordance with Japanese guidelines. The secondary exposure, BMI, remains as a continuous variable. Multiple linear regression is implemented. Sensitivity analyses are stratified by sleep satisfaction. Pre-MetS is significantly associated with occupational stress (7.84 points; 95% CI: 0.17, 15.51). Among participants with low sleep satisfaction, pre-MetS (14.09 points; 95% CI: 1.71, 26.48), MetS (14.72 points; 95% CI: 0.93, 28.51), and BMI (2.54 points; 95% CI: 0.05, 4.99) are all significantly associated with occupational stress. No significant associations are observed in participants with high sleep satisfaction. The findings of this study indicate that sleep satisfaction may modify the association between MetS and BMI, respectively, and occupational stress.

Statistical Methods for Item Reduction in a Representative Lifestyle Questionnaire: Pilot Questionnaire Study.

BACKGROUND: Reducing the number of items in a questionnaire while maintaining relevant information is important as it is associated with advantages such as higher respondent engagement and reduced response error. However, in health care, after the original design, an a posteriori check of the included items in a questionnaire is often overlooked or considered to be of minor importance. When conducted, this is often based on a single selected method. We argue that before finalizing any lifestyle questionnaire, a posteriori validation should always be conducted using multiple approaches to ensure the robustness of the results. OBJECTIVE: The objectives of this study are to compare the results of two statistical methods for item reduction (variance inflation factor [VIF] and factor analysis [FA]) in a lifestyle questionnaire constructed by combining items from different sources and analyze the different results obtained from the 2 methods and the conclusions that can be made about the original items. METHODS: Data were collected from 79 participants (heterogeneous in age and sex) with a high risk of metabolic syndrome working in a financial company based in Tokyo. The lifestyle questionnaire was constructed by combining items (asked with daily, weekly, and monthly frequency) from multiple validated questionnaires and other selected questions. Item reduction was conducted using VIF and exploratory FA. Adequacy tests were used to check the data distribution and sampling adequacy. RESULTS: Among the daily and weekly questions, both VIF and FA identified redundancies in sleep-related items. Among the monthly questions, both approaches identified redundancies in stress-related items. However, the number of items suggested for reduction often differed: VIF suggested larger reductions than FA for daily questions but fewer reductions for weekly questions. Adequacy tests always confirmed that the structural detection was adequate for the considered items. CONCLUSIONS: As expected, our analyses showed that VIF and FA produced both similar and different findings, suggesting that questionnaire designers should consider using multiple methods for item reduction. Our findings using both methods indicate that many questions, especially those related to sleep, are redundant, indicating that the considered lifestyle questionnaire can be shortened.

An Agent-Based Model of the Local Spread of SARS-CoV-2: Modeling Study.

BACKGROUND: The spread of SARS-CoV-2, originating in Wuhan, China, was classified as a pandemic by the World Health Organization on March 11, 2020. The governments of affected countries have implemented various measures to limit the spread of the virus. The starting point of this paper is the different government approaches, in terms of promulgating new legislative regulations to limit the virus diffusion and to contain negative effects on the populations. OBJECTIVE: This paper aims to study how the spread of SARS-CoV-2 is linked to government policies and to analyze how different policies have produced different results on public health. METHODS: Considering the official data provided by 4 countries (Italy, Germany, Sweden, and Brazil) and from the measures implemented by each government, we built an agent-based model to study the effects that these measures will have over time on different variables such as the total number of COVID-19 cases, intensive care unit (ICU) bed occupancy rates, and recovery and case-fatality rates. The model we implemented provides the possibility of modifying some starting variables, and it was thus possible to study the effects that some policies (eg, keeping the national borders closed or increasing the ICU beds) would have had on the spread of the infection. RESULTS: The 4 considered countries have adopted different containment measures for COVID-19, and the forecasts provided by the model for the considered variables have given different results. Italy and Germany seem to be able to limit the spread of the infection and any eventual second wave, while Sweden and Brazil do not seem to have the situation under control. This situation is also reflected in the forecasts of pressure on the National Health Services, which see Sweden and Brazil with a high occupancy rate of ICU beds in the coming months, with a consequent high number of deaths. CONCLUSIONS: In line with what we expected, the obtained results showed that the countries that have taken restrictive measures in terms of limiting the population mobility have managed more successfully than others to contain the spread of COVID-19. Moreover, the model demonstrated that herd immunity cannot be reached even in countries that have relied on a strategy without strict containment measures.

Association of managerial position with cardiovascular risk factors: A fixed-effects analysis for Japanese employees.

OBJECTIVES: Although higher occupational classes have been reported to be associated with better health, researchers do not fully understand whether such associations derive from the position or individual characteristics of the person in that position. We examined the association between being a manager and cardiovascular disease (CVD) risk factors using unique panel data in Japan that annually observed employees' occupational class and health conditions. METHODS: We analyzed data for 45 888 observations from a Japanese company from 2013 through 2017. The association between being a manager and CVD risk factors (metabolic risks and health-related behaviors) were evaluated using simple pooled cross-sectional analyses with adjustment for age, sex, marital status, and overtime-working hours. We further incorporated employee-level fixed-effects into the models to examine whether the associations were subject to individual time-invariant factors. RESULTS: The pooled cross-sectional analyses showed that, compared to non-managers, managers had 2.0 mg/dl lower low density lipoprotein cholesterol (LDL-C) level, 1.4 mmHg-lower systolic blood pressure, and 0.2 kg/m2 lower body mass index (BMI). After adjusting for employee-level fixed-effects, being a manager was associated with a significantly 2.2 mg/dl higher LDL-C level. However, the associations between an individual's management status and blood pressure or BMI were not significant. Furthermore, managers were 5.5% less likely to exercise regularly and 6.1% less likely to report sufficient sleep in the fixed-effects models, although the pooled cross-sectional analyses did not demonstrate these significant associations. CONCLUSIONS: Our findings suggest the necessity of considering these unfavorable health risks associated with being promoted to a manager.

Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.

Identification of medical conditions using claims data is generally conducted with algorithms based on subject-matter knowledge. However, these claims-based algorithms (CBAs) are highly dependent on the knowledge level and not necessarily optimized for target conditions. We investigated whether machine learning methods can supplement researchers' knowledge of target conditions in building CBAs. Retrospective cohort study using a claims database combined with annual health check-up results of employees' health insurance programs for fiscal year 2016-17 in Japan (study population for hypertension, N = 631,289; diabetes, N = 152,368; dyslipidemia, N = 614,434). We constructed CBAs with logistic regression, k-nearest neighbor, support vector machine, penalized logistic regression, tree-based model, and neural network for identifying patients with three common chronic conditions: hypertension, diabetes, and dyslipidemia. We then compared their association measures using a completely hold-out test set (25% of the study population). Among the test cohorts of 157,822, 38,092, and 153,608 enrollees for hypertension, diabetes, and dyslipidemia, 25.4%, 8.4%, and 38.7% of them had a diagnosis of the corresponding condition. The areas under the receiver operating characteristic curve (AUCs) of the logistic regression with/without subject-matter knowledge about the target condition were .923/.921 for hypertension, .957/.938 for diabetes, and .739/.747 for dyslipidemia. The logistic lasso, logistic elastic-net, and tree-based methods yielded AUCs comparable to those of the logistic regression with subject-matter knowledge: .923-.931 for hypertension; .958-.966 for diabetes; .747-.773 for dyslipidemia. We found that machine learning methods can attain AUCs comparable to the conventional knowledge-based method in building CBAs.

Association of Sleep Duration With All- and Major-Cause Mortality Among Adults in Japan, China, Singapore, and Korea.

Importance: The association between long sleep duration and mortality appears stronger in East Asian populations than in North American or European populations. Objectives: To assess the sex-specific association between sleep duration and all-cause and major-cause mortality in a pooled longitudinal cohort and to stratify the association by age and body mass index. Design, Setting, and Participants: This cohort study of individual-level data from 9 cohorts in the Asia Cohort Consortium was performed from January 1, 1984, to December 31, 2002. The final population included participants from Japan, China, Singapore, and Korea. Mean (SD) follow-up time was 14.0 (5.0) years for men and 13.4 (5.3) years for women. Data analysis was performed from August 1, 2018, to May 31, 2021. Exposures: Self-reported sleep duration, with 7 hours as the reference category. Main Outcomes and Measures: Mortality, including deaths from all causes, cardiovascular disease, cancer, and other causes. Sex-specific hazard ratios (HRs) and 95% CIs were estimated using Cox proportional hazards regression with shared frailty models adjusted for age and the key self-reported covariates of marital status, body mass index, smoking status, alcohol consumption, physical activity, history of diabetes and hypertension, and menopausal status (for women). Results: For 322 721 participants (mean [SD] age, 54.5 [9.2] years; 178 542 [55.3%] female), 19 419 deaths occurred among men (mean [SD] age of men, 53.6 [9.0] years) and 13 768 deaths among women (mean [SD] age of women, 55.3 [9.2] years). A sleep duration of 7 hours was the nadir for associations with all-cause, cardiovascular disease, and other-cause mortality in both men and women, whereas 8 hours was the mode sleep duration among men and the second most common sleep duration among women. The association between sleep duration and all-cause mortality was J-shaped for both men and women. The greatest association for all-cause mortality was with sleep durations of 10 hours or longer for both men (hazard ratio [HR], 1.34; 95% CI, 1.26-1.44) and women (HR, 1.48; 95% CI, 1.36-1.61). Sex was a significant modifier of the association between sleep duration and mortality from cardiovascular disease (χ25 = 13.47, P = .02), cancer (χ25 = 16.04, P = .007), and other causes (χ25 = 12.79, P = .03). Age was a significant modifier of the associations among men only (all-cause mortality: χ25 = 41.49, P < .001; cancer: χ25 = 27.94, P < .001; other-cause mortality: χ25 = 24.51, P < .001). Conclusions and Relevance: The findings of this cohort study suggest that sleep duration is a behavioral risk factor for mortality in both men and women. Age was a modifier of the association between sleep duration in men but not in women. Sleep duration recommendations in these populations may need to be considered in the context of sex and age.

eHealth Delivery of Educational Content Using Selected Visual Methods to Improve Health Literacy on Lifestyle-Related Diseases: Literature Review.

BACKGROUND: Lifestyle-related diseases, such as stroke, heart disease, and diabetes, are examples of noncommunicable diseases. Noncommunicable diseases are now the leading cause of death in the world, and their major causes are lifestyle related. The number of eHealth interventions is increasing, which is expected to improve individuals' health literacy on lifestyle-related diseases. OBJECTIVE: This literature review aims to identify existing literature published in the past decade on eHealth interventions aimed at improving health literacy on lifestyle-related diseases among the general population using selected visual methods, such as educational videos, films, and movies. METHODS: A systematic literature search of the PubMed database was conducted in April 2019 for papers written in English and published from April 2, 2009, through April 2, 2019. A total of 538 papers were identified and screened in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. Finally, 23 papers were included in this review. RESULTS: The 23 papers were characterized according to study characteristics (author and year of publication, study design and region where the study was conducted, study objective, service platform, target disease and participant age, research period, outcomes, and research method); the playback time of the educational videos, films, and movies; and the evaluation of the study's impacts on health literacy. A total of 7 studies compared results using statistical methods. Of these, 5 studies reported significant positive effects of the intervention on health literacy and health-related measures (eg, physical activity, body weight). Although most of the studies included educational content aimed at improving health literacy, only 7 studies measured health literacy. In addition, only 5 studies assessed literacy using health literacy measurement tools. CONCLUSIONS: This review found that the provision of educational content was satisfactory in most eHealth studies using selected visual methods, such as videos, films, and movies. These findings suggest that eHealth interventions influence people's health behaviors and that the need for this intervention is expected to increase. Despite the need to develop eHealth interventions, standardized measurement tools to evaluate health literacy are lacking. Further research is required to clarify acceptable health literacy measurements.

Association Between Electroencephalogram-Derived Sleep Measures and the Change of Emotional Status Analyzed Using Voice Patterns: Observational Pilot Study.

BACKGROUND: Measuring emotional status objectively is challenging, but voice pattern analysis has been reported to be useful in the study of emotion. OBJECTIVE: The purpose of this pilot study was to investigate the association between specific sleep measures and the change of emotional status based on voice patterns measured before and after nighttime sleep. METHODS: A total of 20 volunteers were recruited. Their objective sleep measures were obtained using a portable single-channel electroencephalogram system, and their emotional status was assessed using MIMOSYS, a smartphone app analyzing voice patterns. The study analyzed 73 sleep episodes from 18 participants for the association between the change of emotional status following nighttime sleep (Δvitality) and specific sleep measures. RESULTS: A significant association was identified between total sleep time and Δvitality (regression coefficient: 0.036, P=.008). A significant inverse association was also found between sleep onset latency and Δvitality (regression coefficient: -0.026, P=.001). There was no significant association between Δvitality and sleep efficiency or number of awakenings. CONCLUSIONS: Total sleep time and sleep onset latency are significantly associated with Δvitality, which indicates a change of emotional status following nighttime sleep. This is the first study to report the association between the emotional status assessed using voice pattern and specific sleep measures.

Using mHealth to Provide Mobile App Users With Visualization of Health Checkup Data and Educational Videos on Lifestyle-Related Diseases: Methodological Framework for Content Development.

BACKGROUND: The number of people with lifestyle-related diseases continues to increase worldwide. Improving lifestyle behavior with health literacy may be the key to address lifestyle-related diseases. The delivery of educational videos using mobile health (mHealth) services can replace the conventional way of educating individuals, and visualization can replace the provision of health checkup data. OBJECTIVE: This paper aimed to describe the development of educational content for MIRAMED, a mobile app aimed at improving users' lifestyle behaviors and health literacy for lifestyle-related diseases. METHODS: All videos were based on a single unified framework to provide users with a consistent flow of information. The framework was later turned into a storyboard. The final video contents were created based on this storyboard and further discussions with leading experts and specialist physicians on effective communication with app users about lifestyle-related diseases. RESULTS: The app uses visualization of personal health checkup data and educational videos on lifestyle-related diseases based on the current health guidelines, scientific evidence, and expert opinions of leading specialist physicians in the respective fields. A total of 8 videos were created for specific lifestyle-related diseases affecting 8 organs: (1) brain-cerebrovascular disorder, (2) eyes-diabetic retinopathy, (3) lungs-chronic obstructive pulmonary disease, (4) heart-ischemic heart disease, (5) liver-fatty liver, (6) kidneys-chronic kidney disease (diabetic kidney disease), (7) blood vessels-peripheral arterial disease, and (8) nerves-diabetic neuropathy. CONCLUSIONS: Providing enhanced mHealth education using novel digital technologies to visualize conventional health checkup data and lifestyle-related diseases is an innovative strategy. Future studies to evaluate the efficacy of the developed content are planned.

A validation study of a consumer wearable sleep tracker compared to a portable EEG system in naturalistic conditions.

OBJECTIVE: To compare a wearable device, the Fitbit Versa (FV), to a validated portable single-channel EEG system across multiple nights in a naturalistic environment. METHODS: Twenty participants (10 men and 10 women) aged 25-67 years were recruited for the present study. Study duration was 14 days during which participants were asked to wear the FV daily and nightly. The study intended to reproduce free-living conditions; thus, no guidelines for sleep or activity were imposed on the participants. A total of 138 person-nights, equivalent to 76,539 epochs, were used in the validation process. Sleep measures were compared between the FV and portable EEG using Bland-Altman plots, paired t-tests and epoch-by-epoch (EBE) analyses. RESULTS: The FV showed no significant bias with the EEG for the global sleep measures time in bed (TIB) and total sleep time (TST), and for calculated sleep efficiency (cSE = [TST/TIB] x 100). The FV had 92.1% sensitivity, 54.1% specificity, and 88.5% accuracy with a Cohen's kappa of 0.41, but a prevalence- and bias adjusted kappa of 0.77. The predictive values for sleep (PVS; positive predictive value) and wakefulness (PVW; negative predictive value) were 95.0% and 42.0%, respectively. The FV showed significant bias compared to the portable EEG for time spent in specific sleep stages, for SE as provided by FV, for sleep onset latency, sleep period time, and wake after sleep onset. CONCLUSIONS: The consumer sleep tracker could be a useful tool for measuring sleep duration in longitudinal epidemiologic naturalistic studies albeit with some limitations in specificity.

Association measures of claims-based algorithms for common chronic conditions were assessed using regularly collected data in Japan.

OBJECTIVES: Although claims data are widely used in medical research, their ability to identify persons' health-related conditions has not been fully justified. We assessed the validity of claims-based algorithms (CBAs) for identifying people with common chronic conditions in a large population using annual health screening results as the gold standard. STUDY DESIGN AND SETTING: Using a longitudinal claims database (n = 523,267) combined with annual health screening results, we defined the people with hypertension, diabetes, and/or dyslipidemia by applying health screening results as their gold standard and compared them against various CBAs. RESULTS: By using diagnostic and medication code-based CBAs, sensitivity and specificity were 74.5% (95% confidence interval [CI], 74.2%-74.8%) and 98.2% (98.2%-98.3%) for hypertension, 78.6% (77.3%-79.8%) and 99.6% (99.5%-99.6%) for diabetes, and 34.5% (34.2%-34.7%) and 97.2% (97.2%-97.3%) for dyslipidemia, respectively. Sensitivity did not decrease substantially for hypertension (65.2% [95% CI, 64.9%-65.5%]) and diabetes (73.0% [71.7%-74.2%]) when we used the same CBAs without limiting to primary care settings. CONCLUSION: We used regularly collected data to obtain CBA association measures, which are applicable to a wide range of populations. Our framework can be a basis of the validity assessment of CBAs for identifying persons' health-related conditions with regularly collected data.