Relaxing and Communication-Promoting Effects of Wooden Tableware at Workplace Social Gathering

Human beings are thought to have evolved in close contact with wood and touching wood is known to have relaxing effects. In 10 health subjects participating workplace social gathering, the effects of the use of wooden tableware on autonomic functions and communication were examined in comparison to the use of porcelain-metal tableware with a crossover study design. Analysis of pulse rate variability revealed that, compared to porcelain-metal tableware, wooden tableware lowered the amplitude of low-frequency (LF, 0.04-0.15 Hz) component without affecting the amplitude of high-frequency (HF, 0.15-0.45 Hz) component, resulting lower LF-to-HF ratio. Communication measured by the total number of utterances did not differ with the type of tableware. Subjective evaluation by a post questionnaire also showed consistent results, indicating better impression, warmth, relaxation, remission, nostalgic feeling for wooden tableware than porcelain-metal tableware. The use of wooden tableware may reduce sympathetic tone at the workplace social gathering compared to porcelain-metal tableware

Survival predictors of heart rate variability after myocardial infarction with and without low left ventricular ejection fraction

Background: Heart rate variability (HRV) and heart rate (HR) dynamics are used to predict the survival probability of patients after acute myocardial infarction (AMI), but the association has been established in patients with mixed levels of left ventricular ejection fraction (LVEF). Objective: We investigated whether the survival predictors of HRV and HR dynamics depend on LVEF after AMI. Methods: We studied 687 post-AMI patients including 147 with LVEF ≤35% and 540 with LVEF \u3e35%, of which 23 (16%) and 22 (4%) died during the 25 month follow-up period, respectively. None had an implanted cardioverter-defibrillator. From baseline 24 h ECG, the standard deviation (SDNN), root mean square of successive difference (rMSSD), percentage of successive difference \u3e50 ms (pNN50) of normal-to-normal R-R interval, ultra-low (ULF), very-low (VLF), low (LF), and high (HF) frequency power, deceleration capacity (DC), short-term scaling exponent (α Results: The predictors were categorized into three clusters; DC, SDNN, α Conclusion: The mortality risk in post-AMI patients with low LVEF is predicted by indices reflecting decreased HRV or HR responsiveness and cardiac parasympathetic dysfunction, whereas in patients without low LVEF, the risk is predicted by a combination of indices that reflect decreased HRV or HR responsiveness and indicator that reflects abrupt large HR changes suggesting sympathetic involvement

Pitfalls of assessment of autonomic function by heart rate variability

Abstract Although analysis of heart rate variability is widely used for the assessment of autonomic function, its fundamental framework linking low-frequency and high-frequency components of heart rate variability with sympathetic and parasympathetic autonomic divisions has developed in the 1980s. This simplified framework is no longer able to deal with much evidence about heart rate variability accumulated over the past half-century. This review addresses the pitfalls caused by the old framework and discusses the points that need attention in autonomic assessment by heart rate variability

Workout Detection by Wearable Device Data Using Machine Learning

There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable sensors. In this study, we attempted workout detection for one healthy female (40 years old) based on multiple types of biological information, such as the number of steps taken, activity level, and pulse, obtained from a wristband-type wearable sensor using machine learning. Data were recorded intermittently for approximately 64 days and 57 workouts were recorded. Workouts adopted for exercise were yoga and the workout duration was 1 h. We extracted 3416 min of biometric information for each of three categories: workout, awake activities (activities other than workouts), and sleep. Classification was performed using random forest (RF), SVM, and KNN. The detection accuracy of RF and SVM was high, and the recall, precision, and F-score values when using RF were 0.962, 0.963, and 0.963, respectively. The values for SVM were 0.961, 0.962, and 0.962, respectively. In addition, as a result of calculating the importance of the feature values used for detection, sleep state (39.8%), skin temperature (33.3%), and pulse rate (13.2%) accounted for approximately 86.3% of the total. By applying RF or SVM to the biological information obtained from the wearable wristband sensor, workouts could be detected every minute with high accuracy

How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis

Abstract Objective A small electrocardiograph and Holter electrocardiograph can record an electrocardiogram for 24 h or more. We examined whether gender could be verified from such an electrocardiogram and, if possible, how accurate it would be. Results Ten dimensional statistics were extracted from the heart rate data of more than 420,000 people, and gender identification was performed by various major identification methods. Lasso, linear regression, SVM, random forest, logistic regression, k-means, Elastic Net were compared, for Age < 50 and Age ≥ 50. The best Accuracy was 0.681927 for Random Forest for Age < 50. There are no consistent difference between Age < 50 and Age ≥ 50. Although the discrimination results based on these statistics are statistically significant, it was confirmed that they are not accurate enough to determine the gender of an individual

Exposure to blue light during lunch break: effects on autonomic arousal and behavioral alertness

Abstract Background Exposures to melanopsin-stimulating (melanopic) component-rich blue light enhance arousal level. We examined their effects in office workers. Main body of abstract Eight healthy university office workers were exposed to blue and orange lights for 30 min during lunch break on different days. We compared the effects of light color on autonomic arousal level assessed by heart rate variability (HRV) and behavioral alertness by psychomotor vigilance tests (PVT). Heart rate was higher and high-frequency (HF, 0.150.45 Hz) power of HRV was lower during exposure to the blue light than to orange light. No significant difference with light color was observed, however, in any HRV indices during PVT or in PVT performance after light exposure. Short conclusion Exposure to blue light during lunch break, compared with that to orange light, enhances autonomic arousal during exposure, but has no sustained effect on autonomic arousal or behavioral alertness after exposure

Quantitative detection of sleep apnea in adults using inertial measurement unit embedded in wristwatch wearable devices

Abstract Sleep apnea (SA) is associated with risk of cardiovascular disease, cognitive decline, and accidents due to sleepiness, yet the majority (over 80%) of patients remain undiagnosed. Inertial measurement units (IMUs) are built into modern wearable devices and are capable of long-term continuous measurement with low power consumption. We examined if SA can be detected by an IMU embedded in a wristwatch device. In 122 adults who underwent polysomnography (PSG) examinations, triaxial acceleration and triaxial gyro signals from the IMU were recorded during the PSG. Subjects were divided into a training group and a test groups (both n = 61). In the training group, an algorithm was developed to extract signals in the respiratory frequency band (0.13–0.70 Hz) and detect respiratory events as transient (10–90 s) decreases in amplitude. The respiratory event frequency estimated by the algorithm correlated with the apnea–hypopnea index (AHI) of the PSG with r = 0.84 in the test group. With the cutoff values determined in the training group, moderate-to-severe SA (AHI ≥ 15) was identified with 85% accuracy and severe SA (AHI ≥ 30) with 89% accuracy in the test group. SA can be quantitatively detected by the IMU embedded in wristwatch wearable devices in adults with suspected SA

Association of heart rate variability with regional difference in senility death ratio: ALLSTAR big data analysis

Objectives: Senility death is defined as natural death in the elderly who do not have a cause of death to be described otherwise and, if human life is finite, it may be one of the ultimate goals of medicine and healthcare. A recent survey in Japan reports that municipalities with a high senility death ratio have lower healthcare costs per late-elderly person. However, the causes of regional differences in senility death ratio and their biomedical determinants were unknown. In this study, we examined the relationships of the regional difference in senility death ratio with the regional differences in heart rate variability and physical activity. Methods: We compared the age-adjusted senility death ratio of all Japanese prefectures with the regional averages of heart rate variability and actigraphic physical activity obtained from a physiological big data of Allostatic State Mapping by Ambulatory ECG Repository (ALLSTAR). Results: The age-adjusted senility death ratio of 47 Japanese prefectures in 2015 ranged from 1.2% to 3.6% in men and from 3.5% to 7.8% in women. We compared these ratios with the age-adjusted indices of heart rate variability in 108,865 men and 136,536 women and of physical activity level in 16,661 men and 21,961 women. Heart rate variability indices and physical activity levels that are known to be associated with low mortality risk were higher in prefectures with higher senility death ratio. Conclusion: The regional senility death ratio in Japan may be associated with regional health status as reflected in heart rate variability and physical activity levels

Non-REM Sleep Marker for Wearable Monitoring: Power Concentration of Respiratory Heart Rate Fluctuation

A variety of heart rate variability (HRV) indices have been reported to estimate sleep stages, but the associations are modest and lacking solid physiological basis. Non-REM (NREM) sleep is associated with increased regularity of respiratory frequency, which results in the concentration of high frequency (HF) HRV power into a narrow frequency range. Using this physiological feature, we developed a new HRV sleep index named Hsi to quantify the degree of HF power concentration. We analyzed 11,636 consecutive 5-min segments of electrocardiographic (ECG) signal of polysomnographic data in 141 subjects and calculated Hsi and conventional HRV indices for each segment. Hsi was greater during NREM (mean [SD], 75.1 [8.3]%) than wake (61.0 [10.3]%) and REM (62.0 [8.4]%) stages. Receiver-operating characteristic curve analysis revealed that Hsi discriminated NREM from wake and REM segments with an area under the curve of 0.86, which was greater than those of heart rate (0.642), peak HF power (0.75), low-to-high frequency ratio (0.77), and scaling exponent &alpha; (0.77). With a cutoff &gt;70%, Hsi detected NREM segments with 77% sensitivity, 80% specificity, and a Cohen&rsquo;s kappa coefficient of 0.57. Hsi may provide an accurate NREM sleep maker for ECG and pulse wave signals obtained from wearable sensors

Sleep Stage Classification by a Combination of Actigraphic and Heart Rate Signals

Although heart rate variability and actigraphic data have been used for sleep-wake or sleep stage classifications, there are few studies on the combined use of them. Recent wearable sensors, however, equip both pulse wave and actigraphic sensors. This paper presents results on the performance of sleep stage classification by a combination of heart rate and actigraphic signals. We studied 40,643 epochs (length 3 min) of polysomnographic data in 289 subjects. A combined model, consisting of autonomic functional indices from heart rate variability and body movement indices derived from actigraphic data, discriminated non-rapid-eye-movement (REM) sleep from waking/REM sleep with 76.9% sensitivity, 74.5% specificity, 75.8% accuracy, and a Cohen’s kappa of 0.514. The combination was also useful for discriminating between REM sleep and waking at 77.2% sensitivity, 72.3% specificity, 74.5% accuracy, and a kappa of 0.491