Validity of simplified, calibration-less exercise intensity measurement using resting heart rate during sleep: a method-comparison study with respiratory gas analysis

Agreement between Vital Signs Measured Using Mat-Type Noncontact Sensors and Those from Conventional Clinical Assessment

Vital signs are crucial for assessing the condition of a patient and detecting early symptom deterioration. Noncontact sensor technology has been developed to take vital measurements with minimal burden. This study evaluated the accuracy of a mat-type noncontact sensor in measuring respiratory and pulse rates in patients with cardiovascular diseases compared to conventional methods. Forty-eight hospitalized patients were included; a mat-type sensor was used to measure their respiratory and pulse rates during bed rest. Differences between mat-type sensors and conventional methods were assessed using the Bland-Altman analysis. The mean difference in respiratory rate was 1.9 breaths/min (limits of agreement (LOA): -4.5 to 8.3 breaths/min), and proportional bias existed with significance (r = 0.63, p < 0.05). For pulse rate, the mean difference was -2.0 beats/min (LOA: -23.0 to 19.0 beats/min) when compared to blood pressure devices and 0.01 beats/min (LOA: -11.4 to 11.4 beats/min) when compared to 24-h Holter electrocardiography. The proportional bias was significant for both comparisons (r = 0.49, p < 0.05; r = 0.52, p < 0.05). These were considered clinically acceptable because there was no tendency to misjudge abnormal values as normal. The mat-type noncontact sensor demonstrated sufficient accuracy to serve as an alternative to conventional assessments, providing long-term monitoring of vital signs in clinical settings.

Highlighting Unseen Activity Through 48-Hour Continuous Measurement in Subacute Stroke Rehabilitation: Preliminary Cohort Study

BACKGROUND

Motor impairments not only lead to a significant reduction in patient activity levels but also trigger a further deterioration in motor function due to deconditioning, which is an issue that is particularly pronounced during hospitalization. This deconditioning can be countered by sustaining appropriate activity levels. Activities that occur outside of scheduled programs, often overlooked, are critical in this context. Wearable technology, such as smart clothing, provides a means to monitor these activities.

OBJECTIVE

This study aimed to observe activity levels in patients who had strokes during the subacute phase, focusing on both scheduled training sessions and other nontraining times in an inpatient rehabilitation environment. A smart clothing system is used to simultaneously measure heart rate and acceleration, offering insights into both the amount and intensity of the physical activity.

METHODS

In this preliminary cohort study, 11 individuals undergoing subacute stroke rehabilitation were enrolled. The 48-hour continuous measurement system, deployed at admission and reassessed 4 weeks later, monitored accelerometry data for physical activity (quantified with a moving SD of acceleration [MSDA]) and heart rate for intensity (quantified with percent heart rate reserve). The measurements were performed using a wearable activity monitoring system, the hitoe (NTT Corporation and Toray Industries, Inc) system comprising a measuring garment (wear or strap) with integrated electrodes, a data transmitter, and a smartphone. The Functional Independence Measure was used to assess the patients' daily activity levels. This study explored factors such as differences in activity during training and nontraining periods, correlations with activities of daily living (ADLs) and age, and changes observed after 4 weeks.

RESULTS

A significant increase was found in the daily total MSDA after the 4-week program, with the average percent heart rate reserve remaining consistent. Physical activity during training positively correlated with ADL levels both at admission (ρ=0.86, P<.001) and 4 weeks post admission (ρ=0.96, P<.001), whereas the correlation between age and MSDA was not significant during training periods at admission (ρ=-0.41, P=.21) or 4 weeks post admission (ρ=-0.25, P=.45). Conversely, nontraining activity showed a negative correlation with age, with significant negative correlations with age at admission (ρ=-0.82, P=.002) and 4 weeks post admission (ρ=-0.73, P=.01).

CONCLUSIONS

Inpatient rehabilitation activity levels were positively correlated with ADL levels. Further analysis revealed a strong positive correlation between scheduled training activities and ADL levels, whereas nontraining activities showed no such correlation. Instead, a negative correlation between nontraining activities and age was observed. These observations suggest the importance of providing activity opportunities for older patients, while it may also suggest the need for adjusting the activity amount to accommodate the potentially limited fitness levels of this demographic. Future studies with larger patient groups are warranted to validate and further elucidate these findings.

Prediction of stroke patients' bedroom-stay duration: machine-learning approach using wearable sensor data

Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices. Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies. Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman's rank correlation coefficient (R) of 0.56 and -0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics. Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements.

Effect of Combined Exercise Training on Physical and Cognitive Function in Women With Type 2 Diabetes

OBJECTIVES

One of the consequences of old age is cognitive and physical decline, which can cause a wide range of problems. These complications are more pronounced in those with type 2 diabetes (T2D). The aim of this pilot study was to investigate the effect of combined exercise training on blood biomarkers, physical fitness, and cognitive function in elderly women with T2D.

METHODS

Twenty-one elderly women with T2D were randomly allocated to training (n=12) and control (n=9) groups. The exercise training program was a combination of aerobic, resistance, and balance exercises performed 3 times per week over 12 weeks. In the same period, the control group received no training intervention. Blood markers, including brain-derived neurotrophic factor (BDNF), irisin, glycated hemoglobin (A1C), fasting blood sugar (FBS), cardiorespiratory fitness (CRF), lower and upper body strength, and cognitive function, were measured in all participants at baseline and after 12 weeks.

RESULTS

Serum BDNF levels were not significantly different between the exercise and control groups at 12 weeks (p>0.05). FBS and A1C levels in the exercise group decreased significantly compared with the control group (p<0.05). CRF, dynamic balance, and both upper and lower body strength in the exercise group improved significantly compared with the control group (p<0.05). Irisin levels decreased significantly in the control group, but levels did not change significantly in the exercise group. Greater improvements from exercise were observed on the Montreal Cognitive Assessment index compared with the control group (p=0.05), but no other group differences in cognitive function were noted.

CONCLUSIONS

Combined exercise improved some physical fitness and diabetes-related surrogate factors, as well as select cognitive functions, but had no significant effect on cognition-related biochemical factors (i.e. BDNF) in women with T2D.

Commonly Used Subjective Effort Scales May Not Predict Directly Measured Physical Workloads and Fatigue in Hispanic Farmworkers

In North America, Hispanic migrant farmworkers are being exposed to occupational ergonomic risks. Due to cultural differences in the perception and reporting of effort and pain, it was unknown whether standardized subjective ergonomic assessment tools could accurately estimate the directly measured their physical effort. This study investigated whether the subjective scales widely used in exercise physiology were associated with the direct measures of metabolic load and muscle fatigue in this population. Twenty-four migrant apple harvesters participated in this study. The Borg RPE in Spanish and the Omni RPE with pictures of tree-fruit harvesters were used for assessing overall effort at four time points during a full-day 8-h work shift. The Borg CR10 was used for assessing local discomfort at the shoulders. To determine whether there were associations between the subjective and direct measures of overall exertion measures, we conducted linear regressions of the percentage of heart rate reserve (% HRR) on the Borg RPE and Omni RPE. In terms of local discomfort, the median power frequency (MPF) of trapezius electromyography (EMG) was used for representing muscle fatigue. Then full-day measurements of muscle fatigue were regressed on the Borg CR10 changes from the beginning to the end of the work shift. The Omni RPE were found to be correlated with the % HRR. In addition, the Borg RPE were correlated to the % HRR after the break but not after the work. These scales might be useful for certain situations. In terms of local discomfort, the Borg CR10 were not correlated with the MPF of EMG and, therefore, could not replace direct measurement.

Ensemble averaging for categorical variables: Validation study of imputing lost data in 24-h recorded postures of inpatients

Acceleration sensors are widely used in consumer wearable devices and smartphones. Postures estimated from recorded accelerations are commonly used as features indicating the activities of patients in medical studies. However, recording for over 24 h is more likely to result in data losses than recording for a few hours, especially when consumer-grade wearable devices are used. Here, to impute postures over a period of 24 h, we propose an imputation method that uses ensemble averaging. This method outputs a time series of postures over 24 h with less lost data by calculating the ratios of postures taken at the same time of day during several measurement-session days. Whereas conventional imputation methods are based on approaches with groups of subjects having multiple variables, the proposed method imputes the lost data variables individually and does not require other variables except posture. We validated the method on 306 measurement data from 99 stroke inpatients in a hospital rehabilitation ward. First, to classify postures from acceleration data measured by a wearable sensor placed on the patient's trunk, we preliminary estimated possible thresholds for classifying postures as 'reclining' and 'sitting or standing' by investigating the valleys in the histogram of occurrences of trunk angles during a long-term recording. Next, the imputations of the proposed method were validated. The proposed method significantly reduced the missing data rate from 5.76% to 0.21%, outperforming a conventional method.

Determination of Waste Management Workers' Physical and Psychological Load: A Cross-Sectional Study Using Biometric Data

Waste management workers experience high stress and physical strain in their work environment, but very little empirical evidence supports effective health management practices for waste management workers. Hence, this study investigated the effects of worker characteristics and biometric indices on workers' physical and psychological loads during waste-handling operations. A biometric measurement system was installed in an industrial waste management facility in Japan to understand the actual working conditions of 29 workers in the facility. It comprised sensing wear for data collection and biometric sensors to measure heart rate (HR) and physical activity (PA) based on electrocardiogram signals. Multiple regression analysis was performed to evaluate significant relationships between the parameters. Although stress level is indicated by the ratio of low frequency (LF) to high frequency (HF) or high LF power in HR, the results showed that compared with workers who did not handle waste, those who did had lower PA and body surface temperature, higher stress, and lower HR variability parameters associated with higher psychological load. There were no significant differences in HR, heart rate interval (RRI), and workload. The psychological load of workers dealing directly with waste was high, regardless of their PA, whereas others had a low psychological load even with high PA. These findings suggest the need to promote sustainable work relationships and a quantitative understanding of harsh working conditions to improve work quality and reduce health hazards.

The Effect of an 8-Week Rope Skipping Intervention on Standing Long Jump Performance

The purpose of this study was to explore the utility of an 8-week rope skipping intervention in enhancing standing long jump performance was assessed by means of specific kinematic parameters acquired by 3-D space photography. The fifteen male college students from the physical education institute were randomly recruited as the research subjects. Participants first completed a standing long jump test without rope skipping intervention. Participants subsequently took part in a second standing long jump test after rope skipping training. Two high-speed digital cameras with 100 Hz sampling rate were synchronized to capture the movement. The captured images were processed using motion analysis suite, and the markers attached to joints on images were optical auto capture. Based on the results, the velocity of the center of gravity at take-off and landing were significantly improved. In addition, the study confirmed the requirement for forward tilt of the hip joint at landing to increase the velocity of the center of gravity and hence long jump distance. The detailed kinematic analysis described here provided further evidence of the benefits of integrating non-specialized and specialized training activities to enhance athletic performance and offers a contribution to movement theory and practice.

Psychological Effects of Heart Rate and Physical Vibration on the Operation of Construction Machines: Experimental Study

BACKGROUND

A construction method has emerged in which a camera is installed around a construction machine, and the operator remotely controls the machine while synchronizing the vibration of the machine with the images seen from the operator's seat using virtual reality (VR) technology. Indices related to changes in heart rate (HR) and physical vibration, such as heart rate variability (HRV) and multiscale entropy (MSE), can then be measured among the operators. As these indices are quantitative measures of autonomic regulation in the cardiovascular system, they can provide a useful means of assessing operational stress.

OBJECTIVE

In this study, we aimed to evaluate changes in HR and body vibration of machine operators and investigate appropriate methods of machine operation while considering the psychological load.

METHODS

We enrolled 9 remote operators (18-50 years old) in the experiment, which involved 42 measurements. A construction machine was driven on a test course simulating a construction site, and three patterns of operation-riding operation, remote operation using monitor images, and VR operation combining monitor images and machine vibration-were compared. The heartbeat, body vibration, and driving time of the participants were measured using sensing wear made of a woven film-like conductive material and a three-axis acceleration measurement device (WHS-2). We used HRV analysis in the time and frequency domains, MSE analysis as a measure of the complexity of heart rate changes, and the ISO (International Standards Organization) 2631 vibration index. Multiple regression analysis was conducted to model the relationship among the low frequency (LF)/high frequency (HF) HRV, MSE, vibration index, and driving time of construction equipment. Efficiency in driving time was investigated with a focus on stress reduction.

RESULTS

Multiple comparisons conducted via the Bonferroni test and Kruskal-Wallis test showed statistically significant differences (P=.05) in HRV-LF/HF, the vibration index, weighted acceleration, motion sickness dose value (MSDVz), and the driving time among the three operation patterns. The riding operation was found to reduce the driving time of the machine, but the operation stress was the highest in this case; operation based on the monitor image was found to have the lowest operation stress but the longest operation time. Multiple regression analysis showed that the explanatory variables (LH/HF), RR interval, and vibration index (MSDVz by vertical oscillation at 0.5-5 Hz) had a negative effect on the driving time (adjusted coefficient of determination R2=0.449).

CONCLUSIONS

A new method was developed to calculate the appropriate operating time by considering operational stress and suppressing the physical vibration within an acceptable range. By focusing on the relationship between psychological load and physical vibration, which has not been explored in previous studies, the relationship of these variables with the driving time of construction machines was clarified.

Practical Judgment of Workload Based on Physical Activity, Work Conditions, and Worker's Age in Construction Site

It is important for construction companies to sustain a productive workforce without sacrificing its health and safety. This study aims to develop a practical judgement method to estimate the workload risk of individual construction workers. Based on studies, we developed a workload model comprising a hygrothermal environment, behavioral information, and the physical characteristics of workers). The construction workers’ heart rate and physical activity were measured using the data collected from a wearable device equipped with a biosensor and an acceleration sensor. This study is the first report to use worker physical activity, age, and the wet bulb globe temperature (WBGT) to determine a worker’s physical workload. The accuracy of this health risk judgment result was 89.2%, indicating that it is possible to easily judge the health risk of workers even in an environment where it is difficult to measure the subject in advance. The proposed model and its findings can aid in monitoring the health impacts of working conditions during construction activities, and thereby contribute toward determining workers’ health damage. However, the sampled construction workers are 12 workers, further studies in other working conditions are required to accumulate more evidence and assure the accuracy of the models.