Impact of Masticatory Behaviors Measured With Wearable Device on Metabolic Syndrome: Cross-sectional Study

Background

It has been widely recognized that mastication behaviors are related to the health of the whole body and to lifestyle-related diseases. However, many studies were based on subjective questionnaires or were limited to small-scale research in the laboratory due to the lack of a device for measuring mastication behaviors during the daily meal objectively. Recently, a small wearable masticatory counter device, called bitescan (Sharp Co), for measuring masticatory behavior was developed. This wearable device is designed to assess objective masticatory behavior by being worn on the ear in daily life.

Objective

This study aimed to investigate the relation between mastication behaviors in the laboratory and in daily meals and to clarify the difference in mastication behaviors between those with metabolic syndrome (MetS) and those without (non-MetS) measured using a wearable device.

Methods

A total of 99 healthy volunteers (50 men and 49 women, mean age 36.4 [SD 11.7] years) participated in this study. The mastication behaviors (ie, number of chews and bites, number of chews per bite, and chewing rate) were measured using a wearable ear-hung device. Mastication behaviors while eating a rice ball (100 g) in the laboratory and during usual meals for an entire day were monitored, and the daily energy intake was calculated. Participants’ abdominal circumference, fasting glucose concentration, blood pressure, and serum lipids were also measured. Mastication behaviors in the laboratory and during meals for 1 entire day were compared. The participants were divided into 2 groups using the Japanese criteria for MetS (positive/negative for MetS or each MetS component), and mastication behaviors were compared.

Results

Mastication behaviors in the laboratory and during daily meals were significantly correlated (number of chews r=0.36; P<.001; number of bites r=0.49; P<.001; number of chews per bite r=0.33; P=.001; and chewing rate r=0.51; P<.001). Although a positive correlation was observed between the number of chews during the 1-day meals and energy intake (r=0.26, P=.009), the number of chews per calorie ingested was negatively correlated with energy intake (r=–0.32, P=.002). Of the 99 participants, 8 fit the criteria for MetS and 14 for pre-MetS. The number of chews and bites for a rice ball in the pre-MetS(+) group was significantly lower than the pre-MetS(–) group (P=.02 and P=.04, respectively). Additionally, scores for the positive abdominal circumference and hypertension subgroups were also less than the counterpart groups (P=.004 and P=.01 for chews, P=.006 and P=.02 for bites, respectively). The number of chews and bites for an entire day in the hypertension subgroup were significantly lower than in the other groups (P=.02 and P=.006). Furthermore, the positive abdominal circumference and hypertension subgroups showed lower numbers of chews per calorie ingested for 1-day meals (P=.03 and P=.02, respectively).

Conclusions

These results suggest a relationship between masticatory behaviors in the laboratory and those during daily meals and that masticatory behaviors are associated with MetS and MetS components.

Trial Registration

University Hospital Medical Information Network Clinical Trials Registry R000034453; https://tinyurl.com/mwzrhrua

Sensing System for Plegic or Paretic Hands Self-Training Motivation

Patients after stroke with paretic or plegic hands require frequent exercises to promote neuroplasticity and to improve hand joint mobilization. Available devices for hand exercising are intended for persons with some level of hand control or provide continuous passive motion with limited patient involvement. Patients can benefit from self-exercising where they use the other hand to exercise the plegic or paretic one. However, post-stroke neuropsychological complications, apathy, and cognitive impairments such as forgetfulness make regular self-exercising difficult. This paper describes Przypominajka v2-a system intended to support self-exercising, remind about it, and motivate patients. We propose a glove-based device with an on-device machine-learning-based exercise scoring, a tablet-based interface, and a web-based application for therapists. The feasibility of on-device inference and the accuracy of correct exercise classification was evaluated on four healthy participants. Whole system use was described in a case study with a patient with a paretic hand. The anomaly classification has an accuracy of 91.3% and f1 value of 91.6% but achieves poorer results for new users (78% and 81%). The case study showed that patients had a positive reaction to exercising with Przypominajka, but there were issues relating to sensor glove: ease of putting on and clarity of instructions. The paper presents a new way in which sensor systems can support the rehabilitation of after-stroke patients with an on-device machine-learning-based classification that can accurately score and contribute to patient motivation.

A Wearable Breath Sensor Based on Fiber-Tip Microcantilever

Respiration rate is an essential vital sign that requires monitoring under various conditions, including in strong electromagnetic environments such as in magnetic resonance imaging systems. To provide an electromagnetically-immune breath-sensing system, we propose an all-fiber-optic wearable breath sensor based on a fiber-tip microcantilever. The microcantilever was fabricated on a fiber-tip by two-photon polymerization microfabrication based on femtosecond laser, so that a micro Fabry-Pérot (FP) interferometer was formed between the microcantilever and the end-face of the fiber. The cavity length of the micro FP interferometer was reduced as a result of the bending of the microcantilever induced by breath airflow. The signal of breath rate was rebuilt by detecting power variations of the FP interferometer reflected light and applying dynamic thresholds. The breath sensor achieved a high sensitivity of 0.8 nm/(m/s) by detecting the reflection spectrum upon applied flow velocities from 0.53 to 5.31 m/s. This sensor was also shown to have excellent thermal stability as its cross-sensitivity of airflow with respect to the temperature response was only 0.095 (m/s)/°C. When mounted inside a wearable surgical mask, the sensor demonstrated the capability to detect various breath patterns, including normal, fast, random, and deep breaths. We anticipate the proposed wearable breath sensor could be a useful and reliable tool for respiration rate monitoring.

Nighttime Sleep Awakening Frequency and Its Consistency Predict Future Academic Performance in College Students

Although the relationship between sleep and academic performance has been extensively examined, how sleep predicts future academic performance (e.g., 2-3 years) remains to be further investigated. Using wearable smartwatches and a self-report questionnaire, we tracked sleep activities of 45 college students over a period of approximately half a month to see whether their sleep activities predicted their academic performance, which was estimated by grade point average (GPA). Results showed that both nighttime sleep awakening frequency and its consistency in the tracking period were not significantly correlated with the GPA for the courses taken in the sleep tracking semester (current GPA). However, both nighttime sleep awakening frequency and its consistency inversely predicted the GPA for the rest of the courses taken after that semester (future GPA). Moreover, students with more difficulty staying awake throughout the day obtained lower current and future GPAs, and students with higher inconsistency of sleep quality obtained lower future GPA. Together, these findings highlight the importance of nighttime sleep awakening frequency and consistency in predicting future academic performance, and emphasize the necessity of assessing the consistency of sleep measures in future studies.

A Novel Prototype for Safe Driving Using Embedded Smart Box System

Every day, vehicle accidents occur and many of them might be avoided if the drivers demonstrated excellent driving without mistakes. This paper presents a novel prototype applied in a real transportation system, particularly for buses, to avoid accidents, which may involve numerous victims, and even occasionally cause death. This system consists of a wearable device and embedded system with several sensors connected via Bluetooth, similar to the Internet of Things (IoT). Wearable devices are made to monitor the driver's heart rate and alert the driver if they are in a state of sleep deprivation to prevent any potential accidents. The embedded system includes a Global Positioning System (GPS), accelerometers, and gyroscopes attached to a Smart Box mounted on the bus. The embedded system alert function will be triggered if an accident occurs and automatically sends the geolocation of the accident to the registered phone number through a message using a mobile phone. The results for all scenarios were significant when measured by an automatic accident trigger via the smart box if the value of measured values in each axis exceeded 583. In conclusion, the implementation of this innovative solution at the system-level was shown to be satisfactory in terms of the safety mechanism used by the nominated drivers.

Recent Advances of Point-of-Care Devices Integrated with Molecularly Imprinted Polymers-Based Biosensors: From Biomolecule Sensing Design to Intraoral Fluid Testing

Recent developments of point-of-care testing (POCT) and in vitro diagnostic medical devices have provided analytical capabilities and reliable diagnostic results for rapid access at or near the patient's location. Nevertheless, the challenges of reliable diagnosis still remain an important factor in actual clinical trials before on-site medical treatment and making clinical decisions. New classes of POCT devices depict precise diagnostic technologies that can detect biomarkers in biofluids such as sweat, tears, saliva or urine. The introduction of a novel molecularly imprinted polymer (MIP) system as an artificial bioreceptor for the POCT devices could be one of the emerging candidates to improve the analytical performance along with physicochemical stability when used in harsh environments. Here, we review the potential availability of MIP-based biorecognition systems as custom artificial receptors with high selectivity and chemical affinity for specific molecules. Further developments to the progress of advanced MIP technology for biomolecule recognition are introduced. Finally, to improve the POCT-based diagnostic system, we summarized the perspectives for high expandability to MIP-based periodontal diagnosis and the future directions of MIP-based biosensors as a wearable format.

Usability of Wearable Multiparameter Technology to Continuously Monitor Free-Living Vital Signs in People Living With Chronic Obstructive Pulmonary Disease: Prospective Observational Study

Background

Vital signs monitoring (VSM) is routine for inpatients, but monitoring during free-living conditions is largely untested in chronic obstructive pulmonary disease (COPD).

Objective

This study investigated the usability and acceptability of continuous VSM for people with COPD using wearable multiparameter technology.

Methods

In total, 50 people following hospitalization for an acute exacerbation of COPD (AECOPD) and 50 people with stable COPD symptoms were asked to wear an Equivital LifeMonitor during waking hours for 6 weeks (42 days). The device recorded heart rate (HR), respiratory rate (RR), skin temperature, and physical activity. Adherence was defined by the number of days the vest was worn and daily wear time. Signal quality was examined, with thresholds of ≥85% for HR and ≥80% for RR, based on the device’s proprietary confidence algorithm. Data quality was calculated as the percentage of wear time with acceptable signal quality. Participant feedback was assessed during follow-up phone calls.

Results

In total, 84% of participants provided data, with average daily wear time of 11.8 (SD 2.2) hours for 32 (SD 11) days (average of study duration 76%, SD 26%). There was greater adherence in the stable group than in the post-AECOPD group (≥5 weeks wear: 71.4% vs 45.7%; P=.02). For all 84 participants, the median HR signal quality was 90% (IQR 80%-94%) and the median RR signal quality was 93% (IQR 92%-95%). The median HR data quality was 81% (IQR 58%-91%), and the median RR data quality was 85% (IQR 77%-91%). Stable group BMI was associated with HR signal quality (r_s=0.45, P=.008) and HR data quality (r_s=0.44, P=.008). For the AECOPD group, RR data quality was associated with waist circumference and BMI (r_s=–0.49, P=.009; r_s=–0.44, P=.02). In total, 36 (74%) participants in the Stable group and 21 (60%) participants in the AECOPD group accepted the technology, but 10 participants (12%) expressed concerns with wearing a device around their chest.

Conclusions

This wearable multiparametric technology showed good user acceptance and was able to measure vital signs in a COPD population. Data quality was generally high but was influenced by body composition. Overall, it was feasible to continuously measure vital signs during free-living conditions in people with COPD symptoms but with additional challenges in the post-AECOPD context.

On the benefits of wearable devices for Parkinson's disease

Freezing of gait (FOG) is defined as episodic inability to generate an effective movement without any known cause other than parkinson-ism or gait disturbance. FOG is one of the most disabling symptoms of Parkinson's disease (PD), it affects mobility and increases the risk of falling in people with PD, making it a leading cause of hospitalization and of significantly worsening the quality of life (1). In recent years, new non-invasive intervention strategies have been implemented to decrease FOG symptoms. Thanks to technological progress, several devices have been developed as a support for the patients during diag-nosis, treatments and also everyday life. These types of interventions are based on cueing systems that rely on active stimulation. These devices are able to identify FOG states and to operate when this motor blocks occur, providing external stimuli to overcome these episodes. Hence, this work aims to provide a technological review of the literature related to wearable devices and focuses on auditory, visual, virtual and soma-tosensory cueing systems, which can provide a suitable intervention for patients with PD. The paper describes the technical functioning and effectiveness of the different reporting systems in overcoming FOG episodes. Moreover, a classification of existing devices, highlighting their advantages and disadvantages, will be provided in order to identify the ones with the best performance.

A Printed Flexible Humidity Sensor with High Sensitivity and Fast Response Using a Cellulose Nanofiber/Carbon Black Composite

In the emerging Internet of Things (IoT) society, there is a significant need for low-cost, high-performance flexible humidity sensors in wearable devices. However, commercially available humidity sensors lack flexibility or require expensive and complex fabrication methods, limiting their application and widespread use. We report a high-performance printed flexible humidity sensor using a cellulose nanofiber/carbon black (CNF/CB) composite. The cellulose nanofiber enables excellent dispersion of carbon black, which facilitates the ink preparation and printing process. At the same time, its hydrophilic and porous nature provides high sensitivity and fast response to humidity. Significant resistance changes of 120% were observed in the sensor at humidity ranging from 30% RH to 90% RH, with a fast response time of 10 s and a recovery time of 6 s. Furthermore, the developed sensor also exhibited high-performance uniformity, response stability, and flexibility. A simple humidity detection device was fabricated and successfully applied to monitor human respiration and noncontact fingertip moisture as a proof-of-concept.

Ambulatory Heart Rate Variability Monitoring: Comparisons Between the Empatica E4 Wristband and Holter Electrocardiogram

OBJECTIVE

Heart rate variability (HRV) is a useful index of psychological and physiological stress. Although several wristband devices have purported to measure HRV, none have demonstrated reliability when compared with the criterion-standard Holter monitor. We evaluated the reliability of HRV readings from the Empatica E4 wristband compared with a Holter monitor over a 24-hour period of simultaneous monitoring.

METHODS

Agreement between the monitors was assessed by examining correlations and intraclass correlations (ICCs) for fixed sets in 13 individuals in a treatment trial for posttraumatic stress disorder (4 women; mean [standard deviation] age = 51.92 [6.17] years). Agreement was calculated at 1-second and 5-minute intervals for interbeat intervals (IBIs) and for 5-minute intervals of the root mean square of successive differences between normal heartbeats (RMSSD) and standard deviation of all normal R-R intervals (SDNN). Agreement across the entire 24-hour observation period was also measured. Frequency-domain measures of HRV could not be calculated because of too much missing data from the E4.

RESULTS

Although high interdevice correlations and ICCs were observed between the E4 and Holter monitors for IBIs at 1-second (median r = 0.88; median ICC = 0.87) and 5-minute (median r = 0.94; median ICC = 0.94) intervals, reliabilities for 5-minute RMSSD (median r = -0.09; median ICC = -0.05) and 5-minute SDNN (median r = 0.48; median ICC = 0.47) were poor. Agreement between the devices on 24-hour measures of HRV was satisfactory (IBI: r = 0.97, ICC = 0.97; RMSSD: r = 0.77, IBI = 0.76; SDNN: r = 0.92, IBI = 0.89).

CONCLUSIONS

Findings suggest that the low reliability of Empatica E4 as compared with the Holter monitor does not justify its use in ambulatory research for the measurement of HRV over time periods of 5 minutes or less.

Nicotine Sensors for Wearable Battery-Free Monitoring of Vaping

Nicotine, an addictive substance in tobacco products and electronic cigarettes (e-cigs), is recognized for increasing the risk of cardiovascular and respiratory disorders. Careful real-time monitoring of nicotine exposure is critical in alleviating the potential health impacts of not just smokers but also those exposed to second-hand and third-hand smoke. Monitoring of nicotine requires suitable sensing material to detect nicotine selectively and testing under free-living conditions in the standard environment. Here, we experimentally demonstrate a vanadium dioxide (VO₂)-based nicotine sensor and explain its conductometric mechanisms with compositional analysis and density functional theory (DFT) calculations. For real-time monitoring of nicotine vapor from e-cigarettes in the air, the sensor is integrated with an epidermal near-field communication (NFC) interface that enables battery-free operation and data transmission to smart electronic devices to record and store sensor data. Collectively, the technique of sensor development and integration expands the use of wearable electronics for real-time monitoring of hazardous elements in the environment and biosignals wirelessly.

A Rare Case of Ciprofloxacin-Induced Bradycardia Recognized by a Smartwatch

Fluoroquinolones are known to cause cardiac side effects. The most common are ventricular arrhythmias and QT prolongation. We present a case of symptomatic bradycardia secondary to ciprofloxacin use in a patient who presented to the hospital after a smartwatch alert for bradycardia. We believe that the integration of wearable technology in the practice of medicine could provide valuable data and improve patient care in different settings.

Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison With Electrocardiography in Time and Frequency Domains: Comprehensive Analysis

Background

Photoplethysmography is a noninvasive and low-cost method to remotely and continuously track vital signs. The Oura Ring is a compact photoplethysmography-based smart ring, which has recently drawn attention to remote health monitoring and wellness applications. The ring is used to acquire nocturnal heart rate (HR) and HR variability (HRV) parameters ubiquitously. However, these parameters are highly susceptible to motion artifacts and environmental noise. Therefore, a validity assessment of the parameters is required in everyday settings.

Objective

This study aims to evaluate the accuracy of HR and time domain and frequency domain HRV parameters collected by the Oura Ring against a medical grade chest electrocardiogram monitor.

Methods

We conducted overnight home-based monitoring using an Oura Ring and a Shimmer3 electrocardiogram device. The nocturnal HR and HRV parameters of 35 healthy individuals were collected and assessed. We evaluated the parameters within 2 tests, that is, values collected from 5-minute recordings (ie, short-term HRV analysis) and the average values per night sleep. A linear regression method, the Pearson correlation coefficient, and the Bland–Altman plot were used to compare the measurements of the 2 devices.

Results

Our findings showed low mean biases of the HR and HRV parameters collected by the Oura Ring in both the 5-minute and average-per-night tests. In the 5-minute test, the error variances of the parameters were different. The parameters provided by the Oura Ring dashboard (ie, HR and root mean square of successive differences [RMSSD]) showed relatively low error variance compared with the HRV parameters extracted from the normal interbeat interval signals. The Pearson correlation coefficient tests (P<.001) indicated that HR, RMSSD, average of normal heart beat intervals (AVNN), and percentage of successive normal beat-to-beat intervals that differ by more than 50 ms (pNN50) had high positive correlations with the baseline values; SD of normal beat-to-beat intervals (SDNN) and high frequency (HF) had moderate positive correlations, and low frequency (LF) and LF:HF ratio had low positive correlations. The HR, RMSSD, AVNN, and pNN50 had narrow 95% CIs; however, SDNN, LF, HF, and LF:HF ratio had relatively wider 95% CIs. In contrast, the average-per-night test showed that the HR, RMSSD, SDNN, AVNN, pNN50, LF, and HF had high positive relationships (P<.001), and the LF:HF ratio had a moderate positive relationship (P<.001). The average-per-night test also indicated considerably lower error variances than the 5-minute test for the parameters.

Conclusions

The Oura Ring could accurately measure nocturnal HR and RMSSD in both the 5-minute and average-per-night tests. It provided acceptable nocturnal AVNN, pNN50, HF, and SDNN accuracy in the average-per-night test but not in the 5-minute test. In contrast, the LF and LF:HF ratio of the ring had high error rates in both tests.

Improvement in Strain Sensor Stability by Adapting the Metal Contact Layer

Research on stretchable strain sensors is actively conducted due to increasing interest in wearable devices. However, typical studies have focused on improving the elasticity of the electrode. Therefore, methods of directly connecting wire or attaching conductive tape to materials to detect deformation have been used to evaluate the performance of strain sensors. Polyaniline (PANI), a p-type semiconductive polymer, has been widely used for stretchable electrodes. However, conventional procedures have limitations in determining an appropriate metal for ohmic contact with PANI. Materials that are generally used for connection with PANI form an undesirable metal-semiconductor junction and have significant contact resistance. Hence, they degrade sensor performance. This study secured ohmic contact by adapting Au thin film as the metal contact layer (the MCL), with lower contact resistance and a larger work function than PANI. Additionally, we presented a buffer layer using hard polydimethylsiloxane (PDMS) and structured it into a dumbbell shape to protect the metal from deformation. As a result, we enhanced steadiness and repeatability up to 50% strain by comparing the gauge factors and the relative resistance changes. Consequently, adapting structural methods (the MCL and the dumbbell shape) to a device can result in strain sensors with promising stability, as well as high stretchability.

Research and Development of a COVID-19 Tracking System in Order to Implement Analytical Tools to Reduce the Infection Risk

The whole world is currently focused on COVID-19, which causes considerable economic and social damage. The disease is spreading rapidly through the population, and the effort to stop the spread is entirely still failing. In our article, we want to contribute to the improvement of the situation. We propose a tracking system that would identify affected people with greater accuracy than medical staff can. The main goal was to design hardware and construct a device that would track anonymous risky contacts in areas with a highly concentrated population, such as schools, hospitals, large social events, and companies. We have chosen a 2.4 GHz proprietary protocol for contact monitoring and mutual communication of individual devices. The 2.4 GHz proprietary protocol has many advantages such as a low price and higher resistance to interference and thus offers benefits. We conducted a pilot experiment to catch bugs in the system. The device is in the form of a bracelet and captures signals from other bracelets worn at a particular location. In case of contact with an infected person, the alarm is activated. This article describes the concept of the tracking system, the design of the devices, initial tests, and plans for future use.

Prediction of good sleep with physical activity and light exposure: a preliminary study

STUDY OBJECTIVES

Cognitive behavioral treatment for insomnia is performed under the premise that feedback provided by evaluation of sleep diaries written by patients will result in good sleep. The sleep diary is essential for behavior therapy and sleep hygiene education. However, limitations include subjectivity and laborious input. We aimed to develop an artificial intelligence sleep prediction model and to find factors associated with good sleep using a wrist-worn actigraphy device.

METHODS

We enrolled 109 participants who reported having no sleep disturbances. We developed a sleep prediction model using 733 days of actigraphy data of physical activity and light exposure. Twenty-four sleep prediction models were developed based on different data sources (actigraphy alone, sleep diary alone, or combined data), different durations of data (1 or 2 days), and different analysis methods (extreme gradient boosting, convolutional neural network, long short-term memory, logistic regression analysis). The outcome measure of "good sleep" was defined as ≥90% sleep efficiency.

RESULTS

Actigraphy model performance was comparable to sleep diary model performance. Two-day models generally performed better than 1-day models. Among all models, the 2-day, combined (actigraphy and sleep diary), extreme gradient boosting model had the best performance for predicting good sleep (accuracy=0.69, area under the curve=0.70).

CONCLUSIONS

The findings suggested that it is possible to develop automated sleep models with good predictive performance. Further research including patients with insomnia is needed for clinical application.

Sound Source Selection Based on Head Movements in Natural Group Conversation

To optimally improve signal-to-noise ratio in noisy environments, a hearing assistance device must correctly identify what is signal and what is noise. Many of the biosignal-based approaches to solving this question are themselves subject to noise, but head angle is an overt behavior that may be possible to capture in practical devices in the real world. Previous orientation studies have demonstrated that head angle is systematically related to listening target; our study aimed to examine whether this relationship is sufficiently reliable to be used in group conversations where participants may be seated in different layouts and the listener is free to turn their body as well as their head. In addition to this simple method, we developed a source-selection algorithm based on a hidden Markov model (HMM) trained on listeners’ head movement. The performance of this model and the simple head-steering method was evaluated using publicly available behavioral data. Head angle during group conversation was predictive of active talker, exhibiting an undershoot with a slope consistent with that found in simple orientation studies, but the intercept of the linear relationship was different for different talker layouts, suggesting it would be problematic to rely exclusively on this information to predict the location of auditory attention. Provided the location of all target talkers is known, the HMM source selection model implemented here, however, showed significantly lower error in identifying listeners’ auditory attention than the linear head-steering method.

Wrist accelerometry for monitoring dementia agitation behaviour in clinical settings: A scoping review

Agitated behaviour among elderly people with dementia is a challenge in clinical management. Wrist accelerometry could be a versatile tool for making objective, quantitative, and long-term assessments. The objective of this review was to summarise the clinical application of wrist accelerometry to agitation assessments and ways of analysing the data. Two authors independently searched the electronic databases CINAHL, PubMed, PsycInfo, EMBASE, and Web of Science. Nine (n = 9) articles were eligible for a review. Our review found a significant association between the activity levels (frequency and entropy) measured by accelerometers and the benchmark instrument of agitated behaviour. However, the performance of wrist accelerometry in identifying the occurrence of agitation episodes was unsatisfactory. Elderly people with dementia have also been monitored in existing studies by investigating the at-risk time for their agitation episodes (daytime and evening). Consideration may be given in future studies on wrist accelerometry to unifying the parameters of interest and the cut-off and measurement periods, and to using a sampling window to standardise the protocol for assessing agitated behaviour through wrist accelerometry.

Greater variability in daily sleep efficiency predicts depression and anxiety in young adults: Estimation of depression severity using the two-week sleep quality records of wearable devices

OBJECTIVES

Sleep disturbances are associated with both the onset and progression of depressive disorders. It is important to capture day-to-day variability in sleep patterns; irregular sleep is associated with depressive symptoms. We used sleep efficiency, measured with wearable devices, as an objective indicator of daily sleep variability.

MATERIALS AND METHODS

The total sample consists of 100 undergraduate and graduate students, 60% of whom were female. All were divided into three groups (with major depressive disorder, mild depressive symptoms, and controls). Self-report questionnaires were completed at the beginning of the experiment, and sleep efficiency data were collected daily for 2 weeks using wearable devices. We explored whether the mean value of sleep efficiency, and its variability, predicted the severity of depression using dynamic structural equation modeling.

RESULTS

More marked daily variability in sleep efficiency significantly predicted levels of depression and anxiety, as did the average person-level covariates (longer time in bed, poorer quality of life, lower extraversion, and higher neuroticism).

CONCLUSION

Large swings in day-to-day sleep efficiency and certain clinical characteristics might be associated with depression severity in young adults.

Detection of Clenbuterol-Induced Changes in Heart Rate Using At-Home Recorded Smartwatch Data: Randomized Controlled Trial

Background

Although electrocardiography is the gold standard for heart rate (HR) recording in clinical trials, the increasing availability of smartwatch-based HR monitors opens up possibilities for drug development studies. Smartwatches allow for inexpensive, unobtrusive, and continuous HR estimation for potential detection of treatment effects outside the clinic, during daily life.

Objective

The aim of this study is to evaluate the repeatability and sensitivity of smartwatch-based HR estimates collected during a randomized clinical trial.

Methods

The data were collected as part of a multiple-dose, investigator-blinded, randomized, placebo-controlled, parallel-group study of 12 patients with Parkinson disease. After a 6-day baseline period, 4 and 8 patients were treated for 7 days with an ascending dose of placebo and clenbuterol, respectively. Throughout the study, the smartwatch provided HR and sleep state estimates. The HR estimates were quantified as the 2.5th, 50th, and 97.5th percentiles within awake and asleep segments. Linear mixed models were used to calculate the following: (1) the intraclass correlation coefficient (ICC) of estimated sleep durations, (2) the ICC and minimum detectable effect (MDE) of the HR estimates, and (3) the effect sizes of the HR estimates.

Results

Sleep duration was moderately repeatable (ICC=0.64) and was not significantly affected by study day (P=.83), clenbuterol (P=.43), and study day by clenbuterol (P=.73). Clenbuterol-induced changes were detected in the asleep HR as of the first night (+3.79 beats per minute [bpm], P=.04) and in the awake HR as of the third day (+8.79 bpm, P=.001). The median HR while asleep had the highest repeatability (ICC=0.70). The MDE (N=12) was found to be smaller when patients were asleep (6.8 bpm to 11.7 bpm) than while awake (10.7 bpm to 22.1 bpm). Overall, the effect sizes for clenbuterol-induced changes were higher while asleep (0.49 to 2.75) than while awake (0.08 to 1.94).

Conclusions

We demonstrated the feasibility of using smartwatch-based HR estimates to detect clenbuterol-induced changes during clinical trials. The asleep HR estimates were most repeatable and sensitive to treatment effects. We conclude that smartwatch-based HR estimates obtained during daily living in a clinical trial can be used to detect and track treatment effects.

Trial Registration

Netherlands Trials Register NL8002; https://www.trialregister.nl/trial/8002

Wearable E-Textile and CNT Sensor Wireless Measurement System for Real-Time Penile Erection Monitoring

Erection measurements are the most important indicator of male urological disease diagnosis, treatment, and results. Rigiscan has been used widely in studies and diagnoses for nocturnal penile tumescence for evaluating erectile dysfunction by measuring the number and timing of erectile dysfunctions during sleep. However, this device has limitations such as the weight and bulk of the device and has been questioned for its role as a standard for ED Erectile Dysfunction (ED) diagnosis. In this study, we propose a real-time wearable monitoring system that can quantitatively measure the length and circumference of the penis using electronic textiles (E-textile) and carbon nanotube (CNT) sensors. The E-textile sensor is used to measure the length, circumference, and gradient with portability, convenience, and comfort. Sensors were created by coating CNTs on latex for flexibility. The CNT-based latex condom-type sensor in our proposed system shows the length, circumference, and curvature measurements with changes in resistance, and the E-textile performance shows a 1.44% error rate and a cavity radius of 110 to 300. The results of this conceptual study are for supplementary sensor development with a combination of new technologies with alternatives or existing methods for measuring erection function.

Stress Watch: The Use of Heart Rate and Heart Rate Variability to Detect Stress: A Pilot Study Using Smart Watch Wearables

Stress is an inherent part of the normal human experience. Although, for the most part, this stress response is advantageous, chronic, heightened, or inappropriate stress responses can have deleterious effects on the human body. It has been suggested that individuals who experience repeated or prolonged stress exhibit blunted biological stress responses when compared to the general population. Thus, when assessing whether a ubiquitous stress response exists, it is important to stratify based on resting levels in the absence of stress. Research has shown that stress that causes symptomatic responses requires early intervention in order to mitigate possible associated mental health decline and personal risks. Given this, real-time monitoring of stress may provide immediate biofeedback to the individual and allow for early self-intervention. This study aimed to determine if the change in heart rate variability could predict, in two different cohorts, the quality of response to acute stress when exposed to an acute stressor and, in turn, contribute to the development of a physiological algorithm for stress which could be utilized in future smartwatch technologies. This study also aimed to assess whether baseline stress levels may affect the changes seen in heart rate variability at baseline and following stress tasks. A total of 30 student doctor participants and 30 participants from the general population were recruited for the study. The Trier Stress Test was utilized to induce stress, with resting and stress phase ECGs recorded, as well as inter-second heart rate (recorded using a FitBit). Although the present study failed to identify ubiquitous patterns of HRV and HR changes during stress, it did identify novel changes in these parameters between resting and stress states. This study has shown that the utilization of HRV as a measure of stress should be calculated with consideration of resting (baseline) anxiety and stress states in order to ensure an accurate measure of the effects of additive acute stress.

Real-Time PPG Signal Conditioning with Long Short-Term Memory (LSTM) Network for Wearable Devices

This paper presents an algorithm for real-time detection of the heart rate measured on a person’s wrist using a wearable device with a photoplethysmographic (PPG) sensor and accelerometer. The proposed algorithm consists of an appropriately trained LSTM network and the Time-Domain Heart Rate (TDHR) algorithm for peak detection in the PPG waveform. The Long Short-Term Memory (LSTM) network uses the signals from the accelerometer to improve the shape of the PPG input signal in a time domain that is distorted by body movements. Multiple variants of the LSTM network have been evaluated, including taking their complexity and computational cost into consideration. Adding the LSTM network caused additional computational effort, but the performance results of the whole algorithm are much better, outperforming the other algorithms from the literature.

A Wearable Biofeedback Device to Increase Gait Swing Time Could Have Positive Effects on Gait among Older Adults

Older adults walk with a shorter stride length, reduced hip range of motion (ROM) and higher cadence. These are signs of reductions in walking ability. This study investigated whether using a wireless smart insole system that monitored and provided biofeedback to encourage an extension of swing time could increase stride length and hip flexion, while reducing the cadence. Seven older adults were tested in this study, with and without the biofeedback device, in an outdoor environment. Gait analysis was performed by using GaitRite system and Xsens MVN. Repeated measures analysis demonstrated that with biofeedback, the swing time increased by 6.45%, stride length by 4.52% and hip flexion by 14.73%, with statistical significance. It also decreased the cadence significantly by 5.5%. This study has demonstrated that this smart insole system modified positively the studied gait parameters in older adults and has the potential to improve their walking ability.

Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study

BACKGROUND

There is a growing interest in using person-generated wearable device data for biomedical research, but there are also concerns regarding the quality of data such as missing or incorrect data. This emphasizes the importance of assessing data quality before conducting research. In order to perform data quality assessments, it is essential to define what data quality means for person-generated wearable device data by identifying the data quality dimensions.

OBJECTIVE

This study aims to identify data quality dimensions for person-generated wearable device data for research purposes.

METHODS

This study was conducted in 3 phases: literature review, survey, and focus group discussion. The literature review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline to identify factors affecting data quality and its associated data quality challenges. In addition, we conducted a survey to confirm and complement results from the literature review and to understand researchers' perceptions on data quality dimensions that were previously identified as dimensions for the secondary use of electronic health record (EHR) data. We sent the survey to researchers with experience in analyzing wearable device data. Focus group discussion sessions were conducted with domain experts to derive data quality dimensions for person-generated wearable device data. On the basis of the results from the literature review and survey, a facilitator proposed potential data quality dimensions relevant to person-generated wearable device data, and the domain experts accepted or rejected the suggested dimensions.

RESULTS

In total, 19 studies were included in the literature review, and 3 major themes emerged: device- and technical-related, user-related, and data governance-related factors. The associated data quality problems were incomplete data, incorrect data, and heterogeneous data. A total of 20 respondents answered the survey. The major data quality challenges faced by researchers were completeness, accuracy, and plausibility. The importance ratings on data quality dimensions in an existing framework showed that the dimensions for secondary use of EHR data are applicable to person-generated wearable device data. There were 3 focus group sessions with domain experts in data quality and wearable device research. The experts concluded that intrinsic data quality features, such as conformance, completeness, and plausibility, and contextual and fitness-for-use data quality features, such as completeness (breadth and density) and temporal data granularity, are important data quality dimensions for assessing person-generated wearable device data for research purposes.

CONCLUSIONS

In this study, intrinsic and contextual and fitness-for-use data quality dimensions for person-generated wearable device data were identified. The dimensions were adapted from data quality terminologies and frameworks for the secondary use of EHR data with a few modifications. Further research on how data quality can be assessed with respect to each dimension is needed.

Plethysmography System to Monitor the Jugular Venous Pulse: A Feasibility Study

Cerebral venous outflow is investigated in the diagnosis of heart failure through the monitoring of jugular venous pulse, an indicator to assess cardiovascular diseases. The jugular venous pulse is a weak signal stemming from the lying internal jugular vein and often invasive methodologies requiring surgery are mandatory to detect it. Jugular venous pulse can also be extrapolated via the ultrasound technique, but it requires a qualified healthcare operator to perform the examination. In this work, a wireless, user-friendly, wearable device for plethysmography is developed to investigate the possibility of monitoring the jugular venous pulse non-invasively. The proposed device can monitor the jugular venous pulse and the electrocardiogram synchronously. To study the feasibility of using the proposed device to detect physiological variables, several measurements were carried out on healthy subjects by considering three different postures: supine, sitting, and upright. Data acquired in the experiment were properly filtered to highlight the cardiac oscillation and remove the breathing contribution, which causes a considerable shift in the amplitude of signals. To evaluate the proper functioning of the wearable device for plethysmography, a comparison with the ultrasound technique was carried out. As a satisfactory result, the acquired signals resemble the typical jugular venous pulse waveforms found in literature.

Feature Fusion of a Deep-Learning Algorithm into Wearable Sensor Devices for Human Activity Recognition

This paper presents a wearable device, fitted on the waist of a participant that recognizes six activities of daily living (walking, walking upstairs, walking downstairs, sitting, standing, and laying) through a deep-learning algorithm, human activity recognition (HAR). The wearable device comprises a single-board computer (SBC) and six-axis sensors. The deep-learning algorithm employs three parallel convolutional neural networks for local feature extraction and for subsequent concatenation to establish feature fusion models of varying kernel size. By using kernels of different sizes, relevant local features of varying lengths were identified, thereby increasing the accuracy of human activity recognition. Regarding experimental data, the database of University of California, Irvine (UCI) and self-recorded data were used separately. The self-recorded data were obtained by having 21 participants wear the device on their waist and perform six common activities in the laboratory. These data were used to verify the proposed deep-learning algorithm on the performance of the wearable device. The accuracy of these six activities in the UCI dataset and in the self-recorded data were 97.49% and 96.27%, respectively. The accuracies in tenfold cross-validation were 99.56% and 97.46%, respectively. The experimental results have successfully verified the proposed convolutional neural network (CNN) architecture, which can be used in rehabilitation assessment for people unable to exercise vigorously.

A meta-analysis of Fitbit devices: same company, different models, different validity evidence

Fitbit devices are among the most commonly used physical activity devices used by the general public. Multiple studies have examined the validity evidence of Fitbit devices of estimating energy expenditure during physical activity compared to criterion references. However, the literature lacks objective, summary validity evidence that supports the use of various models of Fitbit devices. Therefore, this study aims (a) to examine the validity evidence among the various models of Fitbit devices and (b) to investigate the influence of several device factors on the validity evidence of Fitbit models using meta-analysis. A total of 402 articles were identified through five databases. Upon review of the articles, 29 studies were included in the meta-analysis. Seven different moderator variables, including Fitbit model, device placement, type of device, heart rate capability, release year of devices, activity types and sedentary activity, were identified and included in the meta-analysis to examine their impact on the validity evidence of Fitbit devices. The summarised validity coefficient of energy expenditure during physical activity estimated by Fitbit devices and measured by criterion references was r=.64 (k = 29, 95% CI [.59, .69], p<.001). Fitbit model was not found to be a significant factor impacting validity evidence of Fitbit devices, but heart rate capability, activity types and sedentary activity were found to be significant factors impacting validity evidence. This study found that not all Fitbit models have a similar ability in estimating energy expenditure during physical activity. Continued research is needed in examining the validity evidence of Fitbit devices, especially considering some factors may affect the validity evidence in measuring energy expenditure during physical activity.

Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies

Nowadays, the growing interest in gathering physiological data and human behavior in everyday life scenarios is paralleled by an increase in wireless devices recording brain and body signals. However, the technical issues that characterize these solutions often limit the full brain-related assessments in real-life scenarios. Here we introduce the Biohub platform, a hardware/software (HW/SW) integrated wearable system for multistream synchronized acquisitions. This system consists of off-the-shelf hardware and state-of-art open-source software components, which are highly integrated into a high-tech low-cost solution, complete, yet easy to use outside conventional labs. It flexibly cooperates with several devices, regardless of the manufacturer, and overcomes the possibly limited resources of recording devices. The Biohub was validated through the characterization of the quality of (i) multistream synchronization, (ii) in-lab electroencephalographic (EEG) recordings compared with a medical-grade high-density device, and (iii) a Brain-Computer-Interface (BCI) in a real driving condition. Results show that this system can reliably acquire multiple data streams with high time accuracy and record standard quality EEG signals, becoming a valid device to be used for advanced ergonomics studies such as driving, telerehabilitation, and occupational safety.

Using Inertial Sensors to Determine Head Motion-A Review

Human activity recognition and classification are some of the most interesting research fields, especially due to the rising popularity of wearable devices, such as mobile phones and smartwatches, which are present in our daily lives. Determining head motion and activities through wearable devices has applications in different domains, such as medicine, entertainment, health monitoring, and sports training. In addition, understanding head motion is important for modern-day topics, such as metaverse systems, virtual reality, and touchless systems. The wearability and usability of head motion systems are more technologically advanced than those which use information from a sensor connected to other parts of the human body. The current paper presents an overview of the technical literature from the last decade on state-of-the-art head motion monitoring systems based on inertial sensors. This study provides an overview of the existing solutions used to monitor head motion using inertial sensors. The focus of this study was on determining the acquisition methods, prototype structures, preprocessing steps, computational methods, and techniques used to validate these systems. From a preliminary inspection of the technical literature, we observed that this was the first work which looks specifically at head motion systems based on inertial sensors and their techniques. The research was conducted using four internet databases-IEEE Xplore, Elsevier, MDPI, and Springer. According to this survey, most of the studies focused on analyzing general human activity, and less on a specific activity. In addition, this paper provides a thorough overview of the last decade of approaches and machine learning algorithms used to monitor head motion using inertial sensors. For each method, concept, and final solution, this study provides a comprehensive number of references which help prove the advantages and disadvantages of the inertial sensors used to read head motion. The results of this study help to contextualize emerging inertial sensor technology in relation to broader goals to help people suffering from partial or total paralysis of the body.

Orthogonality-Constrained CNMF-Based Noise Reduction with Reduced Degradation of Biological Sound

The number of deaths due to cardiovascular and respiratory diseases is increasing annually. Cardiovascular diseases with high mortality rates, such as strokes, are frequently caused by atrial fibrillation without subjective symptoms. Chronic obstructive pulmonary disease is another condition in which early detection is difficult owing to the slow progression of the disease. Hence, a device that enables the early diagnosis of both diseases is necessary. In our previous study, a sensor for monitoring biological sounds such as vascular and respiratory sounds was developed and a noise reduction method based on semi-supervised convolutive non-negative matrix factorization (SCNMF) was proposed for the noisy environments of users. However, SCNMF attenuated part of the biological sound in addition to the noise. Therefore, this paper proposes a novel noise reduction method that achieves less distortion by imposing orthogonality constraints on the SCNMF. The effectiveness of the proposed method was verified experimentally using the biological sounds of 21 subjects. The experimental results showed an average improvement of 1.4 dB in the signal-to-noise ratio and 2.1 dB in the signal-to-distortion ratio over the conventional method. These results demonstrate the capability of the proposed approach to measure biological sounds even in noisy environments.

Gait Disorder Detection and Classification Method Using Inertia Measurement Unit for Augmented Feedback Training in Wearable Devices

Parkinson's disease (PD) is a common neurodegenerative disease, one of the symptoms of which is a gait disorder, which decreases gait speed and cadence. Recently, augmented feedback training has been considered to achieve effective physical rehabilitation. Therefore, we have devised a numerical modeling process and algorithm for gait detection and classification (GDC) that actively utilizes augmented feedback training. The numerical model converted each joint angle into a magnitude of acceleration (MoA) and a Z-axis angular velocity (ZAV) parameter. Subsequently, we confirmed the validity of both the GDC numerical modeling and algorithm. As a result, a higher gait detection and classification rate (GDCR) could be observed at a higher gait speed and lower acceleration threshold (AT) and gyroscopic threshold (GT). However, the pattern of the GDCR was ambiguous if the patient was affected by a gait disorder compared to a normal user. To utilize the relationships between the GDCR, AT, GT, and gait speed, we controlled the GDCR by using AT and GT as inputs, which we found to be a reasonable methodology. Moreover, the GDC algorithm could distinguish between normal people and people who suffered from gait disorders. Consequently, the GDC method could be used for rehabilitation and gait evaluation.

Quantifying cricket fast bowling volume, speed and perceived intensity zone using an Apple Watch and machine learning

This study examined whether an inertial measurement unit (IMU) and machine learning models could accurately measure bowling volume (BV), ball release speed (BRS), and perceived intensity zone (PIZ). Forty-four male pace bowlers wore a high measurement range, research-grade IMU (SABELSense) and a consumer-grade IMU (Apple Watch) on both wrists. Each participant bowled 36 deliveries, split into two different PIZs (Zone 1 = 70-85% of maximum bowling effort, Zone 2 = 100% of maximum bowling effort). BRS was measured using a radar gun. Four machine learning models were compared. Gradient boosting models had the best results across all measures (BV: F-score = 1.0; BRS: Mean absolute error = 2.76 km/h; PIZ: F-score = 0.92). There was no significant difference between the SABELSense and Apple Watch on the same hand when measuring BV, BRS, and PIZ. A significant improvement in classifying PIZ was observed for IMUs located on the dominant wrist. For all measures, there was no added benefit of combining IMUs on the dominant and non-dominant wrists.

Colorophone 2.0: A Wearable Color Sonification Device Generating Live Stereo-Soundscapes-Design, Implementation, and Usability Audit

The successful development of a system realizing color sonification would enable auditory representation of the visual environment. The primary beneficiary of such a system would be people that cannot directly access visual information—the visually impaired community. Despite the plethora of sensory substitution devices, developing systems that provide intuitive color sonification remains a challenge. This paper presents design considerations, development, and the usability audit of a sensory substitution device that converts spatial color information into soundscapes. The implemented wearable system uses a dedicated color space and continuously generates natural, spatialized sounds based on the information acquired from a camera. We developed two head-mounted prototype devices and two graphical user interface (GUI) versions. The first GUI is dedicated to researchers, and the second has been designed to be easily accessible for visually impaired persons. Finally, we ran fundamental usability tests to evaluate the new spatial color sonification algorithm and to compare the two prototypes. Furthermore, we propose recommendations for the development of the next iteration of the system.

Blood Pressure Continuous Measurement through a Wearable Device: Development and Validation of a Cuffless Method

The present study aims to develop and validate a cuffless method for blood pressure continuous measurement through a wearable device. The goal is achieved according to the time-delay method, with the guiding principle of the time relation it takes for a blood volume to travel from the heart to a peripheral site. Inversely proportional to the blood pressure, this time relation is obtained as the time occurring between the R peak of the electrocardiographic signal and a marker point on the photoplethysmographic wave. Such physiological signals are recorded by using L.I.F.E. Italia’s wearable device, made of a sensorized shirt and wristband. A linear regression model is implemented to estimate the corresponding blood pressure variations from the obtained time-delay and other features of the photoplethysmographic wave. Then, according to the international standards, the model performance is assessed, comparing the estimates with the measurements provided by a certified digital sphygmomanometer. According to the standards, the results obtained during this study are notable, with 85% of the errors lower than 10 mmHg and a mean absolute error lower than 7 mmHg. In conclusion, this study suggests a time-delay method for continuous blood pressure estimates with good performance, compared with a reference device based on the oscillometric technique.

The Clouclip, a wearable device for measuring near-work and outdoor time: validation and comparison of objective measures with questionnaire estimates

PURPOSE

To validate a novel wearable device that can measure both viewing distance and light exposure, Clouclip, and compare questionnaire estimates regarding near-work and outdoor time with the objective measures obtained using Clouclip.

METHODS

Fifteen Clouclips were selected to measure different distances and levels of illuminance. With each Clouclip, five measurements at different distances and light intensities were measured and recorded. Eighty participants wore Clouclips for a week and completed an activity questionnaire afterwards.

RESULTS

The intra- and inter-Clouclip coefficients were 1.00 and 0.99 for measuring distance and 1.00 and 1.00 for illuminance, respectively. Within the measurement limit, the maximum relative error was 2.07% for distance and 2.23% for illuminance. Assuming that <30 cm was the typical distance for near-work activities and >1000 Lux was the typical cut-off for outdoor environments, the questionnaire showed a trend of overestimation for both. The greatest overestimation of near-work occurred during the school period [Questionnaire: 4.73 hr (4.73, 5.07) versus Clouclip: 2.16 hr (1.74, 2.78); p < 0.01]. The greatest overestimation of outdoor activity also occurred during the school period [Questionnaire: 1.60 hr (1.33, 1.85) versus Clouclip: 1.21 hr (0.96, 1.50); p < 0.01]. Based on Clouclip, the total time spent outdoors was estimated to be 1.55 hr on school days, of which 0.34 hr occurred after school. For weekend days, however, the duration was only 0.17 hr.

CONCLUSIONS

Clouclip had excellent precision and accuracy. Although the agreement between the questionnaire and Clouclip was relatively poor, they were able to complement each other to provide a more logical and feasible assessment of exposure to near-work and outdoor activity. Indoor-oriented lifestyles were found to predominate in Chinese children.

Low Back Pain-Behavior Correction by Providing Haptic Feedbacks: A Preliminary Investigation

The activities performed by nurses in their daily activities involve frequent forward bending and awkward back postures. These movements contribute to the prevalence and development of low back pain (LBP). In previous studies, it has been shown that modifying their posture by education and training in proper lifting techniques decreases the prevalence of LBP. However, this education and training needs to be implemented daily. Hence, implementing the use of a wearable device to monitor the back posture with haptic feedback would be of importance to prevent LBP. This paper proposes a wearable device to monitor the back posture of the user and provide feedback when the participant is performing a possible hurtful movement. In this study, a group of participants was asked to wear the device while performing three of the most common activities performed by nurses. The study was divided into three sessions: In the first session, the participants performed the activities without feedback (baseline). During the second session, the participants received feedback from the wearable device (training) while performing the three tasks. Finally, for the third session, the participants performed the three tasks again, but the haptic feedback was turned off (validation). We found an improvement in the posture of more than 40% for the pitch (lateral bending) and roll (forward/backward bending) axes and 7% for the yaw (twisting) axis when comparing to the results from session 1 and session 2. The comparison between session 1 and session 3 showed an overall improvement of more than 50% for the pitch (lateral bending) and roll (forward/backward bending) axes and more than 20% for the yaw axis. These results hinted at the impact of the haptic feedback on the participants to correct their posture.

Associations of work-related stress and total sleep time with cholesterol levels in an occupational cohort of Japanese office workers

OBJECTIVE

The aim of the study was to investigate the associations of total sleep time (TST) and occupational stress based on the Brief Job Stress Questionnaire (BJSQ) with cholesterol levels in an occupational cohort of Japanese office workers.

METHODS

The present study is a secondary analysis of a subset of participants from a randomized controlled trial. Participants were 179 employees from 5 companies in Tokyo who participated as the intervention group in a 3-month lifestyle intervention study among office workers with metabolic syndrome or at risk of metabolic syndrome. All intervention-group participants used a mobile app and a wearable device. The final population for analysis in the present study were 173 participants. Cholesterol measures were derived from participants' annual health check-up data in the fiscal year preceding their inclusion in the study. Multiple linear regression models were used to determine the association between exposures and outcome.

RESULTS

Overall, stress levels were significantly and inversely associated with LDL-C (-7.12 mg/dl; 95% CI: -11.78, -2.45) and LDL-C/HDL-C ratio (-0.16 mg/dl; 95% CI: -0.27, -0.04) per standard deviation increase. Compared to average TST 5.9-7.2 hours, average TST of 4.0-5.3 hours (-4.82 mg/dl; 95% CI: -9.22, -0.43) was inversely associated with HDL-C.

CONCLUSION

Incremental increases of stress were significantly and inversely associated with LDL-C and LDL-C/HDL-C ratio. The shortest average TST was inversely associated with HDL-C. The results should be interpreted with care given certain methodological limitations.

Automatic Mobile Health Arrhythmia Monitoring for the Detection of Atrial Fibrillation: Prospective Feasibility, Accuracy, and User Experience Study

Background

Atrial fibrillation (AF) is the most common tachyarrhythmia and associated with a risk of stroke. The detection and diagnosis of AF represent a major clinical challenge due to AF’s asymptomatic and intermittent nature. Novel consumer-grade mobile health (mHealth) products with automatic arrhythmia detection could be an option for long-term electrocardiogram (ECG)-based rhythm monitoring and AF detection.

Objective

We evaluated the feasibility and accuracy of a wearable automated mHealth arrhythmia monitoring system, including a consumer-grade, single-lead heart rate belt ECG device (heart belt), a mobile phone application, and a cloud service with an artificial intelligence (AI) arrhythmia detection algorithm for AF detection. The specific aim of this proof-of-concept study was to test the feasibility of the entire sequence of operations from ECG recording to AI arrhythmia analysis and ultimately to final AF detection.

Methods

Patients (n=159) with an AF (n=73) or sinus rhythm (n=86) were recruited from the emergency department. A single-lead heart belt ECG was recorded for 24 hours. Simultaneously registered 3-lead ECGs (Holter) served as the gold standard for the final rhythm diagnostics and as a reference device in a user experience survey with patients over 65 years of age (high-risk group).

Results

The heart belt provided a high-quality ECG recording for visual interpretation resulting in 100% accuracy, sensitivity, and specificity of AF detection. The accuracy of AF detection with the automatic AI arrhythmia detection from the heart belt ECG recording was also high (97.5%), and the sensitivity and specificity were 100% and 95.4%, respectively. The correlation between the automatic estimated AF burden and the true AF burden from Holter recording was >0.99 with a mean burden error of 0.05 (SD 0.26) hours. The heart belt demonstrated good user experience and did not significantly interfere with the patient’s daily activities. The patients preferred the heart belt over Holter ECG for rhythm monitoring (85/110, 77% heart belt vs 77/109, 71% Holter, P=.049).

Conclusions

A consumer-grade, single-lead ECG heart belt provided good-quality ECG for rhythm diagnosis. The mHealth arrhythmia monitoring system, consisting of heart-belt ECG, a mobile phone application, and an automated AF detection achieved AF detection with high accuracy, sensitivity, and specificity. In addition, the mHealth arrhythmia monitoring system showed good user experience.

Trial Registration

ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335

The Significance of Alliance Networks in Research and Development of Digital Health Products for Diabetes: Observational Study

Background

Digital health has been advancing owing to technological progress by means of smart devices and artificial intelligence, among other developments. In the field of diabetes especially, there are many active use cases of digital technology supporting the treatment of diabetes and improving lifestyle. In the innovation ecosystem, new alliance networks are formed not only by medical device companies and pharmaceutical companies, but also by information and communications technology companies and start-ups. While understanding and utilizing the network structure is important to increase the competitive advantage of companies, there is a lack of previous research describing the structure of alliance networks and the factors that lead to their formation in digital health.

Objective

The aim of this study was to explore the significance of alliance networks, focusing on digital health for diabetes, in effectively implementing processes, from the research and development of products or services to their launch and market penetration.

Methods

First, we listed the companies and contracts related to digital health for diabetes, visualized the change in the number of companies and the connections between companies in each industry, and analyzed the overview of the network. Second, we calculated the degree, betweenness centrality, and eigenvector centrality of each company in each year. Next, we analyzed the relationship between network centrality and market competitiveness by using annual sales as a parameter of company competitiveness. We also compared the network centrality of each company by industry or headquarters location (or both) and analyzed the characteristics of companies with higher centrality. Finally, we analyzed the relationship between network centrality and the number of products certified or approved by the US Food and Drug Administration.

Results

We found the degree centrality of companies was correlated with an increase in their sales. The betweenness and eigenvector centralities of medical devices companies located in the United States were significantly higher than those outside the United States (P=.04 and .005, respectively). Finally, the degree, betweenness, and eigenvector centralities were correlated with an increase in the number of Class III, but not of Class I nor II, medical device products.

Conclusions

These findings give rise to new insights into industry ecosystem for digital health and its requirement and expect a contribution to research and development practices in the field of digital health.

Longitudinal Autonomic Nervous System Measures Correlate With Stress and Ulcerative Colitis Disease Activity and Predict Flare

BACKGROUND

Differences in autonomic nervous system function, measured by heart rate variability (HRV), have been observed between patients with inflammatory bowel disease and healthy control patients and have been associated in cross-sectional studies with systemic inflammation. High HRV has been associated with low stress.

METHODS

Patients with ulcerative colitis (UC) were followed for 9 months. Their HRV was measured every 4 weeks using the VitalPatch, and blood was collected at baseline and every 12 weeks assessing cortisol, adrenocorticotropin hormone, interleukin-1β, interleukin-6, tumor necrosis factor-α, and C-reactive protein (CRP). Stool was collected at enrollment and every 6 weeks for fecal calprotectin. Surveys assessing symptoms, stress, resilience, quality of life, anxiety, and depression were longitudinally collected.

RESULTS

Longitudinally evaluated perceived stress was significantly associated with systemic inflammation (CRP, P = 0.03) and UC symptoms (P = 0.02). There was a significant association between HRV and stress (low-frequency to high-frequency power [LFHF], P = 0.04; root mean square of successive differences [RMSSD], P = 0.04). The HRV was associated with UC symptoms (LFHF, P = 0.03), CRP (high frequency, P < 0.001; low frequency, P < 0.001; RMSSD, P < 0.001), and fecal calprotectin (high frequency, P < 0.001; low frequency, P < 0.001; RMSSD, P < 0.001; LFHF, P < 0.001). Significant changes in HRV indices from baseline developed before the identification of a symptomatic or inflammatory flare (P < 0.001).

CONCLUSIONS

Longitudinally evaluated HRV was associated with UC symptoms, inflammation, and perceived and physiological measures of stress. Significant changes in HRV were observed before the development of symptomatic or inflammatory flare.

Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach

BACKGROUND

Wearable devices have been widely used in clinical studies to study daily activity patterns, but the analysis remains a major obstacle for researchers.

OBJECTIVE

This study proposes a novel method to characterize sleep-activity rhythms using actigraphy and further use it to describe early childhood daily rhythm formation and examine its association with physical development.

METHODS

We developed a machine learning-based Penalized Multiband Learning (PML) algorithm to sequentially infer dominant periodicities based on the Fast Fourier Transform (FFT) algorithm and further characterize daily rhythms. We implemented and applied the algorithm to Actiwatch data collected from a cohort of 262 healthy infants at ages 6, 12, 18, and 24 months, with 159, 101, 111, and 141 participants at each time point, respectively. Autocorrelation analysis and Fisher test in harmonic analysis with Bonferroni correction were applied for comparison with the PML. The association between activity rhythm features and early childhood motor development, assessed using the Peabody Developmental Motor Scales-Second Edition (PDMS-2), was studied through linear regression analysis.

RESULTS

The PML results showed that 1-day periodicity was most dominant at 6 and 12 months, whereas one-day, one-third-day, and half-day periodicities were most dominant at 18 and 24 months. These periodicities were all significant in the Fisher test, with one-fourth-day periodicity also significant at 12 months. Autocorrelation effectively detected 1-day periodicity but not the other periodicities. At 6 months, PDMS-2 was associated with the assessment seasons. At 12 months, PDMS-2 was associated with the assessment seasons and FFT signals at one-third-day periodicity (P<.001) and half-day periodicity (P=.04), respectively. In particular, the subcategories of stationary, locomotion, and gross motor were associated with the FFT signals at one-third-day periodicity (P<.001).

CONCLUSIONS

The proposed PML algorithm can effectively conduct circadian rhythm analysis using time-series wearable device data. The application of the method effectively characterized sleep-wake rhythm development and identified the association between daily rhythm formation and motor development during early childhood.

Accuracy and Diversity of Wearable Device-Based Gait Speed Measurement Among Older Men: Observational Study

Background

Gait speed measurements are widely used in clinical practice, as slow gait is a major predictor of frailty and a diagnostic criterion for sarcopenia. With the development of wearable devices, it is possible to estimate the gait speed in daily life by simply wearing the device.

Objective

This study aims to accurately determine the characteristics of daily life gait speed and analyze their association with sarcopenia.

Methods

We invited community-dwelling men aged >50 years who had visited the outpatient clinic at a tertiary university hospital to participate in the study. Daily life gait speed was assessed using a wearable smart belt (WELT) for a period of 4 weeks. Data from participants who wore the smart belt for at least 10 days during this period were included. After 4 weeks, data from a survey about medical and social history, usual gait speed measurements, handgrip strength measurements, and dual-energy x-ray absorptiometry were analyzed.

Results

A total of 217,578 daily life gait speed measurements from 106 participants (mean age 71.1, SD 7.6 years) were analyzed. The mean daily life gait speed was 1.23 (SD 0.26) m/s. The daily life gait speed of the participants varied according to the time of the day and day of the week. Daily life gait speed significantly slowed down with age (P<.001). Participants with sarcopenia had significantly lower mean daily life gait speed (mean 1.12, SD 0.11 m/s) than participants without sarcopenia (mean 1.23, SD 0.08 m/s; P<.001). Analysis of factors related to mean daily life gait speed showed that age and skeletal muscle mass of the lower limbs were significantly associated characteristics.

Conclusions

More diverse and accurate information about gait speed can be obtained by measuring daily life gait speed using a wearable device over an appropriate period, compared with one-time measurements performed in a laboratory setting. Importantly, in addition to age, daily life gait speed is significantly associated with skeletal muscle mass of the lower limbs.

Reactivity to UV Radiation Exposure Monitoring Using Personal Exposure Devices for Skin Cancer Prevention: Longitudinal Observational Study

BACKGROUND

Emerging UV radiation (UVR) monitoring devices may present an opportunity to integrate such technology into skin cancer prevention interventions. However, little is known about the effects of using a wearable UVR monitor on adults' and children's sun protection-related behaviors and attitudes (eg, cancer worry and perceived risk). Understanding the potential role of reactivity and seasonal effects will help inform the use of objective monitors in the context of skin cancer prevention research, including intervention studies.

OBJECTIVE

The aim of this study is to examine the potential reactivity associated with a wearable personal UVR monitor, specifically the effects associated with reported sun-protective behaviors and skin cancer-related attitudes, which are often the targets of skin cancer preventive interventions.

METHODS

Child-parent dyads (n=97 dyads) were asked to wear a UVR monitoring device during waking hours for 2 weeks. Participants were asked to sync the device daily with a smartphone app that stored the UVR exposure data. Participants were blinded to their UVR exposure data during the 2-week period; thus, the smartphone app provided no feedback to the participants on their UVR exposure. Participants completed self-report questionnaires assessing sun-protective behaviors, sunburn, tanning, skin self-examination, skin cancer-related knowledge, perceived risk, cancer worry, response efficacy, and intentions to change behaviors over the 2-week period. Linear regressions were conducted to investigate changes in the outcomes over time and to account for the role of the season of study participation.

RESULTS

Regression results revealed that there was a significant decrease over time for several sun protection outcomes in children, including time spent outdoors on weekends (P=.02) and weekdays (P=.008), sunscreen use (P=.03), reapplication (P<.001), and unintentional tanning (P<.001). There were no significant changes over time in children's and parents' UVR exposure, sunburn occurrence, or sun protection attitudes. Season of participation was associated with several outcomes, including lower sunscreen use (P<.001), reapplication (P<.001), sunburns (P=.01), intentions to change sun-protective behaviors (P=.02), and intentional (P=.008) and unintentional tanning (P=.01) for participants who participated in the fall versus the summer.

CONCLUSIONS

The findings from this study suggest that daily use of a UVR monitoring device over a 2-week period may result in changes in certain sun-protective behaviors. These results highlight the importance of identifying and addressing potential reactivity to UVR monitoring devices, especially in the context of skin cancer preventive intervention research. Ultimately, objectively assessed UVR exposure could be integrated into the outcome assessment for future testing of skin cancer prevention interventions.

Assessing the Outcome of Rehabilitation after Hip Fracture with a Wearable Device-A Study Protocol for a Randomized Control Trial in Community Healthcare

BACKGROUND

The increase of the aging population is a challenge to society, as age is related to dependence. Injuries such as hip fractures cause morbidity, loss of independent life, and mortality. The purpose of this protocol is to describe a randomized control trial, with three intervention arms, aiming at investigating if there are any differences in outcomes after hip fracture between different rehabilitation interventions including (1) High-Intensity Functional Exercise (HIFE), (2) HIFE with the addition of continuous measures of movement and body positions with a wearable device, or (3) standard rehabilitation. A secondary aim is to evaluate physiotherapists' satisfaction with using the wearable device in rehabilitation.

METHOD

Patients with hip fracture that require rehabilitation at home will be invited to participate and randomly assigned to one intervention arm. The primary outcome is balance, measured by postural sway using an Inertial Measurement Unit and by Functional Balance test for Geriatric patients. Secondary outcomes are functional independence in everyday activities, measured with the Barthel Index, and health-related quality of life measured with EuroQol 5 Dimension questionnaire and EuroQol Visual Analogue Scale for health and user satisfaction measured by the User Satisfaction Evaluation Questionnaire.

DISCUSSION

This study protocol is the first step in securing the research process before performing a full randomized controlled trial. The next step will be a pilot- and feasibility study.

Self-Powered Diaper Sensor with Wireless Transmitter Powered by Paper-Based Biofuel Cell with Urine Glucose as Fuel

A self-driven sensor that can detect urine and urine sugar and can be mounted on diapers is desirable to reduce the burden of long-term care. In this study, we created a paper-based glucose biofuel cell that can be mounted on diapers to detect urine sugar. Electrodes for biofuel cells were produced by printing MgO-templated porous carbon on which poly(glycidyl methacrylate) was modified using graft polymerization. A new bioanode was prepared through covalently modifying flavin-adenine-dinucleotide-dependent glucose dehydrogenase and azure A with pendant glycidyl groups of poly(glycidyl methacrylate). We prepared a cathode with covalently bonded bilirubin oxidase. Covalent bonding of enzymes and mediators to both the bioanode and biocathode suppressed elution and improved stability. The biofuel cell could achieve a maximum output density of 0.12 mW cm^–2, and by combining it with a wireless transmission device, the concentration of glucose sensed from the transmission frequency was in the range of 0–10 mM. The sensitivity of the sensor was estimated at 0.0030 ± 0.0002 Hz mmol^–1 dm³. This device is expected to be a new urine-sugar detection device, composed only of organic materials with a low environmental load and it can be useful for detecting postprandial hyperglycemia.

Perspective: Opportunities and Challenges of Technology Tools in Dietary and Activity Assessment: Bridging Stakeholder Viewpoints

The science and tools of measuring energy intake and output in humans have rapidly advanced in the last decade. Engineered devices such as wearables and sensors, software applications, and Web-based tools are now ubiquitous in both research and consumer environments. The assessment of energy expenditure in particular has progressed from reliance on self-report instruments to advanced technologies requiring collaboration across multiple disciplines, from optics to accelerometry. In contrast, assessing energy intake still heavily relies on self-report mechanisms. Although these tools have improved, moving from paper-based to online reporting, considerable room for refinement remains in existing tools, and great opportunities exist for novel, transformational tools, including those using spectroscopy and chemo-sensing. This report reviews the state of the science, and the opportunities and challenges in existing and emerging technologies, from the perspectives of 3 key stakeholders: researchers, users, and developers. Each stakeholder approaches these tools with unique requirements: researchers are concerned with validity, accuracy, data detail and abundance, and ethical use; users with ease of use and privacy; and developers with high adherence and utilization, intellectual property, licensing rights, and monetization. Cross-cutting concerns include frequent updating and integration of the food and nutrient databases on which assessments rely, improving accessibility and reducing disparities in use, and maintaining reliable technical assistance. These contextual challenges are discussed in terms of opportunities and further steps in the direction of personalized health.

Improving Healthy Aging by Monitoring Patients' Lifestyle through a Wearable Device: Results of a Feasibility Study

Population aging is related to a huge growth in healthcare and welfare costs. Therefore, wearable devices could be strategic for minimizing years of disability in old age and monitoring patients’ lifestyles and health. The purpose of this study was to assess the feasibility of using smart devices to monitor patients’ physical activity in a primary care setting. To assess the acceptance of this novel technology from the point of view of both patients and healthcare professionals, two questionnaires (one paper-based and one ex-novo developed) were administered to 11 patients with type 2 diabetes mellitus and a non-compliant behavior towards the therapeutic indications of their general practitioner (GP). Seven participants would continue to use a wearable activity tracker to monitor their health. We observed that 75% of patients reported a device’s characteristics satisfaction level of over 80% of the total score assigned to this dimension. No differences were observed in the questionnaire’s scores between the two professionals categories (GPs and nurses). Three dimensions (equipment characteristics, subjective norm, perceived risks, perceived ease-of-use and facilitating conditions) correlated > 0.5 with the device’s acceptability level. Some weak correlations were observed between healthcare professionals’ perception and patients’ parameters, particularly between the dimensions of collaboration and web interface ease-of-use and patients’ median number of steps and hours of sleep. In conclusion, despite the limited number of subjects involved, a good acceptance level towards these non-medical devices was observed, according to both patients’ and healthcare professionals’ impressions.

Early Detection of Symptom Exacerbation in Patients With SARS-CoV-2 Infection Using the Fitbit Charge 3 (DEXTERITY): Pilot Evaluation

Background

Some patients with COVID-19 experienced sudden death due to rapid symptom deterioration. Thus, it is important to predict COVID-19 symptom exacerbation at an early stage prior to increasing severity in patients. Patients with COVID-19 could experience a unique “silent hypoxia” at an early stage of the infection when they are apparently asymptomatic, but with rather low SpO₂ (oxygen saturation) levels. In order to continuously monitor SpO₂ in daily life, a high-performance wearable device, such as the Apple Watch or Fitbit, has become commercially available to monitor several biometric data including steps, resting heart rate (RHR), physical activity, sleep quality, and estimated oxygen variation (EOV).

Objective

This study aimed to test whether EOV measured by the wearable device Fitbit can predict COVID-19 symptom exacerbation.

Methods

We recruited patients with COVID-19 from August to November 2020. Patients were asked to wear the Fitbit for 30 days, and biometric data including EOV and RHR were extracted. EOV is a relative physiological measure that reflects users’ SpO₂ levels during sleep. We defined a high EOV signal as a patient’s oxygen level exhibiting a significant dip and recovery within the index period, and a high RHR signal as daily RHR exceeding 5 beats per day compared with the minimum RHR of each patient in the study period. We defined successful prediction as the appearance of those signals within 2 days before the onset of the primary outcome. The primary outcome was the composite of deaths of all causes, use of extracorporeal membrane oxygenation, use of mechanical ventilation, oxygenation, and exacerbation of COVID-19 symptoms, irrespective of readmission. We also assessed each outcome individually as secondary outcomes. We made weekly phone calls to discharged patients to check on their symptoms.

Results

We enrolled 23 patients with COVID-19 diagnosed by a positive SARS-CoV-2 polymerase chain reaction test. The patients had a mean age of 50.9 (SD 20) years, and 70% (n=16) were female. Each patient wore the Fitbit for 30 days. COVID-19 symptom exacerbation occurred in 6 (26%) patients. We were successful in predicting exacerbation using EOV signals in 4 out of 5 cases (sensitivity=80%, specificity=90%), whereas the sensitivity and specificity of high RHR signals were 50% and 80%, respectively, both lower than those of high EOV signals. Coincidental obstructive sleep apnea syndrome confirmed by polysomnography was detected in 1 patient via consistently high EOV signals.

Conclusions

This pilot study successfully detected early COVID-19 symptom exacerbation by measuring EOV, which may help to identify the early signs of COVID-19 exacerbation.

Trial Registration

University Hospital Medical Information Network Clinical Trials Registry UMIN000041421; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000047290