Smart wearable body sensors for patient self-assessment and monitoring

Validity and reliability of a wearable-controlled serious game and goniometer for telemonitoring of wrist fracture rehabilitation

PURPOSE

To determine the validity of wrist range of motion (ROM) measurements by the wearable-controlled ReValidate! wrist-rehabilitation game, which simultaneously acts as a digital goniometer. Furthermore, to establish the reliability of the game by contrasting ROM measurements to those found by medical experts using a universal goniometer.

METHODS

As the universal goniometer is considered the reference standard, inter-rater reliability between surgeons was first determined. Internal validity of the game ROM measurements was determined in a test-retest setting with healthy volunteers. The reliability of the game was tested in 34 patients with a restricted range of motion, in whom the ROM was measured by experts as well as digitally. Intraclass-correlation coefficients (ICCs) were determined and outcomes were analyzed using Bland-Altman plots.

RESULTS

Inter-rater reliability between experts using a universal goniometer was poor, with ICCs of 0.002, 0.160 and 0.520. Internal validity testing of the game found ICCs of - 0.693, 0.376 and 0.863, thus ranging from poor to good. Reliability testing of the game compared to medical expert measurements, found that mean differences were small for the flexion-extension arc and the radial deviation-ulnar deviation arc.

CONCLUSION

The ReValidate! game is a reliable home-monitoring device digitally measuring ROM in the wrist. Interestingly, the test-retest reliability of the serious game was found to be considerably higher than the inter-rater reliability of the reference standard, being healthcare professionals using a universal goniometer.

TRIAL REGISTRATION NUMBER

(internal hospital registration only) MEC-AMC W17_003 #17.015.

A Brief Review on the Sensor Measurement Solutions for the Ten-Meter Walk Test

The wide-spread use of wearables and the adoption of the Internet of Things (IoT) paradigm provide an opportunity to use mobile-device sensors for medical applications. Sensors available in the commonly used devices may inspire innovative solutions for physiotherapy striving for accurate and early identification of various pathologies. An essential and reliable performance measure is the ten-meter walk test, which is employed to determine functional mobility, gait, and vestibular function. Sensor-based approaches can identify the various test phases and their segmented duration, among other parameters. The measurement parameter primarily used is related to the tests’ duration, and after identifying patterns, a variety of physical treatments can be recommended. This paper reviews multiple studies focusing on automated measurements of the ten-meter walk test with different sensors. Most of the analyzed studies measure similar parameters as traditional methods, such as velocity, duration, and other involuntary and dangerous patients’ movements after stroke. That provides an opportunity to measure different parameters that can be later fed into machine learning models for analyzing more complex patterns.

Smart Patch for Skin Temperature: Preliminary Study to Evaluate Psychometrics and Feasibility

There is a need for continuous, non-invasive monitoring of biological data to assess health and wellbeing. Currently, many types of smart patches have been developed to continuously monitor body temperature, but few trials have been completed to evaluate psychometrics and feasibility for human subjects in real-life scenarios. The aim of this feasibility study was to evaluate the reliability, validity and usability of a smart patch measuring body temperature in healthy adults. The smart patch consisted of a fully integrated wearable wireless sensor with a multichannel temperature sensor, signal processing integrated circuit, wireless communication feature and a flexible battery. Thirty-five healthy adults were recruited for this test, carried out by wearing the patches on their upper chests for 24 h and checking their body temperature six times a day using infrared forehead thermometers as a gold standard for testing validity. Descriptive statistics, one-sampled and independent t-tests, Pearson’s correlation coefficients and Bland-Altman plot were examined for body temperatures between two measures. In addition, multiple linear regression, receiver operating characteristic (ROC) and qualitative content analysis were conducted. Among the 35 participants, 29 of them wore the patch for over 19 h (dropout rate: 17.14%). Mean body temperature measured by infrared forehead thermometers and smart patch ranged between 32.53 and 38.2 °C per person and were moderately correlated (r = 0.23–0.43) overall. Based on a Bland-Altman plot, approximately 94% of the measurements were located within one standard deviation (upper limit = 4.52, lower limit = −5.82). Most outliers were identified on the first measurement and were located below the lower limit. It is appropriate to use 37.5 °C in infrared forehead temperature as a cutoff to define febrile conditions. Users’ position while checking and ambient temperature and humidity are not affected to the smart patch body temperature. Overall, the participants showed high usability and satisfaction on the survey. Few participants reported discomfort due to limited daily activity, itchy skin or detaching concerns. In conclusion, epidermal electronic sensor technologies provide a promising method for continuously monitoring individuals’ body temperatures, even in real-life situations. Our study findings show the potential for smart patches to monitoring non-febrile condition in the community.

Feasibility of continuous monitoring of vital signs in surgical patients on a general ward: an observational cohort study

OBJECTIVE

To determine feasibility, in terms of acceptability and system fidelity, of continuous vital signs monitoring in abdominal surgery patients on a general ward.

DESIGN

Observational cohort study.

SETTING

Tertiary teaching hospital.

PARTICIPANTS

Postoperative abdominal surgical patients (n=30) and nurses (n=23).

INTERVENTIONS

Patients were continuously monitored with the SensiumVitals wearable device until discharge in addition to usual care, which is intermittent Modified Early Warning Score measurements. Heart rate, respiratory rate and axillary temperature were monitored every 2 min. Values and trends were visualised and alerts sent to the nurses.

OUTCOMES

System fidelity was measured by analysis of the monitoring data. Acceptability by patients and nurses was assessed using questionnaires.

RESULTS

Thirty patients were monitored for a median duration of 81 hours (IQR 47-143) per patient, resulting in 115 217 measurements per parameter. In total, 19% (n=21 311) of heart rate, 51% (n=59 184) of respiratory rate and 9% of temperature measurements showed artefacts (n=10 269). The system algorithm sent 972 alerts (median alert rate of 4.5 per patient per day), of which 90.3% (n=878) were system alerts and 9.7% (n=94) were vital sign alerts. 35% (n=33) of vital sign alerts were true positives. 93% (n=25) of patients rated the patch as comfortable, 67% (n=18) felt safer and 89% (n=24) would like to wear it next time in the hospital. Nurses were neutral about usefulness, with a median score of 3.5 (IQR 3.1-4) on a 7-point Likert scale, ease of use 3.7 (IQR 3.2-4.8) and satisfaction 3.7 (IQR 3.2-4.8), but agreed on ease of learning at 5.0 (IQR 4.0-5.8). Neutral scores were mostly related to the perceived limited fidelity of the system.

CONCLUSIONS

Continuous monitoring of vital signs with a wearable device was well accepted by patients. Nurses' ratings were highly variable, resulting in on average neutral attitude towards remote monitoring. Our results suggest it is feasible to monitor vital signs continuously on general wards, although acceptability of the device among nurses needs further improvement.

Design and validation of a smart garment to measure positioning practices of parents with young infants

The aim of this cross-sectional study was to evaluate the feasibility, construct validity, and reliability of a smart garment to characterize parent-child positioning practices in infants less than six months old. The smart garment (Get Around Garment, GG) was developed through feedback from seven infants and their parents. The final system was then tested with sixteen infants (M = 3.1 ± 1.1 months) assessed in their homes during one visit that consisted of a: 1) Structured Play Assessment (2.5 min): infant was placed in each of five positions (prone, supine, reclined/inclined, and upright) for 30 s, 2) Free Play Assessment (40-60 min): parents were asked to engage in typical daily activities, and 3) second Structured Play Assessment. Infants' body position was both coded from video and identified from sensor data using a custom program. Feasibility was measured by data from a Daily Wearing Log and Garment Perception Questionnaire. Validity was evaluated by comparing the coding and sensor data. Reliability was measured by comparing the sensor data between the two Structured Play Assessments. The GG was considered feasible for use. The smart wearable system showed high levels of accuracy for classifying body position secondby- second and when comparing cumulative duration across time. Reliability of the smart garment was excellent. Young infants spent more time in supine and supported upright positions relative to prone, reclined, or inclined positions. The results suggest that accelerometers can be integrated into garments in a manner that is feasible to provide accurate and consistent data about positioning practices of parents with young infants. Monitoring early positioning practices is important because these practices impact future motor and cognitive developmental trajectories.

The early warning research on nursing care of stroke patients with intelligent wearable devices under COVID-19

Stroke patients under the background of the new crown epidemic need to be home-based care. However, traditional nursing methods cannot take care of the patients' lives in all aspects. Based on this, based on machine learning algorithms, our work combines regression models and SVM to build a smart wearable device system and builds a system prediction module to predict patient care needs. The node is used to collect human body motion and physiological parameter information and transmit data wirelessly. The software is used to quickly process and analyze the various motion and physiological parameters of the patient and save the analysis and processing structure in the database. By comparing the results of nursing intervention experiments, we can see that the smart wearable device designed in this paper has a certain effect in stroke care.

Methods for Adventitious Respiratory Sound Analyzing Applications Based on Smartphones: A Survey

Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.

Bending Analysis of Polymer-Based Flexible Antennas for Wearable, General IoT Applications: A Review

Flexible substrates have become essential in order to provide increased flexibility in wearable sensors, including polymers, plastic, paper, textiles and fabrics. This study is to comprehensively summarize the bending capabilities of flexible polymer substrate for general Internet of Things (IoTs) applications. The basic premise is to investigate the flexibility and bending ability of polymer materials as well as their tendency to withstand deformation. We start by providing a chronological order of flexible materials which have been used during the last few decades. In the future, the IoT is expected to support a diverse set of technologies to enable new applications through wireless connectivity. For wearable IoTs, flexibility and bending capabilities of materials are required. This paper provides an overview of some abundantly used polymer substrates and compares their physical, electrical and mechanical properties. It also studies the bending effects on the radiation performance of antenna designs that use polymer substrates. Moreover, we explore a selection of flexible materials for flexible antennas in IoT applications, namely Polyimides (PI), Polyethylene Terephthalate (PET), Polydimethylsiloxane (PDMS), Polytetrafluoroethylene (PTFE), Rogers RT/Duroid and Liquid Crystal Polymer (LCP). The study includes a complete analysis of bending and folding effects on the radiation characteristics such as S-parameters, resonant frequency deviation and the impedance mismatch with feedline of the flexible polymer substrate microstrip antennas. These flexible polymer substrates are useful for future wearable devices and general IoT applications.

Smartband Use During Enhanced Recovery After Surgery Facilitates Inpatient Recuperation Following Minimally Invasive Colorectal Surgery

Background: Enhanced recovery after surgery (ERAS) is valuable in perioperative care for its ability to improve short-term surgical outcomes and facilitate patient recuperation after major surgery. Early postoperative mobilization is a vital component of the integrated care pathway and is a factor strongly associated with successful outcomes. However, early mobilization still has various definitions and lacks specific strategies. Methods: Patients who underwent minimally invasive surgery for colorectal cancer followed our perioperative ERAS program, including mobilization from the first postoperative day. After perioperative care skills were improved in our well-established program, compliance, inpatient surgical outcomes, and complications associated with adding smartband use were evaluated and compared with the outcomes for standard protocol. Quality of recovery was evaluated using patient-rated QoR-40 questionnaires the day before surgery, on postoperative days 1 and 3, and on the day of discharge. Results: Smartband use after minimally invasive colorectal surgery failed to increase compliance with early mobilization or reduce the occurrence of postoperative complications significantly compared with standard ERAS protocol. However, when smartbands were utilized, quality of recovery was optimized and patients returned to their preoperative status earlier, at postoperative day 3. The length of hospital stay, as defined by discharge criteria, and hospital stay of patients without complications was reduced by 1.1 and 0.9 days, respectively (P = 0.009 and 0.049, respectively). Conclusions: Smartbands enable enhanced communication between patients and surgical teams and strengthen self-management in patients undergoing minimally invasive colorectal resection surgery. Accelerated recovery to preoperative functional status can be facilitated by integrating smartbands into the process of early mobilization during ERAS.

Ethical Challenges With Smartwatch-Based Screening for Atrial Fibrillation: Putting Users at Risk for Marketing Purposes?

Background: Atrial fibrillation is the most common persistent arrhythmia. It is associated with increased mortality and morbidity such as stroke. The early detection of atrial fibrillation can significantly reduce the risk of stroke through preventive anticoagulation. Smartwatches offer the opportunity to screen for atrial fibrillation in the general population. This paper aims to analyze the ethical challenges associated with screening for atrial fibrillation using smartwatches. Methods: This is an ethical analysis. The methodology is based on the principle-orientated approach of Beauchamp and Childress. The principles of beneficence, non-maleficence, justice, and autonomy have to be guaranteed given the influence of private companies, privacy protection, liability and doctor-patient-relationship. The work is based on a systematic literature research. Results: There is currently no evidence that screening for atrial fibrillation with smartwatches improves the outcome and reduces the number of adverse events. The high number of false-positive results can lead to harm. The principle of non-maleficence is violated. The over-reliance on and the lack of adequate education by smartwatches can worsen the doctor-patient relationship. However, the relationship can also be improved by the proactive participation of the patient, which leads to greater autonomy, compliance and in the end beneficence. Since smartwatches are consumer goods, there is a risk for greater disparities in the poor and rich population. There is also a risk of discrimination against ethnic minorities due to underrepresentation in training data and study cohorts. The principle of justice is violated. The storage of sensitive medical data by private companies also raises many ethical and legal concerns. Conclusion: This analysis has shown that the use of smartwatches to detect atrial fibrillation is currently in an ethical perspective problematic. The lack of evidence and the high number of false-positive results can lead to harm. As smartwatches provide only little information about the possible consequences, informed consent cannot be assumed. Ethical implementation could be archived if doctors provide smartwatches to patients who have been shown to benefit from them. The implementation and education should be managed by the doctor.

Wearable Devices for Ambulatory Cardiac Monitoring: JACC State-of-the-Art Review

Ambulatory monitoring devices are enabling a new paradigm of health care by collecting and analyzing long-term data for reliable diagnostics. These devices are becoming increasingly popular for continuous monitoring of cardiac diseases. Recent advancements have enabled solutions that are both affordable and reliable, allowing monitoring of vulnerable populations from the comfort of their homes. They provide early detection of important physiological events, leading to timely alerts for seeking medical attention. In this review, the authors aim to summarize the recent developments in the area of ambulatory and remote monitoring solutions for cardiac diagnostics. The authors cover solutions based on wearable devices, smartphones, and other ambulatory sensors. The authors also present an overview of the limitations of current technologies, their effectiveness, and their adoption in the general population, and discuss some of the recently proposed methods to overcome these challenges. Lastly, we discuss the possibilities opened by this new paradigm, for the future of health care and personalized medicine.

Wearability Testing of Ambulatory Vital Sign Monitoring Devices: Prospective Observational Cohort Study

Background

Timely recognition of patient deterioration remains challenging. Ambulatory monitoring systems (AMSs) may provide support to current monitoring practices; however, they need to be thoroughly tested before implementation in the clinical environment for early detection of deterioration.

Objective

The objective of this study was to assess the wearability of a selection of commercially available AMSs to inform a future prospective study of ambulatory vital sign monitors in an acute hospital ward.

Methods

Five pulse oximeters (4 with finger probes and 1 wrist-worn only, collecting pulse rates and oxygen saturation) and 2 chest patches (collecting heart rates and respiratory rates) were selected to be part of this study: The 2 chest-worn patches were VitalPatch (VitalConnect) and Peerbridge Cor (Peerbridge); the 4 wrist-worn devices with finger probe were Nonin WristOx2 3150 (Nonin), Checkme O2+ (Viatom Technology), PC-68B, and AP-20 (both from Creative Medical); and the 1 solely wrist-worn device was Wavelet (Wavelet Health). Adult participants wore each device for up to 72 hours while performing usual “activities of daily living” and were asked to score the perceived exertion and perception of pain or discomfort by using the Borg CR-10 scale; thoughts and feelings caused by the AMS using the Comfort Rating Scale (CRS); and to provide general free text feedback. Median and IQRs were reported and nonparametric tests were used to assess differences between the devices’ CRS scores.

Results

Quantitative scores and feedback were collected in 70 completed questionnaires from 20 healthy volunteers, with each device tested approximately 10 times. The Wavelet seemed to be the most wearable device (P<.001) with an overall median (IQR) CRS score of 1.00 (0.88). There were no statistically significant differences in wearability between the chest patches in the CRS total score; however, the VitalPatch was superior in the Attachment section (P=.04) with a median (IQR) score of 3.00 (1.00). General pain and discomfort scores and total percentage of time worn are also reflective of this.

Conclusions

Our results suggest that adult participants prefer to wear wrist-worn pulse oximeters without a probe compressing the fingertip and they prefer to wear a smaller chest patch. A compromise between wearability, reliability, and accuracy should be made for successful and practical integration of AMSs within the hospital environment.

A smart wearable device for monitoring and self-management of diabetic foot: A proof of concept study

BACKGROUND AND OBJECTIVE

Diabetic foot is one of the important complications of diabetes, which is occurred due to the destructive parameters in different anatomical sites of feet. Management and monitoring of these parameters are very important to decrease or prevent foot ulcers. We aimed to develop a smart wearable device to monitor these parameters to prevent diabetic foot.

METHODS

Following literature review and expert panel discussions, we considered pressure, temperature and humidity to develop the system. During these sessions, we also developed the system architecture and determined the required technologies. We also developed a mobile application. Finally, all sensors were evaluated for accurate monitoring of pressure, temperature and humidity. A standard protocol was used to evaluate each of these sensors. To this end, five people (four with diabetes and one healthy person) participated. They did a series of movements including walking, sitting, and standing. We considered the pressure measured by Pedar system as the gold standard. Furthermore, we changed the environment temperature and humidity during several experiments and considered the environment temperature and humidity as gold standard. We compared the measured values by sensors with these gold standards.

RESULTS

The evaluation indicated the accurate performance of pressure, humidity and temperature sensors. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the system to provide alarms based on the pressure measured using Pedar were 100, 50, 92.5, 91.8, and 100 %, respectively. The performance of temperature sensors in smart shoes was confirmed by slight differences compared to thermometers. Relatively equal values of humidity measured by two sensors on the left and right feet and the increased difference with the environment humidity showed the exact humidity measured using these sensors.

CONCLUSION

This smart shoes monitors pressure, humidity, and temperature of patients' feet and sends this data to their smart phone by the Bluetooth module. Furthermore, it controls these parameters; as each of these parameters exceeds the defined threshold, alerts are given to patients for self-management.

Clinicians' perspectives on inertial measurement units in clinical practice

Inertial measurement units (IMUs) have been increasingly popular in rehabilitation research. However, despite their accessibility and potential advantages, their uptake and acceptance by health professionals remain a big challenge. The development of an IMU-based clinical tool must bring together engineers, researchers and clinicians. This study is part of a developmental process with the investigation of clinicians’ perspectives about IMUs. Clinicians from four rehabilitation centers were invited to a 30-minute presentation on IMUs. Then, two one-hour focus groups were conducted with volunteer clinicians in each rehabilitation center on: 1) IMUs and their clinical usefulness, and 2) IMUs data analysis and visualization interface. Fifteen clinicians took part in the first focus groups. They expressed their thoughts on: 1) categories of variables that would be useful to measure with IMUs in clinical practice, and 2) desired characteristics of the IMUs. Twenty-three clinicians participated to the second focus groups, discussing: 1) functionalities, 2) display options, 3) clinical data reported and associated information, and 4) data collection duration. Potential influence of IMUs on clinical practice and added value were discussed in both focus groups. Clinicians expressed positive opinions about the use of IMUs, but their expectations were high before considering using IMUs in their practice.

Using Wearable Devices to Monitor Physical Activity in Patients Undergoing Aortic Valve Replacement: Protocol for a Prospective Observational Study

Background

In last few decades, several tools have been developed to measure physical function objectively; however, their use has not been well established in clinical practice.

Objective

This study aims to describe the preoperative physical function and to assess and compare 6-month postoperative changes in the physical function of patients undergoing treatment for aortic stenosis with either surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR). The study also aims to evaluate the feasibility of wearable devices in assessing physical function in such patients.

Methods

This is a prospective observational study. The enrollment will be conducted 1 month before patients’ SAVR/TAVR. Patients will be provided with the wearable device at baseline (activity tracker device, Garmin vívoactive 3). They will be trained in the use of the device, and they will be requested to wear it on the wrist of their preferred hand until 12 months after SAVR/TAVR. After baseline assessment, they will undergo 4 follow-up assessments at 1, 3, 6, and 12 months after SAVR/TAVR. At baseline and each follow-up, they will undergo a set of standard and validated tests to assess physical function, health-related quality of life, and sleep quality.

Results

The ethics committee of Vicenza in Veneto Region in Italy approved the study (Protocol No. 943; January 4, 2019). As of October 2020, the enrollment of participants is ongoing.

Conclusions

The use of the wearable devices for real-time monitoring of physical activity of patients undergoing aortic valve replacement is a promising opportunity for improving the clinical management and consequently, the health outcomes of such patients.

Trial Registration

Clinicaltrials.gov NCT03843320; https://tinyurl.com/yyareu5y

International Registered Report Identifier (IRRID)

DERR1-10.2196/20072

Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification

One class of applications for human activity recognition methods is found in mobile devices for monitoring older adults and people with special needs. Recently, many studies were performed to create intelligent methods for the recognition of human activities. However, the different mobile devices in the market acquire the data from sensors at different frequencies. This paper focuses on implementing four data normalization techniques, i.e., MaxAbsScaler, MinMaxScaler, RobustScaler, and Z-Score. Subsequently, we evaluate the impact of the normalization algorithms with deep neural networks (DNN) for the classification of the human activities. The impact of the data normalization was counterintuitive, resulting in a degradation of performance. Namely, when using the accelerometer data, the accuracy dropped from about 79% to only 53% for the best normalization approach. Similarly, for the gyroscope data, the accuracy without normalization was about 81.5%, whereas with the best normalization, it was only 60%. It can be concluded that data normalization techniques are not helpful in classification problems with homogeneous data.

Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems

Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.

Review on Biomedical Sensors, Technologies and Algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey

This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB.

An API for Wearable Environments Development and Its Application to mHealth Field †

Wearable technologies are transforming research in traditional paradigms of software and knowledge engineering. Among them, expert systems have the opportunity to deal with knowledge bases dynamically varying according to real-time data collected by position sensors, movement sensors, etc. However, it is necessary to design and implement opportune architectural solutions to avoid expert systems are responsible for data acquisition and representation. These solutions should be able to collect and store data according to expert systems desiderata, building a homogeneous framework where data reliability and interoperability among data acquisition, data representation and data use levels are guaranteed. To this aim, the wearable environment notion has been introduced to treat all those information sources as components of a larger platform; a middleware has been designed and implemented, namely WEAR-IT, which allows considering each sensor as a source of information that can be dynamically tied to an expert system application running on a smartphone. As an application example, the mHealth domain is considered.

A survey of research trends in assistive technologies using information modelling techniques

BACKGROUND

Despite the rapid proliferation and emphasis on technology, the use of assistive technology among individuals with varying disabilities and age is different. This situation instigates the need for a systematic review to gain a realistic understanding of prominent issues, research trends and assistive technology applications with minimal bias.

OBJECTIVE

Identification of leading researchers and prominent publications in assistive technologies. Subsequently, semantic relation between qualitative and quantitative research literature on assistive technologies was explored to future research directions.

METHODS

A manual search across reputed research databases was done to find out relevant literature from January 2005 to April 2020. In this paper, latent semantic analysis (LSA) was done to develop an information model for achieving defined objectives.

RESULTS

A corpus of 367 research papers published during 2005-2020 was processed using LSA. Term frequency, inverse document frequency of high loading terms provided five major topic solutions. Marcia Scherer, Rory Cooper and Stefano Federici are most noticed authors in assistive technology research. "Smart Assistive Technologies" and "Wearable Technologies for Rehabilitation" came out as contemporary research trends within assistive technologies.

CONCLUSIONS

The manuscript concludes the fact that assistive technologies for rehabilitation are experiencing a transition from standalone mechanical devices towards smart, wearable and connected devices.Implications for RehabilitationCustomized assistive devices could be programmed for multiple uses.User data privacy and internet dependency of smart assistive technologies must be taken care of while designing smart assistive devices for rehabilitation.Fog devices could eliminate the latency issues associated with cloud-based rehabilitation services.

Using digital health technology to evaluate the impact of chocolate on blood pressure: Results from the COCOA-BP study

Background

High blood pressure (BP) is a major risk factor for cardiovascular disease (CVD). Consumption of dark chocolate, which is high in flavonoids that may reduce CVD risk, is an attractive intervention to reduce to BP. Additionally, the use of mobile health (mHealth) technologies (eg, telehealth, smartphones, and wearable devices) can improve outcomes in patients with CVD.

Objective

The purpose of this study was to investigate the impact of dark chocolate intake on BP, subject use of mHealth, and integration of mHealth into a clinical trial.

Methods

The COCOA-BP (ChOcolate COnsumption And Blood Pressure) study was a prospective, single-center, pre-/postintervention study that enrolled 62 healthy volunteers. The study consisted of 3 phases: smartwatch/smart BP monitor familiarization and washout from chocolate (week 1); control (week 2); and intervention (weeks 3 and 4). During the intervention phase, subjects consumed 50 g of dark chocolate per day. The primary endpoint was change in resting systolic BP between the intervention and control phases. Additional endpoints included device accuracy and correlation with physical activity.

Results

Mean resting systolic BP was 116.4 mm Hg before chocolate intake among 62 participants (mean age 37 years; 61% female). After chocolate intake, mean resting systolic BP was 116.0 mm Hg (difference –0.4; P = .69). These findings suggest that 2 weeks of dark chocolate intake did not reduce resting systolic BP. There was poor agreement between mHealth device and standard (nurse-performed) measurements.

Conclusion

In this study, short-term dark chocolate intake did not seem to reduce BP. mHealth technology shows great potential for use in clinical studies, but challenges related to device accuracy and compliance need to be addressed.

Surface Immobilized Nucleic Acid-Transcription Factor Quantum Dots for Biosensing

Immobilization of biosensors on surfaces is a key step toward development of devices for real-world applications. Here the preparation, characterization, and evaluation of a surface-bound transcription factor-nucleic acid complex for analyte detection as an alternative to conventional systems employing aptamers or antibodies are described. The sensor consists of a gold surface modified with thiolated Cy5 fluorophore-labeled DNA and an allosteric transcription factor (TetR) linked to a quantum dot (QD). Upon addition of anhydrotetracycline (aTc)-the analyte-the TetR-QDs release from the surface-bound DNA, resulting in loss of the Förster resonance energy transfer signal. The sensor responds in a dose-dependent manner over the relevant range of 0-200 µm aTc with a limit of detection of 80 nm. The fabrication of the sensor and the subsequent real-time quantitative measurements establish a framework for the design of future surface-bound, affinity-based biosensors using allosteric transcription factors for molecular recognition.

Microfluidics by Additive Manufacturing for Wearable Biosensors: A Review

Wearable devices are nowadays at the edge-front in both academic research as well as in industry, and several wearable devices have been already introduced in the market. One of the most recent advancements in wearable technologies for biosensing is in the area of the remote monitoring of human health by detection on-the-skin. However, almost all the wearable devices present in the market nowadays are still providing information not related to human ‘metabolites and/or disease’ biomarkers, excluding the well-known case of the continuous monitoring of glucose in diabetic patients. Moreover, even in this last case, the glycaemic level is acquired under-the-skin and not on-the-skin. On the other hand, it has been proven that human sweat is very rich in molecules and other biomarkers (e.g., ions), which makes sweat a quite interesting human liquid with regards to gathering medical information at the molecular level in a totally non-invasive manner. Of course, a proper collection of sweat as it is emerging on top of the skin is required to correctly convey such liquid to the molecular biosensors on board of the wearable system. Microfluidic systems have efficiently come to the aid of wearable sensors, in this case. These devices were originally built using methods such as photolithographic and chemical etching techniques with rigid materials. Nowadays, fabrication methods of microfluidic systems are moving towards three-dimensional (3D) printing methods. These methods overcome some of the limitations of the previous method, including expensiveness and non-flexibility. The 3D printing methods have a high speed and according to the application, can control the textures and mechanical properties of an object by using multiple materials in a cheaper way. Therefore, the aim of this paper is to review all the most recent advancements in the methods for 3D printing to fabricate wearable fluidics and provide a critical frame for the future developments of a wearable device for the remote monitoring of the human metabolism directly on-the-skin.

Using eHealth to Support COVID-19 Education, Self-Assessment, and Symptom Monitoring in the Netherlands: Observational Study

BACKGROUND

The coronavirus disease (COVID-19) situation demands a lot from citizens, health care providers, and governmental institutions. Citizens need to cope with guidelines on social interaction, work, home isolation, and symptom recognition. Additionally, health care providers and policy makers have to cope with unprecedented and unpredictable pressure on the health care system they need to manage. By providing citizens with an app, they always have access to the latest information and can assess their own health. This data could be used to support policy makers and health care providers to get valuable insights in the regional distribution of infection load and health care consumption.

OBJECTIVE

The aim of this observational study is to assess people's use of an app to support them with COVID-19 education, self-assessment, and monitoring of their own health for a 7-day period. In addition, we aim to assess the usability of this data for health care providers and policy makers by applying it to an interactive map and combining it with hospital data. The secondary outcomes of the study were user's satisfaction with the information provided in the app, perceived usefulness of the app, health care providers they contacted, and the follow-up actions from this contact.

METHODS

This observational cohort study was carried out at the nonacademic teaching hospital "Elisabeth Twee Steden" (ETZ) in Tilburg, Netherlands. From April 1, 2020, onwards ETZ offered the COVID-19 education, self-assessment, and symptom tracking diary to their already existing app for patient education and monitoring.

RESULTS

Between April 1 and April 20, 2020, a total of 6194 people downloaded the app. The self-assessment functionality was used abundantly to check one's health status. In total, 5104 people responded to the question about severe symptoms, from which 242 indicated to suffer from severe symptoms. A total of 4929 people responded to the question about mild symptoms, from which 3248 indicated to suffer from these. The data was successfully applied to an interactive map, displaying user demographics and health status. Furthermore, the data was linked to clinical data. App users were satisfied with the information in the app and appreciated the symptom diary functionality. In total, 102 users reached out to a health care provider, leading to 91 contacts.

CONCLUSIONS

Our study demonstrated the successful implementation and use of an app with COVID-19 education, self-assessment, and a 7-day symptom diary. Data collected with the app were successfully applied to an interactive map. In addition, we were able to link the data to COVID-19 screening results from the hospital's microbiology laboratory. This data could be used to support policy makers and health care providers to get valuable insights in the regional distribution of infection load and health care consumption.

TRIAL REGISTRATION

Netherlands Trial Register NL8501; https://www.trialregister.nl/trial/8501.

Current Evidence for Continuous Vital Signs Monitoring by Wearable Wireless Devices in Hospitalized Adults: Systematic Review

BACKGROUND

Continuous monitoring of vital signs by using wearable wireless devices may allow for timely detection of clinical deterioration in patients in general wards in comparison to detection by standard intermittent vital signs measurements. A large number of studies on many different wearable devices have been reported in recent years, but a systematic review is not yet available to date.

OBJECTIVE

The aim of this study was to provide a systematic review for health care professionals regarding the current evidence about the validation, feasibility, clinical outcomes, and costs of wearable wireless devices for continuous monitoring of vital signs.

METHODS

A systematic and comprehensive search was performed using PubMed/MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials from January 2009 to September 2019 for studies that evaluated wearable wireless devices for continuous monitoring of vital signs in adults. Outcomes were structured by validation, feasibility, clinical outcomes, and costs. Risk of bias was determined by using the Mixed Methods Appraisal Tool, quality assessment of diagnostic accuracy studies 2nd edition, or quality of health economic studies tool.

RESULTS

In this review, 27 studies evaluating 13 different wearable wireless devices were included. These studies predominantly evaluated the validation or the feasibility outcomes of these devices. Only a few studies reported the clinical outcomes with these devices and they did not report a significantly better clinical outcome than the standard tools used for measuring vital signs. Cost outcomes were not reported in any study. The quality of the included studies was predominantly rated as low or moderate.

CONCLUSIONS

Wearable wireless continuous monitoring devices are mostly still in the clinical validation and feasibility testing phases. To date, there are no high quality large well-controlled studies of wearable wireless devices available that show a significant clinical benefit or cost-effectiveness. Such studies are needed to help health care professionals and administrators in their decision making regarding implementation of these devices on a large scale in clinical practice or in-home monitoring.

Continuous Monitoring of Vital Signs in the General Ward Using Wearable Devices: Randomized Controlled Trial

Background

Wearable devices can be used for continuous patient monitoring in the general ward, increasing patient safety. Little is known about the experiences and expectations of patients and health care professionals regarding continuous monitoring with these devices.

Objective

This study aimed to identify positive and negative effects as well as barriers and facilitators for the use of two wearable devices: ViSi Mobile (VM) and HealthPatch (HP).

Methods

In this randomized controlled trial, 90 patients admitted to the internal medicine and surgical wards of a university hospital in the Netherlands were randomly assigned to continuous vital sign monitoring using VM or HP and a control group. Users’ experiences and expectations were addressed using semistructured interviews. Nurses, physician assistants, and medical doctors were also interviewed. Interviews were analyzed using thematic content analysis. Psychological distress was assessed using the State Trait Anxiety Inventory and the Pain Catastrophizing Scale. The System Usability Scale was used to assess the usability of both devices.

Results

A total of 60 patients, 20 nurses, 3 physician assistants, and 6 medical doctors were interviewed. We identified 47 positive and 30 negative effects and 19 facilitators and 36 barriers for the use of VM and HP. Frequently mentioned topics included earlier identification of clinical deterioration, increased feelings of safety, and VM lines and electrodes. No differences related to psychological distress and usability were found between randomization groups or devices.

Conclusions

Both devices were well received by most patients and health care professionals, and the majority of them encouraged the idea of monitoring vital signs continuously in the general ward. This comprehensive overview of barriers and facilitators of using wireless devices may serve as a guide for future researchers, developers, and health care institutions that consider implementing continuous monitoring in the ward.

Trial Registration

Clinicaltrials.gov NCT02933307; http://clinicaltrials.gov/ct2/show/NCT02933307.

Patient-Reported Outcome Measures of Utilizing Person-Generated Health Data in the Case of Simulated Stroke Rehabilitation: Development Method

BACKGROUND

Person-generated health data (PGHD) are health data that people generate, record, and analyze for themselves. Although the health benefits of PGHD use have been reported, there is no systematic way for patients to measure and report the health effects they experience from using their PGHD. Patient-reported outcome measures (PROMs) allow patients to systematically self-report their outcomes of a health care service. They generate first-hand evidence of the impact of health care services and are able to reflect the real-world diversity of actual patients and management approaches. Therefore, this paper argues that a PROM of utilizing PGHD, or PROM-PGHD, is necessary to help build evidence-based practice in clinical work with PGHD.

OBJECTIVE

This paper aims to describe a method for developing PROMs for people who are using PGHD in conjunction with their clinical care-PROM-PGHD, and the method is illustrated through a case study.

METHODS

The five-step qualitative item review (QIR) method was augmented to guide the development of a PROM-PGHD. However, using QIR as a guide to develop a PROM-PGHD requires additional socio-technical consideration of the PGHD and the health technologies from which they are produced. Therefore, the QIR method is augmented for developing a PROM-PGHD, resulting in the PROM-PGHD development method.

RESULTS

A worked example was used to illustrate how the PROM-PGHD development method may be used systematically to develop PROMs applicable across a range of PGHD technology types used in relation to various health conditions.

CONCLUSIONS

This paper describes and illustrates a method for developing a PROM-PGHD, which may be applied to many different cases of health conditions and technology categories. When applied to other cases of health conditions and technology categories, the method could have broad relevance for evidence-based practice in clinical work with PGHD.

Two-Electrode ECG for Ambulatory Monitoring with Minimal Hardware Complexity

This article introduces a two-electrode ground-free electrocardiogram (ECG) with minimal hardware complexity, which is ideal for wearable battery-powered devices. The main issue of ground-free measurements is the presence of noise. Therefore, noise suppression methods that can be employed for a two-electrode ECG acquisition system are discussed in detail. Experimental measurements of a living subject and patient simulator are used to investigate and compare the performance of the three proposed methods utilizing the ADS1191 analogue front-end for biopotential measurements. The resulting signals recorded for the simulator indicate that all three methods should be suitable for suppressing power-line noise. The Power Spectral Density (PSD) of the signals measured for a subject exhibits differences across methods; the signal power at 50 Hz is −28, −24.8, and −26 dB for the first, second, and third method, respectively. The digital postprocessing of measured signals acquired a high-quality ECG signal comparable to that of three-electrode sensing. The current consumption measurements demonstrate that all proposed two-electrode ECG solutions are appropriate as a battery-powered device (current consumption < 1.5 mA; sampling rate of 500 SPS). The first method, according to the results, is considered the most effective method in the suppression of power-line noise, current consumption, and hardware complexity.

A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder

OBJECTIVES

Self-monitoring is recommended for individuals with bipolar disorder, with numerous technological solutions available. This study aimed to identify basic components of these solutions that increase engagement with self-monitoring.

METHODS

Participants with bipolar disorder (n = 47) monitored their symptoms with a Fitbit and a smartphone app and were randomly assigned to either review or not review recorded symptoms weekly. We tested whether individuals would better adhere to and prefer monitoring with passive monitoring with an activity tracker compared to active monitoring with a smartphone app and whether individuals would better adhere to self-monitoring if their recorded symptoms were reviewed with an interviewer.

RESULTS

Monitoring with a smartphone app achieved similar adherence and preference to Fitbit (P > .85). Linear mixed effects modeling found adherence decreased significantly more over the study for the Fitbit (12% more, P < .001) even though more participants reported they would use the Fitbit over a year compared to the app (72.3% vs 46.8%). Reviewing symptoms weekly did not improve adherence, but most participants reported they would prefer to review symptoms with a clinician (74.5%) and on monthly basis (57.5%) compared to alternatives. Participants endorsed sleep as the most important symptom to monitor, forgetfulness as the largest barrier to self-monitoring, and raising self-awareness as the best reason for self-monitoring.

CONCLUSIONS

We recommend a combined strategy of wearable and mobile monitoring that includes reminders, targets raising self-awareness, and tracks sleep. A clinician may want to review symptoms on a monthly basis.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03358238.

Protocol for a prospective, controlled, cross-sectional, diagnostic accuracy study to evaluate the specificity and sensitivity of ambulatory monitoring systems in the prompt detection of hypoxia and during movement

INTRODUCTION

Automated continuous ambulatory monitoring may provide an alternative to intermittent manual vital signs monitoring. This has the potential to improve frequency of measurements, timely escalation of care and patient safety. However, a major barrier to the implementation of these wearable devices in the ward environment is their uncertain reliability, efficiency and data fidelity. The purpose of this study is to test performance of selected devices in a simulated clinical setting including during movement and low levels of peripheral oxygen saturation.

METHODS AND ANALYSIS

This is a single centre, prospective, controlled, cross-sectional, diagnostic accuracy study to determine the specificity and sensitivity of currently available ambulatory vital signs monitoring equipment in the detection of hypoxia and the effect of movement on data acquisition. We will recruit up to 45 healthy volunteers who will attend a single study visit; starting with a movement phase and followed by the hypoxia exposure phase where we will gradually decrease saturation levels down to 80%. We will simultaneously test one chest patch, one wrist worn only and three wrist worn with finger probe devices against 'clinical standard 'and 'gold standard' references. We will measure peripheral oxygen saturations, pulse rate, heart rate and respiratory rate continuously and arterial blood gases intermittently throughout the study.

ETHICS AND DISSEMINATION

This study has received ethical approval by the East of Scotland Research Ethics Service REC 2 (19/ES/0008). The results will be broadly distributed through conference presentations and peer-reviewed publications.

TRIAL REGISTRATION NUMBER

ISRCTN61535692 registered on 10/06/2019.

State transition modeling of complex monitored health data

This article considers the analysis of complex monitored health data, where often one or several signals are reflecting the current health status that can be represented by a finite number of states, in addition to a set of covariates. In particular, we consider a novel application of a non-parametric state intensity regression method in order to study time-dependent effects of covariates on the state transition intensities. The method can handle baseline, time varying as well as dynamic covariates. Because of the non-parametric nature, the method can handle different data types and challenges under minimal assumptions. If the signal that is reflecting the current health status is of continuous nature, we propose the application of a weighted median and a hysteresis filter as data pre-processing steps in order to facilitate robust analysis. In intensity regression, covariates can be aggregated by a suitable functional form over a time history window. We propose to study the estimated cumulative regression parameters for different choices of the time history window in order to investigate short- and long-term effects of the given covariates. The proposed framework is discussed and applied to resuscitation data of newborns collected in Tanzania.

Examining the Use of Glucose and Physical Activity Self-Monitoring Technologies in Individuals at Moderate to High Risk of Developing Type 2 Diabetes: Randomized Trial

BACKGROUND

Self-monitoring of behavior (namely, diet and physical activity) and physiology (namely, glucose) has been shown to be effective in type 2 diabetes (T2D) and prediabetes prevention. By combining self-monitoring technologies, the acute physiological consequences of behaviors could be shown, prompting greater consideration to physical activity levels today, which impact the risk of developing diabetes years or decades later. However, until recently, commercially available technologies have not been able to show individuals the health benefits of being physically active.

OBJECTIVE

The objective of this study was to examine the usage, feasibility, and acceptability of behavioral and physiological self-monitoring technologies in individuals at risk of developing T2D.

METHODS

A total of 45 adults aged ≥40 years and at moderate to high risk of T2D were recruited to take part in a 3-arm feasibility trial. Each participant was provided with a behavioral (Fitbit Charge 2) and physiological (FreeStyle Libre flash glucose monitor) monitor for 6 weeks, masked according to group allocation. Participants were allocated to glucose feedback (4 weeks) followed by glucose and physical activity (biobehavioral) feedback (2 weeks; group 1), physical activity feedback (4 weeks) followed by biobehavioral feedback (2 weeks; group 2), or biobehavioral feedback (6 weeks; group 3). Participant usage (including time spent on the apps and number of glucose scans) was the primary outcome. Secondary outcomes were the feasibility (including recruitment and number of sensor displacements) and acceptability (including monitor wear time) of the intervention. Semistructured qualitative interviews were conducted at the 6-week follow-up appointment.

RESULTS

For usage, time spent on the Fitbit and FreeStyle Libre apps declined over the 6 weeks for all groups. Of the FreeStyle Libre sensor scans conducted by participants, 17% (1798/10,582) recorded rising or falling trends in glucose, and 24% (13/45) of participants changed ≥1 of the physical activity goals. For feasibility, 49% (22/45) of participants completed the study using the minimum number of FreeStyle Libre sensors, and a total of 41 sensors were declared faulty or displaced. For acceptability, participants wore the Fitbit for 40.1 (SD 3.2) days, and 20% (9/45) of participants and 53% (24/45) of participants were prompted by email to charge or sync the Fitbit, respectively. Interviews unearthed participant perceptions on the study design by suggesting refinements to the eligibility criteria and highlighting important issues about the usability, wearability, and features of the technologies.

CONCLUSIONS

Individuals at risk of developing T2D engaged with wearable digital health technologies providing behavioral and physiological feedback. Modifications are required to both the study and to commercially available technologies to maximize the chances of sustained usage and behavior change. The study and intervention were feasible to conduct and acceptable to most participants.

TRIAL REGISTRATION

International Standard Randomized Controlled Trial Number (ISRCTN) 17545949; isrctn.com/ISRCTN17545949.

Reduced Graphene Oxide/Mesoporous ZnO NSs Hybrid Fibers for Flexible, Stretchable, Twisted, and Wearable NO2 E-Textile Gas Sensor

E-textiles are gaining growing popularity recently due to low cost, light weight, and conformable compatibility with clothes in wearable and portable smart electronics. Here, an easy-handing, low cost, and scalable fabricating strategy is reported to fabricate conductive, highly flexible, and mechanically stretchable/twisted fiber gas sensor with great wearability and knittability. The proposed gas sensor is built using commercially available cotton/elastic threads as flexible/stretchable templates and reduced graphene oxide/mesoporous zinc oxide nanosheets as sensing layers to form conducting fibers. The as-prepared fiber demonstrates sensitive sensing response, excellent long-term stability (84 days), low theoretical detection limit (43.5 ppb NO₂), great mechanical deformation tolerance (3000 bending cycles, 1000 twisting cycles and 65% strain strength), and washing durability in room-temperature gas detection. More significantly, scalable wearable characteristics including repairability, reliability, stability, and practicability have been efficiently improved, which are achieved by knotting the fractured fibers, incorporating multiple sensors in series/parallel and weaving multisensor array networks integrated into clothes. The good sensing properties, superior flexibility, and scalable applications of wearable fibers may provide a broad window for widespread monitoring of numerous human activities in personal mobile electronics and human-machine interactions.

Precision Medicine in Parkinson's Disease - Exploring Patient-Initiated Self-Tracking

Background:

Individually tailored healthcare, in the form of precision medicine, holds substantial potential for the future of medicine, especially for a complex disorder like Parkinson’s disease (PD). Patient self-tracking is an under-researched area in PD.

Objective:

This study aimed to explore patient-initiated self-tracking in PD and discuss it in the context of precision medicine.

Methods:

The first author used a smartphone app to capture finger-tapping data and also noted times for medication intakes.

Results:

Data were collected during four subsequent days. Only data from the first two days were complete enough to analyze, leading to the realization that the collection of data over a period of time can pose a significant burden to patients. From the first two days of data, a dip in finger function was observed around the time for the second medication dose of the day.

Conclusions:

Patient-initiated self-tracking enabled the first author to glean important insights about how her PD symptoms varied over the course of the day. Symptom tracking holds great potential in precision medicine and can, if shared in a clinical encounter, contribute to the learning of both patient and clinician. More work is needed to develop this field and extra focus needs to be given to balancing the burden of tracking for the patient against any expected benefit.

Mean nocturnal respiratory rate predicts cardiovascular and all-cause mortality in community-dwelling older men and women

Respiratory frequency (f _R) predicts in-hospital and short-term mortality in patients with a variety of pathophysiological conditions, but its predictive value for long-term cardiovascular and all-cause mortality in the general population is unknown. Here, we investigated the relationship between mean nocturnal f _R and mortality in community-dwelling older men and women.We measured mean nocturnal f _R during sleep from overnight polysomnography in 2686 men participating in the Osteoporotic Fractures in Men Study (MrOS) Sleep study and 406 women participating in the Study of Osteoporotic Fractures (SOF) to investigate the relationship between mean nocturnal f _R and long-term cardiovascular and all-cause mortality.166 (6.1%) men in the MrOS cohort (8.9±2.6 years' follow-up) and 46 (11.2%) women in the SOF cohort (6.4±1.6 years' follow-up) died from cardiovascular disease. All-cause mortality was 51.2% and 26.1% during 13.7±3.7 and 6.4±1.6 years' follow-up in the MrOS Sleep study and the SOF cohorts, respectively. Multivariable Cox regression analysis adjusted for significant covariates demonstrated that f _R dichotomised at 16 breaths·min^-1 was independently associated with cardiovascular mortality (MrOS: hazard ratio (HR) 1.57, 95% CI 1.14-2.15; p=0.005; SOF: HR 2.58, 95% CI 1.41-4.76; p=0.002) and all-cause mortality (MrOS: HR 1.18, 95% CI 1.04-1.32; p=0.007; SOF: HR 1.50, 95% CI 1.02-2.20; p=0.04).In community-dwelling older men and women, polysomnography-derived mean nocturnal f _R ≥16 breaths·min^-1 is an independent predictor of long-term cardiovascular and all-cause mortality. Whether nocturnal mean f _R can be used as a risk marker warrants further prospective studies.

Ambulatory cardiac bio-signals: From mirage to clinical reality through a decade of progress

BACKGROUND

Health monitoring is shifting towards continuous, ambulatory and clinically comparable wearable devices. Telemedicine and remote diagnosis could harness the capability of mobile cardiac health information, as the technology on bio-physical signal monitoring has improved significantly.

OBJECTIVES

The purpose of this review article is (1) to systematically assess the viability of ambulatory electrocardiography (ECG), (2) to provide a systems level understanding of a broad spectrum of wearable heart signal monitoring approaches and (3) to identify areas of improvement in the existing technology needed to attain clinical grade diagnosis.

RESULTS

Based on the included literature, we have identified (1) that the developments in ECG monitoring through wearable devices are reaching feasibility, and are capable of delivering diagnostic and prognostic information, (2) that reliable sensing is the major bottleneck in the entire process of ambulatory monitoring, (3) that there is a strong need for artificial intelligence and machine learning techniques to parse and infer the biosignals and (4) that aspects of wearer comfort has largely been ignored in the prevailing developments, which can become a key factor for consumer acceptance.

CONCLUSIONS

Cardiac health information is crucial for diagnosis and prevention of several disease onsets. Mobile and continuous monitoring can aid avoiding risks involved with acute symptoms. The health information obtained through continuous monitoring can serve as the BigData of heart signals, and can facilitate new treatment methods and devise effective health policies.

Genes for Good: Engaging the Public in Genetics Research via Social Media

The Genes for Good study uses social media to engage a large, diverse participant pool in genetics research and education. Health history and daily tracking surveys are administered through a Facebook application, and participants who complete a minimum number of surveys are mailed a saliva sample kit ("spit kit") to collect DNA for genotyping. As of March 2019, we engaged >80,000 individuals, sent spit kits to >32,000 individuals who met minimum participation requirements, and collected >27,000 spit kits. Participants come from all 50 states and include a diversity of ancestral backgrounds. Rates of important chronic health indicators are consistent with those estimated for the general U.S. population using more traditional study designs. However, our sample is younger and contains a greater percentage of females than the general population. As one means of verifying data quality, we have replicated genome-wide association studies (GWASs) for exemplar traits, such as asthma, diabetes, body mass index (BMI), and pigmentation. The flexible framework of the web application makes it relatively simple to add new questionnaires and for other researchers to collaborate. We anticipate that the study sample will continue to grow and that future analyses may further capitalize on the strengths of the longitudinal data in combination with genetic information.

Learning the Orientation of a Loosely-Fixed Wearable IMU Relative to the Body Improves the Recognition Rate of Human Postures and Activities

Features were developed which accounted for the changing orientation of the inertial measurement unit (IMU) relative to the body, and demonstrably improved the performance of models for human activity recognition (HAR). The method is proficient at separating periods of standing and sedentary activity (i.e., sitting and/or lying) using only one IMU, even if it is arbitrarily oriented or subsequently re-oriented relative to the body; since the body is upright during walking, learning the IMU orientation during walking provides a reference orientation against which sitting and/or lying can be inferred. Thus, the two activities can be identified (irrespective of the cohort) by analyzing the magnitude of the angle of shortest rotation which would be required to bring the upright direction into coincidence with the average orientation from the most recent 2.5 s of IMU data. Models for HAR were trained using data obtained from a cohort of 37 older adults (83.9 ± 3.4 years) or 20 younger adults (21.9 ± 1.7 years). Test data were generated from the training data by virtually re-orienting the IMU so that it is representative of carrying the phone in five different orientations (relative to the thigh). The overall performance of the model for HAR was consistent whether the model was trained with the data from the younger cohort, and tested with the data from the older cohort after it had been virtually re-oriented (Cohen's Kappa 95% confidence interval [0.782, 0.793]; total class sensitivity 95% confidence interval [84.9%, 85.6%]), or the reciprocal scenario in which the model was trained with the data from the older cohort, and tested with the data from the younger cohort after it had been virtually re-oriented (Cohen's Kappa 95% confidence interval [0.765, 0.784]; total class sensitivity 95% confidence interval [82.3%, 83.7%]).

Gait Pattern Recognition Using a Smartwatch Assisting Postoperative Physiotherapy

Postoperative rehabilitation is led by physiotherapists and is a vital program that re-establishes joint motion and strengthens the muscles around the joint after an orthopedic surgery. Modern smart devices have affected every aspect of human life. Newly developed technologies have disrupted the way various industries operate, including the healthcare one. Extensive research has been carried out on how smartphone inertial sensors can be used for activity recognition. However, there are very few studies on systems that monitor patients and detect different gait patterns in order to assist the work of physiotherapists during the said rehabilitation phase, even outside the time-limited physiotherapy sessions. In this paper, we are presenting a gait recognition system that was developed to detect different gait patterns. The proposed system was trained, tested and validated with data of people who have undergone lower body orthopedic surgery, recorded by Hirslanden Clinique La Colline, an orthopedic clinic in Geneva, Switzerland. Nine different gait classes were labeled by professional physiotherapists. After extracting both time and frequency domain features from the time series data, several machine learning models were tested including a fully connected neural network. Raw time series data were also fed into a convolutional neural network.

A Commercially Available Capacitive Stretch-Sensitive Sensor for Measurement of Rotational Neck Movement in Healthy People: Proof of Concept

Freedom of neck range of motion has been identified for decades as an important indicator of neck health. In the past, neck motion has been measured in clinical settings using straight-plane movements that do not represent real-world 'ecological' performance. The tools currently used are low-fidelity analog or digital tools that rely greatly on the orientation of the person with respect to gravity, or the evaluator's ability to accurately align protractor arms with key surface markers for angle measurement. A possible solution lies in the use of wearable sensors for tracking the motion of the neck without clinical instruction. For this purpose, the focus of this paper is on the assessment of a commercially available stretch sensitive sensor, C-Stretch® against a gold standard for motion tracking. The sensor's accuracy and agreement for measuring neck rotations were evaluated. The results show that the stretch sensitive sensor was accurate with an average RMSE of 5.86° (SD=$4.38^{\circ}, \mathrm{n}=2$) and highly correlated $r=0.88-0.99,(p\lt0.01)$ with Aurora, an electromagnetic tracking system. This work may lead to using wearable sensors as a cost-effective, lightweight, and safe alternative to assess real-world neck range of motion for clinical application.

Adding wisdom to 'smart' bioelectronic systems: a design framework for physiologic control including practical examples

This perspective provides an overview of how risk can be effectively considered in physiological control loops that strive for semi-to-fully automated operation. The perspective first introduces the motivation, user needs and framework for the design of a physiological closed-loop controller. Then, we discuss specific risk areas and use examples from historical medical devices to illustrate the key concepts. Finally, we provide a design overview of an adaptive bidirectional brain–machine interface, currently undergoing human clinical studies, to synthesize the design principles in an exemplar application.

Technology for Behavioral Change in Rural Older Adults with Obesity

BACKGROUND

Mobile health (mHealth) technologies comprise a multidisciplinary treatment strategy providing potential solutions for overcoming challenges of successfully delivering health promotion interventions in rural areas. We evaluated the potential of using technology in a high-risk population.

METHODS

We conducted a convergent, parallel mixed-methods study using semi-structured interviews, focus groups, and self-reported questionnaires, using purposive sampling of 29 older adults, 4 community leaders and 7 clinicians in a rural setting. We developed codes informed by thematic analysis and assessed the quantitative data using descriptive statistics.

RESULTS

All groups expressed that mHealth could improve health behaviors. Older adults were optimistic that mHealth could track health. Participants believed they could improve patient insight into health, motivating change and assuring accountability. Barriers to using technology were described, including infrastructure.

CONCLUSIONS

Older rural adults with obesity expressed excitement about the use of mHealth technologies to improve their health, yet barriers to implementation exist.

Accuracy of Apple Watch Measurements for Heart Rate and Energy Expenditure in Patients With Cardiovascular Disease: Cross-Sectional Study

Background

Wrist-worn tracking devices such as the Apple Watch are becoming more integrated in health care. However, validation studies of these consumer devices remain scarce.

Objectives

This study aimed to assess if mobile health technology can be used for monitoring home-based exercise in future cardiac rehabilitation programs. The purpose was to determine the accuracy of the Apple Watch in measuring heart rate (HR) and estimating energy expenditure (EE) during a cardiopulmonary exercise test (CPET) in patients with cardiovascular disease.

Methods

Forty patients (mean age 61.9 [SD 15.2] yrs, 80% male) with cardiovascular disease (70% ischemic, 22.5% valvular, 7.5% other) completed a graded maximal CPET on a cycle ergometer while wearing an Apple Watch. A 12-lead electrocardiogram (ECG) was used to measure HR; indirect calorimetry was used for EE. HR was analyzed at three levels of intensity (seated rest, HR1; moderate intensity, HR2; maximal performance, HR3) for 30 seconds. The EE of the entire test was used. Bias or mean difference (MD), standard deviation of difference (SDD), limits of agreement (LoA), mean absolute error (MAE), mean absolute percentage error (MAPE), and intraclass correlation coefficients (ICCs) were calculated. Bland-Altman plots and scatterplots were constructed.

Results

SDD for HR1, HR2, and HR3 was 12.4, 16.2, and 12.0 bpm, respectively. Bias and LoA (lower, upper LoA) were 3.61 (–20.74, 27.96) for HR1, 0.91 (–30.82, 32.63) for HR2, and –1.82 (–25.27, 21.63) for HR3. MAE was 6.34 for HR1, 7.55 for HR2, and 6.90 for HR3. MAPE was 10.69% for HR1, 9.20% for HR2, and 6.33% for HR3. ICC was 0.729 (P<.001) for HR1, 0.828 (P<.001) for HR2, and 0.958 (P<.001) for HR3. Bland-Altman plots and scatterplots showed good correlation without systematic error when comparing Apple Watch with ECG measurements. SDD for EE was 17.5 kcal. Bias and LoA were 30.47 (–3.80, 64.74). MAE was 30.77; MAPE was 114.72%. ICC for EE was 0.797 (P<.001). The Bland-Altman plot and a scatterplot directly comparing Apple Watch and indirect calorimetry showed systematic bias with an overestimation of EE by the Apple Watch.

Conclusions

In patients with cardiovascular disease, the Apple Watch measures HR with clinically acceptable accuracy during exercise. If confirmed, it might be considered safe to incorporate the Apple Watch in HR-guided training programs in the setting of cardiac rehabilitation. At this moment, however, it is too early to recommend the Apple Watch for cardiac rehabilitation. Also, the Apple Watch systematically overestimates EE in this group of patients. Caution might therefore be warranted when using the Apple Watch for measuring EE.

Single-Molecule Detection of DNA in a Nanochannel by High-Field Strength-Assisted Electrical Impedance Spectroscopy

Many researchers have fabricated micro and nanofluidic devices incorporating optical, chemical, and electrical detection systems with the aim of achieving on-chip analysis of macromolecules. The present study demonstrates a label-free detection of DNA using a nanofluidic device based on impedance measurements that is both sensitive and simple to operate. Using this device, the electrophoresis and dielectrophoresis effect on DNA conformation and the length dependence were examined. A low alternating voltage was applied to the nanogap electrodes to generate a high intensity field (>0.5 MV/m) under non-faradaic conditions. In addition, a 100 nm thick gold electrode was completely embedded in the substrate to allow direct measurements of a solution containing the sample passing through the gap, without any surface modification required. The high intensity field in this device produced a dielectrophoretic force that stretched the DNA molecule across the electrode gap at a specific frequency, based on back and forth movements between the electrodes with the DNA in a random coil conformation. The characteristics of 100 bp, 500 bp, 1 kbp, 5 kbp, 10 kbp, and 48 kbp λ DNA associated with various conformations were quantitatively analyzed with high resolution (on the femtomolar level). The sensitivity of this system was found to be more than about 10 orders of magnitude higher than that obtained from conventional linear alternating current (AC) impedance for the analysis of bio-polymers. This new high-sensitivity process is expected to be advantageous with regard to the study of complex macromolecules and nanoparticles.

Relevance of Activity Tracking With Mobile Devices in the Relationship Between Physical Activity Levels and Satisfaction With Physical Fitness in Older Adults: Representative Survey

Background

Physical activity has been shown to positively affect many aspects of life, and the positive relationship between physical activity levels and health is well established. Recently, research on the interrelationship between physical activity levels and subjective experiences has gained attention. However, the underlying mechanisms that link physical activity levels with subjective experiences of physical fitness have not been sufficiently explained.

Objective

This study aimed to explore the role of physical activity tracking (PAT) in the relationship between physical activity levels and satisfaction with physical fitness in older adults. It is hypothesized that higher levels of physical activity are associated with a higher satisfaction with physical fitness in older adults and that this positive association is stronger for older people who use mobile devices for PAT.

Methods

As part of this study, 1013 participants aged 50 years or older and living in Switzerland were interviewed via computer-assisted telephone interviews. Bivariate and multivariate analyses were applied. The interaction effects between physical activity levels and PAT were evaluated using multiple linear regression analysis.

Results

Descriptive analyses showed that 719 participants used at least 1 mobile device and that 136 out of 719 mobile device users (18.9%) used mobile devices for PAT. In the multivariate regression analysis, frequent physical activity was found to have a positive effect on satisfaction with physical fitness (beta=.24, P<.001). A significant interaction effect between physical activity levels and PAT (beta=.30, P=.03) provides some first evidence that the positive effects of physical activity on satisfaction with physical fitness can be enhanced by PAT.

Conclusions

The results indicate the potential of PAT to enhance the physical fitness of older adults. However, the results also raise new issues in this context. Recommendations for further research and practice include the acquisition of longitudinal data, a more detailed observation of durations of use, and the development of devices for PAT considering health psychology and gerontology theories.