Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study

BACKGROUND

Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition.

OBJECTIVE

The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD.

METHODS

Using the All of Us Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and F1-score.

RESULTS

Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method's specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection.

CONCLUSIONS

This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies.

Monitoring Adolescent and Young Adult Patients With Cancer via a Smart T-Shirt: Prospective, Single-Cohort, Mixed Methods Feasibility Study (OncoSmartShirt Study)

Background

Wearables that measure vital parameters can be potential tools for monitoring patients at home during cancer treatment. One type of wearable is a smart T-shirt with embedded sensors. Initially, smart T-shirts were designed to aid athletes in their performance analyses. Recently however, researchers have been investigating the use of smart T-shirts as supportive tools in health care. In general, the knowledge on the use of wearables for symptom monitoring during cancer treatment is limited, and consensus and awareness about compliance or adherence are lacking.

objectives

The aim of this study was to evaluate adherence to and experiences with using a smart T-shirt for the home monitoring of biometric sensor data among adolescent and young adult patients undergoing cancer treatment during a 2-week period.

Methods

This study was a prospective, single-cohort, mixed methods feasibility study. The inclusion criteria were patients aged 18 to 39 years and those who were receiving treatment at Copenhagen University Hospital - Rigshospitalet, Denmark. Consenting patients were asked to wear the Chronolife smart T-shirt for a period of 2 weeks. The smart T-shirt had multiple sensors and electrodes, which engendered the following six measurements: electrocardiogram (ECG) measurements, thoracic respiration, abdominal respiration, thoracic impedance, physical activity (steps), and skin temperature. The primary end point was adherence, which was defined as a wear time of >8 hours per day. The patient experience was investigated via individual, semistructured telephone interviews and a paper questionnaire.

Results

A total of 10 patients were included. The number of days with wear times of >8 hours during the study period (14 d) varied from 0 to 6 (mean 2 d). Further, 3 patients had a mean wear time of >8 hours during each of their days with data registration. The number of days with any data registration ranged from 0 to 10 (mean 6.4 d). The thematic analysis of interviews pointed to the following three main themes: (1) the smart T-shirt is cool but does not fit patients with cancer, (2) the technology limits the use of the smart T-shirt, and (3) the monitoring of data increases the feeling of safety. Results from the questionnaire showed that the patients generally had confidence in the device.

Conclusions

Although the primary end point was not reached, the patients' experiences with using the smart T-shirt resulted in the knowledge that patients acknowledged the need for new technologies that improve supportive cancer care. The patients were positive when asked to wear the smart T-shirt. However, technical and practical challenges in using the device resulted in low adherence. Although wearables might have potential for home monitoring, the present technology is immature for clinical use.

A wearable cardiac ultrasound imager

Continuous imaging of cardiac functions is highly desirable for the assessment of long-term cardiovascular health, detection of acute cardiac dysfunction and clinical management of critically ill or surgical patients1-4. However, conventional non-invasive approaches to image the cardiac function cannot provide continuous measurements owing to device bulkiness5-11, and existing wearable cardiac devices can only capture signals on the skin12-16. Here we report a wearable ultrasonic device for continuous, real-time and direct cardiac function assessment. We introduce innovations in device design and material fabrication that improve the mechanical coupling between the device and human skin, allowing the left ventricle to be examined from different views during motion. We also develop a deep learning model that automatically extracts the left ventricular volume from the continuous image recording, yielding waveforms of key cardiac performance indices such as stroke volume, cardiac output and ejection fraction. This technology enables dynamic wearable monitoring of cardiac performance with substantially improved accuracy in various environments.

Highly efficient patterning technique for silver nanowire electrodes by electrospray deposition and its application to self-powered triboelectric tactile sensor

A patterned transparent electrode is a crucial component of state-of-the-art wearable devices and optoelectronic devices. However, most of the patterning methods using silver nanowires (AgNWs), which is one of the outstanding candidate materials for the transparent electrode, wasted a large amount of unused AgNWs during the patterning process. Here, we report a highly efficient patterning of AgNWs using electrospray deposition with grounded electrolyte solution (EDGE). During electrospray deposition, a patterned electrolyte solution collector attracted AgNWs by strong electrostatic attraction and selectively deposited them only on the patterned collector, minimizing AgNW deposited elsewhere. The enhanced patterning efficiency was verified through a comparison between the EDGE and conventional process by numerical simulation and experimental validation. As a result, despite the same electrospray deposition conditions for both cases except for the existence of the electrolyte solution collector, the coverage ratio of AgNWs fabricated by the EDGE process was at least six times higher than that of AgNWs produced by the conventional process. Furthermore, the EDGE process provided high design flexibility in terms of not only the material of the substrate, including a polymer and a ceramic but also the shape of the substrate, including a 2D flat and 3D curved surface. As an application of the EDGE process, a self-powered touch sensor exploiting the triboelectric effect was demonstrated. Thus, the EDGE process would be utilized in further application in wearable or implantable devices in the field of biomedicine, intelligent robots, and human-machine interface.

A multi-point heart rate monitoring using a soft wearable system based on fiber optic technology

Early diagnosis can be crucial to limit both the mortality and economic burden of cardiovascular diseases. Recent developments have focused on the continuous monitoring of cardiac activity for a prompt diagnosis. Nowadays, wearable devices are gaining broad interest for a continuous monitoring of the heart rate (HR). One of the most promising methods to estimate HR is the seismocardiography (SCG) which allows to record the thoracic vibrations with high non-invasiveness in out-of-laboratory settings. Despite significant progress on SCG, the current state-of-the-art lacks both information on standardized sensor positioning and optimization of wearables design. Here, we introduce a soft wearable system (SWS), whose novel design, based on a soft polymer matrix embedding an array of fiber Bragg gratings, provides a good adhesion to the body and enables the simultaneous recording of SCG signals from multiple measuring sites. The feasibility assessment on healthy volunteers revealed that the SWS is a suitable wearable solution for HR monitoring and its performance in HR estimation is strongly influenced by sensor positioning and improved by a multi-sensor configuration. These promising characteristics open the possibility of using the SWS in monitoring patients with cardiac pathologies in clinical (e.g., during cardiac magnetic resonance procedures) and everyday life settings.

Accuracy and Reliability of Commercial Wrist-Worn Pulse Oximeter During Normobaric Hypoxia Exposure Under Resting Conditions

Purpose: The present study analyzed peripheral blood oxygen saturation (S_pO₂) and heart rate (HR) measurements taken on the Garmin fēnix® 5X Plus watch, comparing them to measurements taken on a standard medical-grade pulse oximeter during normobaric hypoxia exposure under resting conditions. Methods: Thirteen women (mean ± SD: Age 20 ± 1 years, height 165 ± 5 cm, mass, 67 ± 9 kg) and ten men (mean ± SD: Age 21 ± 3 years, height 177 ± 6 cm, mass 78 ± 11 kg) sat inside a customized environmental chamber while the fraction of inspired oxygen (F_IO₂) was adjusted to simulate altitudes of 12,000; 10,000; 8,000; 6,000; and 900 ft. The novel commercial device (Garmin fēnix®) and a medical-grade pulse oximeter (Nonin® 7500) were used to measure S_pO₂ and HR in triplicate at each simulated altitude. Bland-Altman analyses were used to assess differences between methods. Results: Bland-Altman analysis indicated 3.3% bias for S_pO₂ measurements taken on the Garmin fēnix® at 12,000 ft of simulated altitude (limits of agreement: -1.9-8.6%). Mean differences in S_pO₂ measurements were smaller at the remaining simulated altitudes, where bias measurements ranged from 0.7% to 0.8%. The Garmin fēnix® also underestimated heart rate, but those discrepancies were minimal (bias measurements at all simulated altitude exposures were < 1.0 bpm). Conclusions: With the exception of readings taken at 12,000 ft of simulated altitude, the Garmin fēnix® exhibits minimal overestimation of S_pO₂ and minimal underestimation of HR during simulated altitude exposure. These data suggest the Garmin fēnix® watch may be a viable method to monitor S_pO₂ and HR under most ambient environmental conditions.

Automatic methods of hoof-on and -off detection in horses using wearable inertial sensors during walk and trot on asphalt, sand and grass

Detection of hoof-on and -off events are essential to gait classification in horses. Wearable sensors have been endorsed as a convenient alternative to the traditional force plate-based method. The aim of this study was to propose and validate inertial sensor-based methods of gait event detection, reviewing different sensor locations and their performance on different gaits and exercise surfaces. Eleven horses of various breeds and ages were recruited to wear inertial sensors attached to the hooves, pasterns and cannons. Gait events detected by pastern and cannon methods were compared to the reference, hoof-detected events. Walk and trot strides were recorded on asphalt, grass and sand. Pastern-based methods were found to be the most accurate and precise for detecting gait events, incurring mean errors of between 1 and 6ms, depending on the limb and gait, on asphalt. These methods incurred consistent errors when used to measure stance durations on all surfaces, with mean errors of 0.1 to 1.16% of a stride cycle. In conclusion, the methods developed and validated here will enable future studies to reliably detect equine gait events using inertial sensors, under a wide variety of field conditions.

Interventions Using Wearable Physical Activity Trackers Among Adults With Cardiometabolic Conditions: A Systematic Review and Meta-analysis

IMPORTANCE

Wearable physical activity (PA) trackers, such as accelerometers, fitness trackers, and pedometers, are accessible technologies that may encourage increased PA levels in line with current recommendations. However, whether their use is associated with improvements in PA levels in participants who experience 1 or more cardiometabolic conditions, such as diabetes, prediabetes, obesity, and cardiovascular disease, is unknown.

OBJECTIVE

To assess the association of interventions using wearable PA trackers (accelerometers, fitness trackers, and pedometers) with PA levels and other health outcomes in adults with cardiometabolic conditions.

DATA SOURCES

For this systematic review and meta-analysis, searches of MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and PsycINFO were performed from January 1, 2000, until December 31, 2020, with no language restriction. A combination of Medical Subject Heading terms and text words of diabetes, obesity, cardiovascular disease, pedometers, accelerometers, and Fitbits were used.

STUDY SELECTION

Randomized clinical trials or cluster randomized clinical trials that evaluated the use of wearable PA trackers, such as pedometers, accelerometers, or fitness trackers, were included. Trials were excluded if they assessed the trackers only as measuring tools of PA before and after another intervention, they required participants to be hospitalized, assessors were not blinded to the trackers, or they used a tracker to measure the effect of a pharmacological treatment on PA among individuals.

DATA EXTRACTION AND SYNTHESIS

The study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. A random-effects model was used for the meta-analysis.

MAIN OUTCOMES AND MEASURES

The primary outcome was mean difference in PA levels. When the scale was different across studies, standardized mean differences were calculated. Heterogeneity was quantified using the I2 statistic and explored using mixed-effects metaregression.

RESULTS

A total of 38 randomized clinical trials with 4203 participants were eligible in the systematic review; 29 trials evaluated pedometers, and 9 evaluated accelerometers or fitness trackers. Four studies did not provide amenable outcome data, leaving 34 trials (3793 participants) for the meta-analysis. Intervention vs comparator analysis showed a significant association of wearable tracker use with increased PA levels overall (standardized mean difference, 0.72; 95% CI, 0.46-0.97; I2 = 88%; 95% CI, 84.3%-90.8%; P < .001) in studies with short to medium follow-up for median of 15 (range, 12-52) weeks. Multivariable metaregression showed an association between increased PA levels and interventions that involved face-to-face consultations with facilitators (23 studies; β = -0.04; 95% CI, -0.11 to -0.01), included men (23 studies; β = 0.48; 95% CI, 0.01-0.96), and assessed pedometer-based interventions (26 studies; β = 0.20; 95% CI, 0.02-0.32).

CONCLUSIONS AND RELEVANCE

In this systematic review and meta-analysis, interventions that combined wearable activity trackers with health professional consultations were associated with significant improvements in PA levels among people with cardiometabolic conditions.

Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations

Analysis of high-resolution inertial sensor and global navigation satellite system (GNSS) data collected by mobile and wearable devices is a relatively new methodology in forestry and safety research that provides opportunities for modeling work activities in greater detail than traditional time study analysis. The objective of this study was to evaluate whether smartwatch-based activity recognition models could quantify the activities of rigging crew workers setting and disconnecting log chokers on cable logging operations. Four productive cycle elements (travel to log, set choker, travel away, clear) were timed for choker setters and four productive cycle elements (travel to log, unhook, travel away, clear) were timed for chasers working at five logging sites in North Idaho. Each worker wore a smartwatch that recorded accelerometer data at 25 Hz. Random forest machine learning was used to develop predictive models that classified the different cycle elements based on features extracted from the smartwatch acceleration data using 15 sliding window sizes (1 to 15 s) and five window overlap levels (0%, 25%, 50%, 75%, and 90%). Models were compared using multiclass area under the Receiver Operating Characteristic (ROC) curve, or AUC. The best choker setter model was created using a 3-s window with 90% overlap and had sensitivity values ranging from 76.95% to 83.59% and precision values ranging from 41.42% to 97.08%. The best chaser model was created using a 1-s window with 90% overlap and had sensitivity values ranging from 71.95% to 82.75% and precision values ranging from 14.74% to 99.16%. These results have demonstrated the feasibility of quantifying forestry work activities using smartwatch-based activity recognition models, a basic step needed to develop real-time safety notifications associated with high-risk job functions and to advance subsequent, comparative analysis of health and safety metrics across stand, site, and work conditions.

Bio-Inspired Multi-Mode Pain-Perceptual System (MMPPS) with Noxious Stimuli Warning, Damage Localization, and Enhanced Damage Protection

The multi-mode pain-perceptual system (MMPPS) is essential for the human body to perceive noxious stimuli in all circumstances and make an appropriate reaction. Based on the central sensitization mechanism, the MMPPS can switch between different working modes and thus offers a smarter protection mechanism to human body. Accordingly, before injury MMPPS can offer warning of excessive pressure with normal pressure threshold. After injury, extra care on the periphery of damage will be activated by decreasing the pressure threshold. Furthermore, the MMPPS will gradually recover back to a normal state as damage heals. Although current devices can realize basic functions like damage localization and nociceptor signal imitating, the development of a human-like MMPPS is still a great challenge. Here, a bio-inspired MMPPS is developed for prosthetics protection, in which all working modes is realized and controlled by mimicking the central sensitization mechanism. Accordingly, the system warns one of a potential injury, identifies the damaged area, and subsequently offers extra care. The proposed system can open new avenues for designing next-generation prosthetics, especially make other smart sensing systems operate under complete protection against injuries.

Should weight-bearing activity be reduced during healing of plantar diabetic foot ulcers, even when using appropriate offloading devices?

Physical activity is an essential part of general health and diabetes management. However, recommending weight-bearing physical activity for people with plantar diabetic foot ulcers is controversial, even when gold standard offloading devices are used, as it is commonly thought to delay healing. We aimed to narratively review relevant studies investigating the relationship between plantar diabetic foot ulcer healing and weight-bearing activity, plantar pressure and device adherence. We defined relevant studies as those from two systematic reviews, along with those identified since using a similar updated Pubmed search strategy. We identified six studies. One study found that more daily steps were associated with worse ulcer healing, three found no significant association between steps and ulcer healing, and in two others the association was unclear. Thus, there is weak evidence for an inverse relationship between weight-bearing physical activity and plantar ulcer healing while utilizing offloading devices. We propose a Diabetic foot Offloading and Activity framework to guide future research to find the optimal balance between the positive and negative effects of weight-bearing activity in the context of foot ulcers. We hope such future studies will shed more conclusive light on the impact of weight-bearing activity on healing of plantar diabetic foot ulcers.

Can caregivers trust information technology in the care of their patients? A systematic review

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) requires that healthcare providers allow patients to engage in their healthcare by allowing access to their health records. Often patients need informal caregivers including family members or others to help them with their care. This paper explores whether trust is a key factor for informal caregivers' decision to use health information technologies (HIT) including electronic health records (EHR), patient portals, mobile apps, or other devices to care for their patient. Six reviewers conducted a comprehensive search of four literature databases using terms that pertained to a caregiver and trust to investigate the role trust plays when caregivers use HIT. While trust is a key factor for the use of HIT, it the researchers only identified ten articles that met the research question thresholds. Four main topics of trust surfaced including perceived confidentiality, perceived security, technological malfunction, and trustworthiness of the information. Trust is a critical factor for informal caregivers when using HIT to assist in the care of their patient (child, loved one, parent, or acquaintance). Based on the findings, it is clear that more research on the use of HIT by caregivers is needed.

Lower body kinematics estimation from wearable sensors for walking and running: A deep learning approach

BACKGROUND

Inertial measurement units (IMUs) are promising tools for collecting human movement data. Model-based filtering approaches (e.g. Extended Kalman Filter) have been proposed to estimate joint angles from IMUs data but little is known about the potential of data-driven approaches.

RESEARCH QUESTION

Can deep learning models accurately predict lower limb joint angles from IMU data during gait?

METHODS

Lower-limb kinematic data were simultaneously measured with a marker-based motion capture system and running leggings with 5 integrated IMUs measuring acceleration and angular velocity at the pelvis, thighs and tibias. Data acquisition was performed on 27 participants (26.5 (3.9) years, 1.75 (0.07) m, 68.3 (10.0) kg) while walking at 4 and 6 km/h and running at 8, 10, 12 and 14 km/h on a treadmill. The model input consists of raw IMU data, while the output estimates the joint angles of the lower body. The model was trained with a nested k-fold cross-validation and tested considering a user-independent approach. Mean error (ME), mean absolute error (MAE) and Pearson correlation coefficient (r) were computed between the ground truth and predicted joint angles.

RESULTS

MAE for the DOFs ranged from 2.2(0.9) to 5.1(2.7)° with an average of 3.6(2.1)°. r ranged from 0.67(0.23) to 0.99(0.01) with moderate correlation (0.4≤r<0.7) was found for the hip right rotation and lumbar extension, strong correlation (0.7≤r<0.9) was found for the hip left rotation and ankle right/left inversion while all other DOFs showed very strong correlation (r≥0.9).

SIGNIFICANCE

The proposed model can reliably predict joint kinematics for walking, running and gait transitions without specific knowledge about the body characteristics of the wearer, or the position and orientation of the IMU relative to the attached segment. These results have been validated with treadmill gait, and have not yet been confirmed for gait in other settings.

Wearables in the SARS-CoV-2 Pandemic: What Are They Good for?

Recently, companies such as Apple Inc, Fitbit Inc, and Garmin Ltd have released new wearable blood oxygenation measurement technologies. Although the release of these technologies has great potential for generating health-related information, it is important to acknowledge the repercussions of consumer-targeted biometric monitoring technologies (BioMeTs), which in practice, are often used for medical decision making. BioMeTs are bodily connected digital medicine products that process data captured by mobile sensors that use algorithms to generate measures of behavioral and physiological function. These BioMeTs span both general wellness products and medical devices, and consumer-targeted BioMeTs intended for general wellness purposes are not required to undergo a standardized and transparent evaluation process for ensuring their quality and accuracy. The combination of product functionality, marketing, and the circumstances of the global SARS-CoV-2 pandemic have inevitably led to the use of consumer-targeted BioMeTs for reporting health-related measurements to drive medical decision making. In this viewpoint, we urge consumer-targeted BioMeT manufacturers to go beyond the bare minimum requirements described in US Food and Drug Administration guidance when releasing information on wellness BioMeTs. We also explore new methods and incentive systems that may result in a clearer public understanding of the performance and intended use of consumer-targeted BioMeTs.

Mobile Health App With Social Media to Support Self-Management for Patients With Chronic Kidney Disease: Prospective Randomized Controlled Study

BACKGROUND

Chronic kidney disease (CKD) is a global health burden. Self-management plays a key role in improving modifiable risk factors.

OBJECTIVE

The aim of this study was to evaluate the effectiveness of wearable devices, a health management platform, and social media at improving the self-management of CKD, with the goal of establishing a new self-management intervention model.

METHODS

In a 90-day prospective experimental study, a total of 60 people with CKD at stages 1-4 were enrolled in the intervention group (n=30) and control group (n=30). All participants were provided with wearable devices that collected exercise-related data. All participants maintained dietary diaries using a smartphone app. All dietary and exercise information was then uploaded to a health management platform. Suggestions about diet and exercise were provided to the intervention group only, and a social media group was created to inspire the participants in the intervention group. Participants' self-efficacy and self-management questionnaire scores, Kidney Disease Quality of Life scores, body composition, and laboratory examinations before and after the intervention were compared between the intervention and control groups.

RESULTS

A total of 49 participants completed the study (25 in the intervention group and 24 in the control group); 74% of the participants were men and the mean age was 51.22 years. There were no differences in measured baseline characteristics between the groups except for educational background. After the intervention, the intervention group showed significantly higher scores for self-efficacy (mean 171.28, SD 22.92 vs mean 142.21, SD 26.36; P<.001) and self-management (mean 54.16, SD 6.71 vs mean 47.58, SD 6.42; P=.001). Kidney Disease Quality of Life scores were also higher in the intervention group (mean 293.16, SD 34.21 vs mean 276.37, SD 32.21; P=.02). The number of steps per day increased in the intervention group (9768.56 in week 1 and 11,389.12 in week 12). The estimated glomerular filtration rate (eGFR) of the intervention group was higher than that of the control group (mean 72.47, SD 24.28 vs mean 59.69, SD 22.25 mL/min/1.73m2; P=.03) and the decline in eGFR was significantly slower in the intervention group (-0.56 vs -4.58 mL/min/1.73m2). There were no differences in body composition between groups postintervention.

CONCLUSIONS

The use of wearable devices, a health management platform, and social media support not only strengthened self-efficacy and self-management but also improved quality of life and a slower eGFR decline in people with CKD at stages 1-4. These results outline a new self-management model to promote healthy lifestyle behaviors for patients with CKD.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04617431; https://www.clinicaltrials.gov/ct2/show/NCT04617431.

Wearable seizure detection devices in refractory epilepsy

Epilepsy affects 50 million patients and their caregivers worldwide. Devices that facilitate the detection of seizures can have a large influence on a patient's quality of life, therapeutic decisions and the conduct of clinical trials with anti-epileptic drugs. This article provides an up-to-date overview and comparison between wearable seizure detection devices (WSDDs), taking into account the newly proposed standards for testing and clinical validation of devices. 16 devices were included in our comparison. The F1-score, combining the device's accurate recall and precision, was calculated for each of these devices and used to evaluate their performance. The devices were separated by development phase and ranked by F1-score from highest to lowest. We describe 16 WSDDs: 6 of which were accelerometry (ACM)-based, 3 surface electromyography-based, 1 was a wearable application of EEG, 4 had multimodal sensors and 2 other types of sensors. We observed a significant inconsistency in the description of performance measures. The devices in the most advanced development phase with the highest F1-scores incorporated ACM- and sEMG-based sensors to detect tonic-clonic seizures. This review highlights the importance of implementing standards for an optimal comparison and, therefore, improving the research and development of WSDDs. WSDDs can improve the patient's care and quality of life, decrease seizure underreporting and they could potentially prevent sudden-unexpected-death in epilepsy. We discuss the central role of the neurologist in the use of WSDDs, and why a business to business to consumer model is better than the current business to consumer model of most WSDDs.

Accelerometer output and its association with energy expenditure in persons with mild-to-moderate Parkinson's disease

OBJECTIVE

This study examined the association between ActiGraph accelerometer output and energy expenditure across different speeds of walking in persons with Parkinson's disease (PD), and further generated cut-points that represent a metric for quantifying time spent in moderate-to-vigorous physical activity (MVPA) among persons with PD.

METHODS

The sample included 30 persons with mild-to-moderate PD (Hoehn and Yahr stages 2-3) and 30 adults without PD matched by sex and age. All participants completed 5 minutes of quiet, seated rest and then underwent three, 6-minute bouts of walking on a treadmill at three different speeds relative to the individual's self-selected pace. Activity counts were measured using an ActiGraph accelerometer worn at the waist level on the least affected side for persons with PD and the dominant side for controls. The rate of oxygen consumption, or energy expenditure, was measured using a portable, open-circuit spirometry system.

RESULTS

Our results indicated a strong association between activity counts and energy expenditure for persons with PD (R2 = 0.87) and controls (R2 = 0.89). However, the significant difference in slopes resulted in a lower cut-point of 1,354 counts·min-1 for persons with PD than the cut-point of 2,010 counts·min-1 for controls.

CONCLUSION

Our results support the application of the disease-specific cut-point for quantifying the amount of time spent in MVPA using ActiGraph accelerometers among persons with mild-to-moderate PD. Such an application may provide accurate estimates of MVPA in this population, and better inform future research examining the possible determinants and consequences of physical activity as well as testing of interventions for changing MVPA in PD.

Effects of the e-Motivate4Change Program on Metabolic Syndrome in Young Adults Using Health Apps and Wearable Devices: Quasi-Experimental Study

BACKGROUND

The health behaviors of young adults lag behind those of other age groups, and active health management is needed to improve health behaviors and prevent chronic diseases. In addition, developing good lifestyle habits earlier in life could reduce the risk of metabolic syndrome (MetS) later on.

OBJECTIVE

The aim of this study is to investigate the effects of the e-Motivate4Change program, for which health apps and wearable devices were selected based on user needs. The program was developed for the prevention and management of MetS in young adults.

METHODS

This experimental study used a nonequivalent control group. In total, 59 students from 2 universities in Daegu, Korea participated in the study (experimental group n=30; control group n=29). Data were collected over 4 months, from June 1 to September 30, 2018. The experimental group received a 12-week e-Motivate4Change program intervention, and the control group received MetS education and booklets without the e-Motivate4Change program intervention.

RESULTS

After the program, the experimental group had significantly higher scores for health-related lifestyle (t=3.86; P<.001) and self-efficacy (t=6.00; P<.001) than did the control group. Concerning BMI, there were significant effects by group (F=1.01; P<.001) and for the group × time interaction (F=4.71; P=.034). Concerning cholesterol, there were significant main effects for group (F=4.32; P=.042) and time (F=9.73; P<.001).

CONCLUSIONS

The e-Motivate4Change program effectively improved participants' health-related lifestyle scores and self-efficacy, and significantly reduced their BMI and cholesterol levels. The program can be used to identify and prevent MetS among young adults.

Emerging Technologies for Identifying Atrial Fibrillation

Atrial fibrillation (AF) is a major cause of morbidity and mortality globally, and much of this is driven by challenges in its timely diagnosis and treatment. Existing and emerging mobile technologies have been used to successfully identify AF in a variety of clinical and community settings, and while these technologies offer great promise for revolutionizing AF detection and screening, several major barriers may impede their effectiveness. The unclear clinical significance of device-detected AF, potential challenges in integrating patient-generated data into existing healthcare systems and clinical workflows, harm resulting from potential false positives, and identifying the appropriate scope of population-based screening efforts are all potential concerns that warrant further investigation. It is crucial for stakeholders such as healthcare providers, researchers, funding agencies, insurers, and engineers to actively work together in fulfilling the tremendous potential of mobile technologies to improve AF identification and management on a population level.

Identification of Suitable Biomarkers for Stress and Emotion Detection for Future Personal Affective Wearable Sensors

Skin conductivity (i.e., sweat) forms the basis of many physiology-based emotion and stress detection systems. However, such systems typically do not detect the biomarkers present in sweat, and thus do not take advantage of the biological information in the sweat. Likewise, such systems do not detect the volatile organic components (VOC’s) created under stressful conditions. This work presents a review into the current status of human emotional stress biomarkers and proposes the major potential biomarkers for future wearable sensors in affective systems. Emotional stress has been classified as a major contributor in several social problems, related to crime, health, the economy, and indeed quality of life. While blood cortisol tests, electroencephalography and physiological parameter methods are the gold standards for measuring stress; however, they are typically invasive or inconvenient and not suitable for wearable real-time stress monitoring. Alternatively, cortisol in biofluids and VOCs emitted from the skin appear to be practical and useful markers for sensors to detect emotional stress events. This work has identified antistress hormones and cortisol metabolites as the primary stress biomarkers that can be used in future sensors for wearable affective systems.

Design and Realization of an Efficient Large-Area Event-Driven E-Skin

The sense of touch enables us to safely interact and control our contacts with our surroundings. Many technical systems and applications could profit from a similar type of sense. Yet, despite the emergence of e-skin systems covering more extensive areas, large-area realizations of e-skin effectively boosting applications are still rare. Recent advancements have improved the deployability and robustness of e-skin systems laying the basis for their scalability. However, the upscaling of e-skin systems introduces yet another challenge-the challenge of handling a large amount of heterogeneous tactile information with complex spatial relations between sensing points. We targeted this challenge and proposed an event-driven approach for large-area skin systems. While our previous works focused on the implementation and the experimental validation of the approach, this work now provides the consolidated foundations for realizing, designing, and understanding large-area event-driven e-skin systems for effective applications. This work homogenizes the different perspectives on event-driven systems and assesses the applicability of existing event-driven implementations in large-area skin systems. Additionally, we provide novel guidelines for tuning the novelty-threshold of event generators. Overall, this work develops a systematic approach towards realizing a flexible event-driven information handling system on standard computer systems for large-scale e-skin with detailed descriptions on the effective design of event generators and decoders. All designs and guidelines are validated by outlining their impacts on our implementations, and by consolidating various experimental results. The resulting system design for e-skin systems is scalable, efficient, flexible, and capable of handling large amounts of information without customized hardware. The system provides the feasibility of complex large-area tactile applications, for instance in robotics.

Can commercially available wearable EEG devices be used for diagnostic purposes? An explorative pilot study

Electroencephalography (EEG) is a core element in the diagnosis of epilepsy syndromes and can help to monitor antiseizure treatment. Mobile EEG (mEEG) devices are increasingly available on the consumer market and may offer easier access to EEG recordings especially in rural or resource-poor areas. The usefulness of consumer-grade devices for clinical purposes is still underinvestigated. Here, we compared EEG traces of a commercially available mEEG device (Emotiv EPOC) to a simultaneously recorded clinical video EEG (vEEG). Twenty-two adult patients (11 female, mean age 40.2 years) undergoing noninvasive vEEG monitoring for clinical purposes were prospectively enrolled. The EEG recordings were evaluated by 10 independent raters with unmodifiable view settings. The individual evaluations were compared with respect to the presence of abnormal EEG findings (regional slowing, epileptiform potentials, seizure pattern). Video EEG yielded a sensitivity of 56% and specificity of 88% for abnormal EEG findings, whereas mEEG reached 39% and 85%, respectively. Interrater reliability coefficients were better in vEEG as compared to mEEG (ϰ = 0.50 vs. 0.30), corresponding to a moderate and fair agreement. Intrarater reliability between mEEG and vEEG evaluations of simultaneous recordings of a given participant was moderate (ϰ = 0.48). Given the limitations of our exploratory pilot study, our results suggest that vEEG is superior to mEEG, but that mEEG can be helpful for diagnostic purposes. We present the first quantitative comparison of simultaneously acquired clinical and mobile consumer-grade EEG for a clinical use-case.

Quality Assurance of Health Wearables Data: Participatory Workshop on Barriers, Solutions, and Expectations

BACKGROUND

The ubiquity of health wearables and the consequent production of patient-generated health data (PGHD) are rapidly escalating. However, the utilization of PGHD in routine clinical practices is still low because of data quality issues. There is no agreed approach to PGHD quality assurance; therefore, realizing the promise of PGHD requires in-depth discussion among diverse stakeholders to identify the data quality assurance challenges they face and understand their needs for PGHD quality assurance.

OBJECTIVE

This paper reports findings from a workshop aimed to explore stakeholders' data quality challenges, identify their needs and expectations, and offer practical solutions.

METHODS

A qualitative multi-stakeholder workshop was conducted as a half-day event on the campus of an Australian University located in a major health care precinct, namely the Melbourne Parkville Precinct. The 18 participants had experience of PGHD use in clinical care, including people who identified as health care consumers, clinical care providers, wearables suppliers, and health information specialists. Data collection was done by facilitators capturing written notes of the proceedings as attendees engaged in participatory design activities in written and oral formats, using a range of whole-group and small-group interactive methods. The collected data were analyzed thematically, using deductive and inductive coding.

RESULTS

The participants' discussions revealed a range of technical, behavioral, operational, and organizational challenges surrounding PGHD, from the time when data are collected by patients to the time data are used by health care providers for clinical decision making. PGHD stakeholders found consensus on training and engagement needs, continuous collaboration among stakeholders, and development of technical and policy standards to assure PGHD quality.

CONCLUSIONS

Assuring PGHD quality is a complex process that requires the contribution of all PGHD stakeholders. The variety and depth of inputs in our workshop highlighted the importance of co-designing guidance for PGHD quality guidance.

Micro-fabricated electrochemical chloride ion sensors: From the present to the future

The real-time detection and monitoring of chloride ion concentrations play important roles in broad industrial applications, including wearable health care device, environmental pollutant control and infrastructure corrosion monitoring. The development of all-solid-state micro-fabricated electrochemical sensors has enabled the miniaturisation of these testing devices. This study reviewed the micro-fabricated electrochemical chloride sensors developed since 1970s, together with a brief summary regarding the progression of miniaturised electrochemical sensors in the past half century. Three major types of electrochemical chloride sensors with specific ion-selectivity have been discussed, the potentiometric sensors (including both ion-selective electrodes and chemical FETs), the chronopotentiometric sensors and the voltammetric sensors. In addition, colorimetric sensors, an emerging low-cost, portable, fast diagnose sensor technique has been included in this review. Four critical sensor performances have been reviewed and compared systematically, the sensibility (chloride concentration range), selectivity, lifetime and applicable pH ranges. The future perspectives for engineering applications proposed in this review will benefit the further development of integrated multi-functional sensors, as well as new instrumental testing methods.

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar's test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.

Implementing a Wearable Sensor for Lymphedema Garments: A Prospective Study of Training Effectiveness

Lymphedema garments apply therapeutic pressure to maintain minimum leg volume. Practitioners and patients apply these garments and seek to achieve appropriate compression pressure "by feel."

PURPOSE

A study was conducted to assess the feasibility of applying a sensor-feedback device to train staff to accurately apply garments.

METHODS

A convenience sample of wound care and rehabilitation staff volunteered for a prospective, randomized, unblinded, single-center pilot study. Participants were randomized to instruction+feedback (ie, receiving training on compression application and using the device to determine whether they achieved desired pressure) or instruction only groups (n = 6 each). Each volunteer applied hook-and-loop closures on the author's leg pre- and post-training with a target of 35 mm Hg, or |Ppre- 35|= |Ppost- 35|=0. (|P| is absolute value of P). The feedback group used a device to measure the applied compression; the device consists of a capacitive sensor of thin polyurethane foam between conductive fabric layers and a microcomputer/Bluetooth transmitter under a vacuum seal that fits into a fabric pocket of a lymphedema garment at the posterior ankle and pairs with a mobile device. A lymphology-certified therapist coordinated training. Data were collected with a pen/paper tool and analyzed with Student's t test.

RESULTS

The instruction+feedback group was closer to target after training (|Ppre - 35|= 10 ± 12 mm Hg; |Ppost - 35|=5 ± 4 mm Hg; P <.05; paired t test) than the instruction only group (|Ppre- 35|=19 ± 11 mm Hg; |Ppost - 35|=12 ±12 mm Hg; not significant).

CONCLUSION

This wearable mobile pressure sensor device assists practitioners in applying hook-and-loop lymphedema garments closer to target pressure. Larger studies with clinicians and research that involves patient application of compression are warranted.

Subclinical and Device-Detected Atrial Fibrillation: Pondering the Knowledge Gap: A Scientific Statement From the American Heart Association

The widespread use of cardiac implantable electronic devices and wearable monitors has led to the detection of subclinical atrial fibrillation in a substantial proportion of patients. There is evidence that these asymptomatic arrhythmias are associated with increased risk of stroke. Thus, detection of subclinical atrial fibrillation may offer an opportunity to reduce stroke risk by initiating anticoagulation. However, it is unknown whether long-term anticoagulation is warranted and in what populations. This scientific statement explores the existing data on the prevalence, clinical significance, and management of subclinical atrial fibrillation and identifies current gaps in knowledge and areas of controversy and consensus.

Validity and Reliability of the Wristband Activity Monitor in Free-living Children Aged 10-17 Years

OBJECTIVE

In this study we aimed to examine the reliability and validity of the wristband activity monitor against the accelerometer for children..

METHODS

A total of 99 children (mean age = 13.0 ± 2.5 y) wore the two monitors in a free-living context for 7 days. Reliability was measured by intraclass correlation to evaluate consistency over time. Repeated-measures analyses of variance was used to detect differences across days. Spearman's correlation coefficient (rho), median of absolute percentage error, and Bland-Altman analyses were performed to assess the validity of the wristband against the ActiGraph accelerometer. The optimal number of repeated measures for the wristband was calculated by using the Spearman-Brown prophecy formula.

RESULTS

The wristband had high reliability for all variables, although physical activity data were different across 7 days. A strong correlation for steps (rho: 0.72, P < 0.001), and moderate correlations for time spent on total physical activity (rho: 0.63, P < 0.001) and physical activity energy expenditure (rho: 0.57, P < 0.001) were observed between the wristband and the accelerometer. For different intensities of physical activity, weak to moderate correlations were found (rho: 0.38 to 0.55, P < 0.001).

CONCLUSION

The wristband activity monitor seems to be reliable and valid for measurement of overall children's physical activity, providing a feasible objective method of physical activity surveillance in children.

Home rehabilitation supported by a wearable soft-robotic device for improving hand function in older adults: A pilot randomized controlled trial

Background

New developments, based on the concept of wearable soft-robotic devices, make it possible to support impaired hand function during the performance of daily activities and intensive task-specific training. The wearable soft-robotic ironHand glove is such a system that supports grip strength during the performance of daily activities and hand training exercises at home.

Design

This pilot randomized controlled clinical study explored the effect of prolonged use of the assistive ironHand glove during daily activities at home, in comparison to its use as a trainings tool at home, on functional performance of the hand.

Methods

In total, 91 older adults with self-perceived decline of hand function participated in this study. They were randomly assigned to a 4-weeks intervention of either assistive or therapeutic ironHand use, or control group (received no additional exercise or treatment). All participants performed a maximal pinch grip test, Box and Blocks test (BBT), Jebsen-Taylor Hand Function Test (JTHFT) at baseline and after 4-weeks of intervention. Only participants of the assistive and therapeutic group completed the System Usability Scale (SUS) after the intervention period.

Results

Participants of the assistive and therapeutic group reported high scores on the SUS (mean = 73, SEM = 2). The therapeutic group showed improvements in unsupported handgrip strength (mean Δ = 3) and pinch strength (mean Δ = 0.5) after 4 weeks of ironHand use (p≤0.039). Scores on the BBT and JTHFT improved not only after 4 weeks of ironHand use (assistive and therapeutic), but also in the control group. Only handgrip strength improved more in the therapeutic group compared to the assistive and control group. No significant correlations were found between changes in performance and assistive or therapeutic ironHand use (p≥0.062).

Conclusion

This study showed that support of the wearable soft-robotic ironHand system either as assistive device or as training tool may be a promising way to counter functional hand function decline associated with ageing.

Wearable Sleep Technology in Clinical and Research Settings

: The accurate assessment of sleep is critical to better understand and evaluate its role in health and disease. The boom in wearable technology is part of the digital health revolution and is producing many novel, highly sophisticated and relatively inexpensive consumer devices collecting data from multiple sensors and claiming to extract information about users' behaviors, including sleep. These devices are now able to capture different biosignals for determining, for example, HR and its variability, skin conductance, and temperature, in addition to activity. They perform 24/7, generating overwhelmingly large data sets (big data), with the potential of offering an unprecedented window on users' health. Unfortunately, little guidance exists within and outside the scientific sleep community for their use, leading to confusion and controversy about their validity and application. The current state-of-the-art review aims to highlight use, validation and utility of consumer wearable sleep-trackers in clinical practice and research. Guidelines for a standardized assessment of device performance is deemed necessary, and several critical factors (proprietary algorithms, device malfunction, firmware updates) need to be considered before using these devices in clinical and sleep research protocols. Ultimately, wearable sleep technology holds promise for advancing understanding of sleep health; however, a careful path forward needs to be navigated, understanding the benefits and pitfalls of this technology as applied in sleep research and clinical sleep medicine.

Wearable Finger Pulse Oximetry for Continuous Oxygen Saturation Measurements During Daily Home Routines of Patients With Chronic Obstructive Pulmonary Disease (COPD) Over One Week: Observational Study

BACKGROUND

Chronic obstructive pulmonary disease (COPD) patients can suffer from low blood oxygen concentrations. Peripheral blood oxygen saturation (SpO2), as assessed by pulse oximetry, is commonly measured during the day using a spot check, or continuously during one or two nights to estimate nocturnal desaturation. Sampling at this frequency may overlook natural fluctuations in SpO2.

OBJECTIVE

This study used wearable finger pulse oximeters to continuously measure SpO2 during daily home routines of COPD patients and assess natural SpO2 fluctuations.

METHODS

A total of 20 COPD patients wore a WristOx2 pulse oximeter for 1 week to collect continuous SpO2 measurements. A SenseWear Armband simultaneously collected actigraphy measurements to provide contextual information. SpO2 time series were preprocessed and data quality was assessed afterward. Mean SpO2, SpO2 SD, and cumulative time spent with SpO2 below 90% (CT90) were calculated for every (1) day, (2) day in rest, and (3) night to assess SpO2 fluctuations.

RESULTS

A high percentage of valid SpO2 data (daytime: 93.27%; nocturnal: 99.31%) could be obtained during a 7-day monitoring period, except during moderate-to-vigorous physical activity (MVPA) (67.86%). Mean nocturnal SpO2 (89.9%, SD 3.4) was lower than mean daytime SpO2 in rest (92.1%, SD 2.9; P<.001). On average, SpO2 in rest ranged over 10.8% (SD 4.4) within one day. Highly varying CT90 values between different nights led to 50% (10/20) of the included patients changing categories between desaturator and nondesaturator over the course of 1 week.

CONCLUSIONS

Continuous SpO2 measurements with wearable finger pulse oximeters identified significant SpO2 fluctuations between and within multiple days and nights of patients with COPD. Continuous SpO2 measurements during daily home routines of patients with COPD generally had high amounts of valid data, except for motion artifacts during MVPA. The identified fluctuations can have implications for telemonitoring applications that are based on daily SpO2 spot checks. CT90 values can vary greatly from night to night in patients with a nocturnal mean SpO2 around 90%, indicating that these patients cannot be consistently categorized as desaturators or nondesaturators. We recommend using wearable sensors for continuous SpO2 measurements over longer time periods to determine the clinical relevance of the identified SpO2 fluctuations.

Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors

BACKGROUND

It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown.

OBJECTIVE

This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy.

METHODS

A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night's sleep in participants' homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed-rank test was performed to investigate the effect of user-specific factors.

RESULTS

Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0% to 60%. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE>90% (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics.

CONCLUSIONS

Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE.

Wearables for Pediatric Rehabilitation: How to Optimally Design and Use Products to Meet the Needs of Users

This article will define "wearables" as objects that interface and move with users, spanning clothing through smart devices. A novel design approach merging information from across disciplines and considering users' broad needs will be presented as the optimal approach for designing wearables that maximize usage. Three categories of wearables applicable to rehabilitation and habilitation will be explored: (1) inclusive clothing (eg, altered fit, fasteners); (2) supportive wearables (eg, orthotics, exoskeletons); and (3) smart wearables (eg, with sensors for tracking activity or controlling external devices). For each category, we will provide examples of existing and emerging wearables and potential applications for assessment and intervention with a focus on pediatric populations. We will discuss how these wearables might change task requirements and assist users for immediate effects and how they might be used with intervention activities to change users' abilities across time. It is important for rehabilitation clinicians and researchers to be engaged with the design and use of wearables so they can advocate and create better wearables for their clients and determine how to most effectively use wearables to enhance their assessment, intervention, and research practices.

Validation of Polar OH1 optical heart rate sensor for moderate and high intensity physical activities

Background

Optical measurement techniques and recent advances in wearable technology have made heart rate (HR) sensing simpler and more affordable.

Objectives

The Polar OH1 is an arm worn optical heart rate monitor. The objectives of this study are two-fold; 1) to validate the OH1 optical HR sensor with the gold standard of HR measurement, electrocardiography (ECG), over a range of moderate to high intensity physical activities, 2) to validate wearing the OH1 at the temple as an alternative location to its recommended wearing location around the forearm and upper arm.

Methods

Twenty-four individuals participated in a physical exercise protocol, by walking on a treadmill and riding a stationary spin bike at different speeds while the criterion measure, ECG and Polar OH1 HR were recorded simultaneously at three different body locations; forearm, upper arm and the temple. Time synchronised HR data points were compared using Bland-Altman analyses and intraclass correlation.

Results

The intraclass correlation between the ECG and Polar OH1, for the aggregated data, was 0.99 and the estimated mean bias ranged 0.27–0.33 bpm for the sensor locations. The three sensors exhibited a 95% limit of agreement (LoA: forearm 5.22, -4.68 bpm; upper arm 5.15, -4.49; temple 5.22, -4.66). The mean of the ECG HR for the aggregated data was 112.15 ± 24.52 bpm. The intraclass correlation of HR values below and above this mean were 0.98 and 0.99 respectively. The reported mean bias ranged 0.38–0.47 bpm (95% LoA: forearm 6.14, -5.38 bpm; upper arm 6.07, -5.13 bpm; temple 6.09, -5.31 bpm), and 0.15–0.16 bpm (95% LoA: forearm 3.99, -3.69 bpm; upper arm 3.90, -3.58 bpm; temple 4.06, -3.76 bpm) respectively. During different exercise intensities, the intraclass correlation ranged 0.95–0.99 for the three sensor locations. During the entire protocol, the estimated mean bias was in the range -0.15–0.55 bpm, 0.01–0.53 bpm and -0.37–0.48 bpm, for the forearm, upper arm and temple locations respectively. The corresponding upper limits of 95% LoA were 3.22–7.03 bpm, 3.25–6.82 bpm and 3.18–7.04 bpm while the lower limits of 95% LoA were -6.36–(-2.35) bpm, -6.46–(-2.30) bpm and -7.42–(-2.41) bpm.

Conclusion

Polar OH1 demonstrates high level of agreement with the criterion measure ECG HR, thus can be used as a valid measure of HR in lab and field settings during moderate and high intensity physical activities.

[Requirements for CE-marking of apps and wearables]

Depending on the intended use, apps and wearables can be medical devices. In such cases, the manufacturer has to provide evidence that the requirements stated in directive 93/42/EWG are fulfilled. Depending on the classification of the medical device, several so-called conformity assessment procedures are possible. Once the conformity assessment procedure has been finished successfully, the manufacturer attaches the CE-marking to the product. This assures that all requirements of the directive have been fulfilled and the manufacturer is therefore authorized to put the product onto the market in all member states of the European union. In this article, the possible and practical conformity assessment procedures for apps and wearables are described and their implementation is outlined.For medical devices with sufficiently high-risk classification, the manufacturer has to involve a Notified Body. For the conformity assessment procedure according to annex II, the manufacturer implements a full quality management system and compiles technical documentation. These are supervised and evaluated by Notified Body audits. Especially for startups, it is important for the development of apps and wearables to implement a quality management system early and to fulfill the regulatory requirements, for example, related to the software life-cycle model. This also includes considering accompanying processes during development like risk management, usability engineering, and clinical evaluation.Additionally, it should be pointed out, that according to the new medical device regulation almost all apps will fall at least into class IIa. Thus, the involvement of a Notified Body in the related conformity assessment procedures would be required. Apps that have already been put onto the market as class I devices, and are now upgraded to a higher class, need the approval of a notified body starting from 26 May 2020.

Assessment of Accuracy of Overall Energy Expenditure Measurements for the Fitbit Charge HR 2 and Apple Watch

Objectives: In this study, we sought to determine the accuracy of energy expenditure (EE) esti- mation for the Fitbit Charge HR 2 (Fitbit) and the Apple Watch. Design: An observational study. Methods: Thirty young adults (15 men and 15 women, aged 23.5 ± 2.96 years) completed the Bruce treadmill protocol. We measured gross EE by a PARVO metabolic cart (MetCart) and concurrently estimated by the Fitbit and Apple Watch. We calculated concordance correlation coefficients (CCC, r_c) and relative error rates to indicate the difference between each device and the MetCart system. Results: For the Apple Watch and Fitbit, the relative error rate was 24.3%, 20.1% for the pooled sample, 18.6%, 24.2% for men, and 29.9%, 16.7% for women, respectively. The Apple Watch overestimated EE for women and underestimated EE for men; the Fitbit underestimated EE for both. Moderate CCCs between estimated EEs and MetCart measured EEs were found for both Apple Watch (r_c =0.65, 0.43, and 0.39 overall, men and women, respectively) and Fitbit (r_c =0.53, 0.39, and 0.21 overall, men and women, respectively). Conclusion: Neither device showed accurate results compared with EE measured by a MetCart. Users should consider these results when designing programs or personal training plans where physical activity EE is a key outcome assessed with a wearable device.

Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system

Work metabolism (WM) can be accurately estimated by oxygen consumption (VO₂), which is commonly assessed by heart rate (HR) in field studies. However, the VO₂-HR relationship is influenced by individual capacity and activity characteristics. The purpose of this study was to evaluate three models for estimating WM compared with indirect calorimetry, during simulated work activities. The techniques were: the HR-Flex model; HR branched model, combining HR with hip-worn accelerometers (ACC); and HR + arm-leg ACC model, combining HR with wrist- and thigh-worn ACC. Twelve participants performed five simulated work activities and three submaximal tests. The HR + arm-leg ACC model had the overall best performance with limits of agreement (LoA) of -3.94 and 2.00 mL/min/kg, while the HR-Flex model had -5.01 and 5.36 mL/min/kg and the branched model, -6.71 and 1.52 mL/min/kg. In conclusion, the HR + arm-leg ACC model should, when feasible, be preferred in wearable systems for WM estimation. Practitioner Summary: Work with high energy demand can impair employees' health and life quality. Three models were evaluated for estimating work metabolism during simulated tasks. The model combining heart rate, wrist- and thigh-worn accelerometers showed the best accuracy. This is, when feasible, suggested for wearable systems to assess work metabolism.

Effects of Mobile Health Including Wearable Activity Trackers to Increase Physical Activity Outcomes Among Healthy Children and Adolescents: Systematic Review

BACKGROUND

Children and adolescents do not meet the current recommendations on physical activity (PA), and as such, the health-related benefits of regular PA are not achieved. Nowadays, technology-based programs represent an appealing and promising option for children and adolescents to promote PA.

OBJECTIVE

The aim of this review was to systematically evaluate the effects of mobile health (mHealth) and wearable activity trackers on PA-related outcomes in this target group.

METHODS

Electronic databases such as the Cochrane Central Register of Controlled Trials, PubMed, Scopus, SPORTDiscus, and Web of Science were searched to retrieve English language articles published in peer-reviewed journals from January 2012 to June 2018. Those included were articles that contained descriptions of interventions designed to increase PA among children (aged 6 to 12 years) only, or adolescents (aged 13 to 18 years) only, or articles that include both populations, and also, articles that measured at least 1 PA-related cognitive, psychosocial, or behavioral outcome. The interventions had to be based on mHealth tools (mobile phones, smartphones, tablets, or mobile apps) or wearable activity trackers. Randomized controlled trials (RCTs) and non-RCTs, cohort studies, before-and-after studies, and cross-sectional studies were considered, but only controlled studies with a PA comparison between groups were assessed for methodological quality.

RESULTS

In total, 857 articles were identified. Finally, 7 studies (5 with tools of mHealth and 2 with wearable activity trackers) met the inclusion criteria. All studies with tools of mHealth used an RCT design, and 3 were of high methodological quality. Intervention delivery ranged from 4 weeks to 12 months, whereby mainly smartphone apps were used as a tool. Intervention delivery in studies with wearable activity trackers covered a period from 22 sessions during school recess and 8 weeks. Trackers were used as an intervention and evaluation tool. No evidence was found for the effect of mHealth tools, respectively wearable activity trackers, on PA-related outcomes.

CONCLUSIONS

Given the small number of studies, poor compliance with accelerometers as a measuring instrument for PA, risk of bias, missing RCTs in relation to wearable activity trackers, and the heterogeneity of intervention programs, caution is warranted regarding the comparability of the studies and their effects. There is a clear need for future studies to develop PA interventions grounded on intervention mapping with a high methodological study design for specific target groups to achieve meaningful evidence.

Performance of a Factory-Calibrated, Real-Time Continuous Glucose Monitoring System in Pediatric Participants With Type 1 Diabetes

BACKGROUND

The perceived value and consistent use of continuous glucose monitoring (CGM) systems depends in part on their accuracy. We assessed the performance of a sixth-generation CGM system (Dexcom G6) in children and adolescents.

METHODS

Forty-nine participants (6-17 years of age, mean ± SD of 13.5 ± 3.3 years), all with type 1 diabetes, enrolled and data were available from 37. Each participant wore 1 sensor for up to 10 days and was asked to undergo an in-clinic visit lasting 6-12 hours for frequent blood glucose (BG) sample testing on one of the sensor wear days. Estimated glucose values (EGVs) from the G6 system were compared with venous BG values measured with a laboratory reference instrument (YSI).

RESULTS

The overall mean absolute relative difference (MARD) for 1387 EGV-YSI pairs was 7.7%, and the overall percentage of EGVs within 20% or 20 mg/dL of the YSI reference value (for YSI > or ⩽100 mg/dL, respectively, the "%20/20") was 96.2%. The %20/20 was 92.1% on Day 1 and 91.0% on Day 10 of sensor wear. For EGVs <70 mg/dL, 92.6% of the YSI values were within 20 mg/dL and for EGVs >250 mg/dL, 100% of the YSI values were within 20%. Differences between EGVs and YSI values in over 99.9% of the pairs posed no or only slight clinical risk as evaluated by surveillance error grid analysis.

CONCLUSIONS

The accuracy of the G6 CGM system in pediatrics may encourage consistent use of the system and contribute to improved glycemic outcomes in this population.

Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses

Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wearable health devices is their energy efficiency and battery-lifetime, which motivates the recent efforts towards the development of self-powered wearable devices. This article proposes a method for context aware dynamic sensor selection for power optimized physiological prediction using multi-modal wearable data streams. We first cluster the data by physical activity using the accelerometer data, and then fit a group lasso model to each activity cluster. We find the optimal reduced set of groups of sensor features, in turn reducing power usage by duty cycling these and optimizing prediction accuracy. We show that using activity state-based contextual information increases accuracy while decreasing power usage. We also show that the reduced feature set can be used in other regression models increasing accuracy and decreasing energy burden. We demonstrate the potential reduction in power usage using a custom-designed multi-modal wearable system prototype.

Toward all-day wearable health monitoring: An ultralow-power, reflective organic pulse oximetry sensing patch

Pulse oximetry sensors have been playing a key role as devices to monitor elemental yet critical human health states. Conventional pulse oximetry sensors, however, have relatively large power consumption, impeding their use as stand-alone, continuous monitoring systems that can easily be integrated with everyday life. Here, we exploit the design freedom offered by organic technologies to realize a reflective patch-type pulse oximetry sensor with ultralow power consumption. On the basis of flexible organic light-emitting diodes and organic photodiodes designed via an optical simulation of color-sensitive light propagation within human skin, the proposed monolithically integrated organic pulse oximetry sensor heads exhibit successful operation at electrical power as low as 24 μW on average. We thereby demonstrate that organic devices not only have form factor advantages for such applications but also hold great promise as enablers for all-day wearable health monitoring systems.

Validity of contact skin temperature sensors under different environmental conditions with and without fabric coverage: characterisation and correction

Contact skin temperature (T_sk) sensors are calibrated under uniform thermal conditions but used in the presence of a skin-to-environment temperature gradient. We aimed to characterise the validity of contact T_sk sensors when measuring surface temperature under a range of environmental and fabric coverage conditions, to estimate practical temperature limits for a given measurement bias and to explore correcting for bias. Using two types of contact T_sk sensors (thermistors, n = 5; iButtons, n = 5), we performed experiments in three phases: (1) conventional calibration (uniform thermal environment) over 15-40 °C in 5 °C steps (at t = 0, and 24 h, 12 weeks later), (2) surface temperature measurements of a purpose-made aluminium plate (also 15-40 °C) at different environmental temperatures (15, 25, 35 °C) with different sensor attachments and fabric coverings to assess measurement bias and calculate correction factors that account for the next-to-surface microclimate temperature and (3) surface measurements (33.1 °C in 20 °C environment) for assessing generated corrections. The main results were as follows: (1) after initial calibration, T_sk sensors were valid under uniform thermal conditions [mean bias < 0.05 °C, typical error of the estimate < 0.1 °C]. (2) For the surface measurements, bias increased with increasing surface-to-microclimate temperature difference for both sensor types. The range of surface temperatures possible to remain within given bias limits could be estimated for the various conditions. (3) For a given measurement, using corrections encompassing the microclimate temperature (mean difference - 0.1 to 0.5 °C) performed better than conventional calibration alone (mean difference - 2.1 to - 0.3 °C). In conclusion, the bias of T_sk sensors is influenced by the microclimate temperature and, therefore, body coverings. Where excessive bias is expected, the validity can be improved through sensor and attachment selection and by applying corrections that account for the local temperature gradient.

Wearable and Implantable Epidermal Paper-Based Electronics

Traditional manufacturing methods and materials used to fabricate epidermal electronics for physiological monitoring, transdermal stimulation, and therapeutics are complex and expensive, preventing their adoption as single-use medical devices. This work describes the fabrication of epidermal, paper-based electronic devices (EPEDs) for wearable and implantable applications by combining the spray-based deposition of silanizing agents, highly conductive nanoparticles, and encapsulating polymers with laser micromachining. EPEDs are inexpensive, stretchable, easy to apply, and disposable by burning. The omniphobic character and fibrous structure of EPEDs make them breathable, mechanically stable upon stretching, and facilitate their use as electrophysiological sensors to record electrocardiograms, electromyograms, and electrooculograms, even under water. EPEDs can also be used to provide thermotherapeutic treatments to joints, map temperature spatially, and as wirelessly powered implantable devices for stimulation and therapeutics. This work makes epidermal electronic devices accessible to high-throughput manufacturing technologies and will enable the fabrication of a variety of wearable medical devices at a low cost.

Feasibility and Effectiveness of a Wearable Technology-Based Physical Activity Intervention in Preschoolers: A Pilot Study

The purpose of this pilot study was to evaluate the feasibility and the effectiveness of an intervention that employed a technology-based physical activity (PA) monitoring system and teacher-regulated strategies to promote PA in preschoolers. A total of 93 preschoolers (53% girls, 4.7 years) from 5 child care centers were recruited for a one-week intervention and randomly assigned into control (2 centers, n = 45) or intervention (3 centers, n = 48) group. Key intervention components included: (1) wearable device-based, real-time monitoring of children’s PA by classroom teachers and (2) teacher-regulated strategies for providing more opportunities for PA. Sedentary behavior (SED) and PA were measured using accelerometers. Overall, children in the intervention group showed significantly lower level of SED (31.6 vs. 33.6 min/h) and higher level of total PA (28.4 vs. 26.4 min/h) than children in the control group, after adjusting for age, sex, race, parent education level, parent perception of their child’s PA, BMI, and childcare centers. Teachers in the intervention group reported that the intervention was highly feasible to be implemented in their current classroom settings. In conclusion, we observed high acceptability and initial effectiveness of the current intervention. Subsequent research at larger-scale is warranted to fully evaluate the effectiveness of the intervention strategies tested in this study.

Validation of the Apple Watch for Heart Rate Variability Measurements during Relax and Mental Stress in Healthy Subjects

Heart rate variability (HRV) analysis is a noninvasive tool widely used to assess autonomic nervous system state. The market for wearable devices that measure the heart rate has grown exponentially, as well as their potential use for healthcare and wellbeing applications. Still, there is a lack of validation of these devices. In particular, this work aims to validate the Apple Watch in terms of HRV derived from the RR interval series provided by the device, both in temporal (HRM (mean heart rate), SDNN, RMSSD and pNN50) and frequency (low and high frequency powers, LF and HF) domain. For this purpose, a database of 20 healthy volunteers subjected to relax and a mild cognitive stress was used. First, RR interval series provided by Apple Watch were validated using as reference the RR interval series provided by a Polar H7 using Bland-Altman plots and reliability and agreement coefficients. Then, HRV parameters derived from both RR interval series were compared and their ability to identify autonomic nervous system (ANS) response to mild cognitive stress was studied. Apple Watch measurements presented very good reliability and agreement (>0.9). RR interval series provided by Apple Watch contain gaps due to missing RR interval values (on average, 5 gaps per recording, lasting 6.5 s per gap). Temporal HRV indices were not significantly affected by the gaps. However, they produced a significant decrease in the LF and HF power. Despite these differences, HRV indices derived from the Apple Watch RR interval series were able to reflect changes induced by a mild mental stress, showing a significant decrease of HF power as well as RMSSD in stress with respect to relax, suggesting the potential use of HRV measurements derived from Apple Watch for stress monitoring.

Flexible Piezoelectric Sensor-Based Gait Recognition

Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %.

Estimation of Foot Plantar Center of Pressure Trajectories with Low-Cost Instrumented Insoles Using an Individual-Specific Nonlinear Model

Postural control is a complex skill based on the interaction of dynamic sensorimotor processes, and can be challenging for people with deficits in sensory functions. The foot plantar center of pressure (COP) has often been used for quantitative assessment of postural control. Previously, the foot plantar COP was mainly measured by force plates or complicated and expensive insole-based measurement systems. Although some low-cost instrumented insoles have been developed, their ability to accurately estimate the foot plantar COP trajectory was not robust. In this study, a novel individual-specific nonlinear model was proposed to estimate the foot plantar COP trajectories with an instrumented insole based on low-cost force sensitive resistors (FSRs). The model coefficients were determined by a least square error approximation algorithm. Model validation was carried out by comparing the estimated COP data with the reference data in a variety of postural control assessment tasks. We also compared our data with the COP trajectories estimated by the previously well accepted weighted mean approach. Comparing with the reference measurements, the average root mean square errors of the COP trajectories of both feet were 2.23 mm (±0.64) (left foot) and 2.72 mm (±0.83) (right foot) along the medial–lateral direction, and 9.17 mm (±1.98) (left foot) and 11.19 mm (±2.98) (right foot) along the anterior–posterior direction. The results are superior to those reported in previous relevant studies, and demonstrate that our proposed approach can be used for accurate foot plantar COP trajectory estimation. This study could provide an inexpensive solution to fall risk assessment in home settings or community healthcare center for the elderly. It has the potential to help prevent future falls in the elderly.

Location verification algorithm of wearable sensors for wireless body area networks

BACKGROUND

Knowledge of the location of sensor devices is crucial for many medical applications of wireless body area networks, as wearable sensors are designed to monitor vital signs of a patient while the wearer still has the freedom of movement. However, clinicians or patients can misplace the wearable sensors, thereby causing a mismatch between their physical locations and their correct target positions. An error of more than a few centimeters raises the risk of mistreating patients.

OBJECTIVE

The present study aims to develop a scheme to calculate and detect the position of wearable sensors without beacon nodes.

METHODS

A new scheme was proposed to verify the location of wearable sensors mounted on the patient's body by inferring differences in atmospheric air pressure and received signal strength indication measurements from wearable sensors. Extensive two-sample t tests were performed to validate the proposed scheme.

RESULTS

The proposed scheme could easily recognize a 30-cm horizontal body range and a 65-cm vertical body range to correctly perform sensor localization and limb identification.

CONCLUSIONS

All experiments indicate that the scheme is suitable for identifying wearable sensor positions in an indoor environment.