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.