Laboratory Validation of Hexoskin Biometric Shirt at Rest, Submaximal Exercise, and Maximal Exercise While Riding a Stationary Bicycle

Pulmonary function in patients with adolescent idiopathic scoliosis: an explorative study of a wearable smart shirt as a measurement instrument

PURPOSE

Adolescent idiopathic scoliosis (AIS) presents various challenges, including respiratory symptoms that impact pulmonary function. This study aims to explore the feasibility of using a smart shirt for continuous monitoring of lung volumes and heart rate during routine activities in AIS patients.

METHODS

A single-center exploratory feasibility study was conducted with AIS patients aged 16-22 years with a thoracic curvature of ≥ 30 degrees and absence of respiratory comorbidities. A smart shirt was utilized to continuously monitor cardiopulmonary parameters during mild exercise, which included a standardized walking route with the ascent of multiple stairs.

RESULTS

Five participants completed the study. Baseline spirometry measurements showed a range of values for forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and FEV1/FVC ratio. During mild exercise, participants exhibited variability in tidal volume, heart rate, breathing rate, and minute ventilation, with increases observed during stair climbing. Breathlessness levels also varied throughout the activity but did not correlate with the measured lung volumes. Overall, the use of the smart shirt for assessing pulmonary function in AIS patients was deemed feasible and well tolerated by participants during the test activities.

CONCLUSION

The study confirms the feasibility of using a smart shirt for continuous measurement of cardiopulmonary parameters in AIS patients during daily activities. Incongruities between spirometry results and perceived dyspnea exists, which questions the nature of the perceived dyspnea. Further research is needed to validate these findings and explore the impact of AIS characteristics on measurement accuracy.

Increased arthroplasty surgeon energy consumption when performing primary total hip arthroplasty compared to total knee arthroplasty

Introduction

Previously published studies have hypothesized that total hip arthroplasty (THA) requires the surgeon to expend more energy that total knee arthroplasty (TKA). However, techniques for performing these procedures have evolved. Therefore, we sought to compare if primary THA had increased energy expenditure compared to primary TKA.

Methods

We prospectively recorded the heart rate, respiratory rate, minute ventilation, cadence, and energy expenditure of a single fellowship-trained arthroplasty surgeon while performing primary THA and TKA on 372 patients. Patient demographics and operative records were reviewed to evaluate differences in the physical demands of each surgical case. Age (64.3 versus 65.9 years, p = 0.1) and gender (54.8% versus 51.0% female, p = 0.5) were similar between THA and TKA, but TKAs had a higher body mass index (31.1 versus 28.7 kg/m2, p < 0.001). Chi-square and independent-samples t-tests were used to compare cohorts. Significance was set at p < 0.05.

Results

THA tended to have 1.1 times longer operative time than TKA (102.2 versus 88.9 min, p < 0.001). THA had a statistically higher heart rate compared to TKA, although this is unlikely to be clinically significant (82.5 versus 80.7 beats/minute, p < 0.001). Respiratory Rate was 1.1 times higher (15.9 versus 14.9 respirations/minute, p < 0.001) and minute ventilation was 1.2 times higher (19.6 versus 16.9 L/min, p < 0.001) when performing THA. Cadence was 1.5 times higher when performing TKA (4.2 versus 2.8 steps/minute, p < 0.001). THA had a 1.2 times higher energy expenditure/patient (378.8 versus 312.0 Calories/patient, p < 0.001) and a 1.1 times higher energy expenditure/minute (3.7 versus 3.5 Calories/minute, p = 0.01) compared to TKA.

Discussion

THA is associated with longer operative time and increased energy expenditure per compared to TKA. Despite THA and TKA procedures becoming more efficient, arthroplasty surgery continues to have heavy physical burden on the surgeon. Further research is needed to understand ways to decrease surgeon energy expenditure and promote career longevity.

Putting back respiration into respiratory sinus arrhythmia or high-frequency heart rate variability: Implications for interpretation, respiratory rhythmicity, and health

Research on respiratory sinus arrhythmia, or high-frequency heart rate variability (its frequency-domain equivalent), has been popular in psychology and the behavioral sciences for some time. It is typically interpreted as an indicator of cardiac vagal activity. However, as research has shown for decades, the respiratory pattern can influence the amplitude of these noninvasive measures substantially, without necessarily reflecting changes in tonic cardiac vagal activity. Although changes in respiration are systematically associated with experiential and behavioral states, this potential confound in the interpretation of RSA, or HF-HRV, is rarely considered. Interpretations of within-individual changes in these parameters are therefore only conclusive if undertaken relative to the breathing pattern. The interpretation of absolute levels of these parameters between individuals is additionally burdened with the problem of residual inspiratory cardiac vagal activity in humans. Furthermore, multiple demographic, anthropometric, life-style, health, and medication variables can act as relevant third variables that might explain associations of RSA or HF-HRV with experiential and behavioral variables. Because vagal activity measured by these parameters only represents the portion of cardiac vagal outflow that is modulated by the respiratory rhythm, alternative interpretations beyond cardiac vagal activity should be considered. Accumulating research shows that activity of multiple populations of neurons in the brain and the periphery, and with that organ activity and function, are modulated rhythmically by respiratory activity. Thus, observable health benefits ascribed to the cardiac vagal system through RSA or HF-HRV may actually reflect beneficial effects of respiratory modulation. Respiratory rhythmicity may ultimately provide the mechanism that integrates central, autonomic, and visceral activities.

Energy expenditure of femoral broaching in direct anterior total hip replacements-Comparison between manual and automated techniques

INTRODUCTION

Little information is known regarding the energy expenditure of the surgeon during total hip arthroplasty (THA). We sought to compare the energy expenditure associated with femoral broaching using two techniques: manual and automated.

METHODS

We recorded energy expenditure, minute ventilation, heart rate, and total broaching time of a single surgeon while broaching the femoral canal during direct anterior THA using two different techniques: Manual broaching (n = 26) and automated broaching (n = 20).

RESULTS

Manual broaching required a longer time than automated broaching (6.1 ± 1.1 vs. 3.7 ± 0.9 min; p < 0.001) with an increase in energy expenditure (32.6 ± 7.0 vs. 16.0 ± 7.1 Calories; p < 0.001). Heart rate was higher with manual broaching (99.4 ± 9.8 vs. 90.1 ± 9.8 beats per min; p = 0.003), along with minute ventilation (36.5 ± 7.0 vs. 30.3 ± 5.8 L/min; p = 0.003). There were no intraoperative complications.

CONCLUSIONS

Automated femoral broaching during THA can decrease the energy expenditure of broaching by 50% and time of broaching by 40%, when compared to manual technique.

CLINICAL TRIAL REGISTRATION

This research was not a clinical trial.

PreEpiSeizures: description and outcomes of physiological data acquisition using wearable devices during video-EEG monitoring in people with epilepsy

The PreEpiSeizures project was created to better understand epilepsy and seizures through wearable technologies. The motivation was to capture physiological information related to epileptic seizures, besides Electroencephalography (EEG) during video-EEG monitorings. If other physiological signals have reliable information of epileptic seizures, unobtrusive wearable technology could be used to monitor epilepsy in daily life. The development of wearable solutions for epilepsy is limited by the nonexistence of datasets which could validate these solutions. Three different form factors were developed and deployed, and the signal quality was assessed for all acquired biosignals. The wearable data acquisition was performed during the video-EEG of patients with epilepsy. The results achieved so far include 59 patients from 2 hospitals totaling 2,721 h of wearable data and 348 seizures. Besides the wearable data, the Electrocardiogram of the hospital is also useable, totalling 5,838 h of hospital data. The quality ECG signals collected with the proposed wearable is equated with the hospital system, and all other biosignals also achieved state-of-the-art quality. During the data acquisition, 18 challenges were identified, and are presented alongside their possible solutions. Though this is an ongoing work, there were many lessons learned which could help to predict possible problems in wearable data collections and also contribute to the epilepsy community with new physiological information. This work contributes with original wearable data and results relevant to epilepsy research, and discusses relevant challenges that impact wearable health monitoring.

The WE SENSE study protocol: A controlled, longitudinal clinical trial on the use of wearable sensors for early detection and tracking of viral respiratory tract infections

Background

Viral respiratory tract infections (VRTI) are extremely common. Considering the profound social and economic impact of COVID-19, it is imperative to identify novel mechanisms for early detection and prevention of VRTIs, to prevent future pandemics. Wearable biosensor technology may facilitate this. Early asymptomatic detection of VRTIs could reduce stress on the healthcare system by reducing transmission and decreasing the overall number of cases. The aim of the current study is to define a sensitive set of physiological and immunological signature patterns of VRTI through machine learning (ML) to analyze physiological data collected continuously using wearable vital signs sensors.

Methods

A controlled, prospective longitudinal study with an induced low grade viral challenge, coupled with 12 days of continuous wearable biosensors monitoring surrounding viral induction. We aim to recruit and simulate a low grade VRTI in 60 healthy adults aged 18–59 years via administration of live attenuated influenza vaccine (LAIV). Continuous monitoring with wearable biosensors will include 7 days pre (baseline) and 5 days post LAIV administration, during which vital signs and activity-monitoring biosensors (embedded in a shirt, wristwatch and ring) will continuously monitor physiological and activity parameters. Novel infection detection techniques will be developed based on inflammatory biomarker mapping, PCR testing, and app-based VRTI symptom tracking. Subtle patterns of change will be assessed via ML algorithms developed to analyze large datasets and generate a predictive algorithm.

Conclusion

This study presents an infrastructure to test wearables for the detection of asymptomatic VRTI using multimodal biosensors, based on immune host response signature.

CliniclTrials.govregistration:NCT05290792

Integrating Wearable Sensors and Video to Determine Microlocation-Specific Physiologic and Motion Biometrics-Method Development for Competitive Climbing

Competitive indoor climbing has increased in popularity at the youth, collegiate, and Olympic levels. A critical aspect for improving performance is characterizing the physiologic response to different climbing strategies (e.g., work/rest patterns, pacing) and techniques (e.g., body position and movement) relative to location on climbing wall with spatially varying characteristics (e.g., wall inclinations, position of foot/hand holds). However, this response is not well understood due to the limited capabilities of climbing-specific measurement and assessment tools. In this study, we developed a novel method to examine time-resolved sensor-based measurements of multiple personal biometrics at different microlocations (finely spaced positions; MLs) along a climbing route. For the ML-specific biometric system (MLBS), we integrated continuous data from wearable biometric sensors and smartphone-based video during climbing, with a customized visualization and analysis system to determine three physiologic parameters (heart rate, breathing rate, ventilation rate) and one body movement parameter (hip acceleration), which are automatically time-matched to the corresponding video frame to determine ML-specific biometrics. Key features include: (1) biometric sensors that are seamlessly embedded in the fabric of an athletic compression shirt, and do not interfere with climbing performance, (2) climbing video, and (3) an interactive graphical user interface to rapidly visualize and analyze the time-matched biometrics and climbing video, determine timing sequence between the biometrics at key events, and calculate summary statistics. To demonstrate the capabilities of MLBS, we examined the relationship between changes in ML-specific climbing characteristics and changes in the physiologic parameters. Our study demonstrates the ability of MLBS to determine multiple time-resolved biometrics at different MLs, in support of developing and assessing different climbing strategies and training methods to help improve performance.

Accuracy of Heart Rate and Respiratory Rate Measurements Using Two Types of Wearable Devices

Objectives:

Wearable devices such as fitness trackers have become popular in the healthcare field. Tracking heart rate and respiratory rate, in addition to physical activity, may provide an accurate picture of daily health. We believe that a combination of two types of devices can simultaneously measure and record physical activity, heart rate, and respiratory rate. However, the measurement accuracies of these two types of devices are not clear. This study aimed to determine the measurement accuracies of two wearable devices for heart and respiratory rate measurements.

Methods:

Ten healthy men performed incremental load tests (ILTs) and constant load tests (CLTs) on a cycle ergometer. The heart and respiratory rates were measured using wrist-worn (Silmee W22, TDK, Japan, Tokyo) and respiratory tracking devices (Spire Stone, Spire Health, San Francisco, CA, USA), respectively. A 12-lead electrocardiograph and the breath-by-breath method were used as external standards for heart and respiratory rates, respectively.

Results:

Bland–Altman analysis showed that heart rate had a fixed bias at rest and during ILT and CLT and had a proportional bias during CLT. The standard error values of the regression at rest and during CLT were less than 10 bpm for heart rate and less than 5.0 /min for respiratory rate. During ILT, the standard error was greater than 10 bpm for heart rate and approximately 5.0 /min for respiratory rate.

Conclusions:

The heart and respiratory rate measurements obtained using wearable devices were accurate within the practical margin of error.

Wearable technology to inform the prediction and diagnosis of cardiorespiratory events: a scoping review

BACKGROUND

The need for health systems that allow for continuous monitoring and early adverse event detection in individuals outside of the acute care setting has been highlighted by the global rise in chronic cardiorespiratory diseases and the recent COVID-19 pandemic. Currently, it is unclear what type of evidence exists concerning the use of physiological data collected from commercially available wrist and textile wearables to assist in clinical decision making. The aim of this review was therefore to systematically map and summarize the scientific literature surrounding the use of these wearables in clinical decision making as well as identify knowledge gaps to inform further research.

METHODOLOGY

Six electronic bibliographic databases were systematically searched (Ovid MEDLINE, EMBASE, CINAHL, PubMed, Scopus, and SportsDiscus). Publications from database inception to May 6, 2020 were reviewed for inclusion. Non-indexed literature relevant to this review was also searched systematically. Results were then collated, summarized and reported.

RESULTS

A total of 107 citations were retrieved and assessed for eligibility with 31 citations included in the final analysis. A review of the 31 papers revealed three major study designs which included (1) observational studies (n = 19), (2) case control series and reports (n = 8), and (3) reviews (n = 2). All papers examined the use of wearable monitoring devices for clinical decisions in the cardiovascular domain, with cardiac arrhythmias being the most studied. When compared to electrocardiogram (ECG) the performance of the wearables in facilitating clinical decisions varied depending upon the type of wearable, user's activity levels and setting in which they were employed. Observational studies collecting data in the inpatient and outpatient settings were equally represented. Eight case control series and reports were identified which reported on the use of wrist wearables in patients presenting to an emergency department or clinic to aid in the clinical diagnosis of a cardiovascular event. Two narrative reviews were identified which examined the impact of wearable devices in monitoring cardiovascular disease as well as potential challenges they may pose in the future.

CONCLUSIONS

To date, studies employing wearables to facilitate clinical decisions have largely focused upon the cardiovascular domain. Despite the ability of some wearables to collect physiological data accurately, there remains a need for a specialist physician to retrospectively review the raw data to make a definitive diagnosis. Analysis of the results has also highlighted gaps in the literature such as the absence of studies employing wearables to facilitate clinical decisions in the respiratory domain. The disproportionate study of wearables in atrial fibrillation detection in comparison to other cardiac arrhythmias and conditions, as well as the lack of diversity in the sample populations used prevents the generalizability of results.

Enhanced Breathing Pattern Detection during Running Using Wearable Sensors

Breathing pattern (BP) is related to key psychophysiological and performance variables during exercise. Modern wearable sensors and data analysis techniques facilitate BP analysis during running but are lacking crucial validation steps in their deployment. Thus, we sought to evaluate a wearable garment with respiratory inductance plethysmography (RIP) sensors in combination with a custom-built algorithm versus a reference spirometry system to determine its concurrent validity in detecting flow reversals (FR) and BP. Twelve runners completed an incremental running protocol to exhaustion with synchronized spirometry and RIP sensors. An algorithm was developed to filter, segment, and enrich the RIP data for FR and BP estimation. The algorithm successfully identified over 99% of FR with an average time lag of 0.018 s (−0.067,0.104) after the reference system. Breathing rate (BR) estimation had low mean absolute percent error (MAPE = 2.74 [0.00,5.99]), but other BP components had variable accuracy. The proposed system is valid and practically useful for applications of BP assessment in the field, especially when measuring abrupt changes in BR. More studies are needed to improve BP timing estimation and utilize abdominal RIP during running.

Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications

BACKGROUND

Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle.

OBJECTIVES

This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications.

METHODS

We developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols.

RESULTS

The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%.

CONCLUSION

We also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional/routine practice.

Ecological Validation and Reliability of Hexoskin Wearable Body Metrics Tool in Measuring Pre-exercise and Peak Heart Rate During Shuttle Run Test in Professional Handball Players

The aim of the study was to assess the validity and reliability of wearable body metric Hexoskin "smart shirt" in measuring heart rate (HR) at pre-exercise and during peak effort in a field test incorporating vigorous movements of the upper body. Measurements were recorded simultaneously using the Hexoskin and Polar Team Pro. Nine male professional handball players (age: 21.8 ± 2.4 years; weight: 83 ± 10.26 kg; height: 1.81 ± 0.09 m; and BMI: 25.17 ± 2.23) volitionally participated in the study by completing two 400 m shuttle run test trials (10 shuttles), each separated by a 48 to 72 h recovery period. Results indicated significant correlations between Hexoskin and Polar Team Pro system in pre-exercise HR. Hexoskin provided erroneous measurements in four of the nine athletes during peak effort. Subsequent correction yielded no consistency between the Polar Team Pro system and Hexoskin between the first and the second trial. Hexoskin showed significant reliability in pre-exercise HR. However, Hexoskin picked up excessive artifact during vigorous physical activity in four of the nine athletes rendering the results in these cases useless. Nevertheless, in athletes where artifact was not an issue, ICC yielded a good estimate. The main findings indicate that Hexoskin has good validity and reliability in measuring pre-exercise HR in handball players and hence may be used with high confidence in slow motion activities. However, vigorous physical activity with jarring multidirectional upper body movements posed a challenge for Hexoskin.

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.

Smart Shirts for Monitoring Physiological Parameters: Scoping Review

BACKGROUND

The recent trends of technological innovation and widescale digitization as potential solutions to challenges in health care, sports, and emergency service operations have led to the conception of smart textile technology. In health care, these smart textile systems present the potential to aid preventative medicine and early diagnosis through continuous, noninvasive tracking of physical and mental health while promoting proactive involvement of patients in their medical management. In areas such as sports and emergency response, the potential to provide comprehensive and simultaneous physiological insights across multiple body systems is promising. However, it is currently unclear what type of evidence exists surrounding the use of smart textiles for the monitoring of physiological outcome measures across different settings.

OBJECTIVE

This scoping review aimed to systematically survey the existing body of scientific literature surrounding smart textiles in their most prevalent form, the smart shirt, for monitoring physiological outcome measures.

METHODS

A total of 5 electronic bibliographic databases were systematically searched (Ovid Medical Literature Analysis and Retrieval System Online, Excerpta Medica database, Scopus, Cumulative Index to Nursing and Allied Health Literature, and SPORTDiscus). Publications from the inception of the database to June 24, 2019 were reviewed. Nonindexed literature relevant to this review was also systematically searched. The results were then collated, summarized, and reported.

RESULTS

Following the removal of duplicates, 7871 citations were identified. On the basis of title and abstract screening, 7632 citations were excluded, whereas 239 were retrieved and assessed for eligibility. Of these, 101 citations were included in the final analysis. Included studies were categorized into four themes: (1) prototype design, (2) validation, (3) observational, and (4) reviews. Among the 101 analyzed studies, prototype design was the most prevalent theme (50/101, 49.5%), followed by validation (29/101, 28.7%), observational studies (21/101, 20.8%), and reviews (1/101, 0.1%). Presented prototype designs ranged from those capable of monitoring one physiological metric to those capable of monitoring several simultaneously. In 29 validation studies, 16 distinct smart shirts were validated against reference technology under various conditions and work rates, including rest, submaximal exercise, and maximal exercise. The identified observational studies used smart shirts in clinical, healthy, and occupational populations for aims such as early diagnosis and stress detection. One scoping review was identified, investigating the use of smart shirts for electrocardiograph signal monitoring in cardiac patients.

CONCLUSIONS

Although smart shirts have been found to be valid and reliable in the monitoring of specific physiological metrics, results were variable for others, demonstrating the need for further systematic validation. Analysis of the results has also demonstrated gaps in knowledge, such as a considerable lag of validation and observational studies in comparison with prototype design and limited investigation using smart shirts in pediatric, elite sports, and emergency service populations.

Point-of-care technologies in heart, lung, blood and sleep disorders from the Center for Advancing Point-of-Care Technologies

Recent advancements in point-of-care technologies have transformed care for patients with heart, lung, blood, and sleep disorders by providing rapid, cost-effective, and accessible solutions to challenges in the detection and management of many health conditions. However, major barriers exist throughout the technology development process that inhibit the actualization of many promising and potentially successful ideas. The Center for Advancing Point of Care Technologies has established a system for supporting further innovation in this field and bridging the gap between initial idea conception and implementation. We highlight current and emerging point-of-care technologies throughout the development spectrum and emphasize the need for a needs-driven model of health technology development that involve appropriate stakeholders in the process.