Evaluation of a newly designed shirt-based ECG and breathing sensor for home-based training as part of cardiac rehabilitation for coronary artery disease

Validation of a smart shirt for heart rate variability measurements at rest and during exercise

Heart rate variability (HRV) monitoring is a promising option to estimate the autonomic nervous system regulation responding to exercise. Textiles with embedded sensors recording heartbeat intervals are a simple tool for data collection. So-called smart shirts offer comfort for a daily use and are managed easily. Their measurement accuracy for HRV calculation at rest is promising but remains questionable during exercise. Therefore, the present study validated the Ambiotex smart shirt using HRV indices (RMSSD, rel. HF power and rel. LF power) during exercise. Eighty-three healthy participants (31 ± 6 years; 39 females, 44 males) completed an incremental exercise test on a bicycle ergometer wearing the smart shirt and an electrocardiogram simultaneously. We compared HRV indices of segments at rest (5 min), at warm-up (3 min) and twice at the exercise test (each 5 min). At rest and at warm-up, we observed excellent linear relationship (r > 0.96; R² > 0.94), excellent relative reliability (ICC ≥ 0.98; α ≥ 0.98) and acceptable agreement (bias < 10%). During the exercise test, measurement accuracy declined with increasing intensity but remained high (> 0.8), although results for partial HRV indices were insufficient. In addition, percentage bias was unacceptable during exercise test. However, the findings support the validity of the smart shirt for measuring HRV especially at rest and at warm-up. We suggest using the smart shirt for monitoring HRV indices on a daily basis but caution should be taken in the interpretation of HRV indices obtained during moderate to vigorous exercise intensities. This article is protected by copyright. All rights reserved.

mHealth Apps for Self-Management of Cardiovascular Diseases: A Scoping Review

The use of mHealth apps for the self-management of cardiovascular diseases (CVDs) is an increasing trend in patient-centered care. In this research, we conduct a scoping review of mHealth apps for CVD self-management within the period 2014 to 2021. Our review revolves around six main aspects of the current status of mHealth apps for CVD self-management: main CVDs managed, main app functionalities, disease stages managed, common approaches used for data extraction, analysis, management, common wearables used for CVD detection, monitoring and/or identification, and major challenges to overcome and future work remarks. Our review is based on Arksey and O'Malley's methodological framework for conducting studies. Similarly, we adopted the PRISMA model for reporting systematic reviews and meta-analyses. Of the 442 works initially retrieved, the review comprised 38 primary studies. According to our results, the most common CVDs include arrhythmia (34%), heart failure (32%), and coronary heart disease (18%). Additionally, we found that the majority mHealth apps for CVD self-management can provide medical recommendations, medical appointments, reminders, and notifications for CVD monitoring. Main challenges in the use of mHealth apps for CVD self-management include overcoming patient reluctance to use the technology and achieving the interoperability of mHealth applications with other systems.

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.

Home-based telerehabilitation software systems for remote supervising: a systematic review

OBJECTIVES

In the past decade, with the ever-increasing growth of information and communication technologies, telerehabilitation, especially home-based rehabilitation (HBR), has been widely considered by researchers. Many software systems are developed to address HBR programs, which includes various functionalities. The aim of this study is to review the functional features of these systems designed for remote supervising of HBR programs.

METHODS

Scopus, PubMed, EMBASE, ISI Web of Science, Cochrane Library, IEEE Xplore Digital Library, and ProQuest databases were searched for English-language articles published between January 2008 and February 2018 to retrieve studies reported an home-based telerehabilitation software system aiming to remotely supervise HBR program.

RESULTS

A total of fifty studies that reported twenty-two unique systems met the inclusion criteria. Various functional features were identified including but not limited to exercise plan management, report/statistics generating, patient education, and task scheduling. Disorders or diseases addressed by these systems could mainly be grouped into five categories: musculoskeletal, neurological, respiratory, cardiovascular, and other health-related problems. Usability and acceptability, and clinical/patient outcomes were the most reported outcomes and data analysis was used by the majority of included studies to measure the outcomes.

CONCLUSIONS

Systems developed for supervising of HBR program are diverse. However, preliminary results of this review revealed that these systems share more or less common functionalities. However, further research is needed to determine the requirements, structure, and effectiveness of these systems in real-life settings.

Mobile health and cardiac rehabilitation in older adults

With the ubiquity of mobile devices, the availability of mobile health (mHealth) applications for cardiovascular disease (CVD) has markedly increased in recent years. Older adults represent a population with a high CVD burden and therefore have the potential to benefit considerably from interventions that utilize mHealth. Traditional facility-based cardiac rehabilitation represents one intervention that is currently underutilized for CVD patients and, because of the unique barriers that older adults face, represents an attractive target for mHealth interventions. Despite potential barriers to mHealth adoption in older populations, there is also evidence that older patients may be willing to adopt these technologies. In this review, we highlight the potential for mHealth uptake for older adults with CVD, with a particular focus on mHealth cardiac rehabilitation (mHealth-CR) and evidence being generated in this field.

Internet-based training of coronary artery patients: the Heart Cycle Trial

Low adherence to cardiac rehabilitation (CR) might be improved by remote monitoring systems that can be used to motivate and supervise patients and tailor CR safely and effectively to their needs. The main objective of this study was to evaluate the feasibility of a smartphone-guided training system (GEX) and whether it could improve exercise capacity compared to CR delivered by conventional methods for patients with coronary artery disease (CAD). A prospective, randomized, international, multi-center study comparing CR delivered by conventional means (CG) or by remote monitoring (IG) using a new training steering/feedback tool (GEx System). This consisted of a sensor monitoring breathing rate and the electrocardiogram that transmitted information on training intensity, arrhythmias and adherence to training prescriptions, wirelessly via the internet, to a medical team that provided feedback and adjusted training prescriptions. Exercise capacity was evaluated prior to and 6 months after intervention. 118 patients (58 ± 10 years, 105 men) with CAD referred for CR were randomized (IG: n = 55, CG: n = 63). However, 15 patients (27 %) in the IG and 18 (29 %) in the CG withdrew participation and technical problems prevented a further 21 patients (38 %) in the IG from participating. No training-related complications occurred. For those who completed the study, peak VO₂ improved more (p = 0.005) in the IG (1.76 ± 4.1 ml/min/kg) compared to CG (-0.4 ± 2.7 ml/min/kg). A newly designed system for home-based CR appears feasible, safe and improves exercise capacity compared to national CR. Technical problems reflected the complexity of applying remote monitoring solutions at an international level.

A remote quantitative Fugl-Meyer assessment framework for stroke patients based on wearable sensor networks

To extend the use of wearable sensor networks for stroke patients training and assessment in non-clinical settings, this paper proposes a novel remote quantitative Fugl-Meyer assessment (FMA) framework, in which two accelerometer and seven flex sensors were used to monitoring the movement function of upper limb, wrist and fingers. The extreme learning machine based ensemble regression model was established to map the sensor data to clinical FMA scores while the RRelief algorithm was applied to find the optimal features subset. Considering the FMA scale is time-consuming and complicated, seven training exercises were designed to replace the upper limb related 33 items in FMA scale. 24 stroke inpatients participated in the experiments in clinical settings and 5 of them were involved in the experiments in home settings after they left the hospital. Both the experimental results in clinical and home settings showed that the proposed quantitative FMA model can precisely predict the FMA scores based on wearable sensor data, the coefficient of determination can reach as high as 0.917. It also indicated that the proposed framework can provide a potential approach to the remote quantitative rehabilitation training and evaluation.

A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients

Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information.

Evaluation of a New Shirt-Based Electrocardiogram Device for Cardiac Screening in Soccer Players: Comparative Study With Treadmill Ergospirometry

Background

Prevention of cardiac events during competitive sports is fundamental. New technologies with remote monitoring systems integrated into clothing could facilitate the screening of heart disease. Our aim was to evaluate the feasibility of Nuubo system during a field stress test performed by soccer players, comparing results with treadmill ergospirometry as test reference.

Methods

Nineteen male professional soccer players (19.2 ± 1.6 years) were studied. Wireless electrocardiographic monitoring during a Yo-Yo intermittent recovery test level 1 in soccer field and subsequent analysis of arrhythmias were firstly performed. Subsequently, in a period no longer than 4 weeks, each player underwent cardiopulmonary exercise testing in hospital.

Results

During Yo-Yo test, electrocardiogram (ECG) signal was interpretable in 16 players (84.2%). In the other three players, ECG artifacts did not allow a proper analysis. Estimation of maximum oxygen consumption was comparable between two exercise tests (VO₂ max 53.3 ± 2.4 vs. 53.7 ± 3.0 mL/kg/min for Yo-Yo test and ergometry respectively; intra-class correlation coefficient 0.84 (0.63 - 0.93), P < 0.001). No arrhythmias were detected in any player during both tests.

Conclusions

The use of Nuubo’s technology allows an accurate single-lead electrocardiographic recording and estimation of reliable performance variables during exercise testing in field, and provides a new perspective to cardiac remote monitoring in collective sports.