Digital Health Monitoring Research Articles

From Herd Health to Public Health: Digital Tools for Combating Antibiotic Resistance in Dairy Farms.

The emergence of antimicrobial resistance (AMR) is a significant threat to global food security, human health, and the future of livestock production. Higher rates of antimicrobial use in dairy farming and the sheer lack of new antimicrobials available for use focused attention on the question of how the dairy production sector contributed to the development of AMR and paved the path toward taking action to curtail it on the targeted type of farms. This paper aims to provide an introduction to a phenomenon that has gained considerable attention in the recent past due to its ever-increasing impact, the use of antimicrobial drugs, the emergence of antimicrobial resistance (AMR) on dairy farms, and seeks to discuss the possibilities of approaches such as digital health monitoring and precision livestock farming. Using sensors, data, knowledge, automation, etc., digital health monitoring, as well as Precision Livestock Farming (PLF), is expected to enhance health control and minimize disease and antimicrobial usage. The work presents a literature review on the current status and trends of AMR in dairy farms, an understanding of the concept of digital health monitoring and PLF, and the presentation and usefulness of digital health monitoring and PLF in preventing AMR. The study also analyses the strengths and weaknesses of adopting and incorporating digital technologies and artificial intelligence for dairy farming and presents areas for further study and level of use.

Digital Phenotyping of Geriatric Depression Using a Community-Based Digital Mental Health Monitoring Platform for Socially Vulnerable Older Adults and Their Community Caregivers: 6-Week Living Lab Single-Arm Pilot Study.

Despite the increasing need for digital services to support geriatric mental health, the development and implementation of digital mental health care systems for older adults have been hindered by a lack of studies involving socially vulnerable older adult users and their caregivers in natural living environments. This study aims to determine whether digital sensing data on heart rate variability, sleep quality, and physical activity can predict same-day or next-day depressive symptoms among socially vulnerable older adults in their everyday living environments. In addition, this study tested the feasibility of a digital mental health monitoring platform designed to inform older adult users and their community caregivers about day-to-day changes in the health status of older adults. A single-arm, nonrandomized living lab pilot study was conducted with socially vulnerable older adults (n=25), their community caregivers (n=16), and a managerial social worker over a 6-week period during and after the COVID-19 pandemic. Depressive symptoms were assessed daily using the 9-item Patient Health Questionnaire via scripted verbal conversations with a mobile chatbot. Digital biomarkers for depression, including heart rate variability, sleep, and physical activity, were measured using a wearable sensor (Fitbit Sense) that was worn continuously, except during charging times. Daily individualized feedback, using traffic signal signs, on the health status of older adult users regarding stress, sleep, physical activity, and health emergency status was displayed on a mobile app for the users and on a web application for their community caregivers. Multilevel modeling was used to examine whether the digital biomarkers predicted same-day or next-day depressive symptoms. Study staff conducted pre- and postsurveys in person at the homes of older adult users to monitor changes in depressive symptoms, sleep quality, and system usability. Among the 31 older adult participants, 25 provided data for the living lab and 24 provided data for the pre-post test analysis. The multilevel modeling results showed that increases in daily sleep fragmentation (P=.003) and sleep efficiency (P=.001) compared with one's average were associated with an increased risk of daily depressive symptoms in older adults. The pre-post test results indicated improvements in depressive symptoms (P=.048) and sleep quality (P=.02), but not in the system usability (P=.18). The findings suggest that wearable sensors assessing sleep quality may be utilized to predict daily fluctuations in depressive symptoms among socially vulnerable older adults. The results also imply that receiving individualized health feedback and sharing it with community caregivers may help improve the mental health of older adults. However, additional in-person training may be necessary to enhance usability. ClinicalTrials.gov NCT06270121; https://clinicaltrials.gov/study/NCT06270121.

Feasibility of continuous smart health monitoring in pregnant population: A mixed-method approach.

The utilization of smart monitoring technology offers potential for enhancing health outcomes, yet its feasibility and acceptance among Hispanic pregnant individuals remain uncertain. This is particularly crucial to investigate within the context of apparently healthy individuals identified as low risk, who still face a 10% likelihood of complications. Given their frequent underrepresentation in healthcare services and relative lack of attention, improving the feasibility of remote monitoring in this population could yield significant benefits. To address this gap, our study aimed to adapt and evaluate the practicality of a smart monitoring platform among healthy Hispanic pregnant women during the second and third trimesters of pregnancy, as well as one week following childbirth, a period when complications often arise. This longitudinal study followed n = 16 participants for an average of 17 weeks. Participants were instructed to wear the Oura ring for objective data collection, including activity, sleep, and heart rate, and to complete survey questions through REDcap to assess mental health and lifestyle factors. The study framework utilized the RE-AIM approach, with acceptability and adherence as key components of the feasibility evaluation. Our findings revealed that completion rates for biweekly and monthly surveys remained consistently high until after childbirth (approximately 80%), while daily question completion remained above 80% until 38th week of gestation, declining thereafter. The wearing rate of the Oura ring remained consistently above 80% until the 35th gestational week, decreasing to around 31% postpartum. Participants cited barriers to wearing the ring during the postpartum period, including difficulties managing the newborn, forgetfulness, and concerns about scratching the baby's skin. The enrollment rate was 71.42%, with an attrition rate of 6.25%. Thematic analysis of one-on-one interviews identified three main themes: personal desire for health improvement, social acceptability and support, and conditions influencing device/platform efficiency. In conclusion, while adherence varied based on gestational week and survey frequency, the study demonstrated strong acceptability of the smart monitoring platform among the study population, indicated by the high enrollment rate. Qualitative insights underscored the significance of personal motivation, social support, and device/platform efficiency in enhancing patient engagement with digital health monitoring during pregnancy, offering valuable considerations for future healthcare interventions in this domain.

The role of governance in the digital transformation of healthcare: Results of a survey in the WHO Europe Region

Optimal governance is among the key facilitators of the digital transformation of health systems intended to improve access to healthcare, quality, safety, and efficiency, and to attain universal health coverage. This paper highlights the findings of a survey assessing the status of governance of digital health in the WHO European Region.The 2022 survey is a continuation of the 2015 WHO Global Survey on eHealth. The survey focused on national digital health governance, monitoring and evaluation of digital health interventions, and funding and investment in digital health and was conducted through April to October 2022. All 53 Member States of the WHO European Region participated in the survey.The results showed that 83% of the Member States reported having a national digital health strategy in place, and 79% of the Member States reported having a health information system strategy. Most of the priorities of the national digital health strategies were devoted to increasing the accessibility, quality, safety, and efficiency of the health systems, strengthening health information systems, and improving information sharing and interoperability of data. Measures to ensure equity in access to digital healthcare services were directed towards improving broadband connectivity, information and communication technology, digital literacy, and digital health promotion. Oversight for the implementation and operation of the national digital health strategies are mostly delegated to government agencies. The prime source of funding for digital health programs was public funding, though a combination of funding sources was also noted. This analysis revealed increasing adoption of national digital health strategies for access to healthcare and creation of digital health agencies and funding programs for digital health.

Perceptions and Experiences of Healthcare Providers and Patients Towards Digital Health Services in Primary Health Care: A Cross-Sectional Study.

Digital health has the potential to help achieve the Sustainable Development Goals (SDGs) by supporting health systems and enhancing health promotion and disease prevention. However, obstacles such as restricted internet access, inadequate technical assistance, clinical resource disparity, and insufficient user training can impede the utilization and growth of digital health. Researchers should examine healthcare providers' and patients' perspectives to identify challenges and enhance usability. The study was conducted among women health volunteers, staff nurses, and patients who used the VinCense mobile application (MedIoTek Health Systems Private Limited, Chennai, India) to record vital signs. A semi-structured questionnaire was used to evaluate participants' sociodemographic characteristics, perception of digital health monitoring, and patients' attitudes toward digital health monitoring devices. The data were analyzed using R programming, Version 4.3.3 (www.r-project.org). A multinomial logistic regression analysis was used to examine the association between sociodemographic characteristics and attitudes of patients toward digital health monitoring. The study involved 27 healthcare providers and 406 patients. The majority (66.6%) of healthcare providers found the device convenient and efficient. Around 74.1% faced technical difficulties like internet connectivity and device battery issues. Among patients, 79.8% were satisfied with their digital health monitoring experience, 86.2% found device usage comfortable and 78.1% expressed satisfaction with health education and feedback.Around 354 (87.2%) patients stated that technology has improved healthcare, and 326 (80.3%) said that health technologies have improved ease. The results indicate that female gender (p=0.00), age above 50 years (p=0.04), and occupation status as a semiskilled worker (p=0.03), skilled worker (p=0.00), and clerical/shop/farmer (p=0.01) were statistically significant and associated with the positive attitude for digital health monitoring. The digital health monitoring experience was found satisfactory by both patients and healthcare providers. The mobile health (mHealth) has tremendous potential for enhancing patient health. Therefore, it is advisable to contemplate an expansion of the VinCense mHealth Platform and other digital solutions to improve service delivery in primary healthcare setups.

Recent advances in smart wearable sensors for continuous human health monitoring

In recent years, the biochemical and biological research areas have shown great interest in a smart wearable sensor because of its increasing prevalence and high potential to monitor human health in a non-invasive manner by continuous screening of biomarkers dispersed throughout the biological analytes, as well as real-time diagnostic tools and time-sensitive information compared to conventional hospital-centered system. These smart wearable sensors offer an innovative option for evaluating and investigating human health by incorporating a portion of recent advances in technology and engineering that can enhance real-time point-of-care-testing capabilities. Smart wearable sensors have emerged progressively with a mixture of multiplexed biosensing, microfluidic sampling, and data acquisition systems incorporated with flexible substrate and bodily attachments for enhanced wearability, portability, and reliability. There is a good chance that smart wearable sensors will be relevant to the early detection and diagnosis of disease management and control. Therefore, pioneering smart wearable sensors into reality seems extremely promising despite possible challenges in this cutting-edge technology for a better future in the healthcare domain. This review presents critical viewpoints on recent developments in wearable sensors in the upcoming smart digital health monitoring in real-time scenarios. In addition, there have been proactive discussions in recent years on materials selection, design optimization, efficient fabrication tools, and data processing units, as well as their continuous monitoring and tracking strategy with system-level integration such as internet-of-things, cyber-physical systems, and machine learning algorithms.

A278 ACCEPTABILITY, FEASIBILITY, AND IMPACT OF THE MYGUT DIGITAL HEALTH PLATFORM IN THE MONITORING AND MANAGEMENT OF INFLAMMATORY BOWEL DISEASE

Abstract Background Inflammatory bowel disease (IBD) is a chronic gastrointestinal disorder that has been associated with depression, anxiety, and decreased quality of life. With new technological advancements, digital health monitoring platforms offer a promising avenue to integrate self-management strategies into the care of patients with IBD. Aims This study aimed to investigate patients’ acceptability to implementing the MyGut IBD health monitoring platform into their care, evaluate whether its use leads to better quality of life/improved health outcomes, and determine the feasibility of its long-term use. Methods We conducted a multi-centre, single-arm trial at McMaster University and McGill University, among their affiliated hospitals, from September 2020 to September 2023. Patients with IBD were recruited in gastroenterology clinics and asked to install the MyGut application onto their mobile devices. Various metrics such as willingness to use the application, patient satisfaction, symptom control/quality of life scores (measured through the short inflammatory bowel disease questionnaire (SIBDQ)), resource utilization, and feasibility statements were collected throughout the study. Patients exhibiting abnormal SIBDQ scores were identified for follow-up. After one year, patient outcome metrics were compared to baseline values. Results Out of the 84 patients initially enrolled, 58 patients (69%) continued until study completion at one-year. At recruitment, all 84 patients (100%) were willing to use the MyGut application after a brief tutorial and 72.6% (61/84) had already been using technology for health-related purposes. There was a significant improvement in SIBDQ scores after one year of MyGut use (median = 57.5, IQR 50.25–62) when compared to baseline (median = 54.5, IQR 47.25–58.75) (p = 0.01). However, only 42.9% of patients (21/49) were willing to continue using the application after one year, a significant decrease compared to the 71.9% (41/57) who were willing to continue after two months (p = 0.002). No differences were observed in patient satisfaction measures (p = 0.42 to 1) or number of ER visits/hospitalizations (p = 0.78) before and after one year of MyGut use. Conclusions Overall, IBD is a condition that often necessitates frequent medical follow-up and may benefit from proactive symptom management strategies. This study demonstrates that patients with IBD are very willing to integrate digital health monitoring platforms into their care and these may subsequently lead to improved symptom control and enhanced quality of life. Future studies should aim to investigate the subset of patients that would benefit most from these health interventions, and as illustrated, continued efforts must be made to optimize the acceptability/feasibility of its long-term use. Funding Agencies CCC

Advances in Fiber-Based Wearable Sensors for Personal Digital Health Monitoring

With the continuous growth of the global economy, an increasing concern has emerged among individuals with regard to personal digital health. Smart fiber-based sensors meet people’s demands for wearable devices with the advantages of excellent skin-friendliness and breathability, enabling efficient and prompt monitoring of personal digital health signals in daily life. Furthermore, by integrating machine learning and big data analysis techniques, a closed-loop system can be established for personal digital health, covering data collection, data analysis, as well as medical diagnosis and treatment. Herein, we provide a review of the recent research progress on fiber-based wearable sensors for personal digital health. Firstly, a brief introduction is provided to demonstrate the importance of fiber-based wearable sensors in personal digital health. Then, the monitoring of biophysical signals through fiber-based sensors is described, and they are classified based on different sensing principles in biophysical signal monitoring (resistive, capacitive, piezoelectric, triboelectric, magnetoelastic, and thermoelectric). After that, the fiber-based biochemical signal sensors are described through the classification of monitoring targets (biofluids and respiratory gases). Finally, a summary is presented on the application prospects and the prevailing challenges of fiber-based sensors, aiming to implement their future role in constructing personal digital health networks.

Maintaining High-Touch in High-Tech Digital Health Monitoring and Multi-Omics Prognostication: Ethical, Equity, and Societal Considerations in Precision Health for Palliative Care.

Advances in digital health, systems biology, environmental monitoring, and artificial intelligence (AI) continue to revolutionize health care, ushering a precision health future. More than disease treatment and prevention, precision health aims at maintaining good health throughout the lifespan. However, how can precision health impact care for people with a terminal or life-limiting condition? We examine here the ethical, equity, and societal/relational implications of two precision health modalities, (1) integrated systems biology/multi-omics analysis for disease prognostication and (2) digital health technologies for health status monitoring and communication. We focus on three main ethical and societal considerations: benefits and risks associated with integration of these modalities into the palliative care system; inclusion of underrepresented and marginalized groups in technology development and deployment; and the impact of high-tech modalities on palliative care's highly personalized and "high-touch" practice. We conclude with 10 recommendations for ensuring that precision health technologies, such as multi-omics prognostication and digital health monitoring, for palliative care are developed, tested, and implemented ethically, inclusively, and equitably.

Digital Health Intervention of Healthy Indonesia Program with Family Approach: Does it Work?

Background: The digital age holds out the promise of innovative technology. Along with the development of technology, healthcare management has been changing. Indonesia was implementing The Healthy Indonesia Program with a Family Approach (PISPK) that aimed at increasing access of families and their members to comprehensive health services, promotive, preventive, curative, and basic rehabilitative. Every household data related to 12 health indicators was digitally recorded and analyzed on a specific website to provide a database for determining suitable health and empowerment programs according to health problems experienced by the community. Objectives: This study aimed to assess the performance of The Healthy Indonesia Program with a Family Approach website in East Java Province, Indonesia. Method: The study design is cross-sectional with a descriptive exploratory study. Data was collected through an online survey utilizing Google Forms service and analyzed descriptively with a thematic content approach. Result: Respondents of this research involving 232 health workers from community health centers in East Java Province that running Healthy Indonesia with a Family Approach. This research result found that the PIS-PK website met the criteria of a digital health monitoring tool recommended by WHO with the score of Functionality (70.54%), Stability (62.89%), Fidelity (58.17%), Quality (73.81%), Implementation (77.41%), and Judgement (67.92%). Conclusion: Healthy Indonesia with Family Approach Website was beneficiary to be used as the baseline in designing and developing a community empowerment program targeting specific indicators at the community health center level. However, several issues were noticed including, stability, fidelity, privacy, and data governance that should immediately take into account to improve the website’s performance.

Graph Neural Networks (GNNs) based accelerated numerical simulation

Finite element method (FEM) based high-fidelity simulation can be computationally demanding and time-consuming as engineering problems become more complicated. It is thus necessary to develop a surrogate model that only requires a small amount of computational time but retains sufficient accuracy. A graph neural network (GNN) based framework is proposed as a general surrogate model for FEM to simulate the Von Mises stress distribution. The mesh body is embedded to a graph and a novel global attribute representation is introduced to capture the geometry and boundary conditions while overcoming the common problem of over smoothing in graph deep learning. The challenge to deal with varying geometry and boundary conditions is overcome by the proposed model and thus it outperforms existing methods such as proper orthogonal decomposition (POD) and greedy algorithm in terms of generalization. Numerical experiments are given to demonstrate the capability of the model developed and the results show that the proposed model not only accurately predicts the stress distribution but also speed-ups hundreds of times faster compared to a FEM-based simulator, enabling real-time structural response analysis for the application of digital twin and structural health monitoring. It is indicated that GNNs can be a powerful tool for resolving complex physical problems, thereby assisting in advancing science and enhancing engineering productivity.

Developing a digital twin model for monitoring building structural health by combining a building information model and a real-scene 3D model

Structural health monitoring (SHM) is a critical component of ensuring the safety of buildings, yet the effective management of sensors and their corresponding monitoring data is complex. Thus, a digital twin health monitoring information model for buildings was proposed to simultaneously locate sensors in space and monitor data. We described the requirements of this process and designed a conceptual model by combining a building information model (BIM) and a real-scene 3D model. The key steps in the fusion process include reconstructing the BIM into sub-BIMs considering building component types and geometric transformations, 3D spatial position registration with a rigid-body transformation method, and semantic information mapping. After the fusion process, monitoring data from sensors are stored in a database management system and integrated with the fused model. A digital twin model of the Nanjing Museum Old Hall was constructed as a case study, which evaluated the fusion method’s feasibility and usability.

Stool Image Analysis for Digital Health Monitoring By Smart Toilets

Gastrointestinal (GI) conditions are widespread and significantly impact the quality of life and healthcare. Stool appearance is a valuable GI diagnostic indicator, and with the advent of the Internet of Things (IoT), daily monitoring of excreta from a toilet is emerging as a promising digital health tool. This article describes a stool image analysis approach that classifies two physiologically relevant stool characteristics: 1) form and 2) color for an IoT-based smart toilet. We constructed a stool image data set with 3275 images, spanning all seven types of the Bristol stool-form scale (BSFS), a widely used metric for stool consistency and a variety of colors. We used ground-truth data obtained through the annotation of our data set by two gastroenterologists and developed a stool-color card to standardize the labeling of stool colors. We addressed two limitations associated with the application of computer-vision techniques to a smart-toilet system: 1) uneven separability between different stool-form categories and 2) class imbalance in the data set. We present results on hierarchical convolutional neural network (CNN) architectures for training a stool-form classifier and on perceptual color quantization coupled with machine-learning techniques to optimize the color-feature space for the classification of stool color. We utilized an edge–cloud approach to pursue an optimal balance between accuracy and latency and for the classification of stool form, we achieved a balanced accuracy of 84.4% and 84.2% reduction in latency compared to a cloud-model only. For color classification, the logistic-regression (LR) classifier provided 80.8% balanced accuracy and 47.3% reduction in communication latency.

Android Based Digital Contact Tracing and Health Monitoring System for Covid 19 Pandemic

"ENGINEER", established in 1973 by The Institution of Engineers, is a Journal for dissemination of Engineering knowledge, published quarterly.If you are interested in contributing an Original Technical Paper based on research by the author(s) (intended for Section I) or an Original Article of Professional or Technical interest related to Engineering. (Section II) to this journal please go through the publication checklist which can be downloaded from here.Cover DescriptionCover images show a few views of the Maduru Oya reservoir and construction of right bank canal. The Institution of Engineers, Sri Lanka, acknowledges Eng. L.D. Amila Sanjeewa of the Department of Irrigation for providing images and related information of the project.

A digital twin framework for aircraft hydraulic systems failure detection using machine learning techniques

Since the last decade, aircraft systems, such as flight control and landing gear, have been requiring increasing power, and consequently, the complexity of hydraulic aircraft systems has escalated. Inevitably, this complexity has resulted in the need for the troubleshooting of hydraulic aircraft systems that are dispersed around an aircraft and supply power to critical flight systems. This study proposes a novel digital twin-based health monitoring system for aircraft hydraulic systems to enable diagnostics of system failures early in the design cycle using machine learning (ML) methods. The scope of the systems is limited to hydraulic systems at the aircraft level using 20 failure scenarios. The support vector machine and several ensemble learning algorithms of ML methods were used to identify these failures. A comparison of the ML methods revealed that the random forest algorithm performed superior to the other ML algorithms. The developed digital twin framework for hydraulic system of aerial vehicle platforms, can help researchers and engineers to evaluate diagnostics systems early in the design phase.

Validation of Amazon Halo Movement: a smartphone camera-based assessment of movement health

Movement health is understanding our body’s ability to perform movements during activities of daily living such as lifting, reaching, and bending. The benefits of improved movement health have long been recognized and are wide-ranging from improving athletic performance to helping ease of performing simple tasks, but only recently has this concept been put into practice by clinicians and quantitatively studied by researchers. With digital health and movement monitoring becoming more ubiquitous in society, smartphone applications represent a promising avenue for quantifying, monitoring, and improving the movement health of an individual. In this paper, we validate Halo Movement, a movement health assessment which utilizes the front-facing camera of a smartphone and applies computer vision and machine learning algorithms to quantify movement health and its sub-criteria of mobility, stability, and posture through a sequence of five exercises/activities. On a diverse cohort of 150 participants of various ages, body types, and ability levels, we find moderate to strong statistically significant correlations between the Halo Movement assessment overall score, metrics from sensor-based 3D motion capture, and scores from a sequence of 13 standardized functional movement tests. Further, the smartphone assessment is able to differentiate regular healthy individuals from professional movement athletes (e.g., dancers, cheerleaders) and from movement impaired participants, with higher resolution than that of existing functional movement screening tools and thus may be more appropriate than the existing tests for quantifying functional movement in able-bodied individuals. These results support using Halo Movement’s overall score as a valid assessment of movement health.

Factors affecting vaccination demand in Indonesia: a secondary analysis and multimethods national assessment

ObjectivesVaccine hesitancy remains a major barrier to immunisation coverage worldwide. We explored influence of hesitancy on coverage and factors contributing to vaccine uptake during a national measles–rubella (MR) campaign in...

Accuracy and Precision of Wearable Devices for Real-Time Monitoring of Swimming Athletes.

Nowadays, the use of wearable devices is spreading in different fields of application, such as healthcare, digital health, and sports monitoring. In sport applications, the present trend is to continuously monitor the athletes’ physiological parameters during training or competitions to maximize performance and support coaches. This paper aims to evaluate the performances in heart rate assessment, in terms of accuracy and precision, of both wrist-worn and chest-strap commercial devices used during swimming activity, considering a test population of 10 expert swimmers. Three devices were employed: Polar H10 cardiac belt, Polar Vantage V2, and Garmin Venu Sq smartwatches. The former was used as a reference device to validate the data measured by the two smartwatches. Tests were performed both in dry and wet conditions, considering walking/running on a treadmill and different swimming styles in water, respectively. The measurement accuracy and precision were evaluated through standard methods, i.e., Bland–Altman plot, analysis of deviations, and Pearson’s correlation coefficient. Results show that both precision and accuracy worsen during swimming activity (with an absolute increase of the measurement deviation in the range of 13–56 bpm for mean value and 49–52 bpm for standard deviation), proving how water and arms movement act as relevant interference inputs. Moreover, it was found that wearable performance decreases when activity intensity increases, highlighting the need for specific research for wearable applications in water, with a particular focus on swimming-related sports activities.

Patient monitoring and education over a tailored digital application platform for congenital heart disease: A feasibility pilot study

BackgroundPatients with adult congenital heart disease (ACHD) are a rapidly growing cardiovascular population with increasing health needs and co-morbidities. Furthermore, their management requires frequent and ongoing hospital visits which can be burdensome. Digital health and remote monitoring have been shown to have a vast potential to enhance delivery of healthcare for patients, reducing their need for travel to clinic appointments therefore reducing costs to the patient and the healthcare service. MethodsPatients over the age of 16 with a diagnosis of ACHD were invited to use the tailored digital application too. They were monitored for a period of 6 months. Information on patient demographics, time using the application, flagged events that prompted clinical reviews and their feedback through patient surveys were collected. ResultsA total of 103 patients were enrolled and registered to use the digital application tool. There were 57 (56%) males, median age at the time of enrolment was 39 (16–73) years. The majority (96%) had a moderate or complex ACHD according to the ACC/AHA classification. There was a total of 7 modules that were completed on a weekly basis. The median length of a participant session was 2.2 min and the mean time to complete a module was 21 s. In total, 35 (67%) felt that the application helped them better manage their cardiac condition. Almost all (94%) of patients expressed that they would like to continue using the application beyond the pilot. There were 18 flagged events during the 6 month observation period, and 50% of received early clinical intervention. ConclusionApplication based remote monitoring in this select group was well received and potentially holds large benefit to patients both clinically and economically. There were no safety concerns in our pilot feasibility study. Our data may inform much needed and timely investment in digital health.

Design and Analysis of Micro-Vibration Isolation System for Digital Image Correlation System-Based Structural Health Monitoring

Design and Analysis of Micro-Vibration Isolation System for Digital Image Correlation System-Based Structural Health Monitoring