2,847 research outputs found
Heart Failure Monitoring System Based on Wearable and Information Technologies
In Europe, Cardiovascular Diseases (CVD) are the leading source of death, causing 45% of all deceases. Besides, Heart Failure, the paradigm of CVD, mainly affects people older than 65. In the current aging society, the European MyHeart Project was created, whose mission is to empower citizens to fight CVD by leading a preventive lifestyle and being able to be diagnosed at an early stage. This paper presents the development of a Heart Failure Management System, based on daily monitoring of Vital Body Signals, with wearable and mobile technologies, for the continuous assessment of this chronic disease. The System makes use of the latest technologies for monitoring heart condition, both with wearable garments (e.g. for measuring ECG and Respiration); and portable devices (such as Weight Scale and Blood Pressure Cuff) both with Bluetooth capabilitie
Simulated case management of home telemonitoring to assess the impact of different alert algorithms on work-load and clinical decisions
© 2017 The Author(s). Background: Home telemonitoring (HTM) of chronic heart failure (HF) promises to improve care by timely indications when a patient's condition is worsening. Simple rules of sudden weight change have been demonstrated to generate many alerts with poor sensitivity. Trend alert algorithms and bio-impedance (a more sensitive marker of fluid change), should produce fewer false alerts and reduce workload. However, comparisons between such approaches on the decisions made and the time spent reviewing alerts has not been studied. Methods: Using HTM data from an observational trial of 91 HF patients, a simulated telemonitoring station was created and used to present virtual caseloads to clinicians experienced with HF HTM systems. Clinicians were randomised to either a simple (i.e. an increase of 2 kg in the past 3 days) or advanced alert method (either a moving average weight algorithm or bio-impedance cumulative sum algorithm). Results: In total 16 clinicians reviewed the caseloads, 8 randomised to a simple alert method and 8 to the advanced alert methods. Total time to review the caseloads was lower in the advanced arms than the simple arm (80 ± 42 vs. 149 ± 82 min) but agreements on actions between clinicians were low (Fleiss kappa 0.33 and 0.31) and despite having high sensitivity many alerts in the bio-impedance arm were not considered to need further action. Conclusion: Advanced alerting algorithms with higher specificity are likely to reduce the time spent by clinicians and increase the percentage of time spent on changes rated as most meaningful. Work is needed to present bio-impedance alerts in a manner which is intuitive for clinicians
Early indication of decompensated heart failure in patients on home-telemonitoring: a comparison of prediction algorithms based on daily weight and noninvasive transthoracic bio-impedance
Background: Heart Failure (HF) is a common reason for hospitalization. Admissions might be prevented by early detection of and intervention for decompensation. Conventionally, changes in weight, a possible measure of fluid accumulation, have been used to detect deterioration. Transthoracic impedance may be a more sensitive and accurate measure of fluid accumulation.
Objective: In this study, we review previously proposed predictive algorithms using body weight and noninvasive transthoracic bio-impedance (NITTI) to predict HF decompensations.
Methods: We monitored 91 patients with chronic HF for an average of 10 months using a weight scale and a wearable bio-impedance vest. Three algorithms were tested using either simple rule-of-thumb differences (RoT), moving averages (MACD), or cumulative sums (CUSUM).
Results: Algorithms using NITTI in the 2 weeks preceding decompensation predicted events (P<.001); however, using weight alone did not. Cross-validation showed that NITTI improved sensitivity of all algorithms tested and that trend algorithms provided the best performance for either measurement (Weight-MACD: 33%, NITTI-CUSUM: 60%) in contrast to the simpler rules-of-thumb (Weight-RoT: 20%, NITTI-RoT: 33%) as proposed in HF guidelines.
Conclusions: NITTI measurements decrease before decompensations, and combined with trend algorithms, improve the detection of HF decompensation over current guideline rules; however, many alerts are not associated with clinically overt decompensation
Smart nanotextiles: materials and their application
Textiles are ubiquitous to us, enveloping our skin and
surroundings. Not only do they provide a protective
shield or act as a comforting cocoon but they also
serve esthetic appeal and cultural importance. Recent
technologies have allowed the traditional functionality
of textiles to be extended. Advances in materials
science have added intelligence to textiles and created
âsmartâ clothes.
Smart textiles can sense and react to environmental
conditions or stimuli, e.g., from mechanical, thermal,
chemical, electrical, or magnetic sources (Lam Po
Tang and Stylios 2006). Such textiles find uses in many
applications ranging from military and security to
personalized healthcare, hygiene, and entertainment.
Smart textiles may be termed ââpassiveââ or ââactive.ââ A
passive smart textile monitors the wearerâs physiology
or the environment, e.g., a shirt with in-built
thermistors to log body temperature over time. If
actuators are integrated, the textile becomes an active,
smart textile as it may respond to a particular stimulus,
e.g., the temperature-aware shirt may automatically
roll up the sleeves when body temperature rises.
The fundamental components in any smart textile
are sensors and actuators. Interconnections, power
supply, and a control unit are also needed to complete
the system. All these components must be integrated
into textiles while still retaining the usual
tactile, flexible, and comfortable properties that we
expect from a textile. Adding new functionalities to
textiles while still maintaining the look and feel of the
fabric is where nanotechnology has a huge impact on
the textile industry. This article describes current developments
in materials for smart nanotextiles and
some of the many applications where these innovative
textiles are of great benefit
Uncovering Bias in Personal Informatics
Personal informatics (PI) systems, powered by smartphones and wearables,
enable people to lead healthier lifestyles by providing meaningful and
actionable insights that break down barriers between users and their health
information. Today, such systems are used by billions of users for monitoring
not only physical activity and sleep but also vital signs and women's and heart
health, among others. %Despite their widespread usage, the processing of
particularly sensitive personal data, and their proximity to domains known to
be susceptible to bias, such as healthcare, bias in PI has not been
investigated systematically. Despite their widespread usage, the processing of
sensitive PI data may suffer from biases, which may entail practical and
ethical implications. In this work, we present the first comprehensive
empirical and analytical study of bias in PI systems, including biases in raw
data and in the entire machine learning life cycle. We use the most detailed
framework to date for exploring the different sources of bias and find that
biases exist both in the data generation and the model learning and
implementation streams. According to our results, the most affected minority
groups are users with health issues, such as diabetes, joint issues, and
hypertension, and female users, whose data biases are propagated or even
amplified by learning models, while intersectional biases can also be observed
BIOTEX-biosensing textiles for personalised healthcare management.
Textile-based sensors offer an unobtrusive method of continually monitoring physiological parameters during daily activities. Chemical analysis of body fluids, noninvasively, is a novel and exciting area of personalized wearable healthcare systems. BIOTEX was an EU-funded project that aimed to develop textile sensors to measure physiological parameters and the chemical composition of body fluids, with a particular interest in sweat. A wearable sensing system has been developed that integrates a textile-based fluid handling system for sample collection and transport with a number of sensors including sodium, conductivity, and pH sensors. Sensors for sweat rate, ECG, respiration, and blood oxygenation were also developed. For the first time, it has been possible to monitor a number of physiological parameters together with sweat composition in real time. This has been carried out via a network of wearable sensors distributed around the body of a subject user. This has huge implications for the field of sports and human performance and opens a whole new field of research in the clinical setting
Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices
Previous studies have associated COVID-19 symptoms severity with levels of physical activity. We therefore investigated longitudinal trajectories of COVID-19 symptoms in a cohort of healthcare workers (HCWs) with non-hospitalised COVID-19 and their real-world physical activity. 121 HCWs with a history of COVID-19 infection who had symptoms monitored through at least two research clinic visits, and via smartphone were examined. HCWs with a compatible smartphone were provided with an Apple Watch Series 4 and were asked to install the MyHeart Counts Study App to collect COVID-19 symptom data and multiple physical activity parameters. Unsupervised classification analysis of symptoms identified two trajectory patterns of long and short symptom duration. The prevalence for longitudinal persistence of any COVID-19 symptom was 36% with fatigue and loss of smell being the two most prevalent individual symptom trajectories (24.8% and 21.5%, respectively). 8 physical activity features obtained via the MyHeart Counts App identified two groups of trajectories for high and low activity. Of these 8 parameters only âdistance moved walking or runningâ was associated with COVID-19 symptom trajectories. We report a high prevalence of long-term symptoms of COVID-19 in a non-hospitalised cohort of HCWs, a method to identify physical activity trends, and investigate their association. These data highlight the importance of tracking symptoms from onset to recovery even in non-hospitalised COVID-19 individuals. The increasing ease in collecting real-world physical activity data non-invasively from wearable devices provides opportunity to investigate the association of physical activity to symptoms of COVID-19 and other cardio-respiratory diseases
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