Telemonitoring Applications Research Articles

Development and piloting of nurse-led mHealth care for heart failure patients: A feasibility study

Development and piloting of nurse-led mHealth care for heart failure patients: A feasibility study Abstract: Background: Chronic heart failure (cHI) is the most common reason for hospitalization. Telemonitoring (TM) and Heart Failure Nurses (HFN) can detect decompensations and prevent hospitalizations. Aim: The aim was to develop and examine the feasibility of a digitally supported care-guided care model for cHI patients and to evaluate it in terms of adherence, technology usability, satisfaction, self-care and health literacy, disease knowledge, quality of life and health status. Methods: The study was conducted in a user-centered manner using mixed. The 6-month pilot was carried out in the patients' domestics using qualitative (e.g. focus groups) and quantitative methods (validated questionnaires). Results: A total of n = 30 cHI patients (age: M 66, SD 14 years; ♀ n = 10 [31%], ♂ n = 22 [69%]) completed the clinical testing. The intervention consisted of: Training, the TM application, nursing/medical advice and support and a digital library. Adherence to TM was over 80% (relative adherence: M 87, SD 16%; absolute adherence: M 82, SD 19%). Health literacy, disease knowledge and quality of life increased. The state of health improved and those affected felt more confident in dealing with the disease. No changes were recorded for self-care skills. Conclusions: Hybrid care is very well received and demonstrates the potential to promote self-care for those affected.

Efficacy of Remote Health Monitoring in Reducing Hospital Readmissions Among High-Risk Postdischarge Patients: Prospective Cohort Study.

Patients with respiratory or cardiovascular diseases often experience higher rates of hospital readmission due to compromised heart-lung function and significant clinical symptoms. Effective measures such as discharge planning, case management, home telemonitoring follow-up, and patient education can significantly mitigate hospital readmissions. This study aimed to determine the efficacy of home telemonitoring follow-up in reducing hospital readmissions, emergency department (ED) visits, and total hospital days for high-risk postdischarge patients. This prospective cohort study was conducted between July and October 2021. High-risk patients were screened for eligibility and enrolled in the study. The intervention involved implementing home digital monitoring to track patient health metrics after discharge, with the aim of reducing hospital readmissions and ED visits. High-risk patients or their primary caregivers received education on using communication measurement tools and recording and uploading data. Before discharge, patients were familiarized with these tools, which they continued to use for 4 weeks after discharge. A project manager monitored the daily uploaded health data, while a weekly video appointment with the program coordinator monitored the heart and breathing sounds of the patients, tracked health status changes, and gathered relevant data. Care guidance and medical advice were provided based on symptoms and physiological signals. The primary outcomes of this study were the number of hospital readmissions and ED visits within 3 and 6 months after intervention. The secondary outcomes included the total number of hospital days and patient adherence to the home monitoring protocol. Among 41 eligible patients, 93% (n=38) were male, and 46% (n=19) were aged 41-60 years, while 46% (n=19) were aged 60 years or older. The study revealed that home digital monitoring significantly reduced hospitalizations, ED visits, and total hospital stay days at 3 and 6 months after intervention. At 3 months after intervention, average hospitalizations decreased from 0.45 (SD 0.09) to 0.19 (SD 0.09; P=.03), and average ED visits decreased from 0.48 (SD 0.09) to 0.06 (SD 0.04; P<.001). Average hospital days decreased from 6.61 (SD 2.25) to 1.94 (SD 1.15; P=.08). At 6 months after intervention, average hospitalizations decreased from 0.55 (SD 0.11) to 0.23 (SD 0.09; P=.01), and average ED visits decreased from 0.55 (SD 0.11) to 0.23 (SD 0.09; P=.02). Average hospital days decreased from 7.48 (SD 2.32) to 6.03 (SD 3.12; P=.73). By integrating home telemonitoring with regular follow-up, our research demonstrates a viable approach to reducing hospital readmissions and ED visits, ultimately improving patient outcomes and reducing health care costs. The practical application of telemonitoring in a real-world setting showcases its potential as a scalable solution for chronic disease management.

Technologies and main functionalities of the telemonitoring application reCOVeryaID.

The COVID-19 pandemic has highlighted the need to take advantage of specific and effective patient telemonitoring platforms, with specific reference to the constant monitoring of vital parameters of patients most at risk. Among the various applications developed in Italy, certainly there is reCOVeryaID, a web application aimed at remotely monitoring patients potentially, currently or no longer infected with COVID-19. Therefore, in this paper we present a system model, consisting of a multi-platform intelligent telemonitoring application, that enables remote monitoring and provision of integrated home care to both patients symptomatic, asymptomatic and pre-symptomatic with severe acute respiratory infectious disease or syndrome caused by viruses belonging to the Coronavirus family, as well as simply to people with respiratory problems and/or related diseases (chronic obstructive pulmonary disease or asthma). In fact, in this paper we focus on exposing the technologies and various functionalities offered by the system, which constitute the practical implementation of the theoretical framework described in detail in another paper. Specifically, the reCOVeryaID telemonitoring application is a stand-alone, knowledge base-supported application that can promptly react and inform physicians if dangerous trends in a patient's short- and long-term vital signs are detected, thus enabling them to be monitored continuously, both in the hospital and at home. The paper also reports an evaluation of user satisfaction, carried out by actual patients and medical doctors.

A Novel Semi-Supervised Learning Model for Smartphone-Based Health Telemonitoring

Telemonitoring is the use of electronic devices such as smartphones to remotely monitor patients. It provides great convenience and enables timely medical decisions. To facilitate the decision making for each patient, a model is needed to translate the data collected by the patient’s smartphone into a predicted score for his/her disease severity. To train a robust predictive model, semi-supervised learning (SSL) provides a viable approach by integrating both labeled and unlabeled samples to leverage all the available data from each patient. There are two challenging issues that need to be simultaneously addressed in using SSL for this problem: (1) feature selection from high-dimensional noisy telemonitoring data; and (2) instance selection from many, possibly redundant unlabeled samples. We propose a novel SSL model allowing for simultaneous feature and instance selection, namely the S2SSL model. We present a real-data application of telemonitoring for patients with Parkinson’s Disease using their smartphone-collected activity data such as tapping and speaking. A total of 382 features were extracted from the activity data of each patient. 74 labeled and 563 unlabeled instances from 37 patients were used to train S2SSL. The trained model achieved a high accuracy of 0.828 correlation between the true and predicted disease severity scores on a validation dataset. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Telemonitoring is an emerging health care platform enabled by smartphones and wearables. Because it allows for health data to be collected anytime and anywhere, patients can be frequently monitored and medical decisions can be made more timely and effectively. This paper addresses the data science challenges in leveraging the telemonitoring platform to benefit patient care. Specifically, we propose a new model, S2SSL, to tackle these challenges and provide better robustness, accuracy, and efficiency. This paper may be interesting to health care practitioners seeking advanced analytics capabilities to model and integrate the data collected through telemonitoring devices, with ultimate purposes of improving the decision in treating each patient and increasing patient access to specialized care.

Telemonitoring: ethical lessons from the COVID-19 pandemic

BackgroundThe COVID-19 pandemic accelerated the development and application of telemonitoring, enabling health care providers to continue to provide medical care. Telemonitoring oftentimes replaced face-to-face health care services instead of as being offered as a supplement to regular medical care. Given that pressure on hospitals is expected to remain, telemonitoring is seen as an important means of alleviating those pressures. In this paper, we consider the intensified deployment of telemonitoring during the pandemic as an excellent opportunity to learn how telemonitoring can be implemented in a morally responsible way.ResultsIn order to gain concrete, contextual and in-depth knowledge of the ethical issues raised by telemonitoring during the corona pandemic, we explore telemonitoring for two conditions: COVID-19 and Idiopathic pulmonary fibrosis. We conducted interviews with patients and professionals on five important ethical themes: 1. a shift in responsibilities, 2. empowerment and self-management, 3. the value of face-to-face consultations, 4. inclusivity and equal access, and 5. privacy and big data. Based on the findings of this empirical study and medical ethical principles, we formulate lessons for responsible implementation and upscaling: 1. ensure explicit and realistic allocation of responsibilities and avoid expectations that monitoring is more direct and continuous than it actually is; 2. create opportunities for an optimal form of self-management—in particular for patients with chronic conditions—and for meaningful conversations; 3. integrate telemonitoring within an established HCP – patient trust relation and stimulate research on the conditions for face-to-face consultations; 4. take vulnerability into account in inclusion & exclusion criteria and involve patients in design and implementation processes; 5. concerns of collection of data are beyond privacy and identify the risks of dependency on commercial companies.ConclusionsOur findings show that offering patients choices for telemonitoring will not necessarily result in increased and equal accessibility, good quality of care and patient autonomy. Whether or not these aims and promises will be fulfilled, and the ethical challenges adequately met, is highly dependent on choices regarding the design of specific monitoring systems, the development process and the embeddedness in a trusting physician–patient relationship.

Adaptive Smart eHealth Framework for Personalized Asthma Attack Prediction and Safe Route Recommendation

Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual.

A deep learning-based telemonitoring application to automatically assess oral diadochokinesis in patients with bulbar amyotrophic lateral sclerosis

Background and objectivesTimely identification of dysarthria progression in patients with bulbar-onset amyotrophic lateral sclerosis (ALS) is relevant to have a comprehensive assessment of the disease evolution. To this goal literature recognized the utmost importance of the assessment of the number of syllables uttered by a subject during the oral diadochokinesis (DDK) test. MethodsTo support clinicians, this work proposes a remote deep learning-based system, which consists (i) of a web application to acquire audio tracks of bulbar-onset ALS patients and healthy control subjects while performing the oral DDK test (i.e., repeating the /pa/, /pa-ta-ka/ and /oo-ee/ syllables) and (ii) a DDK-AID network designed to process the acquired audio signals which have different duration and to output the number of per-task syllables repeated by the subject. ResultsThe DDK-AID network overcomes the comparative method achieving a mean Accuracy of 90.23 in counting syllables repeated by the eleven bulbar-onset ALS-patients while performing the oral DDK test. ConclusionsThe proposed remote monitoring system, in the light of the achieved performance, represents an important step towards the implementation of self-service telemedicine systems which may ensure customised care plans.

An intelligent telemonitoring application for coronavirus patients: reCOVeryaID.

The COVID-19 emergency underscored the importance of resolving crucial issues of territorial health monitoring, such as overloaded phone lines, doctors exposed to infection, chronically ill patients unable to access hospitals, etc. In fact, it often happened that people would call doctors/hospitals just out of anxiety, not realizing that they were clogging up communications, thus causing problems for those who needed them most; such people, often elderly, have often felt lonely and abandoned by the health care system because of poor telemedicine. In addition, doctors were unable to follow up on the most serious cases or make sure that others did not worsen. Thus, uring the first pandemic wave we had the idea to design a system that could help people alleviate their fears and be constantly monitored by doctors both in hospitals and at home; consequently, we developed reCOVeryaID, a telemonitoring application for coronavirus patients. It is an autonomous application supported by a knowledge base that can react promptly and inform medical doctors if dangerous trends in the patient's short- and long-term vital signs are detected. In this paper, we also validate the knowledge-base rules in real-world settings by testing them on data from real patients infected with COVID-19.

Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network

Human Activity Recognition (HAR) has been proven to be effective in various healthcare and telemonitoring applications. Current HAR methods, especially deep learning, are extensively employed owing to their exceptional recognition capabilities. However, in pursuit of enhancing feature expression abilities, deep learning often introduces a trade-off by increasing Time complexity. Moreover, the intricate nature of human activity data poses a challenge as it can lead to a notable decrease in recognition accuracy when affected by additional noise. These aspects will significantly impair recognition performance. To advance this field further, we present a HAR method based on an edge-computing-assisted and GRU deep-learning network. We initially proposed a model for edge computing to optimize the energy consumption and processing time of wearable devices. This model transmits HAR data to edge-computable nodes, deploys analytical models on edge servers for remote training, and returns results to wearable devices for processing. Then, we introduced an initial convolution method to preprocess large amounts of training data more effectively. To this end, an attention mechanism was integrated into the network structure to enhance the analysis of confusing data and improve the accuracy of action classification. Our results demonstrated that the proposed approach achieved an average accuracy of 85.4% on the 200 difficult-to-identify HAR data, which outperforms the Recurrent Neural Network (RNN) method’s accuracy of 77.1%. The experimental results showcase the efficacy of the proposed method and offer valuable insights for the future application of HAR.

Self-care management and experiences of using telemonitoring as support when living with hypertension or heart failure: A descriptive qualitative study

BackgroundThe need for support in self-care at home will increase with the growing older population with chronic illness. Many people have one or more chronic illnesses and struggle with self-care activities, often supported by informal carers at home. The rapid development of telemonitoring applications in primary care calls for increased knowledge about how people with chronic illness and their informal carers experience the use of telemonitoring applications at home. ObjectiveThis study aims to describe experiences of self-care management at home when living with hypertension or heart failure, with support from primary care through telemonitoring. DesignA descriptive qualitative approach was applied using semi-structured interviews with patients and informal carers in a pilot project on telemonitoring of chronic illness in primary care from October 2019 to June 2021. SettingParticipants were recruited from three primary care settings and one medical department at one hospital in a region in southern Sweden. ParticipantsA purposive sample of patients (n = 20) with chronic illness living at home and their informal carers (n = 4) were recruited. MethodsSemi-structured telephone interviews were conducted, guided by open-ended questions targeting patients’ and informal carers’ experiences of self-care management at home and using telemonitoring applications as support. Transcribed interviews were analyzed through qualitative content analysis. Results‘Developing the capability to perform self-care with technology as both an intruder and an invited guest’ was the unifying theme that tied together the experiences of patients with chronic illness and their informal carers. Experiences of self-care management included acquiring necessary self-care skills, expertise in managing their chronic illness, and reciprocal relationships with healthcare professionals when using telemonitoring application as support in self-care monitoring of vital parameters. However, uncertainty regarding the interpretation of symptoms and a feeling of exclusion were seen. ConclusionsTelemonitoring applications offer potential support for patients with chronic illnesses and their informal carers, enabling them to establish new routines and enhance motivation for self-care activities at home. This study emphasizes the adaptability of telemonitoring applications in meeting the unique support requirements of patients and informal carers when managing self-care at home.

Barriers and facilitators in implementing a telemonitoring application for Chronic Kidney Disease patients and health professionals - An ancillary implementation study of the NeLLY Stepped-Wedge Randomized Controlled Trial (Preprint)

Barriers and facilitators in implementing a telemonitoring application for Chronic Kidney Disease patients and health professionals - An ancillary implementation study of the NeLLY Stepped-Wedge Randomized Controlled Trial (Preprint)

Agreement between patients and physicians on scores of inflammatory bowel disease activity and burden assessed on a telemonitoring platform

Background and aims Telemonitoring is increasingly used in the management of IBD patients. We investigated the agreement between patients and physicians on scores of disease activity and burden. Methods Consecutive outpatients at one IBD clinic were recruited between February and December 2021. Enrolled patients completed a questionnaire for disease activity (Harvey-Bradshaw Index [HBI] for Crohn’s disease or Simple Clinical Colitis Activity Index [SCCAI] for ulcerative colitis) and a test of disease burden (Pictorial Representation of Illness and Self Measure [PRISM]). They did the tests within 5 days of an outpatient visit, working independently on IBD Tool, a new web-based telemonitoring application. Concomitantly, the senior and junior physicians who examined them completed the same tests. The agreement was tested for every pair of scores. Results Five hundred and sixty patients (289 Crohn’s disease; 271 ulcerative colitis) completed disease questionnaires on IBD Tool (in total, 742 times). By Spearman’s correlation, the agreement was substantial both for HBI (rho 0.685–0.837) and SCCAI (rho 0.694–0.888) for comparisons between patients, junior and senior physicians. The agreement was moderate-to-substantial for PRISM (rho 0.406–0.725) for the same comparisons. The correlation between disease activity (HBI/SCCAI) and PRISM scores was substantial for senior (rho 0.757–0.788) or junior (rho 0.746–0.753) physicians and moderate for patients (rho 0.458–0.486). The median PRISM score difference was 2.3–1.6 points lower between patients and senior-junior physicians. Conclusion Agreement between IBD patients and physicians was substantial for disease activity and moderate for disease impact. The inclusion of disease burden scoring in telemonitoring platforms provides important information for the management of IBD patients. Study highlights What IS known •Continuous response to treatments and patient-reported outcomes became an essential goal for IBD patient management. •The use of tele-monitoring and eHealth technologies allows for regular disease assessments and for managing more efficiently IBD patients; disease questionnaires and tests are key to support eHealth tools. What is new here •Agreement between IBD patients and physicians was substantial for disease activity and moderate for disease burden, while agreement among junior and senior physicians was substantial for both. •PRISM performs as well for ulcerative colitis as for Crohn’s patients. •The inclusion of disease burden tests might add to eHealth platforms valuable information, complemental to disease activity questionnaires.

A new real-time lossless data compression algorithm for ECG and PPG signals

ObjectiveData compression is a useful process in tele-monitoring applications, in which lesser number of bits are needed to represent the same data. In this work, a run-time lossless compression of single-channel Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals is proposed, maintaining all dominant features. MethodsThe single-channel data are first quantized using optimal quantization level, so that fewer number of bits are needed to represent it, maintaining low quantization error. Then, second order delta encoding and run-length encoding (RLE) based data compression are proposed in this work. A new approach of using ‘buffer array’ along with RLE is also introduced, so that minimum bits are needed to store. ResultsThis algorithm was tested on various single-lead ECG and PPG signals available in Physionet. An average compression ratio (CR) was achieved of 6.52, 3.82, and 2.49 for 547 PTBDB ECG records, 48 MITDB ECG records, and 53 MIMIC-II PPG records, respectively. This algorithm was also performed on single-channel ECG, collected from 10 healthy volunteers using AD8232 ECG module, with 125 Hz sampling frequency and 10-bit data resolution, which resulted in average CR of 2.34. ConclusionThis algorithm was also performed on a smartphone device that provided user-friendly operation. The low computational complications and standalone operation of data collection, compression, and transmission encouraged its implementation for run-time operation. SignificanceA comparative study of the proposed work with previously published works proved this fact that this algorithm provided better performance in the area of run-time patient health monitoring applications.

Nutrition Telemonitoring for CKD-HD Patients: A Qualitative Study on Patients, Patients’ Families, Medical Teams, and Hospital Management

BACKGROUND: CKD-HD patients require a nutritional monitoring system to control the amount of protein, fluid, sodium, potassium, and phosphate intake to reduce readmission rates and to improve their life quality. The Nutrition Telemonitoring Application (NTA) has a potential to resolve this problem. AIM: Therefore, this study aims to prepare NTA design materials conditioned to the needs of patients, families, medical teams, and hospital management. METHODS: A qualitative study with semi-structured interviews was conducted on 16 informants consisting of patients, families, medical teams, and hospital management teams. Furthermore, a purposive sampling technique was used to recruit participants, and the thematic analysis of the audio recording was conducted. RESULTS: Three main themes and seven sub-themes were identified. First, all participants in support of NTA will be beneficial. Second, NTA implementation requires family, medical team, hospital management, and BPJS (Health Social Security Agency). Third, all participants demand an easy Indonesian language interface equipped with pictures of food portions, additional alarm features, exercise, food recipes, educational videos, and free of charge. CONCLUSIONS: This study provides important insights considering the issues associated with the sustainability of NTA design and long-term benefits for CKD-HD patients.

Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning.

Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram and point clouds for 19 human joints. We developed a multilayer Convolutional Neural Network (CNN) to recognize and classify five different gait patterns with an accuracy of 95.7 to 98.8% using MMW radar data. During training of the CNN algorithm, we used the extracted 3D coordinates of 25 joints using the Kinect V2 sensor and compared them with the point clouds data to improve the estimation. Finally, we performed a real-time simulation to observe the point cloud behavior for different activities and validated our system against the ground truth values. The proposed method demonstrates the ability to distinguish between different human activities to obtain clinically relevant gait information.

Development of an Area Scan Step Length Measuring System Using a Polynomial Estimate of the Heel Cloud Point

Due to impaired mobility caused by aging, it is very important to employ early detection and monitoring of gait parameters to prevent the inevitable huge amount of medical cost at a later age. For gait training and potential tele-monitoring application outside clinical settings, low-cost yet highly reliable gait analysis systems are needed. This research proposes using a single LiDAR system to perform automatic gait analysis with polynomial fitting. The experimental setup for this study consists of two different walking speeds, fast walk and normal walk, along a 5-m straight line. There were ten test subjects (mean age 28, SD 5.2) who voluntarily participated in the study. We performed polynomial fitting to estimate the step length from the heel projection cloud point laser data as the subject walks forwards and compared the values with the visual inspection method. The results showed that the visual inspection method is accurate up to 6 cm while the polynomial method achieves 8 cm in the worst case (fast walking). With the accuracy difference estimated to be at most 2 cm, the polynomial method provides reliability of heel location estimation as compared with the observational gait analysis. The proposed method in this study presents an improvement accuracy of 4% as opposed to the proposed dual-laser range sensor method that reported 57.87 cm ± 10.48, an error of 10%. Meanwhile, our proposed method reported ±0.0633 m, a 6% error for normal walking.

Digital Healthcare for Airway Diseases from Personal Environmental Exposure.

Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, health products and services have expanded by the use and development of artificial intelligence, mobile health applications, and wearable electronic devices. Such advancements have enabled accurate and updated tracking and modeling of health conditions. For instance, digital health technologies are capable of measuring environmental pollution and predicting its adverse health effects. Several health conditions, including chronic airway diseases such as asthma and chronic obstructive pulmonary disease, can be exacerbated by pollution. These diseases impose substantial health burdens with high morbidity and mortality. Recently, efforts have been made to develop digital technologies to alleviate such conditions. Moreover, the COVID-19 pandemic has facilitated the application of telemedicine and telemonitoring for patients with chronic airway diseases. This article reviews current trends and studies in digital technology utilization for investigating and managing environmental exposure and chronic airway diseases. First, we discussed the recent progression of digital technologies in general environmental healthcare. Then, we summarized the capacity of digital technologies in predicting exacerbation and self-management of airway diseases. Concluding these reviews, we provided suggestions to improve digital health technologies’ abilities to reduce the adverse effects of environmental exposure in chronic airway diseases, based on personal exposure-response modeling.

Efficient on-site confirmatory testing for atrial fibrillation with derived 12-lead ECG in a wireless body area network.

Smartphones that can support and assist the screening of various cardiovascular diseases are gaining popularity in recent years. The timely detection, diagnosis, and treatment of atrial fibrillation (AF) are critical, especially for those who are at risk of stroke. AF detection via screening with wearable devices should always be confirmed by a standard 12-lead electrocardiogram (ECG). However, the inability to perform on-site AF confirmatory testing results in increased patient anxiety, followed by unnecessary diagnostic procedures and treatments. Also, the delay in confirmation procedure may conclude the condition as non-AF while it was indeed present at the time of screening. To overcome these challenges, we propose an efficient on-site confirmatory testing for AF with 12-lead ECG derived from the reduced lead set (RLS) in a wireless body area network (WBAN) environment. The reduction in the number of leads enhances the comfort level of patients as well as minimizes the hurdles associated with continuous telemonitoring applications such as data transmission, storage, and bandwidth of the overall system. The proposed method is characterized by segment-wise regression and a lead selection algorithm, facilitating improved P-wave reconstruction. Further, an efficient AF detection algorithm is proposed by incorporating a novel three-level P-wave evidence score with an RR irregularity evidence score. The proposed on-site AF confirmation test reduces false positives and false negatives by 88% and 53% respectively, compared to single lead screening. In addition, the proposed lead derivation method improves accuracy, F_1-score, and Matthews correlation coefficient (MCC) for the on-site AF detection compared to existing related methods.

Prognostic value of signs and symptoms in heart failure patients using remote telemonitoring

Introduction Heart failure is a serious burden on health care systems due to frequent hospital admissions. Early recognition of outpatients at risk for clinical deterioration could prevent hospitalization. Still, the role of signs and symptoms in monitoring heart failure patients is not clear. The heart failure coach is a web-based telemonitoring application consisting of a 9-item questionnaire assessment of heart failure signs and symptoms and developed to identify outpatients at risk for clinical deterioration. If deterioration was suspected, patients were contacted by a heart failure nurse for further evaluation. Methods Heart failure coach questionnaires completed between 2015 and 2018 were collected from 287 patients, completing 18,176 questionnaires. Adverse events were defined as all-cause mortality, heart failure- or cardiac-related hospital admission or emergency cardiac care visits within 30 days after completion of each questionnaire. Multilevel logistic regression analyses were performed to assess the association between the heart failure coach questionnaire items and the odds of an adverse event. Results No association between dyspnea and adverse events was observed (odds ratio 1.02, 95% confidence interval 0.79–1.30). Peripheral edema (odds ratio 2.21, 95% confidence interval 1.58–3.11), persistent chest pain (odds 2.06, 95% confidence interval 1.19–3.58), anxiety about heart failure (odds ratio 2.12, 95% confidence interval 1.44–3.13), and extensive struggle to perform daily activities (odds ratio 2.23, 95% confidence interval 1.38–3.62) were significantly associated with adverse outcome. Discussion Regular assessment of more than the classical signs and symptoms may be helpful to identify heart failure patients at risk for clinical deterioration and should be an integrated part of heart failure telemonitoring programs.

Utility and usability of an automated COVID-19 symptom monitoring system (CoSMoS) in primary care during COVID-19 pandemic: A qualitative feasibility study

BackgroundCOVID-19 telemonitoring applications have been developed and used in primary care to monitor patients quarantined at home. There is a lack of evidence on the utility and usability of telemonitoring applications from end-users’ perspective. ObjectivesThis study aimed to evaluate the feasibility of a COVID-19 symptom monitoring system (CoSMoS) by exploring its utility and usability with end-users. MethodsThis was a qualitative study using in-depth interviews. Patients with suspected COVID-19 infection who used CoSMoS Telegram bot to monitor their COVID-19 symptoms and doctors who conducted the telemonitoring via CoSMoS dashboard were recruited. Universal sampling was used in this study. We stopped the recruitment when data saturation was reached. Patients and doctors shared their experiences using CoSMoS, its utility and usability for COVID-19 symptoms monitoring. Data were coded and analysed using thematic analysis. ResultsA total of 11 patients and 4 doctors were recruited into this study. For utility, CoSMoS was useful in providing close monitoring and continuity of care, supporting patients’ decision making, ensuring adherence to reporting, and reducing healthcare workers’ burden during the pandemic. In terms of usability, patients expressed that CoSMoS was convenient and easy to use. The use of the existing social media application for symptom monitoring was acceptable for the patients. The content in the Telegram bot was easy to understand, although revision was needed to keep the content updated. Doctors preferred to integrate CoSMoS into the electronic medical record. ConclusionCoSMoS is feasible and useful to patients and doctors in providing remote monitoring and teleconsultation during the COVID-19 pandemic. The utility and usability evaluation enables the refinement of CoSMoS to be a patient-centred monitoring system.