The wearable cardioverter defibrillator (WCD) is becoming a more and more widely used instrument for the prevention of sudden cardiac death of patients either with a secondary prevention implantable cardioverter defibrillator indication or with a transient high risk of sudden cardiac death. Although clinical practice has demonstrated a benefit of protecting patients for a period as long as 3-6 months with such devices, the current European guidelines concerning ventricular arrhythmias and sudden cardiac death are still extremely restrictive in the patient selection in part because of the costs derived from such a prevention device, in part because of the lack of robust randomised trials.To illustrate expanded use cases for the WCD, four real-life clinical cases are presented where patients received the device slightly outside the established guidelines. These cases demonstrate the broader utility of WCDs in situations involving acute myocarditis, thyrotoxicosis, pre-excited atrial fibrillation and awaiting staging/prognosis of a lung tumour. The findings prompt expansion of the existing guidelines for WCD use to efficiently protect more patients whose risk of arrhythmic cardiac death is transient or uncertain. This could be achieved by establishing a European register of the patients who receive a WCD for further analysis.

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Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment using AI-driven models. With the absence of labelled data and expert assessment, there is a research gap in providing virtual assessment, especially for unlabeled data. Inspired by the advances in consensus learning, in this paper, we propose a consensus clustering algorithm, PSA-NMF, that combines various clusterings into one united clustering, i.e., cluster consensus, to produce more stable and robust results compared to individual clustering. This paper is the first to investigate severity level using unsupervised learning and trunk displacement features in the frequency domain for post-stroke smart assessment. Two different methods of data collection from the U-limb datasets-the camera-based method (Vicon) and wearable sensor-based technology (Xsens)-were used. The trunk displacement method labelled each cluster based on the compensatory movements that stroke survivors employed for their daily activities. The proposed method uses the position and acceleration data in the frequency domain. Experimental results have demonstrated that the proposed clustering method that uses the post-stroke assessment approach increased the evaluation metrics such as accuracy and F-score. These findings can lead to a more effective and automated stroke rehabilitation process that is suitable for clinical settings, thus improving the quality of life for stroke survivors.

OBJECTIVE: To (1) describe the socio-demographic and behavioural characteristics of older adults who use wearable devices for physical activity monitoring and (2) explore whether wearable device use increases the possibilities of meeting physical activity guideline recommendations among older adults and older adults with known cardiovascular disease or risk.
BACKGROUND: Finding ways to increase physical activity and reduce cardiovascular disease risk among older adults is a public health priority. Wearable technology has great potential for promoting physical activity among older adults.
METHODS: A secondary analysis of the national data.
METHODS: A nationally representative sample of older adults aged 65 years and older (mean age = 73.79 years, N = 1484) and a subsample of older adults with known cardiovascular disease or cardiovascular disease risk (mean = 74.32 years, N = 1098) was used in the analysis. All analyses were weighted to account for the complex survey design. This study was reported according to the STROBE checklist.
RESULTS: The overall prevalence of wearable device use among older adults and older adults with cardiovascular disease risk was 16% and 14%, respectively. Older adults with higher household incomes, better self-rated health, and greater exercise enjoyment were more likely to use wearable devices. Compared with non-users, older adult users were more likely to meet the recommended levels of moderate (55% vs. 31%) and strengthening activity guidelines (46% vs. 25%), but not of the sedentary behaviour guideline (69% vs. 62%). Similar findings were also seen in older adults with known cardiovascular disease or risk.
CONCLUSIONS: The uptake of wearable devices in older adults, particularly those with known cardiovascular disease or risk is still low. The use of wearable devices is an important facilitator of physical activity. It is critical to provide individualised support for their engagement.
CONCLUSIONS: Age-friendly design and individualised support are recommended to increase older adults\' adoption of wearable devices to improve their physical health.
UNASSIGNED: No patient or public contribution was involved in this study since we used publicly available data.

The general public increasingly adopts smart wearable devices to quantify sleep characteristics and dedicated devices for sleep assessment. The rapid evolution of technology has outpaced the ability to implement validation approaches and demonstrate relevant clinical applicability. There are untapped opportunities to validate and refine consumer devices in partnership with scientists in academic institutions, patients, and the private sector to allow effective integration into clinical management pathways and facilitate trust in adoption once reliability and validity have been demonstrated. We call for the formation of a working group involving stakeholders from academia, clinical care and industry to develop clear professional recommendations to facilitate appropriate and optimized clinical utilization of such technologies.

The spread of wearable fitness trackers has contributed to the increase of various fitness studies, utilizing such devices to collect data from a group of users. When these data are disclosed, the lack of sanitization can lead to severe privacy risks. In this paper, we discuss the various threats that are posed by disclosing unaltered information from wearable devices. We also dismiss common fallacies of fitness data sharing and present practical guidelines to preserve user privacy.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a growing public health epidemic. In the UK, over 1.3 million people have a diagnosis of AF and an estimated 400,000 remain undiagnosed. AF-related strokes account for a quarter of all strokes and, as AF episodes are often asymptomatic, are still often the first manifestation of AF. Early diagnosis and initiation of oral anticoagulation, where appropriate, may prevent some of these thromboembolic strokes. Public Health England is committed to decrease the incidence of AF-related strokes and has sponsored initiatives aimed at improving AF detection by promoting the uptake of wearable technologies. However, the National Institute for Health and Care Excellence (NICE) has not recommended wearable technology in their recent AF diagnosis and management guidelines (NG196). Diagnostic accuracy of single-lead electrocardiography (ECG) generated by the latest iteration of wearable devices is excellent and, in many cases, superior to general practitioner interpretation of the 12-lead ECG. High-quality ECG from wearable devices that unequivocally shows AF can expedite AF detection. Otherwise, there is a real risk of delaying AF diagnosis with the potential of devastating consequences for patients and their families.

Wheelchair propulsion interventions typically teach manual wheelchair users to perform wheelchair propulsion biomechanics as recommended by the Clinical Practice Guidelines (CPG). Outcome measures for these interventions are primarily laboratory based. Discrepancies remain between manual wheelchair propulsion (MWP) in laboratory-based examinations and propulsion in the real-world. Current developments in machine learning (ML) allow for monitoring of MWP in the real world. In this study, we collected data from participants enrolled in two wheelchair propulsion interventions, then built an ML algorithm to distinguish CPG recommended MWP patterns from non-CPG-recommended patterns. Eight primary manual wheelchair users did not initially follow CPG recommendations but learned and performed CPG propulsion after the interventions. Participants each wore two inertial measurement units as they propelled their wheelchairs on a roller system, indoors overground, and outdoors. ML models were trained to classify propulsion patterns as following the CPG or not following the CPG. Video recordings were used for reference. For indoor detection, we found that a subject-independent model was able to achieve 85% accuracy. For outdoor detection, we found that the subject-independent model achieved 75.4% accuracy. These results provide further evidence that CPG and non-CPG-recommended MWP patterns can be predicted with wearable sensors using an ML algorithm.

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy, on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found moderate level of evidence for seizure types without GTCs or FBTCs. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.