Long-term adherence to a wearable for continuous behavioural activity measuring in the SafeHeart implantable cardioverter defibrillator population.

Aims: Wearable health technologies are increasingly popular. Yet, wearable monitoring only works when devices are worn as intended, and adherence reporting lacks standardization. In this study, we aimed to explore the long-term adherence to a wrist-worn activity tracker in the prospective SafeHeart study and identify patient characteristics associated with adherence.

Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias.

We aimed to identify and characterise behavioural profiles in patients at high risk of SCD, by using deep representation learning of day-to-day behavioural recordings. We present a pipeline that employed unsupervised clustering on low-dimensional representations of behavioural time-series data learned by a convolutional residual variational neural network (ResNet-VAE). Data from the prospective, observational SafeHeart study conducted at two large tertiary university centers in the Netherlands and Denmark were used.

Behavioural digital biomarkers enable real-time monitoring of patient-reported outcomes: a substudy of the multicentre, prospective observational SafeHeart study.

Aims: Patient-reported outcome measures (PROMs) serve multiple purposes, including shared decision-making and patient communication, treatment monitoring, and health technology assessment. Patient monitoring using PROMs is constrained by recall and non-response bias, respondent burden, and missing data. We evaluated the potential of behavioural digital biomarkers obtained from a wearable accelerometer to achieve personalized predictions of PROMs.

Using Consumer-Wearable Activity Trackers for Risk Prediction of Life-Threatening Heart Arrhythmia in Patients with an Implantable Cardioverter-Defibrillator: An Exploratory Observational Study.

Ventricular arrhythmia (VA) is a leading cause of sudden death and health deterioration. Recent advances in predictive analytics and wearable technology for behavior assessment show promise but require further investigation. Yet, previous studies have only assessed other health outcomes and monitored patients for short durations (7−14 days).

Accelerometer-assessed physical behavior and the association with clinical outcomes in implantable cardioverter-defibrillator recipients: A systematic review.

Background: Current implantable cardioverter-defibrillator (ICD) devices are equipped with a device-embedded accelerometer capable of capturing physical activity (PA). In contrast, wearable accelerometer-based methods enable the measurement of physical behavior (PB) that encompasses not only PA but also sleep behavior, sedentary time, and rest-activity patterns.

Objective: This systematic review evaluates accelerometer-based methods used in patients carrying an ICD or at high risk of sudden cardiac death.

Rationale and design of the SafeHeart study: Development and testing of a mHealth tool for the prediction of arrhythmic events and implantable cardioverter-defibrillator therapy.

Background: Patients with an implantable cardioverter-defibrillator (ICD) are at a high risk of malignant ventricular arrhythmias. The use of remote ICD monitoring, wearable devices, and patient-reported outcomes generate large volumes of potential valuable data. Artificial intelligence-based methods can be used to develop personalized prediction models and improve early-warning systems.

Experiences With Wearable Activity Data During Self-Care by Chronic Heart Patients: Qualitative Study.

Background: Most commercial activity trackers are developed as consumer devices and not as clinical devices. The aim is to monitor and motivate sport activities, healthy living, and similar wellness purposes, and the devices are not designed to support care management in a clinical context. There are great expectations for using wearable sensor devices in health care settings, and the separate realms of wellness tracking and disease self-monitoring are increasingly becoming blurred.

Unpacking telemonitoring work: Workload and telephone calls to patients in implanted cardiac device care.

Objective: Telemonitoring of cardiac implantable electronic devices (CIEDs) has many advantages. However, telemonitoring involves clinical work that is often overlooked or considered a burden, such as the work performed during telephone contact with patients. The objective of this study was to scrutinize telephone calls to and from patients to understand the clinical workload in CIED remote monitoring.

Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning.

Aims: Electrical storm (ES) is a serious arrhythmic syndrome that is characterized by recurrent episodes of ventricular arrhythmias. Electrical storm is associated with increased mortality and morbidity despite the use of implantable cardioverter-defibrillators (ICDs). Predicting ES could be essential; however, models for predicting this event have never been developed.

The agency of patients and carers in medical care and self-care technologies for interacting with doctors.

People living with Parkinson's disease engage in self-care for most of the time but, two or three times a year, they meet with doctors to re-evaluate the condition and adjust treatment. Patients and (informal) carers participate actively in these encounters, but their engagement might change as new patient-centred technologies are integrated into healthcare infrastructures. Drawing on a qualitative study that used observations and interviews to investigate consultations, and digital ethnography to understand interactions in an online community, we describe how patients and carers living with Parkinson's participate in the diagnosis and treatment decisions, engage in discussions to learn about certain topics, and address inappropriate medication.

From Prototype to Product: Making Participatory Design of mHealth Commercially Viable.

This paper delves into the challenges of engaging patients, clinicians and industry stakeholders in the participatory design of an mHealth platform for patient-clinician collaboration. It follows the process from the development of a research prototype to a commercial software product. In particular, we draw attention to four major challenges of (a) aligning the different concerns of patients and clinicians, (b) designing according to clinical accountability, (c) ensuring commercial interest, and (d) dealing with regulatory constraints when prototyping safety critical health Information Technology.

From Research Prototypes to a Marketable eHealth System.

This paper presents three distinct challenges to research and development (R&D) of marketable eHealth systems and suggests strategies to mitigate them. The eHealth system in question is designed to improve self-care and collaboration between remotely monitored heart failure patients and clinicians. By way of introspection and reflection on a current and a previous project, the authors propose solutions for mitigating the central challenges.

Designing for collaborative interpretation in telemonitoring: re-introducing patients as diagnostic agents.

Purpose: We investigate why clinicians experience problems interpreting implantable cardioverter-defibrillator (ICD) data when the patient is absent, and we explore how to re-introduce patients into the socio-technical setup of telemonitored interpretation practices.

Method: An action research study with a design interventionist perspective was conducted to investigate the telemonitoring arrangement for chronic heart patients with ICDs and to identify the nature of the collaborative practices involved in ICD data interpretation. We diagnose the main challenges involved in collaborative interpretation practices.