Prolonged cardiac monitoring improves detection of atrial fibrillation and anti-coagulation use after cryptogenic stroke or TIA and therefore should be considered instead of shorter duration of cardiac monitoring.

Difference in features of voice such as tone, volume, intonation, and rate of speech have been suggested as sensitive and valid measures of mental illness. Researchers have used analysis of voice recordings during phone calls, response to the IVR systems and smartphone based conversational agents as a marker in continuous monitoring of symptoms and effect of treatment in patients with mental illness. While these methods of recording the patient's voice have been considered efficient, they come with a number of issues in terms of adoption, privacy, security, data storage etc. To address these issues we propose a smart speaker based conversational agent -"Hear me out". In this paper, we describe the proposed system, rationale behind using smart speakers, and the challenges we are facing in the design of the system. CCS CONCEPTS• Human-centered computing → Sound-based input / output.

This article presents the design, technical implementation, and feasibility evaluation of mCardia —a context-aware, mobile electrocardiogram (ECG) collection system for longitudinal arrhythmia screening under free-living conditions. Along with ECG, mCardia also records active and passive contextual data, including patient-reported symptoms and physical activity. This contextual data can provide a more accurate understanding of what happens before, during, and after an arrhythmia event, thereby providing additional information in the diagnosis of arrhythmia. By using a plugin-based architecture for ECG and contextual sensing, mCardia is device-agnostic and can integrate with various wireless ECG devices and supports cross-platform deployment. We deployed the mCardia system in a feasibility study involving 24 patients who used the system over a two-week period. During the study, we observed high patient acceptance and compliance with a satisfactory yield of collected ECG and contextual data. The results demonstrate the high usability and feasibility of mCardia for longitudinal ambulatory monitoring under free-living conditions. The article also reports from two clinical cases, which demonstrate how a cardiologist can utilize the collected contextual data to improve the accuracy of arrhythmia analysis. Finally, the article discusses the lessons learned and the challenges found in the mCardia design and the feasibility study.

In clinical cohort studies, researchers analyse the life history of population groups to understand the evolution of diseases. Health research data platforms came to facilitate such studies as they allow multiple projects to share access to cohorts' non-identifiable health information. Some latest initiatives are also starting to include mobile-generated data in their research programmes. Although seemly beneficial, it is not yet clear how potential participants feel about contributing to the new platforms: there is a need to investigate potential factors related to the acceptance in this specific context. In this paper, previous works from related contexts were brought together and, along with a qualitative study, composed a participant-centred perspective of enablers and barriers for contribution. We found that there is an apparent misalignment between current implementations and participants' preferences, leading us to propose design guidelines for future developments which can make participation more ethical and engaging.