JMIR Publications

ABSTRACT

Background:

Heart Failure (HF) is a complex clinical syndrome with a high morbidity and mortality rate. Despite advancements in treatment, the recurrence of HF remains a significant challenge, often leading to deteriorating health conditions and increased pressure on the healthcare system. Early detection of recurrence is pivotal in mitigating and managing the adverse outcomes associated with HF.

Objective:

The primary objective of this study is to collect data that facilitates the identification of digital biomarkers that may indicate deterioration of the heart, and ultimately develop algorithms that can predict HF.

Methods:

This prospective cohort study is conducted in Copenhagen, Denmark, and will recruit individuals diagnosed with decompensated HF. Participants will be followed for a period of one year, during which they will undergo a Quarterly Assessment Period (QAP) every three months. Each QAP spans seven days and involves continuous monitoring using an ambulatory electrocardiogram (ECG) sensor. Throughout each QAP, participants will also complete daily assessments and questionnaires. All data will be collected using a dedicated mobile application installed on the participant's personal smartphone and securely stored in a cloud-based system.

Results:

This study is part of the ‘Cardio-Share Model for Cross-Sectoral Ambulatory Treatment of Congestive Heart Disease based on Personal Health Technology (CATCH)' project. Technical and regulatory preparation started in 2023. Recruitment for this study started in January 2025 and is expected to be completed during the spring of 2026. The dataset will be anonymized and published for further research.

Conclusions:

This study aims to provide a comprehensive longitudinal open-source dataset of HF recorded in real-world ambulatory conditions that enhances our understanding of HF signs and symptoms. This dataset will provide an important source for detailed analysis and understanding of HF based on ambulatory and contextual physiological data. Such insight has the potential to enhance the clinical management of individuals with HF and enable them to handle their condition at home.