JMIR Cardio

Synthetic data offer significant potential for cardiology research by enabling data sharing, preserving privacy, and supporting machine learning model development. By generating artificial patient records that reflect real-world distributions, synthetic data can accelerate clinical research, improve model performance for rare cardiovascular conditions, and facilitate transnational collaborations that would otherwise be restricted by data-sharing barriers. Despite these advantages, the increasing use of synthetic data raises important ethical, regulatory, and methodological concerns that remain insufficiently addressed. Key challenges include assessing the validity and generalizability of synthetic datasets, understanding their limitations in representing complex and heterogeneous patient populations, and preventing the amplification of existing biases in cardiovascular care. Current regulatory frameworks, including the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), do not fully address emerging risks such as reidentification and data leakage, and there is no harmonized guidance to govern the use of synthetic data as stand-alone evidence for medical device evaluation or therapeutic research. In this viewpoint, we argue that responsible integration of synthetic data in cardiology requires, first, clear differentiation between synthetic data as a privacy-preserving distributional substitute and synthetic data as a counterfactual simulation tool, and, second, fit-for-purpose governance frameworks that pair rigorous utility and fidelity testing with explicit, adversary-aware privacy evaluation before synthetic cohorts are accepted as evidence in research or product evaluation. A prerequisite for that governance is conceptual clarity about what synthetic data are being used for. Synthetic data in health care serve 2 fundamentally distinct roles that carry entirely different validity requirements, failure modes, and regulatory implications, yet they are routinely conflated. The first role is as a privacy-preserving distributional substitute: the goal is statistical fidelity to the real data distribution, so that analyses of the synthetic dataset yield results equivalent to those of the original. The second role is as a tool for counterfactual simulation: the goal is to generate data that could not have been observed, such as rare conditions, hypothetical interventions, or extrapolations to new populations. These 2 roles are methodologically distinct. A dataset that accurately reflects real-world distributions may be inadequate for extrapolating findings to underrepresented subgroups. Conversely, a simulator optimized for novel scenario generation may systematically diverge from real-world distributions. This distinction informs every subsequent discussion of validity, bias, and regulation in this viewpoint and our proposed 4 concrete actions for the cardiology research community, including mandatory 3-layer (fidelity, utility, and privacy) validation, systematic subgroup reporting, explicit intended-use scoping, and domain-specific acceptability thresholds for synthetic data–based evidence.

Continuous vital sign monitoring ensures early detection, prevents intensive care unit (ICU) admissions, and improves patient outcomes. Continuous heart rate (HR) monitoring methods often require direct skin contact, which can lead to patient discomfort. The rising popularity of ballistocardiography (BCG) offers a promising, noncontact solution for continuous vital sign monitoring with improved patient comfort.

Patients hospitalized for heart failure (HF) face a high-risk early postdischarge period.

In cardiovascular care, illness and recovery affect both patients and their families, particularly within home-based remote patient management (RPM). A recent scientific statement from the American Heart Association highlighted the importance of family involvement, identifying digital technologies as a key enabling opportunity. Despite this, research into the needs of families and the implications of RPM remains limited.

Heart rate variability (HRV) is a noninvasive indicator of autonomic nervous system activity that is increasingly used for health and performance monitoring. Digital and mobile technologies are increasingly providing opportunities for remote HRV monitoring outside of laboratory-based settings.

Feasible and potentially scalable strategies are needed to address the growing cardiovascular disease (CVD) risk among people living with HIV. Bidirectional automated texting (BAT) programs that remind and encourage adherence to evidence-based CVD-reducing interventions represent a potentially scalable strategy, but data on their feasibility are lacking.

Atrial fibrillation (AF), the most prevalent cardiac arrhythmia, affects 2% to 4% of the global adult population and is associated with an increased risk of stroke. Early diagnosis of AF and atrial flutter (AFL) is crucial due to their association with stroke risk and the challenge posed by their often asymptomatic and episodic nature. Traditional electrocardiogram (ECG) interpretation requires substantial expert input and can be challenging, especially with poor-quality ECGs.

Rapid activation of the cardiac catheterization laboratory (CCL) for ST-segment elevation myocardial infarction (STEMI) is essential to minimize time to reperfusion. However, system-wide efforts to reduce treatment delays have been accompanied by increased false activations, defined as activations that do not result in emergent coronary intervention. False activations contribute to unnecessary team mobilization (UTM), staff fatigue, workflow disruption, and inefficient resource use.

Atrial fibrillation (AF) is the most common sustained heart rhythm disorder and is a challenging chronic disease to manage. Patients’ daily self-care decisions are associated with improved AF outcomes, quality of life, and decreased hospital use and cost. However, many patients find these real-world or naturalistic decisions difficult, often because of their inherent complexity and ambiguity, coupled with the uncertainty of AF. Intervention research using technology to support AF self-care has largely emphasized making decisions with clinicians. Patients with AF are increasingly using consumer technology; yet, little is known about the use of technology by patients with AF in independent self-care decision-making. Addressing this gap will facilitate developing interventions that better leverage technology to enhance patients’ naturalistic decision-making.

Home-based cardiac rehabilitation (CR) using digital health technologies (ie, cardiac telerehabilitation [CTR]) has emerged as a practical alternative to conventional center-based CR, particularly during and after the COVID-19 pandemic. However, maintaining sustained participation in CR remains challenging. Gamification holds the potential to enhance motivation and adherence in CR, but its role in CTR for patients with acute coronary syndrome (ACS) remains under-studied.

Most studies assessing digital interventions for people with heart failure (HF) focus on clinical outcomes, and few include patient perspectives. Understanding patient experiences of the use of a digital HF platform along with community health worker (CHW) care as part of a digitally enabled CHW intervention can inform management of HF at home and improve the postdischarge phase of care.

Social robots (SRs) are innovative tools in health care, offering both medical and psychological support for patients with heart failure (HF). For successful implementation, patient acceptability of SRs is crucial. Living in urban areas and having a lower comorbidity burden have been linked to higher acceptability; however, the role of psychological factors remains underexplored.