JMIR Cardio

Public awareness and understanding of stroke are crucial to reducing its overall burden. Massive open online courses (MOOCs) are free, self-directed online learning platforms that provide accessible education.

Surgical site infection (SSI) in patients with frailty undergoing myocardial revascularization is a major clinical challenge, and there is a gap in the literature related to this topic.

Occlusion myocardial infarction (OMI) on the electrocardiogram (ECG) can present as ST-elevation myocardial infarction (STEMI), non-STEMI, ST depression, or other patterns (eg, de Winter T waves and hyperacute T waves), posing a diagnostic challenge. Even experienced physicians may overlook OMI on ECG, leading to delays in catheterization laboratory activation. Recently, smartphone apps using AI have been shown to exhibit promising accuracy in ECG interpretation in large datasets.

Hospital admission is associated with increased sedentary behavior and low levels of physical activity. Hospitals have developed several strategies and interventions to address this unwanted inactivity and increase patient movement during admission. Self-monitoring of physical activity is a promising approach to support activity during hospital stays.

Wearable monitoring of sedentary behavior is increasingly used in digital cardiovascular care, but the validity of consumer-grade wrist-worn trackers in older patients with cardiac issues is unclear.

Anxiety is common in adolescents with elevated BMI (kg/m), affecting 33%‐43% of adolescents with overweight or obesity (age- and sex-specific BMI ≥85th percentile ). Anxiety has been associated with poorer cardiometabolic health, and disinhibited eating in response to anxiety may be a key explanatory mechanism. Therefore, reducing anxiety in adolescents may improve disinhibited eating patterns and prevent worsening cardiometabolic health.

Long COVID is characterized by persistent fatigue, with disrupted autonomic function and sleep disturbances frequently reported. Whether daily symptom severity influences subsequent sleep and autonomic recovery (the reverse temporal direction) remains underexplored.

Cardiac rehabilitation (CR) improves patient quality of life, morbidity, and mortality. Unfortunately, it is underused by patients. Digital health interventions offer a solution to increase participation in CR. However, patients’ interest in virtual CR, especially among those in the inpatient setting, has not been fully explored. The benefits of CR have been predominantly demonstrated in traditional, center-based CR programs.

Atrial fibrillation (AF) is a major contributor of thromboembolic stroke. However, early detection remains challenging and is severely hindered by structural limitations in health care infrastructure. While current clinical guidelines recognize a 30-second single-lead electrocardiogram (ECG) as a standard diagnostic tool, the actual deployment and real-world utility of mobile screening technologies across health care settings remain poorly understood.

Ambulatory blood pressure monitoring objectively assesses circadian hemodynamic rhythms. However, the joint trajectories of longitudinal blood pressure (BP) and heart rate (HR), and their association with early hypertensive cardiac remodeling, remain incompletely characterized.

Cardiac rehabilitation (CR) is a class IA–recommended treatment and secondary preventive strategy for people with coronary artery disease. eHealth solutions show promise in providing accessible CR. However, few provide a comprehensive and multimodal CR program, and most programs are developed without input from end users.

Cardiovascular biomarkers are increasingly extracted or interpreted using artificial intelligence (AI) applied to electrocardiograms, imaging, laboratory measurements, electronic health records, wearable devices, and longitudinal data. Predictive performance alone, however, does not establish that a measurement is valid, that a model is transportable and calibrated, or that acting on its output improves care. Existing resources provide essential but complementary foundations: the US Food and Drug Administration–National Institutes of Health (FDA-NIH) BEST (Biomarkers, EndpointS, and other Tools) resource standardizes biomarker terminology; the V3 and V3+ frameworks address verification, analytical validation, clinical validation, and usability of digitally measured signals; prediction-model and trustworthy-AI guidance addresses reporting, risk of bias, early clinical evaluation, and deployability; and regulatory qualification pathways evaluate evidence within a defined context of use. A remaining practical challenge is translating these complementary requirements into accountable clinical action within cardiovascular workflows. This viewpoint proposes a clinician-centered operational framework organized around validation, governance, and workflow integration. For each pillar, it identifies accountable actors, required steps, documented outputs, and escalation or stop rules. Validation establishes whether the input measurement and model are fit for the intended population and decision. Governance assigns institutional authorization, clinical ownership, version control, monitoring, and authority to restrict, pause, or withdraw the intervention. Workflow integration specifies who receives the output, what confirmatory action follows, how disagreement is handled, and how decisions are documented. A worked example of an AI-enabled electrocardiographic screening output for possible left ventricular systolic dysfunction illustrates the pathway from local evaluation to echocardiographic confirmation and lifecycle monitoring. The framework does not replace established validation or regulatory standards; it operationalizes them as a clinician-centered and institutionally accountable pathway from validated signal to governed, patient-facing action.