F. N. U. Sawaira

Background: Heart failure with preserved ejection fraction (HFpEF) accounts for nearly half of all global heart failure cases. Historically viewed as a left‐centric diastolic disorder, emerging evidence identifies right ventricular (RV) dysfunction and impaired ventriculo‐arterial (RV‐PA) coupling as central drivers of exercise intolerance, functional decline, and adverse clinical outcomes. Objectives: This review synthesizes current evidence on the pathophysiological mechanisms, advanced diagnostic modalities, prognostic implications, and phenotype‐specific therapies for RV dysfunction in HFpEF, advocating for a transition to systematic biventricular evaluation. Methods: A structured, PRISMA‐informed search of PubMed and Scopus (2015–2024) identified 74 eligible studies evaluating RV structure, function, and hemodynamics in HFpEF cohorts. Results: RV dysfunction is prevalent in 19% to 33% of HFpEF patients and independently predicts all‐cause mortality and heart failure hospitalizations. Key pathophysiological drivers include post‐capillary pulmonary hypertension, impaired RV lusitropy, systemic inflammation, and inter‐ventricular dependence. Non‐invasive markers notably speckle‐tracking echocardiography, 4D‐flow cardiac magnetic resonance (CMR), and the TAPSE/PASP ratio enable early identification of subclinical RV‐PA uncoupling. While traditional heart failure therapies show limited efficacy, emerging options including SGLT2 inhibitors, interatrial shunt devices, GLP‐1 receptor agonists, and targeted pulmonary vasodilators demonstrate potential to preserve RV strain and restore hemodynamics across distinct clinical phenotypes. Conclusions: RV dysfunction is a major prognostic marker in HFpEF. Routine inclusion of RV‐PA coupling indices in diagnostic and risk‐stratification algorithms is essential to guide precision‐based, phenotype‐guided management and improve clinical trajectories.

Traditional biomechanics research has long relied on lab-based systems, bulky, expensive, and often inaccessible to everyday athletes and coaches. This has limited its real-world impact, particularly in fast-paced and dynamic sports environments. However, the landscape is rapidly changing. Recent innovations in wearable technology, including smart textiles, graphene-printed sensors, and compact edge-AI chips, are bringing high-resolution motion analysis directly to the field. These systems can now track and analyse athletic movement in real-time, offering insights that were once only available in specialised labs. This review highlights key developments in sensor fusion, textile-integrated inertial measurement units (IMUs), and AI-driven form recognition. Together, these tools enable more precise monitoring of biomechanics during both training and rehabilitation. With multimodal data streams and machine learning algorithms, wearables can now provide individualised feedback on technique, workload, and recovery, without interrupting performance. The integration of artificial intelligence (AI) and smart fabrics represents a shift toward continuous and scalable biomechanical assessment. For coaches, physiotherapists, and athletes alike, these technologies present new opportunities for injury prevention, skill development, and personalized performance optimisation. As validation studies transition into real-world deployment, AI-powered wearables are poised to advance our ability to analyze and improve human movement in sport.