{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T08:09:01Z","timestamp":1789373341513,"version":"build-2803163510"},"reference-count":135,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:p>Heart rate variability (HRV) is a non-invasive marker of autonomic cardiovascular regulation that reflects beat-to-beat fluctuations in cardiac cycle length. Reduced HRV has been associated with adverse cardiovascular outcomes across multiple disease states, yet its clinical use remains limited by substantial interpretive complexity. Unlike static biomarkers, HRV is highly sensitive to measurement modality, recording duration, circadian timing, behavioral factors, environmental exposures, medications, and comorbidities. Therefore, isolated HRV values cannot be interpreted as direct indicators of autonomic health without careful consideration of context. This review summarizes the physiological basis of HRV, key time-domain, frequency-domain, and nonlinear\/complexity metrics, and major acquisition methods, including 24\u2005h electrocardiography (ECG), short-term ECG, chest strap monitors, and wrist-worn devices. It also examines how sleep, physical activity, diet, psychological stress, substance use, age and sex, medications, and cardiovascular or metabolic diseases influence HRV interpretation. We propose a context-aware framework for interpreting HRV as a dynamic physiological signal rather than a standalone diagnostic marker. When appropriately contextualized, HRV may serve as a complementary tool for cardiovascular risk assessment and provide insight into residual risk not fully captured by conventional clinical models.<\/jats:p>","DOI":"10.3389\/fcvm.2026.1949249","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T07:57:06Z","timestamp":1789372626000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpreting heart rate variability in clinical context: a framework for understanding a multidimensional autonomic biomarker"],"prefix":"10.3389","volume":"13","author":[{"given":"Bahjat Z.","family":"Ghazzal","sequence":"first","affiliation":[{"name":"Department of Medicine, University of Massachusetts Chan Medical School","place":["Worcester, MA, United 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