{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:43:26Z","timestamp":1785545006287,"version":"3.56.0"},"reference-count":82,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Mhealth Uhealth"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Heart rate (HR) monitoring by wearable devices offers a physiological, personalized, and continuous method for assessing physical activity (PA) duration and intensity. However, methods to translate HR data into meaningful PA metrics are diverse and nonstandardized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to provide an overview of how HR data are used to quantify PA behavior and estimate physiological outcomes in adult patients with cardiovascular disease (CVD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A systematic search was performed in PubMed, Web of Science, and CENTRAL for studies published between 2014 and 2024. Eligible studies included adults with CVD or related risk factors wearing HR monitors to estimate PA. Data were synthesized narratively. The methodological quality of the included studies was evaluated using the Crowe Critical Appraisal Tool (CCAT; Michael Crowe).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Twenty studies were included, spanning four HR-based PA estimation methods: (1) HR zone analysis (n=14), which assessed time spent in moderate-to-vigorous zones to evaluate guideline or training adherence; (2) physiological modeling (n=4), estimating outcomes such as energy expenditure (physical activity level) or cardiorespiratory fitness (maximal oxygen uptake); (3) change detection (n=1), using time-series and machine learning algorithms to quantify shifts in PA behavior; and (4) a derived personalized scoring system (n=1). While each approach demonstrated clinical promise of using HR data, external validation, and methodological transparency is often lacking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>HR-based PA estimation holds the promise of physiologically meaningful, personalized PA monitoring in CVD care. Modeling approaches and personalized scoring systems linking PA behavior to cardiovascular outcomes may provide highly needed clinical tools for PA management in patients. Research should prioritize algorithm transparency, clinical validation, and standardization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/79995","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T20:22:01Z","timestamp":1781727721000},"page":"e79995-e79995","source":"Crossref","is-referenced-by-count":0,"title":["Heart Rate Monitors for the Estimation of Physical Activity in Patients With Cardiovascular Disease: Systematic 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