{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T07:20:43Z","timestamp":1787728843517,"version":"build-2784847793"},"reference-count":90,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Heart rate variability (HRV) is a validated biomarker of autonomic and inflammatory regulation and has been associated with both acute COVID-19 and post\u2013COVID-19 condition. Although reverse transcription polymerase chain reaction remains the diagnostic gold standard for acute infection, there is a lack of accessible, noninvasive physiological tools to support ongoing monitoring and stage differentiation of COVID-19 and its sequelae. The growing availability of wearable devices capable of real-time HRV data collection opens up opportunities for early detection and health status classification using machine learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to identify HRV patterns capable of distinguishing individuals with active COVID-19 and post\u2013COVID-19 condition and healthy controls using data collected from wearable devices and processed using machine learning models. A secondary objective was to assess the feasibility of a near\u2013real-time health monitoring system based on these patterns using wearable-derived HRV data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>HRV indexes (SD of the normal-to-normal intervals [SDNN], root mean square of successive differences [RMSSD], low-frequency relative power [LF%], and high-frequency relative power [HF%]) were collected from 61 participants (n=21, 34% with active COVID-19; n=20, 33% with post\u2013COVID-19 condition; and n=20, 33% healthy controls) using 2 standardized datasets. Classification models were developed using supervised machine learning algorithms (decision tree, support vector machines, k-nearest neighbor, and neural networks) and evaluated through cross-validation. A contextual clinical variable indicating recent SARS-CoV-2 infection was incorporated into 1 model configuration to assess its impact on classification performance. In addition, a prototype system for near\u2013real-time monitoring was implemented and tested in a separate group of 4 participants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Participants with active COVID-19 showed significantly lower HRV indexes (SDNN, RMSSD, LF%, and HF%) compared to both participants with post\u2013COVID-19 condition and healthy controls (P&lt;.001), whereas differences between the post\u2013COVID-19 condition and healthy groups were not statistically significant. Decision tree models trained solely on HRV features achieved 76.4% accuracy with high discriminative performance for active COVID-19 (F1-score=88%; area under the curve=0.85) but limited detection of post\u2013COVID-19 condition (F1-score=56%). When a contextual clinical variable indicating recent SARS-CoV-2 infection was included, overall accuracy increased to 87%, and the F1-score for post\u2013COVID-19 condition rose to 92%, with improved area under the curve metrics across classes. A prototype system tested on 4 independent participants correctly classified their status, demonstrating feasibility for near\u2013real-time application.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>HRV patterns collected from wearable devices and analyzed via machine learning successfully distinguished individuals with active COVID-19 from healthy individuals with high accuracy using physiological data alone. When a clinical contextual variable indicating recent infection was added, the model also achieved strong performance in identifying post\u2013COVID-19 condition cases. A prototype system demonstrated feasibility for near\u2013real-time application, reinforcing the potential of HRV for individualized health monitoring.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/76613","type":"journal-article","created":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T10:45:05Z","timestamp":1750761905000},"page":"e76613","source":"Crossref","is-referenced-by-count":1,"title":["Classification of Individuals With COVID-19 and Post\u2013COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near\u2013Real-Time Monitoring Component"],"prefix":"10.2196","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3609-7575","authenticated-orcid":false,"given":"Carlos Alberto","family":"Sanches","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8599-9009","authenticated-orcid":false,"given":"Andre Felipe 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