{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:01:04Z","timestamp":1782846064923,"version":"3.54.5"},"reference-count":36,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Excessive pulmonary congestion (PC) is a major contributor to heart failure (HF) deterioration and often necessitates emergency hospitalization. Early detection of PC-related respiratory abnormalities via wearable devices could enable prompt intervention and reduce admissions. However, the feasibility of using wearables to detect specific respiratory biomarkers of PC remains unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to evaluate the feasibility of using wearables to monitor respiratory data in hospitalized patients with HF and to determine if these signals could distinguish patients with HF and PC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This single-center, observational, exploratory study enrolled hospitalized patients with HF without severe lung diseases or need for intensive care or ventilation. All participants wore a designated device for \u226524 hours starting within 24 hours after admission; only nighttime data were analyzed. Breathing patterns were quantified across the respiratory cycle, amplitude, and multiscale entropy (MSE). Patients underwent comprehensive evaluations, including vital signs, laboratories, echocardiography, and a 28-zone lung ultrasound to define PC (&gt;5 B-lines) within the first 24 hours.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The study enrolled 62 patients with HF between May 2021 and November 2022, including 44 with PC. Compared with patients with non-PC, those with PC demonstrated significantly prolonged mean expiratory time (TE; mean 2.17, SD 0.43 s vs mean 1.94, SD 0.34 s,\n                      <jats:italic>P<\/jats:italic>\n                      =.03), elevated expiratory time ratio (TE_ratio; mean 59.12%, SD 2.94% vs mean 56.4%, SD 3.36%,\n                      <jats:italic>P<\/jats:italic>\n                      =.006), and higher area under the curves (AUC) of MSE values for scales 1 to 5 (area_1_5) and scales 6 to 20 (area_6_20) in respiratory amplitude (RA; area_1_5 mean 4.20, SD 1.52 vs area_1_5 mean 2.93, SD 1.03,\n                      <jats:italic>P<\/jats:italic>\n                      &lt;.001; and area_6_20 mean 8.86, SD 3.14 vs area_6_20 mean 12.28, SD 4.84,\n                      <jats:italic>P<\/jats:italic>\n                      =.002). Logistic regression identified mean TE_ratio, RA area_1_5, and RA area_6_20 as significant predictors of PC (\n                      <jats:italic>P<\/jats:italic>\n                      &lt;.05). After adjusting for clinical confounders, both RA area_1_5 and RA area_6_20 remained independently associated with PC. Receiver operating characteristic analysis revealed that RA area_1_5 had the largest AUC of 0.75 (95% CI 0.63\u20100.88,\n                      <jats:italic>P<\/jats:italic>\n                      =.002), with 65.9% sensitivity and 73.3% specificity. For the multivariate logistic regression model constructed using combined parameters of diastolic blood pressure, logarithmically transformed N-terminal pro-B-type natriuretic peptide, New York Heart Association class IV, mean TE_ratio, RA area_1_5, and RA area_6_20, the AUC was 0.91 (95% CI 0.84-0.98,\n                      <jats:italic>P<\/jats:italic>\n                      &lt;.001), sensitivity of 77.3%, and specificity of 88.9%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In this exploratory study, wearable-based MSE analysis distinguished hospitalized patients with HF with and those without PC, showing prolonged expiratory phases and increased respiratory amplitude complexity in the PC group.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/73488","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T11:35:08Z","timestamp":1750073708000},"page":"e73488-e73488","source":"Crossref","is-referenced-by-count":0,"title":["Wearable Device\u2013Based Respiratory Complexity Analysis for Detecting Pulmonary Congestion in Patients With Heart Failure: Observational Exploratory Study"],"prefix":"10.2196","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7665-236X","authenticated-orcid":false,"given":"Mengwei","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8893-8698","authenticated-orcid":false,"given":"Yu","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5570-7458","authenticated-orcid":false,"given":"Shuanglin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7451-8035","authenticated-orcid":false,"given":"Xiu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8136-357X","authenticated-orcid":false,"given":"Zheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9041-4272","authenticated-orcid":false,"given":"Lixuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5652-4036","authenticated-orcid":false,"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9218-5644","authenticated-orcid":false,"given":"Zhengbo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,8,27]]},"reference":[{"issue":"4","key":"R1","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1007\/s10877-016-9894-9","article-title":"Assessment of an alternative calibration technique to record breathing pattern and its variability with respiratory inductive plethysmography","volume":"31","author":"Lo","journal-title":"J Clin Monit Comput"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106670","article-title":"Breathing patterns recognition: a functional data analysis approach","volume":"217","author":"LoMauro","journal-title":"Comput Methods Programs Biomed"},{"key":"R3","doi-asserted-by":"crossref","unstructured":"Wang J Yang Z Wang Y et al. 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