{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T06:14:27Z","timestamp":1788243267885,"version":"build-2803163510"},"reference-count":86,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"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:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>The aim of this study was to systematically review the evidence for the effectiveness of mHealth self-care management in patients with Heart Failure (HF).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Systematic review and meta-analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data sources<\/jats:title>\n                    <jats:p>A comprehensive search of PubMed, Embase, the Cochrane Library, Web of Science, CINAHL, CNKI, CBM, Wan fang, and VIP databases from February 1, 2025 to January 1, 2026.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study included only randomized controlled trials (RCTs) that compared the effects of mHealth interventions with usual care in adult patients with HF. The revised Cochrane risk-of-bias tool for randomized trials (RoB2) was used to assess the risk of bias for each study. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria were used to rate the certainty of the evidence. Meta-analysis was performed using Review Manager (RevMan v.5.4) and R software.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In total, 28 RCTs met the inclusion criteria. Compared with the usual-care group, nurse-led mHealth self-management interventions could significantly reduce HF-related readmissions [odds ratio (OR) 0.61, 95% CI 0.48\u20130.78,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;lt;\u20090.0001] and all-cause readmissions (OR 0.53, 95% CI 0.41\u20130.69,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;lt;\u20090.00001), but no significant differences were observed in mortality. The meta-analyses also showed that nurse-led mHealth interventions could improve self-care behaviors and quality of life, increase patients\u2019 knowledge of HF and medication adherence, and enhance cardiac function. However, there was no significant improvement in the mental status of the nurse-led mHealth intervention group compared with the control group.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Nurse-led mHealth self-management interventions can benefit the health of patients with heart failure in multiple ways. However, the clinical effects are influenced by various factors, and more high-quality studies are needed to confirm their effectiveness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic Review Registration<\/jats:title>\n                    <jats:p>\n                      PROSPERO CRD420251005175,\n                      <jats:ext-link>https:\/\/www.crd.york.ac.uk\/PROSPERO\/view\/CRD420251005175<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1882692","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:26:35Z","timestamp":1788240395000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Effectiveness of nurse-led mHealth-based self-care management for patients with heart failure: a systematic review and meta-analysis"],"prefix":"10.3389","volume":"13","author":[{"given":"Ting","family":"Wu","sequence":"first","affiliation":[{"name":"Nursing Department, The Second Affiliated Hospital of Zhejiang University School of Medicine","place":["Hangzhou, China"]},{"name":"Zhejiang University School of Medicine","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying Ying","family":"Jia","sequence":"additional","affiliation":[{"name":"Zhejiang University School of Medicine","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruo Han","family":"Fu","sequence":"additional","affiliation":[{"name":"Zhejiang University School of Medicine","place":["Hangzhou, China"]},{"name":"Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Zou","sequence":"additional","affiliation":[{"name":"Nursing Department, The Second Affiliated Hospital of Zhejiang University School of Medicine","place":["Hangzhou, China"]},{"name":"Zhejiang University School of Medicine","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian Ping","family":"Song","sequence":"additional","affiliation":[{"name":"Nursing Department, The Second Affiliated Hospital of Zhejiang University School of Medicine","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.ijcard.2022.07.035","article-title":"Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart 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