{"title":"Effectiveness of Sensor-Based Fall Prevention Systems in Hospitals: A Systematic Review and Meta-Analysis.","authors":"Mingliang Zheng, Tingting Yan, Xingyanan Wang, Yan Yin, Ting Yu, Xiliang Li, Shuli Luo, Zhitian Xiao","doi":"10.1111/wvn.70184","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To synthesize evidence on the effectiveness of hospital sensor-based fall prevention systems and examine whether technology type, patient risk, and implementation strategy modify their impact.</p><p><strong>Methods: </strong>We searched MEDLINE, Embase, CINAHL, CENTRAL, Web of Science, and trial registries through April 30, 2025. Interventional studies evaluating hospital sensor-based fall prevention systems were eligible, including randomized controlled trials and quasi-experimental designs. Two reviewers independently extracted data and assessed risk of bias using the Cochrane Risk of Bias tools. Random-effects meta-analyses applied the Paule-Mandel estimator with Hartung-Knapp-Sidik-Jonkman adjustment. Incidence rate ratios (IRR) were pooled for fall rates and fall-related injuries, and odds ratios (OR) for patients with at least one fall. Subgroup analyses examined technology generation and risk profile, with restricted analyses for mandated implementation. Certainty of evidence was rated with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach.</p><p><strong>Results: </strong>Sixteen studies were included, seven in meta-analysis. No significant impact was found for fall rates (IRR 0.77, 95% CI 0.38-1.58; I<sup>2</sup> = 92%), fallers (OR 1.24, 95% CI 0.76-2.02; I<sup>2</sup> = 73%), or fall-related injuries (IRR 0.98, 95% CI 0.81-1.18; I<sup>2</sup> = 0%). Machine learning-based systems reduced fall rates (IRR 0.50, 95% CI 0.32-0.79), while rule-based systems did not. Mandatory implementation was associated with lower fall rates (IRR 0.51, 95% CI 0.37-0.70) but not fewer fallers. Certainty of evidence ranged from very low to low.</p><p><strong>Conclusion: </strong>Sensor-based systems showed no consistent benefit for preventing hospital falls or injuries, though machine learning and mandated use appeared more promising. Given the limited number of trials, heterogeneity, and low certainty, further research should focus on rigorous evaluation, workflow integration, and cost-effectiveness.</p><p><strong>Register: </strong>This review was registered with PROSPERO (CRD420251123960; registered on 11 August 2025).</p><p><strong>Clinical trial number: </strong>not applicable.</p>","PeriodicalId":49355,"journal":{"name":"Worldviews on Evidence-Based Nursing","volume":"23 5","pages":"e70184"},"PeriodicalIF":5.3000,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Worldviews on Evidence-Based Nursing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/wvn.70184","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: To synthesize evidence on the effectiveness of hospital sensor-based fall prevention systems and examine whether technology type, patient risk, and implementation strategy modify their impact.
Methods: We searched MEDLINE, Embase, CINAHL, CENTRAL, Web of Science, and trial registries through April 30, 2025. Interventional studies evaluating hospital sensor-based fall prevention systems were eligible, including randomized controlled trials and quasi-experimental designs. Two reviewers independently extracted data and assessed risk of bias using the Cochrane Risk of Bias tools. Random-effects meta-analyses applied the Paule-Mandel estimator with Hartung-Knapp-Sidik-Jonkman adjustment. Incidence rate ratios (IRR) were pooled for fall rates and fall-related injuries, and odds ratios (OR) for patients with at least one fall. Subgroup analyses examined technology generation and risk profile, with restricted analyses for mandated implementation. Certainty of evidence was rated with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach.
Results: Sixteen studies were included, seven in meta-analysis. No significant impact was found for fall rates (IRR 0.77, 95% CI 0.38-1.58; I2 = 92%), fallers (OR 1.24, 95% CI 0.76-2.02; I2 = 73%), or fall-related injuries (IRR 0.98, 95% CI 0.81-1.18; I2 = 0%). Machine learning-based systems reduced fall rates (IRR 0.50, 95% CI 0.32-0.79), while rule-based systems did not. Mandatory implementation was associated with lower fall rates (IRR 0.51, 95% CI 0.37-0.70) but not fewer fallers. Certainty of evidence ranged from very low to low.
Conclusion: Sensor-based systems showed no consistent benefit for preventing hospital falls or injuries, though machine learning and mandated use appeared more promising. Given the limited number of trials, heterogeneity, and low certainty, further research should focus on rigorous evaluation, workflow integration, and cost-effectiveness.
Register: This review was registered with PROSPERO (CRD420251123960; registered on 11 August 2025).
期刊介绍：
The leading nursing society that has brought you the Journal of Nursing Scholarship is pleased to bring you Worldviews on Evidence-Based Nursing. Now publishing 6 issues per year, this peer-reviewed journal and top information resource from The Honor Society of Nursing, Sigma Theta Tau International, uniquely bridges knowledge and application, taking a global approach in its presentation of research, policy and practice, education and management, and its link to action in real world settings.
Worldviews on Evidence-Based Nursing is written especially for:
Clinicians
Researchers
Nurse leaders
Managers
Administrators
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Worldviews on Evidence-Based Nursing is a primary source of information for using evidence-based nursing practice to improve patient care by featuring:
Knowledge synthesis articles with best practice applications and recommendations for linking evidence to action in real world practice, administra-tive, education and policy settings
Original articles and features that present large-scale studies, which challenge and develop the knowledge base about evidence-based practice in nursing and healthcare
Special features and columns with information geared to readers’ diverse roles: clinical practice, education, research, policy and administration/leadership
Commentaries about current evidence-based practice issues and developments
A forum that encourages readers to engage in an ongoing dialogue on critical issues and questions in evidence-based nursing
Reviews of the latest publications and resources on evidence-based nursing and healthcare
News about professional organizations, conferences and other activities around the world related to evidence-based nursing
Links to other global evidence-based nursing resources and organizations.
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