Wearable Technologies for Detecting Near‐Falls: A Systematic Review With Implications for Geriatric Nursing Practice.
Background: Near‐falls, defined as events in which individuals momentarily lose their balance but avoid falling, are strong predictors of subsequent falls. Wearable technologies have the potential to accurately detect near‐falls in both laboratory and real‐world settings, providing opportunities for...
| Published in: | Worldviews on Evidence-Based Nursing (John Wiley & Sons, Inc.) Vol. 23; no. 3; pp. 1 - 13 |
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| Main Authors: | , , , |
| Format: | research systematic review tables/charts Journal Article |
| Published: |
John Wiley & Sons, Inc.
Jun2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194919665&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194919665 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17416787 NRNX jtl: Worldviews on Evidence-Based Nursing (John Wiley & Sons, Inc.) issn: 17416787 maglogo: N pubinfo: dt: Jun2026 vid: 23 iid: 3 pid: 52269 pub: John Wiley & Sons, Inc. artinfo: ui: 194919665 194919665 194919665 10.1111/wvn.70143 194919665 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Wearable Technologies for Detecting Near‐Falls: A Systematic Review With Implications for Geriatric Nursing Practice. aug: au: Labrague, Leodoro J. Nguyen, Anna Visbal‐Dionaldo, Liza Ha, David affil: Fran and Earl Ziegler College of Nursing, The University of Oklahoma, Oklahoma Oklahoma, , USA sug: subj: Gerontologic Nursing Nursing Practice Wearable Sensors Accidental Falls Risk Factors Risk Assessment Accidental Falls Prevention and Control Human Systematic Review Male Female Aged PubMed Medline Embase CINAHL Database Movement Biomechanics Checklists Descriptive Statistics Aged: 65+ years Male Female ab: Background: Near‐falls, defined as events in which individuals momentarily lose their balance but avoid falling, are strong predictors of subsequent falls. Wearable technologies have the potential to accurately detect near‐falls in both laboratory and real‐world settings, providing opportunities for early intervention in geriatric nursing practice. Aims: This study has a two‐fold aim: (1) to appraise and synthesize current evidence on wearable sensor technologies for near‐fall detection, and (2) to discuss their potential applications for monitoring near‐fall risk and implementing prevention strategies in older adults. Methods: This is a systematic review. Articles were searched in five electronic databases (PubMed/MEDLINE, Embase, CINAHL, Web of Science, and IEEE Xplore) that explored wearable sensors for near‐fall detection. The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Results: A total of 18 studies, mostly experimental or observational, were included. Inertial Measurement Units (IMUs) were the most commonly used wearable technology, and the most frequently captured biomarker was linear acceleration. Lower‐body placements (feet, ankles, and lower back) demonstrated superior performance in detecting near‐falls. Single‐sensor systems achieved sensitivities of 80%–98%, whereas multi‐sensor configurations achieved 100% sensitivity, 99% specificity, and 100% accuracy. Linking Evidence to Action: Integrating wearable technologies for near‐fall detection into geriatric nursing practice may enhance early identification of older adults at high risk for falls and enable timely, personalized interventions. Future research should validate these technologies in real‐world settings and assess their acceptability among nurses, caregivers, and older adults. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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