Falls among older adults are preventable events and fall prevention programs led by nursing staff are promising and viable programs for preventing falls. This systematic review aimed to gain insight into the effects of nurse-led fall prevention programs for older adults. The Preferred Reporting Items for Systemic Reviews and Meta-Analysis was used as a guideline in reporting this literature search conducted through CINAHL, MEDLINE, Eric, Science Direct, and Google Scholar databases. The Johns Hopkins Nursing Evidence-Based Practice was used to determine the level of evidence and quality rating of the articles, while data extraction was done by a matrix review method. The review included six randomized controlled trials, two non-randomized controlled trials, and three quasi-experimental designs. Six studies directed their education component of the intervention on the nursing staff, while three focused on the older participants. Nurses' roles were patient assessment, patient education, administration of exercise programs, and follow-up post interventions. Fall rates and fall incidents were reduced in five studies, while three studies changed patients' behavior. Fall prevention programs with education components specific for older adults and nursing staff resulted in positive outcomes. Nursing staff make a significant contribution to improving patients' outcomes, and a fall prevention program that focuses on reducing injurious fall rates and enhancing participants' behavior could maximize its effects.
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Otol Neurotol
February 2025
Department of Otolaryngology-Head and Neck Surgery.
Objective: To compare fall risk scores of hearing aids embedded with inertial measurement units (IMU-HAs) and powered by artificial intelligence (AI) algorithms with scores by trained observers.
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Setting: Tertiary referral center.
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Panasonic Corporation, Department Electric Works Company/Engineering Division, Osaka, Japan.
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Virology Department, AP-HP, Hôpital Saint-Louis, Paris, France.
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View Article and Find Full Text PDFSci Rep
January 2025
Department of Exercise Science, Syracuse University, 150 Crouse Dr, Syracuse, NY, 13244, USA.
Analyzing video footage of falls in older adults has emerged as an alternative to traditional lab studies. However, this approach is limited by the labor-intensive process of manually labeling body parts. To address this limitation, we aimed to validate the use of the AI-based pose estimation algorithm (OpenPose) in assessing the hip impact velocity and acceleration of video-captured falls.
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