Purpose Of Study: To describe the ongoing efforts of the Connecticut Collaboration for Fall Prevention (CCFP) to move evidence regarding fall prevention into clinical practice and state policy.
Methods: A university-based team developed methods of networking with existing statewide organizations to influence clinical practice and state policy.
Results: We describe steps taken that led to funding and legislation of fall prevention efforts in the state of Connecticut. We summarize CCFP's direct outreach by tabulating the educational sessions delivered and the numbers and types of clinical care providers that were trained. Community organizations that had sustained clinical practices incorporating evidence-based fall prevention were subsequently funded through mini-grants to develop innovative interventional activities. These mini-grants targeted specific subpopulations of older persons at high risk for falls.
Implications: Building collaborative relationships with existing stakeholders and care providers throughout the state, CCFP continues to facilitate the integration of evidence-based fall prevention into clinical practice and state-funded policy using strategies that may be useful to others.
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http://dx.doi.org/10.1093/geront/gns122 | DOI Listing |
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.
Study Design: Prospective, double-blinded, observational study of fall risk scores between trained observers and those of IMU-HAs.
Setting: Tertiary referral center.
J Occup Health
January 2025
Panasonic Corporation, Department Electric Works Company/Engineering Division, Osaka, Japan.
Background: Falls are among the most prevalent workplace accidents, necessitating thorough screening for susceptibility to falls and customization of individualized fall prevention programs. The aim of this study was to develop and validate a high fall risk prediction model using machine learning (ML) and video-based first three steps in middle-aged workers.
Methods: Train data (n=190, age 54.
Infect Control Hosp Epidemiol
January 2025
Virology Department, AP-HP, Hôpital Saint-Louis, Paris, France.
Objective: Patients with chronic kidney disease suffer from immune dysfunction, increasing susceptibility to infections. The aim of the study was to investigate air contamination with respiratory viruses in a dialysis unit at a quaternary hospital using molecular detection techniques and to analyze airflow dynamics through computational fluid dynamics (CFD) simulations for a comprehensive assessment of air transmission risks.
Methods: We conducted dialysis unit air sampling using AerosolSense™ samplers.
BMC Infect Dis
January 2025
Department of Disease Prevention and Control, Second Affiliated Hospital of Navy Medical University, Shanghai, China.
Background: Limited information is available regarding the changes in blood culture utilization following the COVID-19 pandemic. Blood culture utilization rate is a critical indicator of diagnostic efficiency for infectious diseases. This study aims to describe the impact of the COVID-19 pandemic on blood culture utilization rate in Shanghai.
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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