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Machine learning-based fall risk prediction in community-dwelling older adults using dual-task spatiotemporal gait parameters
CONCLUSIONS: Combining dual-task gait variability with clinical functional assessments via machine learning algorithms provides a robust and interpretable framework for faller identification. The integration of optical gait analysis and machine learning facilitates early risk screening and personalized retrospective evaluation, offering a promising tool to assist in detecting older adults at risk for fall-related injuries in the aging population.
What is the impact of CRP, hs-CRP, IL-6, IL-10, and TNFα in physical performance in community-dwelling older adults: a systematic review
CONCLUSIONS: Findings suggest that hs-CRP and CRP biomarkers are associated with physical performance decline in older adults. Future research including a broader range of inflammation biomarkers should provide insights and understanding of mechanisms linking inflammation and physical decline.