[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-424499-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-424499-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","free-living-hip-accelerometry-detects-and-forecasts-frailty-decline-in-a-sample-of-community-dwelling-older-adults","FREE-LIVING HIP ACCELEROMETRY DETECTS AND FORECASTS FRAILTY DECLINE IN A SAMPLE OF COMMUNITY-DWELLING OLDER ADULTS","","Frailty is a geriatric syndrome marked by reduced strength, exhaustion, slow gait, weight loss, and low activity, which predicts adverse health outcomes. This retrospective study uses 7 days of free-living hip accelerometer data to detect the frailty phenotype concurrently and to forecast frailty worsening after 1 year among community-dwelling older adults. Researchers extracted 115 accelerometry features (activity, sleep, harmonic variables) and trained five machine learning models with demographic and health variables. Best models achieved cross-sectional F1=0.84 and AUROC=0.84 and longitudinal F1=0.87 and AUROC=0.84, supporting wearable real-time screening and clinical monitoring.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/free-living-hip-accelerometry-detects-and-forecasts-frailty-decline-in-a-sample-of-community-dwelling-older-adults/424499/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/free-living-hip-accelerometry-detects-and-forecasts-frailty-decline-in-a-sample-of-community-dwelling-older-adults/424499.png","ImageObject",300,407,{"name":42,"@type":43},"Well 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517  \nAbstract citation ID: igaf122.1766  \nFREE-LIVING HIP ACCELEROMETRY DETECTS AND FORECASTS FRAILTY DECLINE IN A SAMPLE OF COMMUNITY-DWELLING OLDER ADULTS  \nMegan Huisingh-Scheetz1, Benjamin Kramer2, Yanan Long1, Michelangelo Pagan1, Sylvia Brown3, and Andrey Rzhetsky1, 1. The University of Chicago, Chicago, Illinois, United States, 2. Case Western Reserve University, Cleveland, Ohio, United States, 3. National Opinion Research Center at The University of Chicago, Chicago, Illinois, United States  \nFrailty is a geriatric syndrome combining reduced strength, exhaustion, slow gait, weight loss, and low activity, foreboding adverse health outcomes. Early detection of frailty is critical for mitigating risk, but the standard frailty detection is time-consuming. Non-invasive, wearable accelerometers capture physical activity and sleep patterns and may detect frailty changes. In this retrospective study, 7-days of free-living data from hip accelerometers were used to both detect concurrent frailty phenotype (range 0-5, nonfrail=0 vs pre-frail or frail=1-5 points) and forecast frailty increase (1+ point worse frailty score vs no change or improved score) over 1 year in community-dwelling older adults. We extracted 115 accelerometry features from the hip accelerometry readings (activity, sleep, harmonic variables) using open-source code. Five machine learning models included all accelerometry features plus demographic and health characteristics.The models were trained on 85% of the sample and were tested on 15% of the sample. In this presentation, we will share the best-performing cross-sectional model (F1=0.84, AUROC=0.84) and the best performing longitudinal model (F1=0.87,AUROC=0.84).These findings suggest that wearable sensors’ data may be useful for real-time frailty screening and monitoring. Integrating accelerometry into clinical care could facilitate earlier detection of at-risk individuals and improve outcomes.","cbCaioMUf7uuI6WI","https://ap.wps.com/l/cbCaioMUf7uuI6WI","pdf",488135,"English","# Abstract\n## Methods\n## Machine Learning Models and Results\n## Implications","[{\"question\":\"How does the study detect and forecast frailty?\",\"answer\":\"It uses 7 days of free-living hip accelerometer data to detect current frailty phenotype and to forecast whether frailty worsens over the following year.\"},{\"question\":\"What data features were extracted from the hip accelerometer readings?\",\"answer\":\"The study extracted 115 accelerometry features covering activity, sleep, and harmonic variables using open-source code.\"},{\"question\":\"What performance did the best models achieve?\",\"answer\":\"The best cross-sectional model reached F1=0.84 and AUROC=0.84, and the best longitudinal model reached F1=0.87 and AUROC=0.84.\"}]","FREE-LIVING HIP ACCELEROMETRY DETECTS AND FORECASTS FRAILTY DECLINE IN A SAMPLE OF COMMUNITY-DWELLING OLDER ADULTS | PDF",1790634391]