[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-426549-105":3,"doc-detail-426549-en":81,"detail-sidebar-cat-0-en-105":97},{"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","predicting-homebound-status-in-us-older-adults-abstract","Predicting Homebound Status in US Older Adults - Abstract","","At least 5% of U.S. adults aged 65+ are homebound, defined as primarily confined to their residence, a condition linked to worse health outcomes and higher costs. Timely prediction can improve implementation of aging-in-place and long-term care planning. This study aggregates NHATS Round 1 and Round 5 data and uses 43 sociodemographic, clinical, functional, and environmental predictors. CatBoost models evaluate homebound defined as “never/rarely leaves home,” addressing class imbalance, and achieve 0.75 accuracy with AUC 0.82, highlighting modifiable predictors for targeted interventions.",{"@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/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/predicting-homebound-status-in-us-older-adults-abstract/426549/",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/predicting-homebound-status-in-us-older-adults-abstract/426549.png","ImageObject",300,407,{"name":42,"@type":43},"Blitz","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-14","2026-09-29",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",7,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"How is homebound status defined in the study?","Question",{"text":63,"@type":64},"Homebound is defined as “Never/rarely leaves home,” based on NHATS outcomes.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which data are used to build and test the prediction models?",{"text":68,"@type":64},"The study aggregates data from Round 1 and Round 5 of the National Health and Aging Trends Study (NHATS) for Medicare beneficiaries aged 65+.",{"name":70,"@type":61,"acceptedAnswer":71},"What modeling method and evaluation results are reported?",{"text":72,"@type":64},"CatBoost machine learning models are trained using nested cross-validation with hyperparameter tuning and class weights for outcome imbalance. The final model reaches 0.75 accuracy and an AUC of 0.82.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},426549,1790792340,{"code":4,"msg":82,"data":83},"success",{"doc_id":79,"user_id":84,"nickname":42,"user_avatar":85,"doc_module":4,"category_id":86,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":87,"file_id":88,"file_url":89,"file_type":90,"file_size":91,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":22,"language":92,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":93,"faqs":94,"seo_title":95,"seo_description":12,"update_tm":96,"read_time":30},24464137899374,"https://us-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"306 Innovation in Aging, 2025, Vol. 9, No. S2  \nAbstract citation ID: igaf122.1030  \nPREDICTING HOMEBOUND STATUS IN US OLDER  \nADULTS USING DATA FROM THE NATIONAL  \nHEALTH AND AGING TRENDS STUDY  \nAnis Davoudi1, Yifan Liu1, Jennifer Schrack2, and  \nKatherine Ornstein1, 1. Johns Hopkins University, Baltimore, Maryland, United States, 2. Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States  \nAt least 5% of U.S. older adults are homebound—defined as primarily confined to their residence— a condition associated with adverse health outcomes and increased costs. Timely and accurate prediction of homebound status can ensure effective implementation of interventions to support aging in place and long-term care planning. While several risk factors have been identified for homeboundness (e.g., dementia, low income), thereis limited work investigating prediction. We aggregated data from Round 1 and Round 5 of the National Health and Aging Trends Study (NHATS), a nationally representative panel study of U.S.  \nMedicare beneficiaries aged ≥65 years, to predict homebound status. We included 43 sociodemographic, clinical, functional and environmental factors from the NHATS surveys as predictors. Homebound was defined as “Never/rarely leaves home”.  \nWe developed and evaluated CatBoost machine learning models for homebound prediction, using nested cross-validation for hyperparameter tuning and class weights to address outcome imbalance. Our final dataset included 8,651 not-homebound participants at baseline (age mean (SD): 76.6 (7.6) years, 56.2% female) . 11.2% became homebound within the following four years) . The final model achieved 0.75 accuracy, 0.75 specificity, 0.76 sensitivity, 0.28 precision, 0.82 AUC, and 0.41 F1-score.  \nShort Physical Performance Battery (SPPB) score, driving, ability to walk 3 blocks, ADL dependency, and income quartile were the top 5 ranking features. These findings illustrate the feasibility of predicting homebound status among older adults, enabling focused interventions before homeboundness occurs. Moreover, identifying modifiable predictors enables the design of data-driven targeted interventions to enhance mobility and promote functional independence.","cbCaifr8gofobnLZ","https://ap.wps.com/l/cbCaifr8gofobnLZ","pdf",319105,"English","# Abstract\n## Data source and predictors\n## Modeling approach and evaluation\n## Key predictors and implications","[{\"question\":\"How is homebound status defined in the study?\",\"answer\":\"Homebound is defined as “Never/rarely leaves home,” based on NHATS outcomes.\"},{\"question\":\"Which data are used to build and test the prediction models?\",\"answer\":\"The study aggregates data from Round 1 and Round 5 of the National Health and Aging Trends Study (NHATS) for Medicare beneficiaries aged 65+.\"},{\"question\":\"What modeling method and evaluation results are reported?\",\"answer\":\"CatBoost machine learning models are trained using nested cross-validation with hyperparameter tuning and class weights for outcome imbalance. The final model reaches 0.75 accuracy and an AUC of 0.82.\"}]","Predicting Homebound Status in US Older Adults - Abstract | PDF",1790640876,{"code":4,"msg":82,"data":98},[99,103,107,111,116,121,125,128,133,136,140],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":100,"show_sort_weight":101,"slug":102},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":104,"show_sort_weight":105,"slug":106},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},"Exam",70,"exam",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":113,"show_sort_weight":114,"slug":115},5,"Comic",60,"comic",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":118,"show_sort_weight":119,"slug":120},6,"Technology",50,"technology",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":122,"show_sort_weight":123,"slug":124},"Healthcare",40,"healthcare",{"id":86,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":126,"slug":127},30,"research-report",{"id":129,"doc_module":4,"doc_module_name":25,"category_name":130,"show_sort_weight":131,"slug":132},9,"Religion & Spirituality",20,"religion-spirituality",{"id":131,"doc_module":4,"doc_module_name":25,"category_name":134,"show_sort_weight":131,"slug":135},"World Cup","world-cup",{"id":137,"doc_module":4,"doc_module_name":25,"category_name":138,"show_sort_weight":137,"slug":139},10,"Lifestyle","lifestyle",{"id":141,"doc_module":4,"doc_module_name":25,"category_name":142,"show_sort_weight":112,"slug":143},19,"General","general"]