[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124837-en":3,"doc-seo-124837-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124837,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Gait, physical activity and tibiofemoral cartilage damage - a longitudinal machine learning analysis in the Multicenter Osteoarthritis Study","Develop and evaluate an ensemble machine learning model that uses gait and physical activity, alongside clinical and demographic data, to predict medial tibiofemoral cartilage worsening over 2 years in people without advanced knee osteoarthritis. Use repeated cross-validations to assess performance and variable-importance measures across 100 held-out test sets, with g-computation to quantify predictor effects. In 947 legs, 14% worsened; median AUROC was 0.73. Baseline damage, Kellgren-Lawrence grade, walking pain, lateral ground reaction force impulse, lying time, and unloading rate were key predictors, supporting targeted early interventions.","UCSF  \nUC San Francisco Previously Published Works  \nTitle  \nGait, physical activity and tibiofemoral cartilage damage: a longitudinal machine learning analysis in the Multicenter Osteoarthritis Study  \nPermalink  \n[https://escholarship.org/uc/item/56t0b7n0](https://escholarship.org/uc/item/56t0b7n0)  \nJournal  \nBritish Journal of Sports Medicine, 57(16)  \nISSN  \n0306-3674  \nAuthors  \nCostello, Kerry E  \nFelson, David T Jafarzadeh, S Reza et al.  \nPublication Date  \n2023-08-01  \nDOI  \n10.1136/bjsports-2022-106142  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nOriginal research  \nGait, physical activity and tibiofemoral cartilage damage: a longitudinal machine learning analysis in the Multicenter Osteoarthritis Study  \nKerry E Costello  , 1,2,3 David T Felson  ,3 S Reza Jafarzadeh  ,3 Ali Guermazi  ,4 Frank W Roemer  ,5,6 Neil A Segal  ,7,8 Cora E Lewis,9 Michael C Nevitt, 10 Cara L Lewis  ,2,3 Vijaya B Kolachalama  , 11,12 Deepak Kumar  2,3  \n► Additional supplemental material is published online only. To view, please visit the journal online ([http://dx.doi](http://dx.doi). org/10.1136/bjsports-2022- 106142) .  \nFor numbered affiliations see end of article.  \nCorrespondence to  \nDeepak Kumar, Boston University, Boston 02215, Massachusetts, USA; [kumard@bu.edu](kumard@bu.edu)  \nAccepted 20 February 2023 Published Online First  \n3 March 2023  \n© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.  \nTo cite: Costello KE, Felson DT, Jafarzadeh SR, et al. Br J Sports Med 2023;57:1018–1024 .  \nABSTRACT  \nObjective To (1) develop and evaluate a machine learning model incorporating gait and physical activity to predict medial tibiofemoral cartilage worsening over 2 years in individuals without advanced knee osteoarthritis and (2) identify influential predictors in the model and quantify their effect on cartilage worsening. Design An ensemble machine learning model was developed to predict worsened cartilage MRI Osteoarthritis Knee Score at follow-up from gait, physical activity, clinical and demographic data from the Multicenter Osteoarthritis Study. Model performance was evaluated in repeated cross-validations. The top 10 predictors of the outcome across 100 held-out test sets were identified by a variable importance measure. Their effect on the outcome was quantified by g-computation. Results Of 947 legs in the analysis, 14% experienced medial cartilage worsening at follow-up. The median (2 .5–97. 5th percentile) area under the receiver operating characteristic curve across the 100 held-out test sets was 0.73 (0 .65–0. 79) . Baseline cartilage damage, higher Kellgren-Lawrence grade, greater pain during walking, higher lateral ground reaction force impulse, greater time spent lying and lower vertical ground reaction force unloading rate were associated with greater risk of cartilage worsening. Similar results were found for the subset of knees with baseline cartilage damage. Conclusions A machine learning approach incorporating gait, physical activity and clinical/ demographic features showed good performance for predicting cartilage worsening over 2 years. While identifying potential intervention targets from the model is challenging, lateral ground reaction force impulse, time spent lying and vertical ground reaction force unloading rate should be investigated further as potential early intervention targets to reduce medial tibiofemoral cartilage worsening.  \nINTRODUCTION  \nKnee osteoarthritis (OA) is a progressive, painful joint disease and leading cause of disability, affecting over 350 million adults.1 While some individuals with advanced disease undergo knee replacement, there is no cure and many exp","cbCairEpvszzKF4S","https://ap.wps.com/l/cbCairEpvszzKF4S","pdf",1413492,1,9,"English","en",105,"# Abstract\n## Objective and design\n## Results\n## Conclusions","[{\"question\":\"What was the main goal of the study?\",\"answer\":\"To build and evaluate a machine learning model using gait and physical activity to predict medial tibiofemoral cartilage worsening over two years, and to determine influential predictors and their quantified effects.\"},{\"question\":\"How was model performance evaluated?\",\"answer\":\"Performance was assessed using repeated cross-validations, with AUROC reported across 100 held-out test sets and variable importance measures identifying top predictors.\"},{\"question\":\"Which factors were associated with greater risk of cartilage worsening?\",\"answer\":\"Baseline cartilage damage, higher Kellgren-Lawrence grade, greater pain during walking, higher lateral ground reaction force impulse, more time spent lying, and lower vertical ground reaction force unloading rate.\"}]","Gait, physical activity and tibiofemoral cartilage damage - a longitudinal machine learning analysis in the Multicenter Osteoarthritis Study | PDF",1785894917,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"gait-physical-activity-and-tibiofemoral-cartilage-damage-a-longitudinal-machine-learning-analysis-in-the-multicenter-osteoarthritis-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/gait-physical-activity-and-tibiofemoral-cartilage-damage-a-longitudinal-machine-learning-analysis-in-the-multicenter-osteoarthritis-study/124837/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What was the main goal of the study?","Question",{"text":75,"@type":76},"To build and evaluate a machine learning model using gait and physical activity to predict medial tibiofemoral cartilage worsening over two years, and to determine influential predictors and their quantified effects.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was model performance evaluated?",{"text":80,"@type":76},"Performance was assessed using repeated cross-validations, with AUROC reported across 100 held-out test sets and variable importance measures identifying top predictors.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors were associated with greater risk of cartilage worsening?",{"text":84,"@type":76},"Baseline cartilage damage, higher Kellgren-Lawrence grade, greater pain during walking, higher lateral ground reaction force impulse, more time spent lying, and lower vertical ground reaction force unloading rate.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]