[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-456073-105":59,"doc-detail-456073-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","correction-machine-learning-approach-for-frailty-detection-in-long-term-care-using-accelerometer-measured-gait-and-daily-physical-activity-model-development-and-validation-study-revised-results","Correction: Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity - Model Development and Validation Study - Revised Results","","Correction notice for a JMIR Aging study on machine learning-based frailty detection in long-term care using accelerometer-measured gait and daily physical activity. The revised abstract results adjust multiple findings: gait explainable AI interpretation changes stride length variability, sample entropy, and the gait symmetry score direction. Additional wording corrections clarify that higher or lower symmetry metrics correspond differently to frail versus nonfrail classification. The notice also updates a multimedia appendix item through an associated online resource link.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/correction-machine-learning-approach-for-frailty-detection-in-long-term-care-using-accelerometer-measured-gait-and-daily-physical-activity-model-development-and-validation-study-revised-results/456073/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/correction-machine-learning-approach-for-frailty-detection-in-long-term-care-using-accelerometer-measured-gait-and-daily-physical-activity-model-development-and-validation-study-revised-results/456073.png","ImageObject",300,407,{"name":92,"@type":93},"Cart","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What was corrected in the Results section of the abstract?","Question",{"text":113,"@type":114},"The abstract sentence describing gait pattern characteristics was revised, including the direction of the gait symmetry score for older adults with frailty.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How did the gait symmetry interpretation change after the correction?",{"text":118,"@type":114},"Higher gait symmetry score now indicates more symmetry (less symmetry was removed), and the associated metric direction is aligned with the frail versus nonfrailty classification.",{"name":120,"@type":111,"acceptedAnswer":121},"Was the original study updated and how should the corrected paper be cited?",{"text":122,"@type":114},"The correction states that the corrected article was resubmitted to full-text repositories and provides the corrected citation details for JMIR Aging 2025;8:e85173.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},456073,1790792876,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":24,"preview_page_count":4},18829141979164,"https://eur-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","| JMIR AGING Zheng et al\u003Cbr>Corrigenda and Addenda\u003Cbr>Correction: Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study\u003Cbr>Xiaoping Zheng1*, PhD; Ziwei Zeng1*, MSc; Kimberley S van Schooten2,3, PhD; Yijian Yang1,4, MD, PhD |\n| --- |\n| 1Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong) 2Neuroscience Research Australia, University ofNew South Wales, Sydney, Australia\u003Cbr>3School of Population Health, University ofNew South Wales, Sydney, Australia\u003Cbr>4CUHK Jockey Club Institute of Ageing, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)\u003Cbr>*these authors contributed equally\u003Cbr>Corresponding Author:\u003Cbr>Yijian Yang, MD, PhD\u003Cbr>Department of Sports Science and Physical Education The Chinese University of Hong Kong\u003Cbr>G07 Kwok Sports Building. University Ave. The Chinese University of Hong Kong. N.T.\u003Cbr>Hong Kong\u003Cbr>China (Hong Kong) Phone: 852 39434001\u003Cbr>Fax: 852 26035781\u003Cbr>[Email:](Email: yyang@cuhk.edu.hk)[ ](Email: yyang@cuhk.edu.hk)[yyang@cuhk.edu.hk](Email: yyang@cuhk.edu.hk)\u003Cbr>Related Article:\u003Cbr>Correction of: [https://aging.jmir.org/2025/1/e77140](https://aging.jmir.org/2025/1/e77140)[ ](https://aging.jmir.org/2025/1/e77140)(JMIR Aging 2025;8:e85173) doi:  10.2196/85173 |\n\nIn “Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study”(JMIR Aging 2025;1(8) e77140) the authors made a few corrections.  \nIn the Results section of the Abstract, the sentence:  \nExplainable artificial intelligence analysis revealed that older adults with frailty exhibited more variable, complex, and asymmetric gait patterns, which were characterized by higher stride length variability, increased sample entropy, anda higher gait symmetry score.  \nHas been revised to:  \nExplainable artificial intelligence analysis revealed that older adults with frailty exhibited more variable, complex, and asymmetric gait patterns, which were characterized by higher stride length variability, increased sample entropy, anda lower gait symmetry score.  \nIn the Results section, the sentence:  \nHigher gait symmetry score (indicating less symmetry;  \nMultimedia Appendix 5) in the vertical direction contributes to the frail group, while lower values are associated with a nonfrailty classification.  \nHas been revised to:  \nHigher gait symmetry score (indicating more symmetry; Multimedia Appendix 5) in the vertical direction contributes to the frail group, while lower values are associated with a nonfrailty classification.  \nIn the Results section, the sentence:  \nCompared with the nonfrail group, the frail group exhibited higher stride length variability, greater sample entropy in the vertical direction, and increased gait symmetry score in the vertical direction.  \nHas been revised to:  \nCompared with the nonfrail group, the frail group exhibited higher stride length variability, greater sample entropy in the vertical direction, and decreased gait symmetry score in the vertical direction.  \nIn the Multimedia Appendix 5, the formula:  \nHas been revised to:  \n[https://aging.jmir.org/2025/1/e85173](https://aging.jmir.org/2025/1/e85173)  \nXSL• FO  \nRenderX  \nJMIR Aging 2025 | vol. 8 | e85173 | p. 1 (page number not for citation purposes)  \n\n| JMIR AGING\u003Cbr>The correction will appear in the online version of the paper on the JMIR Publications website, together with the publication of this correction notice. Because this was made after submission | Zheng et al\u003Cbr>to full-text repositories, the corrected article has also been resubmitted to those repositories. |\n| --- | --- |\n\nThis is a non–peer-reviewed article. Submitted 02.Oct.2025; accepted 06.Oct.2025; published 29.Oct.2025.  \nPlease cite as:  \nZheng X, Zeng Z, van Schooten KS, Yang Y  \nCorrection: Machine Learning A","cbCaijxeqkIVlD2y","https://ap.wps.com/l/cbCaijxeqkIVlD2y","pdf",58214,"English","# Correction Details\n## Abstract Results Revisions\n## Multimedia Appendix Update\n## Citation and Publication Metadata","[{\"question\":\"What was corrected in the Results section of the abstract?\",\"answer\":\"The abstract sentence describing gait pattern characteristics was revised, including the direction of the gait symmetry score for older adults with frailty.\"},{\"question\":\"How did the gait symmetry interpretation change after the correction?\",\"answer\":\"Higher gait symmetry score now indicates more symmetry (less symmetry was removed), and the associated metric direction is aligned with the frail versus nonfrailty classification.\"},{\"question\":\"Was the original study updated and how should the corrected paper be cited?\",\"answer\":\"The correction states that the corrected article was resubmitted to full-text repositories and provides the corrected citation details for JMIR Aging 2025;8:e85173.\"}]","Correction: Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity - Model Development and Validation Study - Revised Results | PDF",1790745057]