[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123265-en":3,"doc-seo-123265-105":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},123265,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",7,"Healthcare","Toward characterizing cardiovascular fitness using machine learning based on unobtrusive data","Cardiopulmonary exercise testing (CPET) provides a non-invasive measure of maximum oxygen uptake (VO2max), an indicator of cardiovascular fitness (CF), yet it is not universally available and cannot be performed continuously. This study uses wearable technologies and machine learning to predict CF from unobtrusive data collected during 7 days of everyday activity. Forty-three volunteers underwent CPET, while 11 inputs from demographic, respiratory, movement, and heart-related domains were used with support vector regression (SVR), with SHAP used for interpretation.","PLOS ONE  \nOPEN ACCESS  \nCitation: Frade MCM, Beltrame T, Gois MdO, Pinto A, Tonello SCGdM, Torres RdS, et al. (2023) Toward characterizing cardiovascular fitness using machine learning based on unobtrusive data. PLoSONE 18(3): e0282398 . [https://doi.org/10.1371/](https://doi.org/10.1371/)[ ](https://doi.org/10.1371/)[journal.pone.0282398](journal.pone.0282398)  \nEditor: Zulkarnain Jaafar, Universiti Malaya, MALAYSIA  \nReceived: May 11, 2022  \nAccepted: February 14, 2023  \nPublished: March 2, 2023  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here:  \n[https://doi.org/10.1371/journal.pone.0282398](https://doi.org/10.1371/journal.pone.0282398)  \n[Copyright:](Copyright:) © [2023](2023) Frade et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: All relevant data are within the paper and it will be in Supporting Information files.  \nRESEARCH ARTICLE  \nToward characterizing cardiovascular fitness using machine learning based on unobtrusive data  \nMaria Cec´ılia Moraes Frade1, Thomas Beltrame1,2 *, Mariana de Oliveira Gois1, Allan Pinto3, Silvia Cristina Garcia de Moura Tonello1, Ricardo da Silva Torres4, Aparecida Maria Catai1  \n1 Department of Physical Therapy, Federal University of São Carlos, São Carlos, São Paulo, Brazil,  \n2 Samsung R&D Institute Brazil–SRBR, Campinas, São Paulo, Brazil, 3 Brazilian Synchrotron Light Laboratory (LNLS), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo, Brazil, 4 Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, NTNU—Norwegian University of Science and Technology, Ålesund, Norway  \n* [beltramethomas@gmail.com](beltramethomas@gmail.com)  \nAbstract  \nCardiopulmonary exercise testing (CPET) is a non-invasive approach to measure the maxi-_  \nmum oxygen uptake (VO2􀀀 max), which is an index to assess cardiovascular fitness (CF) . However, CPET is not available to all populations and cannot be obtained continuously. Thus, wearable sensors are associated with machine learning (ML) algorithms to investigate CF. Therefore, this study aimed to predict CF by using ML algorithms using data obtained by wearable technologies. For this purpose, 43 volunteers with different levels of aerobic power, who wore a wearable device to collect unobtrusive data for 7 days, were evaluated by CPET. Eleven inputs (sex, age, weight, height, and body mass index, breathing rate, minute ventilation, total hip acceleration, walking cadence, heart rate, and tidal volume) were  \n_  \nused to predict the VO2􀀀 max by support vector regression (SVR). Afterward, the SHapley Additive exPlanations (SHAP) method was used to explain their results. SVR was able to predict the CF, and the SHAP method showed that the inputs related to hemodynamic and anthropometric domains were the most important ones to predict the CF. Therefore, we conclude that the cardiovascular fitness can be predicted by wearable technologies associated with machine learning during unsupervised activities of daily living.  \nIntroduction  \nNoncommunicable chronic diseases (NCDs) are mainly responsible for all causes of death and illness among adults aged between 35–70 years, and cardiovascular diseases are accountable for the main cause of mortality in the world [ 1] . There are some modifiable risk factors associated with NCDs, such as high systolic arterial pressure, high fasting plasma glucose, as well as low physical activity [2, 3] .  \nIt is known that the cardiovascular diseases and their modifiable risk factors lead to a reduction in ","cbCairbasR64rASi","https://ap.wps.com/l/cbCairbasR64rASi","pdf",1768395,1,18,"English","en",105,"# Abstract\n# Introduction\n## Cardiovascular fitness assessment and limitations\n## CPET and VO2max as reference measures\n## Need for continuous CF evaluation","[{\"question\":\"Why is CPET not suitable for continuous cardiovascular fitness assessment?\",\"answer\":\"CPET requires trained professionals and expensive equipment, making VO2max-based assessment unavailable for many populations and impractical for continuous monitoring.\"},{\"question\":\"How was cardiovascular fitness predicted in the study?\",\"answer\":\"The study predicted VO2max, used as the CF reference, by applying support vector regression (SVR) to 11 variables derived from wearable-collected unobtrusive data.\"},{\"question\":\"What did the SHAP analysis show about feature importance?\",\"answer\":\"SHAP indicated that inputs associated with hemodynamic and anthropometric domains were the most influential for predicting cardiovascular fitness.\"}]","Toward characterizing cardiovascular fitness using machine learning based on unobtrusive data | 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is CPET not suitable for continuous cardiovascular fitness assessment?","Question",{"text":75,"@type":76},"CPET requires trained professionals and expensive equipment, making VO2max-based assessment unavailable for many populations and impractical for continuous monitoring.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was cardiovascular fitness predicted in the study?",{"text":80,"@type":76},"The study predicted VO2max, used as the CF reference, by applying support vector regression (SVR) to 11 variables derived from wearable-collected unobtrusive data.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the SHAP analysis show about feature importance?",{"text":84,"@type":76},"SHAP indicated that inputs associated with hemodynamic and anthropometric domains were the most influential for predicting cardiovascular 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