[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119277-en":3,"doc-seo-119277-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119277,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Biophysical versus machine learning models for predicting rectal and skin temperatures in older adults - research article","This study compares the efficacy of machine learning models to traditional biophysical models for predicting rectal (Tre) and skin (Tsk) temperatures in older adults (≥60 years) during prolonged heat exposure. Five machine learning models were trained using 4-fold cross validation from 162 day-long (8–9h) sessions involving 76 participants across six environments, from thermoneutral to heatwave conditions. Ridge regression and other ML approaches showed higher prediction accuracy than JOS-3, the Gagge two-node model, and an optimized two-node model, with clinically meaningful error rates. Results support personalised heat risk assessments and real-time prediction, and motivate future validation across more dynamic, real-world conditions and integration into home monitoring or wearable systems.","Journal of Thermal Biology 128 (2025) 104078  \nContents lists available at ScienceDirect  \nJournal of Thermal Biology  \njournal [homepage: www.elsevier.com/locate/jtherbio](homepage: www.elsevier.com/locate/jtherbio)  \n| Biophysical versus machine learning models for predicting rectal and skin   temperatures in older adults\u003Cbr>Connor Forbes a , Alberto Coccarellib , Zhiwei Xuc,d , Robert D. Meade e,f,\u003Cbr>Glen P. Kenny e , Sebastian Binnewiesa , Aaron J.E. Bach d,g,* \u003Cbr>a School of Information and Communication Technology, Griffith University, Gold Coast, Australia\u003Cbr>b Zienkiewicz Institute for Modelling, AI and Data, Mechanical Engineering Department, Faculty of Science and Engineering, Swansea University, Swansea, UK c School Medicine and Dentistry, Griffith University, Gold Coast, Australia\u003Cbr>d Cities Research Institute, Griffith University, Gold Coast, Australia\u003Cbr>e Human and Environmental Physiology Research Unit, School of Human Kinetics, University of Ottawa, Ottawa, Canada f Harvard T.H. Chan School of Public Health, Harvard University, Boston, United States\u003Cbr>g School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Aged\u003Cbr>Body temperature Extreme heat\u003Cbr>Heat stress disorders Theoretical models |  | This study compares the efficacy of machine learning models to traditional biophysical models in predicting rectal (Tre) and skin (Tsk) temperatures of older adults (≥60 years) during prolonged heat exposure. Five machine learning models were trained on data using 4-fold cross validation from 162 day-long (8–9h) sessions involving 76 older adults across six environments, from thermoneutral to heatwave conditions. These models were compared to three biophysical models: the JOS-3 model, the Gagge two-node model, and an optimised two-node model. Our findings show that machine learning models, particularly ridge regression, outperformed biophysical models in prediction accuracy. The ridge regression model achieved a Root-Mean Squared Error (RMSE) of 0.27 ◦ C for T re, and 0.73 ◦ C for T sk. Among the best biophysical models, the optimised two-node model achieved an RMSE of 0.40 ◦ C for T re, while JOS-3 achieved an RMSE of 0.74 ◦ C for T sk. Of all models, ridge regression had the highest proportion of participants with Tre RMSEs within clinically meaningful thresholds at 70%(\u003C0.3 ◦ C) and the highest proportion for Tsk at 88%(\u003C1.0 ◦ C), tied with the JOS-3 model. Our results suggest machine learning models better capture the complex thermoregulatory responses of older adults during prolonged heat exposure. The study highlights machine learning models’ potential for personalised heat risk assessments and real-time predictions. Future research should expand upon training datasets, incorporate more dynamic conditions, and validate models in real-world settings. Integrating these models into home-based monitoring systems or wearable devices could enhance heat management strategies for older adults. |\n\n1. Introduction  \nHumans deploy both autonomic and behavioural regulation in order to maintain homeothermy and preserve essential physiological functions. Skin temperature plays a crucial role in this process, as it influences, in-part, behavioural thermoregulation (Vargas et al., 2018) and is the site of heat exchange between the body and the surrounding environment. However, prolonged and compensable heat strain leading to relatively small perturbations in core temperature (a rise \u003C1 ◦ C) can lead to severe health consequences in at risk populations (Faunt et al., 1995; Loughnan et al., 2010; Meade et al., 2024c). The measurement of core temperature is invasive, often requiring devices such as rectal  \nthermometers or ingestible sensors, which limits its practicality in many settings. The accurate prediction of core and skin temperatures could help better define individual risk, early warning, and allow for","cbCaiitYPeVQgHU7","https://ap.wps.com/l/cbCaiitYPeVQgHU7","pdf",10974655,1,17,"English","en",105,"# Introduction\n# Models and Methods\n## Machine learning models\n## Biophysical models\n# Results\n## Prediction accuracy comparisons\n## Clinically meaningful error thresholds\n# Implications and Future Work","[{\"question\":\"What does the study compare between machine learning and biophysical models?\",\"answer\":\"It compares machine learning models versus traditional biophysical models for predicting rectal temperature (Tre) and skin temperature (Tsk) in older adults during prolonged heat exposure.\"},{\"question\":\"How were the machine learning models trained and evaluated?\",\"answer\":\"Five machine learning models were trained using data from 162 day-long sessions and evaluated with 4-fold cross validation across six environments.\"},{\"question\":\"Which model performed best and what do the error metrics show?\",\"answer\":\"Ridge regression achieved the lowest reported RMSE for Tre and Tsk and showed the highest proportion of participants within clinically meaningful thresholds, indicating better capture of complex thermoregulatory responses.\"}]","Biophysical versus machine learning models for predicting rectal and skin temperatures in older adults - research article | PDF",1785723462,43,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"biophysical-versus-machine-learning-models-for-predicting-rectal-and-skin-temperatures-in-older-adults-research-article","",{"@graph":36,"@context":86},[37,54,69],{"@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/biophysical-versus-machine-learning-models-for-predicting-rectal-and-skin-temperatures-in-older-adults-research-article/119277/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What does the study compare between machine learning and biophysical models?","Question",{"text":76,"@type":77},"It compares machine learning models versus traditional biophysical models for predicting rectal temperature (Tre) and skin temperature (Tsk) in older adults during prolonged heat exposure.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the machine learning models trained and evaluated?",{"text":81,"@type":77},"Five machine learning models were trained using data from 162 day-long sessions and evaluated with 4-fold cross validation across six environments.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model performed best and what do the error metrics show?",{"text":85,"@type":77},"Ridge regression achieved the lowest reported RMSE for Tre and Tsk and showed the highest proportion of participants within clinically meaningful thresholds, indicating better capture of complex thermoregulatory responses.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]