[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122440-en":3,"doc-seo-122440-105":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},122440,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Web search trends on fibromyalgia - development of a machine learning model - analysis and prediction","Fibromyalgia (FM) is a chronic pain condition marked by widespread musculoskeletal pain, fatigue, and cognitive dysfunction. With increasing reliance on the internet for health information, online search behaviour has become a key signal for patient interest and education needs. This study analyses search behaviours related to FM across multiple countries, determines temporal trends, and evaluates machine learning models for predicting search interest, using time-series and interpretable feature effects.","Web search trends on fibromyalgia: development of a machine learning model  \nM.L.G. Leoni 1 , M. Mercieri 1 , A. Paladini2 , M. Cascella3 , M. Rekatsina4 , F. Atzeni5 ,  \nA. Pasqualucci6 , L. Bazzichi7 , F. Salaffi9 , P. Sarzi-Puttini7,8 , G. Varrassi 10  \n\n| 1Department of Medical and Surgical Sciences and Translational Medicine, Sapienza University of Rome, Italy; 2Department of Life, Health and Environmental Sciences, Università degli Studi dell’Aquila, Italy; 3Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy; 4Department of Anaesthesia and Pain Management, National and Kapodistrian University of Athens, Greece; 5Rheumatology Unit, Department of Experimental and Internal Medicine, University of Messina, Italy; 6Anaesthesiology and Critical Care, University of Perugia, Italy; 7Department of Rheumatology, IRCCS Galeazzi-Sant’Ambrogio Hospital, Milan, Italy; 8Department of Biomedical and Clinical Sciences, University of Milan, Italy; 9Rheumatology Unit, Dipartimento di Scienze Cliniche e Molecolari, Università Politecnica Delle Marche, Ancona;\u003Cbr>10Fondazione Paolo Procacci, Rome, Italy. |\n| --- |\n| Abstract\u003Cbr>Objective\u003Cbr>Fibromyalgia (FM) is a chronic pain condition characterised by widespread musculoskeletal pain, fatigue, and cognitive dysfunction. The growing reliance on the internet for health-related information has transformed how individuals seek medical knowledge, particularly for complex conditions like FM. This study aimed to analyse online search behaviours related to FM across multiple countries, identify temporal trends, and assess machine\u003Cbr>learning models for predicting search interest. |\n| Methods\u003Cbr>Google Trends data (2020–2024) were analysed across sixteen countries. Time-series analysis, linear regression, and the Mann-Kendall trend test assessed monotonic trends, while seasonal decomposition identified periodic fluctuations. An Auto-Regressive Integrated Moving Average (ARIMA) model forecasted search volumes for 2025. Machine learning models, including Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were used to predict search trends, with feature importance evaluated using SHAP (Shapley Additive Explanations) values. |\n| Results\u003Cbr>Search interest in FM varied across countries, with China, the UK, the USA and Canada showing the highest engagement, while Peru, Spain and Turkey had the lowest. Brazil, Italy and the UK exhibited rising search trends, whereas Argentina, Canada, Greece and the USA showed declines. Seasonal analysis revealed mid-year peaks in Brazil and Italy, while Turkey saw late autumn increases. ARIMA forecasting predicted stable or increasing trends in Brazil, Canada and Mexico, while Germany and Venezuela showed slight declines. Machine learning analysis identified short-term search history (search volumes from the previous day, week, and month) as the most\u003Cbr>influential predictor. |\n| Conclusion\u003Cbr>Understanding online search behaviour can enhance FM education. Targeted awareness campaigns and improved digital health literacy initiatives could sustain engagement and improve patient knowledge. Future efforts should focus on optimising online health resources and integrating evidence-based decision aids. |\n| Key words\u003Cbr>fibromyalgia, online search behaviour, chronic pain, Google trends analysis, predictive modelling, machine learning |\n\nClinical and Experimental Rheumatology 2025; 43: 1082-1094.  \nFM trends via machine learning / M.L.G. Leoni et al.  \nMatteo L. G. Leoni, MD Marco Mercieri, MD Antonella Paladini, MD Marco Cascella, MD Martina Rekatsina, MD Fabiola Atzeni, MD Alberto Pasqualucci, MD Laura Bazzichi, MD Fausto Salaffi, MD Piercarlo Sarzi-Puttini, MD Giustino Varrassi, MD  \nPlease address correspondence to: Matteo L. G. Leoni  \nScienze Medico Chirurgiche e Medicina Traslazionale Sapienza Università di Roma, Via Giorgio Nicola Papanicolau, 00189 Roma, Italy.  \n[E-mail: matteolg.leoni@gmail.com](E-mail: matteolg.leoni@gmail.","cbCaii2o2Mb94eFY","https://ap.wps.com/l/cbCaii2o2Mb94eFY","pdf",1655421,1,13,"English","en",105,"# Abstract\n## Objective\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Background on fibromyalgia\n## Potential mechanisms and contributors","[{\"question\":\"What was the objective of analyzing online search behaviour for fibromyalgia?\",\"answer\":\"To analyse online search behaviours related to FM across multiple countries, identify temporal trends, and assess machine learning models for predicting search interest.\"},{\"question\":\"Which data source and time range were used in the study?\",\"answer\":\"Google Trends data from 2020 to 2024 across sixteen countries were analysed.\"},{\"question\":\"Which factors were found to most influence predictions of FM search trends?\",\"answer\":\"Short-term search history—search volumes from the previous day, week, and month—was identified as the most influential predictor.\"}]","Web search trends on fibromyalgia - development of a machine learning model - analysis and prediction | PDF",1785810645,33,{"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},"web-search-trends-on-fibromyalgia-development-of-a-machine-learning-model-analysis-and-prediction","",{"@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/web-search-trends-on-fibromyalgia-development-of-a-machine-learning-model-analysis-and-prediction/122440/",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","2026-08-04",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 objective of analyzing online search behaviour for fibromyalgia?","Question",{"text":75,"@type":76},"To analyse online search behaviours related to FM across multiple countries, identify temporal trends, and assess machine learning models for predicting search interest.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data source and time range were used in the study?",{"text":80,"@type":76},"Google Trends data from 2020 to 2024 across sixteen countries were analysed.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors were found to most influence predictions of FM search trends?",{"text":84,"@type":76},"Short-term search history—search volumes from the previous day, week, and month—was identified as the most influential predictor.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]