[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121426-en":3,"doc-seo-121426-105":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},121426,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Medication adherence among Jordanian adults with chronic conditions - a combined analysis using regression and machine learning","Managing chronic illness depends not only on medication availability but also on patients’ ability to adhere to prescribed therapy. This study analyzed factors affecting medication adherence among Jordanian adults with long-term conditions, combining traditional regression with machine learning. A cross-sectional online survey collected demographic, clinical, and behavioural variables, including HLQ-12 health literacy and MARS-5 adherence. Results from quantile regression and Random Forest models identified key positive and negative associations and used SHAP for interpretation.","Annals of Medicine  \nISSN: 0785-3890 (Print) 1365-2060 (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/iann20)[www.tandfonline.com/journals/iann20](homepage: www.tandfonline.com/journals/iann20)  \nMedication adherence among Jordanian adults with chronic conditions: a combined analysis using regression and machine learning  \nWalid Al-Qerem , AnanJarab , Judith Eberhardt, Salwa Abdo , Lujain Al-sa’di , Razan Al-She hadeh , Dana Khasim , Ruba Zumot & Sarah Khalil  \nTo cite this article: Walid Al-Qerem , AnanJarab , Judith Eberhardt, Salwa Abdo , Lujain Al-sa’di , Razan Al-She hadeh , Dana Khasim , Ruba Zumot & Sarah Khalil (2025) Medication adherence among Jordanian adults with chronic conditions: a combined analysis using regression and machine learning, Annals of Medicine, 57:1, 2548979, DOI: 10.1080/07853890.2025.2548979  \nTo link to this article: [https://doi.org/10.1080/07853890.2025.2548979](https://doi.org/10.1080/07853890.2025.2548979)  \n© 2025 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group  \n\n|  Published online: 20 Aug 2025. |  |\n| --- | --- |\n|  | Submit your article to this journal  |\n|  | View related articles  |\n|  View Crossmark data |  |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=iann20](https://www.tandfonline.com/action/journalInformation?journalCode=iann20)  \nAnnAls of Medicine  \n2025, Vol. 57, no. 1, 2548979  \n[https://doi.org/10.1080/07853890.2025.2548979](https://doi.org/10.1080/07853890.2025.2548979)  \nRESEARCH ARTICLE     \nMedication adherence among Jordanian adults with chronic conditions: a combined analysis using regression and machine learning  \nWalid Al-Qerema, Anan Jarabb, Judith Eberhardtc , Salwa Abdoa, Lujain Al-sa’dia, Razan Al-Shehadeha, Dana Khasima, Ruba Zumota and Sarah Khalila  \nadepartment of Pharmacy, faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman, Jordan; bdepartment of clinical Pharmacy, faculty of Pharmacy, Jordan University of science and Technology, irbid, Jordan; cdepartment of Psychology, school of social sciences, Humanities and law, Teesside University, Middlesbrough, UK  \nABSTRACT  \nBackground: Managing chronic illness effectively depends not only on treatment availability but also on patients’ ability to adhere to prescribed medications. Objectives: This study examined the factors influencing medication adherence among Jordanian adults with long-term conditions, using both traditional regression and machine learning methods.  \nMethod: In this cross-sectional study, patients with chronic conditions completed an online survey that assessed demographic, clinical and behavioural variables, including Health Literacy Questionnaire (HLQ-12) and adherence (MARS-5). Quantile regression and machine learning models were applied.  \nResults: A total of 981 patients (63.1% females) were enrolled in the study. Quantile regression showed that higher health literacy, a diagnosis of diabetes or cardiovascular disease, and fewer prescribed medications were positively associated with adherence. In contrast, being married or having public, military or no insurance was linked to lower adherence scores. The Random Forest model achieved the highest predictive accuracy (R2 = 0.38), and SHAP analysis identified health literacy, disease duration and age as the most influential features.  \nConclusions: These findings highlight the need for targeted interventions that address both individual understanding and structural challenges, such as insurance type and treatment complexity. Improving health literacy, simplifying medication regimens, and ensuring equitable healthcare access may help support better adherence in this population. The use of explainable machine learning, alongside conventional statistical approaches, offers new opportunities to improve the understanding and prediction of adherence behaviours in resource-constrained settings.  \nIntroducti","cbCaiiMx3gw7FBtD","https://ap.wps.com/l/cbCaiiMx3gw7FBtD","pdf",2010280,1,12,"English","en",105,"# ABSTRACT\n# Introduction\n# Methods\n## Study design and measures\n## Regression and machine learning approach\n# Results\n## Quantile regression findings\n## Predictive performance and feature importance\n# Conclusions\n# Keywords","[{\"question\":\"What was the main objective of this study?\",\"answer\":\"To examine factors influencing medication adherence among Jordanian adults with chronic, long-term conditions using both regression and machine learning approaches.\"},{\"question\":\"Which tools measured adherence and health literacy in the study?\",\"answer\":\"Medication adherence was assessed with MARS-5, and health literacy was measured using the HLQ-12 questionnaire.\"},{\"question\":\"What modeling approach produced the best predictive accuracy?\",\"answer\":\"The Random Forest model achieved the highest predictive accuracy, reported as R2 = 0.38.\"}]","Medication adherence among Jordanian adults with chronic conditions - 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