[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120095-en":3,"doc-seo-120095-105":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},120095,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Predicting Adherence to Behavior Change Support Systems Using Machine Learning - Systematic Review","Background: There is a lack of reliable adherence prediction measures in behavior change support systems (BCSSs), and existing reviews often emphasize self-report measures that can overestimate or underestimate actual adherence. Objective: This systematic review identifies and summarizes trends in using machine learning to predict adherence to BCSSs. Methods: Searches in Scopus and PubMed (Jan 2011–Aug 2022) yielded 2182 papers, with 11 eligible studies. Results: Four adherence problem categories were identified, and supervised learning models showed good classification accuracy. Conclusions: Machine learning can support reinforcement of adherence through intelligent, personalized, timely BCSS suggestions.","JMIR AI Ekpezu et al  \nReview  \nPredicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review  \n\n| Akon Obu Ekpezu1, MPhil; Isaac Wiafe2, PhD; Harri Oinas-Kukkonen 1, PhD |\n| --- |\n| 1Oulu Advanced Research on Service and Information Systems, Department of Information Processing Science, University of Oulu, Oulu, Finland 2Department of Computer Science, University of Ghana, Accra, Ghana\u003Cbr>Corresponding Author:\u003Cbr>Akon Obu Ekpezu, MPhil\u003Cbr>Oulu Advanced Research on Service and Information Systems Department of Information Processing Science\u003Cbr>University of Oulu\u003Cbr>Pentti Kaiteran Katu 1 Linnanmaa Oulu, 90570\u003Cbr>Finland\u003Cbr>Phone: 358 468860704\u003Cbr>Email: [akon.ekpezu@oulu.fi](akon.ekpezu@oulu.fi)\u003Cbr>Abstract |\n| Background: There is a dearth of knowledge on reliable adherence prediction measures in behavior change support systems (BCSSs). Existing reviews have predominately focused on self-reporting measures of adherence. These measures are susceptible to overestimation or underestimation of adherence behavior.\u003Cbr>Objective: This systematic review seeks to identify and summarize trends in the use of machine learning approaches to predict adherence to BCSSs.\u003Cbr>Methods: Systematic literature searches were conducted in the Scopus and PubMed electronic databases between January 2011 and August 2022. The initial search retrieved 2182 journal papers, but only 11 of these papers were eligible for this review. Results: A total of 4 categories of adherence problems in BCSSs were identified: adherence to digital cognitive and behavioral interventions, medication adherence, physical activity adherence, and diet adherence. The use of machine learning techniques for real-time adherence prediction in BCSSs is gaining research attention. A total of 13 unique supervised learning techniques were identified and the majority of them were traditional machine learning techniques (eg, support vector machine) . Long short-term memory, multilayer perception, and ensemble learning are currently the only advanced learning techniques. Despite the heterogeneity in the feature selection approaches, most prediction models achieved good classification accuracies. This indicates that the features or predictors used were a good representation of the adherence problem.\u003Cbr>Conclusions: Using machine learning algorithms to predict the adherence behavior of a BCSS user can facilitate the reinforcement of adherence behavior. This can be achieved by developing intelligent BCSSs that can provide users with more personalized, tailored, and timely suggestions.\u003Cbr>(JMIRAI2023;2:e46779) doi:  10.2196/46779 |\n\nKEYWORDS  \nadherence; compliance; behavior change support systems; persuasive systems; persuasive technology; machine learning  \nIntroduction  \nBehavior change support systems (BCSSs) have been effective in improving health and healthier lifestyles. These are persuasive systems that have been designed to change behavior without force or deception [1] . However, the effectiveness of these systems is generally hindered by nonadherence [2-4] . Nonadherence to recommended regimes in BCSSs has the  \n[https://ai.jmir.org/2023/1/e46779](https://ai.jmir.org/2023/1/e46779)  \nXSL• FO  \nRenderX  \npotential to diminish their long-term benefits [5] . It is associated with the increased prevalence of diseases such as hypertension, diabetes, obesity, dementia, bipolar disorder, and heart failure [2,4,6-8], as well as the increased cost of health care. Yet, there are no standardized factors that can reliably predict adherence [9, 10]. Direct adherence monitoring approaches are expensive, burdensome to care providers, and susceptible to distortion by patients, while indirect monitoring approaches such as pill count,  \nJMIR AI 2023 | vol. 2 | e46779 | p. 1  \n(page number not for citation purposes)  \nJMIR AI  \npatient questionnaires, electronic medication monitors, or electronic reporting of daily physical activity are susceptible tomisinterpretati","cbCaiiLdRpAbP1u6","https://ap.wps.com/l/cbCaiiLdRpAbP1u6","pdf",294424,1,12,"English","en",105,"# Abstract\n# Introduction\n## Background and problem of nonadherence\n## Need for reliable adherence assessment\n## Limitations of existing measures and reviews\n# Review scope and approach","[{\"question\":\"Why is adherence prediction in BCSSs difficult today?\",\"answer\":\"Nonadherence reduces the long-term benefits of BCSSs, yet there are no standardized factors that reliably predict adherence. Direct monitoring can be costly and burdensome, while indirect measures may be distorted or misinterpreted.\"},{\"question\":\"How was the systematic review conducted?\",\"answer\":\"Systematic searches were performed in Scopus and PubMed between January 2011 and August 2022. From 2182 initial journal papers, 11 studies met eligibility criteria.\"},{\"question\":\"What did the review find about machine learning approaches for adherence prediction?\",\"answer\":\"The review identified four categories of adherence problems and reported 13 unique supervised learning techniques. Most prediction models achieved good classification accuracies despite heterogeneity in feature selection.\"}]","Predicting Adherence to Behavior Change Support Systems Using Machine Learning - Systematic Review | PDF",1785728142,30,{"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},"predicting-adherence-to-behavior-change-support-systems-using-machine-learning-systematic-review","",{"@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/predicting-adherence-to-behavior-change-support-systems-using-machine-learning-systematic-review/120095/",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","2026-08-03",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},"Why is adherence prediction in BCSSs difficult today?","Question",{"text":75,"@type":76},"Nonadherence reduces the long-term benefits of BCSSs, yet there are no standardized factors that reliably predict adherence. Direct monitoring can be costly and burdensome, while indirect measures may be distorted or misinterpreted.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the systematic review conducted?",{"text":80,"@type":76},"Systematic searches were performed in Scopus and PubMed between January 2011 and August 2022. From 2182 initial journal papers, 11 studies met eligibility criteria.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the review find about machine learning approaches for adherence prediction?",{"text":84,"@type":76},"The review identified four categories of adherence problems and reported 13 unique supervised learning techniques. 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