Evaluation of health recommender systems: a scoping review protocol.

Autor:	Ananthakrishnan A; Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK., Milne-Ives M; Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.; Centre for Health Technology, School of Nursing and Midwifery, University of Plymouth, Plymouth, Devon, UK., Shankar R; Plymouth University Peninsula Medical School, Truro, UK.; CIDER, Cornwall Partnership NHS Foundation Trust, Truro, UK., Meinert E; Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK edward.meinert@newcastle.ac.uk.; Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK.; Faculty of Life Sciences and Medicine, King's College London, London, UK.
Jazyk:	angličtina
Zdroj:	BMJ open [BMJ Open] 2024 Oct 07; Vol. 14 (10), pp. e083359. Date of Electronic Publication: 2024 Oct 07.
DOI:	10.1136/bmjopen-2023-083359
Abstrakt:	Background: People increasingly rely on online health information for their health-related decision-making. Given the overwhelming amount of information available, the risk of misinformation is high. Health recommender systems, which recommend personalised health-related information or interventions using intelligent algorithms, have the potential to address this issue. Many such systems have been developed and evaluated individually, but there is a need to synthesise the evaluation findings to identify gaps and ensure that future recommender systems are designed to have a positive impact on health or target behaviours. Objective: The purpose of this review is to provide an overview of the state of the literature evaluating health recommender systems and highlight lessons learnt, methodological considerations and gaps in current research. Methods and Analysis: The review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews and the Population, Concept, and Context frameworks. Five databases (PubMED, ACM Digital Library Full-Text Collection, IEEE Xplore, Web of Science and ScienceDirect) will be searched for studies published in English that evaluate at least one health recommender system using search terms following the themes reflecting digital health, recommendation systems and evaluations of efficacy and impact. After using EndNote 21 for initial screening, two independent reviewers will screen the titles, abstracts and full texts of the references, and then extract data from included studies related to the recommender system characteristics, evaluation design and evaluation findings into a predetermined form. A descriptive analysis will be conducted to provide an overview of the literature; key themes and gaps in the literature will be discussed. Ethics and Dissemination: Ethical approval is not required as data will be obtained from already published sources. Findings from this study will be disseminated via publication in a peer-reviewed journal. Competing Interests: Competing interests: None declared. (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
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