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健康推荐系统评估:范围综述研究方案

Evaluation of health recommender systems: a scoping review protocol.

机构信息

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.

出版信息

BMJ Open. 2024 Oct 7;14(10):e083359. doi: 10.1136/bmjopen-2023-083359.

Abstract

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.

摘要

背景

人们越来越依赖在线健康信息来做出与健康相关的决策。鉴于可用信息量巨大,错误信息的风险很高。健康推荐系统使用智能算法为个人推荐与健康相关的信息或干预措施,具有解决这一问题的潜力。已经开发和评估了许多此类系统,但需要综合评估结果,以确定差距,并确保未来的推荐系统旨在对健康或目标行为产生积极影响。

目的

本综述的目的是概述评估健康推荐系统的文献现状,并强调所学到的经验教训、方法考虑因素和当前研究中的差距。

方法和分析

本综述将遵循系统评价和荟萃分析扩展的首选报告项目,以及人群、概念和背景框架。将使用反映数字健康、推荐系统以及功效和影响评估主题的搜索词,在五个数据库(PubMed、ACM Digital Library Full-Text Collection、IEEE Xplore、Web of Science 和 ScienceDirect)中搜索以英文发表的评估至少一个健康推荐系统的研究。在使用 EndNote 21 进行初步筛选后,两名独立评审员将筛选参考文献的标题、摘要和全文,然后将包含研究中与推荐系统特征、评估设计和评估结果相关的数据提取到预定的表格中。将进行描述性分析,以提供文献概述;将讨论文献中的关键主题和差距。

伦理和传播

不需要伦理批准,因为数据将从已发表的来源中获取。本研究的结果将通过在同行评议期刊上发表来传播。

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