@文章{信息:doi/10.2196/35831，作者=“Goh, Yong Shian和Ow Yong, Jenna Qing Yun和Chee, Bernice Qian Hui和Kuek, Jonathan Han Loong和Ho, Cyrus Su Hui”，标题=“健康促进和行为改变中的机器学习:范围综述”，期刊=“J医学互联网研究”，年=“2022”，月=“Jun”，日=“2”，卷=“24”，数=“6”，页=“e35831”，关键词=“机器学习;健康促进;健康行为改变;背景:尽管针对慢性疾病潜在的可改变的生活方式因素的健康行为改变干预措施，但个人的辍学率和不依不从率仍然很高。近年来机器学习(ML)的快速发展，以及它为用户提供随时可用的个性化体验的能力，在健康促进和行为改变干预方面具有很大的成功潜力。目的:本文的目的是对ML应用的现有研究进行概述，并利用其在健康促进和行为改变干预方面的潜力。方法:基于Arksey和O'Malley的5阶段框架和PRISMA-ScR(系统评价首选报告项目和范围评价元分析)指南进行范围评价。从成立到2021年2月，共检索了9个数据库(Cochrane Library、CINAHL、Embase、Ovid、ProQuest、PsycInfo、PubMed、Scopus和Web of Science)，不限制出版物的日期和类型。如果研究将ML纳入任何健康促进或行为改变干预，至少研究了一组参与者，并且已以英文发表，则纳入综述。从每项研究中提取与出版相关的信息(作者、年份、目标和发现)、健康促进领域、分析的用户数据、使用的ML类型、遇到的挑战和未来的研究。 Results: A total of 29 articles were included in this review. Three themes were generated, which are as follows: (1) enablers, which is the adoption of information technology for optimizing systemic operation; (2) challenges, which comprises the various hurdles and limitations presented in the articles; and (3) future directions, which explores prospective strategies in health promotion through ML. Conclusions: The challenges pertained to not only the time- and resource-consuming nature of ML-based applications, but also the burden on users for data input and the degree of personalization. Future works may consider designs that correspondingly mitigate these challenges in areas that receive limited attention, such as smoking and mental health. ", issn="1438-8871", doi="10.2196/35831", url="//www.mybigtv.com/2022/6/e35831", url="https://doi.org/10.2196/35831", url="http://www.ncbi.nlm.nih.gov/pubmed/35653177" }