Alice Lee Centre for Nursing Studies, National University of Singapore, Singapore, Singapore.
Faculty of Arts and Social Sciences, National University of Singapore, Singapore, Singapore.
J Med Internet Res. 2022 Jun 2;24(6):e35831. doi: 10.2196/35831.
Despite health behavioral change interventions targeting modifiable lifestyle factors underlying chronic diseases, dropouts and nonadherence of individuals have remained high. The rapid development of machine learning (ML) in recent years, alongside its ability to provide readily available personalized experience for users, holds much potential for success in health promotion and behavioral change interventions.
The aim of this paper is to provide an overview of the existing research on ML applications and harness their potential in health promotion and behavioral change interventions.
A scoping review was conducted based on the 5-stage framework by Arksey and O'Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews) guidelines. A total of 9 databases (the Cochrane Library, CINAHL, Embase, Ovid, ProQuest, PsycInfo, PubMed, Scopus, and Web of Science) were searched from inception to February 2021, without limits on the dates and types of publications. Studies were included in the review if they had incorporated ML in any health promotion or behavioral change interventions, had studied at least one group of participants, and had been published in English. Publication-related information (author, year, aim, and findings), area of health promotion, user data analyzed, type of ML used, challenges encountered, and future research were extracted from each study.
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.
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.
尽管针对慢性病相关可改变生活方式因素的健康行为改变干预措施已经存在，但个体的脱落和不依从率仍然很高。近年来，机器学习（ML）的快速发展，以及其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。
本文旨在概述现有的 ML 应用研究，并利用其潜力促进健康和行为改变干预。
我们按照 Arksey 和 O'Malley 的 5 阶段框架和 PRISMA-ScR（系统评价和荟萃分析的首选报告项目用于范围综述）指南进行了范围综述。从成立到 2021 年 2 月，我们总共在 9 个数据库（Cochrane 图书馆、CINAHL、Embase、Ovid、ProQuest、PsycInfo、PubMed、Scopus 和 Web of Science）中进行了搜索，对出版物的日期和类型没有任何限制。如果研究将 ML 应用于任何健康促进或行为改变干预措施中，研究了至少一组参与者，并且以英文发表，则将其纳入综述。从每项研究中提取了与出版物相关的信息（作者、年份、目的和发现）、健康促进领域、分析的用户数据、使用的 ML 类型、遇到的挑战以及未来研究。
本综述共纳入 29 篇文章。生成了 3 个主题，如下所示：（1）促进因素，即采用信息技术优化系统运行；（2）挑战，包括文章中提出的各种障碍和限制；（3）未来方向，即通过 ML 探索健康促进的未来策略。
挑战不仅涉及基于 ML 的应用程序的耗时和资源密集性质，还涉及用户在数据输入方面的负担和个性化程度。未来的工作可以考虑在关注有限的领域设计相应的方案来减轻这些挑战，例如吸烟和心理健康。
