JMIR Publications

ABSTRACT

Background:

Despite behavioural change interventions targeting modifiable lifestyle factors underlying chronic diseases, dropouts and non-adherence have remained widespread. The rapid development of machine learning (ML) in recent years, alongside its ability to provide readily available personalised experience for users, may address this phenomenon, with much potential in health promotion and behavioural change interventions.

Objective:

To provide an overview of the existing research on ML applications in health promotion and behavioural change in order to inform future development of efficacious interventions.

Methods:

A scoping review was conducted based on the five-stage framework by Arksey and O’Malley and the PRISMA-ScR guidelines. Nine 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 of they had incorporated ML in any health promotion or behavioural change interventions, studied at least one group of participants, and been published in English. Publication-related information (author, year, aim, and findings), area of health promotion, user data analysed, type of ML used, challenges encountered, and future research were extracted from each study.

Results:

Twenty-nine articles were included in this review. Three themes were generated: (i) enablers, i.e. the adoption of information technology for optimising systemic operation; (ii) challenges, i.e. the various hurdles and limitations presented in the articles; and (iii) future directions, i.e. an exploration of 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 personalisation. Future works may consider designs that correspondingly mitigate these challenges in areas that receive limited attention, such as smoking and mental health.