Machine Learning in Health Promotion and Behavioral Change: Scoping Review

Abstract

Background 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. Objective 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. Methods 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. 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.

Salud ocupacional: rol del personal de enfermería en la evaluación y prevención de riesgos

“…En base a estos resultados, se puede recomendar que las diferentes empresas y ambientes laborales incluyan un sistema de salud ocupacional y proporcionen a sus trabajadores seguridad, estos programas de salud ocupacional deben estar encaminados hacia la identificación y manejo adecuado de las exposiciones que son potencialmente perjudiciales para la salud y brindar orientación al personal acerca de medidas profilácticas. Por lo cual, las recomendaciones actuales plantean que en los trabajos futuros se consideren diseños que mitiguen áreas que reciben poca atención como la salud mental y la exposición al humo del tabaco (Goh et al, 2022).…”

SNapp, a Tailored Smartphone App Intervention to Promote Walking in Adults of Low Socioeconomic Position: Development and Qualitative Pilot Study

“…First, we are currently extending SNapp by implementing machine learning techniques to make delivered coaching content more dynamically tailored based on changes in users’ behavior, thereby increasing its efficacy. For example, integrating artificial intelligence techniques that allow SNapp to learn which coaching content is particularly good at triggering walking behavior for individual users within specific contexts can help improve the effectiveness of delivered coaching messages [ 78 , 79 ]. Second, we feel that future iterations may benefit from including more conditional factors (eg, weather, seasonal conditions, or physical environment attributes) to improve the context specificity and relevance of suggestions for walking.…”

Integrating health geography and behavioral economic principles to strengthen context-specific behavior change interventions

“…Machine learning techniques (e.g. artificial intelligence, learning algorithms) are one potential strategy for analyzing information from vast and complex data sets while providing individualized outcomes [ 122 ]. Innovative methods such as just-in-time adaptive interventions are one example of techniques for providing tailored support at opportune times when individuals need assistance with monitoring and reducing harmful substance use [ 123 , 124 ].…”