ArticleJournal of medical Internet research2022
Machine Learning in Health Promotion and Behavioral Change: Scoping Review.
Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- A systematic review and Bayesian network meta-analysis on the efficacy and potential of mobile interventions for stress management.Nature human behaviour · 2025Pooled it
- The impact of machine learning on physical activity-related health outcomes: A systematic review and meta-analysis.International nursing review · 2025Pooled it
- Personalization of Training and Weight Reduction Using Artificial Intelligence: A Scoping Review of Current Evidence and Practical Limitations.Journal of functional morphology and kinesiology · 2026Review
- Designing for Autonomous Motivation: Qualitative Interview Study on Pre-Enrollment Preferences of Survivors of Cancer for Digital Health Behavior Change Programs.Journal of medical Internet research · 2026Article
- Review
- Why they do not move: An explainable machine learning analysis of physical activity barriers in obese adolescents and tool translation.BMC public health · 2026Article
- Predicting college students' exercise dependence: a machine learning approach.Frontiers in psychology · 2026Article
- Artificial Intelligence in Health Promotion and Disease Reduction: Rapid Review.Journal of medical Internet research · 2025Review
- Revealing sleep and pain reciprocity with wearables and machine learning.Communications medicine · 2025Review
- Artificial intelligence in healthcare: rethinking doctor-patient relationship in megacities.Frontiers in health services · 2025Article
- Beyond health: A machine learning analysis of structural barriers to school attainment in Somalia.PloS one · 2025Article
- Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization Overview.Journal of medical Internet research · 2024Article
- Co-Designing a Smoking Cessation Chatbot: Focus Group Study of End Users and Smoking Cessation Professionals.JMIR human factors · 2024Article
- A scoping review of methodologies for applying artificial intelligence to physical activity interventions.Journal of sport and health science · 2024Article
- Integrating health geography and behavioral economic principles to strengthen context-specific behavior change interventions.Translational behavioral medicine · 2024Article
- Precision prevention in worksite health-A scoping review on research trends and gaps.PloS one · 2024Article
- Role of Open AI (Artificial Intelligence)-Innovations in Health Behavior Change Interventions.Healthcare (Basel, Switzerland) · 2023Article
- Technical Report: Machine-Learning Pipeline for Medical Research and Quality-Improvement Initiatives.Cureus · 2023Article
- Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite 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.
objectiveThe 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.
methodsA 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.
resultsA 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.
conclusionsThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.