Artificial Intelligence-Integrated Digital Tools to Promote Physical Activity in People with Multimorbidity: A Rapid Review of Trials

Background: Multimorbidity, the presence of two or more chronic health conditions in an individual, presents a significant challenge for healthcare systems worldwide. Physical activity (PA) is an important intervention for the management of chronic health conditions and prevention of disease complications. However, individuals with multimorbidity face unique barriers to PA participation. Artificial intelligence (AI) has emerged as a promising tool to enhance digital health interventions, offering tailored PA promotion. This review synthesised the current evidence on trials using AI-integrated digital intervention tools (including machine learning, natural language processing and predictive analysis) designed to support PA among individuals with multimorbidity.Methods: A rapid review was conducted following PRISMA guidelines. A comprehensive search was performed across six electronic databases (MEDLINE, EMBASE, CINAHL, OVID, Cochrane Library, PsycINFO, Scopus) covering studies from January 2015 to May 2025. Eligible studies were randomised controlled trials (RCTs) involving adults (≥18 years) with multimorbidity using AI-informed digital health interventions to promote PA. Two reviewers independently screened the articles and extracted the data. Owing to the heterogeneity of the included studies, meta-analysis was not possible, and the results were narratively synthesised.Results: Our initial search identified 276 studies. After removing duplicates and screening titles, abstracts, and full texts, 4 studies met the inclusion criteria. All included studies were RCTs that used AI-integrated digital interventions to promote PA in adults with multimorbidity. AI technology interventions included personalised mobile applications (n=2), decision-support systems (n=1), and socially assistive robotics (n=1). The study populations ranged from generically described multimorbid individuals to those with specific cardiometabolic and respiratory combinations of multimorbidity. PA outcomes were assessed through both self-report questionnaires and objective fitness measures. Attrition was common, particularly in longer-duration studies. While some improvements in PA have been reported, overall evidence remains limited and heterogeneous.Conclusions: The limited number of RCTs suggests emerging but inconclusive evidence on the effectiveness of AI-integrated digital health interventions to support PA in multimorbid individuals. Interventions may offer benefits, but heterogeneity in study design, population, and outcomes limits generalisability. Further research using consistent data collection and outcome measures, as well as longer-term follow-up, is needed.