JMIR Publications

ABSTRACT

Background:

Prevalence of physical inactivity is high and increasing across high-income countries, and interventions to increase physical activity can include behaviour change delivered digitally. Artificial intelligence (AI) is a broad field encompassing various techniques, which include algorithms that learn from data to perform automated tasks without explicit human programming, and could potentially increase the effectiveness of digital behaviour change interventions through personalisation and reducing barriers to engagement.

Objective:

This review aims to summarise the evidence for the effectiveness of AI-assisted interventions to reduce physical inactivity.

Methods:

We conducted a systematic review to identify and summarise evidence from randomised controlled trials (RCTs) of AI-assisted interventions for reducing physical inactivity. Eligible trials were RCTs that reported results of an AI-assisted public health intervention for reducing physical inactivity in a high-income country. We searched Medline (Ovid), Embase (Ovid), Web of Science (Core collection), and Scopus for relevant trials published between 2010 and 14 January 2025. We also searched for reviews of public health interventions for physical inactivity published between 2023 and 14 January 2025, and extracted all references from relevant reviews for screening. We conducted forward and backward citation searching on all included trials (dates of searches: September 2025 to June 2026). Screening for trials was conducted independently by two reviewers. Two reviewers independently assessed risk of bias using the Cochrane Risk of Bias 2 tool. As the included trials were heterogeneous in terms of interventions, outcomes, and timepoints, we synthesised the results narratively.

Results:

We included 17 trials (comprising 48 reports and 3,282 randomised participants). Five trials estimated the effectiveness of apps with chatbots for reducing physical inactivity (one with some concerns of bias, four with high risks of bias), with little evidence to suggest that chatbots increase physical activity. Twelve trials estimated the effectiveness of selection of motivational or other messages using recommender systems, reinforcement learning, machine learning, or case-based reasoning for reducing physical inactivity (four with some concerns of bias, eight with a high risk of bias), with little evidence to suggest an increase in physical activity generally, though some evidence to suggest an increase in step count specifically. All trials had relatively few participants, so results were generally imprecise. There were no trials using large language models.

Conclusions:

There is no strong evidence of a beneficial effect of AI-assistance in public health interventions for reducing physical inactivity in high-income countries, though AI-assisted interventions may increase step count. Future research should better describe public health interventions that use AI and embed equity considerations into their design and analysis to ensure already disadvantaged groups are not harmed further by the adoption of AI-assisted interventions in public health. Clinical Trial: PROSPERO CRD42025642339