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MoveMentor-examining the effectiveness of a machine learning and app-based digital assistant to increase physical activity in adults: protocol for a randomised controlled trial

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dc.contributor.authorVandelanotte, C.
dc.contributor.authorTrost, S.
dc.contributor.authorHodgetts, D.
dc.contributor.authorImam, T.
dc.contributor.authorRashid, M.D.M.
dc.contributor.authorTo, Q.G.
dc.contributor.authorMaher, C.
dc.date.issued2025
dc.descriptionData source: Supplementary information, https://doi.org/10.1186/s13063-025-08926-3
dc.description.abstractBackground: Physical inactivity is prevalent, leading to a high burden of disease and large healthcare costs. Thus, there is a need for affordable, effective and scalable interventions. However, interventions that are affordable and scalable are beset with modest effects and engagement. Interventions that integrate machine learning with real-time data to offer unprecedented levels of personalisation and customisation might offer solutions. The aim of this study is to conduct a randomised controlled trial to evaluate the effectiveness of a machine learning and app-based digital assistant to increase physical activity. Methods: One hundred and ninety-eight participants will be recruited through Facebook advertisements and randomly allocated to an intervention or control group. Intervention participants will gain access to an app-based physical activity digital assistant that can learn and adapt in real-time to achieve high levels of personalisation and user engagement by virtue of applying a range of machine learning techniques (i.e. reinforcement learning, natural language processing and large language models). The digital assistant will interact with participants in 3 main ways: (1) educational conversations about physical activity; (2) just-in-time personalised in-app notifications ('nudges'), cues to action encouraging physical activity and (3) chat-based questions and answers about physical activity. Additionally, the app includes adaptive goal setting and an action planning tool. The control group will gain access to the intervention after the last assessment. Outcomes will be measured at baseline, 3 and 6 months. The primary outcome is device-measured (Axivity AX3) moderate-to-vigorous physical activity. Secondary outcomes include app engagement and retention, quality of life, depression, anxiety, stress, sitting time, sleep, workplace productivity, absenteeism, presenteeism and habit strength. Discussion: The trial presents a unique opportunity to study the effectiveness of a new generation of digital interventions that use advanced machine learning methods to improve physical activity behaviour. By addressing the limitations of existing conversational agents, we aim to pave the way for more effective and adaptable interventions.
dc.identifier.citationTrials, 2025; 26(233):1-13
dc.identifier.doi10.1186/s13063-025-08926-3
dc.identifier.issn1745-6215
dc.identifier.issn1745-6215
dc.identifier.orcidMaher, C. [0000-0002-8676-0224]
dc.identifier.urihttps://hdl.handle.net/11541.2/43836
dc.language.isoen
dc.publisherBioMed Central
dc.relation.fundingNHMRC 2012704
dc.relation.fundingNational Heart Foundation VG105816
dc.relation.fundingARC FT210100234
dc.relation.fundingMedical Research Future Fund 1193862
dc.rightsCopyright 2025 The Authors. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. (http://creativecommons.org/licenses/by/4.0/)
dc.source.urihttps://doi.org/10.1186/s13063-025-08926-3
dc.subjectchatbot
dc.subjectconversational agent
dc.subjectexercise
dc.subjectsteps
dc.subjectartificial intelligence
dc.subjectreinforcement learning
dc.subjectlarge language model
dc.subjectnatural language processing
dc.subjectbehaviour change
dc.subjectintervention
dc.subjectjust-in-time adaptive intervention
dc.subjectmhealth
dc.subjectdigital
dc.subjectpersonalised
dc.subjectcustomised
dc.titleMoveMentor-examining the effectiveness of a machine learning and app-based digital assistant to increase physical activity in adults: protocol for a randomised controlled trial
dc.typeJournal article
pubs.publication-statusPublished
ror.fileinfo12305120440001831 13305110420001831 Open Access Published Version
ror.mmsid9917051667401831

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