Evidence map›Paper›PMID 40597442›Full record

ArticleTrials2025

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.

Corneel Vandelanotte, Stewart Trost, Danya Hodgetts, Tasadduq Imam, M D Mamunur Rashid, Quyen G To, Carol Maher

Abstract readClinical Trial Protocol
PubMed Full text at PMC Publisher
In one paragraph

Article in Trials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Corneel VandelanotteAppleton Institute, Central Queensland University, Bruce Highway, Rockhampton, QLD, 4702, Australia. c.vandelanotte@cqu.edu.au.ORCID http://orcid.org/0000-0002-4445-8094
Stewart TrostSchool of Human Movement and Nutrition Science, The University of Queensland, St Lucia, QLD, 4072, Australia.
Danya HodgettsAppleton Institute, Central Queensland University, Bruce Highway, Rockhampton, QLD, 4702, Australia.
Tasadduq ImamSchool of Business and Law, Central Queensland University, 120 Spencer Street, Melbourne, VIC, 3000, Australia.
M D Mamunur RashidSchool of Engineering and Technology, Central Queensland University, 120 Spencer Street, Melbourne, VIC, 3000, Australia.
Quyen G ToAppleton Institute, Central Queensland University, Bruce Highway, Rockhampton, QLD, 4702, Australia.
Carol MaherAlliance for Research in Exercise, Nutrition and Activity, Allied Health and Human Performance, University of South Australia, City East Campus, Adelaide, SA, 5001, Australia.

Funding

Australian Research Council FT210100234Medical Research Future Fund MRFF1193862National Health and Medical Research Council APP2012704National Heart Foundation of Australia VG105816
6 · The paper itself

Abstract

backgroundPhysical 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.

methodsOne 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.

trial registrationAustralian New Zealand Clinical Trial Registry ACTRN12624000255583p. Registered on 14 March 2024. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=387332 .

Indexed as

ExerciseHealth PromotionMachine LearningMobile ApplicationsAdultHumansRandomized Controlled Trials as TopicArtificial intelligenceBehaviour changeChatbotConversational agentCustomisedDigitalExerciseInterventionJust-in-time adaptive interventionLarge language modelMhealthNatural language processingPersonalisedReinforcement learningSteps

Identifiers

PMID40597442
PMCPMC12220097

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.