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Trial registered on ANZCTR


Registration number
ACTRN12625000102471
Ethics application status
Approved
Date submitted
14/01/2025
Date registered
30/01/2025
Date last updated
30/01/2025
Date data sharing statement initially provided
30/01/2025
Type of registration
Prospectively registered

Titles & IDs
Public title
The feasibility and preliminary effectiveness of a machine learning (ML)-based digital assistant to promote physical activity in older adults
Scientific title
The feasibility and preliminary effectiveness of a machine learning (ML)-based digital assistant intervention to promote physical activity in adults aged 65 years or older
Secondary ID [1] 313705 0
Nil known
Universal Trial Number (UTN)
Trial acronym
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Physical inactivity 336296 0
Condition category
Condition code
Public Health 332832 332832 0 0
Health promotion/education

Intervention/exposure
Study type
Interventional
Description of intervention(s) / exposure
This study will involve adults 65 years of age or older syncing their tracker to an existing ML-based physical activity digital assistant ‘MoveMentor’. The intervention administered to participants is of a 14-week duration. All participants will participate in the intervention. This intervention uses a smartphone app that integrates with a physical activity tracker (such as a Fitbit). The primary feature of this app is a digital assistant for physical activity. The minimum time that participants are asked to interact with this app will last no more than 5-10 minutes, which will be completed at a pace determined by the participants. This study will assess the feasibility (usability, acceptability, and engagement), preliminary effectiveness, and user experience.

The MoveMentor digital assistant incorporates several key features: 1) Generative Pre-trained Transformers (GPT) to simulate natural conversation, 2) reinforcement (machine) learning to optimise the timing and content of messages, and 3) real-time data from a tracker, weather, and Global Positioning System (GPS). The digital assistant will provide tailored advice on physical activity based on user inputs..

Aimed at motivating participants to increase their physical activity and adhere to national physical activity guidelines, the intervention offers detailed sessions delivered as a series of interactive "Conversations," powered by DialogFlow to facilitate natural interaction. During the conversations, the digital assistant engages participants with specific questions (e.g., ‘What stops you from being active?’) about their physical activity, health status, self-efficacy, and social support. Based on their responses, participants will receive personalised advice on the benefits of being active (i.e., why be active) and strategies for increasing activity (i.e., how to be active); for example "Did you know that exercising, walking, and gardening have a very positive effect on wellbeing?". This tailored advice aims to help individuals understand the importance of being physically active and provides evidence-based steps to their engagement in it. The digital assistant will engage participants in personalised conversations providing content tailored to support and improve adherence to the physical activity intervention. These conversations are informed by real-time physical activity data collected from activity trackers.

To encourage and support physical activity, participants will receive just-in-time personalised ‘Nudges’ through smartphone notifications. These nudges are generated by a reinforcement learning algorithm that considers factors like the frequency, timing, and context, to deliver tailored suggestions. Considering a variety of data sources, the algorithm ensures that nudges are sent at the most effective times for each individual. These data sources include: 1) data from preferences and settings during sign-up, 2) personal data (e.g., health status, age) via Conversations, 3) physical activity monitor (e.g., Fitbit), 4) participant preferences (e.g., likes and dislikes, 5) GPS (e.g., at work), and 6) weather conditions (e.g., favourable conditions for outdoor activity).

Additionally, participants can initiate a ‘Questions and Answers’ session with the digital assistant at any time. They can interact through voice or text to ask a variety of physical activity related questions, including: 1) knowledge questions (e.g., ‘I have sprained by ankle, how do I stay active?’), 2) activity status questions (e.g., ‘How many steps did I take yesterday?’), 3) goal-based questions (e.g., ‘Am I meeting my activity goals?’), 4) location-based questions (e.g., ‘Where are there flat walking pathways?’), 5) suggestions (e.g., ‘I feel like being active today, what do you suggest?’).

In addition to interacting with the digital assistant, MoveMentor aids users in goal setting and action planning. Participants receive a daily activity goal that gradually increases over time based on their performance. If they are not consistently meeting the goal, it will gradually decrease. The action plan helps participants outline key activity details, such as: 1) which physical activities they will engage in, 2) when during the day they will do them, and 3) how many times a week they plan to be active.
Intervention code [1] 330301 0
Treatment: Other
Intervention code [2] 330302 0
Behaviour
Intervention code [3] 330300 0
Prevention
Comparator / control treatment
No control group.
Control group
Uncontrolled

Outcomes
Primary outcome [1] 340361 0
Steps per week recorded by activity tracker
Timepoint [1] 340361 0
At baseline (week 0) and in week 13 (post-intervention completion) participants steps per day from the last 7 days
Secondary outcome [1] 443846 0
Physical activity digital assistant usability
Timepoint [1] 443846 0
At week 13 (post-intervention completion) survey
Secondary outcome [2] 443849 0
Physical activity digital assistant acceptability
Timepoint [2] 443849 0
At week 13 (post-intervention completion) survey
Secondary outcome [3] 443848 0
Physical activity digital assistant engagement e.g., number of questions asked, number of nudges read). App usage measures will be assessed as a combined secondary outcome.
Timepoint [3] 443848 0
Cumulative data will be assessed at the conclusion of the 12-week intervention
Secondary outcome [4] 443850 0
Intervention acceptability
Timepoint [4] 443850 0
At week 13 (post-intervention completion) survey
Secondary outcome [5] 443851 0
Steps per day
Timepoint [5] 443851 0
Daily throughout the 12-week intervention
Secondary outcome [6] 443845 0
Frequency of moderate to vigorous physical activity
Timepoint [6] 443845 0
At baseline (week 0) and in week 13 (post-intervention completion) survey
Secondary outcome [7] 444426 0
Duration of moderate to vigorous physical activity
Timepoint [7] 444426 0
At baseline (week 0) and in week 13 (post-intervention completion) survey

Eligibility
Key inclusion criteria
Participants must be aged 65 years of age or older, be inactive (i.e., do not engage in at least 30 minutes of moderate intensity physical activity on most days), be able to speak and read English, are currently residing in Australia, own a smartphone with internet access, be community-dwelling (i.e., not living in a residential aged care facility), and have not been told by their doctor that they are able to increase their physical activity.
Minimum age
65 Years
Maximum age
No limit
Sex
Both males and females
Can healthy volunteers participate?
Yes
Key exclusion criteria
None

Study design
Purpose of the study
Prevention
Allocation to intervention
Non-randomised trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
Masking / blinding
Open (masking not used)
Who is / are masked / blinded?



Intervention assignment
Single group
Other design features
Phase
Not Applicable
Type of endpoint/s
Efficacy
Statistical methods / analysis
A sample size of 30 participants were determined by practical and resource considerations (i.e., duration of the feasibility trial). It is anticipated that this sample size will provide sufficient information to evaluate the feasibility and preliminary effectiveness of the ML-based digital assistant intervention in promoting physical activity in older adults (65 years of age or older). Participants’ sociodemographic data (e.g., age, ethnicity, relationship status) will be described statistically by number and percentage of participants at baseline. A generalised linear model will be used to determine the relationship between the dependent variable physical activity (i.e,, average daily step count) and independent variable of time (i.e., baseline, post-intervention). For usability the System Usability Scale (SUS) scores will be presented to determine if above 68 which represents good usability of the intervention. Of the 14 acceptability statements, the negative statements will be reverse scored with the average score being calculated for each participant to demonstrate participants acceptability of the intervention. Website engagement will be reported descriptively using average number of (e.g., how many nudges were read, how many questions were asked, number of times visited the app/engaged in conversations, number of minutes spent on app).

Recruitment
Recruitment status
Not yet recruiting
Date of first participant enrolment
Anticipated
17/02/2025
Actual
Date of last participant enrolment
Anticipated
19/05/2025
Actual
Date of last data collection
Anticipated
Actual
Sample size
Target
30
Accrual to date
Final
Recruitment in Australia
Recruitment state(s)
ACT,NSW,NT,QLD,SA,TAS,WA,VIC

Funding & Sponsors
Funding source category [1] 318171 0
Government body
Name [1] 318171 0
National Health and Medical Research Council (NHMRC)
Country [1] 318171 0
Australia
Primary sponsor type
University
Name
Central Queensland University (CQU)
Address
Country
Australia
Secondary sponsor category [1] 320555 0
Individual
Name [1] 320555 0
Kim Waters - Central Queensland University
Address [1] 320555 0
Country [1] 320555 0
Australia
Other collaborator category [1] 283359 0
Individual
Name [1] 283359 0
Dr. Danya Hodgetts - Central Queensland University
Address [1] 283359 0
Country [1] 283359 0
Australia
Other collaborator category [2] 283357 0
Individual
Name [2] 283357 0
Dr. Stephanie Alley - Central Queensland University
Address [2] 283357 0
Country [2] 283357 0
Australia
Other collaborator category [3] 283358 0
Individual
Name [3] 283358 0
Dr. Samantha Fien - Central Queensland University
Address [3] 283358 0
Country [3] 283358 0
Australia
Other collaborator category [4] 283360 0
Individual
Name [4] 283360 0
Prof. Corneel Vandelanotte - Central Queensland University
Address [4] 283360 0
Country [4] 283360 0
Australia

Ethics approval
Ethics application status
Approved
Ethics committee name [1] 316821 0
CQ University's Human Research Ethics Committee
Ethics committee address [1] 316821 0
Ethics committee country [1] 316821 0
Australia
Date submitted for ethics approval [1] 316821 0
16/07/2024
Approval date [1] 316821 0
26/08/2024
Ethics approval number [1] 316821 0
0000025067

Summary
Brief summary
Trial website
Trial related presentations / publications
Public notes

Contacts
Principal investigator
Name 139106 0
Ms Kim Waters
Address 139106 0
CQUniversity Mackay Ooralea, 151-171 Boundary Road, Ooralea QLD 4740
Country 139106 0
Australia
Phone 139106 0
+61429463022
Fax 139106 0
Email 139106 0
[email protected]
Contact person for public queries
Name 139107 0
Kim Waters
Address 139107 0
CQUniversity Mackay Ooralea, 151-171 Boundary Road, Ooralea QLD 4740
Country 139107 0
Australia
Phone 139107 0
+61429463022
Fax 139107 0
Email 139107 0
[email protected]
Contact person for scientific queries
Name 139108 0
Kim Waters
Address 139108 0
CQUniversity Mackay Ooralea, 151-171 Boundary Road, Ooralea QLD 4740
Country 139108 0
Australia
Phone 139108 0
+61429463022
Fax 139108 0
Email 139108 0
[email protected]

Data sharing statement
Will the study consider sharing individual participant data?
Yes
Will there be any conditions when requesting access to individual participant data?
Persons/groups eligible to request access:
• Anyone who wishes to access it

Conditions for requesting access:
• -

What individual participant data might be shared?
• De-identified project data underlying published results only

What types of analyses could be done with individual participant data?
• Any purpose

When can requests for individual participant data be made (start and end dates)?
From:
Immediately following publication. Ending 5 years following main results publication.

To:
-

Where can requests to access individual participant data be made, or data be obtained directly?
• Access subject to approvals via Principal Investigator ([email protected])

Are there extra considerations when requesting access to individual participant data?
No


What supporting documents are/will be available?

No Supporting Document Provided


Results publications and other study-related documents

Documents added manually
No documents have been uploaded by study researchers.

Documents added automatically
No additional documents have been identified.