Trial Review

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Trial registered on ANZCTR


Registration number
ACTRN12624000255583p
Ethics application status
Submitted, not yet approved
Date submitted
13/02/2024
Date registered
14/03/2024
Date last updated
14/03/2024
Date data sharing statement initially provided
14/03/2024
Type of registration
Prospectively registered

Titles & IDs
Public title
Do artificially intelligent chatbots work to increase physical activity?
Scientific title
MoveMentor – Examining the effectiveness of a machine learning and app-based digital assistant to increase physical activity in inactive office-based employees: a randomised controlled trial
Secondary ID [1] 311531 0
None
Universal Trial Number (UTN)
Trial acronym
Linked study record

Health condition
Health condition(s) or problem(s) studied:
physical inactivity 332884 0
Condition category
Condition code
Public Health 329600 329600 0 0
Health promotion/education
Physical Medicine / Rehabilitation 329765 329765 0 0
Other physical medicine / rehabilitation

Intervention/exposure
Study type
Interventional
Description of intervention(s) / exposure
Intervention group participants will receive access to a smartphone app that aims to increase their physical activity using a digital assistant (i.e., chatbot or conversational agent) that applies reinforcement learning (a type of machine learning) to personalise content and the timing it is delivered. The app works in conjunction with physical activity trackers (trail participants will receive a Fitbit).

The intervention consists of a stand-alone smartphone app that works in conjunction with a physical activity tracker (i.e., Fitbit). The main feature of the app is the physical activity digital assistant (i.e., chatbot or conversational agent). The name of the app in the app stores is ‘MoveMentor’ and the total duration of the intervention period is 6 months. Engagement with the intervention will be monitored use app usage statistics.

The digital assistant interacts with participants in 3 main ways: 1) conversations; 2) nudges and 3) question and answer (Q&A). The conversations provide participants with educational content in relation to physical activity delivered through interactive ‘conversations’ or ‘chats’. The aim of the conversations is to encourage participants to become more physically active and meet the national physical activity guidelines, as well as to support them in overcoming any difficulties in becoming more active. Example topics of conversations are: ‘Developing a lasting activity habit’, ‘What stops you from being active?’, and ‘Make the most of your surroundings’. During the conversations, the digital assistant will ask participants physical activity questions and participants will be provided with personalised advice, depending on the answers they provide. The conversations are designed to last no longer than 5 or 10 minutes. There is not a set frequency/duration of time that participants must spend with the conversations, they are free to complete as many of the conversations as they see fit whenever it suits them.

Participants will also receive activity 'nudges' delivered as a smartphone app notifications. The timing and content of the nudges is determined by machine learning algorithms that learn what works for whom under what circumstances through trial and error. The nudges that work will be reinforced, those that don’t will be phased out. More specifically, a ‘contextual bandit’ approach is applied to deliver the right nudges at the right time, this is a type of reinforcement learning. Contextual data will be derived from participants app preferences and settings (derived using an onboarding survey), participant interactions with the app and digital assistant, physical activity data from the activity trackers, nudge ratings (i.e., like or dislike), and weather data (to determine whether indoor or outdoor activity is recommended based on the participants' location at that specific time). An extensive library of nudges has been developed to send to users through push notifications. The library consists of a range of nudge categories. Examples of nudge categories are: daily morning check in, weekly action plan review, activity suggestion , motivational, educational, streaks, milestones and more.

Finally, participants can engage with the digital assistant by asking it physical activity-related questions. Answers to questions that have not been pre-programmed (e.g., I have a muscle tear in my calf, how can I still be active?), are being delivered by PaLM2, Google’s machine learning and natural language generation model (it is a large language model comparable to ChatGPT). There is not a set number of Q&A sessions participants must complete. Whenever they have a physical activity-related question, they can just ask the digital assistant as they see fit; this could be a lot of questions or no questions at all.

Beyond interactions with the digital assistant and personalisation features, the MoveMentor apps include additional features of which ongoing self-monitoring, adaptive goal setting and action planning are the most important. Every morning, participants receive a daily activity goal. The goal is designed to slowly increase over time, aligned with how well participants are performing, until it reaches a participant’s ‘long-term activity’ goal at which point the goal stops increasing. If participants are consistently not meeting their daily activity goal, then the goal will gradually decrease.

Participants are encouraged to engage in Action Planning conversations. The action plan helps participants define what physical activities they will engage in, where they will do them, how many times per week, what days of the week, what time of day, how long activity sessions will last and identify any people they may be active together with.
Intervention code [1] 327985 0
Behaviour
Intervention code [2] 327989 0
Prevention
Intervention code [3] 327990 0
Lifestyle
Comparator / control treatment
No treatment: control group participants will not receive access to the intervention until after the 6-month waiting period. They will not receive a Fitbit activity monitor.
Control group
Active

Outcomes
Primary outcome [1] 337384 0
Device-measured change in physical activity
Timepoint [1] 337384 0
Device-measured Physical activity will be measured at baseline, 3-months and 6-months post-commencement of intervention; the primary timepoints is 6-months
Secondary outcome [1] 431635 0
Self-reported change in physical activity
Timepoint [1] 431635 0
Self-reported physical activity will be measured at baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [2] 431636 0
SItting time
Timepoint [2] 431636 0
Sitting time will be measured at baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [3] 431637 0
Sleep behaviour
Timepoint [3] 431637 0
Sleep behaviour will be measured at baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [4] 431638 0
workplace productivity
Timepoint [4] 431638 0
Workplace productiviy outcomes will be measured at baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [5] 432269 0
Absenteeism
Timepoint [5] 432269 0
Absenteeism will be measured at baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [6] 432271 0
Presenteeism
Timepoint [6] 432271 0
Presenteeism will be measured at baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [7] 432272 0
Burnout
Timepoint [7] 432272 0
Burnout will be measured at baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [8] 432273 0
Employee wellbeing
Timepoint [8] 432273 0
Employee wellbeing will be measured at baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [9] 432279 0
Quality of Life
Timepoint [9] 432279 0
Baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [10] 432280 0
Depression
Timepoint [10] 432280 0
Baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [11] 432281 0
Anxiety
Timepoint [11] 432281 0
Baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [12] 432282 0
Stress
Timepoint [12] 432282 0
Baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [13] 432284 0
Satisfaction with Life
Timepoint [13] 432284 0
Baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [14] 432286 0
Positive affect
Timepoint [14] 432286 0
Baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [15] 432288 0
Negative affect
Timepoint [15] 432288 0
Baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [16] 432289 0
Habit Strength
Timepoint [16] 432289 0
Baseline, 3-months and 6-months post-commencement of intervention
Secondary outcome [17] 432290 0
App usability
Timepoint [17] 432290 0
3-months post-commencement of the intervention
Secondary outcome [18] 432292 0
App usefulness
Timepoint [18] 432292 0
3-months post-commencement of the intervention
Secondary outcome [19] 432294 0
App usage statistics
Timepoint [19] 432294 0
Measured continuously during the 6-month intervention period

Eligibility
Key inclusion criteria
Eligible participants will be:
- 18 years and over
- Live anywhere in Australia
- Speak and read the English language
- Engaged in full-time office-based employment
- Have a smartphone (i.e., Apple or Android) with internet access
- Be physically inactive. Participants will be asked: "How many days per week do you engage in at least 30 minutes of activity of a moderate intensity or physical activity higher? For example, brisk walking, swimming, tennis, etc?" Anyone completing 30 minutes more than 2 times per week will be excluded.
- Have no impairments limiting an increase in physical activity
- Did not participate in any physical activity programs in the past 12 months
- Not own or have used a physical activity tracker for at least 12 months
- Not be pregnant
- Have a body mass index over 17.5
Minimum age
18 Years
Maximum age
No limit
Sex
Both males and females
Can healthy volunteers participate?
No
Key exclusion criteria
Interested people that do not meet 1 or more of the eligibility criteria listed above.

Study design
Purpose of the study
Prevention
Allocation to intervention
Randomised controlled trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
The research team member involved with determining eligibility will have no involvement in allocating eligible participants to intervention or control group.
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
Simple randomisation using a randomisation table created by computer software (i.e. computerised sequence generation)
Masking / blinding
Blinded (masking used)
Who is / are masked / blinded?



The people analysing the results/data
Intervention assignment
Single group
Other design features
Phase
Not Applicable
Type of endpoint/s
Efficacy
Statistical methods / analysis

Recruitment
Recruitment status
Not yet recruiting
Date of first participant enrolment
Anticipated
1/04/2024
Actual
Date of last participant enrolment
Anticipated
31/08/2024
Actual
Date of last data collection
Anticipated
31/03/2025
Actual
Sample size
Target
198
Accrual to date
Final
Recruitment in Australia
Recruitment state(s)
ACT,NSW,NT,QLD,SA,TAS,WA,VIC

Funding & Sponsors
Funding source category [1] 315826 0
Government body
Name [1] 315826 0
National Health and Medical Research Council
Country [1] 315826 0
Australia
Funding source category [2] 315833 0
Government body
Name [2] 315833 0
Australian Research Council
Country [2] 315833 0
Australia
Funding source category [3] 315834 0
Charities/Societies/Foundations
Name [3] 315834 0
National Heart Foundation of Australia
Country [3] 315834 0
Australia
Primary sponsor type
Individual
Name
Prof Corneel Vandelanotte
Address
Country
Australia
Secondary sponsor category [1] 317954 0
University
Name [1] 317954 0
Central Queensland University
Address [1] 317954 0
Country [1] 317954 0
Australia

Ethics approval
Ethics application status
Submitted, not yet approved
Ethics committee name [1] 314682 0
CQ University's Human Research Ethics Committee
Ethics committee address [1] 314682 0
https://www.cqu.edu.au/research/current-research/ethics-committees/human-research-ethics
Ethics committee country [1] 314682 0
Australia
Date submitted for ethics approval [1] 314682 0
07/03/2024
Approval date [1] 314682 0
Ethics approval number [1] 314682 0

Summary
Brief summary
Physical inactivity is a leading modifiable cause of death and disease, but less than half of Australian adults are meeting the National Physical Activity Guidelines. Therefore, developing and evaluating new strategies to support people in becoming more active is important. The use of digital assistants is becoming increasingly common and a digital assistant powered by artificial intelligence could potentially offer a cost-effective, scalable solution physical activity program. Therefore, the aim of this study is to conduct a randomised controlled trial to examine the effectiveness of a machine learning and app-based digital assistant to increase physical activity. We hypothesise that participants receiving the intervention will significantly increase their moderate to vigorous physical activity at 3- and 6-month follow-up time-points compared to participants allocated to a no intervention control group.
Trial website
Trial related presentations / publications
Public notes

Contacts
Principal investigator
Name 132386 0
Prof Corneel Vandelanotte
Address 132386 0
Central Queensland University, 554-700 Yaamba Road, Rockhampton QLD 4701
Country 132386 0
Australia
Phone 132386 0
+61 7 4923 2183
Fax 132386 0
Email 132386 0
c.vandelanotte@cqu.edu.au
Contact person for public queries
Name 132387 0
Danya Hodgetts
Address 132387 0
Central Queensland University, 554-700 Yaamba Road, Rockhampton QLD 4701
Country 132387 0
Australia
Phone 132387 0
+61 7 4930 9378
Fax 132387 0
Email 132387 0
d.hodgetts@cqu.edu.au
Contact person for scientific queries
Name 132388 0
Danya Hodgetts
Address 132388 0
Central Queensland University, 554-700 Yaamba Road, Rockhampton QLD 4701
Country 132388 0
Australia
Phone 132388 0
+61 7 4930 9378
Fax 132388 0
Email 132388 0
d.hodgetts@cqu.edu.au

Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
Yes
What data in particular will be shared?
All of the individual participant data collected during the trial, after de-identification.
When will data be available (start and end dates)?
Beginning 3 months and ending 5 years following main results publication
Available to whom?
Only researchers who provide a methodologically sound proposal
Available for what types of analyses?
Any purpose
How or where can data be obtained?
Access subject to approvals by Principal Investigator (c.vandelanotte@cqu.edu.au)


What supporting documents are/will be available?

Doc. No.TypeCitationLinkEmailOther DetailsAttachment
21637Study protocol    We are currently preparing the study protocol for ... [More Details]



Results publications and other study-related documents

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