Trial Review

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Trial registered on ANZCTR


Registration number
ACTRN12625000214437
Ethics application status
Approved
Date submitted
17/01/2025
Date registered
26/03/2025
Date last updated
26/03/2025
Date data sharing statement initially provided
26/03/2025
Type of registration
Prospectively registered

Titles & IDs
Public title
Evaluating the Effectiveness of a Localised Digital Health Program for Behaviour Change in Adults with Type 2 Diabetes
Scientific title
Implementing a Scalable, personalised, behaviour Change digitAL hEalth program in primary care for chronic disease treatment in participants with diabetes – the SCALE cluster-randomised study protocol
Secondary ID [1] 313206 0
Nil Known
Universal Trial Number (UTN)
Trial acronym
SCALE
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Type 2 Diabetes 335503 0
Condition category
Condition code
Metabolic and Endocrine 332063 332063 0 0
Diabetes

Intervention/exposure
Study type
Interventional
Description of intervention(s) / exposure
The Gro-AUS intervention is an AI-enhanced mobile application designed to support self-management among individuals with Type 2 diabetes (T2D). It provides structured education, behavior change tools, and health management services aimed at improving glycaemic control, cardiovascular risk factors, and achieving diabetes remission. The app offers tailored recommendations for diet, physical activity, and mental well-being based on individual health goals, ethnicity, and lifestyle factors.

Participants in the study, recruited from GP practices in Western Sydney, will have access to Gro-AUS for 12 months. The app’s usage will be monitored, and follow-up assessments will be conducted at 3, 6, and 9 months post-recruitment. The app enables users to track their health, receive personalised feedback, and engage with the content in multiple languages. The study aims to evaluate the effectiveness of this digital health intervention compared to standard care.
The Gro Health app is designed to support users in managing their health through a comprehensive, structured approach. It covers a wide range of topics including nutrition, physical activity, sleep, mental wellbeing, stress management, mindfulness, and chronic disease management, such as diabetes, obesity, and hypertension. The app also integrates behavioural change techniques, which help users develop lasting, positive health behaviours.

To engage users, the app offers a variety of educational content, such as interactive tools for food tracking, meal planning, and exercise logging. It also includes evidence-based reading materials, guided meditations, mindfulness exercises, and video clips demonstrating different exercises. Personalised feedback driven by AI ensures that each user receives tailored support to meet their specific health goals.

The resources within the app, which have been developed in collaboration with leading clinicians, were not created specifically for this study. However, they are well-established and cover key areas of health, including sleep, activity, and nutrition. These resources are adaptable, allowing the app to cater to individual users’ needs and preferences, making it an excellent tool for the research.

For daily engagement, participants can use the app for around 15-30 minutes each day or 1-2 hours a week, depending on their schedules and goals. The app is flexible, meaning users can choose whether to engage in quick check-ins or dive deeper into health topics when they have more time.

Beyond the educational content, the app includes a variety of tools that help users make positive changes to their behaviour. These include personalised goal-setting using AI, interactive activities, and a daily wellness score that offers insights into their progress. The app also features smart food tracking, including a food diary, barcode scanner, and AI-powered plate recognition. Additionally, it helps with exercise tracking, offers guided workouts for all fitness levels, provides tools for better sleep, and includes techniques for managing stress. Users can also take part in a peer-to-peer community for motivation, attend live drop-in sessions with health experts, and receive one-on-one guidance from certified coaches.

In this study, participants will use the app in a natural way, without being told how often or for how long they need to engage. This approach will allow the study to assess how people naturally interact with the app and whether it helps them make lasting changes to their health behaviours. Although there are no specific usage requirements, participants may be asked to complete occasional surveys or assessments to track their progress.

The Gro Health app also offers personalised health management services, which help users manage their weight, control blood glucose levels, and improve their mental health and wellbeing. Services include one-on-one coaching with nutritionists and psychotherapists, a clinical dashboard that allows for remote tracking of user progress, and optional integration with wearable devices for more detailed health data. Live drop-in sessions with health experts are also available, along with a peer-to-peer community for additional support.

Recommendations within the app are highly personalised, taking into account factors like self-selected health goals, ethnicity, gender, dietary preferences, activity levels, and other unique aspects of each individual. These recommendations may include personalised diet plans, physical activity goals, and stress management techniques, all tailored to help users meet their health objectives.

Participants will be able to access the app by downloading it from the Apple App Store or Google Play Store. They will receive a code from the researchers to unlock the app for free, and assistance will be provided to help them download and navigate its features.

To measure adherence to the intervention, the study will track app usage data, including completed sessions and activities. Participants’ progress will be monitored through the daily wellness scores and their progress towards their personalised goals. This will help assess how well the app engages participants and whether it helps them achieve their health goals.
Intervention code [1] 329780 0
Lifestyle
Intervention code [2] 329781 0
Behaviour
Comparator / control treatment
In the Gro-AUS study, the control group operates within a stepped-wedge cluster-randomised design. In this model, all participants will eventually receive the Gro-AUS intervention, but at different points in time, depending on the randomisation of their GP practice.

Here’s how the control group functions

Initial Control Period: At the start of the study, each GP practice is allocated to one of three groups. All groups will begin as a control group, where patients continue receiving standard care for managing Type 2 diabetes from their GPs, without access to the Gro-AUS app. This phase allows researchers to compare outcomes from standard care with the outcomes once the intervention is introduced.

Sequential Rollout: Over time, each group (or cluster) will be gradually introduced to the intervention at 3-month intervals. Before the intervention is rolled out to a specific group, that group remains in the control phase, continuing to receive standard care. For example, in the first phase, the first group will start using the app, while the second and third groups will remain in the control phase.

Comparison Between Groups: While in the control phase, researchers will collect data on health outcomes such as blood sugar control (HbA1c levels), cardiovascular risk factors, and other secondary measures. These results will be compared to the intervention group’s outcomes to assess the impact of the Gro-AUS app on patients’ health.

This staggered approach ensures all participants eventually benefit from the intervention, while still allowing a strong comparison to standard care throughout the study.
Control group
Active

Outcomes
Primary outcome [1] 339661 0
Change in HbA1c (glycated haemoglobin) levels
Assessment method [1] 339661 0
HbA1c will be measured using a standard blood test performed by GPs as part of routine diabetes care. HbA1c reflects average blood glucose levels over the previous 6-8 weeks, providing an objective measure of glycaemic control. The test will be conducted via a laboratory-based immunoassay, consistent with Australian clinical guidelines for managing diabetes.
Timepoint [1] 339661 0
Timepoints: Baseline, 3 months, 6 months, and 9 months after intervention commencement (primary timepoint: 9 months).
Secondary outcome [1] 440791 0
Change in lipid profile
Assessment method [1] 440791 0
lipid profiles, including total cholesterol, HDL, LDL, and triglycerides, will be measured through standard blood tests conducted by GPs as part of routine care for patients with Type 2 diabetes.
Timepoint [1] 440791 0
Baseline, 3 months, 6 months, and 9 months post-intervention commencement.
Secondary outcome [2] 440792 0
Change in kidney function
Assessment method [2] 440792 0
Kidney function will be assessed by measuring estimated glomerular filtration rate (eGFR) through routine blood tests performed by GPs.
Timepoint [2] 440792 0
Baseline, 3 months, 6 months, and 9 months post-intervention commencement.
Secondary outcome [3] 440793 0
Proportion of participants achieving diabetes remission
Assessment method [3] 440793 0
Diabetes remission will be assessed based on sustained normal blood glucose levels (HbA1c below 6.5%) without the need for glucose-lowering medication, evaluated through regular HbA1c testing and review of medication records.
Timepoint [3] 440793 0
9 months post-intervention commencement (primary timepoint), with additional assessments at baseline, 3 months, and 6 months.
Secondary outcome [4] 440794 0
Changes in diabetes-related hospitalisations
Assessment method [4] 440794 0
Data on diabetes-related hospitalisations will be obtained through linked medical records using ICD-10 codes E10-E14 to track admissions related to diabetes complications.
Timepoint [4] 440794 0
Assessed continuously throughout the study and reported at 9 months post-intervention commencement.
Secondary outcome [5] 444427 0
Changes in Quality of life
Assessment method [5] 444427 0
EQ-5D-5L is used to assess the quality of life which is a standardised measure of health-related quality of life comprising five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. A visual analogue scale (VAS) complements the descriptive system, allowing patients to rate their overall health.
Timepoint [5] 444427 0
Baseline, 3 months, 6 months, and 9 months post-intervention commencement.
Secondary outcome [6] 444428 0
Changes in diabetes-related emotional distress
Assessment method [6] 444428 0
The diabetes related distress will be measure by The Problem areas in diabetes (PAID) questionnaire which is a self-report questionnaire that contains 20 items that describe negative emotions related to diabetes (e.g. fear, anger, frustration) commonly experienced by patients with diabetes. Completion takes approximately five minutes
Timepoint [6] 444428 0
Baseline, 3 months, 6 months, and 9 months post-intervention commencement.

Eligibility
Key inclusion criteria
Inclusion Criteria:

1- Adults diagnosed with Type 2 diabetes attending GP practices within the Western Sydney Local Health District (WSLHD).
2- Ownership of a smartphone compatible with the Gro-AUS app.
3- Able to speak or read one of the languages supported by the Gro-AUS app (English, Hindi, Chinese, Arabic).
4- HbA1c level greater than 7.5% within 6 months.
5- Diagnosis of Type 2 diabetes within the past 10 years.
Minimum age
18 Years
Maximum age
No limit
Sex
Both males and females
Can healthy volunteers participate?
No
Key exclusion criteria
Exclusion Criteria:

1- Individuals with diabetes types other than Type 2 (e.g. Type 1 or gestational diabetes).
Current pregnancy or breastfeeding.
2- History of severe mental health conditions, particularly eating disorders.
3- No access to a smartphone capable of running the Gro-AUS app.
4- Severe kidney disease (Chronic Kidney Disease stage 2, eGFR <30).
5- Previous bariatric surgery.
6- Genetic metabolic disorders such as porphyria, carnitine deficiency, or fatty acid oxidation defects (e.g. CPT I/II deficiency, multiple acyl-CoA dehydrogenase deficiencies).
7- Severe complications or comorbidities that may affect the participant’s ability to follow the study protocol.

Study design
Purpose of the study
Treatment
Allocation to intervention
Randomised controlled trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
The randomisation schedule will be generated by the study statistician using a custom script in Stata, and allocation will be managed centrally to ensure that the allocation process remains concealed from the researchers involved in participant recruitment and eligibility assessment.
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
The randomisation schedule will be created using a computerised sequence generation method. Specifically, a custom script will be employed in Stata to produce a random order for allocating subjects to different groups. This approach ensures that the allocation process is both random and unbiased.
Masking / blinding
Open (masking not used)
Who is / are masked / blinded?



Intervention assignment
Other
Other design features
Stepped wedge randomised: This design involves recruiting 12 general practices (each representing a cluster) who are then randomised to either receive the intervention at the start of the trial or wait a given time-period until their allocated time-period has ended. GP practices are the designated clustering unit, with each practice aiming to recruit 20 patients. The ‘wedges’ will take place at 3-month intervals, lasting a total of 12 months, with 4 practices randomised per interval.
Phase
Not Applicable
Type of endpoint/s
Efficacy
Statistical methods / analysis
The number of participants required for the Gro-AUS study was determined based on several clinical and statistical assumptions. The primary objective of the study is to evaluate the effectiveness of the Gro-AUS intervention in improving glycaemic control, specifically by measuring changes in HbA1c levels.

Clinical Assumptions: Previous studies using similar digital health interventions have reported an average reduction in HbA1c levels of approximately 0.8% to 1.2% compared to standard care. This reduction is clinically significant and reflects achievable outcomes in the target population of adults with Type 2 diabetes.

Statistical Assumptions:

Effect Size: An effect size of 0.8% for HbA1c reduction was assumed for the sample size calculations.
Standard Deviation: A standard deviation of 1.0% was used based on historical data from comparable studies, allowing for a conservative estimate of variability in HbA1c levels.
Power and Significance Level: The study aimed for a power of 80% to detect significant differences between groups, with a two-sided significance level set at 0.05.
Using these assumptions, the sample size calculation estimated that a total of 240 participants would be necessary, with 20 patients recruited from each of the 12 participating GP practices. This allows for potential dropouts while maintaining adequate power to detect meaningful changes in HbA1c levels.

Statistical Methods and Analysis Plan
The data collected from the study will be analysed using various statistical methods, following the intention-to-treat principle:

Primary Analysis: A within-subjects generalised linear mixed model will be employed to compare changes in HbA1c levels from baseline to follow-up assessments (3, 6, and 9 months). This model will account for the clustering of participants within GP practices and control for potential confounding variables such as age, gender, and baseline HbA1c levels.

Secondary Analyses: Secondary outcomes, including changes in health-related quality of life (assessed via the EQ-5D-5L), diabetes distress (using the PAID scale), and lipid profiles, will be analysed using appropriate statistical techniques such as linear regression for continuous outcomes and logistic regression for categorical outcomes.

Process Measures: Engagement metrics, such as the proportion of participants using the Gro-AUS app, will be summarised and compared across groups using descriptive statistics.

Sensitivity Analyses: To assess the robustness of the findings, sensitivity analyses will be conducted to evaluate the impact of missing data and any assumptions made in the primary analyses.

Recruitment
Recruitment status
Not yet recruiting
Date of first participant enrolment
Anticipated
1/04/2025
Actual
Date of last participant enrolment
Anticipated
1/06/2025
Actual
Date of last data collection
Anticipated
1/04/2026
Actual
Sample size
Target
240
Accrual to date
Final
Recruitment in Australia
Recruitment state(s)
NSW

Funding & Sponsors
Funding source category [1] 317650 0
Charities/Societies/Foundations
Name [1] 317650 0
HCF Research Foundation
Country [1] 317650 0
Australia
Primary sponsor type
Government body
Name
Western Sydney Local Health District
Country
Australia
Secondary sponsor category [1] 319966 0
None
Name [1] 319966 0
Country [1] 319966 0

Ethics approval
Ethics application status
Approved
Ethics committee name [1] 316349 0
Western Sydney Local Health District Human Research Ethics Committee
Ethics committee address [1] 316349 0
Ethics committee country [1] 316349 0
Australia
Date submitted for ethics approval [1] 316349 0
16/10/2024
Approval date [1] 316349 0
17/12/2024
Ethics approval number [1] 316349 0
2024/ETH02078

Summary
Brief summary
Trial website
Public notes

Contacts
Principal investigator
Name 137614 0
Dr Gideon Meyerowitz-Katz
Address 137614 0
Level 3, Administration and Education Building Blacktown Hospital Campus, Blacktown, NSW 2148
Country 137614 0
Australia
Phone 137614 0
+61 2 9881 8878
Email 137614 0
gideon.meyerowitzkatz@health.nsw.gov.au
Contact person for public queries
Name 137615 0
Mahsa Shahidi
Address 137615 0
Level 3, Administration and Education Building Blacktown Hospital Campus, Blacktown, NSW 2148
Country 137615 0
Australia
Phone 137615 0
+61298818878
Email 137615 0
mahsa.shahidi@health.nsw.gov.au
Contact person for scientific queries
Name 137616 0
Gideon Meyerowitz-Katz
Address 137616 0
Level 3, Administration and Education Building Blacktown Hospital Campus, Blacktown, NSW 2148
Country 137616 0
Australia
Phone 137616 0
+61 2 8670 7384
Email 137616 0
gideon.meyerowitzkatz@health.nsw.gov.au

Data sharing statement
Will the study consider sharing individual participant data?
No


What supporting documents are/will be available?

Doc. No.TypeCitationLinkEmailOther DetailsAttachment
24455Study protocol    Gro-Aus RCT Protocol V2 Dated 22.11.2024 Clean.docx
24456Informed consent form    Gro-AUS RCT Master PICF, Version 2 Dated 22.11.2024 Clean.docx



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.