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Trial registered on ANZCTR


Trial ID
ACTRN12616000455460
Ethics application status
Approved
Date submitted
2/04/2016
Date registered
7/04/2016
Date last updated
12/04/2018
Type of registration
Prospectively registered

Titles & IDs
Public title
Gout Self-Management App: eHealth Tool for People with Gout
Scientific title
Patient-centred eHealth approach to improving outcomes for gout sufferers
Secondary ID [1] 288881 0
None
Universal Trial Number (UTN)
U1111-1181-3934
Trial acronym
None
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Gout 298179 0
Condition category
Condition code
Musculoskeletal 298343 298343 0 0
Other muscular and skeletal disorders
Inflammatory and Immune System 298411 298411 0 0
Rheumatoid arthritis

Intervention/exposure
Study type
Interventional
Description of intervention(s) / exposure
The intervention group will be provided with access to the Healthy.me app tailored for the self-management of gout. Healthy.me is a self-management eHealth app available on smartphones and tablets that contains features to help patients manage their chronic diseases. The app has been tailored to promote effective self-management of gout and long-term adherence to ULT. The app was developed, using dimensions of the Health Belief Model: perceived susceptibility, perceived severity, perceived benefits and perceived barriers. While education materials from the app raise awareness of consequences of uncontrolled gout, susceptibility to gout attacks when serum urate concentrations are high, and the importance of keeping a low serum urate level, the key feature of the app, “Uric Acid Tracker”, is designed to provide personalised feedback on susceptibility to and severity of gout by presenting individual patients’ serum urate levels and gout attack data on a graph. This personalised feedback will enable the patients to relate their high serum urate levels to the occurrence of gout attacks (perceived susceptibility and severity), and recognise attack-free benefits of ULT adherence behaviour when their serum urate is consistently low. Low medication adherence is a major barrier to keeping gout under control, and thus the app is designed to include educational content and pop-up alerts to remind the patients to take their ULTs. Specifically, the intervention app contains these features:
• Uric Acid Tracker: Serum urate results will be recorded and graphed under the ‘Uric Acid Tracker’ function. Patients will be asked to obtain their serum urate results from their GP or pathologist after every test, and promptly enter the results into this feature of the app. Alerts within the app will be triggered if serum urate is not in the target range, recommending that patients discuss their gout management with their GP. The patient’s serum urate concentrations will be represented in a graph over time and in relationship to the target serum urate (which is 0.36 mmol/L or lower).
• Gout Facts: Educational materials including written information and a video animation about gout and its management for people with gout will be available via the app. The following topics are covered: what is gout, why take ULT medication, the effects of ULT, what to do in a gout attack, and the importance of ULT adherence.
• Electronic Gout Attacks Diary: This feature provides the ability to record gout attacks including pain intensity, location of attack(s) or joint(s) affected, and triggers. The gout attack data that patients enter will be presented in the graph next to serum urate levels.
• Schedule: This feature can be used as an alternative to the calendar function of smart devices if patients prefer to use it for management of their gout. Patients can schedule their tasks, such as having blood tests and collecting results, seeing their GP or other members of their care team, and recording gout attacks.
• Team: Contact details of GP, pathology service, researchers and others such as rheumatologist, physiotherapist etc. can be listed.
• Diet Tips: An evidence-based list of foods that can raise or lower serum urate will be available.

Patients in the intervention group will be provided with their personal login details to access and download the Healthy.me gout management app and installation guide. They will have access to a short introduction to the ‘Healthy.me app’ and its features via an email or in paper form. There will be an opportunity for patients to ask researchers any questions via the web-site, email, phone or face to face. Patients will have access to the app for 12 months. Patients’ GPs will also have access to the introductory material about the app, and the content of educational materials in the app.
Intervention code [1] 294339 0
Behaviour
Intervention code [2] 294395 0
Treatment: Other
Intervention code [3] 294396 0
Prevention
Comparator / control treatment
The control group patients will be provided with their personal login details to access and download the Healthy.me modified gout app and installation guide. The full version of the Healthy.Me app tailored for patients with gout will be modified to remove all functions except the Gout Attack Diary, which contains fields to record only the date of attack, pain intensity and location of attack, but not triggers. Thus, the control group patients can neither enter their serum urate data into the app nor view the graph. They will be provided with this control app in order to collect gout-attack data as an important secondary outcome measure of this study. They will be informed that the study is tracking their gout experience over the year following entering the study. They will be asked to return to their GP for assessment and serum urate and ULT concentrations at 6 and 12 months independent of other visits they may make to their GP. Reminders to visit their GP and have blood tests will only occur via SMS and/or email immediately prior to 6 and 12 month visits to optimise their retention in the study.
Control group
Active

Outcomes
Primary outcome [1] 297812 0
The proportion of patients whose plasma urate is less than or equal to the target urate of 0.36 mmol/L
Timepoint [1] 297812 0
The primary timepoint is 6 months after provision of the app to the participants (compared to baseline).
Secondary outcome [1] 322320 0
Frequency of acute gout attacks. Participants are asked in a survey how many gout attacks they have had during the past 6 months (at baseline) or since the last survey (at 6- and 12-month follow-ups). This data will be checked against the gout attack data the participants entered into the app, and confirmed with the participants if there is any discrepancies. Using the app, the participants will be able to enter the date and duration of their attack, as well as rate the intensity of the pain using a rating scale from 1-10.
Timepoint [1] 322320 0
The timepoints are 6 and 12 months after provision of the app to the participants (compared to baseline).
Secondary outcome [2] 322552 0
Health-related quality of life (measured using EQ-5D-5L)
Timepoint [2] 322552 0
The timepoints are 6 and 12 months after provision of the app to the participants (compared to baseline).
Secondary outcome [3] 322553 0
Cost-effectiveness of the intervention compared to usual care plus control app will be estimated in terms of the following:
1. The incremental cost per patient achieving the primary outcome, and
2. The incremental cost per quality adjusted life year gained (QALY) using the utility scores derived from the EQ-5D-5L.
Timepoint [3] 322553 0
The secondary timepoint is 12 months after provision of the app to the participants.
Secondary outcome [4] 345470 0
The proportion of patients whose plasma urate is less than or equal to the target urate of 0.36 mmol/L.
Timepoint [4] 345470 0
The secondary timepoint is 12 months after provision of the app to the participants.
Secondary outcome [5] 345471 0
Work productivity impairments and impairment in daily activities, measured using the validated ‘Work Productivity and Activity Impairment’ (WPAI) questionnaire
Timepoint [5] 345471 0
The timepoints are 6 and 12 months after provision of the app to the participants (compared to baseline).
Secondary outcome [6] 345472 0
Adherence to urate-lowering therapy: The proportion of days covered (PDC): the number of days the patient has medication available out of the total days of observation (days with medication available/days of observation)
Timepoint [6] 345472 0
The timepoints are 6 and 12 months after provision of the app to the participants (compared to baseline).

Eligibility
Key inclusion criteria
- Adult (>18 yrs)
- Diagnosis of gout by GP (either new or flare up) and have experienced at least two attacks of acute gout in the previous year
- Receiving ULT treatment or candidate to start ULT treatment
- Regular access to a smartphone device, the Internet and able to download mobile phone applications (apps)
- Sufficient English language to complete questionnaires
- Provide written informed consent
Minimum age
18 Years
Maximum age
No limit
Gender
Both males and females
Can healthy volunteers participate?
No
Key exclusion criteria
- Pregnant or intending to become pregnant during the study
- Psychological condition (i.e. cognitive decline) that may impede participation in the study
- Lack of technological experience so that participation in the study would be difficult

Study design
Purpose of the study
Educational / counselling / training
Allocation to intervention
Randomised controlled trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
An independent researcher will prepare the group allocation list for 20 practices at a time in blocks of 2 or 4 using a computer-generated randomisation sequence. This list will be sent to an administrative officer, not involved in recruitment. Once research personnel have obtained consent from the first patient at a participating GP practice, the administrative officer will provide the allocation details to randomise that practice. After completion of baseline assessments, patients will be sent an email containing a link to install either the intervention app or control app on their smartphone or tablet.
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
GP practices will be randomised by an independent researcher to either intervention or control group with a 1:1 allocation using random block size of 2 or 4 and computer-generated randomisation sequence.
Masking / blinding
Blinded (masking used)
Who is / are masked / blinded?
The people receiving the treatment/s
The people administering the treatment/s
The people assessing the outcomes
The people analysing the results/data
Intervention assignment
Parallel
Other design features
Phase
Not Applicable
Type of endpoint(s)
Efficacy
Statistical methods / analysis
Sample size calculation
An intra-cluster (practice) correlation coefficient of 0.01 for the primary outcome, within the range for similar cluster randomised studies in Australian and international general practices, was used for power calculations. The primary outcome is the achievement of the target serum urate concentration, i.e. less than or equal to 0.36 mmol/L, at 6 months. This serum concentration outcome, if maintained, is a very strong predictor that gout attacks will no longer occur. Rates of achievement of target urate in gout patients, with approximately the same inclusion criteria and offered standard treatment, lie anywhere between 28% and 69%. Based on these studies, we have assumed that 50% of the standard care group will achieve the target urate concentration at 6-months. We expect that this is an optimistic prediction. Although there are few studies published on effect sizes achieved for interventions centred on eHealth tools, this study will attempt to demonstrate that 65% of participants in the intervention group will achieve the target, i.e. a relative increase of 30% compared to the 50% of participants expected to achieve the target in the control group at 6 months (an absolute increase of 15%). The investigators believe that this effect size will be significant clinically and also at a population level. In order to demonstrate an effect of this size with 80% power and statistical significance level for a two-sided test of 5%, we will need 186 patients in each study arm to complete the study. To accommodate a 20% drop-out rate of participants at 6 months, and assuming no GP practices drop-out, but accounting for the effect of the cluster design, 558 patients will be recruited in total. A target of at least 3 patients per practice will mean 186 practices would need to be recruited.

Data will be analysed using SAS version 9.4. Missing-at-random data will be handled using multiple imputation. The intention-to-treat principle will be applied and the significance level will be set at 0.05. Intra-cluster (practice) correlation coefficients will be determined. The primary outcome at 6-months of the control and intervention groups will be compared using the method of general estimating equations, and also for each outcome, to account for the correlation within practices. The effects on adherence will be explored and correlations with primary and secondary outcomes sought.

Baseline comparisons of demographic variables will be performed using independent samples t-tests. Random effects mixed modelling will be used with baseline, 6 and 12-months and group allocation (Intervention, Control) as the fixed factors. Socio-demographic factors will be controlled for in the analysis. If the proportion of participant’s baseline serum urate less than 0.36mmol/L is greater than 20%, stratification will be performed.

Economic and Process Evaluations
An economic evaluation will be conducted to understand the potential investment case of the intervention for the Australian health system. First, the within trial cost-effectiveness of the intervention compared to usual care plus control app will be estimated in terms of the incremental cost per a) patient achieving the primary outcome and b) quality adjusted life year gained (QALY) using the utility scores derived from the EQ-5D-5L (EQ-5D), given the previous use of EQ-5D in economic evaluations of ULT. Costs will be derived from all aspects of the intervention (e.g. software implementation and maintenance, training) as determined from project files, and direct medical costs (e.g. GP visits, medications, pathology tests, hospitalisations) as determined from GP records and valued at prevailing rates. In addition, indirect (productivity) costs incurred (or avoided) by patients will be collected by participant self-report using the WPAI questionnaire that will be administered at baseline, 6 and 12 months. These costs will be included as a sensitivity analysis to estimate the impact of these costs on cost-effectiveness estimates.

Second, to understand the cost-effectiveness of the Healthy.me gout app beyond the trial, a Markov model will be used to track a group of surviving patients at the end of the trial over time in which they potentially progress through a number of health states including serum urate concentrations controlled, uncontrolled and death. The transition probabilities across various defined health states, costs and quality of life attached to various health states will be based on published evidence. Using appropriate discounting, estimates of long-term costs and outcomes will emerge from the model and an incremental cost-effectiveness ratio estimated. A series of sensitivity analyses will be conducted including on discount rates, uncertainty in outcome estimates and variations in costs that may occur across different settings, so as to explore important questions about the potential scalability and generalisability of this intervention.

A process evaluation will also be undertaken using qualitative research methods to understand how and why the intervention was (or was not) working in practice. At the completion of the study, a random sample of patients and GPs will be interviewed employing a semi-structured interview. Interview questions will be open-ended and will be developed in order to appreciate participants’ views on the app and its value to them. Interviews will be audio recorded and transcribed verbatim. A general inductive approach will be used for analysis. Two researchers will independently analyse transcripts and meet periodically throughout data collection to discuss emerging themes, resolve any discrepancies and determine when theme saturation occurs, and thus no further interviews are needed. Back-end data from the app, which give information on the app usage, will also be included in the analysis. These data will be considered secondary outcomes of the study.

Recruitment
Recruitment status
Recruiting
Date of first participant enrolment
Anticipated
Actual
Date of last participant enrolment
Anticipated
Actual
Date of last data collection
Anticipated
Actual
Sample size
Target
Accrual to date
Final
Recruitment in Australia
Recruitment state(s)
NSW
Recruitment postcode(s) [1] 12992 0
2000 - Sydney

Funding & Sponsors
Funding source category [1] 293231 0
Government body
Name [1] 293231 0
National Health and Medical Research Council
Address [1] 293231 0
National Health and Medical Research Council
GPO Box 1421
Canberra ACT 2601
Country [1] 293231 0
Australia
Primary sponsor type
University
Name
University of New South Wales
Address
St Vincent’s Clinical School | UNSW Australia
Department of Clinical Pharmacology and Toxicology
Therapeutics Centre | Level 2 Xavier Building
St Vincent’s Hospital
Victoria Street | Darlinghurst NSW 2010 | Australia
Country
Australia
Secondary sponsor category [1] 292034 0
None
Name [1] 292034 0
None
Address [1] 292034 0
Country [1] 292034 0

Ethics approval
Ethics application status
Approved
Ethics committee name [1] 294711 0
University of New South Wales Human Research Ethics Comittee
Ethics committee address [1] 294711 0
Research Ethics and Compliance Support (RECS)
University of New South Wales
Level 3, Rupert Myers Building (South Wing)
Sydney NSW 2052
Ethics committee country [1] 294711 0
Australia
Date submitted for ethics approval [1] 294711 0
15/04/2016
Approval date [1] 294711 0
27/05/2016
Ethics approval number [1] 294711 0

Summary
Brief summary
Background:
Gout is increasing despite effective therapies to lower serum urate concentrations to 0.36 mmol/L or less, which, if sustained, significantly reduces acute attacks of gout. Adherence to urate lowering therapy (ULT) is poor, with rates of less than 50% one year after initiation of ULT. Attempts to increase adherence in gout patients have been disappointing.

Aims:
We aim to evaluate the effectiveness of use of a personal, self-management, ‘smartphone’ application (app) to achieve target serum urate concentrations in people with gout. We hypothesise that personalised feedback of serum urate concentrations will improve adherence to ULT.

Method:
A prospective, cluster randomised controlled trial is conducted in primary care setting. GP practices are randomised to either intervention or control clusters with their patients allocated to the same cluster. The intervention group has access to the Healthy.me app tailored for the self-management of gout. The control group patients have access to the same app modified to remove all functions except the Gout Attack Diary.

Outcome measures:
Primary outcome is the proportion of patients whose serum urate concentrations are less than or equal to 0.36 mmol/L after 6 months. Secondary outcomes are proportions of patients achieving target urate concentrations at 12 months, ULT adherence rates, serum urate concentrations at 6 and 12 months, rates of attacks of gout, quality of life estimations and process and economic evaluations. The study is designed to detect a =30% improvement in the intervention group above the expected 50% achievement of target serum urate at 6 months in the control group: power 0.80, significance level 0.05, assumed ‘drop-out’ rate 20%.

Conclusion:
The study is being conducted. Involvement of GPs is essential to the management of people with gout in the community and in the evaluation of usefulness of new technology in primary care.
Trial website
mygoutapp.com
Trial related presentations / publications
Publications
Day, R. O., Frensham, L. J., Nguyen, A. D., Baysari, M. T., Aung, E., Lau, A. Y. S., . . . Westbrook, J. I. (2017). Effectiveness of an electronic patient-centred self-management tool for gout sufferers: a cluster randomised controlled trial protocol. BMJ Open, 7(10), e017281. doi:10.1136/bmjopen-2017-017281

Presentations
Day, R. O., Frensham, L. J., Nguyen, A. D., Baysari, M. T., Aung, E., Lau, A. Y. S., . . . Westbrook, J. I. Effectiveness of an electronic patient-centred self-management tool for gout sufferers. In: “2018 ANZMUSC Annual Scientific Meeting ”. The Australia & New Zealand Musculoskeletal Clinical Trials Network (ANZMUSC). Melbourne, Australia (March 21-22, 2018)
Public notes

Contacts
Principal investigator
Name 64778 0
Prof Richard Day
Address 64778 0
St Vincent’s Clinical School, UNSW Australia
Department of Clinical Pharmacology and Toxicology
Therapeutics Centre, Level 2 Xavier Building
St Vincent’s Hospital
Victoria Street, Darlinghurst NSW 2010
Country 64778 0
Australia
Phone 64778 0
+61 2 8382 2331
Fax 64778 0
Email 64778 0
r.day@unsw.edu.au
Contact person for public queries
Name 64779 0
Dr Eindra Aung
Address 64779 0
St Vincent’s Clinical School, UNSW Australia
Department of Clinical Pharmacology and Toxicology
Therapeutics Centre, Level 2 Xavier Building
St Vincent’s Hospital
Victoria Street, Darlinghurst NSW 2010
Country 64779 0
Australia
Phone 64779 0
+61 2 8382 2199
Fax 64779 0
Email 64779 0
eindra.aung@unsw.edu.au
Contact person for scientific queries
Name 64780 0
Dr Eindra Aung
Address 64780 0
St Vincent’s Clinical School, UNSW Australia
Department of Clinical Pharmacology and Toxicology
Therapeutics Centre, Level 2 Xavier Building
St Vincent’s Hospital
Victoria Street, Darlinghurst NSW 2010
Country 64780 0
Australia
Phone 64780 0
+61 2 8382 2199
Fax 64780 0
Email 64780 0
eindra.aung@unsw.edu.au