Diabetes Mellitus, Type 2EMPOWERNCT04518566
Empowering Patients With Chronic Disease Using Profiling and Targeted Feedbacks Delivered Through Wearable Device
A study testing Nudges in people with Diabetes Mellitus, Type 2, run by Singapore General Hospital at 7 sites in Singapore.
Trial summary
Chronic diseases are the leading cause of deaths in Singapore. The rising prevalence in chronic diseases with age and Singapore's rapidly aging population calls for new models of care to effectively prevent the onset and delay the progression of these diseases. Read more about this study
- Who is it for?
- Ages 40 to 120 years
- Conditions the study is for
- Diabetes Mellitus, Type 2
- What is being tested?
- Nudges
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,000 participants
- Where
- 7 sitesSingapore
- Study dates
- 11 May 2021to 31 Aug 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 120 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Aged 40 and above at time of recruitment
- Have been diagnosed with diabetes at time of recruitment
- Most recent HbA1c more than or equal to 7.0% mmol/l
- Physically able to exercise
- Literate in English
- Agreeable to be monitored by FitBit and adaptive intervention platform
- Able to conform to the FitBit monitoring schedule
Who cannot join
- On insulin treatment
- Require assistance with basic activities of daily living (BADL)
- Have planned major operation or surgical procedure in the coming year at the time of recruitment
- Cognitively impaired (scored more than or equal to 6 on the Abbreviated Mental Test)
Trial locations and contacts
7 sites in 1 country.
7 sites in 1 country
Singapore7 sites
Singapore General Hospital
Singapore, 486838
Duke-NUS Medical School
Singapore
National University of Singapore - Saw Swee Hock School of Public Health
Singapore
National University of Singapore - School of Computing
Singapore
SingHealth Polyclinic (Bedok)
Singapore
SingHealth Polyclinic (Punggol)
Singapore
SingHealth Polyclinic (Tampines)
Singapore
About this study
Official title: Empowering Patients With Chronic Disease Using Profiling and Targeted Feedbacks Delivered Through Wearable Device (EMPOWER)
Chronic diseases are the leading cause of deaths in Singapore. The rising prevalence in chronic diseases with age and Singapore's rapidly aging population calls for new models of care to effectively prevent the onset and delay the progression of these diseases. Advancement in medical technology has offered new innovations that aid healthcare systems in coping with the rapid rising in healthcare needs. These include mobile applications, wearable technologies and machine learning-derived personalized behaviorial interventions. The overall goal of the project is to improve health outcomes in chronic disease patients through delivering targeted nudges via mobile application and wearable to sustain behavioral change. The objective is to design, develop and evaluate an adaptive interventional platform that is capable of delivering personalized behavioral nudges to promote and sustain healthy behavioral changes in senior patients with diabetes. The aim is to assess the clinical effectiveness of real-time personalized educational and behavioral interventions delivered through wearable (FitBit) and an in-integrative mobile application in improving patient activation scores measured using the patient activation measure (PAM). Secondary outcome measures include cost-effectiveness, quality of life, medication adherence, healthcare cost, utilization and lab results. Together with the experts from the SingHealth Regional Health System and National University of Singapore, the investigators will conduct a randomized controlled trial of 1,000 eligible patients. This proposal aims to achieve sustainable and cost-effective behavioral change in diabetes patients through patient-empowerment and targeted chronic disease care.
Read the detailed description
Traditional healthcare facility-based consultation model of episodic contact in managing chronic disease patients have limited exposure to monitor and intervene patients' lifestyle factors. These factors have been found to be more effective in managing 3H than medication. The proposed adaptive platform will utilize wearable and mobile application technologies which has the ability to continuous track several physiological and lifestyle factors data (e.g. moderate to vigorous active minutes, resting heart rate, sleep hours and quality and dietary habits)
Similarly, due to the limited exposure that healthcare workers have with patients under the current consultation model, current health education and intervention tends to be "one size fits all", passive and "top down" knowledge-loading. Patients are expected to change their behavior or to remember health education knowledge after a consultation session. The proposed adaptive platform will be built using educational and behavioral cues obtained from multiple stakeholders (including patients) and multiple data sources with the aim to gather more comprehensive and targeted feedback that is relevant to patients' needs in their management of their 3H condition. As changes in lifestyle factors and habits takes time, the proposed platform can also provide timely and appropriate feedbacks and reminders to patients at a more constant interval as compared to current model of care when advice was only given during consultation follow-up
To be able to add healthy years to the life of the current and future seniors,behavioral interventions that are closely studied and carefully implemented without disruption to the daily activity of the seniors is needed to achieve a revolutionary improvement in current primary care management.
The investigators will conduct a qualitative study to have a deep and enriched understanding of the types of nudges that are suited for patients with chronic diseases. Through modelling approach using the electronic medical records, the proposed adaptive platform will profile patients into groups and pre-set the nudges that are suitable for them. This allows the investigators to identify patients that have a higher risk of complications of 3H and quickly match the desired nudges to change behavior.
The proposed adaptive platform also aims to empower patients by providing patients with automated bite-sized knowledge of their health conditions. Coupled with real-time personalized feedback to their health behaviors, patients will be equipped with the knowledge to take charge of their health using far lesser healthcare manpower and resources.
The proposed adaptive platform will be integrated into common mobile wearable which are readily available devices that are widely used by many Singaporeans now. As such it can also be scaled up relatively easily with minimal resources and education.
Therefore, the proposed adaptive intervention will improve health outcomes and reduce healthcare utilization. An empowered patient will result in lesser complications and improve health outcomes, resulting in lower patient and caregiver burden, improving quality of life.
- Conditions
- Diabetes Mellitus, Type 2
- Being tested
- Nudges
- Keywords
- wearables, nudges, gamification, behavioural change
- Medical subject headings
- Diabetes Mellitus, Type 2
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
NudgesBehavioural
Behavioral nudges will be delivered to patients' FitBit device through adaptive intervention platform via notification syncing. To ensure the delivered nudges are timely and personalized, predictive nudges will be developed based on patterns in patients' sociodemographic, clinical and baseline activity tracking. These nudges will be sent automatically to patients upon specific triggers. The nudges will also be assessed for its effectiveness in behavior change. For example, a predictive nudge to encourage patients to take a short walk after detecting long periods of sedentary time will be assessed for its effects by step counts data after delivery of nudge. An iterative approach will be used to generate an effective set of nudges and its most appropriate delivery times for specific activity patterns.
| Study group | Role | What they receive |
|---|---|---|
| Placebo | No intervention | Patients in control arm will have FitBit. However, there are no personalised nudges given to the patients in the control arm. Occasional reminders to encourage adherence to wearing of the FitBit will be sent. |
| Nudges | Experimental | Patients in the intervention arm will be given a FitBit device and will be encouraged to wear it as often as possible. Using FitBit built-in tracking technologies such as PurePulse and SmartTrack54, patient's daily activities such as number of steps taken, sedentary time, heart rate, sleep time and exercise will be captured and synced to the adaptive intervention platform as developed in Phase 2 for real-time tracking. Receives: Behavioral: Nudges |
What is being measured
The primary outcome is the main question the study is designed to answer.
Patient activation score as measured by patient activation measure
Difference in patient activation score between intervention and control at 12 months
Measured: 12 months
Secondary outcomes (9)
Medication adherence as measured by Voils Scale
Difference in medication adherence between intervention and control at 6 months and 12 months
Measured: 6 months, 12 months
Medication adherence as measured by Adherence to Refills and Medications Scale
Difference in medication adherence between intervention and control at 6 months and 12 months
Measured: 6 months, 12 months
Quality of life as measured by SF36-v2
Difference in quality of life between intervention and control at 12 months
Measured: 12 months
Quality of life as measured by EQ-5D-5L
Difference in quality of life between intervention and control at 6 months and 12 months
Measured: 6 months, 12 months
Healthcare cost
Healthcare cost throughout 12 months
Measured: 12 months
Physical activity as measured by number of steps
Number of steps throughout 12 months
Measured: 12 months
Physical activity as measured by moderate to vigorous active minutes
Moderate to vigorous active minutes throughout 12 months
Measured: 12 months
Diet as measured by calorie intake, carbohydrates and sugar intake
Diet throughout 12 months
Measured: 12 months
HbA1c
HbA1c throughout 12 months
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Aug 2020
- Study start11 May 2021
- Primary completion31 Aug 2023
- Study completion31 Aug 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Diabetes Mellitus, Type 2Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- A pragmatic 2-arm (1:1) randomized controlled trial (RCT) on 1,000 eligible diabetes patients using the Pragmatic Explanatory Continuum Indicator Summary Framework-2 (PRECIS-2) criteria for pragmatic trials. Patients with diabetes will be randomly allocated in a 1:1 ratio to either the intervention or control group. The intervention group will receive the personalized feedback intervention through the personalized and adaptive intervention platform app on a FitBit wearable on top of their usual clinical care for their diabetes condition. The control group will receive the FitBit wearable on top of their usual clinical care for their diabetes condition but will not receive the personalized and adaptive intervention platform.
- Masking
- Triple blind
- Who is masked
- Care provider, Investigator, Outcomes assessor
- Enrolment
- 1,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Singapore General Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- National University of Singapore, SingHealth Polyclinics, Duke-NUS Graduate Medical School
- Responsible party
- Sponsor
- Investigators
- Lian Leng Low, Principal investigator, Singhealth Foundation
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04518566
- Sponsor's protocol ID
- 202004-00158
- Acronym
- EMPOWER
- Status last verified
- Apr 2024
- First submitted
- 14 Aug 2020
- Last updated
- 17 Apr 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Empowering Patients With Chronic Disease Using Profiling and Targeted Feedbacks Delivered Through Wearable Device (EMPOWER)
- ClinicalTrials.gov ID
- NCT04518566
- Acronym / sponsor ID
- EMPOWER / 202004-00158
- Conditions
- Diabetes Mellitus, Type 2
- Keywords
- wearables, nudges, gamification, behavioural change
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 1,000 participants (actual)
- Lead sponsor
- Singapore General HospitalUniversity, hospital or other
- Collaborators
- National University of Singapore, SingHealth Polyclinics, Duke-NUS Graduate Medical School
- Study start
- 11 May 2021 (actual)
- Primary completion
- 31 Aug 2023 (actual)
- Study completion
- 31 Aug 2023 (actual)
- First posted
- 19 Aug 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Chronic diseases are the leading cause of deaths in Singapore. The rising prevalence in chronic diseases with age and Singapore's rapidly aging population calls for new models of care to effectively prevent the onset and delay the progression of these diseases. Advancement in medical technology has offered new innovations that aid healthcare systems in coping with the rapid rising in healthcare needs. These include mobile applications, wearable technologies and machine learning-derived personalized behaviorial interventions. The overall goal of the project is to improve health outcomes in chronic disease patients through delivering targeted nudges via mobile application and wearable to sustain behavioral change. The objective is to design, develop and evaluate an adaptive interventional platform that is capable of delivering personalized behavioral nudges to promote and sustain healthy behavioral changes in senior patients with diabetes. The aim is to assess the clinical effectiveness of real-time personalized educational and behavioral interventions delivered through wearable (FitBit) and an in-integrative mobile application in improving patient activation scores measured using the patient activation measure (PAM). Secondary outcome measures include cost-effectiveness, quality of life, medication adherence, healthcare cost, utilization and lab results. Together with the experts from the SingHealth Regional Health System and National University of Singapore, the investigators will conduct a randomized controlled trial of 1,000 eligible patients. This proposal aims to achieve sustainable and cost-effective behavioral change in diabetes patients through patient-empowerment and targeted chronic disease care.
Detailed description
Traditional healthcare facility-based consultation model of episodic contact in managing chronic disease patients have limited exposure to monitor and intervene patients' lifestyle factors. These factors have been found to be more effective in managing 3H than medication. The proposed adaptive platform will utilize wearable and mobile application technologies which has the ability to continuous track several physiological and lifestyle factors data (e.g. moderate to vigorous active minutes, resting heart rate, sleep hours and quality and dietary habits)
Similarly, due to the limited exposure that healthcare workers have with patients under the current consultation model, current health education and intervention tends to be "one size fits all", passive and "top down" knowledge-loading. Patients are expected to change their behavior or to remember health education knowledge after a consultation session. The proposed adaptive platform will be built using educational and behavioral cues obtained from multiple stakeholders (including patients) and multiple data sources with the aim to gather more comprehensive and targeted feedback that is relevant to patients' needs in their management of their 3H condition. As changes in lifestyle factors and habits takes time, the proposed platform can also provide timely and appropriate feedbacks and reminders to patients at a more constant interval as compared to current model of care when advice was only given during consultation follow-up
To be able to add healthy years to the life of the current and future seniors,behavioral interventions that are closely studied and carefully implemented without disruption to the daily activity of the seniors is needed to achieve a revolutionary improvement in current primary care management.
The investigators will conduct a qualitative study to have a deep and enriched understanding of the types of nudges that are suited for patients with chronic diseases. Through modelling approach using the electronic medical records, the proposed adaptive platform will profile patients into groups and pre-set the nudges that are suitable for them. This allows the investigators to identify patients that have a higher risk of complications of 3H and quickly match the desired nudges to change behavior.
The proposed adaptive platform also aims to empower patients by providing patients with automated bite-sized knowledge of their health conditions. Coupled with real-time personalized feedback to their health behaviors, patients will be equipped with the knowledge to take charge of their health using far lesser healthcare manpower and resources.
The proposed adaptive platform will be integrated into common mobile wearable which are readily available devices that are widely used by many Singaporeans now. As such it can also be scaled up relatively easily with minimal resources and education.
Therefore, the proposed adaptive intervention will improve health outcomes and reduce healthcare utilization. An empowered patient will result in lesser complications and improve health outcomes, resulting in lower patient and caregiver burden, improving quality of life.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind (care provider, investigator, outcomes assessor)
- Number of arms
- 2
- Model description
- A pragmatic 2-arm (1:1) randomized controlled trial (RCT) on 1,000 eligible diabetes patients using the Pragmatic Explanatory Continuum Indicator Summary Framework-2 (PRECIS-2) criteria for pragmatic trials. Patients with diabetes will be randomly allocated in a 1:1 ratio to either the intervention or control group. The intervention group will receive the personalized feedback intervention through the personalized and adaptive intervention platform app on a FitBit wearable on top of their usual clinical care for their diabetes condition. The control group will receive the FitBit wearable on top of their usual clinical care for their diabetes condition but will not receive the personalized and adaptive intervention platform.
| Arm | Type | Receives |
|---|---|---|
| Placebo | No intervention | –Patients in control arm will have FitBit. However, there are no personalised nudges given to the patients in the control arm. Occasional reminders to encourage adherence to wearing of the FitBit will be sent. |
| Nudges | Experimental | Behavioral: NudgesPatients in the intervention arm will be given a FitBit device and will be encouraged to wear it as often as possible. Using FitBit built-in tracking technologies such as PurePulse and SmartTrack54, patient's daily activities such as number of steps taken, sedentary time, heart rate, sleep time and exercise will be captured and synced to the adaptive intervention platform as developed in Phase 2 for real-time tracking. |
Who may qualify
- Ages
- 40 to 120 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (7 listed)
- Aged 40 and above at time of recruitment
- Have been diagnosed with diabetes at time of recruitment
- Most recent HbA1c more than or equal to 7.0% mmol/l
- Physically able to exercise
- Literate in English
Key exclusion criteria (4 listed)
- On insulin treatment
- Require assistance with basic activities of daily living (BADL)
- Have planned major operation or surgical procedure in the coming year at the time of recruitment
- Cognitively impaired (scored more than or equal to 6 on the Abbreviated Mental Test)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| NudgesBehavioral nudges will be delivered to patients' FitBit device through adaptive intervention platform via notification syncing. To ensure the delivered nudges are timely and personalized, predictive nudges will be developed based on patterns in patients' sociodemographic, clinical and baseline activity tracking. These nudges will be sent automatically to patients upon specific triggers. The nudges will also be assessed for its effectiveness in behavior change. For example, a predictive nudge to encourage patients to take a short walk after detecting long periods of sedentary time will be assessed for its effects by step counts data after delivery of nudge. An iterative approach will be used to generate an effective set of nudges and its most appropriate delivery times for specific activity patterns. | Behavioural | Nudges |
Primary outcome
Patient activation score as measured by patient activation measure
Time frame: 12 months
Difference in patient activation score between intervention and control at 12 months
9 secondary outcomes
Medication adherence as measured by Voils Scale
Time frame: 6 months, 12 months
Medication adherence as measured by Adherence to Refills and Medications Scale
Time frame: 6 months, 12 months
Quality of life as measured by SF36-v2
Time frame: 12 months
Quality of life as measured by EQ-5D-5L
Time frame: 6 months, 12 months
Healthcare cost
Time frame: 12 months
Physical activity as measured by number of steps
Time frame: 12 months
Physical activity as measured by moderate to vigorous active minutes
Time frame: 12 months
Diet as measured by calorie intake, carbohydrates and sugar intake
Time frame: 12 months
HbA1c
Time frame: 12 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Apr 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Lian Leng Low principal investigator
Singhealth Foundation
7 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Nudges in people with Diabetes Mellitus, Type 2, run by Singapore General Hospital at 7 sites in Singapore.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 17 Apr 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 7 sites in Singapore. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Patient activation score as measured by patient activation measure (time frame: 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Apr 2024.