Empowering Patients With Chronic Disease Using Profiling and Targeted Feedbacks Delivered Through Wearable Device (EMPOWER)

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.

Study Overview

• Status: Completed
• Conditions: Diabetes Mellitus, Type 2
• Intervention / Treatment: Behavioral: Nudges

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 Type: Interventional
• Enrollment (Actual): 1000
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Lian Leng Low; Phone Number: 91051097; Email: low.lian.leng@singhealth.com.sg

Study Locations

• Singapore
  • Singapore, Singapore, 486838
    • Singapore General Hospital
  • Singapore, Singapore
    • Duke-NUS Medical School
  • Singapore, Singapore
    • National University of Singapore - Saw Swee Hock School of Public Health
  • Singapore, Singapore
    • National University of Singapore - School of Computing
  • Singapore, Singapore
    • SingHealth Polyclinic (Bedok)
  • Singapore, Singapore
    • SingHealth Polyclinic (Punggol)
  • Singapore, Singapore
    • SingHealth Polyclinic (Tampines)

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 40 years to 120 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• 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

Exclusion Criteria:

• 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)

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Triple

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
No Intervention: Placebo; 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.
Experimental: Nudges; 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.	Behavioral: Nudges; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Patient activation score as measured by patient activation measure	Difference in patient activation score between intervention and control at 12 months	12 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Medication adherence as measured by Voils Scale	Difference in medication adherence between intervention and control at 6 months and 12 months	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	6 months, 12 months
Quality of life as measured by SF36-v2	Difference in quality of life between intervention and control at 12 months	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	6 months, 12 months
Healthcare cost	Healthcare cost throughout 12 months	12 months
Physical activity as measured by number of steps	Number of steps throughout 12 months	12 months
Physical activity as measured by moderate to vigorous active minutes	Moderate to vigorous active minutes throughout 12 months	12 months
Diet as measured by calorie intake, carbohydrates and sugar intake	Diet throughout 12 months	12 months
HbA1c	HbA1c throughout 12 months	12 months

Collaborators and Investigators

• Sponsor: Singapore General Hospital
• Collaborators: Duke-NUS Graduate Medical School; SingHealth Polyclinics; National University of Singapore
• Investigators: Principal Investigator: Lian Leng Low, Singhealth Foundation

Study record dates

Study Major Dates

• Study Start (Actual): May 11, 2021
• Primary Completion (Actual): August 31, 2023
• Study Completion (Actual): August 31, 2023

Study Registration Dates

• First Submitted: August 14, 2020
• First Submitted That Met QC Criteria: August 17, 2020
• First Posted (Actual): August 19, 2020

Study Record Updates

• Last Update Posted (Actual): April 17, 2024
• Last Update Submitted That Met QC Criteria: April 16, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Keywords: gamification; wearables; nudges; behavioural change
• Additional Relevant MeSH Terms: Pathologic Processes; Glucose Metabolism Disorders; Metabolic Diseases; Endocrine System Diseases; Disease Attributes; Diabetes Mellitus; Diabetes Mellitus, Type 2; Chronic Disease
• Other Study ID Numbers: 202004-00158

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No