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

NCT04518566 | Completed DMC

• 1 other identifier: 202004-00158
• Study Type: interventional
• Target: 1,000
• Locations: 1 country
• Sites: 7

Brief 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. 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.

Conditions

Diabetes Mellitus, Type 2

Keywords

wearables; nudges; gamification; behavioural change

Outcome Measures

Primary Outcomes (1)

• Patient activation score as measured by patient activation measure

  Difference in patient activation score between intervention and control at 12 months

  12 months

Secondary Outcomes (9)

• Medication adherence as measured by Voils Scale

  6 months, 12 months

• Medication adherence as measured by Adherence to Refills and Medications Scale

  6 months, 12 months

• Quality of life as measured by SF36-v2

  12 months

• Quality of life as measured by EQ-5D-5L

  6 months, 12 months

• Healthcare cost

  12 months

Study Arms (2)

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.

Behavioral: Nudges

Interventions

Nudges BEHAVIORAL

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.

Nudges

Eligibility Criteria

• Age: 40 Years - 120 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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)

Sponsors & Collaborators

• Singapore General Hospital: lead
• National University of Singapore: collaborator
• SingHealth Polyclinics: collaborator
• Duke-NUS Graduate Medical School: collaborator

Study Sites (7)

Singapore General Hospital

Singapore, 486838, Singapore

Location

Duke-NUS Medical School

Singapore, Singapore

Location

National University of Singapore - Saw Swee Hock School of Public Health

Singapore, Singapore

Location

National University of Singapore - School of Computing

Singapore, Singapore

Location

SingHealth Polyclinic (Bedok)

Singapore, Singapore

Location

SingHealth Polyclinic (Punggol)

Singapore, Singapore

Location

SingHealth Polyclinic (Tampines)

Singapore, Singapore

Location

MeSH Terms

Conditions

Diabetes Mellitus, Type 2

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Study Officials

• Lian Leng Low

  Singhealth Foundation

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: TRIPLE
• Who Masked: CARE PROVIDER, INVESTIGATOR, OUTCOMES ASSESSOR
• Masking Details: Patients will be screened and recruited for the RCT by research coordinators positioned in the SingHealth polyclinics. They will identify eligible patients according to the inclusion and exclusion criteria. Informed consent will be taken and they will be referred to the research coordinators who will randomly assign the patients to the intervention or control arm using a site-specific pre-generated randomization list. A research coordinator will keep custody of the 3 randomization lists (1 for each recruitment site), and assign treatment accordingly to the intervention listed and not be involved in the recruitment or assessment of patients.
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: 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.

Study Record Dates

• First Submitted: August 14, 2020
• First Posted: August 19, 2020
• Study Start: May 11, 2021
• Primary Completion: August 31, 2023
• Study Completion: August 31, 2023
• Last Updated: April 17, 2024

Record last verified: 2024-04

Data Sharing

• IPD Sharing: Will not share

Locations

SG (7)