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

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

Interventions

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.

Sponsors & Collaborators

• National University of Singapore

  collaborator OTHER

• SingHealth Polyclinics

  collaborator OTHER

• Duke-NUS Graduate Medical School

  collaborator OTHER

• Singapore General Hospital

  lead OTHER

Principal Investigators

• Lian Leng Low · Singhealth Foundation

Study Design

Allocation

RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

TRIPLE

Model

PARALLEL

Eligibility

Min Age

40 Years

Max Age

120 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2021-05-11

Primary Completion

2023-08-31

Completion

2023-08-31

Countries

• Singapore

Study Locations