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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT04518566
Other study ID # 202004-00158
Secondary ID
Status Completed
Phase N/A
First received
Last updated
Start date May 11, 2021
Est. completion date August 31, 2023

Study information

Verified date April 2024
Source Singapore General Hospital
Contact n/a
Is FDA regulated No
Health authority
Study type Interventional

Clinical 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. 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.


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.


Recruitment information / eligibility

Status Completed
Enrollment 1000
Est. completion date August 31, 2023
Est. primary completion date August 31, 2023
Accepts healthy volunteers No
Gender All
Age group 40 Years to 120 Years
Eligibility 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 Design


Related Conditions & MeSH terms


Intervention

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.

Locations

Country Name City State
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 Singapore General Hospital Singapore
Singapore SingHealth Polyclinic (Bedok) Singapore
Singapore SingHealth Polyclinic (Punggol) Singapore
Singapore SingHealth Polyclinic (Tampines) Singapore

Sponsors (4)

Lead Sponsor Collaborator
Singapore General Hospital Duke-NUS Graduate Medical School, National University of Singapore, SingHealth Polyclinics

Country where clinical trial is conducted

Singapore, 

Outcome

Type Measure Description Time frame Safety issue
Primary Patient activation score as measured by patient activation measure Difference in patient activation score between intervention and control at 12 months 12 months
Secondary 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
Secondary 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
Secondary Quality of life as measured by SF36-v2 Difference in quality of life between intervention and control at 12 months 12 months
Secondary 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
Secondary Healthcare cost Healthcare cost throughout 12 months 12 months
Secondary Physical activity as measured by number of steps Number of steps throughout 12 months 12 months
Secondary Physical activity as measured by moderate to vigorous active minutes Moderate to vigorous active minutes throughout 12 months 12 months
Secondary Diet as measured by calorie intake, carbohydrates and sugar intake Diet throughout 12 months 12 months
Secondary HbA1c HbA1c throughout 12 months 12 months
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