Better Risk Perception Via Patient Similarity to Control Hyperglycemia and Sustained by Telemonitoring

Status: Recruiting
Location: See location...
Intervention Type: Other
Study Type: Interventional
Study Phase: Not Applicable
SUMMARY

Background: Diabetes significantly raises the likelihood of complications, thereby increasing the risk of diabetes-related mortality, particularly due to vascular complications. It is vital to address this rising trend of mortality, by enhancing awareness of diabetes complications to improve risk perception and ultimately reduce mortality rates. Managing diabetes effectively requires interventions addressing both risk communication and monitoring, helping patients better understand and make informed decisions about their health.

Objectives: The primary aim is to evaluate and compare the effectiveness of combined risk communication session using an AI module (PERDICT.AI) and home-based diabetes monitoring (PTEC-DM) versus a standalone risk communication session in improving health outcomes (risk perception, medication adherence, self-care activities and glycaemic control) among poorly controlled diabetes patients. Secondary aims are to explore participants' views and experiences of risk communication session using PERDICT.AI, PTEC-DM and usual care and clinician' views on utility of the new approach to improve risk perception.

Methods: A mixed-method study design will be employed to conduct a multi-arm randomized controlled trial across four of the SingHealth Polyclinics cluster (Pasir Ris, Eunos, Sengkang, Tampines North). Patient participants will be randomly allocated in a 1:1:1 ratio to one of the three arms. Arm 1 will receive risk communication session using PERDICT.AI and home-based diabetes monitoring using PTEC-DM alongside usual care. Arm 2 participants will undergo a standalone risk communication session using PERDICT.AI with usual care while arm 3 will serve as the control group with usual care. A total of 360 (120 in each group) participants will be enrolled by simple randomization. Eligible patient must be of age between 36 and 65 years with HbA1c \>8.0% within the last 6 months. Significance of the study: Findings from the study may add evidence to the scientific knowledge of using these approaches to improve risk perception and recommend development of similar interventions.

Eligibility
Participation Requirements
Sex: All
Minimum Age: 36
Maximum Age: 65
Healthy Volunteers: f
View:

• Type 2 Diabetes Mellitus on follow-up at the study site for at least 12 months

• Age 36 to 65 years

• At least one HbA1c reading ≥ 8.0% within the last 6 months

• Able to read and speak English

Locations
Other Locations
Singapore
SingHealth Polyclinics
RECRUITING
Singapore
Contact Information
Primary
Kalaipriya Gunasekaran, MD
kalaipriya.gunasekaran@singhealth.com.sg
(65)98071122
Backup
Ngiap Chuan Tan, MMed
tan.ngiap.chuan@singhealth.com.sg
Time Frame
Start Date: 2024-07-15
Estimated Completion Date: 2025-09-30
Participants
Target number of participants: 360
Treatments
Experimental: Arm 1
In arm 1, participants will attend the risk communication session utilizing AI module (PERDICT.AI) delivered by the study team integrated with Home-based Diabetes Monitoring (PTEC-DM) providing personalized guidance through teleconsultation in addition to usual care. Screen activity of PERDICT.AI will be recorded using a screen capture software. The entire session will be audio recorded.
Experimental: Arm 2
In arm 2, participants will attend the risk communication session utilising AI module (PERDICT.AI) without PTEC-DM. Screen activity of PERDICT.AI will be recorded using a screen capture software. The entire session will be audio recorded.
No_intervention: Arm 3
Arm 3 will be the active control group, receiving only standard care
Related Therapeutic Areas
Sponsors
Leads: SingHealth Polyclinics
Collaborators: AISG Health Grand Challenge

This content was sourced from clinicaltrials.gov