| CTRI Number |
CTRI/2024/11/077368 [Registered on: 26/11/2024] Trial Registered Prospectively |
| Last Modified On: |
26/11/2024 |
| Post Graduate Thesis |
Yes |
| Type of Trial |
Interventional |
|
Type of Study
|
Other (Specify) [App] |
| Study Design |
Randomized, Parallel Group Trial |
|
Public Title of Study
|
A AI based app for type 2 diabetes mellitus patient for complication prevention with notification bot. |
|
Scientific Title of Study
|
Deep Learning Based Intelligent Recommendation Model For Type 2 Diabetes Mellitus complication Prevention with notification caller bot.A Randomized controlled trail |
| Trial Acronym |
Nil |
|
Secondary IDs if Any
|
| Secondary ID |
Identifier |
| NIL |
NIL |
|
|
Details of Principal Investigator or overall Trial Coordinator (multi-center study)
|
| Name |
DrKarthikKR |
| Designation |
Post Graduate |
| Affiliation |
Saveetha medical college |
| Address |
Department of community medicine
Saveetha Medical college,
Thandalam,
Chennai,
Tamil nadu,India
Department of community medicine
Saveetha Medical college,
Thandalam,
Chennai,
Tamil nadu,India
Kancheepuram
TAMIL NADU
602105
India |
| Phone |
9940544662 |
| Fax |
|
| Email |
k.r.karthik27@gmail.com |
|
Details of Contact Person
Scientific Query
|
| Name |
DrNisha B |
| Designation |
Associate professor |
| Affiliation |
Saveetha medical college |
| Address |
Department of community medicine,
Saveetha Medical college,
Thandalam,
Chennai,Tamil nadu,India
Department of community medicine,
Saveetha Medical college,
Thandalam,
Chennai,Tamil nadu,India
Kancheepuram
TAMIL NADU
602105
India |
| Phone |
9940544662 |
| Fax |
|
| Email |
drnishacm2014@gmail.com |
|
Details of Contact Person
Public Query
|
| Name |
DrKarthikKR |
| Designation |
Post Graduate |
| Affiliation |
Saveetha medical college |
| Address |
Department of community medicine
Saveetha Medical college,
Thandalam,
Chennai,
Tamil nadu,India
Department of community medicine
Saveetha Medical college,
Thandalam,
Chennai,
Tamil nadu,India
Kancheepuram
TAMIL NADU
602105
India |
| Phone |
9940544662 |
| Fax |
|
| Email |
k.r.karthik27@gmail.com |
|
|
Source of Monetary or Material Support
|
| Saveetha Medical college and hospital,saveetha nagar,Thandalam,chennai 602105,Tamil nadu,India |
|
|
Primary Sponsor
|
| Name |
Nil |
| Address |
Nil |
| Type of Sponsor |
Other [Nil] |
|
|
Details of Secondary Sponsor
|
|
|
Countries of Recruitment
|
India |
|
Sites of Study
|
| No of Sites = 1 |
| Name of Principal
Investigator |
Name of Site |
Site Address |
Phone/Fax/Email |
| DrKarthikKR |
Saveetha medical college |
Department of community medicine,
Saveetha medical college,
saveeetha nagar,
thandalam,chennai 602105
Thiruvallur
TAMIL NADU |
9940544662
k.r.karthik27@gmail.com |
|
|
Details of Ethics Committee
|
| No of Ethics Committees= 1 |
| Name of Committee |
Approval Status |
| saveetha medical college and hospital institutional ethics committee |
Approved |
|
|
Regulatory Clearance Status from DCGI
|
|
|
Health Condition / Problems Studied
|
| Health Type |
Condition |
| Patients |
(1) ICD-10 Condition: E119||Type 2 diabetes mellitus without complications, |
|
|
Intervention / Comparator Agent
|
| Type |
Name |
Details |
| Intervention |
Deep learning based intelligent recommendation model for type 2 Diabetes mellitus complication prevention with notification caller bot.A Randomized controlled trail. |
The notification caller bot will be integrated into the system to deliver call reminders, reminders messages .It helps to know whether patient taken medication regularly or not. In case of emergency like giddiness, if patient didn’t attend the call, it gives notification call to patient attenders.
Total duration -4weeks |
| Comparator Agent |
Health App for T2DM patient |
Remind medication timing for patients through notification
Total duration-4weeks |
|
|
Inclusion Criteria
|
| Age From |
18.00 Year(s) |
| Age To |
80.00 Year(s) |
| Gender |
Both |
| Details |
1)Adult individuals with Type 2 Diabetes mellitus over 18years of age.
2)Old and newly diagnosed Type 2 diabetes patients. |
|
| ExclusionCriteria |
| Details |
1)Patients not willing to participate in the study
2)Patients not aware of using mobile app. |
|
|
Method of Generating Random Sequence
|
Computer generated randomization |
|
Method of Concealment
|
Sequentially numbered, sealed, opaque envelopes |
|
Blinding/Masking
|
Not Applicable |
|
Primary Outcome
|
| Outcome |
TimePoints |
| It will helps to prevent T2DM complication by notification |
4 weeks |
|
|
Secondary Outcome
|
| Outcome |
TimePoints |
| It reminds patients to take medication through reminder message and call reminder. |
3 months |
|
|
Target Sample Size
|
Total Sample Size="120"
Sample Size from India="120"
Final Enrollment numbers achieved (Total)= "Applicable only for Completed/Terminated trials"
Final Enrollment numbers achieved (India)="Applicable only for Completed/Terminated trials" |
|
Phase of Trial
|
N/A |
|
Date of First Enrollment (India)
|
07/12/2024 |
| Date of Study Completion (India) |
Applicable only for Completed/Terminated trials |
| Date of First Enrollment (Global) |
07/12/2024 |
| Date of Study Completion (Global) |
Applicable only for Completed/Terminated trials |
|
Estimated Duration of Trial
|
Years="0"
Months="3"
Days="0" |
|
Recruitment Status of Trial (Global)
|
Not Yet Recruiting |
| Recruitment Status of Trial (India) |
Not Yet Recruiting |
|
Publication Details
|
N/A |
|
Individual Participant Data (IPD) Sharing Statement
|
Will individual participant data (IPD) be shared publicly (including data dictionaries)?
Response - NO
|
|
Brief Summary
|
The prevention of Type 2 Diabetes mellitus is a critical public health concern globally. This study proposes an artificial intelligence (AI)-based recommendation model for Diabetes prevention, utilizing deep learning techniques, and integrating a notification audio caller bot. The goal is to develop a personalized and effective intervention to combat diabetes complication through timely reminders, education, and behavioral recommendations. A randomized controlled trial (RCT) will be conducted to assess the effectiveness of the AI recommendation model with the notification caller bot. The target population was comprised of patients with type 2 Diabetes mellitus. The notification caller bot will be integrated into the system to deliver call reminders, reminders messages and educational content to individuals. It helps to know whether patient taken medication regularly or not. In case of emergency like giddiness ,if patient didn’t attend the call, it gives notification call to patient attenders.
|