Ignite Creation Date:
2024-05-06 @ 3:46 PM
Last Modification Date:
2024-10-26 @ 1:57 PM
Study NCT ID:
NCT04757233
Status:
COMPLETED
Last Update Posted:
2023-03-29
First Post:
2018-12-05
Brief Title:
Personalizing Self-management in Diabetes - Pilot Study
Sponsor:
Columbia University
Organization:
Columbia University
Study Overview
Official Title:
Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data
Status:
COMPLETED
Status Verified Date:
2023-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The goal of this study is to conduct a pilot feasibility study a novel informatics intervention GlucoType also called Platano for Latino users that incorporates computational analysis of self-monitoring data to help individuals with type 2 diabetes personalize diabetes self-management strategies This study will include 20 individuals with type 2 diabetes mellitus T2DM recruited from economically disadvantaged and medically underserved communities to test Platano for 4 weeks to assess its acceptability and feasibility The main outcome measures include problem-solving abilities in diabetes Diabetes Problem-Solving Inventory DPSA and self-reported diabetes self-care Summary of Diabetes Self-Care Activities Questionnaire SDSCA In addition this study will include a controlled laboratory experiment to assess whether participants can understand and follow personalized nutritional goals generated by Platano
Detailed Description:
Growing evidence highlights significant differences in individuals physiology and glycemic function and their cultural social and economical circumstances that impact diabetes self-management These discoveries paved the way for precision medicine-an approach to personalizing medical treatment to an individuals genetic makeup clinical history and lifestyle Computational learning methods have been successfully used for identifying clinical phenotypes-observable manifestations of diseases Studies showed the benefits of tailoring not only medical treatment but also behavioral interventions however tailoring typically relies on expert identification of tailoring variables and decision rules and on standard surveys Data collected with self-monitoring can more accurately reflect an individuals behaviors and glycemic patterns thus highlighting their behavioral phenotypes yet such data are rarely utilized in tailoring
The ongoing focus of this research is on facilitating problem-solving in diabetes self-management Well-developed problem-solving skills are essential to diabetes management result in better diabetes self-care behaviors lead to improvements in clinical outcomes and can be fostered with face-to-face interventions Previous research suggested problem identification and generation of alternatives as critical steps in problem-solving in diabetes In previous work the investigators developed an informatics intervention that relied on expert-generated knowledge for assisting individuals on these steps of problem-solving In this pilot feasibility study the investigators study an alternative solution that relies on computational pattern analysis of data collected with self-monitoring technologies to tailor the problem-solving assistance to individuals unique behavioral phenotypes The intervention GlucoType uses computational learning methods to identify systematic patterns in individuals diet physical activity and sleep captured with custom-built and commercial self-monitoring technologies and correlates these patterns with fluctuations in individuals blood glucose levels GlucoType then uses this information to 1 identify behavioral patterns associated with high glycemic excursion 2 formulate personalized goals to modify these behaviors 3 provide in-the-moment decision support to help individuals be more consistent in meeting their goals
The ongoing focus of this research is on facilitating problem-solving in diabetes self-management Well-developed problem-solving skills are essential to diabetes management result in better diabetes self-care behaviors lead to improvements in clinical outcomes and can be fostered with face-to-face interventions Previous research suggested problem identification and generation of alternatives as critical steps in problem-solving in diabetes In previous work the investigators developed an informatics intervention that relied on expert-generated knowledge for assisting individuals on these steps of problem-solving In this pilot feasibility study the investigators study an alternative solution that relies on computational pattern analysis of data collected with self-monitoring technologies to tailor the problem-solving assistance to individuals unique behavioral phenotypes The intervention GlucoType uses computational learning methods to identify systematic patterns in individuals diet physical activity and sleep captured with custom-built and commercial self-monitoring technologies and correlates these patterns with fluctuations in individuals blood glucose levels GlucoType then uses this information to 1 identify behavioral patterns associated with high glycemic excursion 2 formulate personalized goals to modify these behaviors 3 provide in-the-moment decision support to help individuals be more consistent in meeting their goals
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| R56DK113189 | NIH | None | httpsreporternihgovquickSearchR56DK113189 |