NCT04757233

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.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
20

participants targeted

Target at below P25 for not_applicable type-2-diabetes-mellitus

Timeline
Completed

Started Feb 2018

Shorter than P25 for not_applicable type-2-diabetes-mellitus

Geographic Reach
1 country

2 active sites

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

February 1, 2018

Completed
3 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

April 30, 2018

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

April 30, 2018

Completed
7 months until next milestone

First Submitted

Initial submission to the registry

December 5, 2018

Completed
2.2 years until next milestone

First Posted

Study publicly available on registry

February 17, 2021

Completed
Last Updated

December 12, 2024

Status Verified

December 1, 2024

Enrollment Period

3 months

First QC Date

December 5, 2018

Last Update Submit

December 9, 2024

Conditions

Type 2 Diabetes Mellitus

Keywords

GlucoTypeInformaticsDiabetes

Outcome Measures

Primary Outcomes (1)

  • Change in score on Summary of Diabetes Self-Care Activities Questionnaire (SDSCA)

    Change in score on Summary of Diabetes Self-Care Activities Questionnaire (SDSCA) - 12-item with 5 sub-scales (diet, exercise, home blood glucose testing, foot care, smoking status). The respondent is asked how many days in the past week he/she performed the behavior (ranges from 0 to 7); higher scores indicates higher performance.

    From Baseline to 4 weeks

Study Arms (1)

Single arm

OTHER

Intervention: GlucoType Single arm study; all participants assigned to use the intervention

Behavioral: GlucoType

Interventions

GlucoTypeBEHAVIORAL

GlucoType is an mobile Health intervention for facilitating self-management in T2DM built for iPhone and Android smartphones. GlucoType includes a custom-built interface for low-burden capture of diet and blood glucose (BG) levels and relies on a commercial activity tracker, FitBit, for capture of sleep and physical activity. It then applies computational phenotyping techniques to identify patterns of associations between daily activities and changes in BG levels. GlucoType uses an expert system developed by our research team to translate identified phenotypes into automatically-generated personalized behavioral goals for improving glycemic control formulated in natural language.

Single arm

Eligibility Criteria

Age18 Years - 65 Years
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • Age 18-65 years
  • A diagnosis of Type 2 Diabetes.
  • A participant of the Washington Heights/Inwood Informatics Infrastructure for Comparative Effectiveness Research (WICER), a patient of the AIM clinic, or a patient of a participating Federally Qualified Health Center (FQHC) health center for at least 6 months
  • Has participated in at least one diabetes education session at the participating site in the last 6 months
  • Proficient in either English or Spanish
  • Must own a basic cell phone

You may not qualify if:

  • Pregnancy
  • Presence of serious illness (e.g. cancer diagnosis with active treatment, advanced stage heart failure, multiple sclerosis)
  • Presence of cognitive impairment
  • Plans for leaving their healthcare provider in the next 12 months
  • Does not have a computer and/or Internet access

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (2)

Clinical Directors Network

New York, New York, 10018, United States

Location

Columbia University Medical Center

New York, New York, 10032, United States

Location

Related Publications (6)

  • Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalova L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. Personalized Nutrition by Prediction of Glycemic Responses. Cell. 2015 Nov 19;163(5):1079-1094. doi: 10.1016/j.cell.2015.11.001.

    PMID: 26590418BACKGROUND

  • Haas L, Maryniuk M, Beck J, Cox CE, Duker P, Edwards L, Fisher EB, Hanson L, Kent D, Kolb L, McLaughlin S, Orzeck E, Piette JD, Rhinehart AS, Rothman R, Sklaroff S, Tomky D, Youssef G; 2012 Standards Revision Task Force. National standards for diabetes self-management education and support. Diabetes Care. 2013 Jan;36 Suppl 1(Suppl 1):S100-8. doi: 10.2337/dc13-S100. No abstract available.

    PMID: 23264420BACKGROUND

  • Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015 Feb 26;372(9):793-5. doi: 10.1056/NEJMp1500523. Epub 2015 Jan 30.

    PMID: 25635347BACKGROUND

  • Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning [Internet]. New York, NY: Springer New York; 2009 [cited 2016 Jun 4]. (Springer Series in Statistics)

    BACKGROUND

  • Liao KP, Cai T, Savova GK, Murphy SN, Karlson EW, Ananthakrishnan AN, Gainer VS, Shaw SY, Xia Z, Szolovits P, Churchill S, Kohane I. Development of phenotype algorithms using electronic medical records and incorporating natural language processing. BMJ. 2015 Apr 24;350:h1885. doi: 10.1136/bmj.h1885.

    PMID: 25911572BACKGROUND

  • Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc. 2013 Jan 1;20(1):117-21. doi: 10.1136/amiajnl-2012-001145. Epub 2012 Sep 6.

    PMID: 22955496BACKGROUND

MeSH Terms

Conditions

Diabetes Mellitus, Type 2Diabetes Mellitus

Condition Hierarchy (Ancestors)

Glucose Metabolism DisordersMetabolic DiseasesNutritional and Metabolic DiseasesEndocrine System Diseases

Study Officials

  • Olena Mamykina, Ph.D.

    Columbia University

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NA
Masking
NONE
Purpose
OTHER
Intervention Model
SINGLE GROUP
Model Details: Pre-post pilot study
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

December 5, 2018

First Posted

February 17, 2021

Study Start

February 1, 2018

Primary Completion

April 30, 2018

Study Completion

April 30, 2018

Last Updated

December 12, 2024

Record last verified: 2024-12

Locations

US(2)