Personalizing Self-management in Diabetes - Pilot Study
NCT ID: NCT04757233
Last Updated: 2024-12-12
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
20 participants
INTERVENTIONAL
2018-02-01
2018-04-30
Brief Summary
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Detailed Description
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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.
Conditions
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Keywords
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Study Design
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NA
SINGLE_GROUP
OTHER
NONE
Study Groups
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Single arm
Intervention: GlucoType Single arm study; all participants assigned to use the intervention
GlucoType
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.
Interventions
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GlucoType
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.
Eligibility Criteria
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Inclusion Criteria
* 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
Exclusion Criteria
* 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
18 Years
65 Years
ALL
No
Sponsors
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National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
NIH
Columbia University
OTHER
Responsible Party
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Principal Investigators
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Olena Mamykina, Ph.D.
Role: PRINCIPAL_INVESTIGATOR
Columbia University
Locations
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Clinical Directors Network
New York, New York, United States
Columbia University Medical Center
New York, New York, United States
Countries
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References
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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.
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.
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.
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)
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.
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.
Other Identifiers
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AAAM0057(a)
Identifier Type: -
Identifier Source: org_study_id