Enhancing Diabetes Care: Exposome &Amp; Sensors
Leveraging the Exposome and Patient Sensor Data to Enhance Personalized Diabetes Care Across Diverse Communities
1 other identifier
observational
20
1 country
2
Brief Summary
The study aims to integrate various data types, such as electronic health records, wearable device data, and environmental data, to create a comprehensive, personalized diabetes care model. The study will focus on people with type 2 diabetes living in specified vulnerable zip codes.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at below P25 for all trials
Started Mar 2025
Shorter than P25 for all trials
2 active sites
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
March 25, 2025
CompletedFirst Submitted
Initial submission to the registry
April 28, 2025
CompletedFirst Posted
Study publicly available on registry
May 25, 2025
CompletedPrimary Completion
Last participant's last visit for primary outcome
January 1, 2026
CompletedStudy Completion
Last participant's last visit for all outcomes
January 31, 2026
CompletedMay 25, 2025
May 1, 2025
9 months
April 28, 2025
May 16, 2025
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
The primary outcome is to collect and analyze continuous interstitial glucose measurements in 20 individuals with type 2 diabetes over a period of 20 days using a DEXCOM G7 continuous glucose monitor (CGM).
The primary outcomes will include the following CGM metrics: mean glucose "Glucose Management Indicator" which is an estimate of A1C (%); Coefficient of Variation which is an estimate of glycemic variability; Very High Time Above Range which is the percent of time above range including the % of readings and time \> 250 mg/dl; High Time Above Range which is the percent of time above range including the % of readings and time 181-250 mg/dl; Time in Range which is the % of readings and time 70-180 mg/dl; Low Time Below Range which is the percent of time below range including the % of readings and time 54 - 69 mg/dl; and Very Low Time Below Range which is the percent of time below range including the % of readings and time \< 54 mg/dl.
20 days
Secondary Outcomes (3)
The subject's perception of their "Quality of Life" will be measured at the start of the study.
1 time
The subject's physical activity and sleep will be measured continuously throughout the study.
20 days
The subject's air quality will be measured continuously throughout the study.
20 days
Study Arms (1)
Type 2 Diabetes
The study will include adults with Type 2 Diabetes living in an urban setting characterized by poor health outcomes.
Interventions
This is a single group observational study with no interventions.
Eligibility Criteria
The study aims to enroll participants with type 2 diabetes who reside in an urban area that is associated with poor health outcomes as characterized by vulnerable zip codes.
You may qualify if:
- People with diabetes in the 18-65 years of age range
- Diagnosed with type 2 diabetes
- Resides in the specified high social vulnerability zip codes - 60619, 60620, 60621, 60636, 60644, 60624, 60609, 60612, 60617, 60623, 60628, 60629, 60639, 60645, 60649, 60651, 60652, 60653
- Speak and understand the English language
- Willing to wear various devices (CGM and sports wristband)
You may not qualify if:
- Subjects will be excluded from the study for the following reasons:
- Any concern of not understanding informed consent
- Unable to understand or unwilling to follow research protocol
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (2)
University of Illinois - Chicago
Chicago, Illinois, 60612, United States
University of Illinois College of Nursing
Chicago, Illinois, 60612, United States
Related Publications (6)
Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput Sci. 2021;2(3):160. doi: 10.1007/s42979-021-00592-x. Epub 2021 Mar 22.
PMID: 33778771BACKGROUNDWu Y, Ding Y, Tanaka Y, Zhang W. Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. Int J Med Sci. 2014 Sep 6;11(11):1185-200. doi: 10.7150/ijms.10001. eCollection 2014.
PMID: 25249787BACKGROUNDVrijheid M, Slama R, Robinson O, Chatzi L, Coen M, van den Hazel P, Thomsen C, Wright J, Athersuch TJ, Avellana N, Basagana X, Brochot C, Bucchini L, Bustamante M, Carracedo A, Casas M, Estivill X, Fairley L, van Gent D, Gonzalez JR, Granum B, Grazuleviciene R, Gutzkow KB, Julvez J, Keun HC, Kogevinas M, McEachan RR, Meltzer HM, Sabido E, Schwarze PE, Siroux V, Sunyer J, Want EJ, Zeman F, Nieuwenhuijsen MJ. The human early-life exposome (HELIX): project rationale and design. Environ Health Perspect. 2014 Jun;122(6):535-44. doi: 10.1289/ehp.1307204. Epub 2014 Mar 7.
PMID: 24610234BACKGROUNDSevil M, Rashid M, Maloney Z, Hajizadeh I, Samadi S, Askari MR, Hobbs N, Brandt R, Park M, Quinn L, Cinar A. Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems. IEEE Sens J. 2020 Nov;20(21):12859-12870. doi: 10.1109/jsen.2020.3000772. Epub 2020 Jun 8.
PMID: 33100923BACKGROUNDLi X, Dunn J, Salins D, Zhou G, Zhou W, Schussler-Fiorenza Rose SM, Perelman D, Colbert E, Runge R, Rego S, Sonecha R, Datta S, McLaughlin T, Snyder MP. Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information. PLoS Biol. 2017 Jan 12;15(1):e2001402. doi: 10.1371/journal.pbio.2001402. eCollection 2017 Jan.
PMID: 28081144BACKGROUNDSevil M, Rashid M, Hajizadeh I, Askari MR, Hobbs N, Brandt R, Park M, Quinn L, Cinar A. Discrimination of simultaneous psychological and physical stressors using wristband biosignals. Comput Methods Programs Biomed. 2021 Feb;199:105898. doi: 10.1016/j.cmpb.2020.105898. Epub 2020 Dec 17.
PMID: 33360529BACKGROUND
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Andy Boyd, MD
University of Illinois Chicago
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- CROSS SECTIONAL
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
April 28, 2025
First Posted
May 25, 2025
Study Start
March 25, 2025
Primary Completion
January 1, 2026
Study Completion
January 31, 2026
Last Updated
May 25, 2025
Record last verified: 2025-05
Data Sharing
- IPD Sharing
- Will share
- Shared Documents
- STUDY PROTOCOL, CSR
- Time Frame
- The data will will be available 6 months after publication.
- Access Criteria
- The study protocol, clinical study report, data elements, will be available through contacting Dr. Andy Boyd at boyda@uic.edu
Data obtained through this study may be provided to qualified researchers with academic interest in type 2 diabetes. Data or samples shared will be coded, with no PHI included. Approval of the request and execution of all applicable agreements (i.e. a material transfer agreement) are prerequisites to the sharing of data with the requesting party.