Enhancing Diabetes Care: Exposome &Amp; Sensors

NCT ID: NCT06989008

Last Updated: 2025-05-25

Study Results

Results pending

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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Recruitment Status

RECRUITING

Total Enrollment

20 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-03-25

Study Completion Date

2026-01-31

Brief Summary

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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.

Detailed Description

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The study procedures will commence with an initial screening to confirm participants' eligibility based on the inclusion criteria, followed by the signing of Informed Consent forms. Baseline data including medical records and quality of life questionnaires will be collected. A continuous glucose monitoring (CGM - Dexcom 7) will be inserted; a sport wristband (Fitbit Sense 2) will be provided; and environmental sensor for home data collection, will be distributed and set up in the home setting by the participant. The study will require two CGMs (10 days for each CGM). The 1st CGM will be inserted during the initial visit to the clinic. After 10 days, participants will return to the clinic, and the 2nd CGM will be inserted. The investigators will also send text reminders two days before the scheduled 2nd CGM insertion. Communication with participants will be maintained via text messages, and for those who request it, virtual meetings can be arranged (via HIPAA-protected Zoom). The subjects will be provided with a mailing envelope with postage to return the 2nd CGM, Fitbit Sense 2 and environmental sensors. If the subject is returning to clinic within the next week, they can instead return the devices during their visit, and a member of the study team will meet them in the clinic.

Throughout the study, there will be integration of real-time data from various sources, including electronic health records, wearables, and environmental sensor. The environmental sensor is a climate sensor called Airthings. The Airthings View Plus is an advanced indoor air quality monitor that tracks various environmental parameters to ensure healthy indoor air conditions. It features sensors for radon, particulate matter (PM2.5), carbon dioxide (CO2), volatile organic compounds (VOCs), humidity, temperature, and air pressure. This monitor is connected to Wi-Fi, allowing real-time access to air quality data via a smartphone app. The device is designed for ease of use with an eInk display for clear visibility of air quality readings and simple setup instructions. The Airthings View Plus is battery-operated with an option for USB power, providing flexibility in how and where the device can be used within a home.

Conditions

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Type 2 Diabetes

Study Design

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Observational Model Type

COHORT

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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Type 2 Diabetes

The study will include adults with Type 2 Diabetes living in an urban setting characterized by poor health outcomes.

Observational Study: Relationships Among Glucose, Physical Activity and Environmental Influences

Intervention Type OTHER

This is a single group observational study with no interventions.

Interventions

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Observational Study: Relationships Among Glucose, Physical Activity and Environmental Influences

This is a single group observational study with no interventions.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* 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)

Exclusion Criteria

* 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
Minimum Eligible Age

18 Years

Maximum Eligible Age

65 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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University of Illinois at Chicago

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Andy Boyd, MD

Role: PRINCIPAL_INVESTIGATOR

University of Illinois Chicago

Locations

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University of Illinois - Chicago

Chicago, Illinois, United States

Site Status RECRUITING

University of Illinois College of Nursing

Chicago, Illinois, United States

Site Status RECRUITING

Countries

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United States

Central Contacts

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Laurie Quinn, PhD

Role: CONTACT

312 771 6497

Sulaimon Balogun, PhD Student

Role: CONTACT

773-971-0503

Facility Contacts

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Laurie Quinn, PhD RN

Role: primary

312-996-7906

Laurie Quinn Clinical Professor, PhD

Role: primary

312-771-6497

Andy Boyd, MD

Role: backup

312-996-8339

References

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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.

Reference Type BACKGROUND
PMID: 33778771 (View on PubMed)

Wu 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.

Reference Type BACKGROUND
PMID: 25249787 (View on PubMed)

Vrijheid 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.

Reference Type BACKGROUND
PMID: 24610234 (View on PubMed)

Sevil 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.

Reference Type BACKGROUND
PMID: 33100923 (View on PubMed)

Li 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.

Reference Type BACKGROUND
PMID: 28081144 (View on PubMed)

Sevil 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.

Reference Type BACKGROUND
PMID: 33360529 (View on PubMed)

Other Identifiers

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2024-0372

Identifier Type: -

Identifier Source: org_study_id

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