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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Type 2 Diabetes | The study will include adults with Type 2 Diabetes living in an urban setting characterized by poor health outcomes. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Observational Study: Relationships Among Glucose, Physical Activity and Environmental Influences | Other | This is a single group observational study with no interventions. |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| The subject's perception of their "Quality of Life" will be measured at the start of the study. | Quality of Life will be measured by scores on the Diabetes Quality of Life (DQOL) questionnaire. | 1 time |
| The subject's physical activity and sleep will be measured continuously throughout the study. |
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Inclusion Criteria
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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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Laurie Quinn, PhD | Contact | 312 771 6497 | lquinn1@uic.edu | |
| Sulaimon Balogun, PhD Student | Contact | 773-971-0503 | sbalog6@uic.edu |
| Name | Affiliation | Role |
|---|---|---|
| Andy Boyd, MD | University of Illinois at Chicago | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Illinois - Chicago | Recruiting | Chicago | Illinois | 60612 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 33778771 | Background | 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. | |
| 25249787 | Background | 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. |
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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.
The data will will be available 6 months after publication.
The study protocol, clinical study report, data elements, will be available through contacting Dr. Andy Boyd at boyda@uic.edu
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| ID | Term |
|---|---|
| D003924 | Diabetes Mellitus, Type 2 |
| D009043 | Motor Activity |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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| ID | Term |
|---|---|
| D015444 | Exercise |
| ID | Term |
|---|---|
| D009043 | Motor Activity |
| D009068 | Movement |
| D009142 | Musculoskeletal Physiological Phenomena |
| D055687 | Musculoskeletal and Neural Physiological Phenomena |
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The subjects activity and sleep will be measured continuously throughout the study using a FitbitSense 2 Smartwatch. Physical activity will be quantified as time spent in sedentary, low, moderate and vigorous activity; sleep will be quantified by time spent awake, light sleep, deep sleep, and REM sleep. |
| 20 days |
| The subject's air quality will be measured continuously throughout the study. | The air quality will be measured by an environmental sensor called an Airthing's View Plus that will monitor radon, particulate matter (PM2.5), carbon dioxide (CO2), volatile organic compounds (VOCs), humidity, temperature, and air pressure. | 20 days |
| University of Illinois College of Nursing | Recruiting | Chicago | Illinois | 60612 | United States |
|
| 24610234 | Background | 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. |
| 33100923 | Background | 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. |
| 28081144 | Background | 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. |
| 33360529 | Background | 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. |
| D004700 | Endocrine System Diseases |
| D001519 | Behavior |