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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05211869
Other study ID # 2021-13714
Secondary ID R01DK132302
Status Recruiting
Phase N/A
First received
Last updated
Start date November 7, 2022
Est. completion date March 2025

Study information

Verified date February 2024
Source Albert Einstein College of Medicine
Contact Shivani Agarwal, MD, MPH
Phone 844-556-6683
Email shivani.agarwal@einsteinmed.org
Is FDA regulated No
Health authority
Study type Interventional

Clinical Trial Summary

The objective of this study is to test the early effects and implementation of an enhanced community health worker (CHW) model (T1D-CATCH) that encourages and supports diabetes technology use in young adults from underrepresented minority groups (YA-URMs) with type 1 diabetes (T1D). The investigators will conduct a 9-month randomized controlled trial in which YA-URMs will be randomized to T1D-CATCH or usual care. The investigators will recruit from adult and pediatric endocrinology and primary care practices in a large safety-net health system in the Bronx, New York. Our specific aims are to 1) evaluate T1D-CATCH effects on technology initiation and continued use over 6 months and 2) evaluate T1D-CATCH implementation using Proctor's Taxonomy of Implementation Outcomes: feasibility, adoption, fidelity, and cost.


Description:

The study will involve a 9-month randomized control trial of usual care versus T1D-CATCH, an intervention that enhances core community health worker (CHW) service roles to support increased use of T1D technology in young adults (underrepresented minorities)(YA_URM's). Participants will be recruited from primary and specialty care practices at Montefiore Medical Center in the Bronx, NY, which is a large safety-net hospital system in one of the poorest counties in the U.S. Two young adult-aged CHWs from the Montefiore CHW program will be trained extensively per our Supporting Emerging Adults with Diabetes (SEAD) program manuals. For YA-URMs, CHWs will conduct hands-on diabetes technology education, goal-setting, peer support, and social service linkage. CHWs will also help shift insurance approval tasks away from busy providers and better align patient-provider priorities through close communication between the YA-URM and provider. Group sessions will be optional and will follow the YA-centric education curriculum developed in Dr. Agarwal's Supporting Emerging Adults with Diabetes (SEAD) program.


Recruitment information / eligibility

Status Recruiting
Enrollment 130
Est. completion date March 2025
Est. primary completion date January 2025
Accepts healthy volunteers No
Gender All
Age group 18 Years to 35 Years
Eligibility Inclusion Criteria: - T1D duration =6 months - 18-35 years old - Self-identified URM status: non-Hispanic Black or Hispanic - English- or Spanish-speaking - Not currently on a connected diabetes technology system (includes never offered, prescribed but not started within 3 months of receiving the device, discontinued, or previously refused technology) Exclusion Criteria: - Developmental or sensory disability interfering with study participation - Current pregnancy - Participation in another behavioral or diabetes technology intervention study in the past 6 months.

Study Design


Intervention

Behavioral:
T1D-CATCH
As defined by the CDC, a CHW is "a frontline public health worker who is a trusted member of a community or who has a thorough understanding of the community being served, and leverages this unique position to link health systems, social services, and communities". CHWs engender trust with patients by having direct community and lived experience, offering specific support and empathy that may be difficult for other diabetes care professionals to provide. In addition, CHWs have firsthand understanding of cultural barriers to traditional western healthcare and can promote patient-centered culturally-relevant care. They enhance team-based care by helping providers with extra outreach, social needs management, time-consuming tasks, and aligning patient-provider priorities. CHWs in this project will provide social needs assessment and management, introduction to diabetes technologies, and support for onboarding to technology.

Locations

Country Name City State
United States Albert Einstein College of Medicine Bronx New York

Sponsors (2)

Lead Sponsor Collaborator
Albert Einstein College of Medicine National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

Country where clinical trial is conducted

United States, 

References & Publications (24)

Addala A, Auzanneau M, Miller K, Maier W, Foster N, Kapellen T, Walker A, Rosenbauer J, Maahs DM, Holl RW. A Decade of Disparities in Diabetes Technology Use and HbA1c in Pediatric Type 1 Diabetes: A Transatlantic Comparison. Diabetes Care. 2021 Jan;44(1):133-140. doi: 10.2337/dc20-0257. Epub 2020 Sep 16. — View Citation

Addala A, Hanes S, Naranjo D, Maahs DM, Hood KK. Provider Implicit Bias Impacts Pediatric Type 1 Diabetes Technology Recommendations in the United States: Findings from The Gatekeeper Study. J Diabetes Sci Technol. 2021 Sep;15(5):1027-1033. doi: 10.1177/19322968211006476. Epub 2021 Apr 15. — View Citation

Agarwal S, Crespo-Ramos G, Long JA, Miller VA. "I Didn't Really Have a Choice": Qualitative Analysis of Racial-Ethnic Disparities in Diabetes Technology Use Among Young Adults with Type 1 Diabetes. Diabetes Technol Ther. 2021 Sep;23(9):616-622. doi: 10.1089/dia.2021.0075. — View Citation

Agarwal S, Schechter C, Gonzalez J, Long JA. Racial-Ethnic Disparities in Diabetes Technology use Among Young Adults with Type 1 Diabetes. Diabetes Technol Ther. 2021 Apr;23(4):306-313. doi: 10.1089/dia.2020.0338. Epub 2020 Dec 1. — View Citation

Ballard M, Westgate C, Alban R, Choudhury N, Adamjee R, Schwarz R, Bishop J, McLaughlin M, Flood D, Finnegan K, Rogers A, Olsen H, Johnson A, Palazuelos D, Schechter J. Compensation models for community health workers: Comparison of legal frameworks across five countries. J Glob Health. 2021 Feb 15;11:04010. doi: 10.7189/jogh.11.04010. — View Citation

Berkowitz SA, Kalkhoran S, Edwards ST, Essien UR, Baggett TP. Unstable Housing and Diabetes-Related Emergency Department Visits and Hospitalization: A Nationally Representative Study of Safety-Net Clinic Patients. Diabetes Care. 2018 May;41(5):933-939. doi: 10.2337/dc17-1812. Epub 2018 Jan 4. — View Citation

Berkowitz SA, Meigs JB, DeWalt D, Seligman HK, Barnard LS, Bright OJ, Schow M, Atlas SJ, Wexler DJ. Material need insecurities, control of diabetes mellitus, and use of health care resources: results of the Measuring Economic Insecurity in Diabetes study. JAMA Intern Med. 2015 Feb;175(2):257-65. doi: 10.1001/jamainternmed.2014.6888. — View Citation

C3 Project. The Community Health Worker Core Consensus Project (C3): Roles and Competencies. C3 Project Findings. Published 2018. Accessed April 29, 2021. https://www.c3project.org/roles-competencies

Findley SE, Matos S, Hicks AL, Campbell A, Moore A, Diaz D. Building a consensus on community health workers' scope of practice: lessons from New York. Am J Public Health. 2012 Oct;102(10):1981-7. doi: 10.2105/AJPH.2011.300566. Epub 2012 Aug 16. — View Citation

Foster NC, Beck RW, Miller KM, Clements MA, Rickels MR, DiMeglio LA, Maahs DM, Tamborlane WV, Bergenstal R, Smith E, Olson BA, Garg SK. State of Type 1 Diabetes Management and Outcomes from the T1D Exchange in 2016-2018. Diabetes Technol Ther. 2019 Feb;21(2):66-72. doi: 10.1089/dia.2018.0384. Epub 2019 Jan 18. Erratum In: Diabetes Technol Ther. 2019 Apr;21(4):230. — View Citation

Franklin CM, Bernhardt JM, Lopez RP, Long-Middleton ER, Davis S. Interprofessional Teamwork and Collaboration Between Community Health Workers and Healthcare Teams: An Integrative Review. Health Serv Res Manag Epidemiol. 2015 Mar 16;2:2333392815573312. doi: 10.1177/2333392815573312. eCollection 2015 Jan-Dec. — View Citation

Hagiwara N, Elston Lafata J, Mezuk B, Vrana SR, Fetters MD. Detecting implicit racial bias in provider communication behaviors to reduce disparities in healthcare: Challenges, solutions, and future directions for provider communication training. Patient Educ Couns. 2019 Sep;102(9):1738-1743. doi: 10.1016/j.pec.2019.04.023. Epub 2019 Apr 19. — View Citation

Hall WJ, Chapman MV, Lee KM, Merino YM, Thomas TW, Payne BK, Eng E, Day SH, Coyne-Beasley T. Implicit Racial/Ethnic Bias Among Health Care Professionals and Its Influence on Health Care Outcomes: A Systematic Review. Am J Public Health. 2015 Dec;105(12):e60-76. doi: 10.2105/AJPH.2015.302903. Epub 2015 Oct 15. — View Citation

Lai CW, Lipman TH, Willi SM, Hawkes CP. Racial and Ethnic Disparities in Rates of Continuous IRB NUMBER: 2021-13714 IRB APPROVAL DATE: 03/23/2022 Glucose Monitor Initiation and Continued Use in Children With Type 1 Diabetes. 2020;(Online ahead of print). doi:10.2337/dc20-1663

Livingstone SJ, Levin D, Looker HC, Lindsay RS, Wild SH, Joss N, Leese G, Leslie P, McCrimmon RJ, Metcalfe W, McKnight JA, Morris AD, Pearson DW, Petrie JR, Philip S, Sattar NA, Traynor JP, Colhoun HM; Scottish Diabetes Research Network epidemiology group; Scottish Renal Registry. Estimated life expectancy in a Scottish cohort with type 1 diabetes, 2008-2010. JAMA. 2015 Jan 6;313(1):37-44. doi: 10.1001/jama.2014.16425. — View Citation

McKergow E, Parkin L, Barson DJ, Sharples KJ, Wheeler BJ. Demographic and regional disparities in insulin pump utilization in a setting of universal funding: a New Zealand nationwide study. Acta Diabetol. 2017 Jan;54(1):63-71. doi: 10.1007/s00592-016-0912-7. Epub 2016 Sep 20. — View Citation

Palmas W, March D, Darakjy S, Findley SE, Teresi J, Carrasquillo O, Luchsinger JA. Community Health Worker Interventions to Improve Glycemic Control in People with Diabetes: A Systematic Review and Meta-Analysis. J Gen Intern Med. 2015 Jul;30(7):1004-12. doi: 10.1007/s11606-015-3247-0. Epub 2015 Mar 4. — View Citation

Saydah S, Imperatore G, Cheng Y, Geiss LS, Albright A. Disparities in Diabetes Deaths Among Children and Adolescents - United States, 2000-2014. MMWR Morb Mortal Wkly Rep. 2017 May 19;66(19):502-505. doi: 10.15585/mmwr.mm6619a4. — View Citation

Schaaf M, Warthin C, Freedman L, Topp SM. The community health worker as service extender, cultural broker and social change agent: a critical interpretive synthesis of roles, intent and accountability. BMJ Glob Health. 2020 Jun;5(6):e002296. doi: 10.1136/bmjgh-2020-002296. — View Citation

Spencer MS, Kieffer EC, Sinco B, Piatt G, Palmisano G, Hawkins J, Lebron A, Espitia N, Tang T, Funnell M, Heisler M. Outcomes at 18 Months From a Community Health Worker and Peer Leader Diabetes Self-Management Program for Latino Adults. Diabetes Care. 2018 Jul;41(7):1414-1422. doi: 10.2337/dc17-0978. Epub 2018 Apr 27. — View Citation

Walker AF, Hood KK, Gurka MJ, Filipp SL, Anez-Zabala C, Cuttriss N, Haller MJ, Roque X, Naranjo D, Aulisio G, Addala A, Konopack J, Westen S, Yabut K, Mercado E, Look S, Fitzgerald B, Maizel J, Maahs DM. Barriers to Technology Use and Endocrinology Care for Underserved Communities With Type 1 Diabetes. Diabetes Care. 2021 Jul;44(7):1480-1490. doi: 10.2337/dc20-2753. Epub 2021 May 17. — View Citation

Walker RJ, Gebregziabher M, Martin-Harris B, Egede LE. Independent effects of socioeconomic and psychological social determinants of health on self-care and outcomes in Type 2 diabetes. Gen Hosp Psychiatry. 2014 Nov-Dec;36(6):662-8. doi: 10.1016/j.genhosppsych.2014.06.011. Epub 2014 Jul 9. — View Citation

Walker RJ, Gebregziabher M, Martin-Harris B, Egede LE. Quantifying direct effects of social determinants of health on glycemic control in adults with type 2 diabetes. Diabetes Technol Ther. 2015 Feb;17(2):80-7. doi: 10.1089/dia.2014.0166. Epub 2014 Oct 31. — View Citation

Willi SM, Miller KM, DiMeglio LA, Klingensmith GJ, Simmons JH, Tamborlane WV, Nadeau KJ, Kittelsrud JM, Huckfeldt P, Beck RW, Lipman TH; T1D Exchange Clinic Network. Racial-ethnic disparities in management and outcomes among children with type 1 diabetes. Pediatrics. 2015 Mar;135(3):424-34. doi: 10.1542/peds.2014-1774. — View Citation

* Note: There are 24 references in allClick here to view all references

Outcome

Type Measure Description Time frame Safety issue
Other Feasibility Check Post-intervention interviews examining intervention content, complexity, comfort, delivery, and credibility 6 months (post-intervention)
Other Adoption Measured by recruitment logs 9 months (post-intervention)
Other Adoption Measured by electronic medical records (EMR) 9 months (post-intervention)
Other Adoption Measured by young adult participant consent rates 9 months (post-intervention)
Other Adoption Measured by percentage of provider opt-in 9 months (post-intervention)
Other Adoption Measured by CHW communications 9 months (post-intervention)
Other Fidelity Measured by the community health worker (CHW) dashboard Baseline
Other Fidelity Measured by electronic medical records (EMR) to analyze session attendance Baseline
Other Fidelity Measured by content delivery Baseline
Other Fidelity Measured by community health workers (CHW) dashboard 3 month mark
Other Fidelity Measured by CHW session recordings 3 month mark
Other Fidelity Measured by EMR to analyze session attendance 3 month mark
Other Fidelity Measured by content delivery 3 month mark
Other Fidelity Measured by insurance tasks 3 month mark
Other Fidelity Measured by CHW dashboard 6 month mark
Other Fidelity Measured by CHW session recordings 6 month mark
Other Fidelity Measured by EMR to analyze session attendance 6 month mark
Other Fidelity Measured by content delivery 6 month mark
Other Fidelity Measured by insurance tasks 6 month mark
Other Cost Measured by time sheets, receipts, and budget to analyze CHW salary/benefits 6 months (post-intervention)
Other Cost Measured by time sheets, receipts, and budget to analyze CHW equipment 6 months (post-intervention)
Other Cost Measured by time sheets, receipts, and budget to analyze CHW consumables 6 months (post-intervention)
Primary Technology Use Technology use tracked using EMR prescriptions, self-reporting, CHW records, and device platforms and will be measured as a binary variable (yes/no), days of wear (% use) 9 month mark
Secondary YA-URM Autonomy/ Competence, Social Support Measured using the Healthcare Self-Determination survey Baseline
Secondary YA-URM Autonomy/ Competence, Social Support Measured using the Healthcare Self-Determination survey 3 month mark
Secondary YA-URM Autonomy/ Competence, Social Support Measured using the Healthcare Self-Determination survey 6 month mark
Secondary YA-URM Autonomy/ Competence, Social Support Measured using the Healthcare Self-Determination survey 9 month mark
Secondary Hemoglobin A1c Obtained by POC (in clinic) or laboratory (DCA Vantage) Baseline
Secondary Hemoglobin A1c Obtained by POC (in clinic) or laboratory (DCA Vantage) 3 month mark
Secondary Hemoglobin A1c Obtained by POC (in clinic) or laboratory (DCA Vantage) 6 month mark
Secondary Hemoglobin A1c Obtained by POC (in clinic) or laboratory (DCA Vantage) 9 month mark
Secondary Quality of Life (Diabetes Distress) Validated survey: Type 1 Diabetes and Life Scale - Young Adult (T1DAL-YA)
Likert Scale: 1-5 (1= no, not at all true, 2=no, not very true, 3=sometimes true, sometimes not true, 4=yes, a little true, 5= yes, very true)
Baseline
Secondary Quality of Life (Diabetes Distress) Validated surveys: Problem Areas in Diabetes (PAID)
The scores for each item are summed, then multiplied by 1.25 to generate a total score out of 100.
Total scores of 40 and above: severe diabetes distress
Individual items scored 3 or 4: moderate to severe distress to be discussed during the appointment following completion of the questionnaire.
Baseline
Secondary Quality of Life (Diabetes Distress) Validated survey: Diabetes Self-Management Questionnaire (DSMQ) Baseline
Secondary Quality of Life (Diabetes Distress) Validated survey: Healthcare Climate Questionnaire (HCCQ)
Likert Scale (1-7) 1= strongly disagree ---- 7= strongly agree
Higher average scores represent a higher level of perceived autonomy support.
Baseline
Secondary Quality of Life (Diabetes Distress) Validated survey: Type 1 Diabetes and Life Scale - Young Adult (T1DAL-YA)
Likert Scale: 1-5 (1= no, not at all true, 2=no, not very true, 3=sometimes true, sometimes not true, 4=yes, a little true, 5= yes, very true)
3 month follow-up
Secondary Quality of Life (Diabetes Distress) Validated surveys: Problem Areas in Diabetes (PAID)
The scores for each item are summed, then multiplied by 1.25 to generate a total score out of 100.
Total scores of 40 and above: severe diabetes distress
Individual items scored 3 or 4: moderate to severe distress to be discussed during the appointment following completion of the questionnaire.
3 month follow-up
Secondary Quality of Life (Diabetes Distress) Validated survey: Diabetes Self-Management Questionnaire (DSMQ) 3 month follow-up
Secondary Quality of Life (Diabetes Distress) Validated survey: Healthcare Climate Questionnaire (HCCQ)
Likert Scale (1-7) 1= strongly disagree ---- 7= strongly agree
Higher average scores represent a higher level of perceived autonomy support.
3 month follow-up
Secondary Quality of Life (Diabetes Distress Validated survey: Type 1 Diabetes and Life Scale - Young Adult (T1DAL-YA)
Likert Scale: 1-5 (1= no, not at all true, 2=no, not very true, 3=sometimes true, sometimes not true, 4=yes, a little true, 5= yes, very true)
6 month follow-up
Secondary Quality of Life (Diabetes Distress) Validated surveys: Problem Areas in Diabetes (PAID)
The scores for each item are summed, then multiplied by 1.25 to generate a total score out of 100.
Total scores of 40 and above: severe diabetes distress
Individual items scored 3 or 4: moderate to severe distress to be discussed during the appointment following completion of the questionnaire.
6 month follow-up
Secondary Quality of life (Diabetes Distress) Validated survey: Diabetes Self-Management Questionnaire (DSMQ) 6 month follow-up
Secondary Quality of Life (Diabetes Distress) Validated survey: Healthcare Climate Questionnaire (HCCQ)
Likert Scale (1-7) 1= strongly disagree ---- 7= strongly agree
Higher average scores represent a higher level of perceived autonomy support.
6 month follow-up
Secondary Technology Use Technology use tracked using EMR prescriptions, self-reporting, CHW records, and device platforms and will be measured as a binary variable (yes/no), days of wear (% use) 3 month mark
Secondary Technology Use Technology use tracked using EMR prescriptions, self-reporting, CHW records, and device platforms and will be measured as a binary variable (yes/no), days of wear (% use) 6 month mark
Secondary Technology Use Technology use tracked using EMR prescriptions, self-reporting, CHW records, and device platforms and will be measured as a binary variable (yes/no), days of wear (% use) 9 month mark
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