T1DTechCHW: Enhancing the Community Health Worker Model to Promote Diabetes Technology Use in Young Adults From Underrepresented Minority Groups (T1DTechCHW)

T1DTechCHW: Enhancing the Community Health Worker (CHW) Model to Promote Diabetes Technology Use in Young Adults From Underrepresented Minority Groups (YA-URMs) With Type 1 Diabetes (T1D)

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.

Study Overview

• Status: Completed
• Conditions: Diabetes Mellitus; Diabetes; Type 1 Diabetes; Young Adult
• Intervention / Treatment: Behavioral: T1D-CATCH

Detailed 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, New York, 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.

• Study Type: Interventional
• Enrollment (Actual): 119
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • New York
    • The Bronx, New York, United States, 10461
      • Albert Einstein College of Medicine

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 30 years (Adult)
• Accepts Healthy Volunteers: No

Description

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 Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Double

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: T1D-CATCH; The CHW intervention will consist of both individual and optional group sessions with YA-URMs with T1D. In individual sessions, CHWs will provide T1D technology education, peer support, and social needs management. Over the 9-month study period, session frequency will involve weekly individual sessions based on participant technology milestones and an optional monthly CHW-led peer group support session. CHW individual and group sessions will be held via videoconferencing or in person, per participant preference and institutional COVID-19 rules.	Behavioral: T1D-CATCH; As defined by the Centers for Disease Control and Prevention (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.
No Intervention: Usual Care Control Condition; Control arm participants will receive usual primary or endocrine care at Montefiore. Usual care consists of a physician or nurse practitioner visit with review of blood sugars and treatment decisions based on provider experience. Physicians in endocrinology practices are nested within a diabetes center with access to diabetes nurse practitioners/educators, dieticians, a psychologist, and nurses. In all practices, patients are recommended to see their physician or nurse practitioner every 3 months and attend individual or group sessions.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Technology Initiation	Technology use will be tracked using EMR prescriptions, self-reporting, CHW records, and device platforms and will be measured and reported as a binary variable (yes/no). Technology will include any combination of continuous glucose monitor (CGM), pump, or CGM and pump. Technology initiation will be defined as at least 1 week of first use in the 6-month period. Variables will be described using summary statistics with counts/percentages.	6 months following intervention

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Technology Initiation	Technology use will be tracked using EMR prescriptions, self-reporting, CHW records, and device platforms and will be measured and reported as a binary variable (yes/no). Technology will include any combination of continuous glucose monitor (CGM), pump, or CGM and pump. Technology use will be defined as at least 1 week of first use in the 3-month period. Variables will be described using summary statistics with counts/percentages.	3 months following intervention
Continued Technology Use - Percent Use	Continued technology use by study end will be determined from device platform metrics detailing days/percent time used, in addition to self-report. Per standard diabetes technology clinical trial guidelines, CGM use is defined as pump use >90% time. The percentage of time used (% used) will be summarized by study arm using basic descriptive statistics. A target of at least 90% CGM wear-time is associated with the best outcomes for glycemic control.	9 months following intervention
Continued Technology Use - Days of wear	Continued technology use by study end will be determined from device platform metrics detailing days/percent time used, in addition to self-report. Per standard diabetes technology clinical trial guidelines, CGM use is defined as ≥ 5/7 days wear over the 9 month period. Days of wear will be summarized study arm using basic descriptive statistics. A target of at least 5/7 days of CGM wear-time is associated with the best outcomes for glycemic control.	9 months following intervention

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
YA-URM Autonomy/ Competence, Social Support	Measured using the Healthcare Self-Determination survey	Baseline
YA-URM Autonomy/ Competence, Social Support	Measured using the Healthcare Self-Determination survey	3 month mark
YA-URM Autonomy/ Competence, Social Support	Measured using the Healthcare Self-Determination survey	6 month mark
YA-URM Autonomy/ Competence, Social Support	Measured using the Healthcare Self-Determination survey	9 month mark
Hemoglobin A1c	Obtained by POC (in clinic) or laboratory (DCA Vantage)	Baseline
Hemoglobin A1c	Obtained by POC (in clinic) or laboratory (DCA Vantage)	3 month mark
Hemoglobin A1c	Obtained by POC (in clinic) or laboratory (DCA Vantage)	6 month mark
Hemoglobin A1c	Obtained by POC (in clinic) or laboratory (DCA Vantage)	9 month mark
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
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
Quality of Life (Diabetes Distress)	Validated survey: Diabetes Self-Management Questionnaire (DSMQ)	Baseline
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
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
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
Quality of Life (Diabetes Distress)	Validated survey: Diabetes Self-Management Questionnaire (DSMQ)	3 month follow-up
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
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
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
Quality of life (Diabetes Distress)	Validated survey: Diabetes Self-Management Questionnaire (DSMQ)	6 month follow-up
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
Feasibility Check	Post-intervention interviews examining intervention content, complexity, comfort, delivery, and credibility	6 months (post-intervention)
Fidelity	Measured by the community health worker (CHW) dashboard	Baseline
Fidelity	Measured by electronic medical records (EMR) to analyze session attendance	Baseline
Fidelity	Measured by content delivery	Baseline
Fidelity	Measured by community health workers (CHW) dashboard	3 month mark
Fidelity	Measured by CHW session recordings	3 month mark
Fidelity	Measured by EMR to analyze session attendance	3 month mark
Fidelity	Measured by content delivery	3 month mark
Fidelity	Measured by insurance tasks	3 month mark
Fidelity	Measured by CHW dashboard	6 month mark
Fidelity	Measured by CHW session recordings	6 month mark
Fidelity	Measured by EMR to analyze session attendance	6 month mark
Fidelity	Measured by content delivery	6 month mark
Fidelity	Measured by insurance tasks	6 month mark
Cost	Measured by time sheets, receipts, and budget to analyze CHW salary/benefits	6 months (post-intervention)
Cost	Measured by time sheets, receipts, and budget to analyze CHW equipment	6 months (post-intervention)
Cost	Measured by time sheets, receipts, and budget to analyze CHW consumables	6 months (post-intervention)
Adoption	Measured by recruitment logs	9 months (post-intervention)
Adoption	Measured by electronic medical records (EMR)	9 months (post-intervention)
Adoption	Measured by young adult participant consent rates	9 months (post-intervention)
Adoption	Measured by percentage of provider opt-in	9 months (post-intervention)
Adoption	Measured by CHW communications	9 months (post-intervention)

Collaborators and Investigators

• Sponsor: Albert Einstein College of Medicine
• Collaborators: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); The Leona M. and Harry B. Helmsley Charitable Trust
• Investigators: Principal Investigator: Shivani Agarwal, MD, MPH, Albert Einstein College of Medicine

Publications and helpful links

General Publications

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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
• 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.
• 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.
• Lai CW, Lipman TH, Willi SM, Hawkes CP. Racial and Ethnic Disparities in Rates of Continuous Glucose Monitor Initiation and Continued Use in Children With Type 1 Diabetes. Diabetes Care. 2021 Jan;44(1):255-257. doi: 10.2337/dc20-1663. Epub 2020 Nov 11.
• 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.

Study record dates

Study Major Dates

• Study Start (Actual): November 7, 2022
• Primary Completion (Actual): September 8, 2025
• Study Completion (Actual): December 8, 2025

Study Registration Dates

• First Submitted: December 16, 2021
• First Submitted That Met QC Criteria: January 26, 2022
• First Posted (Actual): January 27, 2022

Study Record Updates

• Last Update Posted (Estimated): January 12, 2026
• Last Update Submitted That Met QC Criteria: January 9, 2026
• Last Verified: January 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Endocrine System Diseases; Metabolic Diseases; Autoimmune Diseases; Immune System Diseases; Glucose Metabolism Disorders; Nutritional and Metabolic Diseases; Diabetes Mellitus; Diabetes Mellitus, Type 1
• Other Study ID Numbers: 2021-13714; R01DK132302 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No