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| ID | Type | Description | Link |
|---|---|---|---|
| 7-25-JDFPC-403 | Other Grant/Funding Number | American Diabetes Association |
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| Name | Class |
|---|---|
| American Diabetes Association | OTHER |
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Continuous glucose monitoring (CGM) is a technology that helps individuals with diabetes track their sugar levels in real-time, leading to more in-range blood sugars, fewer episodes of dangerously low blood sugar, and improved quality of life. Despite these benefits, CGM is not widely used in primary care settings, where most people receive their diabetes care. The investigators aim to make CGM more accessible and equitably prescribed in primary care practices. The study team will support primary care to increase CGM use with a program called SPARK-CGM (Supporting Primary Care Adoption, Resources, and Knowledge for CGM) across a large network of primary care clinics at Montefiore Medical Center. This program will provide primary care providers (PCPs) with education, tools, and support to incorporate CGM into their routine care for people with diabetes. Investigators plan to test SPARK-CGM to evaluate whether it increases CGM prescriptions who are eligible to receive this technology.
Continuous glucose monitoring (CGM) has been shown to reduce HbA1c levels, hypoglycemia, and improve quality of life, representing a powerful tool to improve population-level outcomes in diabetes. The American Diabetes Association (ADA) recommends CGM for all individuals with any type of diabetes on insulin, but its widespread adoption into primary care remains low.
SPARK-CGM (Supporting Primary care Adoption, Resources, and Knowledge for CGM) is a hybrid effectiveness-implementation trial evaluating a multifaceted practice transformation package designed to increase new continuous glucose monitoring (CGM) prescriptions among adult patients with diabetes treated wtih insulin. This includes: (1) strengthening clinic capacity by streamlining CGM prescribing pathways; (2) optimizing pharmacy and insurance authorization workflows to reduce administrative burden; (3) providing prescriber-focused training on CGM workflows, data access, and interpretation to support effective clinical use; (4) training nursing and support staff to facilitate device initiation, patient education, and data management; and (5) delivering regular performance feedback on prescribing rates across clinics and providers.
The effectiveness component will be evaluated using a stepped-wedge cluster randomized trial in which primary care clinic clusters sequentially transition from usual care to the SPARK-CGM implementation intervention over the study period until all clinics receive the intervention. The implementation component will include mixed-methods evaluation using surveys and semi-structured interviews with patients, providers and clinic staff to assess implementation outcomes.
Based on pre-implementation clinic counts, the stepped-wedge trial will include 14 non-pilot primary care clinics. The investigators expect to include up to approximately 20,000 adult patients with insulin-treated diabetes who may contribute data to the effectiveness analysis (Aim 1) during the study period . For implementation evaluation and semi-structured interviews (Aim 2), investigators plan to recruit up to 40 adults with insulin-treated diabetes (10 per cluster) and 15-20 clinicians or clinic staff participants (4-5 per cluster) in order to reach thematic saturation.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Implementation Phase | Experimental | Clinics will sequentially transition from the usual care (control) condition to the intervention phase at three-month intervals until all clusters receive the intervention. The active implementation phase at each cluster will span six months, providing sufficient time to equip clinics to use CGM. The intervention includes development of clinic workflows for CGM prescribing and onboarding, provider education on CGM use and interpretation, training of clinic staff to support CGM initiation, and regular performance feedback on CGM prescribing rates. |
|
| Pre-implementation phase | Active Comparator | Patients in pre-implementation practices will receive the usual care under the direction of their primary care provider and practice. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Enhanced CGM Interventions | Other | The study intervention involves creating a streamlined workflow for CGM prescribing that does not restrict the treatment options available to patients or clinicians. SPARK-CGM implementation strategy includes three core practice transformations: (1) building clinic infrastructure by establishing CGM prescription workflows, and by training clinic staff to place CGM devices at the point of care, (2) provider training on accessing and using CGM data effectively in practice, and (3) regular feedback on prescription rates across the network. |
| Measure | Description | Time Frame |
|---|---|---|
| Time to first CGM prescription by primary care provider | Time to first CGM prescription initiated by a primary care provider (PCP) will be defined as the date from a patient's first eligible primary care encounter during the study period to the date of the first CGM prescription, up to 18 months following intervention initiation, as recorded in the electronic health record. All CGM orders are captured in the EHR. Results will be summarized by study arm using descriptive statistics and analyzed using Cox proportional hazards models. | Up to 18 months following initiation of intervention |
| Measure | Description | Time Frame |
|---|---|---|
| CGM Utilization | A dichotomous (binary) measure of CGM usage obtained from device platforms and/or EHR. CGM utilization will be summarized by study arm using descriptive statistics. | Up to 18 months following initiation of intervention |
| HbA1c |
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Inclusion Criteria:
Clinic level:
Patient level:
Exclusion Criteria:
Clinic level:
- Sites participating in pilot phase of CGM initiative
Patient level:
- Existing CGM prescription within 24 months before the study start
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Rosina Antwi | Contact | 646-515-8399 | rosina.antwi@einsteinmed.edu |
| Name | Affiliation | Role |
|---|---|---|
| Jovan Milosavljevic, MD | Montefiore Medical Center | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Montefiore Medical Group (MMG) | Recruiting | The Bronx | New York | 10467 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 34077499 | Background | Martens T, Beck RW, Bailey R, Ruedy KJ, Calhoun P, Peters AL, Pop-Busui R, Philis-Tsimikas A, Bao S, Umpierrez G, Davis G, Kruger D, Bhargava A, Young L, McGill JB, Aleppo G, Nguyen QT, Orozco I, Biggs W, Lucas KJ, Polonsky WH, Buse JB, Price D, Bergenstal RM; MOBILE Study Group. Effect of Continuous Glucose Monitoring on Glycemic Control in Patients With Type 2 Diabetes Treated With Basal Insulin: A Randomized Clinical Trial. JAMA. 2021 Jun 8;325(22):2262-2272. doi: 10.1001/jama.2021.7444. | |
| 18779236 |
| Label | URL |
|---|---|
| U.S. Census Bureau QuickFacts: Bronx County, New York. | View source |
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The data sharing plan includes the following:
In Specific Aim 1, valuable SPARK-CGM efficacy data may be of interest to other diabetes, health equity and primary care researchers.
In Specific Aim 2, in-depth qualitative data of PCP and patients with diabetes may be of interest to investigators who want to apply similar interventions to new populations. Information on analytic methods will be shared with other collaborators, including providing statistical code and process logs that could be used to either confirm our results or apply similar statistical methods to related projects. Additional resources developed during this project, such as study protocols, educational materials for primary care providers, will be shared upon request or made available through appropriate channels
De-identified individual participant data will be deposited in an approved open data repository within six months of publication or within eighteen months of the conclusion of the funding period if the study remains unpublished.
Data sharing will require:
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| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| ID | Term |
|---|---|
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D004700 | Endocrine System Diseases |
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| ID | Term |
|---|---|
| D059039 | Standard of Care |
| ID | Term |
|---|---|
| D019984 | Quality Indicators, Health Care |
| D011787 | Quality of Health Care |
| D006298 | Health Services Administration |
| D017530 | Health Care Quality, Access, and Evaluation |
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This study uses type II hybrid effectiveness-implementation design combining (1) stepped-wedge cluster randomized trial (SW-CRT) evaluating effectiveness of SPARK-CGM and (2) post-intervention mixed methods implementation evaluation including interviews and surveys with patients, providers and clinic staff. Participating clinics will be randomized into four mutually exclusive cluster waves, stratified by clinic size, defined by the number of patients with insulin-treated diabetes, and by federally qualified health center status to ensure balance across waves. Clinics will sequentially transition from the usual care (control) condition to the intervention phase at three-month intervals until all clusters receive the intervention.
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| Standard of Care | Other | Usual care |
|
Longitudinal repeated HbA1c measures over time will be obtained from EHR. HbA1c results will be summarized by study arm using descriptive statistics.
| Up to 18 months following initiation of intervention |
| Hospitalizations | The number/percentage of patients who were admitted for inpatient hospitalization will be assessed based on data available from EHR from the initiation of the intervention to the end of the study, up to 18 months. | Up to 18 months following initiation of intervention |
| Emergency Department (ED) visits | The number/percentage of patients who visited an ED will be assessed based on data available from EHR from the initiation of the intervention to the end of the study, up to 18 months. | Up to 18 months following initiation of intervention |
| CGM prescription rate by race/ethnicity and payor | The number/percentage of participants with CGM will be evaluated by race/ethnicity and payor based on data available from EHR from the initiation of the intervention to the end of the study, up to 18 months. | Up to 18 months following initiation of intervention |
| Longitudinal glucose time in range (TIR) | TIR (i.e., percent time spent in glucose range 70-180 mg/dL) will be collected longitudinally among patients using CGM in the post-implementation trial phase. Repeated TIR measures will be obtained from device platforms and/or the EHR at baseline and throughout follow-up. Average TIR levels over time will be analyzed to assess within-participant change over time. | Up to 18 months following initiation of intervention |
| Longitudinal glucose time below range (TBR) | TBR (i.e., percent time spent in glucose range <70 mg/dL) will be collected longitudinally among patients using CGM in the post-implementation trial phase. Repeated TBR measures will be obtained from device platforms and/or the EHR at baseline and throughout the follow-up. Average TBR levels over time will be analyzed to assess within-participant change over time. | Up to 18 months following initiation of intervention |
| Longitudinal glucose time above range (TAR) | TAR (i.e., percent time spent in glucose range >180 mg/dL) will be collected longitudinally among patients using CGM in the post-implementation trial phase. Repeated TAR measures will be obtained from device platforms and/or the EHR at baseline and throughout the follow-up. Average TAR levels over time will be analyzed to assess within-participant change over time. | Up to 18 months following initiation of intervention |
| Longitudinal glucose management indicator (GMI) | GMI measures will be collected longitudinally among patients using CGM in the post-implementation trial phase. Repeated GMI measures will be obtained from device platforms and/or the electronic health record at baseline and throughout the follow-up. Average GMI levels over time will be analyzed to assess within-participant change over time. | Up to 18 months following initiation of intervention |
| Background |
| Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group; Tamborlane WV, Beck RW, Bode BW, Buckingham B, Chase HP, Clemons R, Fiallo-Scharer R, Fox LA, Gilliam LK, Hirsch IB, Huang ES, Kollman C, Kowalski AJ, Laffel L, Lawrence JM, Lee J, Mauras N, O'Grady M, Ruedy KJ, Tansey M, Tsalikian E, Weinzimer S, Wilson DM, Wolpert H, Wysocki T, Xing D. Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med. 2008 Oct 2;359(14):1464-76. doi: 10.1056/NEJMoa0805017. Epub 2008 Sep 8. |
| 28118453 | Background | Beck RW, Riddlesworth T, Ruedy K, Ahmann A, Bergenstal R, Haller S, Kollman C, Kruger D, McGill JB, Polonsky W, Toschi E, Wolpert H, Price D; DIAMOND Study Group. Effect of Continuous Glucose Monitoring on Glycemic Control in Adults With Type 1 Diabetes Using Insulin Injections: The DIAMOND Randomized Clinical Trial. JAMA. 2017 Jan 24;317(4):371-378. doi: 10.1001/jama.2016.19975. |
| 32518063 | Background | Wada E, Onoue T, Kobayashi T, Handa T, Hayase A, Ito M, Furukawa M, Okuji T, Okada N, Iwama S, Sugiyama M, Tsunekawa T, Takagi H, Hagiwara D, Ito Y, Suga H, Banno R, Kuwatsuka Y, Ando M, Goto M, Arima H. Flash glucose monitoring helps achieve better glycemic control than conventional self-monitoring of blood glucose in non-insulin-treated type 2 diabetes: a randomized controlled trial. BMJ Open Diabetes Res Care. 2020 Jun;8(1):e001115. doi: 10.1136/bmjdrc-2019-001115. |
| 25759183 | Background | Heinemann L, Deiss D, Hermanns N, Graham C, Kaltheuner M, Liebl A, Price D. HypoDE: Research Design and Methods of a Randomized Controlled Study Evaluating the Impact of Real-Time CGM Usage on the Frequency of CGM Glucose Values <55 mg/dl in Patients With Type 1 Diabetes and Problematic Hypoglycemia Treated With Multiple Daily Injections. J Diabetes Sci Technol. 2015 May;9(3):651-62. doi: 10.1177/1932296815575999. Epub 2015 Mar 9. |
| 28000140 | Background | Haak T, Hanaire H, Ajjan R, Hermanns N, Riveline JP, Rayman G. Flash Glucose-Sensing Technology as a Replacement for Blood Glucose Monitoring for the Management of Insulin-Treated Type 2 Diabetes: a Multicenter, Open-Label Randomized Controlled Trial. Diabetes Ther. 2017 Feb;8(1):55-73. doi: 10.1007/s13300-016-0223-6. Epub 2016 Dec 20. |
| Background | Johnston AR, Poll JB, Hays EM, Jones CW. Perceived impact of continuous glucose monitor use on quality of life and self-care for patients with type 2 diabetes. Diabetes Epidemiology and Management. 2022;6:100068. doi:10.1016/j.deman.2022.100068 |
| 23427866 | Background | Polonsky WH, Hessler D. What are the quality of life-related benefits and losses associated with real-time continuous glucose monitoring? A survey of current users. Diabetes Technol Ther. 2013 Apr;15(4):295-301. doi: 10.1089/dia.2012.0298. Epub 2013 Feb 21. |
| 28389582 | Background | Polonsky WH, Hessler D, Ruedy KJ, Beck RW; DIAMOND Study Group. The Impact of Continuous Glucose Monitoring on Markers of Quality of Life in Adults With Type 1 Diabetes: Further Findings From the DIAMOND Randomized Clinical Trial. Diabetes Care. 2017 Jun;40(6):736-741. doi: 10.2337/dc17-0133. Epub 2017 Apr 7. |
| 36507635 | Background | ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, Collins BS, Hilliard ME, Isaacs D, Johnson EL, Kahan S, Khunti K, Leon J, Lyons SK, Perry ML, Prahalad P, Pratley RE, Seley JJ, Stanton RC, Gabbay RA, on behalf of the American Diabetes Association. 7. Diabetes Technology: Standards of Care in Diabetes-2023. Diabetes Care. 2023 Jan 1;46(Suppl 1):S111-S127. doi: 10.2337/dc23-S007. |
| 31975751 | Background | Grunberger G, Sze D, Ermakova A, Sieradzan R, Oliveria T, Miller EM. Treatment Intensification With Insulin Pumps and Other Technologies in Patients With Type 2 Diabetes: Results of a Physician Survey in the United States. Clin Diabetes. 2020 Jan;38(1):47-55. doi: 10.2337/cd19-0008. |
| 37254011 | Background | Mayberry LS, Guy C, Hendrickson CD, McCoy AB, Elasy T. Rates and Correlates of Uptake of Continuous Glucose Monitors Among Adults with Type 2 Diabetes in Primary Care and Endocrinology Settings. J Gen Intern Med. 2023 Aug;38(11):2546-2552. doi: 10.1007/s11606-023-08222-3. Epub 2023 May 30. |
| 36443083 | Background | Oser TK, Hall TL, Dickinson LM, Callen E, Carroll JK, Nease DE Jr, Michaels L, Oser SM. Continuous Glucose Monitoring in Primary Care: Understanding and Supporting Clinicians' Use to Enhance Diabetes Care. Ann Fam Med. 2022 Nov-Dec;20(6):541-547. doi: 10.1370/afm.2876. |
| 21106869 | Background | Davidson JA. The increasing role of primary care physicians in caring for patients with type 2 diabetes mellitus. Mayo Clin Proc. 2010 Dec;85(12 Suppl):S3-4. doi: 10.4065/mcp.2010.0466. Epub 2010 Nov 24. No abstract available. |
| 30719646 | Background | Pilla SJ, Segal JB, Maruthur NM. Primary Care Provides the Majority of Outpatient Care for Patients with Diabetes in the US: NAMCS 2009-2015. J Gen Intern Med. 2019 Jul;34(7):1089-1091. doi: 10.1007/s11606-019-04843-9. No abstract available. |
| 24940655 | Background | Vigersky RA, Fish L, Hogan P, Stewart A, Kutler S, Ladenson PW, McDermott M, Hupart KH. The clinical endocrinology workforce: current status and future projections of supply and demand. J Clin Endocrinol Metab. 2014 Sep;99(9):3112-21. doi: 10.1210/jc.2014-2257. Epub 2014 Jun 18. |
| 37356446 | Background | GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023 Jul 15;402(10397):203-234. doi: 10.1016/S0140-6736(23)01301-6. Epub 2023 Jun 22. |
| 36054022 | Background | Mathias P, Mahali LP, Agarwal S. Targeting Technology in Underserved Adults With Type 1 Diabetes: Effect of Diabetes Practice Transformations on Improving Equity in CGM Prescribing Behaviors. Diabetes Care. 2022 Oct 1;45(10):2231-2237. doi: 10.2337/dc22-0555. |
| Background | November 14-15, 2023, T1DX-QI Learning Session, Journal of Diabetes Abstracts. J Diabetes. 2023;15(Suppl 1):4-31. doi:10.1111/1753-0407.13488 |
| Behavioral Risk Factor Surveillance System (BRFSS) Health Indicators by County and Region \| State of New York. | View source |
| Glucose\_Monitoring\_Criteria\_-\_10-2-23.pdf. | View source |