The Invested in Diabetes Study Protocol: a cluster randomized pragmatic trial comparing standardized and patient-driven diabetes shared medical appointments

Bethany M Kwan, L Miriam Dickinson, Russell E Glasgow, Martha Sajatovic, Mark Gritz, Jodi Summers Holtrop, Don E Nease Jr, Natalie Ritchie, Andrea Nederveld, Dennis Gurfinkel, Jeanette A Waxmonsky, Bethany M Kwan, L Miriam Dickinson, Russell E Glasgow, Martha Sajatovic, Mark Gritz, Jodi Summers Holtrop, Don E Nease Jr, Natalie Ritchie, Andrea Nederveld, Dennis Gurfinkel, Jeanette A Waxmonsky

Abstract

Background: Shared medical appointments (SMAs) have been shown to be an efficient and effective strategy for providing diabetes self-management education and self-management support. SMA features vary and it is not known which features are most effective for different patients and practice settings. The Invested in Diabetes study tests the comparative effectiveness of SMAs with and without multidisciplinary care teams and patient topic choice for improving patient-centered and clinical outcomes related to diabetes.

Methods: This study compares the effectiveness of two SMA approaches using the Targeted Training for Illness Management (TTIM) curriculum. Standardized SMAs are led by a health educator with a set order of TTIM topics. Patient-driven SMAs are delivered collaboratively by a multidisciplinary care team (health educator, medical provider, behavioral health provider, and a peer mentor); patients select the order and emphasis on TTIM topics. Invested in Diabetes is a cluster randomized pragmatic trial involving approximately 1440 adult patients with type 2 diabetes. Twenty primary care practices will be randomly assigned to either standardized or patient-driven SMAs. A mixed-methods evaluation will include quantitative (practice- and patient-level data) and qualitative (practice and patient interviews, observation) components. The primary patient-centered outcome is diabetes distress. Secondary outcomes include autonomy support, self-management behaviors, clinical outcomes, patient reach, and practice-level value and sustainability.

Discussion: Practice and patient stakeholder input guided protocol development for this pragmatic trial comparing SMA approaches. Implementation strategies from the enhanced Replicating Effective Programs framework will help ensure practices maintain fidelity to intervention protocols while tailoring workflows to their settings. Invested in Diabetes will contribute to the literature on chronic illness management and implementation science using the RE-AIM model.

Trial registration: ClinicalTrials.gov, NCT03590041. Registered on 5 July 2018.

Keywords: Cluster randomized pragmatic trial; Diabetes; Diabetes distress; Diabetes self-management; Implementation; Mixed methods; Peer mentors; RE-AIM; Replicating effective programs; Shared medical appointments.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Invested in Diabetes study conceptual model
Fig. 2
Fig. 2
SPIRIT Figure for Invested in Diabetes project timeline

References

    1. Centers for Disease Control and Prevention . National Diabetes Statistics Report: estimates of diabetes and its burden in the United States. Atlanta: CDC; 2017.
    1. Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. 2014;103(2):137–149. doi: 10.1016/j.diabres.2013.11.002.
    1. Ali MK, Bullard KM, Saaddine JB, Cowie CC, Imperatore G, Gregg EW. Achievement of goals in U.S. diabetes care, 1999-2010. N Engl J Med. 2013;368(17):1613–1624. doi: 10.1056/NEJMsa1213829.
    1. Boyle JP, Thompson TJ, Gregg EW, Barker LE, Williamson DF. Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metrics. 2010;8:29. doi: 10.1186/1478-7954-8-29.
    1. Gregg EW, Li Y, Wang J, Burrows NR, Ali MK, Rolka D, et al. Changes in diabetes-related complications in the United States, 1990-2010. N Engl J Med. 2014;370(16):1514–1523. doi: 10.1056/NEJMoa1310799.
    1. Li R, Barker LE, Shrestha S, Zhang P, Duru OK, Pearson-Clarke T, et al. Changes over time in high out-of-pocket health care burden in U.S. adults with diabetes, 2001-2011. Diabetes Care. 2014;37(6):1629–1635. doi: 10.2337/dc13-1997.
    1. Koopmanschap M. Coping with Type II diabetes: the patient's perspective. Diabetologia. 2002;45(7):S18–S22.
    1. Rabi DM, Edwards AL, Southern DA, Svenson LW, Sargious PM, Norton P, et al. Association of socio-economic status with diabetes prevalence and utilization of diabetes care services. BMC Health Serv Res. 2006;6:124. doi: 10.1186/1472-6963-6-124.
    1. Fisher L, Skaff MM, Mullan JT, Arean P, Mohr D, Masharani U, et al. Clinical depression versus distress among patients with type 2 diabetes: not just a question of semantics. Diabetes Care. 2007;30(3):542–548. doi: 10.2337/dc06-1614.
    1. Fisher L, Glasgow RE, Strycker LA. The relationship between diabetes distress and clinical depression with glycemic control among patients with type 2 diabetes. Diabetes Care. 2010;33(5):1034–1036. doi: 10.2337/dc09-2175.
    1. Inzucchi SE, Bergenstal RM, Buse JB, Diamant M, Ferrannini E, Nauck M, et al. Management of hyperglycemia in type 2 diabetes, 2015: a patient-centered approach: update to a position statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care. 2015;38(1):140–149. doi: 10.2337/dc14-2441.
    1. Nutting PA, Dickinson WP, Dickinson LM, Nelson CC, King DK, Crabtree BF, et al. Use of chronic care model elements is associated with higher-quality care for diabetes. Ann Fam Med. 2007;5(1):14–20. doi: 10.1370/afm.610.
    1. Coleman K, Austin BT, Brach C, Wagner EH. Evidence on the Chronic Care Model in the new millennium. Health Aff (Millwood) 2009;28(1):75–85. doi: 10.1377/hlthaff.28.1.75.
    1. Austin B, Wagner E, Hindmarsh M, Davis C. Elements of effective chronic care: a model for optimizing outcomes for the chronically Ill. Epilepsy Behav. 2000;1(4):S15–S20. doi: 10.1006/ebeh.2000.0105.
    1. Wagner EH. Chronic disease care. BMJ. 2004;328(7433):177–178. doi: 10.1136/bmj.328.7433.177.
    1. Wagner EH, Grothaus LC, Sandhu N, Galvin MS, McGregor M, Artz K, et al. Chronic care clinics for diabetes in primary care: a system-wide randomized trial. Diabetes Care. 2001;24(4):695–700. doi: 10.2337/diacare.24.4.695.
    1. Norris SL, Lau J, Smith SJ, Schmid CH, Engelgau MM. Self-management education for adults with type 2 diabetes: a meta-analysis of the effect on glycemic control. Diabetes Care. 2002;25(7):1159–1171. doi: 10.2337/diacare.25.7.1159.
    1. Haas L, Maryniuk M, Beck J, Cox CE, Duker P, Edwards L, et al. National standards for diabetes self-management education and support. Diabetes Care. 2013;36(Suppl 1):S100–S108. doi: 10.2337/dc13-S100.
    1. Fisher L, Hessler D, Glasgow RE, Arean PA, Masharani U, Naranjo D, et al. REDEEM: a pragmatic trial to reduce diabetes distress. Diabetes Care. 2013;36(9):2551–2558. doi: 10.2337/dc12-2493.
    1. Glasgow RE, Davis CL, Funnell MM, Beck A. Implementing practical interventions to support chronic illness self-management. Jt Comm J Qual Patient Saf. 2003;29(11):563–574.
    1. Edelman D, Gierisch JM, McDuffie JR, Oddone E, Williams JW., Jr Shared medical appointments for patients with diabetes mellitus: a systematic review. J Gen Intern Med. 2015;30(1):99–106. doi: 10.1007/s11606-014-2978-7.
    1. Kwan BM, Jortberg B, Warman MK, Kane I, Wearner R, Koren R, et al. Stakeholder engagement in diabetes self-management: patient preference for peer support and other insights. Fam Pract. 2017;34(3):358–363.
    1. Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55(1):68–78. doi: 10.1037/0003-066X.55.1.68.
    1. Mead N, Bower P. Patient-centredness: a conceptual framework and review of the empirical literature. Soc Sci Med. 2000;51(7):1087–1110. doi: 10.1016/S0277-9536(00)00098-8.
    1. Williams GC, Patrick H, Niemiec CP, Williams LK, Divine G, Lafata JE, et al. Reducing the health risks of diabetes: how self-determination theory may help improve medication adherence and quality of life. Diabetes Educ. 2009;35(3):484–492. doi: 10.1177/0145721709333856.
    1. Teixeira PJ, Carraca EV, Markland D, Silva MN, Ryan RM. Exercise, physical activity, and self-determination theory: a systematic review. Int J Behav Nutr Phys Act. 2012;9:78. doi: 10.1186/1479-5868-9-78.
    1. Williams GC, McGregor HA, Zeldman A, Freedman ZR, Deci EL. Testing a self-determination theory process model for promoting glycemic control through diabetes self-management. Health Psychol. 2004;23(1):58. doi: 10.1037/0278-6133.23.1.58.
    1. Williams GC, Lynch M, Glasgow RE. Computer-assisted intervention improves patient-centered diabetes care by increasing autonomy support. Health Psychol. 2007;26(6):728. doi: 10.1037/0278-6133.26.6.728.
    1. Li F, Lokhnygina Y, Murray DM, Heagerty PJ, DeLong ER. An evaluation of constrained randomization for the design and analysis of group-randomized trials. Stat Med. 2016;35(10):1565–79.
    1. Moulton LH. Covariate-based constrained randomization of group-randomized trials. Clin Trials. 2004;1(3):297–305. doi: 10.1191/1740774504cn024oa.
    1. Dickinson LM, Beaty B, Fox C, Pace W, Dickinson WP, Emsermann C, et al. Pragmatic cluster randomized trials using covariate constrained randomization: a method for Practice-based Research Networks (PBRNs) J Am Board Fam Med. 2015;28(5):663–672. doi: 10.3122/jabfm.2015.05.150001.
    1. Kaddumukasa M, Nakibuuka J, Mugenyi L, Namusoke O, Birungi D, Kabaala B, et al. Feasibility study of a targeted self-management intervention for reducing stroke risk factors in a high-risk population in Uganda. J Neurol Sci. 2018;386:23–28. doi: 10.1016/j.jns.2017.12.032.
    1. Sajatovic M, Colon-Zimmermann K, Kahriman M, Fuentes-Casiano E, Liu H, Tatsuoka C, et al. A 6-month prospective randomized controlled trial of remotely delivered group format epilepsy self-management versus waitlist control for high-risk people with epilepsy. Epilepsia. 2018;59(9):1684–1695. doi: 10.1111/epi.14527.
    1. Sajatovic M, Gunzler DD, Kanuch SW, Cassidy KA, Tatsuoka C, McCormick R, et al. A 60-week prospective RCT of a self-management Intervention for individuals with serious mental illness and diabetes mellitus. Psychiatr Serv. 2017;68(9):883–890. doi: 10.1176/appi.ps.201600377.
    1. Sajatovic M, Needham K, Colon-Zimmermann K, Richter N, Liu H, Garrity J, et al. The Community-targeted Self-management of Epilepsy and Mental Illness (C-TIME) initiative: a research, community, and healthcare administration partnership to reduce epilepsy burden. Epilepsy Behav. 2018;89:175–180. doi: 10.1016/j.yebeh.2018.10.004.
    1. Sajatovic M, Tatsuoka C, Welter E, Colon-Zimmermann K, Blixen C, Perzynski AT, et al. A targeted self-management approach for reducing stroke risk factors in African American men who have had a stroke or transient ischemic attack. Am J Health Promot. 2018;32(2):282–293. doi: 10.1177/0890117117695218.
    1. Kilbourne AM, Neumann MS, Pincus HA, Bauer MS, Stall R. Implementing evidence-based interventions in health care: application of the replicating effective programs framework. Implement Sci. 2007;2(1):1. doi: 10.1186/1748-5908-2-42.
    1. Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. 1999;89(9):1322–1327. doi: 10.2105/AJPH.89.9.1322.
    1. Glasgow RE, Harden SM, Gaglio B, Rabin B, Smith ML, Porter GC, et al. RE-AIM planning and evaluation framework: adapting to new science and practice with a 20-year review. Front Public Health. 2019;7:64. doi: 10.3389/fpubh.2019.00064.
    1. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619. doi: 10.1016/0895-4356(92)90133-8.
    1. Toobert DJ, Hampson SE, Glasgow RE. The summary of diabetes self-care activities measure: results from 7 studies and a revised scale. Diabetes Care. 2000;23(7):943–950. doi: 10.2337/diacare.23.7.943.
    1. Gittell JH, Godfrey M, Thistlethwaite J. Interprofessional collaborative practice and relational coordination: improving healthcare through relationships. J Interprof Care. 2013;27(3):210–213. doi: 10.3109/13561820.2012.730564.
    1. Bonomi AE, Wagner EH, Glasgow RE, VonKorff M. Assessment of chronic illness care (ACIC): a practical tool to measure quality improvement. Health Serv Res. 2002;37(3):791–820. doi: 10.1111/1475-6773.00049.
    1. Kaplan RS, Haas DA, Warsh J. Adding value by talking more. N Engl J Med. 2016;375(20):1918–20. doi: 10.1056/NEJMp1607079.
    1. Dickinson LM, Dickinson WP, Nutting PA, Fisher L, Harbrecht M, Crabtree BF, et al. Practice context affects efforts to improve diabetes care for primary care patients: a pragmatic cluster randomized trial. J Gen Intern Med. 2015;30(4):476–482. doi: 10.1007/s11606-014-3131-3.
    1. Noel PH, Lanham HJ, Palmer RF, Leykum LK, Parchman ML. The importance of relational coordination and reciprocal learning for chronic illness care within primary care teams. Health Care Manag Rev. 2013;38(1):20–28. doi: 10.1097/HMR.0b013e3182497262.
    1. Dickinson WP, Dickinson LM, Nutting PA, Emsermann CB, Tutt B, Crabtree BF, et al. Practice facilitation to improve diabetes care in primary care: a report from the EPIC randomized clinical trial. Ann Fam Med. 2014;12(1):8–16. doi: 10.1370/afm.1591.
    1. Gilchrist V, Williams R. Key informant interviews. In: Crabtree B, Miller W, editors. Doing qualitative research. 2. Thousand Oaks: Sage; 1999. pp. 71–88.
    1. Potworowski G, Green L. Cognitive task analysis: methods to improve patient-centered medical home models by understanding and leveraging its knowledge work. Rockville: Agency for Healthcare Research and Quality; 2013.
    1. Fisher L, Hessler DM, Polonsky WH, Mullan J. When is diabetes distress clinically meaningful?: establishing cut points for the Diabetes Distress Scale. Diabetes Care. 2012;35(2):259–264. doi: 10.2337/dc11-1572.
    1. Polonsky WH, Fisher L, Earles J, Dudl RJ, Lees J, Mullan J, et al. Assessing psychosocial distress in diabetes: development of the diabetes distress scale. Diabetes Care. 2005;28(3):626–631. doi: 10.2337/diacare.28.3.626.
    1. Williams GC, Freedman ZR, Deci EL. Supporting autonomy to motivate patients with diabetes for glucose control. Diabetes Care. 1998;21(10):1644–1651. doi: 10.2337/diacare.21.10.1644.
    1. Hwang W, Weller W, Ireys H, Anderson G. Out-of-pocket medical spending for care of chronic conditions. Health Aff. 2001;20(6):267–278. doi: 10.1377/hlthaff.20.6.267.
    1. Russell LB, Ibuka Y, Carr D. How much time do patients spend on outpatient visits? Patient. 2008;1(3):211–222. doi: 10.2165/1312067-200801030-00008.
    1. Sarkar U, Schillinger D, Lopez A, Sudore R. Validation of self-reported health literacy questions among diverse English and Spanish-speaking populations. J Gen Intern Med. 2011;26(3):265–271. doi: 10.1007/s11606-010-1552-1.
    1. Hedeker D, Gibbons RD, Waternaux C. Sample size estimation for longitudinal designs with attrition: comparing time-related contrasts between two groups. J Educ Behav Stat. 1999;24(1):70–93. doi: 10.3102/10769986024001070.
    1. SAS Institute Inc. SAS V 9.3. Cary: SAS Institute, Inc.
    1. Schilling LM, Kwan BM, Drolshagen CT, Hosokawa PW, Brandt E, Pace WD, et al. Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) technology infrastructure for a distributed data network. eGEMs. 2013;1(1):11. doi: 10.13063/2327-9214.1027.
    1. Brown J, Kahn M, Toh S. Data quality assessment for comparative effectiveness research in distributed data networks. Med Care. 2013;51(8 0 3):S22. doi: 10.1097/MLR.0b013e31829b1e2c.
    1. Dempster AP, Laird NM, Rubin DB. Maximum likelihood estimation from incomplete data via the EM algorithm. J R Stat Soc Ser B. 1977;39:1–38.
    1. Diggle P, Kenward MG. Informative drop-out in longitudinal data analysis. Appl Stat. 1994;43:49–93. doi: 10.2307/2986113.
    1. Little, Roderick JA and Donald B Rubin. Statistical analysis with missing data. New York: Wiley; 1987.
    1. Fairclough DL. Design and analysis of quality of life studies in clinical trials. New York: Chapman & Hall/CRC; 2010.
    1. Hedeker D, Gibbons RD. Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychol Methods. 1997;2(1):64–78. doi: 10.1037/1082-989X.2.1.64.
    1. Wright N, Ivers N, Eldridge S, Taljaard M, Bremner S. A review of the use of covariates in cluster randomized trials uncovers marked discrepancies between guidance and practice. J Clin Epidemiol. 2015;68(6):603–609. doi: 10.1016/j.jclinepi.2014.12.006.
    1. Bryk AS, Raudenbush SW, editors. Hierarchical linear models: applications and data analysis methods. 2. Newbury Park: Sage Publications; 2000.
    1. Murray D, editor. Design and analysis of group-randomized trials. New York: Oxford University Press; 1998.
    1. Hedeker D, Gibbons RD. Longitudinal data analysis. Hoboken: Wiley; 2006.
    1. Giraudeau B, Ravaud P. Preventing bias in cluster randomised trials. PLoS Med. 2009;6(5):e1000065. doi: 10.1371/journal.pmed.1000065.
    1. Puffer S, Torgerson D, Watson J. Evidence for risk of bias in cluster randomised trials: Review of recent trials published in three general medical journals. BMJ. 2003;327(7418):785–789. doi: 10.1136/bmj.327.7418.785.
    1. Campbell MK, Elbourne DR, Altman DG. CONSORT statement: extension to cluster randomised trials. BMJ. 2004;328(7441):702–708. doi: 10.1136/bmj.328.7441.702.
    1. Murray DM. Design and analysis of group-randomized trials. USA: Oxford University Press; 1998.
    1. Littell RC, Stroup WW, Milliken GA, Wolfinger RD, Schabenberger O. SAS for mixed models. Cary: SAS Institute; 2006.
    1. Gaglio B, Shoup JA, Glasgow RE. The RE-AIM framework: a systematic review of use over time. Am J Public Health. 2013;103(6):e38–e46. doi: 10.2105/AJPH.2013.301299.
    1. Dempster A, Laird N, Rubin D. Maximum likelihood estimation from incomplete data. J R Stat Soc B. 1977;39(1):1–38.
    1. Little RJ, Rubin DB. Statistical analysis with missing data. Chichester: Wiley; 2014.
    1. Fairclough DL. Design and analysis of quality of life studies in clinical trials. Boca Raton: CRC Press; 2010.
    1. Liu L, Lee MJ, Brateanu A. Improved A1C and lipid profile in patients referred to diabetes education programs in a wide health care network: a retrospective study. Diabetes Spectr. 2014;27(4):297–303. doi: 10.2337/diaspect.27.4.297.
    1. Hawkins J, Kieffer EC, Sinco B, Spencer M, Anderson M, Rosland AM. Does gender influence participation? Predictors of participation in a community health worker diabetes management intervention with African American and Latino adults. Diabetes Educ. 2013;39(5):647–654. doi: 10.1177/0145721713492569.
    1. Schillinger D, Grumbach K, Piette J, Wang F, Osmond D, Daher C, et al. Association of health literacy with diabetes outcomes. JAMA. 2002;288(4):475–482. doi: 10.1001/jama.288.4.475.
    1. Addison R. A grounded hermenuetic editing approach. In: Crabtree B, Miller W, editors. Doing qualitative research. 2. Thousand Oaks: Sage; 1999. pp. 145–161.
    1. Stirman SW, Baumann AA, Miller CJ. The FRAME: an expanded framework for reporting adaptations and modifications to evidence-based interventions. Implement Sci. 2019;14(1):58. doi: 10.1186/s13012-019-0898-y.

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