The Patient - Patient-Centered Outcomes Research

, Volume 10, Issue 6, pp 773–783 | Cite as

Identifying and Prioritizing the Barriers and Facilitators to the Self-Management of Type 2 Diabetes Mellitus: A Community-Centered Approach

  • Allison H. OakesEmail author
  • Vincent S. Garmo
  • Lee R. Bone
  • Daniel R. Longo
  • Jodi B. Segal
  • John F. P. Bridges
Original Research Article



Self-management of type 2 diabetes mellitus is crucial to controlling the disease and preventing harm. Multiple factors have been identified in the literature as potential barriers and facilitators to self-management, but the magnitude and directionality of these factors are seldom studied. We sought to develop and test an instrument to identify and quantify the barriers and facilitators to self-management of type 2 diabetes.


A community-centered approach was used to design, implement, and interpret the results of a stated-preference study. All activities were guided by a diverse stakeholder board. Based on previously reported development work, a novel survey instrument consisting of 13 potential barriers and facilitators was pretested and piloted in our local community. Participants were asked to discuss, rate, and rank each factor. A simple self-explicated method was used to quantify the data and Z scores were used for hypothesis testing.


In total, 25 patients with self-reported type 2 diabetes (64% female; 92% minorities) participated in the pretest and pilot. Time commitments (Z = −3.72), lack of active support groups (Z = −3.39) and other resources in the local community (Z = −2.96), and language/culture (Z = −2.69) were identified as barriers to self-management. Access to healthy food (Z = +5.68), personal understanding (Z = +4.81), and communication with healthcare providers (Z = +4.62) were identified as facilitators.


We demonstrate that factors impacting self-management can be quantified and categorized as barriers and facilitators. While further refinement to some factors and investigation into alternative prioritization methods is necessary, our stakeholder board endorsed moving this to a large nationally representative study to see how these factors vary across different people.


Strong Positive Effect Rating Exercise Family Commitment Personal Understanding Ranking Exercise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research team sincerely thanks the Johns Hopkins Institute for Clinical and Translational Research Community Research Advisory Council and members of the Diabetes Action Board for their valuable contributions and engagement in this study. The authors also thank Ellen M. Janssen and Thomas Lynch for their involvement in the project. Finally, the authors thank the respondents who participated in the study.

Author Contributions

JFPB contributed to the study design and conceptualization and is the guarantor of this work. VSG, LRB, DRL, JBS, and JFPB were responsible for the acquisition of the data, interpretation of data, and manuscript preparation. AHO was responsible for data analysis, interpretation of results, and synthesizing the discussion and conclusions.

Compliance with Ethical Standards


This work was supported through the Patient-Centered Outcomes Research Institute Methods Program Award (ME-1303-5946) titled “Advancing stated-preference methods for measuring the preferences of patients with type 2 diabetes”, the Johns Hopkins Center of Excellence in Regulatory Science and Innovation and the US Food and Drug Administration (1U01FD004977-01), and the Agency for Healthcare Research and Quality (T32HS000029). The statements in this work are solely the responsibility and views of the authors.

Conflict of interest

Allison H. Oakes, Vincent S. Garmo, Lee R. Bone, Daniel R. Longo, Jodi B. Segal, and John F. P. Bridges have no competing financial or non-financial interests to disclose.

Ethics approval

This research was conducted in accordance with the Declaration of Helsinki and the study protocol was reviewed by the Johns Hopkins Institutional Review Board (IRB 6001).


  1. 1.
    Centers for Disease Control and Prevention. National vital statistics report (NVSR). Deaths: final data for 2013. Accessed 8 May 2017.
  2. 2.
    Centers for Disease Control and Prevention. National diabetes statistics report: estimates of diabetes and its burden in the United States, 2014. Atlanta: US Department of Health and Human Services; 2014. Accessed 8 May 2017.
  3. 3.
    American Diabetes Association. Economic costs of diabetes in the US in 2012. Diabetes Care. 2013;36(4):1033–46. doi: 10.2337/dc12-2625.CrossRefPubMedCentralGoogle Scholar
  4. 4.
    Centers for Disease Control and Prevention. Common eye disorders. Accessed 14 May 2017.
  5. 5.
    American Diabetes Association. Position statement: nephropathy in diabetes. Diabetes Care. 2004;27(Suppl. 1):s79–83. doi: 10.2337/diacare.27.2007.S79.Google Scholar
  6. 6.
    Li Y, Burrows NR, Gregg EW, et al. Declining rates of hospitalization for nontraumatic lower-extremity amputation in the diabetic population aged 40 years or older: US, 1988–2008. Diabetes Care. 2012;35(2):273–7. doi: 10.2337/dc11-1360.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    American Heart Association. Cardiovascular disease and diabetes. Last reviewed August 2015. Accessed 14 May 2017.
  8. 8.
    van den Arend IJ, Stolk RP, Krans HM, et al. Management of type 2 diabetes: a challenge for patient and physician. Patient Educ Couns. 2000;40(2):187–94.CrossRefPubMedGoogle Scholar
  9. 9.
    Haas L, Maryniuk M, Beck J, Cox CE. National standards for diabetes self-management education and support. Diabetes Care. 2014;37(Suppl. 1):S144–53. doi: 10.2337/dc14-S144.CrossRefPubMedGoogle Scholar
  10. 10.
    Glasgow RE, Hampson SE, Strycker LA, Ruggiero L. Personal-model beliefs and social-environmental barriers related to diabetes self-management. Diabetes Care. 1997;20(4):556–61.CrossRefPubMedGoogle Scholar
  11. 11.
    Onwudiwe NC, Mullins CD, Winston RA. Barriers to self-management of diabetes: a qualitative study among low-income minority diabetics. Ethn Dis. 2011;21(1):27–32.PubMedGoogle Scholar
  12. 12.
    Johnson AE, Boulware LE, Anderson CA. Perceived barriers and facilitators of using dietary modification for CKD prevention among African Americans of low socioeconomic status: a qualitative study. BMC Nephrol. 2014;15(1):194. doi: 10.1186/1471-2369-15-194.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Samuel-Hodge CD, Headen SW, Skelly AH. Influences on day-to-day self-management of type 2 diabetes among African–American women: spirituality, the multi-caregiver role, and other social context factors. Diabetes Care. 2000;23(7):928–33.CrossRefPubMedGoogle Scholar
  14. 14.
    Karimi MH, Namdar AH, Jouybari L. Facilitators and barriers of adaptation to diabetes: experiences of Iranian patients. J Diabetes Metab Disord. 2014;13(1):17. doi: 10.1186/2251-6581-13-17.CrossRefGoogle Scholar
  15. 15.
    Carolan M. Women’s experiences of gestational diabetes self-management: a qualitative study. Midwifery. 2013;29(6):637–45. doi: 10.1016/j.midw.2012.05.013.CrossRefPubMedGoogle Scholar
  16. 16.
    Nagelkerk J, Reick K, Meengs L. Perceived barriers and effective strategies to diabetes self-management. J Adv Nurs. 2006;54(2):151–8.CrossRefPubMedGoogle Scholar
  17. 17.
    Dailey G, Kim MS, Lian JF. Patient compliance and persistence with anti-hyperglycemic therapy: evaluation of a population of type 2 diabetic patients. J Int Med Res. 2002;30(1):71–9.CrossRefPubMedGoogle Scholar
  18. 18.
    Rovner BW, Casten RJ, Harris LF. Sociocultural influences on diabetes self-management behaviors in older African Americans. Diabetes Spectr. 2013;26(1):29–33.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Vanstone M, Giacomini M, Smith A, et al. How diet challenges are magnified in vulnerable or marginalized people with diabetes and heart disease: a systematic review and qualitative meta-synthesis. Ont Health Technol Assess Ser. 2013;13(14):1–40.Google Scholar
  20. 20.
    Lawton J, Ahmad N, Hanna L, et al. ‘We should change ourselves, but we can’t’: accounts of food and eating practices amongst British Pakistanis and Indians with type 2 diabetes. Ethn Health. 2008;13(4):305–19. doi: 10.1080/13557850701882910.CrossRefPubMedGoogle Scholar
  21. 21.
    Ahola AJ, Groop PH. Review article: barriers to self-management of diabetes. Diabetes Med. 2013;30(4):413–20. doi: 10.1111/dme.12105.CrossRefGoogle Scholar
  22. 22.
    Purnell TS, Lynch TJ, Bone L, et al. Perceived barriers and potential strategies to improve self-management among adults with type 2 diabetes: a community-engaged research approach. Patient. 2016;9(4):349–58. doi: 10.1007/s40271-016-0162-3.CrossRefPubMedGoogle Scholar
  23. 23.
    Bridges JF. Stated preference methods in health care evaluation: an emerging methodological paradigm in health economics. Appl Health Econ Health Policy. 2003;2(4):213–24.PubMedGoogle Scholar
  24. 24.
    Bridges JF, Hauber AB, Marshall D, et al. Conjoint analysis applications in health: a checklist. A report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health. 2011;14(4):403–13.CrossRefPubMedGoogle Scholar
  25. 25.
    Coast J, Al-Janabi H, Sutton EJ, et al. Using qualitative methods for attribute development for discrete choice experiments: issues and recommendations. Health Econ. 2012;21(6):730–41.CrossRefPubMedGoogle Scholar
  26. 26.
    Janssen EM, Segal JB, Bridges JF. A framework for instrument development of a choice experiment: an application to type 2 diabetes. Patient. 2016;9(5):465–79. doi: 10.1007/s40271-016-0170-3.CrossRefPubMedGoogle Scholar
  27. 27.
    Hollin IL, Young C, Hanson C, et al. Developing a patient-centered benefit-risk survey: a community-engaged process. Value Health. 2016;19(6):751–7.CrossRefPubMedGoogle Scholar
  28. 28.
    Peay HL, Hollin I, Fischer R, Bridges JF. A community-engaged approach to quantifying caregiver preferences for the benefits and risks of emerging therapies for Duchenne muscular dystrophy. Clin Ther. 2014;36(5):624–37.CrossRefPubMedGoogle Scholar
  29. 29.
    Johns Hopkins Institute for Clinical and Translational Research. Community Research Advisory Council. Accessed 3 Mar 2017.
  30. 30.
    Johns Hopkins School of Public Health. Type 2 diabetes stated preferences research. Diabetes Action Board. Accessed 3 Mar 2017.
  31. 31.
  32. 32.
    Fleurence R, Selby JV, Odom-Walker K, et al. How the Patient-Centered Outcomes Research Institute is engaging patients and others in shaping its research agenda. Health Affairs (Millwood). 2013;32(2):393–400.CrossRefPubMedGoogle Scholar
  33. 33.
    Green PE, Srinivasan V. Conjoint analysis in marketing: new developments with implications for research and practice. J Mark. 1990;54:3–19.CrossRefGoogle Scholar
  34. 34.
    Bridges JF, Joy SM, Gallego G, et al. Priorities for hepatocellular carcinoma (HCC) control: a comparison of policy needs in five European countries. J Comp Policy Anal Res Pract. 2012;14(4):352–68.CrossRefGoogle Scholar
  35. 35.
    Netzer O, Srinivasan V. Adaptive self-explication of multiattribute preferences. J Mark Res. 2011;48(1):140–56.CrossRefGoogle Scholar
  36. 36.
    Potoglou D, Burge P, Flynn T, et al. Best–worst scaling vs. discrete choice experiments: an empirical comparison using social care data. Soc Sci Med. 2011;72(10):1717–27. doi: 10.1016/j.socscimed.2011.03.027.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Allison H. Oakes
    • 1
    Email author
  • Vincent S. Garmo
    • 1
  • Lee R. Bone
    • 2
    • 4
  • Daniel R. Longo
    • 3
  • Jodi B. Segal
    • 1
    • 2
  • John F. P. Bridges
    • 1
    • 4
  1. 1.Department of Health Policy and ManagementJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  2. 2.Department of MedicineJohns Hopkins School of MedicineBaltimoreUSA
  3. 3.Department of Family Medicine and Population HealthVirginia Commonwealth UniversityRichmondUSA
  4. 4.Department of Health Behavior and SocietyJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA

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