Skip to main content

Advertisement

Log in

Understanding social forces involved in diabetes outcomes: a systems science approach to quality-of-life research

  • Commentary
  • Published:
Quality of Life Research Aims and scope Submit manuscript

Abstract

Purpose

The field of quality-of-life (QOL) research would benefit from learning about and integrating systems science approaches that model how social forces interact dynamically with health and affect the course of chronic illnesses. Our purpose is to describe the systems science mindset and to illustrate the utility of a system dynamics approach to promoting QOL research in chronic disease, using diabetes as an example.

Methods

We build a series of causal loop diagrams incrementally, introducing new variables and their dynamic relationships at each stage.

Results

These causal loop diagrams demonstrate how a common set of relationships among these variables can generate different disease and QOL trajectories for people with diabetes and also lead to a consideration of non-clinical (psychosocial and behavioral) factors that can have implications for program design and policy formulation.

Conclusions

The policy implications of the causal loop diagrams are discussed, and empirical next steps to validate the diagrams and quantify the relationships are described.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Ahmed, S., et al. (2012). The use of patient-reported outcomes (PRO) within comparative effectiveness research: Implications for clinical practice and health care policy. Medical Care, 50(12), 1060–1070.

    PubMed  Google Scholar 

  2. Fleming, B. B., et al. (2001). The Diabetes Quality Improvement Project: Moving science into health policy to gain an edge on the diabetes epidemic. Diabetes Care, 24(10), 1815–1820.

    CAS  PubMed  Google Scholar 

  3. Velikova, G., et al. (2004). Measuring quality of life in routine oncology practice improves communication and patient well-being: A randomized controlled trial. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 22(4), 714–724.

    Google Scholar 

  4. FDA, Guidance for Industry. Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims, F.a.D. Administration, Editor. 2009, U.S. Dept. of Health and Human Services: Washington, D.C.

  5. Wagner, E. H., et al. (2001). Improving chronic illness care: Translating evidence into action. Health Affairs, 20(6), 64–78.

    CAS  PubMed  Google Scholar 

  6. Wagner, E. H., et al. (1999). A survey of leading chronic disease management programs: are they consistent with the literature? Managed Care Quarterly, 7(3), 56–66.

    CAS  PubMed  Google Scholar 

  7. Stellefson, M., Dipnarine, K., & Stopka, C. (2013). The chronic care model and diabetes management in US primary care settings: A systematic review. Preventing Chronic Disease, 10, 120180.

    Google Scholar 

  8. Allen, I. HHS Secretary calls on corporate America and government to help fight obesity. Center for the Advancement of Health.

  9. Huang, T. T., et al. (2009). A systems-oriented multilevel framework for addressing obesity in the 21st century. Preventing Chronic Disease: Public Health Research, Practice, and Policy, 6(3), 1–10.

    Google Scholar 

  10. Milstein, B., Homer, J., & Hirsch, G. B. (2010). Analyzing national health reform strategies with a dynamic simulation model. American Journal of Public Health, 100(5), 811–819.

    PubMed Central  PubMed  Google Scholar 

  11. Roux, A. V. D. (2011). Complex systems thinking and current impasses in health disparities research. American Journal of Public Health, 101(9), 1627–1634.

    Google Scholar 

  12. Metcalf, S. S., Northridge, M. E., & Lamster, I. B. (2011). A systems perspective for dental health in older adults. American Journal of Public Health, 101(10), 1820–1822.

    PubMed Central  PubMed  Google Scholar 

  13. Luke, D. A., & Stamatakis, K. A. (2012). Systems science methods in public health: Dynamics, networks, and agents. Annual Review of Public Health, 33, 357–376.

    PubMed Central  PubMed  Google Scholar 

  14. Mabry, P. L., et al. (2008). Interdisciplinarity and systems science to improve population health: A view from the NIH office of behavioral and social sciences research. American Journal of Preventive Medicine, 35(S2), S211–S224.

    PubMed Central  PubMed  Google Scholar 

  15. Hirsch, G. B., Levine, R., & Miller, R. L. (2007). Using system dynamics modeling to understand the impact of social change initiatives. American Journal of Community Psychology, 39(3–4), 239–253.

    PubMed  Google Scholar 

  16. Homer, J., et al. (2004). Models for collaboration: How system dynamics helped a community organize cost-effective care for chronic illness. System Dynamics Review, 20(3), 199–222.

    Google Scholar 

  17. Forrester, J. W. (1987). Nonlinearity in high-order models of social systems. European Journal of Operational Research, 30(2), 104–109.

    Google Scholar 

  18. Maani, K. E., & Cavana, R. Y. (2000). Systems thinking modeling: Understanding change and complexity. Auckland: Pearson Education New Zealand Limited.

    Google Scholar 

  19. Sterman, J. (1994). Learning in and about complex systems. System Dynamics Review, 10(2–3), 291–330.

    Google Scholar 

  20. Forrester, J. W. (1987). The model versus a modeling process. System Dynamics Review, 1(1), 133–134.

    Google Scholar 

  21. Repenning, N. A. (2002). A simulation-based approach to understanding the dynamics of innovation implementation. Organization Science, 13, 109–127.

    Google Scholar 

  22. Richardson, G. P. (1991). Feedback thought in social science and systems theory. Waltham: Pegasus Communications, Inc.

    Google Scholar 

  23. Richardson, G. P., & Pugh, A. L., I. I. I. (1981). Introduction to system dynamics modeling. Portland: Productivity Press.

    Google Scholar 

  24. Homer, J. B., & Hirsch, G. (2006). System dynamics modeling for public health: Background and opportunities. American Journal of Public Health, 96(3), 452–458.

    PubMed Central  PubMed  Google Scholar 

  25. Arboleda, C. A., Abraham, D. M., & Lubitz, R. (2007). Simulation as a tool to assess the vulnerability of the operation of a health care facility. Journal of Performance of Constructed Facilities, 21(4), 302–312.

    Google Scholar 

  26. Cavana, R. Y., et al. (1999). Drivers of quality in health services: Different worldviews of clinicians and policy managers revealed. System Dynamics Review, 15(3), 331–340.

    Google Scholar 

  27. Hirsch, G., & Immediato, C. S. (1999). Microworlds and generic structures as resources for integrating care and improving health. System Dynamics Review, 15(3), 315–330.

    Google Scholar 

  28. Hirsch, G., & Miller, S. (1974). Evaluating HMO Policies with a Computer Simulation Model. Medical Care, 12(8), 668–681.

    CAS  PubMed  Google Scholar 

  29. Hovmand, P. S., & Gillespie, D. F. (2010). Implementation of evidence-based practice and organizational performance. Journal of Behavioral Health Services and Research, 37(1), 79–94.

    PubMed  Google Scholar 

  30. Royston, G., et al. (1999). Using system dynamics to help develop and implement policies and programmes in health care in England. System Dynamics Review, 15(3), 293–313.

    Google Scholar 

  31. Wolstenholme, E., et al. (2007). Coping but not coping in health and social care: Masking the reality of running organisations beyond safe design capacity. System Dynamics Review, 23(4), 371–389.

    Google Scholar 

  32. Gonzalez-Busto, B., & Garcia, R. (1999). Waiting lists in Spanish public hospitals: A system dynamics approach. System Dynamics Review, 15(3), 201–224.

    Google Scholar 

  33. Lane, D. C., & Husemann, E. (2008). System dynamics mapping of acute patient flows. Journal of the Operational Research Society, 59(2), 213–224.

    Google Scholar 

  34. Fernandez, M. I. T., Vasquez, O. C., & Martinic, J. (2010). Computer modeling and simulation of the patient-visit network within a Chilean public health service. Revista Panamericana De Salud Publica-Pan American Journal of Public Health., 27(3), 203–210.

    PubMed  Google Scholar 

  35. van Ackere, A., & Smith, P. C. (1999). Towards a macro model of national health service waiting lists. System Dynamics Review, 15(3), 225–252.

    Google Scholar 

  36. Vanderby, S., & Carter, M. W. (2010). An evaluation of the applicability of system dynamics to patient flow modelling. Journal of the Operational Research Society, 61(11), 1572–1581.

    Google Scholar 

  37. Wolstenholme, E. (1999). A patient flow perspective of UK Health Services: Exploring the case for new “intermediate care” initiatives. System Dynamics Review, 15(3), 253–271.

    Google Scholar 

  38. Bliss, J. R., Gillespie, D. F., & Gongaware, N. K. (2010). Dynamics of caseworker turnover and clinical knowledge. Administration in Social Work, 34(1), 4–26.

    Google Scholar 

  39. McGregor, M. (2010). A system dynamics approach to jurisdictional conflict between a major and a minor healthcare profession. Systems Research and Behavioral Science, 27(6), 639–652.

    Google Scholar 

  40. Vanderby, S. A., et al. (2010). Modeling the cardiac surgery workforce in Canada. Annals of Thoracic Surgery, 90(2), 467–473.

    PubMed  Google Scholar 

  41. Braithwaite, J., et al. (2009). The development, design, testing, refinement, simulation and application of an evaluation framework for communities of practice and social-professional networks. BMC Health Services Research, 9(1), 162.

    PubMed Central  PubMed  Google Scholar 

  42. Elf, M., Poutilova, M., & Ohrn, K. (2007). A dynamic conceptual model of care planning. Scandinavian Journal of Caring Sciences, 21(4), 530–538.

    PubMed  Google Scholar 

  43. Taylor, K., & Dangerfield, B. (2005). Modelling the feedback effects of reconfiguring health services. Journal of the Operational Research Society, 56(6), 659–675.

    Google Scholar 

  44. Homer, J., et al. (2004). Models for collaboration: How system dynamics helped a community organize cost-effective care for chronic illness. System Dynamics Review, 20(3), 199–222.

    Google Scholar 

  45. Brailsford, S. C., et al. (2004). Emergency and on-demand health care: Modelling a large complex system. Journal of the Operational Research Society, 55(1), 34–42.

    Google Scholar 

  46. Lane, D. C., Monefeldt, C., & Rosenhead, J. V. (2000). Looking in the wrong place for healthcare improvements: A system dynamics study of an accident and emergency department. Journal of the Operational Research Society, 51(5), 518–531.

    Google Scholar 

  47. Storrow, A. B., et al. (2008). Decreasing lab turnaround time improves emergency department throughput and decreases emergency medical services diversion: A Simulation Model. Academic Emergency Medicine, 15(11), 1130–1135.

    PubMed  Google Scholar 

  48. Abdel-Hamid, T. K. (2002). Modeling the dynamics of human energy regulation and its implications for obesity treatment. System Dynamics Review, 18(4), 431–471.

    Google Scholar 

  49. Karanfil, O., & Barlas, Y. (2008). A dynamic simulator for the management of disorders of the body water homeostasis. Operations Research, 56(6), 1474–1492.

    Google Scholar 

  50. Liu, H., & Shi, P. (2009). Maximum a posteriori strategy for the simultaneous motion and material property estimation of the heart. IEEE Transactions on Biomedical Engineering, 56(2), 378–389.

    PubMed  Google Scholar 

  51. Dangerfield, B. C., Fang, Y. X., & Roberts, C. A. (2001). Model-based scenarios for the epidemiology of HIV/AIDS: The consequences of highly active antiretroviral therapy. System Dynamics Review, 17(2), 119–150.

    Google Scholar 

  52. Flessa, S. (2003). Decision support for AIDS control programmes in eastern Africa. OR Spectrum, 25(2), 265–291.

    Google Scholar 

  53. Lebcir, R. M., Atun, R. A., & Coker, R. J. (2010). System dynamic simulation of treatment policies to address colliding epidemics of tuberculosis, drug resistant tuberculosis and injecting drug users driven HIV in Russia. Journal of the Operational Research Society, 61(8), 1238–1248.

    Google Scholar 

  54. Roberts, C., & Dangerfield, B. (1990). Modeling the epidemiologic consequences of HIV-infection and aids—a contribution from operational-research. Journal of the Operational Research Society, 41(4), 273–289.

    Google Scholar 

  55. Roberts, E. B., et al. (1982). A systems view of the smoking problem: Perspective and limitations of the role of science in decision-making. International Journal of Bio-Medical Computing, 13(1), 69–86.

    CAS  PubMed  Google Scholar 

  56. Chick, S. E., Mamani, H., & Simchi-Levi, D. (2008). Supply chain coordination and influenza vaccination. Operations Research, 56(6), 1493–1506.

    Google Scholar 

  57. Homer, J., et al. (2000). Toward a dynamic theory of antibiotic resistance. System Dynamics Review, 16(4), 287–319.

    Google Scholar 

  58. Thompson, K. M., & Tebbens, R. J. D. (2008). Using system dynamics to develop policies that matter: Global management of poliomyelitis and beyond. System Dynamics Review, 24(4), 433–449.

    Google Scholar 

  59. Ahmad, S. (2005). The cost-effectiveness of raising the legal smoking age in California. Medical Decision Making, 25(3), 330–340.

    PubMed  Google Scholar 

  60. Ahmad, S. (2005). Closing the youth access gap: The projected health benefits and cost savings of a national policy to raise the legal smoking age to 21 in the United States. Health Policy, 75(1), 74–84.

    PubMed  Google Scholar 

  61. Cavana, R. Y., & Clifford, L. V. (2006). Demonstrating the utility of system dynamics for public policy analysis in New Zealand: the case of excise tax policy on tobacco. System Dynamics Review, 22(4), 321–348.

    Google Scholar 

  62. Tengs, T. O., Osgood, N. D., & Chen, L. L. (2001). The cost-effectiveness of intensive national school-based anti-tobacco education: Results from the Tobacco Policy Model. Preventive Medicine, 33(6), 558–570.

    CAS  PubMed  Google Scholar 

  63. Homer, J. B. (1993). A system dynamics model of national cocaine prevalence. System Dynamics Review, 9(1), 49–78.

    Google Scholar 

  64. Homer, J. B., & StClair, C. L. (1991). A model of hiv transmission through needle sharing. Interfaces, 21(3), 26–49.

    Google Scholar 

  65. Smith, P. C., & van Ackere, A. (2002). A note on the integration of system dynamics and economic models. Journal of Economic Dynamics and Control, 26(1), 1–10.

    Google Scholar 

  66. Wakeland, W., et al. (2011). System dynamics modeling as a potentially useful tool in analyzing mitigation strategies to reduce overdose deaths associated with pharmaceutical opioid treatment of chronic pain. Pain Medicine, 12, S49–S58.

    PubMed  Google Scholar 

  67. Huz, S., et al. (1997). A framework for evaluating systems thinking interventions: An experimental approach to mental health system change. System Dynamics Review, 13(2), 149–169.

    Google Scholar 

  68. Smits, M. (2010). Impact of policy and process design on the performance of intake and treatment processes in mental health care: a system dynamics case study. Journal of the Operational Research Society, 61(10), 1437–1445.

    Google Scholar 

  69. Homer, J., Hirsch, G., & Milstein, B. (2007). Chronic illness in a complex health economy: The perils and promises of downstream and upstream reforms. System Dynamics Review, 23(2–3), 313–343.

    Google Scholar 

  70. Siegel, C. A., et al. (2011). Real-time tool to display the predicted disease course and treatment response for children with Crohn’s disease. Inflammatory Bowel Diseases, 17(1), 30–38.

    PubMed Central  PubMed  Google Scholar 

  71. Wild, S., et al. (2004). Global prevalence of diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care, 27(5), 1047–1053.

    PubMed  Google Scholar 

  72. Shaw, J. E., Sicree, R. A., & Zimmet, P. Z. (2010). Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Research and Clinical Practice, 87(1), 4–14.

    CAS  PubMed  Google Scholar 

  73. Owen, J., & Reisin, E. (2012). Non-communicable disease: A welcome and long needed addition to the WHO’s 2012 world heath statistics. Current Hypertension Reports, 14(6), 475–477.

    PubMed  Google Scholar 

  74. Prevention, C.f.D.C.a., National Diabetes Fact Sheet. 2011, Centers for Disease Control and Prevention: Atlanta, GA.

  75. Rubin, R. R., & Peyrot, M. (1999). Quality of life and diabetes. Diabetes/Metabolism Research and Reviews, 15, 205.

    CAS  PubMed  Google Scholar 

  76. Kim, M., D. Berger, and T. Matte, Diabetes in New York city: Public health burden and disparities., D.o.H.a.M. Hygiene., Editor. 2006, New York City NY.

  77. Glasgow, R., et al. (1999). If diabetes is a public health problem, why not treat it as one? A population-based approach to chronic illness. Annals of Behavioral Medicine, 21(2), 159–170.

    CAS  PubMed  Google Scholar 

  78. Ayalon, L., et al. (2008). Determinants of quality of life in primary care patients with diabetes: Implications for social workers. Health and Social Work, 33(3), 229–236.

    PubMed  Google Scholar 

  79. Hinder, S., Greenhalgh, T. (2012). “This does my head in”. Ethnographic study of self-management by people with diabetes. BMC Health Services Research. 12(Journal Article), 83–83.

  80. Primozic, S., et al. (2012). Specific cognitive abilities are associated with diabetes self-management behavior among patients with type 2 diabetes. Diabetes Research and Clinical Practice, 95(1), 48–54.

    PubMed  Google Scholar 

  81. Brod, M. (1998). Pilot study—quality of life issues in patients with diabetes and lower extremity ulcers: Patients and care givers. Quality of Life Research, 7(4), 365–375.

    CAS  PubMed  Google Scholar 

  82. Chiu, C., & Wray, L. A. (2011). Healthy lifestyle and psychological well-being buffer diabetes-related cognitive decline: Findings from the taiwan longitudinal study on aging. Gerontologist, 51, 518.

    Google Scholar 

  83. Hernandez-Tejada, M. A., et al. (2012). Effect of perceived control on quality of life in indigent adults with type 2 diabetes. The Diabetes Educator, 38(2), 256.

    PubMed Central  PubMed  Google Scholar 

  84. Wändell, P. E. (2005). Quality of life of patients with diabetes mellitus. Scandinavian Journal of Primary Health Care, 23(2), 68–74.

    PubMed  Google Scholar 

  85. Bower, P., et al. (2012). A cluster randomised controlled trial of the clinical and cost-effectiveness of a ‘whole systems’ model of self-management support for the management of long- term conditions in primary care: trial protocol. Implementation Science, 7(7), 26.

    Google Scholar 

  86. Dickman, K., et al. (2012). Behavior changes in patients with diabetes and hypertension after experiencing shared medical appointments. Journal of the American Academy of Nurse Practitioners, 24(1), 43–51.

    PubMed  Google Scholar 

  87. Gagliardino, J. J., et al. (2012). Patients’ education, and its impact on care outcomes, resource consumption and working conditions: Data from the International Diabetes Management Practices Study (IDMPS). Diabetes and Metabolism, 38(2), 128–134.

    CAS  PubMed  Google Scholar 

  88. Hecht, L. (2012). Informed shared decision making. Where are we in diabetology? Diabetologe, 8(3), 222.

    Google Scholar 

  89. Jonkers, C. C. M., et al. (2012). The effectiveness of a minimal psychological intervention on self-management beliefs and behaviors in depressed chronically ill elderly persons: A randomized trial. International Psychogeriatrics, 24(2), 288–297.

    PubMed  Google Scholar 

  90. Feinglass, J., et al. (2012). How ‘preventable’ are lower extremity amputations? A qualitative study of patient perceptions of precipitating factors. Disability and Rehabilitation, 34(25), 2158–2165.

    PubMed  Google Scholar 

  91. Fransen, M. P., von Wagner, C., & Essink-Bot, M. L. (2012). Diabetes self-management in patients with low health literacy: Ordering findings from literature in a health literacy framework. Patient Education and Counseling, 88(1), 44–53.

    PubMed  Google Scholar 

  92. Richardson, G. (1986). Problems with causal loop diagrams. System Dynamics Review, 2(2), 158–170.

    Google Scholar 

  93. Hernandez-Tejada, M. A., et al. (2012). Effect of perceived control on quality of life in indigent adults with type 2 diabetes. The Diabetes Educator, 38(2), 256.

    PubMed Central  PubMed  Google Scholar 

  94. Rubin, R. R., Peyrot, M. Quality of life and diabetes. Diabetes/Metabolism Research and Reviews. 15(Journal Article), 205.

  95. Jelsness-Jorgensen, L.-P., et al. (2011). Measuring health-related quality of life in non-complicated diabetes patients may be an effective parameter to assess patients at risk of a more serious disease course: a cross-sectional study of two diabetes outpatient groups. Journal of Clinical Nursing. 20(Journal Article), 1255.

    Google Scholar 

  96. Stern, Y., ed. Cognitive Reserve: Theory and Applications. 2007, Taylor and Francis: New York.

  97. Stern, Y. (2009). Cognitive Reserve. Neuropsychologia, 47, 2015–2028.

    PubMed Central  PubMed  Google Scholar 

  98. Penner, I.-K., et al. (2006). Therapy-induced plasticity of cognitive functions in MS patients: Insights from fMRI. Journal of Physiology—Paris, 99, 455–462.

    Google Scholar 

  99. Perneczky, R., et al. (2008). Activities of daily living, cerebral glucose metabolism, and cognitive reserve in Lewy body and Parkinson’s disease. Dementia and Geriatric Cognitive Disorders, 26(5), 475–481.

    CAS  PubMed  Google Scholar 

  100. Nithianantharajah, J., & Hannan, A. J. (2009). The neurobiology of brain and cognitive reserve: mental and physical activity as modulators of brain disorders. Progress in Neurobiology, 89(4), 369–382.

    PubMed  Google Scholar 

  101. Schwartz, C. E., et al. (2013). Cognitive reserve and patient-reported outcomes. MS Journal, 19(1), 87–105.

    Google Scholar 

  102. Schwartz, C.E., et al. (2013). Cognitive reserve and symptom experience in multiple sclerosis: A buffer to disability progression over time? Archives of Physical Medicine and Rehabilitation (in press).

  103. Schwartz, C. E., et al. (2013). Cognitive reserve and appraisal in multiple sclerosis. Multiple Sclerosis and Related Disorders, 2, 36–44.

    Google Scholar 

  104. Schwartz, C. E., et al. (2013). Altruism and health outcomes in multiple sclerosis: The effect of cognitive reserve. Journal of Positive Psychology, 8(2), 144–152.

    Google Scholar 

  105. Gagliardino, J. J., et al. (2000). Evaluation and cost of diabetes care. Medicina-Buenos Aires, 60(6), 880–888.

    CAS  Google Scholar 

  106. Venkatesh, S., & Weatherspoon, L. (2013). Social and health care provider support in diabetes self-management. American Journal of Health Behavior, 37(1), 112–121.

    PubMed  Google Scholar 

  107. McFadden, E., et al. (2012). Screening for the risk of job loss in multiple sclerosis (MS): Development of an MS-specific Work Instability Scale (MS-WIS). Multiple Sclerosis, 18, 862–870.

    PubMed  Google Scholar 

  108. IOM. (2012). Living well with chronic illness: A call for public health action. Washington, DC: National Academy of Science Press.

    Google Scholar 

  109. Zagarins, S. E., et al. (2012). Improvement in glycemic control following a diabetes education intervention is associated with change in diabetes distress but not change in depressive symptoms. Journal of Behavioral Medicine, 35(3), 299–304.

    PubMed  Google Scholar 

  110. Jacobs-Van, D. B., et al. (2007). Lifestyle interventions are cost-effective in people with different levels of diabetes risk. Diabetes Care, 30(1), 128–134.

    Google Scholar 

  111. van der Wulp, I., et al. (2012). Effectiveness of peer-led self-management coaching for patients recently diagnosed with Type 2 diabetes mellitus in primary care: A randomized controlled trial. Diabetic Medicine, 29(10), e390–e397.

    PubMed  Google Scholar 

  112. Osborn, C. Y., & Egede, L. E. (2012). The relationship between depressive symptoms and medication nonadherence in type 2 diabetes: The role of social support. General Hospital Psychiatry, 34(3), 249–253.

    PubMed Central  PubMed  Google Scholar 

  113. Homer, J. B., & Hirsch, G. B. (2006). System dynamics modeling for public health: Background and opportunities. American Journal of Public Health, 96(3), 452–458.

    PubMed Central  PubMed  Google Scholar 

  114. Forrester, J. W. (1987). Lessons from system dynamics modelling. System Dynamics Review, 3, 136–149.

    Google Scholar 

  115. Rahmandad, H., Repenning, N., & Sterman, J. (2009). Effects of feedback delay on learning. System Dynamics Review, 25, 309–338.

    Google Scholar 

Download references

Acknowledgments

We gratefully acknowledge Joel Zonszein, M.D., and Judith Wylie-Rosett, Ed.D., for helpful discussions during the writing of this manuscript, and Brian Quaranto for assistance with manuscript preparation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carolyn E. Schwartz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lounsbury, D.W., Hirsch, G.B., Vega, C. et al. Understanding social forces involved in diabetes outcomes: a systems science approach to quality-of-life research. Qual Life Res 23, 959–969 (2014). https://doi.org/10.1007/s11136-013-0532-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-013-0532-4

Keywords

Navigation