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ANALYSIS OF PSYCHOSOCIAL INFLUENCES ON GLYCEMIC CONTROL AMONG ADULTS WITH T2DM AND DEPRESSIONJennifer Bauman, BA, RN1 Kimberly Frier, MSN, FNP-BC, ACHPN1 Celia E. Wills,PhD, RN1 Carla Miller, PhD, RD2 William F. Miser, MD3

Background

Self-management of type 2 diabetes (T2DM) is influenced by a multitude of factors. Autonomy and decision support from health care providers and important extra personal others influences confidence in capabilities, or self-efficacy,1,2,3 which in turn impacts engagement in self-management behaviors (SMB) and behavior change.4,5,6 However, engagement in SMBs is reduced in patients with coexisting depression and chronic illness.7 Engagement in SMBs affects patient-centered outcomes, such as hemoglobin A1C (HbA1C).8,9 Because of the complex context in which self-management of T2DM occurs, more research is needed on the influence of psychosocial variables on clinical outcomes.

Purpose

To explore the relationships among depressive symptoms, autonomy and decision support, self-efficacy, and engagement in SMBs in predicting extent of glycemic control.

Implications

The combination of depressive symptoms, autonomy and decision support, and self-efficacy accounted for over two-thirds of the variance in baseline HbA1c, highlighting the importance of these potentially modifiable variables in predicting glycemic control. Interventions to enhance self-management of T2DM should address these variables.

Acknowledgements 1College of Nursing, Ohio State University, Columbus, OH, 2Department of Human Nutrition, Ohio State University, Columbus, OH, 3College of Medicine, Ohio State University, Columbus, OH Authors listed on the poster abstract are research team members for a study led by Dr. Celia E. Wills, Principal Investigator, entitled, "Support Intervention for Shared Decision-making about Depressive Symptoms in Diabetes Mellitus: A comparative Effectiveness Research Pilot Study."  The project was supported by Award Number ULRR025755 to The Ohio State University Center for Clinical Translational Science from the U.S. National Center for Research Resources.  The content of the poster is solely the responsibility of the authors' and does not necessarily represent the official views of the U.S. National Center for Research Resources of the National Institutes of Health.

References

1. Williams, G.C., Lynch, M.F., McGregor, H.A., Ryan, R.M., Sharp, D., & Deci, E.L. (2006). Validation of the “Important Other” Climate Questionnaire: Assessing autonomy support for health-related change. Families, Systems, & Health, 24(2), 179-194.2. Williams, G.C., McGregor, H.A., King, D., Nelson, C.C., & Glasgow, R.E. (2005). Variation in perceived competence, glycemic control, and patient satisfaction: Relationship to autonomy support from physicians. Patient Education and Counseling, 57, 39-45.3. Williams, G.C., Patrick, H., Niemiec, C.P., Williams, L.K., Divine, G., Lafata, J.E., & Heisler, M. (2009). Reducing the health risks of diabetes: How Self-determination Theory may help improve medication adherence and quality of life. Diabetes Educator, 35(3), 484-492.4. Lorig, K., & Holman, H. (2004). Patient self-management: A key to effectiveness and efficiency in care of chronic diseases. Public Health Reports, 119, 239-243.5. Lorig, K.R., & Holman, H.R. (2003). Self-management education: History, definition, outcomes, and measurement. Annals of Behavioral Medicine, 26(1). 6. Leventhal, H., Weinman, J., Leventhal, E.A., & Phillips, L.A. (2008). Health psychology: The search for pathways between behavior and health. Annual Review of Psychology, 59, 477–505. doi: 10.1146/annurev.psych.59.103006.093643.7. Harrison, M., Reeves, D., Harkness, E., Valderas, J., Kennedy, A., Rogers, A., Hann, M., & Bower, P. (2012). A secondary analysis of the moderating effects of depression and multimorbidity on the effectiveness of a chronic disease self-management programme. Patient Education and Counseling, 87, 67-73. 8. Al-Khawaldeh, O.A., Al-Hassan, M.A., & Froelicher, E.S. (2012). Self-efficacy, self-management, and glycemic control in adults with type 2 diabetes mellitus. Journal of Diabetes and Its Complications, 26, 10-16.  9. Gao, J., Wang, J., Zheng, P., Haardorfer, R., Kegler, M.C., Yapcjemg. Z., & Fu, H. (2013). Effects of self-care, self-efficacy, and social support on glycemic control in adults with type 2 diabetes. British Medical Journal Family Practice, 14, 66.

Methods

•Baseline data from a pilot intervention study for supporting effective decision-making about managing depressive symptoms were analyzed for 20 socio-demographically diverse adults (76.2% female, 38.1% non-white, mean 48 years, 42.9% with less than a college degree) with inadequately-controlled T2DM and depressive symptoms.

•Standardized validated measures included the following:

• Depressive symptoms: Patient Health Questionnaire-9 (PHQ-9, range 0-27)

• Autonomy support: Important Other Climate Questionnaire (IOCQ, range 0-60) and Modified Health Care Climate Questionnaire (MHCCQ, range 0-60)

• Diabetes self-efficacy: Diabetes Self-Efficacy scale (DSES, range 0-80)

• Glycemic control: fingerstick HbA1c

•Baseline narrative case notes were dichotomously coded for mention of any SMB and presence/absence of positive extra personal decision support (0 = not mentioned, 1 = mentioned).

•A linear regression analysis was done to model the direction and extent of relationships among decision and autonomy support, depressive symptoms, self-efficacy, and engagement in SMBs in predicting baseline glycemic control (HbA1c), after controlling for socio-demographic factors or age, sex, and educational level (0 = “some college/technical school or less, 1 = completed college degree or higher).

Results

•After controlling for socio-demographic factors, the combination of depressive symptoms and autonomy and decision support accounted for an additional 11.8% (R2 Δ = .118) of the variance in HbA1c.

•The addition of self-efficacy to the model accounted for an additional 46.9% of the variance in HbA1c; this was statistically significant (Δ R2 = .469, F1, 11 Δ = 16.686, p = .002). The addition of SBM code did not contribute significant variance beyond the variables already included.

•The overall model accounted for 69.7% (p = .661) of the variance in baseline HbA1c.

Model R R2 R2 Δ Cumulative R2 Δ

F Δ df1

df2

Sig.

1AgeSexEducational level

.333 .111 .111 .111 .664 3 16 .586

2PHQ-9

.344 .118 .007 .007 .124 1 15 .730

3MHCCQIOCQInterpersonal support code

.472 .222 .104 .111 .537 3 12 .666

4DSES

.831 .691 .469 .580 16.686 1 11 .002

5SBM code

.835 .697 .006 .586 .204 1 10 .661

Variable Mean SD N

HbA1c 8.11 2.23 20

PHQ-9 11.70 5.81 20

MHCCQ 29.99 9.75 20

IOCQ 26.20 9.86 20

DSES 43.85 15.68 20

Age 48.25 9.24 20