Large clinical trials demonstrated that glycemic control improves health outcomes in persons with type 2 diabetes, but fewer than 30% of these individuals achieve glycemic goals. To achieve glycemic control, individuals must adapt dietary preferences, increase physical activity, accurately adhere to prescribed medications, and self-monitor glucose levels several times per day. A large body of correlational and/or predictive research has explored intervening variables that may affect behavior change and health outcomes. This extensive literature has not been systematically reviewed nor synthesized. The proposed study will: 1) describe the research literature that examines associations among psychological factors, motivational factors, diabetes-related knowledge, and health behaviors and outcomes of weight loss, metabolic control, and quality of life in type 2 diabetes;and 2) using data synthesized from previously reported studies, test a series of predictive models that involve these variables. Meta-analytic and model- testing procedures will be used to synthesize both published and unpublished research data. Comprehensive literature search strategies will be used. Each primary study selected will involve: a) a sample of subjects diagnosed with type 2 diabetes;b) data reported between 1960 and the present;c) English language;d) weight loss, HbA1c, FBG, and/or quality of life measured as the criterion variable(s);e) a measure of at least one of the following predictors: psychological factors (stress, depression, anxiety), motivational factors (self-efficacy, empowerment/locus of control, readiness for change, health beliefs), diabetes-related knowledge, and/or behavioral factors (diet, physical activity, medication adherence, and self-monitoring);f) data in the form of correlation matrices among predictor and criterion variables or a simple correlation between at least one predictor and a criterion variable;and g) a minimum of 20 subjects. Rigorous procedures for establishing reliable and valid data extraction/coding will be employed. Established causal modeling procedures will be used to combine correlations across studies, test for heterogeneity of the correlation matrices, estimate the between-studies covariance components matrix, and estimate the random-effects of the common correlation matrix, along with either fixed- or random-effects estimation of the path coefficients of the predictive model, and the associated standard errors and significance tests. It is imperative to synthesize these studies to inform clinical guidelines so that clinicians can effectively address the growing global diabetes epidemic.
Despite recent technological and pharmaceutical advancements, fewer than thirty percent of individuals diagnosed with type 2 diabetes achieve glycemic goals. To achieve glycemic control, individuals must adapt dietary preferences, increase physical activity, accurately adhere to prescribed medications, and self- monitor glucose levels several times per day. The proposed study involves a systematic synthesis of the large body of research conducted on the psychological, motivational, and behavioral factors that need to be emphasized in diabetes interventions in order to improve diabetes health outcomes, particularly those related to glycemic control and quality of life. The information obtained will inform clinical guidelines so that clinicians can effectively address the growing global diabetes epidemic.
| Brown, Sharon A; García, Alexandra A; Brown, Adama et al. (2016) Biobehavioral determinants of glycemic control in type 2 diabetes: A systematic review and meta-analysis. Patient Educ Couns 99:1558-67 |
| Brown, Sharon A; Becker, Betsy Jane; García, Alexandra A et al. (2015) Model-driven meta-analyses for informing health care: a diabetes meta-analysis as an exemplar. West J Nurs Res 37:517-35 |
| Sumlin, Lisa L; Garcia, Theresa J; Brown, Sharon A et al. (2014) Depression and adherence to lifestyle changes in type 2 diabetes: a systematic review. Diabetes Educ 40:731-44 |
| Brown, Sharon A; Martin, Ellen E; Garcia, Theresa J et al. (2013) Managing complex research datasets using electronic tools: a meta-analysis exemplar. Comput Inform Nurs 31:257-65 |
