Meta-Analysis of Biobehavioral Determinants of Health Outcomes in Type 2 Diabetes

描述（由申请人提供）：大型临床试验表明，血糖控制可以改善2型糖尿病患者的健康结果，但只有不到30%的患者达到血糖控制目标。为了实现血糖控制，个人必须调整饮食偏好，增加体育活动，准确地坚持服用处方药，每天多次自我监测血糖水平。大量相关和/或预测性研究探索了可能影响行为改变和健康结果的干预变量。这些广泛的文献还没有被系统地回顾和综合。本研究将：1)描述研究2型糖尿病患者心理因素、动机因素、糖尿病相关知识、健康行为和减肥、代谢控制和生活质量之间关系的研究文献；2)利用先前报道的研究合成的数据，测试一系列涉及这些变量的预测模型。荟萃分析和模型检验程序将用于综合已发表和未发表的研究数据。将使用综合文献检索策略。选择的每个初步研究将包括：a)诊断为2型糖尿病的受试者样本；B) 1960年至今报告的数据；c)英语语言；d)体重减轻、糖化血红蛋白（HbA1c）、空腹血糖（FBG）和/或生活质量作为标准变量；E)对以下预测因素中至少一项的测量：心理因素（压力、抑郁、焦虑）、动机因素（自我效能、授权/控制点、改变准备、健康信念）、糖尿病相关知识和/或行为因素（饮食、身体活动、药物依从性和自我监控）；F)预测变量与标准变量之间的相关矩阵形式的数据，或至少一个预测变量与标准变量之间的简单相关性；g)至少20个科目。将采用严格的程序建立可靠和有效的数据提取/编码。已建立的因果建模程序将用于组合研究间的相关性，检验相关矩阵的异质性，估计研究间协方差成分矩阵，估计共同相关矩阵的随机效应，以及预测模型路径系数的固定或随机效应估计，以及相关的标准误差和显著性检验。综合这些研究为临床指导提供信息，以便临床医生能够有效地应对日益增长的全球糖尿病流行是势在必行的。

DESCRIPTION (provided by applicant): 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 Meta-analytic and model- testing procedures will be used to synthesize both published and unpublished research data.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. PUBLIC HEALTH RELEVANCE: 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.