Impact of Cancer on Adherence in Diabetes

描述（由申请人提供）：Jenny J. Lin，医学博士，是西奈山医学院的普通内科医生。候选人的长期目标是成为一名独立的临床研究人员，在癌症生存和慢性疾病管理之间的交叉点工作。更具体地说，候选人有兴趣了解影响癌症幸存者对慢性共病自我管理的认知和情感因素，并利用这些知识来开发，测试和传播行为干预措施，以改善癌症幸存者对共病的管理。候选人参与了这一领域，因为她从一个既定的临床医生，教育者转变为临床医生，研究者，并开始研究乳腺癌幸存者的激素治疗药物依从性的障碍和促进因素。拟议的职业发展计划将密切指导的癌症控制和行为科学项目与健康心理学（包括行为改变和健康信念），调查设计和复杂数据结果分析的多学科教学培训相结合。糖尿病是影响癌症幸存者的最常见的共病之一。这种慢性疾病及其并发症与癌症幸存者相当大的发病率和死亡率有关。此外，糖尿病也可能影响癌症风险和预后。然而，人们对新的癌症诊断对糖尿病癌症幸存者自我管理行为的影响以及癌症对糖尿病控制的影响知之甚少。在癌症研究人员的指导下，Juan Wisnivesky，MD，DrPH; Nina Bickell，MD，MPH;和Michael Diefenbach，PhD，候选人将研究早期乳腺癌或前列腺癌的新诊断对糖尿病管理和结果的短期和长期影响。研究部分的目标是：（1）评估新的癌症诊断与糖尿病控制之间的关系;（2）评估癌症诊断对糖尿病自我管理行为的影响，如药物依从性、血糖自我监测、饮食控制和运动;（3）阐明认知和情感因素（如健康信念，抑郁，焦虑和压力），介导癌症与较差的糖尿病自我管理行为和控制的关联。为了实现这些目标，候选人将通过血红蛋白A1 c水平前瞻性评估糖尿病控制，并监测200名新诊断早期乳腺癌或前列腺癌的糖尿病患者和150名年龄和性别匹配的对照糖尿病患者的口服糖尿病药物依从性。将使用电子监测帽和患者自我报告（使用标准化调查工具）测量依从性。自我管理行为将使用标准化的调查措施进行评估，有效的调查工具将用于评估抑郁症，压力和焦虑，健康信念和疾病的心理模型的症状。将在入组时以及3、6和12个月时对患者进行调查。我们将使用多元方法和结构方程模型来测试所提出的目标。

DESCRIPTION (provided by applicant): Jenny J. Lin, MD, is a general internist at the Mount Sinai School of Medicine. The candidate's long-term goal is to become an independent clinician-investigator working at the intersection between cancer survivorship and chronic disease management. More specifically, the candidate is interested in understanding the cognitive and emotional factors that affect cancer survivors' self-management of chronic comorbid diseases and to use this knowledge to develop, test, and disseminate behavioral interventions that can improve cancer survivors' management of comorbid illnesses. The candidate became involved in this area as she transitioned from being an established clinician-educator to a clinician-investigator and started to examine barriers to and facilitators of medication adherence to hormonal therapies in breast cancer survivors. The proposed career development plan combines a closely-mentored cancer control and behavioral sciences project with multidisciplinary, didactic training in health psychology (including behavior change and health beliefs), survey design, and complex data outcomes analysis. Diabetes is one of the most common comorbid illnesses affecting cancer survivors. This chronic disease and its complications are associated with considerable morbidity and mortality in cancer survivors. Additionally, diabetes may also impact cancer risk and prognosis. However, little is known about the impact of a new cancer diagnosis on patient self-management behaviors for cancer survivors with diabetes and of the impact that cancer has on diabetes control. Under the mentorship of cancer researchers, Juan Wisnivesky, MD, DrPH; Nina Bickell, MD, MPH; and Michael Diefenbach, PhD, the candidate will study the short- and longer-term impact of a new diagnosis of early-stage breast or prostate cancer on diabetes management and outcomes. The goals of the research component will be (1) to assess the relationship between new cancer diagnosis and diabetes control; (2) to evaluate the impact of cancer diagnosis on diabetes self-management behaviors, such as medication adherence, glucose self-monitoring, diet control and exercise; and (3) to elucidate the cognitive and emotional factors (such as health beliefs, depression, anxiety and stress) that mediate the association of cancer with poorer diabetes self-management behaviors and control. To achieve these goals, the candidate will prospectively assess diabetes control via hemoglobin A1c levels and monitor adherence to oral diabetes medications in a cohort of 200 diabetic patients with newly diagnosed early-stage breast or prostate cancer and 150 age- and gender-matched control diabetic patients without cancer. Adherence will be measured with electronic monitoring caps and via patient self-report using a standardized survey instrument. Self-management behaviors will be assessed using standardized survey measures, and validated survey instruments will be used to assess symptoms of depression, stress and anxiety, health beliefs and mental models of disease. Patients will be surveyed at enrollment and at 3, 6 and 12 months. We will use multivariate methods and structural equation modeling to test the proposed aims.