XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines

The Common-Sense Model of Self-Regulation (the "Common-Sense Model", CSM) is a widely used theoretical framework that explicates the processes by which patients become aware of a health threat, navigate affective responses to the threat, formulate perceptions of the threat and potential treatment actions, create action plans for addressing the threat, and integrate continuous feedback on action plan efficacy and threat-progression. A description of key aspects of the CSM's history-over 50 years of research and theoretical development-makes clear the model's dynamic underpinnings, characteristics, and assumptions. The current article provides this historical narrative and uses that narrative to highlight dynamic aspects of the model that are often not evaluated or utilized in contemporary CSM-based research. We provide suggestions for research advances that can more fully utilize these dynamic aspects of the CSM and have the potential to further advance the CSM's contribution to medical practice and patients' self-management of illness.

The Common-Sense Model of Self-Regulation (CSM): a dynamic framework for understanding illness self-management.

Many hopes exist regarding the opportunities that the internet can offer to young people as well as fears about the risks it may bring. Informed by research on media literacy, this article examines the role of selected measures of internet literacy in relation to teenagers’ online experiences. Data from a national survey of teenagers in the UK (N = 789) are analyzed to examine: first, the demographic factors that influence skills in using the internet; and, second (the main focus of the study), to ask whether these skills make a difference to online opportunities and online risks. Consistent with research on the digital divide, path analysis showed the direct influence of age and socioeconomic status on young people’s access, the direct influence of age and access on their use of online opportunities, and the direct influence of gender on online risks. The importance of online skills was evident insofar as online access, use and skills were found to mediate relations between demographic variables and young people’s experience of online opportunities and risks. Further, an unexpected positive relationship between online opportunities and risks was found, with implications for policy interventions aimed at reducing the risks of internet use.

Balancing opportunities and risks in teenagers' use of the internet: the role of online skills and internet self-efficacy

Shared decision-making is increasingly advocated as an ideal model of treatment decision-making in the medical encounter. To date, the concept has been rather poorly and loosely defined. This paper attempts to provide greater conceptual clarity about shared treatment decision-making, identify some key characteristics of this model, and discuss measurement issues. The particular decision-making context that we focus on is potentially life threatening illnesses, where there are important decisions to be made at key points in the disease process, and several treatment options exist with different possible outcomes and substantial uncertainty. We suggest as key characteristics of shared decision-making (1) that at least two participants-physician and patient be involved; (2) that both parties share information; (3) that both parties take steps to build a consensus about the preferred treatment; and (4) that an agreement is reached on the treatment to implement. Some challenges to measuring shared decision-making are discussed as well as potential benefits of a shared decision-making model for both physicians and patients.

Shared Decision-making in the Medical Encounter: What Does It Mean? (Or, It Takes at Least Two to Tango)

OBJECTIVE Diabetes devices (insulin pumps, continuous glucose monitors [CGMs]) are associated with benefits for glycemic control, yet uptake of these devices continues to be low. Some barriers to device uptake may be modifiable through psychosocial intervention, but little is known about which barriers and which patients to target. RESEARCH DESIGN AND METHODS We surveyed 1,503 adult T1D Exchange participants (mean age 35.3 [SD 14.8] years, diagnosis duration 20.4 [SD 12.5] years) to investigate barriers to device uptake, understand profiles of device users versus nonusers, and explore differences by age and sex. Scales used were the Diabetes Distress Scale, technology use attitudes (general and diabetes-specific), and barriers to device use and reasons for discontinuing devices. RESULTS Most commonly endorsed modifiable barriers were related to the hassle of wearing devices (47%) and disliking devices on one’s body (35%). CGM users (37%) were older than nonusers (mean 38.3 vs. 33.5 years), had diabetes for longer (22.9 vs. 18.8 years), had more positive technology attitudes than older age-groups (22.6–26.0 vs. 21.4–24.8), and reported fewer barriers to using diabetes technology than nonusers (3.3 vs. 4.3). The youngest age-group (18–25 years) had the lowest CGM (26% vs. 40–48%) and insulin pump (64% vs. 69–77%) uptake, highest diabetes distress (2.2 vs. 1.8–2.1), and highest A1C levels (8.3% [67 mmol/mol] vs. 7.2–7.4% [55–57 mmol/mol]). CONCLUSIONS Efforts to increase device use need to target physical barriers to wearing devices. Because young adults had the lowest device uptake rates, highest distress, and highest A1C compared with older age-groups, they should be the focus of future interventions to increase device use.

https://care.diabetesjournals.org/content/diacare/40/2/181.full.pdf

Diabetes Device Use in Adults With Type 1 Diabetes: Barriers to Uptake and Potential Intervention Targets.

Diabetes is a chronic illness that places a significant self-management burden on affected individuals and families. Given the importance of health behaviors-such as medication adherence, diet, physical activity, blood glucose self-monitoring-in achieving optimal glycemic control in diabetes, interventions designed and delivered by psychologists hold promise in assisting children, adolescents, and adults with diabetes in improving their health status and lowering their risk of serious complications. This article first provides an overview of diabetes self-management and associated challenges and burdens. Socioeconomic status factors that may influence diabetes management and outcomes are briefly highlighted. We then review the evidence base for select psychosocial factors that may be implicated in diabetes self-management. Modifiable targets of psychological intervention are presented across 3 overarching domains: (a) knowledge, beliefs, and related cognitive constructs; (b) emotional distress and well-being; and (c) behavioral skills and coping. Important methodological issues facing future research are discussed, along with opportunities for psychologists in improving the care and treatment outcomes of individuals and families living with diabetes. In conclusion, we advocate for continued research emphasis on improving psychosocial aspects of living with diabetes, with greater attention to the situational context in which the self-regulatory processes underlying self-management occur. Psychologists have important roles to play in reducing emotional distress, improving patient knowledge, and providing training in behavioral skills to promote successful self-management and to support patient-centered diabetes care. (PsycINFO Database Record

Psychosocial factors in medication adherence and diabetes self-management: Implications for research and practice.

Few studies have examined protective factors for diabetes distress. This study aimed to examine the moderating role of social support in the relationship between the burden of diabetes and diabetes distress. Adults with type 2 diabetes (N = 119; 29 % Latino, 61 % Black, 25 % White) completed validated measures of diabetes distress and social support. Multiple linear regression evaluated the moderating role of social support in the relationship between diabetes burden, indicated by prescription of insulin and presence of complications, and distress. Greater support satisfaction was significantly associated with lower distress after controlling for burden. Support satisfaction and number of supports significantly moderated the relationship between diabetes burden and distress. Post hoc probing revealed a consistent pattern: Insulin was significantly associated with more diabetes distress at low levels of support but was not at high levels of support. Findings support the stress-buffering hypothesis and suggest that social support may protect against diabetes distress.

Diabetes Burden and Diabetes Distress: the Buffering Effect of Social Support

With the evolution of diabetes technology, those living with Type 1 diabetes are given a wider arsenal of tools with which to achieve glycaemic control and improve patient-reported outcomes. Furthermore, the use of these technologies may help reduce the risk of acute complications, such as severe hypoglycaemia and diabetic ketoacidosis, as well as long-term macro- and microvascular complications. In addition, diabetes technology can have a beneficial impact on psychosocial health by reducing the burden of diabetes. Unfortunately, diabetes goals are often unmet and people with Type 1 diabetes too frequently experience acute and long-term complications of this condition, in addition to often having less than ideal psychosocial outcomes. Increasing realization of the importance of patient-reported outcomes is leading to diabetes care delivery becoming more patient-centred. Diabetes technology in the form of medical devices, digital health and big data analytics have the potential to improve clinical care and psychosocial support, resulting in lower rates of acute and chronic complications, decreased burden of diabetes care, and improved quality of life.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/dme.13588

Diabetes technology: improving care, improving patient-reported outcomes and preventing complications in young people with Type 1 diabetes.

Patients managing type 1 diabetes have access to new technologies to assist in management. This manuscript has two aims: 1) to briefly review the literature on diabetes technology use and how this relates to psychological factors and 2) to present an example of human factors research using our data to examine psychological factors associated with technology use. Device/technology uptake and use has increased over the years and at present day is a common clinical practice. There are mixed results in terms of health and psychosocial outcomes, with specific subgroups doing better than others with technology. Our data demonstrated that patients have moderately elevated diabetes distress across differing types of technology used, from low-tech to high-tech options, possibly meaning that technology does not add or take away distress. In addition, users on multiple daily injections compared to all other technology groups have less positive attitudes about technology. Finally, we discuss implications for clinical practice and future research.

https://journals.sagepub.com/doi/pdf/10.1177/1932296816650900

Molly L. Tanenbaum

Papers

Diabetes Device Use in Adults With Type 1 Diabetes: Barriers to Uptake and Potential Intervention Targets.

Psychosocial factors in medication adherence and diabetes self-management: Implications for research and practice.

Diabetes Burden and Diabetes Distress: the Buffering Effect of Social Support

Diabetes technology: improving care, improving patient-reported outcomes and preventing complications in young people with Type 1 diabetes.

Diabetes Technology Uptake, Outcomes, Barriers, and the Intersection With Distress