Diane K. King

Self-Efficacy, Problem Solving, and Social-Environmental Support Are Associated With Diabetes Self-Management Behaviors

OBJECTIVE To evaluate associations between psychosocial and social-environmental variables and diabetes self-management, and diabetes control. RESEARCH DESIGN AND METHODS Baseline data from a type 2 diabetes self-management randomized trial with 463 adults having elevated BMI (M = 34.8 kg/m2) were used to investigate relations among demographic, psychosocial, and social-environmental variables; dietary, exercise, and medication-taking behaviors; and biologic outcomes. RESULTS Self-efficacy, problem solving, and social-environmental support were independently associated with diet and exercise, increasing the variance accounted for by 23 and 19%, respectively. Only diet contributed to explained variance in BMI (β = −0.17, P = 0.0003) and self-rated health status (β = 0.25, P < 0.0001); and only medication-taking behaviors contributed to lipid ratio (total–to–HDL) (β = −0.20, P = 0.0001) and A1C (β = −0.21, P < 0.0001). CONCLUSIONS Interventions should focus on enhancing self-efficacy, problem solving, and social-environmental support to improve self-management of diabetes.

Use of Chronic Care Model Elements Is Associated With Higher-Quality Care for Diabetes

PURPOSE In 30 small, independent primary care practices, we examined the association between clinician-reported use of elements of the Chronic Care Model (CCM) and diabetic patients’ hemoglobin A1c (HbA1c) and lipid levels and self-reported receipt of care. METHODS Ninety clinicians (60 physicians, 17 nurse-practitioners, and 13 physician’s assistants) completed a questionnaire assessing their use of elements of the CCM on a 5-point scale (never, rarely, occasionally, usually, and always). A total of 886 diabetic patients reported their receipt of various diabetes care services. We computed a clinical care composite score that included patient-reported assessments of blood pressure, lipids, microalbumin, and HbA1c; foot examinations; and dilated retinal examinations. We computed a behavioral care composite score from patient-reported support from their clinician in setting self-management goals, obtaining nutrition education or therapy, and receiving encouragement to self-monitor their glucose. HbA1c values and lipid profiles were obtained by independent laboratory assay. We used multilevel regression models for analyses to account for the hierarchical nature of the data. RESULTS Clinician-reported use of elements of CCM was significantly associated with lower HbA1c values (P = .002) and ratios of total cholesterol to high-density lipoprotein cholesterol (P = .02). For every unit increase in clinician-reported CCM use (eg, from “rarely” to “occasionally”), there was an associated 0.30% reduction in HbA1c value and 0.17 reduction in the lipid ratio. Clinician use of the CCM elements was also significantly associated with the behavioral composite score (P = .001) and was marginally associated with the clinical care composite score (P = .07). CONCLUSIONS Clinicians in small independent primary care practices are able to incorporate elements of the CCM into their practice style, often without major structural change in the practice, and this incorporation is associated with higher levels of recommended processes and better intermediate outcomes of diabetes care.

Engagement in a Diabetes Self-management Website: Usage Patterns and Generalizability of Program Use

Background Increased access to the Internet and the availability of efficacious eHealth interventions offer great promise for assisting adults with diabetes to change and maintain health behaviors. A key concern is whether levels of engagement in Internet programs are sufficient to promote and sustain behavior change. Objective This paper used automated data from an ongoing Internet-based diabetes self-management intervention study to calculate various indices of website engagement. The multimedia website involved goal setting, action planning, and self-monitoring as well as offering features such as “Ask an Expert” to enhance healthy eating, physical activity, and medication adherence. We also investigated participant characteristics associated with website engagement and the relationship between website use and 4-month behavioral and health outcomes. Methods We report on participants in a randomized controlled trial (RCT) who were randomized to receive (1) the website alone (n = 137) or (2) the website plus human support (n = 133) that included additional phone calls and group meetings. The website was available in English and Spanish and included features to enhance engagement and user experience. A number of engagement variables were calculated for each participant including number of log-ins, number of website components visited at least twice, number of days entering self-monitoring data, number of visits to the “Action Plan” section, and time on the website. Key outcomes included exercise, healthy eating, and medication adherence as well as body mass index (BMI) and biological variables related to cardiovascular disease risk. Results Of the 270 intervention participants, the average age was 60, the average BMI was 34.9 kg/m2, 130 (48%) were female, and 62 (23%) self-reported Latino ethnicity. The number of participant visits to the website over 4 months ranged from 1 to 119 (mean 28 visits, median 18). Usage decreased from 70% of participants visiting at least weekly during the first 6 weeks to 47% during weeks 7 to 16. There were no significant differences between website only and website plus support conditions on most of the engagement variables. In total, 75% of participants entered self-monitoring data at least once per week. Exercise action plan pages were visited more often than medication taking and healthy eating pages (mean of 4.3 visits vs 2.8 and 2.0 respectively, P < .001). Spearman nonparametric correlations indicated few significant associations between patient characteristics and summary website engagement variables, and key factors such as ethnicity, baseline computer use, age, health literacy, and education were not related to use. Partial correlations indicated that engagement, especially in self-monitoring, was most consistently related to improvement in healthy eating (r = .20, P = .04) and reduction of dietary fat (r = -.31, P = .001). There was also a significant correlation between self-monitoring and improvement in exercise (r = .20, P = .033) but not with medication taking. Conclusions Participants visited the website fairly often and used all of the theoretically important sections, but engagement decreased over 4 months. Usage rates and patterns were similar for a wide range of participants, which has encouraging implications for the potential reach of online interventions. Trial Registration NCT00987285; http://clinicaltrials.gov/show/NCT00987285 (Archived by WebCite at http://www.webcitation.org/5vpe4RHTV)

A practical randomized trial to improve diabetes care

AbstractOBJECTIVE: There is a well-documented gap between diabetes care guidelines and the services received by patients in almost all health care settings. This project reports initial results from a computer-assisted, patient-centered intervention to improve the level of recommended services received by patients from a wide variety of primary care providers. DESIGN AND SETTINGS: Eight hundred eighty-six patients with type 2 diabetes under the care of 52 primary care physicians participated in the Diabetes Priority Program. Physicians were stratified and randomized to intervention or control conditions and evaluated on 2 primary outcomes: number of recommended laboratory screenings and recommended patient-centered care activities completed. Secondary outcomes were evaluated using the Problem Areas in Diabetes scale and the Patient Health Questionnaire (PHQ)-9 depression scale, and the RE-AIM framework was used to evaluate potential for dissemination. RESULTS: The program was well-implemented and significantly improved both number of recommended laboratory assays (3.4 vs 3.1; P<.001) and patient-centered aspects of diabetes care patients received (3.6 vs 3.2; P<.001) compared to those in randomized control practices. Activities that were increased most were foot exams (follow-up rates of 80% vs 52%; P<.003) and nutrition counseling (76% vs 52%; P<.001). CONCLUSIONS: Patients are very willing to participate in a brief computer-assisted intervention that is effective in enhancing quality of diabetes care. Staff in primary care offices can consistently deliver an intervention of this nature, but most physicians were unwilling to participate in this translation research study.

Effects of a brief computer-assisted diabetes self-management intervention on dietary, biological and quality-of-life outcomes

Objective: There is a need for practical, efficient and broad-reaching diabetes self-management interventions that can produce changes in lifestyle behaviours such as healthy eating and weight loss. The objective of this study was to evaluate such a computer-assisted intervention. Methods: Type 2 diabetes primary care patients (n=335) from fee-for-service and health maintenance organization settings were randomized to social cognitive theory-based tailored self-management (TSM) or computer-aided enhanced usual care (UC). Intervention consisted of computer-assisted self-management assessment and feedback, tailored goal-setting, barrier identification, and problem-solving, followed by health counsellor interaction and follow-up calls. Outcomes were changes in dietary behaviours (fat and fruit/vegetable intake), haemoglobin A1c (HbA1c), lipids, weight, quality of life, and depression. Results: TSM patients reduced dietary fat intake and weight significantly more than UC patients at the 2-month follow-up. Among patients having elevated levels of HbA1c, lipids or depression at baseline, there were consistent directional trends favouring intervention, but these differences did not reach significance. The intervention proved feasible and was implemented successfully by a variety of staff. Conclusions: This relatively low-intensity intervention appealed to a large, generally representative sample of patients, was well implemented, and produced improvement in targeted behaviours. Implications of this practical clinical trial for dissemination are discussed.

Reaiming RE-AIM: using the model to plan, implement, and evaluate the effects of environmental change approaches to enhancing population health.

The RE-AIM (reach, effectiveness, adoption, implementation, maintenance) framework, which provides a practical means of evaluating health interventions, has primarily been used in studies focused on changing individual behaviors. Given the importance of the built environment in promoting health, using RE-AIM to evaluate environmental approaches is logical. We discussed the benefits and challenges of applying RE-AIM to evaluate built environment strategies and recommended modest adaptations to the model. We then applied the revised model to 2 prototypical built environment strategies aimed at promoting healthful eating and active living. We offered recommendations for using RE-AIM to plan and implement strategies that maximize reach and sustainability, and provided summary measures that public health professionals, communities, and researchers can use in evaluating built environment interventions.

Understanding Who Benefits at Each Step in an Internet-Based Diabetes Self-Management Program Application of a Recursive Partitioning Approach

Background. Efforts to predict success in chronic disease management programs have been generally unsuccessful. Objective. To identify patient subgroups associated with success at each of 6 steps in a diabetes self-management (DSM) program. Design. Using data from a randomized trial, recursive partitioning with signal detection analysis was used to identify subgroups associated with 6 sequential steps of program success: agreement to participate, completion of baseline, initial website engagement, 4-month behavior change, later engagement, and longer-term maintenance. Setting. The study was conducted in 5 primary care clinics within Kaiser Permanente Colorado. Patients. Different numbers of patients participated in each step, including 2076, 544, 270, 219, 127, and 89. All measures available were used to address success at each step. Intervention. Participants were randomized to receive either enhanced usual care or 1 of 2 Internet-based DSM programs: 1) self-administered, computer-assisted self-management and 2) the self-administered program with the addition of enhanced social support. Measurements. Two sets of potential predictor variables and 6 dichotomous outcomes were created. Results. Signal detection analysis differentiated successful and unsuccessful subgroups at all but the final step. Different patient subgroups were associated with success at these different steps. Demographic factors (education, ethnicity, income) were associated with initial participation but not with later steps, and the converse was true of health behavior variables. Limitations. Analyses were limited to one setting, and the sample sizes for some of the steps were modest. Conclusions. Signal detection and recursive partitioning methods may be useful for identifying subgroups that are more or less successful at different steps of intervention and may aid in understanding variability in outcomes.

Implications of Active Living by Design for Broad Adoption, Successful Implementation, and Long-Term Sustainability

It is clear that there is a need to pay attention to the contextual factors that will promote the broad adoption, successful implementation, and long-term sustainability of community-based environment and policy change. Reviews of both clinical and community-based interventions have shown that information about the intervention setting, how a program or policy is implemented, and how it is institutionalized are reported much less often than individual-level factors.1 Thus, as best practices for active living are identified, there is an equally important opportunity to identify ways to ensure that these best practices will be adopted, implemented, and maintained. A key strength of the Active Living by Design (ALbD) program is its inclusion of a variety of types of communities. By purposively including both urban and rural communities, variation in climate and geographic locations, and both low- and moderate-income settings, this greatly enhances confidence that success is generalizable. Robustness of results across different types of communities is an important criterion for widespread public health impact, and the ALbD program receives high marks on this dimension. The 5P model (preparation, promotion, programs, policy, and physical projects) used in ALbD2 should enhance adoption and implementation. Like the 5A’s model for smoking cessation and chronic illness self-management,3 this alliterative mnemonic facilitates understanding and reduces the complexity of multi-level interventions—one of Rogers’s key criteria for successful adoption.4 The 5P model should also help implementation as the framework serves as a reminder to community partners that it is important to address all of the Ps. It is clear from the papers in this issue that this framework was widely used by communities. A first step to increasing the likelihood that the individuals who are reached (i.e., those who are exposed to the ALbD interventions) include those who could most benefit is through the strategic composition of the community partnership. Use of a multi-level socio-ecologic approach to identify those partners who represent, or have access to, the venues and organizations in which the target population(s) live, work, and play will greatly increase the likelihood of success. The variety of partners included in the ALbD communities, such as city planners, law enforcement, schools, residents, and neighborhood associations, bodes well for decreasing any skepticism that the partnership has a pre-determined agenda and for identifying issues and solutions that resonate with the target population.5 Another way to assure a broad reach for strategies that are ultimately sustained is to implement changes at the policy (e.g., zoning laws, school physical education policies) and environment (e.g., biking and pedestrian infrastructure) levels. While strategic use of programs is appropriate to build awareness, knowledge, and skills, assuring that organizations will continue to offer and finance them into the future is often challenging, particularly during tight times. Policies and environment changes, while more likely to be sustained once implemented, also require upkeep, enforcement, and periodic evaluation to assure they are effective. Although far fewer resources are required on an ongoing basis than for program activities, an unfunded policy mandate or a well-maintained trail that cannot be accessed without a car may not reach those most in need. Of course, the real test of maintenance will be another 5 or 10 years to see if these communities have integrated ALbD goals, values, and activities into their vision of their communities. Encouraging shared leadership among partners, such as partner-led workgroups, will help to sustain partner engagement and foster adoption and maintenance of strategies beyond the grant.6 In addition, to the degree that partners are able to identify with and integrate the ALbD vision into their own organizations, the work of the partnership will continue even if the partnership is disbanded. It will be of interest to see how many of the partnerships established for ALbD have continued or evolved into something different. One example of how the 5P framework has already achieved maintenance and the related goal of generalization is the report that the framework has been used by ALbD communities for other grants and projects.

Multiple-Behavior–Change Interventions for Women With Type 2 Diabetes

Type 2 diabetes is a costly chronic illness that is increasing in prevalence and associated with significant health problems, including heart disease.1 Furthermore, type 2 diabetes and heart disease share multiple lifestyle risk factors that tend to co-occur for many adults.2 Research has established the health benefits of adopting Mediterranean-style eating practices,3 engaging in physical activity,4,5 managing stress,6,7 and using social-environmental support to initiate and sustain health-related behaviors.8–10 Nevertheless, multiple–risk-factor intervention studies are rare11-13 and sorely needed, particularly to understand their potential to reach and benefit underserved populations. Multiple-behavior–change interventions appear necessary in light of the co-occurrence of risk factors,2 yet they are complex and demanding for both service providers and patients. Providers must have multiple competencies or the resources to assemble an interdisciplinary team with expertise in nutrition, exercise, smoking cessation, stress management, and motivational strategies. They must weigh the costs and benefits of sequential and simultaneous approaches to changing multiple behaviors, although thus far, those approaches seem to produce comparable effects.14 Patients must comprehend many intervention methods and devote sufficient effort to each. The purpose of this article is to describe the structure and content of the Mediterranean Lifestyle Program (MLP) and its results during 7 years of assessments, as well as a cultural adaptation of the MLP for Latinas (iViva Bien!) that was offered to members of a large health maintenance organization (HMO) and a community health center in the Denver, Colo., metropolitan area and its results during 2 years of evaluation. The adaptation of the MLP into iViva Bien! was intended to expand the generalizability of the intervention and to test its application in collaboration with health organizations, two important steps in developing an intervention that can be disseminated …