Behavioral Programs for Type 2 Diabetes Mellitus

In 2012, 29.1 million Americans had diabetes with costs of $245 billion (1), representing 11% of the total U.S. health care expenditure (2). Although tight glycemic control may reduce the risk for microvascular complications in type 2 diabetes mellitus (T2DM) (3), behavioral and pharmacologic management of body weight, blood pressure, and cholesterol levels are often needed to reduce the risk for mortality and macrovascular complications. Moreover, other patient-centered outcomes, such as diabetes-related distress and depression, are important to address (4). Health care experts recommend that anyone with diabetes adopt and adhere to multiple self-care behaviors, including healthy eating, being active, monitoring, taking medication, problem-solving, healthy coping, and reducing risks (5). Approaches to support behavior change include diabetes self-management education (DSME) with or without an added support (clinical, behavioral, psychosocial, or educational) phase, and lifestyle programs. Because knowledge acquisition insufficiently promotes behavioral changes (6), recommendations for DSME have shifted from traditional didactic educational services to more patient-centered methodologies that incorporate interaction, problem-solving, and other behavioral approaches. Although evidence shows that diabetes-specific behavioral interventions can be effective, which combination of program components and delivery mechanisms is most effective is unclear (711). We conducted a network meta-analysis to identify factors related to program components and delivery mechanisms that moderate the effectiveness of multicomponent behavioral programs for T2DM. Methods Key informants, a technical expert panel, and public commentary informed our methods. A protocol and a peer- and public-reviewed technical report were produced for the Agency for Healthcare Research and Quality (AHRQ) and are available online (www.ahrq.gov/research/findings/evidence-based-reports/). Data Sources and Searches A research librarian searched the following bibliographic databases from 1993 to January 2015: Ovid MEDLINE (Appendix Table 1) and Ovid MEDLINE In-Process & Other Non-Indexed Citations, Cochrane Central Register of Controlled Trials via the Cochrane Library, EMBASE via Ovid, CINAHL Plus with Full Text via EBSCOhost, PsycINFO via Ovid, Scopus, and PubMed via the National Center for Biotechnology Information Databases. We reviewed the reference lists of relevant systematic reviews and of all included studies. We also searched ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform, relevant conference proceedings (2011 through 2014), and the U.S. Federal Register. Appendix Table 1. Search Strategy for MEDLINE* Study Selection We included studies conducted in highly developed countries published in English after 1993 (because medical management for diabetes intensified after this time). We included randomized, controlled trials done in community or outpatient health settings and involving adults that compared a behavioral program with usual care (medical management provided to all participants), an active control (intervention not meeting our definition of behavioral program), or another behavioral program (comparative effectiveness study). A behavioral program was a multicomponent, diabetes-specific program that included repeated interactions with trained individuals over at least 4 weeks, and that consisted of DSME using a behavioral approach or another program format including at least a structured dietary or physical activity intervention with another component (Appendix). We excluded abstracts and studies in which the intervention was a disease or care management program (for example, with active adjustment of diabetes-related medications) (12) or a quality improvement program incorporating strategies targeting health systems or providers (13). Other exclusion criteria included studies 1) focusing on patients with newly diagnosed (1 year) disease; 2) with no outcome of interest to this review (for example, only C-reactive protein), or in which the only difference between the study groups was a factor outside of the review's scope (for example, low- vs. high-fat diet); and 3) in which 25% or more of the participants had type 1 diabetes mellitus (unless results were reported for those with T2DM). Two reviewers independently screened all titles and abstracts, and the full text of any publication marked for inclusion was retrieved. Two reviewers independently assessed the full texts by using a priori inclusion criteria and a standard form. We resolved disagreements by consensus or consultation with a third reviewer. Data Extraction and Quality Assessment One reviewer extracted data by using a structured form created in the Systematic Review Data Repository (available at http://srdr.ahrq.gov/) (14); a second reviewer verified data. Two reviewers independently applied the Cochrane risk of bias tool (15). Discrepancies were resolved through discussion. Data Synthesis and Analysis With input from technical experts, we categorized behavioral programs by various component and delivery factors (Table). We separated DSME and DSME plus support, in recognition that the support phase of the latter was often of lower intensity (less frequent contacts) and focused on different content, such as psychosocial support. Table. Categorization of Program Components and Delivery Factors To serve as an overview of program effectiveness and help interpret our primary analysis of program moderation, we performed pairwise meta-analyses by using the HartungKnappSidikJonkman random-effects model (16, 17) for multiple behavioral, clinical, and health outcomes, as well as health care utilization and program acceptability (the full report is available at www.ahrq.gov/research/findings/evidence-based-reports/). We defined thresholds for clinical importance where there was guidance: For hemoglobin A1c (HbA1c), we used a reduction of at least 0.4% (for example, 7.6% vs. 8.0%) (18); for quality-of-life measures and other patient-reported outcomes, we used a conservative value of one-half SD (19, 20). We then conducted a network meta-analysis that allowed simultaneous evaluation of a suite of comparisons and considered both direct and indirect evidence while preserving the within-study randomization. To assure the transitivity within the network, we categorized all behavioral programs and comparators into nodes. The nodes for behavioral programs were formed on the basis of different combinations of variables in our program categorization (Appendix Table 2); we identified all plausible nodes differing by only one variable (for example, a level within the intensity category) and then filled the nodes with the applicable interventions on the basis of our coding. The nodes for the comparator groups were categorized as usual care, active non-DSME control (education interventions not meeting our criteria), and active other control (for example, stand-alone dietary or physical activity interventions). Appendix Table 2. Characteristics of Studies of Behavioral Programs for T2DM Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued The analysis was conducted by using a Bayesian network model to compare all interventions simultaneously and to use all available information on treatment effects in a single analysis (21, 22). These methods ensure that correlation in multigroup trials is preserved. Mean differences (MDs) were modeled using noninformative prior distributions. A normal prior distribution with mean 0 and large variance (10000) was used for each of the trial means, whereas their between study variance had a uniform prior with range 0 to 2. These priors were checked for influence with sensitivity analyses. Markov chain Monte Carlo simulations using WinBugs software were performed to obtain simultaneous estimates of all interventions compared with placebo, as well as estimates of which interventions were the best. A burn-in sample of 20000 iterations was followed by 300000 iterations used to compute estimates. A sensitivity analysis that thinned the amount of used data to every 10th iteration was also conducted to check for proper chain convergence. The analysis was checked for consistency by contrasting direct and indirect estimates in each triangular and quadratic loop by using the methods described by Veroniki and colleagues (23). Results are presented as estimates of the treatment effects (MD) relative to usual care, with 95% credible intervals. To examine different population subgroups, we conducted subgroup analyses of the pairwise meta-analysis results for HbA1c at longest follow-up in comparison with usual care and active controls; subgroups were defined on the basis of study-level baseline HbA1c (<7% vs. 7%), age (<65 vs. 65 years), and ethnicity (75 vs. <75% nonwhite), according to categories that were defined a priori. For baseline HbA1c level and age, we performed subgroup analyses of the network meta-analysis; the analysis was rerun for studies having a mean baseline HbA1c level of 7% or greater and for those with a mean participant age younger than 65 years. For subgroups based on race/ethnicity, the number of trials in either subgroup was insufficient to perform a network meta-analysis. Role of the Funding Source This project was funded under contract 290-2012-000131 from the AHRQ, U.S. Department of Health and Human Services. Staff at AHRQ participated in development of the scope of the work and reviewed drafts of the manuscript. Approval by AHRQ was required before the manuscript could be submitted for publication, but the authors are solely responsible for its content and the decision to submit for publication. AHRQ staff did not participate in the conduct of the review,