Behavioral Programs for Diabetes Mellitus

Key Points: Body Mass Index
• Lifestyle programs resulted in the highest effect sizes for BMI.
• Program intensity appeared less important than method of delivery; providing some
individual (rather than solely group-based) delivery appears beneficial.
Detailed Synthesis
We conducted network meta-analyses for the outcomes of HbA 1c and BMI. These outcomes
represent two of our key outcomes that were reported by the most studies. Tables 12 (HbA 1c ) and
13 (BMI) provide descriptions of the nodes (no two containing the same combination of
variables), and include the results including the rank order of each node, the MD relative to usual
care, the associated 95 percent credibility interval, and a percentage referring to the node’s
“probability of being best” (PB). These tables also indicate which studies contributed to each
node, with the sample size of the applicable study arms, although it should be noted that the
network approach accounts for direct and indirect comparisons such that other information
contributes to the results. We summarize our approach and the results for each outcome below.
Figures 19 and 20 contain the plots showing the relative ranking of the different nodes; the
studies within each node are cited in the accompanying tables. A consistency analysis was
performed for the HbA 1c analysis and it was found that only two quadratic loops (of a total of 43
total quadratic and triangular loops) showed statistically significant inconsistency.
HbA 1c
Accounting for all variables of program components and delivery variables (Table 3) when
creating the network was deemed not appropriate for various reasons. When choosing which
variables to use, we prioritized them by considering factors including the: reliability and
specificity with which we could categorize programs in each variable based on extent of
reporting, overlap in meaning between variables, and the ability to inform those individuals
making decisions to implement these programs in community settings. Deciding between
program duration (months) and intensity (contact hours), the latter was chosen because it
accounts for duration to some extent, aligns with our focus on interactive programs, and better
enables one to estimate resource requirements in terms of personnel and space. Degree of
tailoring was not chosen because every program incorporated this to some extent and
categorizing this (e.g., minimal versus moderate in terms of content and delivery) was considered
unreliable based on study reporting. Moreover, the use of technology (captured in the delivery
method variable) was also considered a way to tailor the program to individuals, particularly in
cases of poor access due to travel or time constraints. The level of community engagement was
also not used because, when incorporated, this was largely via use of lay or peer providers which
was captured in the delivery personnel variable. The remaining variables were placed in order
(program components, program intensity, method of communication, method of delivery, and
delivery personnel) and we then created nodes trying to incorporate as many variables as
possible without having numerous nodes either empty (a theoretical grouping of variables that
did not represent a studied program), or with only one or two programs. Dividing the data by the
first variable of program components (DSME, DSME and support, and lifestyle) resulted in a
relatively large number of DSME comparisons. For this group, we were able to use all five
variables to create 24 potential nodes (18 which contained comparisons). We did not capture the
variable of delivery personnel for the DSME and support, and lifestyle groups because most
nodes would in this case contain at most one comparison.
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