A problem-solving intervention for cardiovascular disease risk reduction in veterans: Protocol for a randomized controlled trial

Effectiveness of a Problem-Solving Program in Improving Problem-Solving Ability and Glycemic Control for Diabetics with Hypoglycemia

The purpose of this study was to evaluate the effects of a hypoglycemia problem-solving program (HPSP) on problem-solving ability and glycemic control in diabetics with hypoglycemia. This was a prospective, quasi-experimental study with two groups, using a pre- and post-repeated measures design. A total of 71 diabetic patients with hypoglycemia were purposively assigned to an experimental group (n = 34) and a control group (n = 37). The experimental group participated in an 8-week HPSP, and each weekly session lasted approximately 90 min, while the control group received usual care. Participants were assessed at baseline, 1, 3, and 6 months after intervention care. In the experimental group, 6 months after the HPSP intervention, HbA1c was superior to that before the intervention. In both groups, the score obtained using the hypoglycemia problem-solving scale (HPSS) was low before the intervention. In the experimental group, HPSS tracking improved at all stages after the intervention compared to before the intervention. In the control group, the HPSS score improved slightly in the first month and sixth months after usual care. There were significant differences between and within groups in HbA1c levels and HPSS score over time. The intervention based on the HPSP effectively improves HbA1c level and hypoglycemia problem-solving ability in patients with hypoglycemia.

A data-driven examination of which patients follow trial protocol

Protocol adherence in behavioral intervention clinical trials is critical to trial success. There is increasing interest in understanding which patients are more likely to adhere to trial protocols. The objective of this study was to demonstrate the use of a data-driven approach to explore patient characteristics associated with the lowest and highest rates of adherence in three trials assessing interventions targeting behaviors related to lifestyle and risk for cardiovascular disease. Each trial included a common set of baseline variables. Model-based recursive partitioning (MoB) was applied in each trial to identify participant characteristics of subgroups characterized by these baseline variables with differences in protocol adherence. Bootstrap resampling was conducted to provide optimism-corrected c-statistics of the final solutions. In the three trials, rates of protocol adherence varied from 56.9% to 87.5%. Evaluation of heterogeneity of protocol adherence via MoB in each trial resulted in trees with 2–4 subgroups based on splits of 1–3 variables. In two of the three trials, the first split was based on pain in the past week, and those reporting lower pain were less likely to be adherent. In one of these trials, the second and third splits were based on education and employment, where those with lower education levels and who were employed were less likely to be adherent. In the third trial, the two splits were based on smoking status and then marriage status, where smokers who were married were least likely to be adherent. Optimism-corrected c-statistics ranged from 0.54 to 0.63. Model-based recursive partitioning can be a useful approach to explore heterogeneity in protocol adherence in behavioral intervention trials. An important next step would be to assess whether patterns hold in other similar studies and samples. Identifying subgroups who are less likely to be adherent to an intervention can help inform modifications to the intervention to help tailor the intervention to these subgroups and increase future uptake and impact.

Trial registration

ClinicalTrials.gov identifiers: NCT01828567, NCT02360293, and NCT01838226.