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BACKGROUND: Overtesting and treatment of older patients is common and may lead to harms. The Choosing Wisely campaign has provided recommendations to reduce overtesting and overtreatment of older adults. Behavioral economics-informed interventions embedded within the electronic health record (EHR) have been shown to reduce overuse in several areas. Our objective is to conduct a parallel arm, pragmatic cluster-randomized trial to evaluate the effectiveness of behavioral-economics-informed clinical decision support (CDS) interventions previously piloted in primary care clinics and designed to reduce overtesting and overtreatment in older adults. METHODS/DESIGN: This trial has two parallel arms: clinician education alone vs. clinician education plus behavioral-economics-informed CDS. There are three co-primary outcomes for this trial: (1) prostate-specific antigen (PSA) screening in older men, (2) urine testing for non-specific reasons in older women, and (3) overtreatment of diabetes in older adults. All eligible primary care clinics from a large regional health system were randomized using a modified constrained randomization process and their attributed clinicians were included. Clinicians were recruited to complete a survey and educational module. We randomized 60 primary care clinics with 374 primary care clinicians and achieved adequate balance between the study arms for prespecified constrained variables. Baseline annual overuse rates for the three co-primary outcomes were 25%, 23%, and 17% for the PSA, urine, and diabetes measures, respectively. DISCUSSION: This trial is evaluating behavioral-economics-informed EHR-embedded interventions to reduce overuse of specific tests and treatments for older adults. The study will evaluate the effectiveness and safety of these interventions.

Background Hypertension is a major cause of morbidity and mortality but frequently remains uncontrolled. A smartphone application that provides coaching regarding home blood pressure monitoring and other aspects of hypertension self-care and related behavior change may be a scalable way to help manage hypertension. Methods/design The Smart Hypertension Control Study is a prospective, randomized controlled trial to assess the effects of a hypertension personal control program (HPCP), which consists of an automated artificial intelligence smartphone application that provides individualized support and coaching to promote home monitoring and healthy behavior changes related to hypertension self-management. Enrolled adults with uncontrolled hypertension will be randomized in a 1:1 fashion to the HPCP with home blood pressure monitoring or to home monitoring alone. We plan to enroll 350 participants, with a target of 300 participants with complete six-month follow-up data. The primary study outcome will be systolic blood pressure at six months. Additional outcomes include measures of antihypertensive medication adherence, home blood pressure monitoring practices, self-management practices, weight, and self-reported health behaviors. Conclusion The Smart Hypertension Control Study will evaluate blood pressure and hypertension self-management behavior outcomes in participants with uncontrolled hypertension exposed to a smartphone-based hypertension health coaching application in addition to home blood pressure monitoring compared to those exposed to home blood pressure monitoring alone.

Background The Healthy Hearts in the Heartland (H3) study is part of a nationwide effort, EvidenceNOW, seeking to better understand the ability of small primary care practices to improve “ABCS” clinical quality measures: appropriate Aspirin therapy, Blood pressure control, Cholesterol management, and Smoking cessation. H3 aimed to assess feasibility of implementing Point-of-Care (POC) or POC plus Population Management (POC + PM) quality improvement (QI) strategies to improve ABCS at practices in Illinois, Indiana, and Wisconsin. We describe the design and randomization of the H3 study. Methods We conducted a two-arm (1:1, POC:POC + PM), practice-randomized, comparative effectiveness study in 226 primary care practices across four “waves” of randomization with a 12-month intervention period, followed by a six-month sustainability period. Randomization controlled imbalance in nine baseline variables through a modified constrained algorithm. Among others, we used initial, unverified estimates of baseline ABCS values. Results We randomized 112 and 114 practices to POC and POC + PM arms, respectively. Randomization ensured baseline comparability for all nine key variables, including the ABCS measures indicating proportion of patients at the practice level meeting each quality measure. Median(Inner Quartile Range) values were A: 0.78(0.66–0.86) in POC arm vs. 0.77(0.63–0.86) in POC + PM arm, B: 0.64(0.53–0.73) vs. 0.64(0.53–0.75), C: 0.78(0.63–0.86) vs. 0.75(0.64–0.81), S: 0.80(0.65–0.81) vs. 0.79(0.61–0.91). Discussion Surrogate estimates for the true ABCS at baseline coupled with the unique randomization logic achieved adequate baseline balance on these outcomes. Similar practice- or cluster-randomized trials may consider adaptations of this design. Final analyses on 12- and 18-month ABCS outcomes for the H3 study are forthcoming. Trial registration This trial is registered on ClinicalTrials.gov (Initial post: 11/05/2015; identifier: NCT02598284 ; https://clinicaltrials.gov/ct2/show/NCT02598284?term=NCT02598284&rank=1 ).

Oftentimes valid statistical analyses for clinical trials involve adjustment for known influential covariates, regardless of imbalance observed in these covariates at baseline across treatment groups. Thus, it must be the case that valid interim analyses also properly adjust for these covariates. There are situations, however, in which covariate adjustment is not possible, not planned, or simply carries less merit as it makes inferences less generalizable and less intuitive. In this case, covariate imbalance between treatment groups can have a substantial effect on both interim and final primary outcome analyses. This paper illustrates the effect of influential continuous baseline covariate imbalance on unadjusted conditional power (CP), and thus, on trial decisions based on futility stopping bounds. The robustness of the relationship is illustrated for normal, skewed, and bimodal continuous baseline covariates that are related to a normally distributed primary outcome. Results suggest that unadjusted CP calculations in the presence of influential covariate imbalance require careful interpretation and evaluation.

Motivated by potentially serious imbalances of continuous baseline covariates in clinical trials, we investigated the cost in statistical power of ignoring the balance of these covariates in treatment allocation design for a logistic regression model. Based on data from a clinical trial of acute ischemic stroke treatment, computer simulations were used to create scenarios varying from the best possible baseline covariate balance to the worst possible imbalance, with multiple balance levels between the two extremes. The likelihood of each scenario occurring under simple randomization was evaluated. The power of the main effect test for treatment was examined. Our simulation results show that the worst possible imbalance is highly unlikely, but it can still occur under simple random allocation. Also, power loss could be nontrivial if balancing distributions of important continuous covariates were ignored even if adjustment is made in the analysis for important covariates. This situation, although unlikely, is more serious for trials with a small sample size and for covariates with large influence on primary outcome. These results suggest that attempts should be made to balance known prognostic continuous covariates at the design phase of a clinical trial even when adjustment is planned for these covariates at the analysis.