A Computer-Assisted CBT Tool to Enhance Fidelity in CBOCs

Depression

Background and Significance: Mental health (MH) providers in VA Community-Based-Outpatient-Clinics (CBOCs) are often located in rural areas and isolated from educational opportunities. Almost half of Veterans now use CBOCs. Studies have shown that the quality of delivery of EBPs (fidelity) impacts clinical outcomes. This study will test a computer-assisted tool (CALM Tools for Living) that increases fidelity to CBT in treating depression and four common anxiety disorders, including PTSD. Although results of a large RCT, the CALM study, suggested that the tool contributed to fidelity to the CBT protocol, this hypothesis has not been tested. This study will test the tool in primarily rural CBOCs in VA VISN16. Objective: To modify a computer-assisted CBT tool to meet the needs of CBOC MH providers and Veterans, to evaluate the impact on providers' fidelity to the CBT model and clinical outcomes, and to assess how best to support future implementation. Specific Aims/Hypothesis: (1) Engage CBOC MH providers in modifying the computer-assisted CBT tool such that its content is relevant and acceptable to Veterans and providers. The investigators hypothesize that the modified tool will be acceptable to both Veterans and providers. (2) Compare MH provider fidelity to CBT and clinical outcomes among providers who used the tool and those who did not. The investigators hypothesize that clinicians who use the tool will have a higher fidelity to CBT and clinical outcomes among patients will be superior. (3) Prepare for future implementation of the tool in the VA. Methodology: This study will use a Type III hybrid effectiveness design. Methods common to the field of Instructional Design and Technology (IDT) will be used to modify the tool. Thirty-four CBOC MH providers will be trained in CBT and randomized to use the tool or not. Both groups will receive external facilitation to encourage the full implementation of CBT into practice on the clinic level. MH providers will treat 10 patients each. Patients will be assessed at baseline, 3, 6, and 12 months. Provider fidelity to the CBT protocol will be measured, and finally, a tool kit for future implementation of the tool will be disseminated. Impact: The investigators expect the intervention to improve the technical quality of MH treatment in CBOCs and improve clinical outcomes among Veterans.

Power analysis for primary outcome We aimed to recruit a total sample of 34 (17 per condition) providers to achieve a statistical power of 0.94 for analysis comparing our primary outcome of CBT fidelity between the two conditions. We used a general linear mixed model to account for patients clustered within the same providers and a type I error rate of .05. This initial sample size was also determined by assuming an effect size of 1.0 (1.3 point difference on a scale of CBT fidelity; 5.3 of 6 for the computer condition versus 4 of 6 for manual condition), a medium intraclass correlation of 0.5, and four patients per provider. A total of 16 of 32 clinicians self-selected to provide audiotaped sessions that were assessed for fidelity. Assuming an effect size of 1.0 for condition on the fidelity outcome, an intraclass correlation of 0.5, four participants per provider, and a type I error rate of .05, our statistical power for comparing our primary outcome of fidelity between conditions using general linear mixed model is 0.65. Statistical analysis For the primary outcome of providers' CBT fidelity, descriptive statistics were calculated for the entire sample and by session. The association between condition and the outcome of fidelity was examined using a general linear mixed model to account for Veteran participants clustered within providers. The treatment session and strata variables were also included in the model. For the patient-level secondary outcomes, bivariate analysis was performed using generalized estimating equations due to the clustered structure of Veterans within the same providers. Associations between condition and potential covariates and between outcomes and potential covariates were examined. Covariates with p-values less than 0.10 were included in the multivariate models for associations between condition and outcomes over time. Generalized linear mixed models were used to account for the correlations for patients within providers as well as the correlations of multiple assessments within patients. Gamma distribution was specified for BSI-18 GSI scores after a small rescale for zero value due to its violation of normality and normal distribution was specified for the remaining outcomes as they were approximately normally distributed. All the models included the condition indicator variable, time (for the three interviews), strata, and covariates identified in the bivariate analysis. The covariates associated with condition (gender and primary diagnosis) were included in all the models with the exception of primary diagnosis not being included in the subgroup specific diagnosis group analysis. The covariates associated with the outcomes were also included in the corresponding outcome models. The interaction between condition and time was included in all of the models as hypothesized. General linear mixed models were also fit for disorder specific outcomes for subgroups of Veterans with the corresponding specific disorders as they were approximately normally distributed. The LS mean differences (or ratios depending on the outcomes) between the two conditions and their corresponding 95% confidence intervals were calculated for evaluating the effect of condition. Similar differences (or ratios) between each follow-up and baseline by each condition and their corresponding 97.5% confidence intervals were also calculated for evaluating the effect of time. A narrower confidence interval (equivalent to a p-value of 0.025) was used to adjust for multiple comparisons. All the analyses were performed using SAS 9.4.

Participants by arm