(PS12-A22) Exploring Treatment Trajectories in Adult Clients with Depressive and Anxiety Disorders: A Hierarchical Linear Modeling Approach Examining Clinical and Demographic Factors

Depressive and anxiety disorders are highly prevalent psychopathologies that significantly impact individuals' adjustment and cognitive behavioral therapy (CBT) is widely used as a gold standard for these disorders. However, there is still a need to better understand the therapy process and outcomes, particularly in community-based mental health settings, where clients present diverse demographic and clinical characteristics. This study aims to explore the treatment trajectories of adult clients with depressive, anxiety, and comorbid disorders who received evidence-based psychotherapies (EBPs) in a community clinic, where the primary orientation of clinicians was CBT. This study investigates how trajectories of psychological adjustment are influenced by a variety of clinical and demographic characteristics to determine which factors have a greater impact on treatment outcomes. 

Thirty-six participants with depressive (N = 6), anxiety (N = 7), or comorbid disorders (N = 23) were recruited from a community clinic, and they were predominantly White (N = 28; 77.8%) and the gender was equally distributed (male: N = 19; 52.8%). The Brief Adjustment Scale-6 (BASE-6) was used as an outcome measure and completed every session, as measurement-based care (MBC) was part of the usual care. The deviance test using unconditional growth models by hierarchical linear modeling (HLM) was conducted to evaluate the trajectory of overall functioning, and specifically compared the linear (Model 1) and quadratic unconditional growth model (Model 2). Then, each of the three level-2 predictors for two different models, the clinical model (Model 3) and the demographic model (Model 4), were selected to explain the treatment outcome. The demographic model included income, educational level, and age, and the clinical model included diagnosis type, working alliance, and total session numbers. The fixed effect coefficients of these predictors were tested by HLM.  

The unconditional growth model showed that the improvements follow a significant quadratic change in multilevel growth modeling rather than a linear trajectory based on the deviance test (χ2 = 13.725, df = 4, p = 0.008). Regarding the analysis that explored the impact of clinical and demographic variables, the results supported the demographic model rather than the clinical model. Younger clients showed a better linear rate of improvement (β = 0.06, t (22) = 0.02, p = 0.023), and those with a higher education level showed a better quadratic rate of improvement (β = -0.84, t (22) = 0.38, p = 0.038) throughout the treatment. Though the HLM supported the demographic model, the clinical model demonstrated the different trajectories based on the diagnosis, and clients with depressive disorders showed a faster rate of acceleration in improvement (β = 1.64, t (27) = 2.42, p = 0.023) compared to non-depressive disorder clients. 

The present study provides insights and evidence of treatment trajectories with adult clients with the most commonly occurring mental health disorders, and highlights the importance of considering both demographic and clinical features when working with this clientele in a community setting. Data is collected on a continuous basis, and final results are subject to change.