The reciprocal relationship between alliance and symptom improvement across the treatment of childhood anxiety

Abstract

This study examined changes in the therapeutic alliance and in self-reported anxiety over the course of 16 weeks of manual-based family treatment for child anxiety disorders. Eighty-six children (51.3% female; aged 7.15-14.44; 86.2% Caucasian, 14.8% minority) with a principal diagnosis of separation anxiety disorder, generalized anxiety disorder, and/or social phobia, and their parents, received family treatment for anxiety disorders in youth. Child, therapist, and parent ratings of therapeutic alliance and child ratings of state anxiety were measured each session. Latent difference score growth modeling investigated the interacting relationship. Therapeutic alliance change, as rated by the mother and by the therapist, was a significant predictor (medium effect) of latter change in child anxiety (with greater therapeutic alliance leading to later reduction in anxiety). However, changes in child-reported anxiety also predicted latter change in father- and therapist-reported alliance (small-to-medium effect). Prospective relationships between child-reported therapeutic alliance and child-reported symptom improvement were not significant. Results provide partial support for a reciprocal model in which therapeutic alliance improves outcome, and anxiety reduction improves therapeutic alliance.

Figure 1

Bivariate latent difference score model of the State Anxiety Scale for Children (STAIC) and Therapist Alliance Scale for Children (TASC-R). Previous score on alliance is a significant predictor of later change on the anxiety. Additionally, previous score on anxiety is a significant predictor of later change on alliance. Note. The role of the latent intercepts and slopes is to describe change in a manner similar to a latent growth curve model (i.e., to take into account the starting point and overall linear change process for each measure separately, as we look at our primary question of how the change processes across variables are predictive of one another). The α refers to alpha (estimate to model straight-line growth) and the β refers beta, which is a proportional change (allowing the trajectory to have a non-linear slope).