Exploring the Factor Structure of Child- and Caregiver-Directed Treatment Strategy Delivery in an Individualized Mental Health Intervention for ASD (AIM HI)

Poster Presentation
Friday, May 3, 2019: 5:30 PM-7:00 PM
Room: 710 (Palais des congres de Montreal)
K. Martinez1, E. Hurwich-Reiss2, T. Lind2, C. Chlebowski3 and L. Brookman-Frazee4,5, (1)Clinical Psychology, San Diego State University / University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, (2)University of California, San Diego, La Jolla, CA, (3)National Institute of Mental Health, Bethesda, MD, (4)Psychiatry, University of California, San Diego, San Diego, CA, (5)Child and Adolescent Services Research Center, San Diego, CA
Background: AIMHI (“An Individualized Mental Health Intervention for ASD”), a parent mediated and child-focused behavioral intervention for children 5-13 years of age with ASD, has been shown to have a long-lasting impact in reducing challenging behaviors when delivered by therapists in publicly-funded mental health (MH) settings (Brookman-Frazee et al., under review). AIMHI intervention strategies are theoretically conceptualized into two broad categories (Active Teaching & Within Session Elements); however, statistical exploration of the factor structure of these treatment elements has not been conducted.

Objectives: The current study aimed to explore the factor structure of therapists’ delivery of child- and parent-directed AIMHI intervention strategies during therapy sessions.

Methods: Data were drawn from a large-scale effectiveness trial of AIMHI; in the intervention condition therapists received training in AIMHI and delivered the intervention to their client for 6 months. Therapists’ use of child- and parent-directed AIMHI strategy delivery was evaluated using observational coding of therapy sessions. Participants included 143 therapist-child dyads in the intervention condition of the trial. The child sample (n=143) consisted of 83% males, with a mean age of 9.05 (SD=2.5). Therapists (n=126) were 34 years of age (SD=8.30), and ranged in experience (0-55 years). Seventy-two percent of the children and fifty-nine percent of the therapists were from an ethnic minority background. The number of coded sessions per child was 8.06 sessions (SD=3.2). Multilevel exploratory factor analyses (MEFA) with geomin rotation were run on Mplus to explore the factor structure of child-directed and parent-directed therapist strategies in the delivery of AIMHI.

Results: The Root Mean Square Error of Approximation (RMSEA) and the Comparative Fit Index (CFI) were used to evaluate model fit, with an RMSEA of ≤ 0.08 and a CFI > 0.95 indicative of good model fit. For the child-directed strategies, MEFA indicated preference for a two-factor model with unrestricted within-level covariance. The two-factor model demonstrated excellent fit Χ2(19) = 97.55, p <.001; CFI = .96., RMSEA = .02. Six items loaded onto the first factor, termed Engagement Strategies, while three items loaded onto the second factor, termed Active Teaching Strategies (see Table 1). For the parent-directed therapist strategies, a one-factor model with unrestricted covariance was revealed. Model fit was excellent Χ2(20) = 36.24, p <.05; CFI = .99, RMSEA = .00. The factor included 8 items. See Table 1 for more details.

Conclusions: Findings based on MEFA suggest that AIMHI child-directed strategies are best characterized by a two-factor model, whereas a unidimensional model best captures parent directed strategies. These results confirm the importance of using active teaching strategies (modeling, behavioral rehearsal, feedback and reinforcement) to teach child and parent skills and highlight the importance of using specific engagement strategies in MH delivery for an ASD population. Next steps will include confirming the factor structure of child- and parent-directed strategy delivery in AIMHI using multilevel confirmatory factor analyses. Long-term, these factors might be used to understand which factors best predict AIMHI treatment response as well as to understand which child and parent characteristics best predict strategy delivery.