Poster #54 - Family Environment, RSA, and Pre-Adolescents' Social-Behavioral Outcomes: Evidence of Differential Susceptibility

• In Event: 2-163 - Poster Session 08
  In Poster Session: PS 08 Section - Developmental Psychopathology

Integrative Statement

Introduction
Children vary in their susceptibility to family influences. To explain these individual differences, the traditional diathesis-stress model hypothesized that some children are disproportionately more vulnerable to adverse environments (Zuckerman, 1999). However, this diathesis-stress model was challenged by the biological sensitivity to context theory (BSCT; Boyce & Ellis, 2005; Ellis et al., 2005) and the differential susceptibility theory (DST; Belsky, 1997, 2005). These theories purported that youth vary in their neurobiological susceptibility to both negative (risk-promoting) and positive (development-enhancing) contexts. Specifically, highly susceptible children are not only more vulnerable to toxic rearing environments but also benefit exponentially in more supportive and nurturing environments.
Research testing differential susceptibility has yielded mixed results, with some studies, in fact, supporting the diathesis-stress model. A failure to capture the full spectrum of rearing environments (e.g. Davis et al., 2017), from negative to positive, could cause these results. In addition, most studies have tested differential susceptibility among early-childhood youth and lacked stress-reactivity data. To fill in these gaps, the current study employed the Family Adaptability and Cohesion Scale (FACES-IV; Olson, 2015) to assess family environments from problematic to healthy in a sample of rural pre-adolescence youth. Respiratory Sinus Arrhythmia (RSA) baseline, a stress-reactivity index, was measured as youth’s neurobiological susceptibility indicator to the differential susceptibility. Overall, the present study aimed to examine whether family cohesion and flexibility (measured as presence or absence), differentially influenced youth’s positive and negative social-behavioral outcomes (e.g., Benzies, & Mychasiuk, 2009; Leve et al., 2005).
Methods
The current study assessed 101 rural parent-child dyads (Mchildage=10.28, 52% female, 78% African American) in a longitudinal (two time-points with one year in-between), multi-reporter, and multi-method (experimental task, electrocardiogram data, and surveys) design. Family environment was measured through the parent-report FACES-IV circumplex total ratio, assessing family cohesion and flexibility, with higher scores indicating healthier family systems. Youth internalizing and externalizing problems were assessed at both time-points through the parent-report Child Behavior Checklist (Achenbach & Rescorla, 2001; αINT=.69, αEXT=.81). Future Orientation was measured using the child-report Future Orientation Scale (Steinberg et al., 2009; α=.65) at both time-points. Youth’s RSA baseline data were obtained by electrocardiogram during a relaxation task.
Results
A Structural Equation Model was used to test the hypotheses (Figure 1). The interaction between RSA baseline and the family environment was associated negatively with T2 internalizing problems (β=-.26, p<.05) and positively with T2 future orientation (β=.23, p<.05) significantly, after controlling for these variables at T1. Probing the interaction effects (Figure 2) indicated that youth with higher RSA baseline (i.e. higher neurobiological susceptibility) exhibited more internalizing problems and less future orientation in problematic family environments, but showed less internalizing problems and more future orientation in healthy family environments, compared to youth with lower RSA baseline.
Conclusions
The current study corroborated previous research using RSA as a neurobiological susceptibility indicator (e.g. Eisenberg et al., 2012) and supported differential susceptibility among rural pre-adolescence youth. In addition, this study demonstrated the importance of employing measures that can assess both positive and negative environments for youth when testing differential susceptibility.

Authors

• Sihong Liu, University of Georgia
  Presenting Author

• Assaf Oshri, University of Georgia
  Non-Presenting Author

• Kimberly Osborne, University of Georgia
  Non-Presenting Author

• Zehua Cui, University of Georgia
  Non-Presenting Author

• Emilie Mylene Ellis, University of Georgia
  Non-Presenting Author