Description

The proposed RCT will test the impacts of the Attachment Biobehavioral Catch-up (ABC) intervention program on child health outcomes. A total of 260 male and female infants will be enrolled in this study, with an age range of 8-12 months (infant) and 13 months- 23 months (toddler). This study will enroll primiparous and multiparous mothers who identify as Latina, speak English or Spanish, and have a 9-month-old child enrolled in Medicaid at the start of the study. Participants will be recruited in collaboration with the Children's Medical Practice (CMP) at Johns Hopkins Bayview Medical Center and matriculated on a rolling basis. Each participant will participate for approximately 15 months; the time required to complete the Time 1 assessment, either 10-week condition, and the two post-intervention assessments. The entire study is anticipated to be completed in 5 years. This RCT will test maternal sensitivity and child stress regulation as mediators of intervention effects. It will also examine the extent to which sociocultural factors moderate the effects of ABC. Behavioral methods and procedures will include surveys/questionnaires, audio/video recordings, individual or group behavioral observations, psychosocial or behavioral interventions, and other psychosocial or behavioral procedures. This RCT is powered to detect small-medium intervention effects (Cohen's d's= 0.35 - 0.50). These effect sizes align with previous studies, which have shown small to large effects on maternal sensitivity (d's = 0.23 - 0.77) and small to medium effects on main and moderated child behavioral stress regulation (d's= 0.15 - 0.48) and child physiological (cortisol) regulation, via maternal sensitivity (d=-0.36). For .80 power or higher and a two-tailed significance level of .05, power analyses performed using Optimal Design, Webpower, and Gpower software indicated a post-attrition sample size of N=221 will adequately detect main, mediated, and moderated effects of comparable size. Analyses will be conducted in SAS 9.4, SPSS 25.0, and Mplus 8 and will include regression and structural equation models. Missing data will be accommodated using multiple imputations and/or full information maximum likelihood (FIML) and estimators that are robust to non-normality when missing data are present (e.g., MLR). Data safety monitoring will be conducted by the Principal Investigators and the external Data Safety and Monitoring Board, who will review adverse events, enrollment numbers, procedure reports, raw data, outcomes, preliminary analyses, and other data which will be completed on an ongoing basis and reported to the IRB and a sponsor. Study findings stand to inform the nature and timing of preventive interventions to reduce health disparities that disproportionately impact Latino families and will advance understanding of early social influences that promote health development across the lifespan.