UCLA

Daily living skills (DLS) predict adult success for autistic individuals. However, most research on DLS in autism relies on cross-sectional data and DLS summary scores, limiting understanding of how specific skills across development relate to adult outcomes. Using longitudinal data from autistic adults (n = 232, 19% female, 69% White) followed from early childhood into their early thirties (m_age = 32.6 years, SD = 7.58, range 19-35), this study used machine learning techniques to identify item-level DLS from ages 5 (n = 123), 9 (n = 152), 14 (n = 154), and 18 (n = 143) that predicted adult employment, residential status, social relationships, and well-being. DLS items demonstrated predictive utility for employment (age 9 model R² = .557) and social relationships (age 14 model ROC AUC = .839). Notably, DLS measured at age 5 explained a large proportion of variance in adult vocational outcomes (R² = .507). Predictive strength for residential status increased across development (age 18 model ROC AUC = .906), whereas DLS showed limited association with subjective well-being (age 14 model R² = .096). Community-based skills (e.g., money use, telephone skills) consistently emerged as top predictors. Findings are interpreted within a developmental cascade framework, highlighting how DLS competencies in childhood and adolescence may inform experiences for autistic adults.