Testing a Biopsychosocial Model of Outcome in Schizophrenia: Integrating Electrodermal, Neurocognitive, and Social Cognitive Variables for Predicting Functional Outcome Over 12 Months

Background: Social work has a history of using biopsychosocial models, but biological factors are rarely integrated into empirical models. Heterogeneity is common in the functional outcome of schizophrenia. It is believed that psychophysiology, neurocognition, and social cognition are three significant predictors for the functional outcome of schizophrenia. However, the patterns of their relationships and their combined predictive value for the functional outcome of schizophrenia are not yet clearly understood.

Objective: First, to explore the relationships between the baseline electrodermal activity (EDA), neurocognition, and social cognition of people with schizophrenia before receiving rehabilitation. Second, to test a predictive model consisting of electrodermal activity, neurocognition, and social cognition for predicting the functional outcome of schizophrenia during 12 months of community-based psychosocial rehabilitation.

Methods: One hundred and three subjects at baseline were admitted to four highly similar psychosocial rehabilitation programs and were followed prospectively for 12 months during rehabilitation in this NIMH-funded protocol. Ninety subjects completed the follow-up functional outcome evaluation after 6 months and 82 subjects completed the follow-up functional outcome and neuropsychological evaluation after 12 months. EDA measures of resting arousal and stress reactivity were taken at baseline. Neurocognition at baseline was represented with measures of working and secondary memory, vigilance and speed of processing. Social cognition at baseline was measured using emotion recognition tests. Role functioning in independent living, social and work domains were measured with the Role Functioning Scale at baseline, 6 and 12 months. Structural Equation Modeling using EQS version 6.1 for windows was used to test the predictive model over time.

Results: The model combining EDA, neurocognition, and social cognition was predictive of functional outcomes at baseline (χ2 = 18.095, df = 14, p = 0.203, CFI = 0.946, RMSEA = 0.054), 6 months (χ2 = 12.915, df =14, p = 0.533, CFI = 1.000, RMSEA = 0.000) and 12 months (χ2 = 16.184, df =14, p = 0.302, CFI = 0.966, RMSEA = 0.044). All parameters in the model were significant at each time point. The model shows that higher EDA is positively related to better neurocognition, better neurocognition is related to higher social cognition, and the total model significantly predicts functional outcomes over time.

Conclusions: This model showed robustness in predicting both 6 and 12 month prospective outcomes in schizophrenia. Higher EDA represents a capacity to engage with environmental stimuli, which contributes to better neurocognition and social cognition, resulting in higher functional outcomes. This model has relevance to holistic psychosocial interventions in schizophrenia by illustrating the multi-level biopsychosocial factors that predict functional outcomes.