Predictors of Self-Stigma in Schizophrenia: New Insights Using Mobile Technologies

J Dual Diagn. 2012 Oct;8(4):305-314. doi: 10.1080/15504263.2012.723311. Epub 2012 Sep 10.

Abstract

Objective: Self-stigma has significant negative impact on the recovery of individuals with severe mental illness, but its varying course is not well understood. Individual levels of self-stigma may vary over time and fluctuate in response to both external/contextual (i.e., location, activity, social company) and internal (i.e., psychiatric symptoms, mood) factors. The aim of this study was to examine the relationship between self-stigmatizing beliefs and these factors, as they occur in the daily life of individuals with schizophrenia.

Methods: Mobile technologies were used to longitudinally track momentary levels of self-stigma, psychotic symptoms, negative affect, positive affect, activity, and immediate social and physical environment in twenty-four individuals with schizophrenia, multiple times daily, over a one-week period.

Results: Multilevel modeling showed that participants' current activity was associated with changes in self-stigma (χ2= 10.53, p <0.05), but immediate location and social company were not. Time-lagged analyses found that increases in negative affect (β=0.11, p<0.01) and psychotic symptom severity (β=0.16, p<0.01) predicted increases in the intensity of self-stigmatizing beliefs. Psychotic symptoms were found to be both an antecedent and a consequence (β=0.08, p<0.01) of increased self-stigma.

Conclusions: Our findings support a framework for understanding self-stigma as an experience that changes based on alterations in internal states and external circumstances. Mobile technologies are an effective methodology to study self-stigma and have potential to be used to deliver clinical interventions.

Keywords: ecological momentary assessment (EMA); mHealth; mobile technologies; schizophrenia; stigma.