Objective: To study the longitudinal patterns of subjective wellbeing in schizophrenia using cluster analysis and their relation to recovery criteria, further to examine predictors for cluster affiliation, and to evaluate the sensitivity and specificity of baseline subjective wellbeing cut-offs for cluster affiliation.
Methods: Data was collected in an observational 36-month follow-up study of 2842 patients with schizophrenia in Germany. Subjective wellbeing was assessed using the SWN-K scale. Cluster analyses were applied based on Ward's procedure. Predictors were analyzed using logistic regression models. Optimal SWN-K total score cut-off points for cluster affiliation were analyzed using Cohen's kappa.
Results: 4 distinct clusters were identified: a stable low (33%), a stable moderate (31%), a stable high (16%), and a cluster with distinct initial improvement and then stable high subjective wellbeing (20%). Highly concordant patterns were also observed for symptoms, social functioning, and quality of life. Sensitivity and specificity of SWN-K total score cut-offs at baseline were 82.8% and 63.8% for <or=60 points for the stable low cluster and 84.7% and 95.4% for >or=80 points for the stable high cluster. Affiliation to the stable low cluster was related to a 0.6% chance of being in recovery at 3-year endpoint.
Conclusions: Long-term patterns of subjective wellbeing are stable and highly concordant with course of symptoms, functioning level, and quality of life. Baseline subjective wellbeing cut-off points were found to be sufficient predictors of outcome, which, particularly in case of impaired subjective wellbeing and low baseline functioning level, make early treatment adaptations mandatory.