Cluster Hidden Markov Models: An Application to Ecological Momentary Assessment of Schizophrenia

Ecological Momentary Assessment (EMA) tools are used to monitor the thoughts and feelings of people in their everyday lives over time. In this paper we examine the feasibility of multi-item, multi-subject Hidden Markov Models (HMMs) to identify response clusters in people with schizophrenia. Data comprise 49 participants from two randomised clinical trials using the mobile app ClinTouch, an EMA tool for daily monitoring of schizophrenia symptoms. The app was used for up to 12 weeks (median follow-up 83 days, 78% response rate). We find that a 3-cluster model with 3 states per cluster performs best amongst the configurations tested, and the feasibility of HMMs as applied to multi-item EMA data is demonstrated. However, there is substantial heterogeneity between participants within each hidden state for which sampling error due to short observation periods is a likely contributor. More data are needed to validate and refine the modelling approach taken here.