Uncovering social states in healthy and clinical populations using digital phenotyping and Hidden Markov Models

Abstract

Brain related disorders are characterised by observable behavioural symptoms. Smartphones can passively collect objective behavioural data, avoiding recall bias. Despite promising clinical utility, analysing smartphone data is challenging as datasets often include a range of missingness-prone temporal features. Hidden Markov Models (HMMs) provide interpretable, lower-dimensional temporal representations of data, allowing missingness. We applied an HMM to an aggregate dataset of smartphone measures designed to assess social functioning in healthy controls (HCs) (n=247), participants with schizophrenia (n=18), Alzheimer’s disease (AD) (n=26) and memory complaints (n=57). We selected a model with socially “active” and “inactive” states, generated hidden state sequences per participant and calculated their “dwell time”, i.e. the percentage of time spent in the socially active state. We identified lower dwell times in AD versus HCs and higher dwell times related to increased social functioning questionnaire scores in HCs, finding the HMM to be a practical method for digital phenotyping analysis.