2023/12/02 by Andy Davies, Eiko I. Fried, Davies, Andy +5
Neuroscience · Physics and Astronomy · Psychology · #Complex Network Analysis Techniques #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Human-Computer Interaction (cs.HC) #Mental Health Research Topics #Social and Information Networks (cs.SI)
paper · doi:10.48550/arxiv.2312.01216
openalex publication_date 2023/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Digital phenotyping in mental health often consists of collecting behavioral and experience-based information through sensory and self-reported data from devices such as smartphones. Such rich and comprehensive data could be used to develop insights into the relationships between daily behavior and a range of mental health conditions. However, current analytical approaches have shown limited application due to these datasets being both high dimensional and multimodal in nature. This study demonstrates the first use of a principled method which consolidates the complexities of subjective self-reported data (Ecological Momentary Assessments - EMAs) with concurrent sensor-based data. In this study the CrossCheck dataset is used to analyse data from 50 participants diagnosed with schizophrenia. Network Analysis is applied to EMAs at an individual (n-of-1) level while sensor data is used to identify periods of various behavioral context. Networks generated during periods of certain behavioral contexts, such as variations in the daily number of locations visited, were found to significantly differ from baseline networks and networks generated from randomly sampled periods of time. The framework presented here lays a foundation to reveal behavioural contexts and the concurrent impact of self-reporting at an n-of-1 level. These insights are valuable in the management of serious mental illnesses such as schizophrenia.