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.

翻译：数字表型分析在心理健康领域通常通过智能手机等设备收集传感器数据和自我报告数据，以获取行为和经验信息。这些丰富全面的数据可用于揭示日常行为与多种心理健康状况之间的关系。然而，由于这些数据集具有高维度和多模态特性，当前的分析方法应用有限。本研究首次采用一种系统化方法，将主观自我报告数据（生态瞬时评估，EMA）与同步传感器数据加以整合。研究使用CrossCheck数据集分析了50名精神分裂症患者的资料，在个体层面（n-of-1）对EMA进行网络分析，同时利用传感器数据识别不同行为背景时段。研究发现，在特定行为背景（如每日访问地点数量的变化）下生成的网络，与基线网络及随机抽样时段生成的网络存在显著差异。本文提出的框架为揭示个体层面上的行为背景及其对自我报告的并发影响奠定了基础，这些见解对于精神分裂症等严重精神疾病的管理具有重要价值。