HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning

The collection of data from a personal digital device to characterize current health conditions and behaviors that determine how an individual's health will evolve has been called digital phenotyping. In this paper, we describe the development of and early experiences with a comprehensive digital phenotyping platform: Health Outcomes through Positive Engagement and Self-Empowerment (HOPES). HOPES is based on the open-source Beiwe platform but adds a wider range of data collection, including the integration of wearable devices and further sensor collection from smartphones. Requirements were partly derived from a concurrent clinical trial for schizophrenia that required the development of significant capabilities in HOPES for security, privacy, ease of use, and scalability, based on a careful combination of public cloud and on-premises operation. We describe new data pipelines to clean, process, present, and analyze data. This includes a set of dashboards customized to the needs of research study operations and clinical care. A test use case for HOPES was described by analyzing the digital behavior of 22 participants during the SARS-CoV-2 pandemic.

Automated summary

This paper describes the development and early experiences with a comprehensive digital phenotyping platform called Health Outcomes through Positive Engagement and Self-Empowerment (HOPES). Based on the open-source Beiwe platform, HOPES integrates a wider range of data collection methods, including wearable devices and smartphone sensor data. Requirements were partly derived from a concurrent clinical trial for schizophrenia, leading to the development of significant capabilities in HOPES for security, privacy, ease of use, and scalability.