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

TL;DR

HOPES is a comprehensive, open-source digital phenotyping platform that integrates diverse data sources, including wearables and smartphones, to facilitate data collection, monitoring, and machine learning for clinical research and care.

Contribution

It extends the Beiwe platform by adding extensive data collection, security, privacy, and scalability features tailored for clinical trials and real-world monitoring.

Findings

Successful deployment in a schizophrenia trial

Analysis of digital behaviors during COVID-19 pandemic

Enhanced data pipelines and dashboards for research and clinical use

Abstract

We describe the development of, and early experiences with, 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 much wider range of data collection, including the integration of wearable data sources and further sensor collection from the smartphone. Requirements were in part derived from a concurrent clinical trial for schizophrenia. This trial required development of significant capabilities in HOPES in 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 the research study operations and for clinical care. A test use of HOPES is described by analyzing the digital behaviors of 20 participants during the SARS-CoV-2 pandemic.