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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Aug 31, 2020
Date Accepted: Jan 18, 2021

The final, peer-reviewed published version of this preprint can be found here:

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

Wang X, Vouk N, Heaukulani C, Buddhika T, Martanto W, Lee J, Morris RJ

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

J Med Internet Res 2021;23(3):e23984

DOI: 10.2196/23984

PMID: 33720028

PMCID: 8074871

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

  • Xuancong Wang; 
  • Nikola Vouk; 
  • Creighton Heaukulani; 
  • Thisum Buddhika; 
  • Wijaya Martanto; 
  • Jimmy Lee; 
  • Robert JT Morris

ABSTRACT

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 much wider range of data collection, including the integration of wearable devices 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 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 for clinical care. A test use case for HOPES is described by analyzing the digital behavior of 20 participants during the SARS-CoV-2 pandemic.


 Citation

Please cite as:

Wang X, Vouk N, Heaukulani C, Buddhika T, Martanto W, Lee J, Morris RJ

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

J Med Internet Res 2021;23(3):e23984

DOI: 10.2196/23984

PMID: 33720028

PMCID: 8074871

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