HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning.
J Med Internet Res
; 23(3): e23984, 2021 03 15.
Article
in English
| MEDLINE | ID: covidwho-1133814
ABSTRACT
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Data Collection
/
Machine Learning
/
Wearable Electronic Devices
Type of study:
Diagnostic study
/
Experimental Studies
/
Prognostic study
/
Qualitative research
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
J Med Internet Res
Journal subject:
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
23984
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