Outcome measures based on digital health technology sensor data: data- and patient-centric approaches

Digital health technology tools (DHTT) are technologies such as apps, smartphones, and wearables that remotely acquire health-related information from individuals. They have the potential advantages of objectivity and sensitivity of measurement, richness of high-frequency sensor data, and opportunit...

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Autores principales:	Kirsten I. Taylor, Hannah Staunton, Florian Lipsmeier, David Nobbs, Michael Lindemann
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/754c85fde89e4efea3f6b564c217b4ac
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spelling	oai:doaj.org-article:754c85fde89e4efea3f6b564c217b4ac2021-12-02T16:17:26ZOutcome measures based on digital health technology sensor data: data- and patient-centric approaches10.1038/s41746-020-0305-82398-6352https://doaj.org/article/754c85fde89e4efea3f6b564c217b4ac2020-07-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0305-8https://doaj.org/toc/2398-6352Digital health technology tools (DHTT) are technologies such as apps, smartphones, and wearables that remotely acquire health-related information from individuals. They have the potential advantages of objectivity and sensitivity of measurement, richness of high-frequency sensor data, and opportunity for passive collection of health-related data. Thus, DHTTs promise to provide patient phenotyping at an order of granularity several times greater than is possible with traditional clinical research tools. While the conceptual development of novel DHTTs is keeping pace with technological and analytical advancements, an as yet unaddressed gap is how to develop robust and meaningful outcome measures based on sensor data. Here, we describe two roadmaps which were developed to generate outcome measures based on DHTT data: one using a data-centric approach and the second a patient-centric approach. The data-centric approach to develop digital outcome measures summarizes those sensor features maximally sensitive to the concept of interest, exemplified with the quantification of disease progression. The patient-centric approach summarizes those sensor features that are optimally relevant to patients’ functioning in everyday life. Both roadmaps are exemplified for use in tracking disease progression in observational and clinical interventional studies, and with a DHTT designed to evaluate motor symptom severity and symptom experience in Parkinson’s disease. Use cases other than disease progression (e.g., case-finding) are considered summarily. DHTT research requires methods to summarize sensor data into meaningful outcome measures. It is hoped that the concepts outlined here will encourage a scientific discourse and eventual consensus on the creation of novel digital outcome measures for both basic clinical research and clinical drug development.Kirsten I. TaylorHannah StauntonFlorian LipsmeierDavid NobbsMichael LindemannNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Kirsten I. Taylor Hannah Staunton Florian Lipsmeier David Nobbs Michael Lindemann Outcome measures based on digital health technology sensor data: data- and patient-centric approaches
description	Digital health technology tools (DHTT) are technologies such as apps, smartphones, and wearables that remotely acquire health-related information from individuals. They have the potential advantages of objectivity and sensitivity of measurement, richness of high-frequency sensor data, and opportunity for passive collection of health-related data. Thus, DHTTs promise to provide patient phenotyping at an order of granularity several times greater than is possible with traditional clinical research tools. While the conceptual development of novel DHTTs is keeping pace with technological and analytical advancements, an as yet unaddressed gap is how to develop robust and meaningful outcome measures based on sensor data. Here, we describe two roadmaps which were developed to generate outcome measures based on DHTT data: one using a data-centric approach and the second a patient-centric approach. The data-centric approach to develop digital outcome measures summarizes those sensor features maximally sensitive to the concept of interest, exemplified with the quantification of disease progression. The patient-centric approach summarizes those sensor features that are optimally relevant to patients’ functioning in everyday life. Both roadmaps are exemplified for use in tracking disease progression in observational and clinical interventional studies, and with a DHTT designed to evaluate motor symptom severity and symptom experience in Parkinson’s disease. Use cases other than disease progression (e.g., case-finding) are considered summarily. DHTT research requires methods to summarize sensor data into meaningful outcome measures. It is hoped that the concepts outlined here will encourage a scientific discourse and eventual consensus on the creation of novel digital outcome measures for both basic clinical research and clinical drug development.
format	article
author	Kirsten I. Taylor Hannah Staunton Florian Lipsmeier David Nobbs Michael Lindemann
author_facet	Kirsten I. Taylor Hannah Staunton Florian Lipsmeier David Nobbs Michael Lindemann
author_sort	Kirsten I. Taylor
title	Outcome measures based on digital health technology sensor data: data- and patient-centric approaches
title_short	Outcome measures based on digital health technology sensor data: data- and patient-centric approaches
title_full	Outcome measures based on digital health technology sensor data: data- and patient-centric approaches
title_fullStr	Outcome measures based on digital health technology sensor data: data- and patient-centric approaches
title_full_unstemmed	Outcome measures based on digital health technology sensor data: data- and patient-centric approaches
title_sort	outcome measures based on digital health technology sensor data: data- and patient-centric approaches
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/754c85fde89e4efea3f6b564c217b4ac
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Outcome measures based on digital health technology sensor data: data- and patient-centric approaches

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