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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378210/ https://www.ncbi.nlm.nih.gov/pubmed/32715091 http://dx.doi.org/10.1038/s41746-020-0305-8 |
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author | Taylor, Kirsten I. Staunton, Hannah Lipsmeier, Florian Nobbs, David Lindemann, Michael |
author_facet | Taylor, Kirsten I. Staunton, Hannah Lipsmeier, Florian Nobbs, David Lindemann, Michael |
author_sort | Taylor, Kirsten I. |
collection | PubMed |
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 | Online Article Text |
id | pubmed-7378210 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73782102020-07-24 Outcome measures based on digital health technology sensor data: data- and patient-centric approaches Taylor, Kirsten I. Staunton, Hannah Lipsmeier, Florian Nobbs, David Lindemann, Michael NPJ Digit Med Comment 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. Nature Publishing Group UK 2020-07-23 /pmc/articles/PMC7378210/ /pubmed/32715091 http://dx.doi.org/10.1038/s41746-020-0305-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Comment Taylor, Kirsten I. Staunton, Hannah Lipsmeier, Florian Nobbs, David Lindemann, Michael Outcome measures based on digital health technology sensor data: data- and patient-centric approaches |
title | 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_short | 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 |
topic | Comment |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378210/ https://www.ncbi.nlm.nih.gov/pubmed/32715091 http://dx.doi.org/10.1038/s41746-020-0305-8 |
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