Vollständiger Abstract
Worum geht es in dieser Arbeit?
Abstract Digital health technologies (DHTs) enable continuous and remote measurement in clinical research, but DHT-derived measures require evidence of accuracy, usability, generalizability, and regulatory suitability. Without structured guidance, studies risk generating data that are technically sophisticated but clinically ambiguous and difficult to reproduce. This Perspective presents a six-component framework for selecting, validating, and optimizing DHT-derived measures to the appropriate research question, endpoint role, context of use, and evidentiary purpose.
Bibliografischer Nachweis
Publikationsdaten
- Autor:innen
- David Zahavi, Audie A. Atienza, Caitlin P. Bailey, Adam Berger, Yuxia Cui, Pablo Cure, Nicole Dickerman, Michael Espey, Josh Fessel, Caroline Hagedorn, Orlando Lopez, James J. McClain, Thomas Radman, Carol Shreffler, Dana L. Wolff-Hughes, Christopher M. Hartshorn
- Quelle
- npj Digital Medicine
- Publikation
- 2026-01-01
- Band / Ausgabe
- Nicht angegeben
- Seiten
- Nicht angegeben
- ISSN / ISBN
- 2398-6352
- Zitationen
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Zitierfähiger Nachweis
David Zahavi, Audie A. Atienza, Caitlin P. Bailey, Adam Berger, Yuxia Cui, Pablo Cure, Nicole Dickerman, Michael Espey, Josh Fessel, Caroline Hagedorn, Orlando Lopez, James J. McClain, Thomas Radman, Carol Shreffler, Dana L. Wolff-Hughes, Christopher M. Hartshorn (2026). A framework for selecting, validating, and optimizing DHT-derived measures in clinical research. npj Digital Medicine. https://doi.org/10.1038/s41746-026-03176-2
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