Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study.

Autor:	Polhemus AM; Merck Research Labs Information Technology, Merck Sharpe & Dohme, Prague, Czech Republic.; Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zürich, Switzerland., Novák J; Merck Research Labs Information Technology, Merck Sharpe & Dohme, Prague, Czech Republic.; Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Prague, Czech Republic., Ferrao J; Merck Research Labs Information Technology, Merck Sharpe & Dohme, Prague, Czech Republic., Simblett S; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., Radaelli M; Neurology Services, San Raffaele Hospital Multiple Sclerosis Centre, Milan, Italy., Locatelli P; Department of Engineering and Applied Science, University of Bergamo, Bergamo, Italy., Matcham F; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom., Kerz M; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., Weyer J; Patient Advisory Board, Remote Assessment of Disease and Relapse Research Program, King's College London, London, United Kingdom., Burke P; Patient Advisory Board, Remote Assessment of Disease and Relapse Research Program, King's College London, London, United Kingdom., Huang V; Merck Research Labs Information Technology, Merck Sharpe & Dohme, Singapore, Singapore., Dockendorf MF; Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co, Inc, Kenilworth, NJ, United States., Temesi G; Merck Research Labs Information Technology, Merck Sharpe & Dohme, Prague, Czech Republic., Wykes T; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom., Comi G; Neurology Services, San Raffaele Hospital Multiple Sclerosis Centre, Milan, Italy., Myin-Germeys I; Department for Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium., Folarin A; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom., Dobson R; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom., Manyakov NV; Janssen Pharmaceutica NV, Beerse, Belgium., Narayan VA; Research and Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ, United States., Hotopf M; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom.
Jazyk:	angličtina
Zdroj:	JMIR mHealth and uHealth [JMIR Mhealth Uhealth] 2020 May 07; Vol. 8 (5), pp. e16043. Date of Electronic Publication: 2020 May 07.
DOI:	10.2196/16043
Abstrakt:	Background: Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these technologies persist. Many health care programs that use RMT rely on commercially available, "off-the-shelf" devices to collect patient data. However, validation of these devices is sparse, the technology landscape is constantly changing, relative benefits between device options are often unclear, and research on patient and health care provider preferences is often lacking. Objective: To address these common challenges, we propose a novel device selection framework extrapolated from human-centered design principles, which are commonly used in de novo digital health product design. We then present a case study in which we used the framework to identify, test, select, and implement off-the-shelf devices for the Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) consortium, a research program using RMT to study central nervous system disease progression. Methods: The RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. The framework guides study designers through stakeholder engagement, technology landscaping, rapid proof of concept testing, and creative problem solving to develop device selection criteria and a robust implementation strategy. It also describes a method for considering compromises when tensions between stakeholder needs occur. Results: The framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis. In the initial stage, we engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals. We desired regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data. In the second stage, we iteratively refined our strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, we used this method to devise compromises that addressed conflicting stakeholder needs. We then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program. Conclusions: The RADAR device selection framework provides a structured yet flexible approach to device selection for health care programs and can be used to systematically approach complex decisions that require teams to consider patient experiences alongside scientific priorities and logistical, technical, or regulatory constraints. (©Ashley Marie Polhemus, Jan Novák, Jose Ferrao, Sara Simblett, Marta Radaelli, Patrick Locatelli, Faith Matcham, Maximilian Kerz, Janice Weyer, Patrick Burke, Vincy Huang, Marissa Fallon Dockendorf, Gergely Temesi, Til Wykes, Giancarlo Comi, Inez Myin-Germeys, Amos Folarin, Richard Dobson, Nikolay V Manyakov, Vaibhav A Narayan, Matthew Hotopf. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 07.05.2020.)
Databáze:	MEDLINE
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