TY  - JOUR
AU  - Polhemus, Ashley Marie
AU  - Novák, Jan
AU  - Ferrao, Jose
AU  - Simblett, Sara
AU  - Radaelli, Marta
AU  - Locatelli, Patrick
AU  - Matcham, Faith
AU  - Kerz, Maximilian
AU  - Weyer, Janice
AU  - Burke, Patrick
AU  - Huang, Vincy
AU  - Dockendorf, Marissa Fallon
AU  - Temesi, Gergely
AU  - Wykes, Til
AU  - Comi, Giancarlo
AU  - Myin-Germeys, Inez
AU  - Folarin, Amos
AU  - Dobson, Richard
AU  - Manyakov, Nikolay V
AU  - Narayan, Vaibhav A
AU  - Hotopf, Matthew
PY  - 2020
DA  - 2020/5/7
TI  - Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study
JO  - JMIR Mhealth Uhealth
SP  - e16043
VL  - 8
IS  - 5
KW  - human-centric design
KW  - design thinking
KW  - patient centricity
KW  - device selection
KW  - technology selection
KW  - remote patient monitoring
KW  - remote measurement technologies
AB  - 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. 
SN  - 2291-5222
UR  - https://mhealth.jmir.org/2020/5/e16043
UR  - https://doi.org/10.2196/16043
UR  - http://www.ncbi.nlm.nih.gov/pubmed/32379055
DO  - 10.2196/16043
ID  - info:doi/10.2196/16043
ER  -