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.
To address these common challenges, the RADAR-CNS team propose a novel device selection framework deduced from human-centered design principles, which are commonly used in new digital health product design.
The paper’s author Ashley Polhemus, Merck Research Labs Information Technology, from our Patient Involvement work package says, “Our RADAR device selection framework provides a structured yet flexible approach to choosing devices 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.”
The RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. To develop device selection criteria and a robust implementation strategy the framework guides study designers through:
- stakeholder engagement
- technology landscaping
- rapid proof of concept testing
- and creative problem solving.
It also describes a method for considering compromises when tensions between stakeholder needs occur.
MS Case Study
The framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis.
In the initial stage, the team engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals.
They wanted regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the RADAR-MS study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data.
In the second stage, the researchers refined their strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, they used this method to devise compromises that addressed conflicting stakeholder needs. They then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program.
The new paper was published in JMIR Mhealth Uhealth. To read the paper, visit: https://mhealth.jmir.org/2020/5/e16043/