JMIR Publications

ABSTRACT

Background:

Despite growing use of remote measurement technologies (RMT) such as wearables or biosensors in healthcare programs, challenges associated with selecting and implementing these technologies persist. Many healthcare 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 healthcare 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 RADAR-CNS (Remote Assessment of Disease and Relapse – Central Nervous System), 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 mHealth 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, healthcare professionals, researchers, and technologists to identify our primary device-related goals. We desired home-based measurements of gait, balance, fatigue, heart rate, and sleep regularly 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 healthcare 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.