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

Abstract

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). 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.