Despite growing use of remote measurement technologies (RMT) such as wearables or biosensors in healthcare programs, challenges associated with selecting and implementing technologies in these programs 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 landscape is constantly changing, and relative benefits between different device options are often unclear. Further, research on patient and healthcare provider preferences is often lacking. To address these and other common challenges with device selection, we aimed to identify and synthesize existing methods or best practices. A review of published literature and industry guidance confirmed that few relevant best practices exist. Therefore, we proposed a novel device selection framework extrapolated from human-centric design principles commonly used in de-novo digital health product design. The framework describes a three-stage approach to device selection based on stakeholder engagement, iterative design, and rapid learning. We then used the framework to successfully identify, test, select, and implement off-the-shelf devices for RADAR-CNS (Remote Assessment of Disease and Relapse–Central Nervous System), a collaborative research program using RMT to study central nervous system disease progression. 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.