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首页> 外文期刊>JMIR mHealth and uHealth >Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study
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Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study

机译：MHEATH计划中的设备选择的以人为本的设计策略：新颖框架和案例研究的发展

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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.
机译：背景技术尽管使用远程测量技术（RMT），如医疗保健计划中的可穿戴设备或生物传感器，但与选择和实施这些技术相关的挑战仍然存在。许多使用RMT依赖市售的“现货”设备的医疗保健程序，以收集患者数据。然而，这些设备的验证是稀疏的，技术景观不断变化，设备选项之间的相对益处通常不明确，并且往往缺乏患者和医疗保健提供者偏好的研究。目的旨在解决这些共同挑战，提出了一种新颖的设备选择框架，从以人为本的设计原则推断，这常用于De Novo数字健康产品设计。然后，我们提供了一个案例研究，我们使用该框架来识别，测试，选择和实施现成的设备，用于远程评估疾病和复发 - 中枢神经系统（RADAR-CNS）联盟，其中一项研究计划RMT学习中枢神经系统疾病进展。方法雷达-CNS设备选择框架描述了一种用于移动健康计划的设备选择的以人为本的方法。框架指南通过利益相关者参与，技术景观，概念测试快速证明，以及解决设备选择标准和强大的实施策略的创造性问题以及强大的实施策略的创新问题。它还描述了一种考虑妥协时，当需要发生利益相关者之间的紧张局势时。结果框架成功引导了雷达-CNS研究多发性硬化中复发研究的设备选择。在初始阶段，我们从事患者，医疗保健专业人员，研究人员和技术人员的多学科团队，以确定我们的主要设备有关的目标。我们期望定期的基于家庭的步态测量，平衡，疲劳，心率，并在研究过程中睡觉。但是，设备和测量方法必须用户友好，安全，能够产生高质量的数据。在第二阶段，我们迭代地基于技术和监管限制，用户反馈和研究目标来改进我们的战略和选定的设备。在几点时，我们使用这种方法来制定妥协，以解决冲突的利益相关者需求。然后，我们将反馈机制实施到了研究中，以收集有关设备的课程以改善雷达-CNS程序的未来版本。结论雷达器件选择框架提供了用于保健计划的设备选择的结构化且灵活的方法，可用于系统地接近复杂的决策，这些决策要求团队考虑科学优先事项和后勤，技术或监管制约的患者体验。

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