Assessing the performance at self-management of patients with CKD is essential for effective strategies to safeguard care. We evaluated the use of a sick-day protocol with structured usability testing by patients with CKD. The Sick-Day Protocol is a selfmanagement protocol developed by National Health Service Highland in Scotland to prevent AKI, and it is made available through the Scottish Patient Safety Program on a "Medicine Sick Day Rules" card (1). The Sick-Day Protocol card describes a gastrointestinal or fever-related and volume-depleting illness; then, it directs patients to withhold specified medications and resume them when well (after 24-48 hours of eating and drinking normally). Five medication classes and examples are listed, including angiotensinconverting enzyme inhibitors, angiotensin receptor blockers, diuretics, nonsteroidal anti-inflammatory drugs, and metformin. We recruited volunteers with stages 3-5 CKD from the Safe Kidney Care program (2). All participants independently scheduled and completed testing. The protocol was deemed exempt by the University of Maryland Baltimore Institutional Review Board. Usability testing sessions were conducted in a private room by a moderator and recorder. Participants were educated about the purpose of the Sick-Day Protocol and qualifying illnesses. The moderator presented the card to all participants and requested each to summarize it in their own words. The study team developed four scenarios representing commonly encountered medical problems. All were reviewed and adjudicated by two physicians (C.J.D. and J.C.F.) for clinical validity and population suitability. Participants were presented the scenarios, including one for which the Sick-Day Protocol would be appropriately activated (index) and one with nonqualifying illnesses, including nephrolithiasis, congestive heart failure, and a "minor" gastrointestinal illness. One half of the sample had scenarios presented in a fixed sequence, starting with the index. The remainder had the scenarios presented randomly. The moderator reviewed the mock patient's medications and comorbidities in each scenario and then, provided mock medication bottles. Participants were asked about the appropriateness of the Sick-Day Protocol, if any of the mock medications should be withheld in each scenario, and if so, which ones. Completion of each task was classified as a success or error. After completing

Objective We present examples of laboratory and remote studies, with a focus on studies appropriate for medical device design and evaluation. From this review and description of extant options for remote testing, we provide methods and tools to achieve research goals remotely. Background The FDA mandates human factors evaluation of medical devices. Studies show similarities and differences in results collected in laboratories compared to data collected remotely in non-laboratory settings. Remote studies show promise, though many of these are behavioral studies related to cognitive or experimental psychology. Remote usability studies are rare but increasing, as technologies allow for synchronous and asynchronous data collection. Method We reviewed methods of remote evaluation of medical devices, from testing labels and instruction to usability testing and simulated use. Each method was coded for the attributes (e.g., supported media) that need consideration in usability studies. Results We present examples of how published usability studies of medical devices could be moved to remote data collection. We also present novel systems for creating such tests, such as the use of 3D printed or virtual prototypes. Finally, we advise on targeted participant recruitment. Conclusion Remote testing will bring opportunities and challenges to the field of medical device testing. Current methods are adequate for most purposes, excepting the validation of Class III devices. Application The tools we provide enable the remote evaluation of medical devices. Evaluations have specific research goals, and our framework of attributes helps to select or combine tools for valid testing of medical devices.

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Automation can be utilized to relieve humans of difficult and repetitive tasks in many domains, presenting the opportunity for safer and more efficient systems. This increase in automation has led to new supervisory roles for human operators where humans monitor feedback from autonomous systems and provide input when necessary. Optimizing these roles requires tools for evaluation of task complexity and resulting operator cognitive workload. Cognitive task analysis is a process for modeling the cognitive actions required of a human during a task. This work presents an enhanced version of this process: Cognitive Task Analysis and Workload Classification (CTAWC). The goal of developing CTAWC was to provide a standardized process to decompose cognitive tasks in enough depth to allow for precise identification of sources of cognitive workload. CTAWC has the following advantages over conventional CTA methodology: Integrates standard terminology from existing taxonomies for task classification to describe expected operator cognitive workload during task performance. Provides a framework to evaluate adequate cognitive depth when decomposing cognitive tasks. Provides a standard model upon which to build an empirical study to evaluate task complexity.

Mental health and substance abuse patients face many challenges in receiving effective long-term outpatient behavioral therapies, including issues related to accessibility and personalized care. Mobile health technologies, particularly those integrating virtual reality (VR), are increasingly becoming more accessible and affordable, thus providing a potential avenue to deploy outpatient behavioral therapy. This paper proposes a method to address the aforementioned challenges by personalizing and validating VR simulation content for behavioral therapy. An initial demonstration will be performed for tobacco cessation, which is a critical public health treatment area for mental illness and substance abuse. The method empirically builds smoker personas from theoretically grounded survey content. The personas are then used to design and pilot VR simulation modules tailored to behavioral interventions, which will be tested in the patient population. The VR simulation will record a subject’s emotions and brain activities in real-time through subjective (surveys) and objective (neurophysiology) measures of emotional response. The overall goal of the study is to validate the VR content by demonstrating that significant differences are seen in emotional response when presenting content personalized for the patient.

Humans can contribute to error at all stages of the medical device product life-cycle. Use error associated with medical devices can result in catastrophic consequences for end users and inefficient use of healthcare system resources. Industry-wide statistics about medical device use error has the potential to aid in identifying opportunities for human factors intervention, however publicly available statistics are sparse. The Food and Drug Administration (FDA) requires medical device manufactures, importers, and device user facilities to track and report adverse events for post-market surveillance through medical device reports (MDRs). This data is available in an online database: Manufacturer and User Facility Experience (MAUDE). This study provides a comprehensive evaluation of use error adverse events in MAUDE (2010-2018) based on device class, device operator, and event outcome, to address the lack of industry-wide statistics on medical device use error. Results indicate that use error is significantly represented in adverse event reporting, constituting 28.1% of reports labeled with device problem codes. Events associated with patient device operators were predominately associated with diabetes-related medical devices, while provider operators were associated with a wider array of devices. Additionally, it was found that most use error reports were attributed to issues with device output; using the device in accordance with manufacturer expectations; and physically activating, positioning, or separating device components. This work demonstrates the viability of using MAUDE to attain industry wide statistics on medical device use error for later integration in industry-wide or device-specific risk mitigation strategies.

Virtual reality is being used to aid in prototyping of advanced limb prostheses with anthropomorphic behavior and user training. A virtual version of a prosthesis and testing environment can be programmed to mimic the appearance and interactions of its real-world counterpart, but little is understood about how task selection and object design impact user performance in virtual reality and how it translates to real-world performance. To bridge this knowledge gap, we performed a study in which able-bodied individuals manipulated a virtual prosthesis and later a real-world version to complete eight activities of daily living. We examined subjects' ability to complete the activities, how long it took to complete the tasks, and number of attempts to complete each task in the two environments. A notable result is that subjects were unable to complete tasks in virtual reality that involved manipulating small objects and objects flush with the table, but were able to complete those tasks in the real world. The results of this study suggest that standardization of virtual task environment design may lead to more accurate simulation of real-world performance.