BackgroundHealth apps and Web-based interventions designed for patients with diabetes offer novel and scalable approaches to engage patients and improve outcomes. However, careful attention to the design and usability of these apps and Web-based interventions is essential to reduce the barriers to engagement and maximize use.ObjectiveThe aim of this study was to apply design sprint methodology paired with mixed-methods, task-based usability testing to design and evaluate an innovative, patient-facing diabetes dashboard embedded in an existing patient portal and integrated into an electronic health record.MethodsWe applied a 5-day design sprint methodology developed by Google Ventures (Alphabet Inc, Mountain View, CA) to create our initial dashboard prototype. We identified recommended strategies from the literature for using patient-facing technologies to enhance patient activation and designed a dashboard functionality to match each strategy. We then conducted a mixed-methods, task-based usability assessment of dashboard prototypes with individual patients. Measures included validated metrics of task performance on 5 common and standardized tasks, semistructured interviews, and a validated usability satisfaction questionnaire. After each round of usability testing, we revised the dashboard prototype in response to usability findings before the next round of testing until the majority of participants successfully completed tasks, expressed high satisfaction, and identified no new usability concerns (ie, stop criterion was met).ResultsThe sample (N=14) comprised 5 patients in round 1, 3 patients in round 2, and 6 patients in round 3, at which point we reached our stop criterion. The participants’ mean age was 63 years (range 45-78 years), 57% (8/14) were female, and 50% (7/14) were white. Our design sprint yielded an initial patient-facing diabetes dashboard prototype that displayed and summarized 5 measures of patients’ diabetes health status (eg, hemoglobin A1c). The dashboard used graphics to visualize and summarize health data and reinforce understanding, incorporated motivational strategies (eg, social comparisons and gamification), and provided educational resources and secure-messaging capability. More than 80% of participants were able to successfully complete all 5 tasks using the final prototype. Interviews revealed usability concerns with design, the efficiency of use, and content and terminology, which led to improvements. Overall satisfaction (0=worst and 7=best) improved from the initial to the final prototype (mean 5.8, SD 0.4 vs mean 6.7, SD 0.5).ConclusionsOur results demonstrate the utility of the design sprint methodology paired with mixed-methods, task-based usability testing to efficiently and effectively design a patient-facing, Web-based diabetes dashboard that is satisfying for patients to use.

Recognition of human gestures is an active area of research integral to the development of intuitive human-machine interfaces for ubiquitous computing and assistive robotics. In particular, such systems are key to effective environmental designs which facilitate aging in place. Typically, gesture recognition takes the form of template matching in which the human participant is expected to emulate a choreographed motion as prescribed by the researchers. The robotic response is then a one-to-one mapping of the template classification to a library of distinct responses. In this paper, we explore a recognition scheme based on the Growing Neural Gas (GNG) algorithm which places no initial constraints on the user to perform gestures in a specific way. Skeletal depth data collected using the Microsoft Kinect sensor is clustered by GNG and used to refine a robotic response associated with the selected GNG reference node. We envision a supervised learning paradigm similar to the training of a service animal in which the response of the robot is seen to converge upon the user's desired response by taking user feedback into account. This paper presents initial results which show that GNG effectively differentiates between gestured commands and that, using automated (policy based) feedback, the system provides improved responses over time.

Evidence-based design, human factors, patient-centered care, quality care, technology.

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Evidence-based design, hospital, human factors, patient-centered care, quality care, technology.

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When compared to the rehabilitation hospital's current OBT, the developed ART in this study was successful. Designing novel features is dependent upon ensuring patient safety. The inclusion of clinicians in the participatory iterative design and evaluation process and the use of personas provided a broadened list of specifications for the successful implementation of assistive robotic furniture.

Designing a more useful, usable, and actively assistive over-the-bed table is primarily about the ability to position it optimally with respect to the user for any task, as well as improving ease of use and usability. It is also important to make explicit and discuss the differences in priorities and perspectives demonstrated between research and design teams and their clients.