Understanding how to design health data visualizations for Chilean older adults on mobile devices

Mobile devices, including activity trackers and smartwatches, can help older adults monitor health parameters passively and unobtrusively. Most user interactions with small devices consist of brief glances at the time or notifications. Consuming information from small displays poses challenges, which have been seldom studied from the perspective of older users. In this paper, we worked with older adults towards creating health data visualizations for them for small devices. We conducted a mixed-methods study with 30 older adults, in which we (1) conducted group discussions to understand participants’ opinions, (2) measured times taken to interpret health data visualizations with and without progress information, (3) measured how much information they could manage to see during brief glances. When data was visualized without progress indicators, participants took less time to understand the data and made fewer errors. Participants preferred health data visualizations that featured peaceful, and positive pictorial representations. We present design opportunities for older adults’ data visualizations in small devices

Health State Estimation

Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.Comment: Ph.D. Dissertation @ University of California, Irvin

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe