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International audience; We detail and illustrate glanceability as a crucial requirement for several types of mobile visualizations. For example, in a difficult terrain, a runner can only check a smartwatch for elevation or heart rate data for a few hundred milliseconds before the eyes need to refocus on the trail ahead. Such quick information needs differ from those in traditional visualizations that are meant for deep analysis and interaction with possibly large and complex datasets. Visualizations designed for quick information needs are described under a variety of terms in the literature such as glanceablevisualizations, glanceable displays, peripheral displays, ambient visualizations, or sometimes as forms of casual visualizations. To clarify how glanceability is used in the field of Visualization, we discuss these related terms with respect to visualization concepts, drawing from not only Visualization but also, Vision Sciences, HumanComputer Interaction, and Ubiquitous Computing, revealing how the use of the term glanceable differs in these communities. Drawing from these different perspectives, we discuss specific values for glanceable mobile visualizations: presence & accessibility, simplicity & understandability, as well as suitability & purposefulness. Based on these values, we explore different evaluation methodologies, ranging from lab studies, to online experiments, to evaluation in the field and conclude with a discussion of open challenges in the design of future glanceable mobile visualizations.

Background: Subjective symptoms, which are retrospectively assessed during clinical interviews in the office, may be influenced by patient recall in Parkinson’s disease (PD). Prospective collection of subjective data might be an effective tool to overcome this bias. Objective: We investigated the correspondence between prospectively and retrospectively assessed motor symptoms in PD. Methods: Forty-two consecutive patients (9 females, 67±9.8 years old) with mild to moderate PD reported their symptoms four times a day for two weeks, using the “SleepFit” application (app) for tablets. This app incorporates a new Visual Analogue Scale assessing global mobility (m-VAS), and the Scales for Outcome in Parkinson Assessment Diary Card (SCOPA-DC). At day 14, the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) parts II and IV questionnaires were completed at the hospital. Agreement (root mean square difference) and the tendency to under- or overestimate their symptoms by patients (relative difference after normalization) were calculated to compare prospectively vs. retrospectively collected information. Results: Although agreement was good for overall scores (m-VAS: 10.0%; SCOPA-DC: 18.3%), and for single motor symptoms (involuntary movements, hand dexterity, walking, changing position; each

Background Parkinson disease (PD) is a common, multifaceted neurodegenerative disorder profoundly impacting patients' autonomy and quality of life. Assessment in real-life conditions of subjective symptoms and objective metrics of mobility and nonmotor symptoms such as sleep disturbance is strongly advocated. This information would critically guide the adaptation of antiparkinsonian medications and nonpharmacological interventions. Moreover, since the spread of the COVID-19 pandemic, health care practices are being reshaped toward a more home-based care. New technologies could play a pivotal role in this new approach to clinical care. Nevertheless, devices and information technology tools might be unhandy for PD patients, thus dramatically limiting their widespread employment. Objective The goals of the research were development and usability evaluation of an application, SleepFit, for ecological momentary assessment of objective and subjective clinical metrics at PD patients’ homes, and as a remote tool for researchers to monitor patients and integrate and manage data. Methods An iterative and user-centric strategy was employed for the development of SleepFit. The core structure of SleepFit consists of (1) an electronic finger-tapping test; (2) motor, sleepiness, and emotional subjective scales; and (3) a sleep diary. Applicable design, ergonomic, and navigation principles have been applied while tailoring the application to the specific patient population. Three progressively enhanced versions of the application (alpha, v1.0, v2.0) were tested by a total of 56 patients with PD who were asked to perform multiple home assessments 4 times per day for 2 weeks. Patient compliance was calculated as the proportion of completed tasks out of the total number of expected tasks. Satisfaction on the latest version (v2.0) was evaluated as potential willingness to use SleepFit again after the end of the study. Results From alpha to v1.0, SleepFit was improved in graphics, ergonomics, and navigation, with automated flows guiding the patients in performing tasks throughout the 24 hours, and real-time data collection and consultation were made possible thanks to a remote web portal. In v2.0, the kiosk-mode feature restricts the use of the tablet to the SleepFit application only, thus preventing users from accidentally exiting the application. A total of 52 (4 dropouts) patients were included in the analyses. Overall compliance (all versions) was 88.89% (5707/6420). SleepFit was progressively enhanced and compliance increased from 87.86% (2070/2356) to 89.92% (2899/3224; P=.04). Among the patients who used v2.0, 96% (25/26) declared they would use SleepFit again. Conclusions SleepFit can be considered a state-of-the-art home-based system that increases compliance in PD patients, ensures high-quality data collection, and works as a handy tool for remote monitoring and data management in clinical research. Thanks to its user-friendliness and modular structure, it could be employed in other clinical studies with minimum adaptation efforts. Trial Registration ClinicalTrials.gov NCT02723396; https://clinicaltrials.gov/ct2/show/NCT02723396

Background Although the use of patient-generated data (PGD) in the optimization of patient care shows great promise, little is known about whether patients who track their PGD necessarily share the data with their clinicians. Meanwhile, health literacy—an important construct that captures an individual’s ability to manage their health and to engage with their health care providers—has often been neglected in prior studies focused on PGD tracking and sharing. To leverage the full potential of PGD, it is necessary to bridge the gap between patients’ data tracking and data sharing practices by first understanding the interrelationships between these practices and the factors contributing to these practices. Objective This study aims to systematically examine the interrelationships between PGD tracking practices, data sharing practices, and health literacy among individual patients. Methods We surveyed 109 patients at the time they met with a clinician at a university health center, unlike prior research that often examined patients’ retrospective experience after some time had passed since their clinic visit. The survey consisted of 39 questions asking patients about their PGD tracking and sharing practices based on their current clinical encounter. The survey also contained questions related to the participants’ health literacy. All the participants completed the survey on a tablet device. The onsite survey study enabled us to collect ecologically valid data based on patients’ immediate experiences situated within their clinic visit. Results We found no evidence that tracking PGD was related to self-reports of having sufficient information to manage one’s health; however, the number of data types participants tracked positively related to their self-assessed ability to actively engage with health care providers. Participants’ data tracking practices and their health literacy did not relate to their data sharing practices; however, their ability to engage with health care providers positively related to their willingness to share their data with clinicians in the future. Participants reported several benefits of, and barriers to, sharing their PGD with clinicians. Conclusions Although tracking PGD could help patients better engage with health care providers, it may not provide patients with sufficient information to manage their health. The gaps between tracking and sharing PGD with health care providers call for efforts to inform patients of how their data relate to their health and to facilitate efficient clinician-patient communication. To realize the full potential of PGD and to promote individuals’ health literacy, empowering patients to effectively track and share their PGD is important—both technologies and health care providers can play important roles.

Background: As people increasingly receive personal health information through technology, there is increased importance for this information to be communicated with empathy and consideration for the patient’s experience of consuming it. Although technology enables people to have more frequent and faster access to their health information, it could also cause unnecessary anxiety, distress, or confusion because of the sensitive and complex nature of the information and its potential to provide information that could be considered bad news. Objective: The aim of this study was to uncover insights for the design of health information technologies that potentially communicate bad news about health such as the result of a diagnosis, increased risk for a chronic or terminal disease, or overall declining health. Methods: On the basis of a review of established guidelines for clinicians on communicating bad news, we developed an interview guide and conducted interviews with patients, patients’ family members, and clinicians on their experience of delivering and receiving the diagnosis of a serious disease. We then analyzed the data using a thematic analysis to identify overall themes from a perspective of identifying ways to translate these strategies to technology design. Results: We describe qualitative results combining an analysis of the clinical guidelines for sharing bad health news with patients and interviews on clinicians’ specific strategies to communicate bad news and the emotional and informational support that patients and their family members seek. Specific strategies clinicians use included preparing for the patients’ visit, anticipating patients’ feelings, building a partnership of trust with patients, acknowledging patients’ physical and emotional discomfort, setting up a scene where patients can process the information, helping patients build resilience and giving hope, matching the level of information to the patients’ level of understanding, communicating face-to-face, if possible, and using nonverbal means. Patient and family member experiences included internal turmoil and emotional distress when receiving bad news and emotional and informational support that patients and family members seek. Conclusions: The results from this study identify specific strategies for health information technologies to better promote empathic communication when they communicate concerning health news. We distill the findings from our study into design hypotheses for ways technologies may be able to help people better cope with the possibility of receiving bad health news, including tailoring the delivery of information to the patients’ individual preferences, supporting interfaces for sharing patients’ context, mitigating emotional stress from self-monitoring data, and identifying clear, actionable steps patients can take next.

International audience; As mobile visualization is increasingly used and new mobile device form factors and hardware capabilities continuously emerge, it is timely to reflect on what has been discovered to date and to look into the future. This workshop will bring together researchers, designers, and practitioners from relevant application and research fields, including visualization, personal informatics, and data journalism. We will work on identifying a research agenda for mobile data visualization as well as to collect and propagate practical guidance for mobile visualization design. Our overarching goal is to bring us closer to making an effective use of ubiquitous mobile devices as data visualization platforms.