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This forum is dedicated to personal health in all its many facets: decision making, goal setting, celebration, discovery, reflection, and coordination, among others. We look at innovations in interactive technologies and how they help address current critical healthcare challenges. --- Yunan Chen, Editor

International audience; In the first crowdsourced visualization experiment conducted exclusively on mobile phones, we compare approaches to visualizing ranges over time on small displays. People routinely consume such data via a mobile phone, from temperatures in weather forecasting apps to sleep and blood pressure readings in personal health apps. However, we lack guidance on how to effectively visualize ranges on small displays in the context of different value retrieval and comparison tasks, or with respect to different data characteristics such as periodicity, seasonality, or the cardinality of ranges. Central to our experiment is a comparison between two ways to lay out ranges: a more conventional linear layout strikes a balance between quantitative and chronological scale resolution, while a less conventional radial layout emphasizes the cyclicality of time and may prioritize discrimination between values at its periphery. With results from 87 crowd workers, we found that while participants completed tasks more quickly with linear layouts than with radial ones, there were few differences in terms of error rate between layout conditions. We also found that participants performed similarly with both layouts in tasks that involved comparing superimposed observed and average ranges.

The factors influencing people’s food decisions, such as one’s mood and eating environment, are important information to foster self-reflection and to develop personalized healthy diet. But, it is difficult to consistently collect them due to the heavy data capture burden. In this work, we examine how speech input supports capturing everyday food practice through a week-long data collection study (N = 11). We deployed FoodScrap, a speech-based food journaling app that allows people to capture food components, preparation methods, and food decisions. Using speech input, participants detailed their meal ingredients and elaborated their food decisions by describing the eating moments, explaining their eating strategy, and assessing their food practice. Participants recognized that speech input facilitated self-reflection, but expressed concerns around re-recording, mental load, social constraints, and privacy. We discuss how speech input can support low-burden and reflective food journaling and opportunities for effectively processing and presenting large amounts of speech data.

Most mobile health apps employ data visualization to help people view their health and activity data, but these apps provide limited support for visual data exploration. Furthermore, despite its huge potential benefits, mobile visualization research in the personal data context is sparse. This work aims to empower people to easily navigate and compare their personal health data on smartphones by enabling flexible time manipulation with speech. We designed and developed Data@Hand, a mobile app that leverages the synergy of two complementary modalities: speech and touch. Through an exploratory study with 13 long-term Fitbit users, we examined how multimodal interaction helps participants explore their own health data. Participants successfully adopted multimodal interaction (i.e., speech and touch) for convenient and fluid data exploration. Based on the quantitative and qualitative findings, we discuss design implications and opportunities with multimodal interaction for better supporting visual data exploration on mobile devices., Comment: To appear in ACM CHI 2021 Conference on Human Factors in Computing Systems; 16 pages, 6 figures, 5 tables

Polycystic Ovary Syndrome (PCOS) is a condition that causes hormonal imbalance and infertility in women and people with female reproductive organs. PCOS causes different symptoms for different people, with no singular or universal cure. Being a stigmatized and enigmatic condition, it is challenging to discover, diagnose, and manage PCOS. This work aims to inform the design of inclusive health technologies through an understanding of people’s lived experiences and challenges with PCOS. We conducted semi-structured interviews with 10 women diagnosed with PCOS and analyzed a PCOS-specific subreddit forum. We report people’s support-seeking, sense-making, and self-experimentation practices, and find uncertainty and stigma to be key in shaping their unique experiences of the condition. We further identify potential avenues for designing technology to support their diverse needs, such as personalized and contextual tracking, accelerated self-discovery, and co-management, contributing to a growing body of HCI literature on stigmatized topics in women’s health and well-being.

Smart speakers such as Amazon Echo present promising opportunities for exploring voice interaction in the domain of in-home exercise tracking. In this work, we examine if and how voice interaction complements and augments a mobile app in promoting consistent exercise. We designed and developed TandemTrack, which combines a mobile app and an Alexa skill to support exercise regimen, data capture, feedback, and reminder. We then conducted a four-week between-subjects study deploying TandemTrack to 22 participants who were instructed to follow a short daily exercise regimen: one group used only the mobile app and the other group used both the app and the skill. We collected rich data on individuals' exercise adherence and performance, and their use of voice and visual interactions, while examining how TandemTrack as a whole influenced their exercise experience. Reflecting on these data, we discuss the benefits and challenges of incorporating voice interaction to assist daily exercise, and implications for designing effective multimodal systems to support self-tracking and promote consistent exercise.

The visualization research community can and should reach broader audiences beyond data-savvy groups of people, because these audiences could also greatly benefit from visual access to data. In this article, we discuss four research topics—personal data visualization, data visualization on mobile devices, inclusive data visualization, and multimodal interaction for data visualization—that, individually and collaboratively, would help us reach broader audiences with data visualization, making data more accessible.

The authors report on the PervasiveHealth 2017 workshop, Leveraging Patient-Generated Data (PGD) for Collaborative Decision Making in Healthcare. They discuss characteristics of PGD, followed by scenarios demonstrating the data-sharing practice among patients, clinicians, and caregivers. The authors also highlight current challenges and opportunities, and outline a future research agenda to envision ways to harness the power of PGD.

We now see an increasing number of self-tracking apps and wearable devices. Despite the vast number of available tools, however, it is still challenging for self-trackers to find apps that suit their unique tracking needs, preferences, and commitments. Furthermore, people are bounded by the tracking tools’ initial design because it is difficult to modify, extend, or mash up existing tools. In this paper, we present OmniTrack, a mobile self-tracking system, which enables self-trackers to construct their own trackers and customize tracking items to meet their individual tracking needs. To inform the OmniTrack design, we first conducted semi-structured interviews (N = 12) and analyzed existing mobile tracking apps (N = 62). We then designed and developed OmniTrack as an Android mobile app, leveraging a semi-automated tracking approach that combines manual and automated tracking methods. We evaluated OmniTrack through a usability study (N = 10) and improved its interfaces based on the feedback. Finally, we conducted a 3-week deployment study (N = 21) to assess if people can capitalize on OmniTrack’s flexible and customizable design to meet their tracking needs. From the study, we showed how participants used OmniTrack to create, revise, and appropriate trackers—ranging from a simple mood tracker to a sophisticated daily activity tracker. We discuss how OmniTrack positively influences and supports self-trackers’ tracking practices over time, and how to further improve OmniTrack by providing more appropriate visualizations and sharable templates, incorporating external contexts, and supporting researchers’ unique data collection needs.

The authors present an approach for designing self-monitoring technology called "semi-automated tracking," which combines both manual and automated data collection methods. Through this approach, they aim to lower the capture burdens, collect data that is typically hard to track automatically, and promote awareness to help people achieve their self-monitoring goals. They first specify three design considerations for semi-automated tracking: data capture feasibility, the purpose of self-monitoring, and the motivation level. They then provide examples of semi-automated tracking applications in the domains of sleep, mood, and food tracking to demonstrate strategies they developed to find the right balance between manual tracking and automated tracking, combining each of their benefits while minimizing their associated limitations.

Productivity tracking tools often determine productivity based on the time interacting with work-related applications. To deconstruct productivity's diverse and nebulous nature, we investigate how knowledge workers conceptualize personal productivity and delimit productive tasks in both work and non-work contexts. We report a 2-week diary study followed by a semi-structured interview with 24 knowledge workers. Participants captured productive activities and provided the rationale for why the activities were assessed to be productive. They reported a wide range of productive activities beyond typical desk-bound work-ranging from having a personal conversation with dad to getting a haircut. We found six themes that characterize the productivity assessment-work product, time management, worker's state, attitude toward work, impact & benefit, and compound task and identified how participants interleaved multiple facets when assessing their productivity. We discuss how these findings could inform the design of a comprehensive productivity tracking system that covers a wide range of productive activities.

We report co-design workshops with registered dietitians conducted to identify opportunities for designing customizable food trackers. Dietitians typically see patients who have different dietary problems, thus having different information needs. However, existing food trackers such as paper-based diaries and mobile apps are rarely customizable, making it difficult to capture necessary data for both patients and dietitians. During the co-design sessions, dietitians created representative patient personas and designed food trackers for each persona. We found a wide range of potential tracking items such as food, reflection, symptom, activity, and physical state. Depending on patients' dietary problems and dietitians' practice, the necessity and importance of these tracking items vary. We identify opportunities for patients and healthcare providers to collaborate around data tracking and sharing through customization. We also discuss how to structure co-design workshops to solicit the design considerations of self-tracking tools for patients with specific health problems.

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.

We compare the efficacy of animated and small multiples variants of scatterplots on mobile phones for comparing trends in multivariate datasets. Visualization is increasingly prevalent in mobile applications and mobile-first websites, yet there is little prior visualization research dedicated to small displays. In this paper, we build upon previous experimental research carried out on larger displays that assessed animated and non-animated variants of scatterplots. Incorporating similar experimental stimuli and tasks, we conducted an experiment where 96 crowdworker participants performed nine trend comparison tasks using their mobile phones. We found that those using a small multiples design consistently completed tasks in less time, albeit with slightly less confidence than those using an animated design. The accuracy results were more task-dependent, and we further interpret our results according to the characteristics of the individual tasks, with a specific focus on the trajectories of target and distractor data items in each task. We identify cases that appear to favor either animation or small multiples, providing new questions for further experimental research and implications for visualization design on mobile devices. Lastly, we provide a reflection on our evaluation methodology., Comment: Accepted for presentation at IEEE VIS 2019, October 20-25 in Vancouver, Canada. To appear in IEEE Transactions on Visualization and Computer Graphics

Objective Despite the potential values self-tracking data could offer, we have little understanding of how much access people have to “their” data. Our goal of this article is to unveil the current state of the data accessibility—the degree to which people can access their data—of personal health apps in the market. Materials and Methods We reviewed 240 personal health apps from the App Store and selected 45 apps that support semi-automated tracking. We characterized the data accessibility of these apps using two dimensions—data access methods and data types. Results More than 90% of our sample apps (n = 41) provide some types of data access support, which include synchronizing data with a health platform (ie, Apple Health), file download, and application program interfaces. However, the two approachable data access methods for laypeople—health platform and file download—typically put a significant limit on data format, granularity, and amount, which constrains people from easily repurposing the data. Discussion Personal data should be accessible to the people who collect them, but existing methods lack sufficient support for people in accessing the fine-grained data. Lack of standards in personal health data schema as well as frequent changes in market conditions are additional hurdles to data accessibility. Conclusions Many stakeholders including patients, healthcare providers, researchers, third-party developers, and the general public rely on data accessibility to utilize personal data for various goals. As such, improving data accessibility should be considered as an important factor in designing personal health apps and health platforms.

Family relationships are resources to foster healthy behaviors in family contexts. However, we have little understanding of ways to leverage intergenerational communication between elderly parents and adult children to improve family health and the roles technology plays in supporting this process. In this study, we examine how intergenerational family relationships and technology use influence health communication and collaboration among family members, from which we identify design opportunities to promote a culture of health within the family. We conducted 10 focus group interviews with independent living elderly parents (n = 12) and adult children (n = 25). We present different types of health information sharing and motivations to curate what health-related information to be shared. We also explore how family members deal with obstacles in health information sharing. Based on our findings, we suggest design implications to promote effective sharing, to support different types of sharing, and to enhance family sharing to build a culture of health within the family.

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.

Parental controls allow parents to set limits on children's use of technology, but prior work suggests that controlling children alone is unlikely to foster the development of healthy media habits. We took elements from evidence-based preschool curricula that teach self-regulation and translated them to the digital space by creating a tool for preschoolers and parents to plan their device-based playtime. In an observational lab study with 11 parent-child dyads and follow-up interviews with 14 parents, we found that children demonstrated intentionality and made goal-directed choices as they planned, the mediating factor in developing self-regulation. We observed that parents prompted their child to be intentional and solicited children's input. When children played through their plan, they transitioned to the next activity without intervention 93% of the time. Our results suggest that evidence-based practices for teaching self-regulation in a non-digital context can be applied productively to children's use of technology. As parents supported children in trying the tool for the first time, a further contribution of this work is a hierarchical model of parents' approaches to scaffolding children's use of a novel technology.

An explosion of affordable commercial wearable sensing devices and mobile health applications has opened up new possibilities to practice self-tracking and enjoy its benefits. However, elders often do not engage with health tracking technologies because they do not see much benefits. Leveraging the inherent reciprocal relationship among family members is one potential approach to promote the practice of health tracking. In this paper, we aim to understand and support intergenerational sharing of health data as a collective and collaborative family project of mutual support. Based on interviews and scenario-based focus group discussions, we report on family members' understanding of one another's health and well-being, their current health-related practices, and issues around health management as a means to facilitate intergenerational health collaboration.

Rapid advancements in consumer technologies enable people to collect a wide range of personal data. With a proper means for people to ask questions and explore their data, longitudinal data feeds from multiple self-tracking tools pose great opportunities to foster deep self-reflection. However, most self-tracking tools lack support for self-reflection beyond providing simple feedback. Our overarching goal is to support self-trackers in reflecting on their data and gaining rich insights through visual data exploration. As a first step toward the goal, we built a web-based application called Visualized Self, and conducted an in-lab think-aloud study (N = 11) to examine how people reflect on their personal data and what types of insights they gain throughout the reflection. We discuss lessons learned from studying with Visualized Self, and suggest directions for designing visual data exploration tools for fostering self-reflection.

Insomnia can drastically affect individuals' overall well-being and work performance, with substantial costs to society and industry. Cognitive behavioral therapy for insomnia (CBT-I) is a psychotherapeutic treatment, which requires patients to track sleep and share the data with CBT-I clinicians. However, the number of specialists who can provide CBT-I limits the number of patients who can receive it. In this paper, we aim to identify opportunities to leverage technology to assist clinicians in delivering quality and effective CBT-I services to broader populations. Toward this goal, we conducted formative studies, including 11 CBT-I clinic observations and 17 semi-structured interviews, to understand the current workflow of CBT-I and associated challenges. We discuss how technology can assist clinicians and patients throughout the various steps of CBT-I workflow while addressing some of the identified challenges, and more broadly, how technology can make space for clinicians and patients to build quality therapeutic relationships.

Annotation plays an important role in conveying key points in visual data-driven storytelling; it helps presenters explain and emphasize core messages and specific data. However, the visualization research community has a limited understanding of annotation and its role in data-driven storytelling, and existing charting software provides limited support for creating annotations. In this paper, we characterize a design space of chart annotations, one informed by a survey of 106 annotated charts published by six prominent news graphics desks. Using this design space, we designed and developed ChartAccent, a tool that allows people to quickly and easily augment charts via a palette of annotation interactions that generate manual and data-driven annotations. We also report on a study in which participants reproduced a series of annotated charts using ChartAccent, beginning with unadorned versions of the same charts. Finally, we discuss the lessons learned during the process of designing and evaluating ChartAccent, and suggest directions for future research.

Personal visualizations have the great potential to provide the benefits of visualizations to everyone in their everyday lives. Their diverse goals combined with the personal data they contain and the contexts in which they are being used, however, make their evaluation particularly challenging and call for a wider perspective on empirical approaches. We need to devise new methods and adapt existing methods from other fields to account for the specific goals and challenges in this emerging research area. An open-minded approach to empirical methods may help us gain a more realistic understanding of personal visualizations.