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Communications of the ACM

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Shelfie: A Framework for Designing Material Representations of Physical Activity Data

Self-monitoring devices are becoming increasingly popular in the support of physical activity experiences. These devices mostly represent on-screen data using numbers and graphs and in doing so, they may miss multi-sensorial methods for engaging with data. Embracing the opportunity for pleasurable interactions with one's own data through the use of different materials and digital fabrication technology, we designed and studied three systems that turn this data into 3D-printed plastic artifacts, sports drinks, and 3D-printed chocolate treats. We utilize the insights gained from associated studies, related literature, and our experiences in designing these systems to develop a conceptual framework, “Shelfie.” The “Shelfie” framework has 13 cards that convey key themes for creating material representations of physical activity data. Through this framework, we present a conceptual understanding of relationships between material representation and physical activity data and contribute guidelines to the design of meaningful material representations of physical activity data.


The Role of Conversational Grounding in Supporting Symbiosis Between People and Digital Assistants

In "smart speaker'' digital assistant systems such as Google Home, there is no visual user interface, so people must learn about the system's capabilities and limitations by experimenting with different questions and commands. However, many new users give up quickly and limit their use to a few simple tasks. This is a problem for both the user and the system. Users who stop trying out new things cannot learn about new features and functionality, and the system receives less data upon which to base future improvements. Symbiosis---a mutually beneficial relationship---between AI systems like digital assistants and people is an important aspect of developing systems that are partners to humans and not just tools. In order to better understand requirements for symbiosis, we investigated the relationship between the types of digital assistant responses and users' subsequent questions, focusing on identifying interactions that were discouraging to users when speaking with a digital assistant. We conducted a user study with 20 participants who completed a series of information seeking tasks using the Google Home, and analyzed transcripts using a method based on applied conversation analysis. We found that the most common response from the Google Home, a version of "Sorry, I'm not sure how to help'', provided no feedback for participants to build on when forming their next question. However, responses that provided somewhat strange but tangentially related answers were actually more helpful for conversational grounding, which extended the interaction. We discuss the connection between grounding and symbiosis, and present recommendations for requirements for forming partnerships with digital assistants.


From Paper Flight Strips to Digital Strip Systems: Changes and Similarities in Air Traffic Control Work Practices

To increase capacity and safety in air traffic control, digital strip systems have superseded paper strips in lower airspace control centers in Europe. Previous ethnographic studies on paper strip systems anticipated a radical change in work practices with digital strip systems, but we are not aware of any studies that evaluated these predictions. We carried out contextual inquiries with controllers and focused on face-to-face and radio communication, interactions with the digital strip system and the workspace in general. In turn, we contribute (1) detailed descriptions of controllers' work practices, such as using tacit information from radio communication and 'standard advocates vs. tinkerers' operation modes, (2) respective implications for design and (3) discuss how the observed work practices are similar or different from the reported practices in the literature of the two preceding decades. Our key insights are, that documentation speed is faster with digital strips, although a high load in the case of radio frequency persists. Controllers retrieve tacit information from the radio communication and combine it with scattered cues from several displays to form empathic decisions that sometimes exceed the standard protocol. We conclude that the role of tacit information holds opportunities for future flight systems and should be considered in a holistic approach to individualized workspaces for controllers.


I Share, You Care: Private Status Sharing and Sender-Controlled Notifications in Mobile Instant Messaging

While mobile instant messaging (MIM) facilitates ubiquitous interpersonal communication, its constant connectivity could build the expectation of an immediate response to messages, and its notifications flood could cause interruptions at inopportune moments. We examine two design concepts for MIM-private status sharing and sender-controlled notifications-that aim to lower the pressure for an immediate reply and reduce unnecessary interruptions by untimely notifications. Private status sharing reactively reveals a customized status with a selected partner(s) only when the partner has sent a message. Sender-controlled notifications give senders the control of choosing whether to send a notification for their own messages. We built MyButler, an Android app prototype that instantiates these two concepts and integrated it with KakaoTalk, a commercial MIM app. During a two-week field study with 11 pairs (5 couples and 6 friend pairs), participants expressed themselves through a total of 210 different statuses, 64.3% of which indicated the current activity or task of the user. Participants reported that private status sharing enabled them to explain their unavailability and relieved the pressure and expectations for timely attendance. We reveal more findings on the types of privately shared statuses and their roles in MIM communication; the in-situ behaviors and patterns of using sender-controlled notifications; and the motivations of MIM users in choosing whether to alert their messages. In terms of message notifications, senders chose to send 25.4% of the messages without any notification. We found that senders' decisions to alert are affected by the receiver's status, their own status to chat, and the possibility of message content exposure to others through notifications. Based on our findings, we draw insights into how the concepts of private status sharing and sender-controlled notifications can be applied in future designs and explorations.


MANTIS: time-shifted prefetching of YouTube videos to reduce peak-time cellular data usage

The load on wireless cellular networks is not uniformly distributed through the day, and is significantly higher during peak periods. In this context, we present MANTIS, a time-shifted prefetching solution that prefetches content during off-peak periods of network connectivity. We specifically focus on YouTube given that it represents a significant portion of overall wireless data-usage. We make the following contributions: first, we collect and analyze a real-life dataset of YouTube watch history from 206 users comprised of over 1.8 million videos spanning over a 1-year period and present insights on a typical user's viewing behavior; second, we develop an accurate prediction algorithm using a K-nearest neighbor classifier approach; third, we evaluate the prefetching algorithm on two different datasets and show that MANTIS is able to reduce the traffic during peak periods by 34%; and finally, we develop a proof-of-concept prototype for MANTIS and perform a user study.


Context-aware Location Search on Maps

Location searching by keywords has immense demands in location-based services (LBSs). In this paper, we study the context-aware location search problem based on maps. Specifically, given a primary keyword and a set of contexts keywords as constraints, the objective is to search for the best-fit location that meets the user's requirements. In order to improve the performance of the search process, we propose an index structure to reduce the workload of querying. In particular, we consider max distance among the locations corresponding to the primary keyword and all surrounding contexts keywords. Extensive experiments are conducted on multiple datasets to validate the effectiveness of our proposed index structure and searching algorithm.


Understanding Participant Needs for Engagement and Attitudes towards Passive Sensing in Remote Digital Health Studies

Digital psychiatry is a rapidly growing area of research. Mobile assessment, including passive sensing, could improve research into human behavior and may afford opportunities for rapid treatment delivery. However, retention is poor in remote studies of depressed populations in which frequent assessment and passive monitoring are required. To improve engagement and understanding participant needs overall, we conducted semi-structured interviews with 20 people representative of a depressed population in a major metropolitan area. These interviews elicited feedback on strategies for long-term remote research engagement and attitudes towards passive data collection. Our results found participants were uncomfortable sharing vocal samples, need researchers to take a more active role in supporting their understanding of passive data collection, and wanted more transparency on how data were to be used in research. Despite these findings, participants trusted researchers with the collection of passive data. They further indicated that long term study retention could be improved with feedback and return of information based on the collected data. We suggest that researchers consider a more educational consent process, giving participants a choice about the types of data they share in the design of digital health apps, and consider supporting feedback in the design to improve engagement.


Understanding Reflection Needs for Personal Health Data in Diabetes

To empower users of wearable medical devices, it is important to enable methods that facilitate reflection on previous care to improve future outcomes. In this work, we conducted a two-phase user-study involving patients, caregivers, and clinicians to understand gaps in current approaches that support reflection and user needs for new solutions. Our results show that users desire to have specific summarization metrics, solutions that minimize cognitive effort, and solutions that enable data integration to support meaningful reflection on diabetes management. In addition, we developed and evaluated a visualization called PixelGrid that presents key metrics in a matrix-based plot. Majority of users (84%) found the matrix-based approach to be useful for identifying salient patterns related to certain times and days in blood glucose data. Through our evaluation we identified that users desire data visualization solutions with complementary textual descriptors, concise and flexible presentation, contextually-fitting content, and informative and actionable insights. Directions for future research on tools that automate pattern discovery, detect abnormalities, and provide recommendations to improve care were also identified.


Designing Everyday Conversational Agents for Managing Health and Wellness: A Study of Alexa Skills Reviews

Conversational agents have been developed for supporting a wide array of areas, including autonomous vehicles, decision making, and health behavior change. In the last few years, conversational agents increasingly became available as everyday technologies. This phenomenon enables opportunities for finding novel ways to support health and wellness in everyday contexts. By conducting a content analysis of 433 user reviews of Amazon Alexa's Skills, the goal of this study is two-fold: (1) Extract users' perceived strengths of conversational agents in everyday health and wellness management, (2) develop design heuristics for developing conversational agents for health and wellness. We found that the role of trustworthy content providers is critical during the adoption. The Skills enabled people to overcome logistical barriers to improving daily health and wellness routines. The findings also revealed the importance of transparency in the limitations of the Skill and how to better design command dialogues. We present the design heuristics of conversational agents, building on Nielsen's Usability Heuristics, and discuss implications for designing conversational agents that support health and wellness.


Enter Your Dinner Now!: Uncovering Persuasive Message Attributes in Tracking Reminders that Motivate Logging

Continuous tracking of information is critical for meaningful self-reflection and self-monitoring, but people often forget to log their information in tracking devices. Research indicates that tracking reminders can successfully remind people to log their information, yet, little is known about what make reminders (in)effective. We extend prior work by identifying message attributes in tracking reminders that people find most effective in motivating them to log. To address this overarching research goal, we conducted two online studies where participants evaluated and designed tracking reminders. In Study 1, participants (N = 135) evaluated a set of tracking reminders for different behaviors (e.g., breakfast, weight) from popular fitness tracking apps on several dimensions such as the persuasiveness of each reminder. We found that participants liked reminders that were straightforward, encouraging, goal specific, and positive. In Study 2, participants (N = 100) designed a reminder for different behaviors (i.e., breakfast, lunch, dinner, weight, and exercise) that would successfully motivate them to log. Through thematic analysis of participants' self-created reminders, we again found prominent message attributes that had emerged in Study 1 and also uncovered novel message attributes, including personalization, humor, and friend-like. Design implications are discussed in light of our findings.