Peer Reviewed

Owing to the multi-factorial nature of fatigue, leveraging context to effectively monitor and intervene with fatigue symptoms presents a significant challenge. This paper aimed to understand how to incorporate context into designing mHealth systems for fatigue management. We conducted a two-week field study with 20 fatigue-vulnerable individuals using an activity-tracking sensor and self-reporting. We conducted data-prompted interviews to explore phenomena about participants’ fatigue experiences. Findings show a heterogeneous relationship between context and fatigue, which can be attributed to the phenomena that: (1) participants were influenced by multiple fatigue-inducing factors for different durations; (2) broad contexts moderated participants’ perceptions and coping strategies in response to local contexts; (3) the predictability and repetition of activities influenced participants’ fatigue perception and coping strategies. We propose a context-informed layer-based framework integrating these phenomena and discuss implications for designing fatigue management tools informed by our framework.

Efficient Type 1 Diabetes (T1D) management necessitates comprehensive tracking of various factors that influence blood sugar levels. However, tracking health data for children with T1D poses unique challenges, as it requires the active involvement of both children and their parents. This study aims to uncover the benefits, challenges, and strategies associated with collaborative tracking for children (ages 6-12) with T1D and their parents. Over a three-week data collection probe study with 22 child-parent pairs, we found that collaborative tracking, characterized by the shared responsibility of tracking management and data provision, yielded positive outcomes for both children and their parents. Drawing from these findings, we delineate four distinct tracking approaches: child-independent, child-led, parent-led, and parent-independent. Our study offers insights for designing health technologies that empower both children and parents in learning and encourage the sharing of different perspectives through collaborative tracking.

Many people do not get enough physical activity. Establishing routines to incorporate physical activity into people’s daily lives is known to be effective, but many people struggle to establish and maintain routines when facing disruptions. In this paper, we build on prior self-experimentation work to assist people in establishing or improving physical activity routines using a framework we call “reflective iteration.” This framework encourages individuals to articulate, reflect upon, and iterate on high-level “strategies” that inform their day-to-day physical activity plans. We designed and deployed a mobile application, Planneregy, that implements this framework. Sixteen U.S. college students used the Planneregy app for 42 days to reflectively iterate on their weekly physical exercise routines. Based on an analysis of usage data and interviews, we found that the reflective iteration approach has the potential to help people find and maintain effective physical activity routines, even in the face of life changes and temporary disruptions.

OBJECTIVES: To understand healthcare providers' experiences of using GlucoGuide, a mockup tool that integrates visual data analysis with algorithmic insights to support clinicians' use of patientgenerated data from Type 1 diabetes devices. MATERIALS AND METHODS: This qualitative study was conducted in three phases. In Phase 1, 11 clinicians reviewed data using commercial diabetes platforms in a think-aloud data walkthrough activity followed by semistructured interviews. In Phase 2, GlucoGuide was developed. In Phase 3, the same clinicians reviewed data using GlucoGuide in a think-aloud activity followed by semistructured interviews. Inductive thematic analysis was used to analyze transcripts of Phase 1 and Phase 3 think-aloud activity and interview. RESULTS: 3 high level tasks, 8 sub-tasks, and 4 challenges were identified in Phase 1. In Phase 2, 3 requirements for GlucoGuide were identified. Phase 3 results suggested that clinicians found GlucoGuide easier to use and experienced a lower cognitive burden as compared to the commercial diabetes data reports that were used in Phase 1. Additionally, GlucoGuide addressed the challenges experienced in Phase 1. DISCUSSION: The study suggests that the knowledge of analytical tasks and task-specific visualization strategies in implementing features of data interfaces can result in tools that lower the perceived burden of engaging with data. Additionally, supporting clinicians in contextualizing algorithmic insights by visual analysis of relevant data can positively influence clinicians' willingness to leverage algorithmic support. CONCLUSION: Task-aligned tools that combine multiple data-driven approaches, such as visualization strategies and algorithmic insights, can improve clinicians' experience in reviewing device data.

Children with Type 1 Diabetes (T1D) face many challenges with keeping their blood glucose levels within a healthy range because they cannot manage their illness by themselves. To prevent children’s blood glucose from becoming too high or too low, parents apply different strategies to avoid risky situations. To understand how parents of children with T1D manage these risks, we conducted semi-structured interviews with children with T1D (ages 6-12) and their parents (N=41). We identified four types of strategies used by parents (i.e., educated guessing game, contingency planning, experimentation, and reaching out for help) that can be categorized according to two dimensions: 1) the cause of risk (known or unknown) and 2) the occurrence of risk (predictable or unpredictable). Based on our findings, we provide design implications for collaborative health technologies that support parents in better planning for contingencies and identifying unknown causes of risks together with their children.

Engaging with multiple streams of personal health data to inform self-care of chronic health conditions remains a challenge. Existing informatics tools provide limited support for patients to make data actionable. To design better tools, we conducted two studies with Type 1 diabetes patients and their clinicians. In the first study, we observed data review sessions between patients and clinicians to articulate the tasks involved in assessing different types of data from diabetes devices to make care decisions. Drawing upon these tasks, we designed novel data interfaces called episode-driven data narratives and performed a task-driven evaluation. We found that as compared to the commercially available diabetes data reports, episode-driven data narratives improved engagement and decision-making with data. We discuss implications for designing data interfaces to support interaction with multidimensional health data to inform self-care.

Social media platforms exacerbate trauma, and many users experience various forms of trauma unique to them (e.g., doxxing and swatting). Trauma is the psychological and physical response to experiencing a deeply disturbing event. Platforms’ failures to address trauma threaten users’ well-being globally, especially amongst minoritized groups. Platform policies also expose moderators and designers to trauma through content they must engage with as part of their jobs (e.g., child sexual abuse). We consider how a trauma-informed approach might help address or decrease the likelihood of (re)experiencing trauma online. A trauma-informed approach to social media recognizes that everyone likely has a trauma history and that trauma is experienced at the individual, secondary, collective, and cultural levels. This paper proceeds by detailing trauma and its impacts. We then describe how the six trauma-informed principles can be applied to social media design, content moderation, and companies. We conclude by offering recommendations that balance platform responsibility and accountability with well-being and healing for all.

Although child participation is required for successful Type 1 Diabetes (T1D) management, it is challenging because the child’s young age and immaturity make it difficult to perform self-care. Thus, parental caregivers are expected to be heavily involved in their child’s everyday illness management. Our study aims to investigate how children and parents collaborate to manage T1D and examine how the children become more independent in their self-management through the support of their parents. Through semi-structured interviews with children with T1D and their parents (N=41), our study showed that children’s knowledge of illness management and motivation for self-care were crucial for their transition towards independence. Based on these two factors, we identified four types of children’s collaboration (i.e., dependent, resistant, eager, and independent) and parents’ strategies for supporting their children’s independence. We suggest design implications for technologies to support collaborative care by improving children’s transition to independent illness management.

Making and executing physical activity plans can help people improve their physical activity levels. However, little is known about how people make physical activity plans in everyday settings and how people can be assisted in creating more successful plans. In this paper, we developed and deployed a mobile app as a probe to investigate the in-the-wild physical activity planning experience for 28 days with 17 participants. Additionally, we explored the impact of presenting successful and unsuccessful planning records on participants’ planning behaviors. Based on interviews before, during, and after the deployment, we offer a description of what factors participants considered to fit their exercise plans into their existing routines, as well as factors leading to plan failures and dissatisfaction with planned physical activity. With access to historical records, participants derived insights to improve their plans, including trends in successes and failures. Based on those findings, we discuss the implications for better supporting people to make and execute physical activity plans, including suggestions for incorporating historical records into planning tools.

The operation of wireless devices is often affected by their physical locations and spatial relationships to other objects and phenomena in the environment. As devices become increasing small and mobile, these relationships can change rapidly. We need tools that can provide an at-a-glance operating picture of each moment and highlight spatial patterns. Such insights are poorly supported by existing tools that abstract away the physical reality of the underlying network. Fortunately, recent advances in localization technologies and mixed reality platforms provide an opportunity for a new approach: mixed reality network introspection. Mixed reality allows users to see network traffic situated directly in the real world, taking advantage of human spatial intuition and visual processing to quickly identify behavior of interest. We propose an architecture for mixed reality network introspection, and implement a prototype called XRShark, a network visualizer with augmented reality and virtual reality modes. Initial evaluations of XRShark show promise in enabling users to identify behaviors of local networks that would be difficult to detect using traditional tools.

In-situ self-reporting on smartwatches has been widely-used to collect data about participants’ experience. Yet it places a burden on participants by requiring immediate response in context. Such user burden can be studied through users’ performance and perceptions. This paper evaluates six interaction techniques to study user burden of in-situ self-reporting from both performance and perception aspects under three simulated scenarios (i.e., gaming, social chatting, and walking) through an in-lab experiment with twenty-four participants. Findings show that user performance was not always aligned with their perceived burden owing to factors such as users’ acceptable interaction technique features for self-reporting, the context of use, and users’ acquired skills over time. This study also sheds lights onto how user burden is experienced across different stages of a self-reporting microinteraction. Reflecting on findings, we discuss the implications for understanding and minimizing user burden related to in-situ self-reporting and as well as other types of microinteractions.

Tools for self-care of chronic conditions often do not fit the contexts in which self-care happens because the influence of context on self-care practices is unclear. We conducted a diary study with 15 adolescents with Type 1 Diabetes and their caregivers to understand how context affects self-care. We observed different contextual settings, which we call contextual frames, in which diabetes self-management varied depending on certain factors - physical activity, food, emotional state, insulin, people, and attitudes. The relative prevalence of these factors across contextual frames impacts self-care necessitating different types of support. We show that contextual frames, as phenomenological abstractions of context, can help designers of context-aware systems systematically explore and model the relation of context with behavior and with technology supporting behavior. Lastly, considering contextual frames as sensitizing concepts, we provide design direction for using context in technology design.

Independence is a central concern for people in the care of many chronic conditions. It is often viewed as a goal that can be facilitated with the use of patient data. It is also viewed, especially from the medical side, as something an individual achieves. However, in the lived experience, independence is often a set of collaborative practices. In this paper, we unpack findings from an interview-based study of Spinal Cord Injury (SCI) self-care. We found that independence - both functional and in the form of effecting agency - must be co-constructed by the choices and activities of the care network, including the person with disability, caregivers, and clinicians. This collaboratively shaped independence also affects potential collection and use of data in support of self-care. We describe how collaboratively shaped independence informs requirements and constraints for the design of sensor-based networks for self-care in long-term chronic disability.

Smart thermostats have been proposed as a way to reduce energy consumption in the home. While occupancy-based thermostat control and scheduling has been shown to provide energy savings, more recent work in comfort-aware thermostats promises to provide even greater savings. Comfort awareness and adaptive thermal comfort models, combined with the mixed-initiative eco-coaching approach to thermostat control, offer a promising approach to optimizing savings by offering both schedule and setpoint recommendations and actionable plans. In this paper, we investigate the design space of comfort-aware eco-coaching thermostats. Through a user enactment study wherein 11 participants encountered fifteen design probes covering various design attributes and interaction scenarios, we uncover insights on how to design such thermostats in a way that respect people's values relating to comfort, sustainability, control, convenience, and allocation of agency while also encouraging more energy efficient behaviors.

Making effective use of patient-generated data (PGD) is challenging for both patients and providers. Designing systems to support collaborative and individual use of PGD is a topic of importance in CSCW, considering the limitations of informatics tools. To inform better system design, we conducted a study including focus groups, observations and interviews with patients and providers to understand how PGD is interpreted and used. We found that while PGD is useful for identifying and solving disease-related problems, the following differences in patient-provider perceptions challenge its effective use - different perceptions about what is a problem, selecting what kinds of problems to focus on, and using different data representations. Drawing on these insights, we reflect on two specific conceptualizations of disease management behavior (sensemaking and problem-solving) as they relate to data specific activities of patients and providers and provide design suggestions for tools to support collaborative and individual use of PGD.

Personal informatics systems are becoming increasingly prevalent as their price, form, and ease of use improves. Though these systems offer great potential value to users, many systems are hampered by issues that limit their ability to foster engagement, and people often abandon use of these systems without garnering meaningful outcomes. While continued use of these systems is not necessary for all people, there is an opportunity to better support people working towards achievement-based goals.

This paper presents findings from a field deployment that explored a design approach we call eco-coaching: giving personalized suggestions for specific actions that would reduce wasted energy. We studied ThermoCoach, which performs eco-coaching for thermostat scheduling. It senses and models occupancy patterns in a home, and provides occupants alternative suggestions for configuring their thermostat. Our study shows that eco-coaching accomplished four things. First, it made it easier for users to implement an effective thermostat schedule. Second, it supported user agency in negotiating energy savings and comfort goals. Third, it facilitated learning different scheduling strategies as well as weighing different options. Finally, it challenged users' beliefs about how well they were doing. These outcomes, in turn, were successful in getting users to employ and experiment with more efficient setback strategies. Going forward, we propose ways that eco-coaching systems could better support users in customizing and assessing the systems' recommendations.

In this paper, we examine whether the use of expertise ratings can help crowd-sourcing systems. We show, using simulations, that a crowd-sourcing system based in social navigation works better when users’ expertise levels are taken into account.

Collecting ground-truth annotations for contextual data is vital to context-aware system development. However, current research lacks a systematic analysis of different approaches to collecting such data. We present a field experiment comparing three approaches: Participatory, Context-Triggered In Situ, and Context-Triggered Post Hoc, which involved users in recording and annotating activity data in real-world settings. We compared the quantity and quality of collected data using each approach, as well as the participant experience. We found Context-Triggered approaches produced more recordings, whereas the Participatory approach produced a greater amount of data with higher completeness and precision. Moreover, while participants appreciated automated recording and reminders for convenience, they highly valued having control over what and when to record and annotate. We conclude that user burden and user control are key aspects to consider when collecting and annotating contextual data with participants, and suggest features for a future tool focused on these two aspects.

In this work, we designed and field-tested a system called Home Trivia to explore how we can use activity traces captured in the home to allow household members to reflect on how they use technology, which has become an issue of increasing concern among families that have seen their home lives intertwined with Internet-enabled devices. Home Trivia captures traces of using technology at home and then shows those traces to family members as content of a puzzle game they can play together. The results of testing Home Trivia in the field show that the design of the game allows engagement and reflection to reinforce each other. Moreover, our work enriches and further develops the idea of using ambiguity as a resource for design with the insight that allowing users to reduce ambiguity through recollecting past events and communicating with others can help trigger reflection.

For decades, researchers have investigated ways to infer human thermal comfort. Studies have usually required cumbersome sensors and human observers, making them inappropriate for use in naturalistic settings such as the home. Emerging wearable and smart home sensing devices offer the opportunity to develop new models of thermal comfort based on data collected in-situ. To explore this opportunity, we deployed a sensing system in seven homes and collected self-report data from 11 participants for four weeks. Our system captures many factors employed in previous thermal comfort research, as well as new factors (e.g., activity level, sweat level). Machine learning-based models derived from the collected data show improvement over previous techniques, however significant prediction errors remain. In analyzing these errors we identify six problems that pose challenges for inferring comfort in the wild. Based on our findings, we suggest techniques to improve future in-situ thermal comfort modeling efforts.

Personal tracking technologies allow users to monitor and reflect on their physical activities and fitness. However, users are uncertain about how accurately their devices track their data. In order to better understand this challenge, we analyzed 600 product reviews and conducted 24 interviews with tracking device users. In this paper, we describe what methods users used to assess accuracy of their tracking devices and identify seven problems they encountered. We found that differences in users' expectations, physical characteristics, types of activities and lifestyle led them to have different perceptions of the accuracy of their devices. With the absence of sound mental models and unclear understanding of the concepts of accuracy and experimental controls, users designed faulty tests and came to incorrect conclusions. We propose design recommendations to better support end-users' efforts to assess and improve the accuracy of their tracking devices as required to suit their individual characteristics and purposes.

Learning storytelling is beneficial for children's development. Various tools have been proposed to expand the set of materials children can use to compose their stories. However, most previous research focuses on enhancing the visual aspect of storytelling and underexplores the acoustic elements needed for children's stories. In this paper, Olegoru1, a sound composition tool in the form of magic gloves and soul stones, is proposed to augment children's storytelling when using physical objects. Children can create contextual and regional sound effects as well as event-based acoustics through speech, non-verbal and gestural interaction, and could potentially enable children to create more immersive story-worlds. To investigate the technical feasibility of such tool, a preliminary prototype was built that accepts a limited number of vocalized sound effects and vocabulary.

This work explores the potential value of using the enormous amount of activity traces latest ubicomp environments have started to capture. We sought to understand potential practices of using these traces in the long term through a field-based study in the USA that examines how today's people use traces left by their predecessors in the houses where they live.

The smart home is here. One area where smart home devices promise to deliver great benefits is in the control of home heating, ventilation, and cooling (HVAC) systems. In this paper, we seek to inform the design of future heating and cooling systems by investigating users' experiences with the Nest Learning Thermostat, a commercially available smart home device. We conducted a qualitative study where we compared people's interactions with conventional thermostats with interactions with the Nest. A key finding was that the Nest impacted users' pattern of HVAC control, but only for a while, and caused new problems in unrealized energy savings. In leveraging these findings, we create a set of design implications for Eco-Interaction, the design of features and human-system interactions with the goal of saving energy.

Context has long been considered an important component of design, but as technology becomes more capable of inferring the user's behavior and environment, what constitutes context has become an increasingly pressing concern to designers. Although design frameworks and models have been proposed for context-aware computing systems, there has not yet been research that focuses on understanding context empirically from the perspective of the designer. To address this, we present an analysis of 11 in-depth interviews we conducted with designers of a variety of context-aware systems. Our analysis of the artifacts and interviews reveal five concerns designers address in their work. Furthermore, we present a process model that illustrates how context-aware system designers address these concerns. Our findings demonstrate the central role that designers' views of context plays in (a) framing a design space, (b) encoding the relevant features of context, (c) unifying possible solutions within that design space, and (d) evaluating designs. These findings suggest that context is a dynamic concept that evolves over the course of a design project, generally from a more phenomenological perspective toward a positivist interpretation. This, and the process by which it occurs, contributes insight into context-aware design with implications for both academics and practitioners.

Everyday systems and devices in the home are becoming smarter. In order to better understand the challenges of deploying an intelligent system in the home, we studied the experience of living with an advanced thermostat, the Nest. The Nest utilizes machine learning, sensing, and networking technology, as well as eco-feedback features. We conducted interviews with 23 participants, ten of whom also participated in a three-week diary study. Our findings show that while the Nest was well-received overall, the intelligent features of the Nest were not perceived to be as useful or intuitive as expected, in particular due to the system's inability to understand the intent behind sensed behavior and users' difficulty in understanding how the Nest works. A number of participants developed workarounds for the shortcomings they encountered. Based on our observations, we propose three avenues for future development of interactive intelligent technologies for the home: exception flagging, incidental intelligibility, and constrained engagement.

We propose using discovery-based learning games to teach people how to use complex software. Specifically, we developed Jigsaw, a learning game that asks players to solve virtual jigsaw puzzles using tools in Adobe Photoshop. We conducted an eleven-person lab study of the prototype, and found the game to be an effective learning medium that can complement demonstration-based tutorials. Not only did the participants learn about new tools and techniques while actively solving the puzzles in Jigsaw, but they also recalled techniques that they had learned previously but had forgotten.

Transportation mode (TM) detection is one of the activity recognition tasks in ubiquitous computing. A number of previous studies have compared the performance of various classifiers for TM detection. However, the current study is the first work aiming to understand how TM detection performance is impacted by how the recorded location traces are segmented into data segments for training a classifier. In our preliminary experiments we examine three trace segmentation (TS) methods---Uniform Duration (UniDur), Uniform Number of Location Points (UniNP), and Uniform Distance (UniDis)---and compare their performance on detecting different transportation modes. The results indicate that while driving can be more accurately detected by using UniDis method, walking and bus can be more accurately detected by using UniDur method. This suggests that choosing a right TS method for training a TM classifier is an important step to accurately detect particular transportation modes.

In order to better understand the opportunities and challenges of an intelligent system in the home, we studied the lived experience of a thermostat, the Nest. The Nest utilizes machine learning, sensing, and networking technology, as well as eco-feedback features. To date, we have conducted six interviews and one diary study. Our findings show that improved interfaces through web and mobile applications changed the interactions between users and their home system. Intelligibility and accuracy of the machine learning and sensing technology influenced the way participants perceive and adapt to the system. The convenient control over the system combined with limitations of the technology may have prevented the desired energy savings. These findings assert that thoughtful, continuous involvement from users is critical to the desired system performance and the success of interventions to promote sustainable choices. We suggest that an intelligent system in the home requires improved intelligibility and a better way in which users can provide deliberate input to the system.

The popularization of Location Based Services (LBS) has created new challenges for interaction designers in validating the design of their applications. Existing tools designed to play back GPS location traces data streams have shown potential for testing LBS applications and for supporting rapid and reflective prototyping. However, selecting a useful set of location traces from among a large collection remains a difficult task. In this paper, we present TraceViz, the first system that is aimed specifically at supporting LBS designers in exploring, filtering, and selecting location traces. TraceViz employs dynamic queries and "brushing" to allow LBS designers to flexibly adjust their trajectory filter criteria to find location traces of interest. An evaluation performed with eight LBS designers and developers indicates that TraceViz is helpful for rapidly locating useful traces and also highlights areas for future improvement.

Much of the information designed to help people navigate the built environment is conveyed through visual channels, which means it is not accessible to people with visual impairments. Due to this limitation, travelers with visual impairments often have difficulty navigating and discovering locations in unfamiliar environments, which reduces their sense of independence with respect to traveling by foot. In this paper, we examine how mobile location-based computing systems can be used to increase the feeling of independence in travelers with visual impairments. A set of formative interviews with people with visual impairments showed that increasing one's general spatial awareness is the key to greater independence. This insight guided the design of Talking Points 3 (TP3), a mobile location-aware system for people with visual impairments that seeks to increase the legibility of the environment for its users in order to facilitate navigating to desired locations, exploration, serendipitous discovery, and improvisation. We conducted studies with eight legally blind participants in three campus buildings in order to explore how and to what extent TP3 helps promote spatial awareness for its users. The results shed light on how TP3 helped users find destinations in unfamiliar environments, but also allowed them to discover new points of interest, improvise solutions to problems encountered, develop personalized strategies for navigating, and, in general, enjoy a greater sense of independence.

In this paper, we seek to advance the research around utilizing collaborative help for supporting individualized use of technologies. We do this by shedding light on the ways that users of MythTV, a highly flexible open-source software system for home entertainment enthusiasts, collaboratively help one another in maintaining their individualized MythTV systems. By analyzing the MythTV user community's mailing list archive, documentation, and wiki, coupled with user interviews we discuss how the community utilizes configuration artifacts as proxies to easily mobilize and exchange knowledge. While exchanging concrete artifacts such as scripts and configuration files was seen to sometimes increase the efficiency of knowledge transfer, it also presented several challenges. Negotiating the transparency of configuration artifacts, navigating the customization and appropriation gulfs, and aligning usage trajectories all emerged as problematic areas. We discuss design implications that address these challenges. Our findings provide a crucial understanding for how to support users in their individualized use of systems.

To understand why and how people share health information online, we interviewed fourteen people with significant health concerns who participate in both online health communities and Facebook. Qualitative analysis of these interviews highlighted the ways that people think about with whom and how to share different types of information as they pursue social goals related to their personal health, including emotional support, motivation, accountability, and advice. Our study suggests that success in these goals depends on how well they develop their social networks and how effectively they communicate within those networks. Effective communication is made more challenging by the need to strike a balance between sharing information related to specific needs and the desire to manage self-presentation. Based on these observations, we outline a set of design opportunities for future systems to support health-oriented social interactions online, including tools to help users shape their social networks and communicate effectively within those.

HCI endeavors to create human-centered computer systems, but underlying technological infrastructures often stymie these efforts. We outline three specific classes of user experience difficulties caused by underlying technical infrastructures, which we term constrained possibilities, unmediated interaction, and interjected abstractions. We explore how prior approaches in HCI have addressed these issues, and discuss new approaches that will be required for future progress. We argue that the HCI community must become more deeply involved with the creation of technical infrastructures. Doing so, however, requires a substantial expansion to the methodological toolbox of HCI.

For emerging group technologies that require evaluations on long-term use and social norms, assumptions, and implicit rules that develop around the technologies, standard usability testing may not be adequate. At the same time, field based research that allows for observing technology use over long-term is costly in terms of time. In this paper, we present a rapid method that we call progressive scenarios, which could help replicate the processes by which interpretations evolve over time in natural settings and how invisible assumptions and social norms dictate the technology use. Using a preliminary design concept of a publicly available ambient personal information and communication system, we demonstrate how the method helped to elicit design implications.

Real-time, character-by-character transmission of messages in synchronous forms of text-based communications has seen a recent resurgence in CMC. We evaluated the impact of real-time text display on the usability of an instant messaging (IM) client. Participants were randomly assigned to dyads to participate in two discussion tasks using IM with both real-time text and enhanced message-by-message display (i.e., line-by-line display with additional cues to show when the remote party is typing). We found that real-time text helped users better coordinate turns and lead to less self-editing of messages, but had no overall influence on users' typing ability and provided minimal support for collaborative completion of sentences. Users who typed less or had less experience with IM tended to prefer real-time text. These findings have significance for several forms text-based CMC, including IM, chat, text telephony, and collaborative document editing.

When designing context-aware applications, it is difficult to for designers in the studio or lab to envision the contextual conditions that will be encountered at runtime. Designers need a tool that can create/re-create naturalistic contextual states and transitions, so that they can evaluate an application under expected contexts. We have designed and developed RePlay: a system for capturing and playing back sensor traces representing scenarios of use. RePlay contributes to research on ubicomp design tools by embodying a structured approach to the capture and playback of contextual data. In particular, RePlay supports: capturing naturalistic data through Capture Probes, encapsulating scenarios of use through Episodes, and supporting exploratory manipulation of scenarios through Transforms. Our experiences using RePlay in internal design projects illustrate its potential benefits for ubicomp design.

This article describes an infrastructure that supports the creation of interoperable systems while requiring only limited prior agreements about the specific forms of communication between these systems. Conceptually, our approach uses a set of “meta-interfaces”—agreements on how to exchange new behaviors necessary to achieve compatibility at runtime , rather than requiring that communication specifics be built in at development time —to allow devices on the network to interact with one another. While this approach to interoperability can remove many of the system-imposed constraints that prevent fluid, ad hoc use of devices now, it imposes its own limitations on the user experience of systems that use it. Most importantly, since devices may be expected to work with peers about which they have no detailed semantic knowledge, it is impossible to achieve the sort of tight semantic integration that can be obtained using other approaches today, despite the fact that these other approaches limit interoperability. Instead, under our model, users must be tasked with performing the sense-making and semantic arbitration necessary to determine how any set of devices will be used together. This article describes the motivation and details of our infrastructure, its implications on the user experience, and our experience in creating, deploying, and using applications built with it over a period of several years.

This paper presents a context-aware mobile recommender system, codenamed Magitti. Magitti is unique in that it infers user activity from context and patterns of user behavior and, without its user having to issue a query, automatically generates recommendations for content matching. Extensive field studies of leisure time practices in an urban setting (Tokyo) motivated the idea, shaped the details of its design and provided data describing typical behavior patterns. The paper describes the fieldwork, user interface, system components and functionality, and an evaluation of the Magitti prototype.

Many mobile devices have touch-sensitive screens that people interact with using fingers or thumbs. However, such interaction is difficult because targets become occluded, and because fingers and thumbs have low input resolution. Recent research has addressed occlusion through visual techniques. However, the poor resolution of finger and thumb selection still limits selection speed. In this paper, we address the selection speed problem through a new target selection technique called Escape. In Escape, targets are selected by gestures cued by icon position and appearance. A user study shows that for targets six to twelve pixels wide, Escape performs at a similar error rate and at least 30% faster than Shift, an alternative technique, on a similar task. We evaluate Escape's performance in different circumstances, including different icon sizes, icon overlap, use of color, and gesture direction. We also describe an algorithm that assigns icons to targets, thereby improving Escape's performance.

We present Talking Points, an urban orientation system based on the idea that an individual's walking journey can be enhanced by providing contextual information about points of interest (POIs) along their route. Our formative research revealed numerous ways to provide serendipitous and task-critical information for both sighted and visually impaired users as they navigate through an urban environment on foot. Based on this, we developed a prototype system comprised of the following: an unobtrusive mobile device to present the user with contextual information; a socially maintained online database containing information about POIs; software that is accessible via both a graphical and a speech user interface; and location "tags" to be detected by the unobtrusive device. This socially maintained urban orientation and contextual information system offers relevant, dynamic, and up-to-date information, a combination which may not otherwise be accessible.

A proactive display is an application that selects content to display based on the set of users who have been detected nearby. For example, the Ticket2Talk [17] proactive display application presented content for users so that other people would know something about them.

With the rapid growth of personal computer networks and the Internet, sharing files has become a central activity in computer use. The ways in which users control the what, how, and with whom of sharing are dictated by the tools they use for sharing; there are a wide range of sharing practices, and hence a wide range of tools to support these practices. In practice, users' requirements for certain sharing features may dictate their choice of tool, even though the other affordances available through that tool may not be an ideal match to the desired manner of sharing.In this paper, we explore users' current practices in file sharing and examine the tools used to share files. Based on our findings, we unpack the features and affordances of these tools into a set of dimensions along which sharing tools can be characterized. Then, we present the set of user interface features we have prototyped in an interface called a sharing palette, which provides a platform for exploration and experimentation with new modalities of sharing. We briefly present the tool as a whole and then focus on the individual features of the sharing palette that support reported styles of sharing.

This paper presents a descriptive account of the social practices surrounding the iTunes music sharing of 13 participants in one organizational setting. Specifically, we characterize adoption, critical mass, and privacy; impression management and access control; the musical impressions of others that are created as a result of music sharing; the ways in which participants attempted to make sense of the dynamic system; and implications of the overlaid technical, musical, and corporate topologies. We interleave design implications throughout our results and relate those results to broader themes in a music sharing design space.

Infrastructure software comprises code libraries or runtime processes that support the development or operation of application software. A particular infrastructure system may support certain styles of application, and may even determine the features of applications built using it. This poses a challenge: although we have good techniques for designing and evaluating interactive applications, our techniques for designing and evaluating infrastructure intended to support these applications are much less well formed. In this paper, we reflect on case studies of two infrastructure systems for interactive applications. We look at how traditional user-centered techniques, while appropriate for application design and evaluation, fail to properly support infrastructure design and evaluation. We present a set of lessons from our experience, and conclude with suggestions for better user-centered design and evaluation of infrastructure software.

Through a study of Web site design practice, we observed that designers employ multiple representations of Web sites as they progress through the design process and that these representations allow them to focus on different aspects of the design. In particular, we observed that Web site designers focus their design efforts at 3 different levels of granularity-site map, storyboard, and individual page-and that designers sketch at all levels during the early stages of design. Sketching on paper is especially important during the early phases of a project, when designers wish to explore many design possibilities quickly without focusing on low-level details. Existing Web design tools do not support such exploration tasks well, nor do they adequately integrate multiple site representations. Informed by these observations we developed DENIM: an informal Web site design tool that supports early phase information and navigation design of Web sites. It supports sketching input, allows design at different levels of granularity, and unifies the levels through zooming. Designers are able to interact with their sketched designs as if in a Web browser, thus allowing rapid creation and exploration of interactive prototypes. Based on an evaluation with professional designers as well as usage feedback from users who have downloaded DENIM from the Internet, we have made numerous improvements to the system and have received many positive reactions from designers who would like to use a system like DENIM in their work.

Peer-to-peer systems appear promising in terms of their ability to support ad hoc, spontaneous collaboration. However, current peer-to-peer systems suffer from several deficiencies that diminish their ability to support this domain, such as inflexibility in terms of discovery protocols, network usage, and data transports. We have developed the Speakeasy framework, which addresses these issues, and supports these types of applications. We show how Speakeasy addresses the shortcomings of current peer-to-peer systems, and describe a demonstration application, called Casca, that supports ad hoc peer-to-peer collaboration by taking advantages of the mechanisms provided by Speakeasy.

Interoperability among a group of devices, applications, and services is typically predicated on those entities having some degree of prior knowledge of each another. In general, they must be written to understand the type of thing with which they will interact, including the details of communication as well as semantic knowledge such as when and how to communicate. This paper presents a case for "recombinant computing" -- a set of common interaction patterns that leverage mobile code to allow rich interactions among computational entities with only limited a priori knowledge of one another. We have been experimenting with a particular embodiment of these ideas, which we call Speakeasy. It is designed to support ad hoc, end user configurations of hardware and software, and provides patterns for data exchange, user control, discovery of new services and devices, and contextual awareness.

Users in ubiquitous computing environments need to be able to make serendipitous use of resources that they did not anticipate and of which they have no prior knowledge. The Speakeasy recombinant computing framework is designed to support such ad hoc use of resources on a network. In addition to other facilities, the framework provides an infrastructure through which device and service user interfaces can be made available to users on multiple platforms. The framework enables UIs to be provided for connections involving multiple entities, allows these UIs to be delivered asynchronously, and allows them to be injected by any party participating in a connection.

In our previous studies into web design, we found that pens, paper, walls, and tables were often used for explaining, developing, and communicating ideas during the early phases of design. These wall-scale paper-based design practices inspired The Designers' Outpost, a tangible user interface that combines the affordances of paper and large physical workspaces with the advantages of electronic media to support information design. With Outpost, users collaboratively author web site information architectures on an electronic whiteboard using physical media (Post-it notes and images), structuring and annotating that information with electronic pens. This interaction is enabled by a touch-sensitive SMART Board augmented with a robust computer vision system, employing a rear-mounted video camera for capturing movement and a front-mounted high-resolution camera for capturing ink. We conducted a participatory design study with fifteen professional web designers. The study validated that Outpost supports information architecture work practice, and led to our adding support for fluid transitions to other tools.

Through a study of web site design practice, we observed that web site designers design sites at different levels of refinement—site map, storyboard, and individual page—and that designers sketch at all levels during the early stages of design. However, existing web design tools do not support these tasks very well. Informed by these observations, we created DENIM, a system that helps web site designers in the early stages of design. DENIM supports sketching input, allows design at different refinement levels, and unifies the levels through zooming. We performed an informal evaluation with seven professional designers and found that they reacted positively to the concept and were interested in using such a system in their work.

We present a Java-based framework, SWAMI (Shared Wisdom through the Amalgamation of Many Interpretations) for building and studying collaborative filtering systems. SWAMI consists of three components: a prediction engine, an evaluation system, and a visualization component. The prediction engine provides a common interface for implementing different prediction algorithms. The evaluation system provides a standardized testing methodology and metrics for analyzing the accuracy and run-time performance of prediction algorithms. The visualization component suggests how graphical representations can inform the development and analysis of prediction algorithms. We demonstrate SWAMI on the Each Movie data set by comparing three prediction algorithms: a traditional Pearson correlation-based method, support vector machines, and a new accurate and scalable correlation-based method based on clustering techniques.