New parents often turn to baby-tracking technology to monitor and reflect on the daily routines of their infants. However, we lack understanding of how tracking practices evolve as children grow and develop, with caregivers adopting, using, and eventually abandoning baby tracking. We analyze the logs of 60 parents and 71 children who used the popular baby-tracking app Huckleberry for an average of 12 months, combined with re-analyzing interviews with 20 parents who used various baby-tracking technologies. We find that parents start tracking at different ages, track habitually and intermittently, change and swap what and how they track, and often gradually abandon the practice. Through unpacking why these patterns occur, we find that parents effectively self-manage what data categories are worthwhile to continue tracking. We point out lessons that domains outside baby tracking can take from the evolving, longitudinal process, and present design recommendations to better support caregivers across phases.

Shared gaze, where collaborators can see each other's point of gaze visualized on their screen in real time, is a novel non-verbal mechanism that augments remote collaborations and increases shared awareness and common grounding. While past studies have focused on well-structured tasks and analyzed task performance and efficiency, our study explores the domain of collaborative drawing for recreational purposes and focuses on collaborators' own perceptions. We surveyed 75 users of online collaborative drawing platforms who mostly drew collaboratively for recreation and artistic growth; they reported the importance of communication but also of retaining individual space despite the collaborative setting. Informed by this and prior research on shared gaze, we evaluate collaboration by allowing two collaborators to draw synchronously on a shared canvas and share their point of gaze. We conducted a study with 24 pairs that drew collaboratively under all combinations of shared gaze and voice communication. Combining voice and shared gaze was perceived to reach the best balance between tightly coupled collaboration and parallel individual execution. Shared gaze led to higher spatial awareness and less turn-taking was observed in conditions that shared gaze was present. Surprisingly, many participants found the lack of any communication medium to afford the highest degree of divergent thinking. Our findings provide guidelines for adaptive tools that consider individual preferences as well as the nature of the task to better support remote collaborations that are open-ended and prize creativity.

Patients' self-reports are crucial for effective care management of clinical conditions involving subjective symptoms. While patients often value the ability to bring in different forms of self-report data to convey their lived experiences, they often struggle to make their data practically usable in clinical settings. To better center patient needs in communicating illness experiences in clinical contexts, we explore the idea of patient annotations to digital clinical self-report measures, specifically in the context of discontinuing antidepressants. Through interviews with 20 patients with AT Annotator, a digital aid to introduce the concept of annotations, we found that participants perceived annotations to digital clinical measures as a means to enrich self-report measures and reduce the cognitive and emotional burden of logging. However, concerns were raised regarding potential disruptions in patient-provider relationships and the sensitive and complex nature of mental health contexts. We discuss opportunities for annotations to promote patient-centered communication by balancing with clinical practicality and incorporating customization support for patients' communication needs.

Recommendation systems are essential for music platforms to drive exploration and discovery for users. Little work has been done in exploring cross-language music recommendation systems, which represent another avenue for music exploration. In this paper, we collected and created a database of over 200,000 artists, which includes subsets of artists that sing in 8 different languages other than English. Our goal was to recommend artists in those 8 other language subsets for a given English-speaking artist. Using Spotify’s API-related artists feature, we implemented two approaches: a matrix factorization model using alternating least squares and a breadth-first search system. Both systems perform significantly better than a random baseline based on accuracy of the base artist’s genre with the breadth-first search model outperforming the matrix factorization technique. We conclude with suggestions for improving the performance and reach of cross-language music recommendation systems.

Clinical decision support tools have typically focused on one-time support for diagnosis or prognosis, but have the ability to support providers in longitudinal planning of patient care regimens amidst infrastructural challenges. We explore an opportunity for technology support for discontinuing antidepressants, where clinical guidelines increasingly recommend gradual discontinuation over abruptly stopping to avoid withdrawal symptoms, but providers have varying levels of experience and diverse strategies for supporting patients through discontinuation. We conducted two studies with 12 providers, identifying providers' needs in developing discontinuation plans and deriving design guidelines. We then iteratively designed and implemented AT Planner, instantiating the guidelines by projecting taper schedules and providing flexibility for adjustment. Provider feedback on AT Planner highlighted that discontinuation plans required balancing interpersonal and infrastructural constraints and surfaced the need for different technological support based on clinical experience. We discuss the benefits and challenges of incorporating flexibility and advice into clinical planning tools.

People often turn to online health communities (OHCs) for peer support on their specific medical conditions and health-related concerns. Over time, core members in OHCs build a shared understanding of the medical conditions they support. Although prior work has studied how individuals function differently in active sensemaking mode compared to habitual mode, little is known about how OHCs disseminate their advice once their core members operate primarily in habitual mode. We qualitatively observe one such OHC, 'Surviving Antidepressants', to understand how collectively-built protocols are disseminated in the important domain of discontinuing psychiatric drugs. Psychiatric drugs are widely prescribed to treat mental health diagnoses, but, in certain cases, discontinuation might be clinically advisable. Unfortunately, some people experience severe withdrawal symptoms upon discontinuation, even when following medical advice, and thus turn to OHCs for support. We find that collectively-built protocols resemble medical advice and are delivered in a top-down fashion, with staff members being the primary source of informational support. In contrast, all members provide emotional support and exchange advice on navigating the medical system, while many express their distrust of the medical community and pharmaceutical companies. We also discuss the implications of OHCs offering advice outside of the medical system and offer suggestions for how OHCs can collaborate with healthcare providers to advance scientific knowledge and better support people living with medical conditions.

The life cycle of a peer-produced dataset follows the phases of growth, maturity, and decline. Paying crowdworkers is a proven method to collect and organize information into structured tables. However, these tabular representations may contain inaccuracies due to errors or data changing over time. Thus, the maturation phase of a dataset can benefit from the additional human examination. One method to improve accuracy is to recruit additional paid crowdworkers to verify and correct errors. An alternative method relies on unpaid contributors, collectively editing the dataset during regular use. We describe two case studies to examine different strategies for human verification and maintenance of in-flux tabular datasets. The first case study examines traditional micro-task verification strategies with paid crowdworkers, while the second examines long-term maintenance strategies with unpaid contributions from non-crowdworkers. Two paid verification strategies that produced more accurate corrections at a lower cost per accurate correction were redundant data collection followed by final verification from a trusted crowdworker and allowing crowdworkers to review any data freely. In the unpaid maintenance strategies, contributors provided more accurate corrections when asked to review data matching their interests. This research identifies considerations and future approaches to collectively improving information accuracy and longevity of tabular information.

Remote collaborations are becoming ubiquitous, but, despite their many advantages, face unique challenges compared to collocated collaborations. Visualizing the collaborator's point of gaze on a shared screen has been explored as a promising way to alleviate some of these limitations by increasing shared awareness. However, prior studies on shared gaze have not considered the medium of communication and have only studied its effect on audio. This paper presents a study that compares the effects of shared gaze on collaboration performance during audio- and text-based communication. We find that for text, shared gaze improved task correctness and led collaborators to look at and talk more about shared content. Similar trends are found for gaze-augmented voice communication, but contrary to the slower performance in text, it also saw improvements in completion time as well as in cognitive workload. Our findings demonstrate the differences in how shared gaze impacts audio- versus text-based communication and highlight the need to further understand the nuances of the medium of communication when designing novel tools to support remote collaborators.

Online collaborative writing is an increasingly common practice. Despite its positive effect on productivity and quality of work, it poses challenges to co-authors in remote settings because of limitations in conversational grounding and activity awareness. This paper presents Eye-Write, a novel system which allows two co-authors to see at will the location of their partner's gaze within a text editor. To investigate the effect of shared gaze on collaboration, we conducted a study on synchronous remote collaborative writing in academic settings with 20 dyads. Gaze sharing improved five aspects of perceived collaboration quality: mutual understanding, level of joint attention, flow of communication, level of negotiation, and awareness of the co-author's activity. Furthermore, dyads whose participants deactivated the gaze visualization showed a smaller degree of collaboration. Our findings offer insights for future text editors by outlining the benefits of at-will gaze sharing in collaborative writing.

We examine the relationship between eye gaze and typing, focusing on the differences between touch and non-touch typists. To enable typing-based research, we created a 51-participant benchmark dataset for user input across multiple tasks, including user input data, screen recordings, webcam video of the participant's face, and eye tracking positions. There are patterns of eye movements that differ between the two types of typists, representing glances at the keyboard, which can be used to identify touch-.typed strokes with 92% accuracy. Then, we relate eye gaze with cursor activity, aligning both pointing and typing to eye gaze. One demonstrative application of the work is in extending WebGazer, a real-time web-browser-based webcam eye tracker. We show that incorporating typing behavior as a secondary signal improves eye tracking accuracy by 16% for touch typists, and 8% for non-touch typists.

We introduce SearchGazer, a web-based eye tracker for remote web search studies using common webcams already present in laptops and some desktop computers. SearchGazer is a pure JavaScript library that infers the gaze behavior of searchers in real time. The eye tracking model self-calibrates by watching searchers interact with the search pages and trains a mapping of eye features to gaze locations and search page elements on the screen. Contrary to typical eye tracking studies in information retrieval, this approach does not require the purchase of any additional specialized equipment, and can be done remotely in a user's natural environment, leading to cheaper and easier visual attention studies.

As crowdsourcing has gained prominence in recent years, an increasing number of people turn to popular crowdsourcing platforms for their many uses. Experienced members of the crowdsourcing community have developed numerous systems both separately and in conjunction with these platforms, along with other tools and design techniques, to gain more specialized functionality and overcome various shortcomings. It is unclear, however, how novice requesters using crowdsourcing platforms for general tasks experience existing platforms and how, if at all, their approaches deviate from the best practices established by the crowdsourcing research community. We conduct an experiment with a class of 19 students to study how novice requesters design crowdsourcing tasks. Each student tried their hand at crowdsourcing a real data collection task with a fixed budget and realistic time constraint. Students used Amazon Mechanical Turk to gather information about the academic careers of over 2,000 professors from 50 top Computer Science departments in the U.S. In addition to curating this dataset, we classify the strategies which emerged, discuss design choices students made on task dimensions, and compare these novice strategies to best practices identified in crowdsourcing literature. Finally, we summarize design pitfalls and effective strategies observed to provide guidelines for novice requesters.

A key challenge in genomics is to identify genetic variants that distinguish patients with different survival time following diagnosis or treatment. While the log-rank test is widely used for this purpose, nearly all implementations of the log-rank test rely on an asymptotic approximation that is not appropriate in many genomics applications. This is because: the two populations determined by a genetic variant may have very different sizes; and the evaluation of many possible variants demands highly accurate computation of very small p-values. We demonstrate this problem for cancer genomics data where the standard log-rank test leads to many false positive associations between somatic mutations and survival time. We develop and analyze a novel algorithm, Exact Log-rank Test (ExaLT), that accurately computes the p-value of the log-rank statistic under an exact distribution that is appropriate for any size populations. We demonstrate the advantages of ExaLT on data from published cancer genomics studies, finding significant differences from the reported p-values. We analyze somatic mutations in six cancer types from The Cancer Genome Atlas (TCGA), finding mutations with known association to survival as well as several novel associations. In contrast, standard implementations of the log-rank test report dozens-hundreds of likely false positive associations as more significant than these known associations.