Challenges in engagement with digital mental health (DMH) tools are commonly addressed through technical enhancements and algorithmic interventions. This paper shifts the focus towards the role of users' broader social context as a significant factor in engagement. Through an eight-week text messaging program aimed at enhancing psychological wellbeing, we recruited 20 participants to help us identify situational engagement disruptors (SEDs), including personal responsibilities, professional obligations, and unexpected health issues. In follow-up design workshops with 25 participants, we explored potential solutions that address such SEDs: prioritizing self-care through structured goal-setting, alternative framings for disengagement, and utilization of external resources. Our findings challenge conventional perspectives on engagement and offer actionable design implications for future DMH tools.
CSCW research is increasingly interested in the ways that people use technology to discuss health and disability online. In addition to studying how people share information and seek and provide emotional support, a growing area of interest is health activism. In this paper, we analyze how a project centered around sharing "real and raw" experiences with dementia provides a safe platform for people to share their authentic experiences. These accounts counter predominant depictions of dementia and push back on tokenistic involvement of people with this condition. In a study involving observations and interviews with members of this project, we find that people with dementia must negotiate several goals which at times compete with each other: sharing a "real and raw" look at dementia, changing attitudes, showcasing a polished presentation, and inhabiting a safe space. The paper concludes with a discussion of future directions for CSCW on configuring a space for dialogue on sensitive topics, health activism, and sharing online with dementia.
In online communities, antisocial behavior such as trolling disrupts constructive discussion. While prior work suggests that trolling behavior is confined to a vocal and antisocial minority, we demonstrate that ordinary people can engage in such behavior as well. We propose two primary trigger mechanisms: the individual's mood, and the surrounding context of a discussion (e.g., exposure to prior trolling behavior). Through an experiment simulating an online discussion, we find that both negative mood and seeing troll posts by others significantly increases the probability of a user trolling, and together double this probability. To support and extend these results, we study how these same mechanisms play out in the wild via a data-driven, longitudinal analysis of a large online news discussion community. This analysis reveals temporal mood effects, and explores long range patterns of repeated exposure to trolling. A predictive model of trolling behavior shows that mood and discussion context together can explain trolling behavior better than an individual's history of trolling. These results combine to suggest that ordinary people can, under the right circumstances, behave like trolls.
Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.
Patient-generated data can allow patients and providers to collaboratively develop accurate diagnoses and actionable treatment plans. Unfortunately, patients and providers often lack effective support to make use of such data. We examine patient-provider collaboration to interpret patient-generated data. We focus on irritable bowel syndrome (IBS), a chronic illness in which particular foods can exacerbate symptoms. IBS management often requires patient-provider collaboration using a patient's food and symptom journal to identify the patient's triggers. We contribute interactive visualizations to support exploration of such journals, as well as an examination of patient-provider collaboration in interpreting the journals. Drawing upon individual and collaborative interviews with patients and providers, we find that collaborative review helps improve data comprehension and build mutual trust. We also find a desire to use tools like our interactive visualizations within and beyond clinic appointments. We discuss these findings and present guidance for the design of future tools.
Important work rooted in psychological theory posits that health behavior change occurs through a series of discrete stages. Our work builds on the field of social computing by identifying how social media data can be used to resolve behavior stages at high resolution (e.g. hourly/daily) for key population subgroups and times. In essence this approach opens new opportunities to advance psychological theories and better understand how our health is shaped based on the real, dynamic, and rapid actions we make every day. To do so, we bring together domain knowledge and machine learning methods to form a hierarchical classification of Twitter data that resolves different stages of behavior. We identify and examine temporal patterns of the identified stages, with alcohol as a use case (planning or looking to drink, currently drinking, and reflecting on drinking). Known seasonal trends are compared with findings from our methods. We discuss the potential health policy implications of detecting high frequency behavior stages.
The growing amount of data collected by quantified self tools and social media hold great potential for applications in personalized medicine. Whereas the first includes health-related physiological signals, the latter provides insights into a user's behavior. However, the two sources of data have largely been studied in isolation. We analyze public data from users who have chosen to connect their MyFitnessPal and Twitter accounts. We show that a user's diet compliance success, measured via their self-logged food diaries, can be predicted using features derived from social media: linguistic, activity, and social capital. We find that users with more positive affect and a larger social network are more successful in succeeding in their dietary goals. Using a Granger causality methodology, we also show that social media can help predict daily changes in diet compliance success or failure with an accuracy of 77%, that improves over baseline techniques by 17%. We discuss the implications of our work in the design of improved health interventions for behavior change.
Informal caregivers, such as close friends and family, play an important role in a hospital patient's care. Although CSCW researchers have shown the potential for social computing technologies to help patients and their caregivers manage chronic conditions and support health behavior change, few studies focus on caregivers' role during a multi-day hospital stay. To explore this space, we conducted an interview and observation study of patients and caregivers in the inpatient setting. In this paper, we describe how caregivers and patients coordinate and collaborate to manage patients' care and wellbeing during a hospital stay. We define and describe five roles caregivers adopt: companion, assistant, representative, navigator, and planner, and show how patients and caregivers negotiate these roles and responsibilities throughout a hospital stay. Finally, we identify key design considerations for technology to support patients and caregivers during a hospital stay.
Unlike most social media, where automatic archiving of data is the default, Snapchat defaults to ephemerality: deleting content shortly after it is viewed by a receiver. Interviews with 25 Snapchat users show that ephemerality plays a key role in shaping their practices. Along with friend-adding features that facilitate a network of mostly close relations, default deletion affords everyday, mundane talk and reduces self-consciousness while encouraging playful interaction. Further, although receivers can save content through screenshots, senders are notified; this selective saving with notification supports complex information norms that preserve the feel of ephemeral communication while supporting the capture of meaningful content. This dance of giving and taking, sharing and showing, and agency for both senders and receivers provides the basis for a rich design space of mechanisms, levels, and domains for ephemerality.
Patient-generated data is increasingly common in chronic disease care management. Smartphone applications and wearable sensors help patients more easily collect health information. However, current commercial tools often do not effectively support patients and providers in collaboration surrounding these data. This paper examines patient expectations and current collaboration practices around patient-generated data. We survey 211 patients, interview 18 patients, and re-analyze a dataset of 21 provider interviews. We find that collaboration occurs in every stage of self-tracking and that patients and providers create boundary negotiating artifacts to support the collaboration. Building upon current practices with patient-generated data, we use these theories of patient and provider collaboration to analyze misunderstandings and privacy concerns as well as identify opportunities to better support these collaborations. We reflect on the social nature of patient-provider collaboration to suggest future development of the stage-based model of personal informatics and the theory of boundary negotiating artifacts.
MoodLight is an interactive ambient lighting system that responds to biosensor input related to an individual's current level of arousal. Changes in levels of arousal correspond to fluctuations in the color of light provided by the system, altering the immediate environment in ways intimately related to the user's private internal state. We use this intervention to explore personal and social implications of the ambient display of biosensor data. This study provides greater understanding of the ways in which the representations of personal informatics, with a focus on ambient feedback, influence our perceptions of ourselves and those around us.
What is the role of shared calendars for home health management? Utilizing a maximum variation sampling method, we interviewed 20 adult individuals with diabetes and 20 mothers of children with asthma to understand calendar use in the context of chronic disease home health management. In comparing the experiences of these two groups, we explore participants' use of tools for organizing tasks and appointments, their strategies for capturing health and non-health events in the family calendar system, the ecology of artifacts that intersect with their scheduling tools, and the failures they experienced while managing their calendar systems. Through this work, we offer a context-specific perspective of schedule management strategies for individuals and families who must integrate their handling of chronic illnesses with everyday living.
This paper describes the role of temporal information in emergency medical teamwork and how time-based features can be designed to support the temporal awareness of clinicians in this fast-paced and dynamic environment. Engagement in iterative design activities with clinicians over the course of two years revealed a strong need for time-based features and mechanisms, including timestamps for tasks based on absolute time and automatic stopclocks measuring time by counting up since task performance. We describe in detail the aspects of temporal awareness central to clinicians' awareness needs and then provide examples of how we addressed these needs through the design of a shared information display. As an outcome of this process, we define four types of time representation techniques to facilitate the design of time-based features: (1) timestamps based on absolute time, (2) timestamps relative to the process start time, (3) time since task performance, and (4) time until the next required task.
Online health communities are known to provide psychosocial support. However, concerns for misinformation being shared around clinical information persist. An existing practice addressing this concern includes monitoring and, as needed, discouraging asking clinical questions in the community. In this paper, I examine such practice where moderators redirected patients to see their health care providers instead of consulting the community. I observed that, contrary to common beliefs, community members provided constructive tips and persuaded the patients to see doctors rather than attempting to make a diagnosis or give medical advice. Moderators' posts on redirecting patients to see their providers were highly associated with no more follow up replies, potentially hindering active community dynamic. The findings showed what is previously thought of as a solution-quality control through moderation-might not be best and that the community, in coordination with moderators, can provide critical help in addressing clinical questions and building constructive information sharing community environment.
We discover patterns related to depression in the social graph of an online community of approximately 20,000 lesbian, gay, and bisexual, transgender, and questioning youth. With survey data on fewer than two hundred community members and the network graph of the entire community (which is completely anonymous except for the survey responses), we detected statistically significant correlations between a number of graph properties and those TrevorSpace users showing a higher likelihood of depression, according to the Patient Healthcare Questionnaire-9, a standard instrument for estimating depression. Our results suggest that those who are less depressed are more deeply integrated into the social fabric of TrevorSpace than those who are more depressed. Our techniques may apply to other hard-to-reach online communities, like gay men on Facebook, where obtaining detailed information about individuals is difficult or expensive, but obtaining the social graph is not.
Social Internet content plays an increasingly critical role in many domains, including public health, disaster management, and politics. However, its utility is limited by missing geographic information; for example, fewer than 1.6% of Twitter messages (tweets) contain a geotag. We propose a scalable, content-based approach to estimate the location of tweets using a novel yet simple variant of gaussian mixture models. Further, because real-world applications depend on quantified uncertainty for such estimates, we propose novel metrics of accuracy, precision, and calibration, and we evaluate our approach accordingly. Experiments on 13 million global, comprehensively multi-lingual tweets show that our approach yields reliable, well-calibrated results competitive with previous computationally intensive methods. We also show that a relatively small number of training data are required for good estimates (roughly 30,000 tweets) and models are quite time-invariant (effective on tweets many weeks newer than the training set). Finally, we show that toponyms and languages with small geographic footprint provide the most useful location signals.
Administrators of online communities face the crucial issue of understanding and developing their user communities. Will new users become committed members? What types of roles are particular individuals most likely to take on? We report on a study that investigates these questions. We administered a survey (based on standard psychological instruments) to nearly 4000 new users of the MovieLens film recommendation community from October 2009 to March 2010 and logged their usage history on MovieLens. We found that general volunteer motivations, pro-social behavioral history, and community-specific motivations predicted both the amount of use and specific types of activities users engaged in after joining the community. These findings have implications for the design and management of online communities.
Coping with chronic illness disease is a long and lonely journey, because the burden of managing the illness on a daily basis is placed upon the patients themselves. In this paper, we present our findings for how diabetes patient support groups help one another find individualized strategies for managing diabetes. Through field observations of face-to-face diabetes support groups, content analysis of an online diabetes community, and interviews, we found several help interactions that are critical in helping patients in finding individualized solutions. Those are: (1) patients operationalize their experiences to easily contextualize and share executable strategies; (2) operationalization has to be done within the larger context of sharing illness trajectories; and (3) the support groups develop common understanding towards diabetes management. We further discuss how our findings translate into design implications for supporting chronic illness patients in online community settings.
Researchers and practitioners show increasing interest in utilizing patient-generated information on the Web. Although the HCI and CSCW communities have provided many exciting opportunities for exploring new ideas and building broad agenda in health, few venues offer a platform for interdisciplinary and collaborative brainstorming about design challenges and opportunities in this space. The goal of this workshop is to provide participants with opportunities to interact with stakeholders from diverse backgrounds and practices - researchers, practitioners, designers, programmers, and ethnographers - and together generate tangible design outcomes that utilize patient-generated information on the Web. Through small multidisciplinary group work, we will provide participants with new collaboration opportunities, understanding of the state of the art, inspiration for future work, and ideally avenues for continuing to develop research and design ideas generated at the workshop.
