Self-experimentation, or using tracked data to systematically answer health and wellbeing questions via hypothesis testing, has significant potential to support personal health. However, technological support for self-experimentation has focused on expert-designed self-experiments for specific health conditions, limiting people’s ability to design their own rigorous experiments. To address this gap, we developed CASEbot (Conversation Agent for Self-Experimentation), an LLM-powered chatbot using a theory-driven approach to guide users through designing well-structured, personalized, and safe self-experiments. We conducted a within-subjects, mixed-methods study with 42 participants comparing CASEbot to a traditional worksheet-based approach. When formally comparing the experiment rigor and specificity, most participants designed better experiments using CASEbot. They appreciated CASEbot’s conversational approach, which prompted them to surface everyday constraints and proactively raised safety concerns, but some found the platform too rigid in its recommendations. We discuss opportunities for future generative AI self-experimentation systems for health to balance structured guidance with user autonomy.

Personal informatics literature has typically examined how fitness trackers can support understanding of cardio-based exercises, like running, which are closely associated with the step counts central to these devices. Although this focus has supported individuals in gleaning fruitful data discoveries, it confines the manner in which a body moves to these movement domains. In this paper, we contend with pole dance, a physical activity whose movement affordances (e.g., rotational, artistic, feminine) differ greatly from those centralized in the self-tracking technologies. We qualitatively interviewed 20 polers, and gathered their reflection on pole dance and how they view the use self-tracking devices for the activity. Using their insights on the inherent dynamicity of poling, independently, and upon interaction with fitness trackers, we offer suggestions to our discipline to challenge ourselves to re-imagine sensing as a pluralistic endeavor.

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.

Family health informatics tools can help support well-being with shared data tracking. Prior work typically focused on shared data review, but often in specific moments, like bedtime, or centered on caregiving of children or elderly members. To investigate how tracking can support mutual health collaboration between family members pervasively across daily contexts, we designed and deployed FamilyBloom, a glanceable smartwatch and home display system for mood and goal tracking. Twelve families with both neurotypical and ADHD members used FamilyBloom for three months on average. Our findings reveal how family-centered tracking created collaboration opportunities and tensions across multiple ecological systems: individual self-regulation, collaborations within family dynamics, involvement of care networks with varying trust levels, institutional school constraints and cultural stigma, and temporality of regular routines and crisis periods. We discuss an ecosystem-aware approach to family informatics, wherein design can attend to how families navigate multiple contexts while sustaining family-level collaboration.

Sharing activities that people do in everyday life, such as physical activity, health management, or hobbies, help people receive benefits like social support and positive self-presentation. Short-form videos present new opportunities for activity-sharing, which has traditionally been studied in static contexts like text- and image-sharing. We therefore aim to understand what information people incorporate into short-form activity videos, and how. We qualitatively analyzed 420 short-form activity videos on TikTok across three domains: running, studying, and sketching. We found people often present information before, during, and after activities, developing strategies for qualitatively and quantitatively incorporating activity-relevant information in each. We also uncover practices for aligning the sharing of activity-relevant information with the nature of short-form videos, such as modifying broader-scale goals into video-scale goals. We further discuss design opportunities and challenges for designers to create tools that support the practice, such as closer integration with tracking tools and encouraging narrative structure.

Recent large language models (LLMs) offer the potential to support public health monitoring by facilitating health disclosure through open-ended conversations but rarely preserve the knowledge gained about individuals across repeated interactions. Augmenting LLMs with long-term memory (LTM) presents an opportunity to improve engagement and self-disclosure, but we lack an understanding of how LTM impacts people’s interaction with LLM-driven chatbots in public health interventions. We examine the case of CareCall—an LLM-driven voice chatbot with LTM—through the analysis of 1,252 call logs and interviews with nine users. We found that LTM enhanced health disclosure and fostered positive perceptions of the chatbot by offering familiarity. However, we also observed challenges in promoting self-disclosure through LTM, particularly around addressing chronic health conditions and privacy concerns. We discuss considerations for LTM integration in LLM-driven chatbots for public health monitoring, including carefully deciding what topics need to be remembered in light of public health goals.

Wearable technology for physical activity promotion is a frequent research topic within HCI and health, and researchers have documented that much of our knowledge is sourced from understanding the needs of populations from college educated, racially privileged, Western backgrounds. However socioeconomic class, a core component for how people perceive physical activity, wearables, and even wearable studies, has not often been contended with. In this critical discussion of the literature, incorporating examples from over 30 deployment studies involving wearables and over 70 other related works, we investigate how socioeconomic class shows up in study design and identify how class cultures are embedded in the design of wearable technology. We hypothesize that common study components related to time and activity type engenders high SES class cultures and ultimately risk creating intervention generated inequalities. We discuss the implications of ignoring class such as further perpetuating inequities in subsequent waves of wearable device maturity.

Pregnancy brings physical, emotional, and economic challenges for expectant parent(s), close relatives, and friends. Existing technology support, including tracking technology, largely targets pregnant people and ignores other stakeholders. We therefore lack an understanding of how to approach designing collaborative pregnancy tracking technology. To understand how people collaborate around pregnancy tracking and wish to do so, we interviewed 13 pregnant people and 11 non-pregnant stakeholders in the U.S., including partners, friends, and grandparents-to-be. We find that people collaborate for goals like social bonding and jointly managing various pregnancy data. Stakeholders collaborated by either dividing up data types or collectively monitoring the same information. We also identify tensions and challenges, such as pregnant people’s privacy concerns and stakeholders’ varied levels of interest in tracking. In light of socio-cultural norms and stakeholders’ distinctive roles around pregnancy, we point to opportunities for designing collaborative technology that aligns with as well as challenges socio-cultural practices around pregnancy tracking.

Family informatics often uses shared data dashboards to promote awareness of each other’s health-related behaviors. However, these interfaces often stop short of providing families with needed guidance around how to improve family functioning and health behaviors. We consider the needs of family co-regulation with ADHD children to understand how in-home displays can support family well-being. We conducted three co-design sessions with each of eight families with ADHD children who had used a smartwatch for self-tracking. Results indicate that situated displays could nudge families to jointly use their data for learning and skill-building. Accommodating individual needs and preferences when family members are alone is also important, particularly to support parents exploring their co-regulation role, and assisting children with data interpretation and guidance on self and co-regulation. We discuss opportunities for displays to nurture multiple intents of use, such as joint or independent use, while potentially connecting with external expertise.

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.

Pregnancy is a significant but stressful life transition, requiring effort from multiple stakeholders including expectant parents, family members, and friends to navigate. Existing work has primarily focused on understanding and supporting the technology use of pregnant people, neglecting other stakeholders' needs and participation. We therefore consider how pregnancy tracking apps both improve and interfere with the reconfiguration of social relationships caused by pregnancy, drawing on insights from family sociology to examine how these relationships evolve over pregnancy and the transition to parenthood. We reviewed the features of 20 pregnancy tracking apps, and analyzed 4,709 public reviews of them, finding that stakeholders used apps to bond with one another around the excitement of pregnancy, build a prenatal relationship with the fetus, and co-manage pregnancy-related logistical tasks. We find that not accounting for fetal demographics and users' identities, along with socio-cultural norms around gender and parenting roles, often inhibit these collaborative practices. We therefore suggest designing collaborative pregnancy tracking technology that considers both inclusiveness and specificity regarding stakeholders' different roles and relationships to pregnancy.

People often leverage multiple platforms to share activities they undertake in their lives, from music listening to eating. Broad-purpose platforms, which people use to share a wide variety of activities with a diverse audience, and dedicated platforms, which often focus on tracking and sharing a specific activity with connections with similar interests, both help individuals seeking social benefits from sharing their activity. Researchers designing systems for activity sharing have often reflected on whether to support sharing on dedicated or broad-purpose platforms, suggesting a need to better understand their relative utility. We collected and compared the responses received between 700,000 pairs of activity-sharing posts on four sets of broad-purpose and dedicated platforms across two domains: physical activity (Strava, MapMyRun) and creativity (Dribbble, Behance). Results showed that dedicated platforms were more likely to receive responses (likes and comments), and comments were more likely to be encouraging and refer to specific qualities of the activities being shared. We reflect on the tradeoff between sheer audience volume and likelihood of response, and discuss how to design prompts and templates into sharing features which better align with the norms of respective platforms.

Child-owned activity trackers are not only devices for self-tracking, but they are also co-used by children and parents for family-centered health and wellbeing. This presents a challenge for the design of this technology as children's and parents’ wants and needs from this technology are not always aligned. To further understand children's and parents’ ideas and expectations, we conducted a qualitative study utilizing co-design sessions and semantic differential scales. Data from five families show four trends: 1) representation of parental worries and values as tracking metrics, 2) wish to access the unknown, 3) the significance of smartphones and touchscreen imagery on children's visual language, and 4) concerns around child's privacy and autonomy. These trends can be interpreted as a potential for activity trackers to mediate family interaction and to structure family conversations around worries, values, and privacy during co-use.

People engage in self-tracking with diverse data collection and visualisation needs and preferences. Customisable self-tracking tools offer the potential to support individualized preferences by letting people make changes to the aesthetics and functionality of tracker displays. In this paper, we use the customisation options offered by the displays of commercial fitness smartwatches as a lens to investigate when, why and how 386 self-trackers engage in customisations in their daily lives. We find that people largely customise their trackers’ display frequently, multiple times a day, or not at all, with frequent customisations reflecting situational data, aesthetic and personal meaning needs. We discuss implications for the design of tracking tools aiming to support customisation and discuss the utility of customisations towards goal scaffolding and maintaining interest in tracking.

Recent large language models (LLMs) have advanced the quality of open-ended conversations with chatbots. Although LLM-driven chatbots have the potential to support public health interventions by monitoring populations at scale through empathetic interactions, their use in real-world settings is underexplored. We thus examine the case of CareCall, an open-domain chatbot that aims to support socially isolated individuals via check-up phone calls and monitoring by teleoperators. Through focus group observations and interviews with 34 people from three stakeholder groups, including the users, the teleoperators, and the developers, we found CareCall offered a holistic understanding of each individual while offloading the public health workload and helped mitigate loneliness and emotional burdens. However, our findings highlight that traits of LLM-driven chatbots led to challenges in supporting public and personal health needs. We discuss considerations of designing and deploying LLM-driven chatbots for public health intervention, including tensions among stakeholders around system expectations.

Challenges associated with ADHD affect children’s daily routines and response to environmental stimuli, and support from parents is helpful in managing and overcoming behavior regulation challenges. Positive reinforcement is increasingly integrated into family technologies for teaching regulation skills, but typically support specific co-located activities. To better understand how technology can support co-regulation within families with ADHD children, we deployed CoolTaco, a smartwatch and phone system to support collaboration in creating tasks, gaining points for achieving them, and redeeming rewards. Ten families with ADHD children used CoolTaco in their daily routines. By qualitatively analyzing family interviews and usage logs, we find that smartwatches can help provide pervasive regulation support to children, but the division across devices and parent-child roles interfere with developing independence. We discuss how technology should support co-regulation while also fostering future self-regulation, such as by guiding children in goal setting and helping them reflect on progress and achievements.

Access to undergraduate research is limited. One approach to broaden access is scaling up the mentee-to-mentor ratio (e.g., course-based undergraduate research experiences have a classroom of student mentees being led by one professor mentor). However, some mentors and mentees may prefer apprentice-style research, which is defined as research with a small mentee-to-mentor ratio. Pulling influences from non-scaled and scaled approaches, we implemented and evaluated the Community College to PhD (CC2PhD) Scholars Program, which was a community college research program. CC2PhD was designed to scale up non-personalized aspects of apprentice-style research while maintaining personalized one-on-one mentoring. The scaled non-personalized aspects of CC2PhD included the predefined mentoring curriculum and the research methods workshops. They were "scaled" in the sense that few people were involved in curriculum development and workshop instruction. These scaled resources can then be used by a large number of mentor-mentee pairs. We interviewed and surveyed seven mentor alumni and six mentee alumni to understand the effects of the scaled aspects of CC2PhD. We identified four themes: (1) improved time-related issues by saving time and facilitating time management, (2) influenced meeting content, (3) helped beginner mentors and mentees, and (4) increased mentors' willingness to volunteer. Future researchers can further scale-up and digitize our scaled research-apprentice model. For example, the mentoring curriculum and workshops can be adapted into a MOOC, which mentor-mentee pairs can reference from.

Digital food journaling can support personal goals, such as weight loss and developing healthy eating behaviors. However, traditional manual tracking demands great effort, often leading to lapses or abandonment. We explore opportunities for journaling with multiple input modalities and devices, leveraging people's daily interactions with a range of technologies. We report on an extended analysis of 15 participants' experiences with ModEat, a prototype supporting journaling with several input modalities on phone, computer, and voice assistants. Participants' modality and device preferences were largely influenced by their goals, but they frequently deviated from those preferences depending on device availability, perceived affordances, and characteristics of foods eaten. Participants rarely combined input modalities in entries, but some described that doing so allowed for more detailed journaling or serve as a placeholder for later. We discuss advantages and drawbacks of multimodal tracking and potential strategies for improving interactions.

COVID-19 has demonstrated the importance of digital contact tracing apps in reducing the spread of disease. Despite people widely expressing interest in using contact tracing apps, actual installation rates have been low in many parts of the world. Prior studies suggest that decisions to use these apps are largely shaped by pandemic beliefs, social influences, perceived benefits and harms, and other factors. However, there is a gap in understanding what factors motivate intention, but not subsequent behavior of actual adoption. Reporting on a survey of 290 U.S. residents, we disentangle the intention-behavior gap by investigating factors associated with installing a contact tracing app from those associated with intending to install, but not actually installing. Our results suggest that social norms can be leveraged to span the intention-behavior gap, and that a privacy paradox may influence people’s adoption decisions. We present recommendations for technologies that enlist individuals to address collective challenges.

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.

The CSCW community has a history of designing, implementing, and evaluating novel social interactions in technology, but the process requires significant technical effort for uncertain value. We discuss the opportunities and applications of "piggyback prototyping", building and evaluating new ideas for social computing on top of existing ones, expanding on its potential to contribute design recommendations. Drawing on about 50 papers which use the method, we critically examine the intellectual and technical benefits it provides, such as ecological validity and leveraging well-tested features, as well as research-product and ethical tensions it imposes, such as limits to customization and violation of participant privacy. We discuss considerations for future researchers deciding whether to use piggyback prototyping and point to new research agendas which can reduce the burden of implementing the method.

Challenging behaviors significantly impact learning and socialization of autistic children and can stress and burden their caregivers. Documentation of challenging behaviors is fundamental for identifying what environmental factors influence them, such as how others respond to a child's such behaviors. Caregiver-tracked data on their child's challenging behaviors can help clinical experts make informed recommendations about how to manage such behaviors. To support caregivers in recording their children's challenging behaviors, we developed GeniAuti, a mobile-based data-collection tool built upon a clinical data collection form to document challenging behaviors and other clinically relevant contextual information such as place, duration, intensity, and what triggers such behaviors. Through an open-ended deployment with 19 parent-child pairs and three expert collaborators, caregivers found GeniAuti valuable for (1) becoming more attentive and reflective to behavioral contexts, including their own response strategies, (2) discovering positive aspects of their children's behaviors, and (3) promoting collaboration with clinical experts around the caregiver-tracked data to develop tailored intervention strategies for their children. However, participant experiences surface challenges of logging behaviors in social circumstances, conflicting views between caregivers and clinical experts around the structured recording process, and emotional struggles resulting from recording and reflecting on intensely negative experiences. Considering the complex nature of caregiver-based health tracking and caregiver--clinician collaboration, we suggest design opportunities for facilitating negotiations between caregivers and clinicians and accounting for caregivers' emotional needs.

Contact tracing, a major way to curb COVID-19 and other epidemics, has been employed worldwide, with human interviewing and proximity tracing technology as two major approaches. While previous research has contributed some understanding of people's perspectives on contact tracing technology, much of this is based in single countries or regions where technology has been deployed. To understand how culture influences people's perceptions toward human tracing and digital tracing, we replicated a mixed-methods survey study conducted in the U.S. in South Korea and compared participants' perspectives. South Korean participants preferred digital tracing to human tracing, contrasting with the U.S. context where no strong preference was observed. We discuss how observed differences in perspective align and contrast with the country's typical cultural dimensions, such as high power distance, informing the perspective that human tracing will have greater accuracy. We emphasize the need for culturally designing contact tracing technology to highlight personal benefits regardless of cultural dimensions, and leverage technology to support social interaction in human tracing.

Sharing personal informatics data can support accountability, connectedness, and self-expression, but people often find their data too trivial to share on social media. Ephemeral social platforms like Snapchat and Story features have emerged as spaces for sharing more trivial life events, presenting an opportunity to incorporate self-tracked data into sharing. Past work suggests that including data-driven stickers on these platforms can help add additional context to what people share, but little is known about the benefits and challenges of people's everyday experiences with this concept. To understand people's everyday use of data-driven stickers, we designed and developed SnapPI, an app for flexibly incorporating data into stickers for Snapchat. We deployed SnapPI to 21 participants for two weeks, finding that participants value aligning data sharing with Snapchat's communication and stylistic norms. Perceiving Snapchat as a playful platform, participants connected data stickers with various visual components of their Snaps. Stickers were used to incorporate personal informatics data into their existing conversations, and were edited to align with different audience needs or to be expressive. We discuss recommendations for personal data sharing, suggesting supporting flexibility in presentation and aligning with the norms of existing platforms.

Persuasive features are often used in food journaling apps to help people reach various personal goals. Our understanding of persuasive design in food journaling apps has primarily been built from studying and designing apps in Western contexts. However, varied cultural perceptions around journaling goals, such as healthy eating and weight management, suggest that the design of persuasive features may differ across cultures. We therefore investigate how culture influences the use of persuasive techniques in Chinese food journaling apps and consequently people's journaling experiences. Through reviewing features of Chinese apps and interviewing people who have used them, we find that some Chinese apps heavily emphasize body shape, and people's motivations for journaling and desires for journaling apps similarly focus more on body shape than diet. We discuss tensions and opportunities for food journaling apps which align with or account for cultural norms while challenging unhealthy or problematic aspects of them.

Journaling of consumed foods through digital devices is a popular self-tracking strategy for weight loss and eating mindfulness. Research has explored modalities, like photos and open-ended text and voice descriptions, to make journaling less burdensome and more descriptive than traditional barcode and database searches. However, less is known about how people prefer to journal foods when less constrained by limitations of databases, natural language processing, and image recognition. We deployed a food journal prototype supporting varied devices and input modalities, which 15 participants used to journal 1008 food logs over two weeks. Participants had diverse strategies for indicating what and how much they ate, varying from ambiguous foods to specifying varieties and using different measurements for clarifying amount. Some strategies were interpretable by natural language food identification and image classification services, while others point to open research questions. We finally discuss opportunities for accounting for variance in food journaling.

Various contact tracing approaches have been applied to help contain the spread of COVID-19, with technology-based tracing and human tracing among the most widely adopted. However, governments and communities worldwide vary in their adoption of digital contact tracing, with many instead choosing the human approach. We investigate how people perceive the respective benefits and risks of human and digital contact tracing through a mixed-methods survey with 291 respondents from the United States. Participants perceived digital contact tracing as more beneficial for protecting privacy, providing convenience, and ensuring data accuracy, and felt that human contact tracing could help provide security, emotional reassurance, advice, and accessibility. We explore the role of self-tracking technologies in public health crisis situations, highlighting how designs must adapt to promote societal benefit rather than just self-understanding. We discuss how future digital contact tracing can better balance the benefits of human tracers and technology amidst the complex contact tracing process and context.

Children are increasingly using wearables with physical activity tracking features. Although research has designed and evaluated novel features for supporting parent-child collaboration with these wearables, less is known about how families naturally adopt and use these technologies in their everyday life. We conducted interviews with 17 families who have naturally adopted child-owned wearables to understand how they use wearables individually and collaboratively. Parents are primarily motivated to use child-owned wearables for children's long-term health and wellbeing, whereas children mostly seek out entertainment and feeling accomplished through reaching goals. Children are often unable to interpret or contextualize the measures that wearables record, while parents do not regularly track these measures and focus on deviations from their children's routines. We discuss opportunities for making naturally-occurring family moments educational to positively contribute to children's conceptual understanding of health, such as developing age-appropriate trackable metrics for shared goal-setting and data reflection.

Contact tracing apps have been suggested as a promising approach towards containing viral spread during pandemics, yet their actual use in the COVID-19 pandemic has been low. While researchers have examined reasons for or against installing contact tracing apps, we have less understanding of their ongoing use and how they interact with everyday pressures related to work, communities, and mental well-being. Through a survey of 153 working people in Japan and 15 follow-up interviews, we investigated attitudes toward installing and using Japan's national contact tracing app, COCOA, and how these related to respondents' daily lives, work structures, and general attitudes about the pandemic. We found that motivations about installing the app differed from those related to ongoing usage. Specifically, we identified ways that people navigate uncertain norms of behaviour during the pandemic, and how people consider individual risks such as COVID-related stigmas, anxiety, and financial precarity when deciding if and how to use COCOA. In light of these, we discuss the tension between COCOA's design and desires to protect oneself by selective controlling disclosures. We note that perceived risks are closely tied to respondents' local contexts, and based on our analysis, we identify ways to address these challenges and tensions through design interventions at multiple scales.

Studies of personal informatics systems primarily examine people's use or non-use, but people often leverage other technology towards their long-term behavior change processes such as social platforms. We explore how tracking technologies and social platforms together help people build healthy eating behaviors by interviewing 18 people who use Chinese food journaling apps. We contribute a Model of Socially Sustained Self-Tracking in personal informatics, building on the past model of Personal Informatics and the learning components of Social Cognitive Theory. The model illustrates how people get advice from social platforms on when and how to track, transfer data to and apply knowledge from social platforms, evolve to use social platforms after tracking, and occasionally resume using tracking tools. Observational learning and enactive learning are central to these processes, with social technologies helping people to gain deeper and more reliable domain knowledge. We discuss how lapsing and abandoning of tracking can be viewed as evolving to social platforms, offering recommendations for how technology can better facilitate this evolution.

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.

People often do not receive the reactions they desire when they use social networking sites to share data collected through personal tracking tools like Fitbit, Strava, and Swarm. Although some people have found success sharing with close connections or in finding online communities, most audiences express limited interest and rarely respond. We report on findings from a human-centered design process undertaken to examine how tracking tools can better support people in telling their story using their data formative interviews contribute design goals for telling stories of accomplishment, including a need to include relevant data. We implement these goals in Yarn, a mobile app that offers structure for telling stories of accomplishment around training for running races and completing Do-It-Yourself projects.1 participants used Yarn for 4 weeks across two studies. Although Yarn's structure led some participants to include more data or explanation in the moments they created, many felt like the structure prevented them from telling their stories in the way they desired. In light of participant use, we discuss additional challenges to using personal data to inform and target an interested audience.

As self-tracking has evolved from a niche movement to a mass-market phenomenon, it has become possible for people to track a broad range of activities and vital parameters over years, even decades. The associated opportunities, as well as the challenges, have had very little research attention so far. With the phenomenon of long-term tracking becoming widespread and important, we have identified its key characteristics by drawing on work from UbiComp, HCI, and health informatics. We identify important differences between long- and short-term tracking, and discuss consequences for the tracking process. Going beyond previous models for short-term tracking, we now present a model for long-term tracking, integrating its distinctive characteristics in purposeful and incidental tracking. Finally, we present major topics for future research.

People often do not receive the engagement or responses they desire when they share on broad social media platforms. Sharers are hesitant to share trivial accomplishments, and the emphasis on data often results posts that audiences find repetitive or unengaging. Ephemeral social media's focus on self-authored content and sharing trivial accomplishments has the potential to ameliorate these challenges. We explore design principles for incorporating personal informatics data like steps, heart rate, or duration in data-driven stickers as a first step towards integrating these data into ephemeral social media. We examine the effect of a sticker's presentation style, domain, domain-relevance, and background through three surveys with 506 total participants. We uncover the importance of domain-relevant backgrounds and stickers, identify the situational value of stickers styled as analogies, embellished, and badges, and demonstrate that data-driven stickers can make ephemeral content more informative and entertaining, discussing implications for platforms and tools.

Commercial activity trackers are increasingly being designed for children as young as 3 years old. However, we have limited understanding of family use practices around these trackers. To provide an overall view of how families naturally use activity trackers towards collaborative management of family health, we systematically identified 9 trackers designed for children available on 4 consumer electronics retailers. Our data is composed of 2,628 user reviews both from the consumer retailers (for the wearables) and mobile application stores (for the associated apps). Our findings indicate children's and parents' collaborative use of these technologies beyond health and wellness. Parents state that their children enjoy practicing independence and rewards while contributing to family health management and daily life requirements. Parents expect these devices to ease their life and to teach their children to become more responsible for their health, daily tasks, and schedule. However, the current designs give limited agency on child's side and require parents' active participation for wearable-app coordination. For these reasons, they do not fully address parents' expectations in decreasing their workload. On the other hand, they have the potential to facilitate family interaction with challenges structured around the data reported through trackers.

Self-tracked health data can help people and their health providers understand and manage chronic conditions. This paper examines personal informatics practices and challenges in migraine, a condition characterized by unpredictable, intermittent, and poorly-understood symptoms. To investigate how people with migraine track and use data related to their condition, we surveyed 279 people with migraine and conducted semi-structured interviews with 13 survey respondents and 6 health providers. We find four distinct goals people bring to tracking and data: 1) answering questions about migraines, 2) predicting and preventing migraines, 3) monitoring and managing migraines over time, and 4) enabling motivation and social recognition. Each goal suggests different needs for the design of tools to support migraine tracking. We also find needs resulting from an individual's goals evolving over time, their varied personal experiences, and their communication and collaboration with providers. We discuss these goals and needs in terms of opportunities for personal informatics tools to facilitate learning to: 1) avoid common pitfalls; 2) support customization and flexibility; 3) account for burden, negativity, and lapsing; and 4) support management with uncertainty.

We consider why and how women track their menstrual cycles, examining their experiences to uncover design opportunities and extend the field's understanding of personal informatics tools. To understand menstrual cycle tracking practices, we collected and analyzed data from three sources: 2,000 reviews of popular menstrual tracking apps, a survey of 687 people, and follow-up interviews with 12 survey respondents. We find that women track their menstrual cycle for varied reasons that include remembering and predicting their period as well as informing conversations with healthcare providers. Participants described six methods of tracking their menstrual cycles, including use of technology, awareness of their premenstrual physiological states, and simply remembering. Although women find apps and calendars helpful, these methods are ineffective when predictions of future menstrual cycles are inaccurate. Designs can create feelings of exclusion for gender and sexual minorities. Existing apps also generally fail to consider life stages that women experience, including young adulthood, pregnancy, and menopause. Our findings encourage expanding the field's conceptions of personal informatics.

Diagnostic self-tracking, the recording of personal information to diagnose or manage a health condition, is a common practice, especially for people with chronic conditions. Unfortunately, many who attempt diagnostic self-tracking have trouble accomplishing their goals. People often lack knowledge and skills needed to design and conduct scientifically rigorous experiments, and current tools provide little support. To address these shortcomings and explore opportunities for diagnostic self-tracking, we designed, developed, and evaluated a mobile app that applies a self-experimentation framework to support patients suffering from irritable bowel syndrome (IBS) in identifying their personal food triggers. TummyTrials aids a person in designing, executing, and analyzing self-experiments to evaluate whether a specific food triggers their symptoms. We examined the feasibility of this approach in a field study with 15 IBS patients, finding that participants could use the tool to reliably undergo a self-experiment. However, we also discovered an underlying tension between scientific validity and the lived experience of self-experimentation. We discuss challenges of applying clinical research methods in everyday life, motivating a need for the design of self-experimentation systems to balance rigor with the uncertainties of everyday life.

We study the inclusion of a social awareness stream (SAS) in the peer-to-peer payment app Venmo. While SASs are prominent in many social network sites, such as Facebook and Twitter, Venmo's use offers an illustrative example of how SASs can be used in task-oriented apps, particularly in a domain, finance, which people often view as sensitive. Through interviews with 14 Venmo users and surveys of 164 peer-to-peer payment app users and 80 Venmo users, we find uses consistent with other SASs and uncover novel uses that reflect the unusual inclusion of an SAS within a utilitarian app for personal finance. For many users, the SAS is a flexible feature that creates an experience that blends their task-driven use with social benefits. People write purely functional transaction descriptions with strangers, while in transactions with friends, they sometimes craft playful descriptions that enhance their experience or perform their social relationships. The SAS provides opportunities for learning about how to use the application and for keeping up with friends. The results of this study extend the CSCW community's knowledge of SASs and offer guidance to designers considering use of SAS in a variety of applications.

Taking breaks from work is an essential and universal practice. In this paper, we extend current research on productivity in the workplace to consider the break habits of knowledge workers and explore opportunities of break logging for personal informatics. We report on three studies. Through a survey of 147 U.S.-based knowledge workers, we investigate what activities respondents consider to be breaks from work, and offer an understanding of the benefit workers desire when they take breaks. We then present results from a two-week in-situ diary study with 28 participants in the U.S. who logged 800 breaks, offering insights into the effect of work breaks on productivity. We finally explore the space of information visualization of work breaks and productivity in a third study. We conclude with a discussion of implications for break recommendation systems, availability and interuptibility research, and the quantified workplace.

Many people struggle with efforts to make healthy behavior changes, such as healthy eating. Several existing approaches promote healthy eating, but present high barriers and yield limited engagement. As a lightweight alternative approach to promoting mindful eating, we introduce and examine crumbs: daily food challenges completed by consuming one food that meets the challenge. We examine crumbs through developing and deploying the iPhone application Food4Thought. In a 3 week field study with 61 participants, crumbs supported engagement and mindfulness while offering opportunities to learn about food. Our 2x2 study compared nutrition versus non-nutrition crumbs coupled with social versus non-social features. Nutrition crumbs often felt more purposeful to participants, but non-nutrition crumbs increased mindfulness more than nutrition crumbs. Social features helped sustain engagement and were important for engagement with non-nutrition crumbs. Social features also enabled learning about the variety of foods other people use to meet a challenge.

Recent research examines how and why people abandon self-tracking tools. We extend this work with new insights drawn from people reflecting on their experiences after they stop tracking, examining how designs continue to influence people even after abandonment. We further contrast prior work considering abandonment of health and wellness tracking tools with an exploration of why people abandon financial and location tracking tools, and we connect our findings to models of personal informatics. Surveying 193 people and interviewing 12 people, we identify six reasons why people stop tracking and five perspectives on life after tracking. We discuss these results and opportunities for design to consider life after self-tracking.

stage of their tool use. We explore how designs can support people when they lapse in tracking, considering how to design data representations for a person who lapses in Fitbit use. Through a survey of 141 people who had lapsed in using Fitbit, we identified three use patterns and four perspectives on tracking. Participants then viewed seven visual representations of their Fitbit data and seven approaches to framing this data. Participant Fitbit use and perspective on tracking influenced their preference, which we surface in a series of contrasts. Specifically, our findings guide selecting appropriate aggregations from Fitbit use (e.g., aggregate more when someone has less data), choosing an appropriate framing technique from tracking perspective (e.g., ensure framing aligns with how the person feels about tracking), and creating appropriate social comparisons (e.g., portray the person positively compared to peers). We conclude by discussing how these contrasts suggest new designs and opportunities in other tracking domains.

Although food journaling is understood to be both important and difficult, little work has empirically documented the specific challenges people experience with food journals. We identify key challenges in a qualitative study combining a survey of 141 current and lapsed food journalers with analysis of 5,526 posts in community forums for three mobile food journals. Analyzing themes in this data, we find and discuss barriers to reliable food entry, negative nudges caused by current techniques, and challenges with social features. Our results motivate research exploring a wider range of approaches to food journal design and technology.

Many research applications and popular commercial applications include features for sharing personally collected data with others in social awareness streams. Prior work has identified several barriers to use as well as discrepancies between designer goals and how these features are used in practice. We develop a framework for designing and evaluating these features based on an extensive review of prior literature. We demonstrate the value of this framework by analyzing physical activity sharing on Twitter, coding 4,771 tweets and their responses and gathering 444 reactions from 97 potential tweet recipients, learning that specific user-generated content leads to more responses and is better received by the post audience. We conclude by extending our findings to other sharing problems and discussing the value of our design framework.

Current models of how people use personal informatics systems are largely based in behavior change goals. They do not adequately characterize the integration of self-tracking into everyday life by people with varying goals. We build upon prior work by embracing the perspective of lived informatics to propose a new model of personal informatics. We examine how lived informatics manifests in the habits of self-trackers across a variety of domains, first by surveying 105, 99, and 83 past and present trackers of physical activity, finances, and location and then by interviewing 22 trackers regarding their lived informatics experiences. We develop a model characterizing tracker processes of deciding to track and selecting a tool, elaborate on tool usage during collection, integration, and reflection as components of tracking and acting, and discuss the lapsing and potential resuming of tracking. We use our model to surface underexplored challenges in lived informatics, thus identifying future directions for personal informatics design and research.

As people continue to adopt technology based self tracking devices and applications, questions arise about how personal informatics tools can better support self tracker goals. This paper extends prior work on analyzing and summarizing self tracking data, with the goal of helping self trackers identify more meaningful and actionable findings. We begin by surveying physical activity self trackers to identify their goals and the factors they report influence their physical activity. We then define a cut as a subset of collected data with some shared feature, develop a set of cuts over location and physical activity data, and visualize those cuts using a variety of presentations. Finally, we conduct a month long field deployment with participants tracking their location and physical activity data and then using our methods to examine their data. We report on participant reactions to our methods and future design opportunities suggested by our work.

Personal informatics applications in a variety of domains are increasingly enabled by low cost personal sensing. Although applications capture fine-grained activity for self reflection, sharing is generally limited to high level summaries. There are potential advantages to fine-grained sharing, but also potential harms. To help investigate this complex design space, we employ Value Sensitive Design to consider whether and how to share fine grained step activity. We identify key values and value tensions, and we develop scenarios to highlight these. We then design a set of data transformations that seek to maximize the benefits while minimizing the harms of detailed sharing. These include a novel approach to interactive modification of fine grained step data, allowing people to remove private data and using motif discovery to generate realistic replacement data. Finally, we conduct semi structured interviews with 12 participants examining these scenarios and transformations. We distill results into a set of design considerations for fine-grained physical activity sharing.