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Procrastination is a prevalent issue among university students and leads to long-term negative impacts on academic performance as well as mental health and quality of life. This paper investigated StudyTracker, a self-tracking digital application (app) that we developed for university students to use to track their study sessions. The app provided feedback to the student in the form of text and charts. We investigated the impact that StudyTracker had on procrastination habits, and explored how students interpreted their feedback data from the app. Results indicated that both the control group (N=9) and the experimental group (N=8) experienced a significant decrease in procrastination scores, however there was no significant difference between the two groups. The design implications of our findings are discussed.

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This study introduces a Generative Artificial Intelligence (GenAI) assistant designed to address key challenges in Remote Patient Monitoring (RPM) for hypertension. After a comprehensive needs assessment from clinicians and patients, we identified pivotal issues in RPM data management and patient engagement. The GenAI RPM assistant integrates a patient-facing chatbot, clinician-facing smart summaries, and automated draft portal messages to enhance communication and streamline data review. Validated through six rounds of testing and evaluations by ten participants, the initial prototype was positively received, highlighting the importance of personalized interactions. Our findings demonstrate GenAI's potential to improve RPM by optimizing data management and enhancing patient-provider communication. [ABSTRACT FROM AUTHOR]

Sleep-tracking products are promising their users an improvement to their sleep by focusing on behavior change but often neglecting the contextual and individual factors contributing to sleep quality and quantity. Making good sleep for productive scheduling a personal responsibility does not necessarily lead to better sleep and may cause stress and anxiety. In an autoethnographic study, the first author of this paper tracked her sleep for one month using a diary, body maps and an Oura ring and compared her subjectively felt sleep experience with the data produced by the Oura app. A thematic analysis of the data resulted in four themes describing the relationship between the user-researcher and her wearable sleep-tracker: (1) good sleep scores are motivating, (2) experience that matches the data leads to sense-making, (3) contradictory information from the app leads to frustration, and (4) the sleep-tracker competes with other social agents. A diffractive reading of the data and research process, following Karen Barad's methodology, resulted in a discussion of how data passes through the analog and digital apparatus and what contextual factors are left out but still significantly impact sleep quality and quantity. We add to a canon of sleep research recommending a move away from representing sleep in terms of comparison and competition, uncoupling it from neoliberal capitalistic productivity and self-improvement narratives which are often key contributing factors to bad sleep in the first place.

Background: Consumer technology is increasingly being adopted to support personal stress management, including by teachers. Multidisciplinary research has contributed some knowledge of design and features that can help detect and manage workplace stress. However, there is less understanding of what facilitates engagement with ubiquitous "off the shelf" technologies, particularly in a specific occupational setting. An understanding of features that facilitate or inhibit technology use, and the influences of contexts on the manner of interaction, could improve teachers' stress-management opportunities. Objective: The aim of the study was to investigate the interaction features that facilitated or inhibited engagement with 4 consumer technologies chosen by teachers for stress management, as well as the influence of the educational contexts on their engagement. We also examined how use of well-being technology could be better supported in the school. Methods: The choice of consumer technologies was categorized in a taxonomy for English secondary school teachers according to stress-management strategies and digital features. Due to the COVID-19 pandemic, we adapted the study so that working from home in the summer could be contrasted with being back in school. Thus, a longitudinal study intended for 6 weeks in the summer term (in 2020) was extended into the autumn term, lasting up to 27 weeks. Teachers chose to use either a Withings smartwatch or Wysa, Daylio, or Teacher Tapp apps. Two semistructured interviews and web-based surveys were conducted with 8 teachers in England in the summer term, and 6 (75%) of them took part in a third interview in the autumn term. Interviews were analyzed using reflexive thematic analysis informed by interpretive phenomenological analysis. Results: Technology elements and characteristics such as passive data collation, brevity of interaction, discreet appearance, reminders, and data visualization were described by teachers as facilitators. Lack of instructions and information on features, connectivity, extended interaction requirements, and nondifferentiation of activity and exercise data were described as barriers. Mesocontextual barriers to engagement were also reported, particularly when teachers were back on school premises, including temporal constraints, social stigma, and lack of private space to de-stress. Teachers had ideas for feature improvements and how educational leadership normalizing teachers' stress management with consumer technologies could benefit the school culture. Conclusions: Having preselected their stress-management strategies, teachers were able to harness design features to support themselves over an extended period. There could be an important role for digital interventions as part of teachers' stress management, which the school leadership would need to leverage to maximize their potential. The findings add to the holistic understanding of situated self-care and should inform developers' considerations for occupational digital stress support. [ABSTRACT FROM AUTHOR]

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.

The research community on the study and design of systems for personal informatics has grown over the past decade. To take stock of what the topics the field has studied and methods the field has used, we map and label 523 publications from ACM's library, IEEE Xplore, and PubMed. We surface that the literature has focused on studying and designing for health and wellness domains, an emphasis on understanding and overcoming barriers to data collection and reflection, and progressively fewer contributions involving artifacts being made. Our mapping review suggests directions future research could explore, such as identifying and resolving barriers to tracking stages beyond collection and reflection, engaging more with domain experts, and further discussing the privacy and ethical concerns around tracked data.

Identifying and planning strategies that support a healthy lifestyle or manage a chronic disease often require patient-provider collaboration. For example, people with healthy eating goals often share everyday food, exercise, or sleep data with health coaches or nutritionists to find opportunities for change, and patients with irritable bowel syndrome (IBS) often gather food and symptom data as part of working with providers to diagnose and manage symptoms. However, a lack of effective support often prevents health experts from reviewing large amounts of data in time-constrained visits, prevents focusing on individual goals, and prevents generating correct, individualized, and actionable recommendations. To examine how to design photo-based diaries to help people and health experts exchange knowledge and focus on collaboration goals when reviewing the data together, we designed and developed Foodprint, a photo-based food diary. Foodprint includes three components: (1) A mobile app supporting lightweight data collection, (2) a web app with photo-based visualization and quantitative visualizations supporting collaborative reflection, and (3) a pre-visit note communicating an individuals expectations and questions to experts. We deployed Foodprint in two studies: (1) with 17 people with healthy eating goals and 7 health experts, and (2) with 16 IBS patients and 8 health experts. Building upon the lens of boundary negotiating artifacts and findings from two field studies, our research contributes design principles to (1) prepare individuals to collect data relevant to their health goals and for collaboration, (2) help health experts focus on an individuals eating context, experiences, and goals in collaborative review, and (3) support individuals and experts to develop individualized, actionable plans and strategies.

Sleep studies suggest that exams, jobs, and technologies keep teens up at night, but little research exists to engage teens in reflecting on their own sleep. We designed a set of cards and a web-based app 'SleepBeta' to support reflection by inviting teens to ask questions, explore, track, and experiment with sleep and related technology and lifestyle habits. Through card sorting, we invited teens to identify technology and lifestyle habits they wished to track. SleepBeta let teens track various habits and sleep whilst visualizing interrelationships between these data. Twelve teens and 11 parents participated in interviews before and after a 3-week field trial of SleepBeta. Our findings highlighted four distinct modes of reflection: reflection in preparation, reflection in action, reflection upon revisiting data, and reflection through social interaction. We discuss how our findings provide sensitizing concepts that reframe reflection from a post hoc activity with personal data, to an ongoing process that starts before technologies are used to generate data. We highlight design opportunities for scaffolding reflection in preparation, and we reflect on design choices that give teens control over their data. [ABSTRACT FROM AUTHOR]

With the proliferation of activity-tracking devices and other smart tools, more users leverage these personal informatics technologies to track their physical and fitness-related activities. The research on the benefits (and limitations) of these devices tends to focus on the use of a single tool, leaving out the interactions among multiple technologies, and how these interactions influence the way users perceive their affordances. Building from an ecological perspective, I extend this research by providing insight into the competitive and complementary relationships among activity tracking devices and other fitness-related and personal informatics technologies within the device ecology of technologies around the user. The affordances of these devices are therefore not enacted in isolation but are relational to understanding of other technological options and differing personal preferences and goals of the user.

Predicting future moods with machine learning can help plan appropriate strategies for mental health; this can be facilitated by collecting future event information from calendar applications. However, a model for mood prediction from calendar event information has not yet been developed. In addition, because the dataset of mood values is characterized by the tendency to collect values close to either the mean or the most recent value, a mood prediction model employing past moods as input is likely to overfit the distribution of the training dataset. This makes it difficult to capture changes in mood due to external factors such as events, especially on days when mood fluctuates greatly. In this study, we propose a mood prediction deep learning model with an event feature extractor. For introducing a multitask learning method, we add not only mood but also the effects of events on mood as objective variables, and we achieve higher prediction accuracy than existing machine learning models. Given the small initial weights in deep learning, the middle layer tends to output values near zero when learning is insufficient. With the introduction of multitask learning, the output values of the event feature extractor in the prediction model output non-zero values, and the prediction accuracy is improved on days featuring a large fluctuation in mood from the previous day. Our proposal is expected to be a more efficient learning method for deep learning models to predict human mental states from external factors.

The practice of quantified-self sleep tracking has become increasingly common among healthy individuals as well as patients with sleep problems. However, existing sleep-tracking technologies only support simple data collection and visualization and are incapable of providing actionable recommendations that are tailored to users' physical, behavioral, and environmental context. A promising solution to address this gap is the context-aware sleep health recommender system (CASHRS), an emerging research field that bridges ubiquitous sleep computing and context-aware recommender systems. This paper presents a narrative review to analyze the type of contextual information, the recommendation algorithms, the context filtering techniques, the behavior change techniques, the system evaluation, and the challenges identified in peer-reviewed publications that meet the characteristics of CASHRS. The analysis results identified current research trends, the knowledge gap, and future research opportunities in CASHRS. [ABSTRACT FROM AUTHOR]

Many people appropriate social media and online communities in their pursuit of personal health goals, such as healthy eating or increased physical activity. However, people struggle with impression management, and with reaching the right audiences when they share health information on these platforms. Instagram, a popular photo-based social media platform, has attracted many people who post and share their food photos. We aim to inform the design of tools to support healthy behaviors by understanding how people appropriate Instagram to track and share food data, the benefits they obtain from doing so, and the challenges they encounter. We interviewed 16 women who consistently record and share what they eat on Instagram. Participants tracked to support themselves and others in their pursuit of healthy eating goals. They sought social support for their own tracking and healthy behaviors and strove to provide that support for others. People adapted their personal tracking practices to better receive and give this support. Applying these results to the design of health tracking tools has the potential to help people better access social support.

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 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.

People stop using personal tracking tools over time, referred to as the lapsing 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.

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 2×2 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.

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.

Personal informatics research helps people track personal data for the purposes of self-reflection and gaining self-knowledge. This field, however, has predominantly focused on the data collection and insight-generation elements of self-tracking, with less attention paid to flexible data analysis. As a result, this inattention has led to inflexible analytic pipelines that do not reflect or support the diverse ways people want to engage with their data. This paper contributes a review of personal informatics and visualization research literature to expose a gap in our knowledge for designing flexible tools that assist people engaging with and analyzing personal data in personal contexts, what we call the personal informatics analysis gap. We explore this gap through a multistage longitudinal study on how asthmatics engage with personal air quality data, and we report how participants: were motivated by broad and diverse goals; exhibited patterns in the way they explored their data; engaged with their data in playful ways; discovered new insights through serendipitous exploration; and were reluctant to use analysis tools on their own. These results present new opportunities for visual analysis research and suggest the need for fundamental shifts in how and what we design when supporting personal data analysis. [ABSTRACT FROM AUTHOR]