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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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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.

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.

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. [ABSTRACT FROM AUTHOR]

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.

Internet services have collected our personal data since their inception. In the beginning, the personal data collection was uncoordinated and was limited to a few selected data types such as names, ages, birthdays, etc. Due to the widespread use of social media, more and more personal data has been collected by different online services. We increasingly see that Internet of Things (IoT) devices are also being adopted by consumers, making it possible for companies to capture personal data (including very sensitive data) with much less effort and autonomously at a very low cost. Current systems architectures aim to collect, store, and process our personal data in the cloud with very limited control when it comes to giving back to citizens. However, Personal Data Stores (PDS) have been proposed as an alternative architecture where personal data will be stored within households, giving us complete control (self-sovereignty) over our data. This paper surveys the current literature on Personal Data Stores (PDS) that enable individuals to collect, control, store, and manage their data. In particular, we provide a comprehensive review of related concepts and the expected benefits of PDS platforms. Further, we compare and analyse existing PDS platforms in terms of their capabilities and core components. Subsequently, we summarise the major challenges and issues facing PDS platforms’ development and widespread adoption.

INTRODUCTION: Quantified-self application is widely used in sports and health management; the type and amount of data that can be detected and fed back to the user are growing rapidly. However, only a few studies discussed the social attributes of quantified-self data, especially in the context of cycling. OBJECTIVES: To design and evaluate SocialBike - a digitally augmented bicycle that aims to increase user’s motivation in physical activity by showing their quantified-self data to each other. METHODS: We conducted a within-subject experiment on a cycling simulation system with 20 participants. The on-bike display of SocialBike was compared to a control display to evaluate its impact on two dependent variables, including user’s intrinsic motivation in physical activity and social relatedness with other cyclists nearby. Intrinsic Motivation Inventory (IMI) was used to measure users’ self-reported intrinsic motivation and social relatedness. The user’s cycling behaviour data were also recorded by the simulation system. We extracted six indicators about the two dependent variables from the behaviour data. Qualitative data were collected through semi-structured interviews. We conducted a paired sample T-test on both types of quantitative data. Qualitative data were analysed by the method of thematic analysis. RESULTS: The results of the Intrinsic Motivation Inventory show that SocialBike’s on-bike display has significant positive effects on four subscales, including intrinsic motivation, perceived competence, values/usefulness, and relatedness. The results of user cycling behaviour indicate that SocialBike increased user’s social relatedness with other cyclists nearby by significantly affecting three indicators. Three themes were identified from qualitative data, including motivation, social interaction, and data visualization. The qualitative result provides supplementary evidence for the quantitative result and additional insights into the overall design of SocialBike. CONCLUSION: SocialBike as a platform for sharing quantified-self data have positive effects in enhancing users’ intrinsic motivation in physical activity and social relatedness with other cyclists nearby.

With the vision of ubiquitous computing becoming increasingly realized through smart city solutions, the proliferation of smartphones and smartwatches, and the rise of the quantified-self movement, a new technological layer is being added to the urban environment. This technological layer offers the possibility to capture, track, measure, visualize, and augment our experience of the urban environment. But to that end, there is a growing need to better understand the triangular relationship between person, place, and technology. Urban HCI studies are increasingly focusing on emotion and affect to create a better understanding of people’s experience of the city, and to investigate how technology could potentially play a role in augmenting this urban lived experience. Artist Christian Nold for example, used wearable technology to measure people's arousal levels as they walked freely through the urban environment, identifying locations in the city that evoked an emotional response from people. After these walks, people’s arousal levels were superimposed on a map of the city and participants were asked to interpret their own data, resulting in aggregated, fully annotated, and beautifully visualized emotion maps of the city. Based on a systematic review of emotions maps in existing literature, and our own work which seeks to understand how people’s experiences of places in the urban environment that are meaningful to them on a personal level, for example the place where they have met their partner, could potentially inform the design of future technological devices and services, this journal paper discusses the strengths, limitations and potential of capturing, representing, exploring and sharing this personal, geo-located emotion data with other people using emotion maps. Although our analysis seems to indicate that emotion maps in their current form are only of limited efficacy in accurately capturing, representing and communicating the profound, complex emotional bond that people have with personally meaningful places in the city, there appears to be potential for the use of emotion maps as a provocation in a speculative design approach.

The financial market is extremely attractive since it moves trillion dollars per year. Many investors have been exploring ways to predict future prices by using different types of algorithms that use fundamental analysis and technical analysis. Many professional speculators or amateurs had been analysing the price movement of some financial assets using these algorithms. The use of genetic algorithms, neural networks, genetic programming combined with these tools in an attempt to find a profitable solution is very common. This study presents a prototype that utilizes genetic algorithms (GAs) and personal informatics system (PI) for short-term stock index forecast. The prototype works according to the following steps. Firstly, a collection of input variables is defined through technical data analysis. Secondly, GA is applied to determine an optimal set of input variables for a one-day forecast. The data is gathered from the Saudi Stock Exchange as being the target market. Thirdly, PI is utilised to create a smart environment, which enables visualisation of stock prices. The outcome indicates that this approach of forecasting the stock price is positive. The highest accuracy obtained is 64.67% and the lowest one is 48.06%.

Lifelogging enables people to digitally record aspects of their daily life events in varying amounts of detail, for a variety of purposes. This continuous record can support human memory or can act as a form of surrogate memory. As such, it somehow constitutes a comprehensive «black box» of certain daily activities and therefore can potentially mine knowledge about how we live our lives. Although «total capture» seems unfeasible for the time being, it is possible to generate records that, despite being inevitably partial or fragmented, are very useful in a good number of scenarios in which users can infer knowledge about themselves for their own benefit. However, keeping this personal data on remote hard disks can be risky and should be carefully considered. [ABSTRACT FROM AUTHOR]

Consumer sleep tracking devices are known to be inaccurate, but there is a lack of understanding of how user characteristics may affect the accuracy of these devices. This study aims to examine the effect of age, gender, subjective sleep quality, sleep hygiene and sleep structure on the accuracy of two consumer sleep trackers, i.e. Fitbit Charge 2 and Neuroon. Sleep data were collected from 27 healthy participants using consumer devices and a medical device concurrently. Analysis found that age, sleep hygiene and sleep structure were significantly associated to the accuracy of consumer sleep trackers, whereas no association was found on gender and subjective sleep quality. Both consumer devices had improved accuracy on total sleep time and sleep efficiency for participants who had longer, deeper and less interrupted sleep. Our findings suggest that consumer devices may not be suited for young adults and for people with short and fragmented sleep.

A first-year undergraduate course in Information Systems in a South African university includes an opportunity for students to reflect on their own use of information and personal information systems. Their reflections provide data about the technologies and tools that they use to find and manage everyday life information, as well as academic information, and about the sources of information they draw on. This paper analyses data collected over three years and reports on the dominant technologies and information sources that students use. We then adapt the ecological model of information seeking and use developed by Williamson (1998) to make sense of the diversity of information sources and students’ choices in engaging with them. The results show that students rely to a very small degree on traditional university information sources. The study offers insights into the information contexts and behaviour of students and argues for the importance of a flexible range of information sources to support students in the complex process of managing information for academic success. The results will be of interest to those involved in designing and delivering undergraduate programmes, as well as those providing information services and infrastructures.