Uncovering the Hidden Biases in Personal Informatics

The design and usage of Personal Informatics (PI) systems have been subjects of rapidly growing interest in recent years. PI systems are typically designed to monitor individuals’ physical activity and encourage them to be more active, thereby ‘hacking’ the habit of prolonged sitting. Most PI systems focus solely on collecting quantitative data to encourage self-reflection and are therefore sometimes discussed in terms of the Quantified Self movement. However, this perspective is wholly focused on individual bodily movements and neglects the role of architectural spaces. This paper discusses an ongoing project focused on PI systems design at the intersection of bodily movements and the office as an architectural space. Taking this as a point of departure, we introduce a simple prototype interactive lamp known as the NEAT lamp, which was designed, implemented and evaluated in relation to everyday office work. The rationale underpinning the prototype’s design is presented, followed by the results of a real-world evaluation of its effects in practice. We also discuss the role of the NEAT lamp as an ambient light that promotes awareness of sedentary behavior in the office as an open architectural space. Finally, we highlight the role of ambient displays as a medium for creating a sense of unity between the self and the architectural space, and propose that this observation suggests that we should move the discussion away from “quantified selves” towards qualitative spaces.

Reflecting on stress-related data is critical in addressing one’s mental health. Personal Informatics (PI) systems augmented by algorithms and sensors have become popular ways to help users collect and reflect on data about stress. While prediction algorithms in the PI systems are mainly for diagnostic purposes, few studies examine how the explainability of algorithmic prediction can support user-driven self-insight. To this end, we developed MindScope, an algorithm-assisted stress management system that determines user stress levels and explains how the stress level was computed based on the user’s everyday activities captured by a smartphone. In a 25-day field study conducted with 36 college students, the prediction and explanation supported self-reflection, a process to re-establish preconceptions about stress by identifying stress patterns and recalling past stress levels and patterns that led to coping planning. We discuss the implications of exploiting prediction algorithms that facilitate user-driven retrospection in PI systems.

Personal informatics (PI) systems are designed for diverse users in the real world. Even when these systems are usable, people encounter barriers while engaging with them in ways designers cannot anticipate, which impacts the system's effectiveness. Although PI literature extensively reports such barriers, the volume of this information can be overwhelming. Researchers and practitioners often find themselves repeatedly addressing the same challenges since sifting through this enormous volume of knowledge looking for relevant insights is often infeasible. We contribute to alleviating this issue by conducting a meta-synthesis of the PI literature and categorizing people's barriers and facilitators to engagement with PI systems into eight themes. Based on the synthesized knowledge, we discuss specific generalizable barriers and paths for further investigations. This synthesis can serve as an index to identify barriers pertinent to each application domain and possibly to identify barriers from one domain that might apply to a different domain. Finally, to ensure the sustainability of the syntheses, we propose a Design Statements (DS) block for research articles.

Personal informatics (PI) systems, powered by smartphones and wearables, enable people to lead healthier lifestyles by providing meaningful and actionable insights that break down barriers between users and their health information. Today, such systems are used by billions of users for monitoring not only physical activity and sleep but also vital signs and women's and heart health, among others. Despite their widespread usage, the processing of sensitive PI data may suffer from biases, which may entail practical and ethical implications. In this work, we present the first comprehensive empirical and analytical study of bias in PI systems, including biases in raw data and in the entire machine learning life cycle. We use the most detailed framework to date for exploring the different sources of bias and find that biases exist both in the data generation and the model learning and implementation streams. According to our results, the most affected minority groups are users with health issues, such as diabetes, joint issues, and hypertension, and female users, whose data biases are propagated or even amplified by learning models, while intersectional biases can also be observed.

Personal Informatics (PI) systems help individuals collect and reflect on personal physiological, behavioral and/or contextual data. Typically, these systems offer users interactive visualizations that allow meaningful exploration of the data. Through this exploration, PI systems have great potential to facilitate self-reflection and encourage behavior change.

Personal Informatics (PI) systems allow their users to collect data from a variety of sources for the purpose of extracting meaningful insights and making positive changes in their lives. Emerging consumer-grade Brain-Computer Interface (BCI)/EEG devices may provide an additional source of data for incorporating into PI systems. To explore users' expectations for brain-related PI systems we provided participants with a consumer-grade BCI headset and prototype mobile application capable of visualizing and recording their brain waves. Participants were interviewed to assess expectations for this type of technology. Our work contributes an understanding of users' various motivations for tracking brain activity data within a personal informatics system. We present our findings so far and discuss their implications for the design of a Cognitive Personal Informatics system, which we intend to deploy in a follow-up longitudinal field study.

Psychophysiological Therapy from a Distance: The Art of Sharing

An expanding range of apps supported by wearable and mobile devices are being used by people engaged in personal informatics in order to track and explore data about themselves and their everyday activities. While the aspect of data collection is easier than ever before through these technologies, more advanced forms of support from personal informatics systems are not presently available. This lack of next generation personal informatics systems presents research with an important role to fill, and this paper presents a two-step contribution to this effect. The first step is to present a new model of human cooperation with intelligent computing, which collates key issues from the literature. The second step is to apply this model to personal informatics, identifying twelve key considerations for integrating intelligent computing in the design of future personal informatics systems. These design considerations are also applied to an example system, which illustrates their use in eliciting new design directions.

Thanks to the advancements in ubiquitous and wearable technologies, Personal Informatics (PI) systems can now reach a larger audience of users. However, it is not still clear whether this kind of tool can fit the needs of their daily lives. Our research aims at identifying specific barriers that may prevent the widespread adoption of PI and finding solutions to overcome them. We requested users without competence in self-tracking to use different PI instruments during their daily practices, identifying five user requirements by which to design novel PI tools. On such requirements, we developed a new system that can stimulate the use of these technologies, by enhancing the perceived benefits of collecting personal data. Then, we explored how naïve and experienced users differently explore their personal data in our system through a user trial. Results showed that the system was successful at helping individuals manage and interpret their own data, validated the usefulness of the requirements found and inspired three further design opportunities that could orient the design of future PI systems.

Personal informatics systems help people collect and reflect on behavioral information to better understand their own behavior. Because most systems only show one type of behavioral information, finding factors that affect one's behavior is difficult. Supporting exploration of multiple types of contextual and behavioral information in a single interface may help. To explore this, I developed prototypes of IMPACT, which supports reflection on physical activity and multiple types of contextual information. I conducted field studies of the prototypes, which showed that such a system could increase people's awareness of opportunities for physical activity. However, several limitations affected the usage and value of these prototypes. To improve support for such systems, I conducted a series of interviews and field studies. First, I interviewed people about their experiences using personal informatics systems resulting in the Stage-Based Model of Personal Informatics Systems, which describes the different stages that systems need to support, and a list of problems that people experience in each of the stages. Second, I identified the kinds of questions people ask about their personal data and found that the importance of these questions differed between two phases: Discovery and Maintenance. Third, I evaluated different visualization features to improve support for reflection on multiple kinds of data. Finally, based on this evaluation, I developed a system called Innertube to help people reflect on multiple kinds of data in a single interface using a visualization integration approach that makes it easier to build such tools compared to the more common data integration approach.

Though some work has looked at the implementation of personal informatics tools with youth and in schools, the approach has been prescriptive; students are pushed toward behaviour change intervention or otherwise use the data for prescribed learning in a particular curriculum area. This has left a gap around how young people may themselves choose to use personal informatics tools in ways relevant to their own concerns. We gave workshops on personal informatics to 13 adolescents at two secondary schools in London, UK. We asked them to use a commercial personal informatics app to track something they chose that they thought might impact their learning. Our participants proved competent and versatile users of personal informatics tools. They tracked their feelings, tech activity, physical activity, and sleep with many using the process as a system for understanding and validating aspects of their own lives, rather than changing them.

Personal informatics (PI) systems allow users to collect and review personally relevant information. The purpose commonly envisioned for these systems is that they provide users with actionable, data-driven self-insight to help them change their behavioral patterns for the better. Here, we review relevant theory as well as empirical evidence for this self-improvement hypothesis. From a corpus of 6,568, only 24 studies met the selection criteria of being a peer-reviewed empirical study reporting on actionable, data-driven insights from PI data, using a “clean” PI system with no other intervention techniques (e.g., additional coaching) on a nonclinical population. First results are promising—many of the selected articles report users gaining actionable insights—but we do note a number of methodological issues that make these results difficult to interpret. We conclude that more work is needed to investigate the self-improvement hypothesis and provide a set of recommendations for future work.

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.

Personal informatics (PI) systems have emerged as powerful tools for helping people to be aware of their behaviors in diverse domains. Nevertheless, little attention has been devoted to educational scenarios. For this reason, this paper presents an educational PI system called Glance and, moreover, evaluates its usefulness in two fully online undergraduate courses.

Personal informatics systems are tools that capture, aggregate, and analyze data from distinct facets of their users’ lives. This article adopts a mixed-methods approach to understand the problem of information overload in personal informatics systems. We report findings from a 3-month study in which 20 participants collected multifaceted personal tracking data and used a system called Exist to reveal statistical correlations within their data. We explore the challenges that participants faced in reviewing the information presented by Exist, and we identify characteristics that exemplify “interesting” correlations. Based on these findings, we develop automated filtering mechanisms that aim to prevent information overload and support users in extracting interesting insights. Our approach deals with information overload by reducing the number of correlations shown to users by about 55% on average and increases the percentage of displayed correlations rated as interesting to about 81%, representing a 34 percentage point improvement over filters that only consider statistical significance at p < .05. We demonstrate how this curation can be achieved using objective data harvested by the system, including the use of Google Trends data as a proxy for subjective user interest.