Reflections on 5 Years of Personal Informatics: Rising Concerns and Emerging Directions

The real world use and design of personal informatics has been increasingly explored in HCI research in the last five years. However, personal informatics research is still a young multidisciplinary area of concern facing unrecognised methodological differences and offering unarticulated design challenges. In this review, we analyse how personal informatics has been approached so far using the Grounded Theory Literature Review method. We identify a (1) psychologically, (2) phenomenologically, and (3) humanistically informed stream and provide guidance on the design of future personal informatics systems by mapping out rising concerns and emerging research directions

Informing the Design of Personal Informatics Technologies for Unpredictable Chronic Conditions

Personal informatics technologies, such as consumer fitness tracking devices, have an enormous potential to transform the self-management of chronic conditions. However, it is unclear how people living with relapsing and progressive illnesses experience personal informatics tools in everyday life: what values and challenges are associated with their use? This research informs the design of future health tracking technologies through an ethnographic design study of the use and experience of personal informatics tools in multiple sclerosis (MS) self-management. Initial findings suggest that future health tracking technologies should acknowledge people’s emotional wellbeing and foster flexible and mindful self-tracking, rather than focusing only on tracking primary

Uncovering Bias in Personal Informatics

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 particularly sensitive personal data, and their proximity to domains known to be susceptible to bias, such as healthcare, bias in PI has not been investigated systematically. 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

Enhancing Personal Informatics Through Social Sensemaking

Personal informatics practices are increasingly common, with a range of consumer technologies available to support, largely individual, interactions with data (e.g., performance measurement and activity/health monitoring). In this paper, we explore the concept of social sensemaking. In contrast to high-level statistics, we posit that social networking and reciprocal sharing of fine-grained self-tracker data can provide valuable context for individuals in making sense of their data. We present the design of an online platform called Citizense Makers (CM), which facilitates group sharing, annotating and discussion of self-tracker data. In a field trial of CM, we explore design issues around willingness to share data reciprocally; the importance of familiarity between individuals; and understandings of common activities in contextualising one's own data

How dangerous is your life? Personalising Government open crime data

This paper discusses the use of Government Open Data and how public services based on this data can and should encourage data personalisation. We present our case study Fearsquare, an application that allows people to interact with public UK crime statistics in a way that is specific to their own, individual, everyday life by leveraging the popular social media service FourSquare. This service is used as an example of how Open Data can be tailored for used in the field of personal informatics. Results suggest that the ability to personalise Government Open Crime Data using Foursquare user location history data provides an added value to an already publically available dataset

How do technological properties influence user affordance of wearable technologies?

© John Benjamins Publishing Company. The Internet of things (IoT) affords people plenty of opportunities and a higher quality of life as well as drives a huge amount of data. By drawing on the concept of affordances, this study examines the user experience of personal informatics focusing on the technological and affective nature of affordance. A multi-mixed approach is used by combining qualitative methods and a quantitative survey. Results of the qualitative methods revealed a series of factors that related to the affordance of personal informatics, whereas results of the user model confirmed a significant role for connectivity, control, and synchronicity affordance regarding their underlying link to other variables, namely, expectation, confirmation, and satisfaction. The experiments showed that users\u27 affordances are greatly influenced by personal traits with interactivity tendency. The findings imply the embodied cognition process of personal informatics in which technological qualities are shaped by users\u27 perception, traits, and context. The results establish a foundation for wearable technologies through a heuristic quality assessment tool from a user embodied cognitive process. They confirm the validity and utility of applying affordances to the design of IoT as a useful concept, as well as prove that the optimum mix of affordances is crucial to the success or failure of IoT design