Self-tracking Research Articles

Socio-Cognitive Framework for Personal Informatics: A Preliminary Framework for Socially-Enabled Health Technologies

Socio-Cognitive Framework for Personal Informatics: A Preliminary Framework for Socially-Enabled Health Technologies

MigraineTracker: Examining Patient Experiences with Goal-Directed Self-Tracking for a Chronic Health Condition.

Self-tracking and personal informatics offer important potential in chronic condition management, but such potential is often undermined by difficulty in aligning self-tracking tools to an individual's goals. Informed by prior proposals of goal-directed tracking, we designed and developed MigraineTracker, a prototype app that emphasizes explicit expression of goals for migraine-related self-tracking. We then examined migraine patient experiences in a deployment study for an average of 12+ months, including a total of 50 interview sessions with 10 patients working with 3 different clinicians. Patients were able to express multiple types of goals, evolve their goals over time, align tracking to their goals, personalize their tracking, reflect in the context of their goals, and gain insights that enabled understanding, communication, and action. We discuss how these results highlight the importance of accounting for distinct and concurrent goals in personal informatics together with implications for the design of future goal-directed personal informatics tools.

Lateralization Effects in Electrodermal Activity Data Collected Using Wearable Devices

Electrodermal activity (EDA) is a physiological signal that can be used to infer humans' affective states and stress levels. EDA can nowadays be monitored using unobtrusive wearable devices, such as smartwatches, and leveraged in personal informatics systems. A still largely uncharted issue concerning EDA is the impact on real applications of potential differences observable on signals measured concurrently on the left and right side of the human body. This phenomenon, called lateralization, originates from the distinct functions that the brain's left and right hemispheres exert on EDA. In this work, we address this issue by examining the impact of EDA lateralization in two classification tasks: a cognitive load recognition task executed in the lab and a sleep monitoring task in a real-world setting. We implement a machine learning pipeline to compare the performance obtained on both classification tasks using EDA data collected from the left and right sides of the body. Our results show that using EDA from the side that is not associated with the specific hemisphere activation leads to a significant decline in performance for the considered classification tasks. This finding highlights that researchers and practitioners relying on EDA data should consider possible EDA lateralization effects when deciding on sensor placement.

Designing Data Visualisations for Self-Compassion in Personal Informatics

Wearable personal trackers offer exciting opportunities to contribute to one's well-being, but they also can foster negative experiences. It remains a challenge to understand how we can design personal informatics experiences that help users frame their data in a positive manner and foster self-compassion. To explore this, we conducted a study where we compared different visualisations for user-generated screen time data. We examined positive, neutral and negative framings of the data and whether or not a point of reference was provided in a visualisation. The results show that framing techniques have a significant effect on reflection, rumination and self-compassion. We contribute insights into what design features of data representations can support positive experiences in personal informatics.

TextureSight

Objects engaged by users' hands contain rich contextual information for their strong correlation with user activities. Tools such as toothbrushes and wipes indicate cleansing and sanitation, while mice and keyboards imply work. Much research has been endeavored to sense hand-engaged objects to supply wearables with implicit interactions or ambient computing with personal informatics. We propose TextureSight, a smart-ring sensor that detects hand-engaged objects by detecting their distinctive surface textures using laser speckle imaging on a ring form factor. We conducted a two-day experience sampling study to investigate the unicity and repeatability of the object-texture combinations across routine objects. We grounded our sensing with a theoretical model and simulations, powered it with state-of-the-art deep neural net techniques, and evaluated it with a user study. TextureSight constitutes a valuable addition to the literature for its capability to sense passive objects without emission of EMI or vibration and its elimination of lens for preserving user privacy, leading to a new, practical method for activity recognition and context-aware computing.

‘Um, so like, is this how I speak?’: design implications for automated visual feedback systems on speech

ABSTRACT At a time when diverse forms of automated visual feedback systems on speech are introduced to the Human–Computer Interaction (HCI) community, our research investigates the possible design spaces and design implications to consider when creating them. We utilised a Wizard of Oz prototype that recognises fillers during speech and sends out real-time feedback in the form of a flickering screen and a post-report with graphic charts. We let 27 participants use this prototype and interviewed them about their general experience of using such systems. Based on thematic analysis, we propose eight design implications to consider when designing an automated visual feedback system on speech. Additionally, we discuss the possible future for automated visual feedback systems on speech where they collaborate with fields such as personal informatics (quantified self), self-determination theory (SDT) and motivation, and other socio-ethnic areas

A Meta-Synthesis of the Barriers and Facilitators for Personal Informatics 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.

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 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 Health Tracking: A Paradigm Shift in the Self-Care Models in Nursing.

The rapidly evolving digital health landscape necessitates updates to existing self-care models in nursing. This viewpoint paper revisits and evaluates prevalent models, recognizing their comprehensive exploration of self-care concepts while also identifying a gap in the incorporation of personal informatics. It underscores the missing link of human-technology interplay, an essential aspect in understanding self-care practices within digital generations. The author delineates the role of personal health tracking in self-care and the achievement of desired health outcomes. Based on these insights, the author proposes a refined, digitized self-care model that incorporates mobile health (mHealth) technologies and self-tracking behaviors. The paper concludes by advocating the application of this model for future mHealth nursing interventions, providing a framework for facilitating patient self-care and improving health and well-being in the era of digital health.

14 Years of Self-Tracking Technology for mHealth—Literature Review: Lessons Learned and the PAST SELF Framework

In today’s connected society, many people rely on mHealth and self-tracking (ST) technology to help them adopt healthier habits with a focus on breaking their sedentary lifestyle and staying fit. However, there is scarce evidence of such technological interventions’ effectiveness, and there are no standardized methods to evaluate their impact on people’s physical activity and health. This work aims to help ST practitioners and researchers by empowering them with systematic guidelines and a framework for designing and evaluating technological interventions to facilitate health behavior change and user engagement, focusing on increasing physical activity and decreasing sedentariness. To this end, we conduct a literature review of 129 papers between 2008 and 2022, which identifies the core ST design principles and their efficacy, as well as the most comprehensive list to date of user engagement evaluation metrics for ST. Based on the review’s findings, we propose PAST SELF, a framework to guide the design and evaluation of ST technology that has potential applications in industrial and scientific settings. Finally, to facilitate researchers and practitioners, we complement this article with an open corpus and an online, adaptive exploration tool for the PAST SELF data.

Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping Review.

Over the past few decades, there has been a rapid increase in the number of wearable sleep trackers and mobile apps in the consumer market. Consumer sleep tracking technologies allow users to track sleep quality in naturalistic environments. In addition to tracking sleep per se, some sleep tracking technologies also support users in collecting information on their daily habits and sleep environments and reflecting on how those factors may contribute to sleep quality. However, the relationship between sleep and contextual factors may be too complex to be identified through visual inspection and reflection. Advanced analytical methods are needed to discover new insights into the rapidly growing volume of personal sleep tracking data. This review aimed to summarize and analyze the existing literature that applies formal analytical methods to discover insights in the context of personal informatics. Guided by the problem-constraints-system framework for literature review in computer science, we framed 4 main questions regarding general research trends, sleep quality metrics, contextual factors considered, knowledge discovery methods, significant findings, challenges, and opportunities of the interested topic. Web of Science, Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Fitbit Research Library, and Fitabase were searched to identify publications that met the inclusion criteria. After full-text screening, 14 publications were included. The research on knowledge discovery in sleep tracking is limited. More than half of the studies (8/14, 57%) were conducted in the United States, followed by Japan (3/14, 21%). Only a few of the publications (5/14, 36%) were journal articles, whereas the remaining were conference proceeding papers. The most used sleep metrics were subjective sleep quality (4/14, 29%), sleep efficiency (4/14, 29%), sleep onset latency (4/14, 29%), and time at lights off (3/14, 21%). Ratio parameters such as deep sleep ratio and rapid eye movement ratio were not used in any of the reviewed studies. A dominant number of the studies applied simple correlation analysis (3/14, 21%), regression analysis (3/14, 21%), and statistical tests or inferences (3/14, 21%) to discover the links between sleep and other aspects of life. Only a few studies used machine learning and data mining for sleep quality prediction (1/14, 7%) or anomaly detection (2/14, 14%). Exercise, digital device use, caffeine and alcohol consumption, places visited before sleep, and sleep environments were important contextual factors substantially correlated to various dimensions of sleep quality. This scoping review shows that knowledge discovery methods have great potential for extracting hidden insights from a flux of self-tracking data and are considered more effective than simple visual inspection. Future research should address the challenges related to collecting high-quality data, extracting hidden knowledge from data while accommodating within-individual and between-individual variations, and translating the discovered knowledge into actionable insights.

Designing Reflective Derived Metrics for Fitness Trackers

Personal tracking devices are equipped with more and more sensors and offer an ever-increasing level of accuracy. Yet, this comes at the cost of increased complexity. To deal with that problem, fitness trackers use derived metrics---scores calculated based on sensor data, e.g. a stress score. This means that part of the agency in interpreting health data is transferred from the user to the tracker. In this paper, we investigate the consequences of that transition and study how derived metrics can be designed to offer an optimal personal informatics experience. We conducted an online survey and a series of interviews which examined a health score (a hypothetical derived metric) at three levels of abstraction. We found that the medium abstraction level led to the highest level of reflection. Further, we determined that presenting the metric without contextual information led to decreased transparency and meaning. Our work contributes guidelines for designing effective derived metrics.

SnapPI: Understanding Everyday Use of Personal Informatics Data Stickers on Ephemeral Social Media

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.

From self‐quantification to self‐objectification? Framework and research agenda on consequences for well‐being

AbstractMeasuring, recording, and visualizing a consumer's physiological, behavioral, and psychological features with the use of technologies is a growing phenomenon. Within academic literature and popular press, this phenomenon has been termed self‐quantification, lifelogging, self‐tracking, personal informatics, among others. This research conceptualizes consumer psycho‐physiological quantification and explores the unique consumer journey with quantification practices. This research introduces a conceptual framework that accounts for distinct stages and aspects of consumer quantification at the acquisition/adoption, usage, and disposal stages of consumption. Adopting a novel theoretical perspective, the authors draw on objectification theory to examine the consequences of consumer psycho‐physiological quantification and develop propositions that link it to self‐objectification and consumer well‐being. Finally, the authors outline evolving issues around the consumer quantification framework and identify a research agenda. Taken together, this research provides a foundation for future research into the unique facets of consumer psycho‐physiological quantification and consequences for consumer well‐being.

A Review of Personal Informatics Research for People with Motor Disabilities

Personal informatics (PI) has become an area of significant research over the past decade, maturing into a sub-field that seeks to support people from many backgrounds and life contexts in collecting and finding value in their personal data. PI research includes a focus on people with chronic conditions as a monolithic group, but currently fails to distinguish the needs of people with motor disabilities (MD). To understand how current PI literature addresses those needs, we conducted a mapping review on PI publications engaged with people with MD. We report results from 50 publications identified in the ACM DL, Pubmed, JMIR, SCOPUS, and IEEE Xplore. Our analysis shows significant incompatibilities between the needs of individuals with MD and the ways that PI literature supports them. We also found inconsistencies in the ways that disability levels are reported, that PI literature for MD excludes non-health-related data domains, and an insufficient focus on PI tools' accessibility and usability for some MD users. In contrast with Epstein et al.'s [36] recent PI review, behavior change and habit awareness were the most common motivation in these publications. Finally, many of the reviewed articles reported involvement by caregivers, trainers, healthcare providers, and researchers across the PI stages. In addition to these insights, we provide recommendations for designing PI technology through a user-centric lens that will broaden the scope of PI and include people regardless of their motor abilities.

Shared motivations, goals and values in the practice of personal science: a community perspective on self-tracking for empirical knowledge

Some individuals do not limit their self-tracking efforts to passively collecting and observing gathered data about themselves, but rather develop it into forms of self-research and self-experimentation, also called “personal science”. This type of N-of-1 research is relevant to the fields of personal informatics, patient-led research and social studies of science, but as a knowledge generation practice is still poorly understood. To fill this gap, we conducted 22 semi-structured interviews to investigate the intrinsic and extrinsic motivations of individuals engaging in personal science activities, as well as shared goals and values present in self-research communities. Our analysis is based on a conceptual framework that integrates previous approaches in self-research, as well as in connection with citizen science, the scientific ethos and cooperation in peer production. We identify how self-researchers seek to go beyond personal metrics about their health and wellbeing regarding data provided by wearables, are engaged over time by individual involvement in technology and scientific-related activity, and collaborate following similar goals and values when learning and sharing empirical knowledge with peers. In this sense, personal science can be understood as a specific type of citizen science and an example of a more participatory and inclusive scientific culture driven by self-reflection, critical thinking and openness.

The Role of Model Personalization for Sleep Stage and Sleep Quality Recognition Using Wearables

Personal informatics systems can help people promote their health and well-being. Recent studies have shown that such systems can be used to infer relevant health indicators such as, e.g., stress, anxiety, and sleeping habits. While automatic detection of sleep has been studied extensively, there is a lack of studies exploring how population and personalized models influence the performance of sleep detection. In this article, we address this challenge by investigating the recognition of sleep/wake stages and high/low sleep quality with a focus on the impact of personalized models. To evaluate our approach, we collect a dataset of physiological signals and self-reports about sleep/wake times and sleep quality score. The dataset contains 6557 hours of sensor data collected using wristbands from 16 participants over one month. Our results show that personalized models perform significantly better than population models for sleep quality recognition, and are comparably good for sleep stage detection. The balanced accuracy for sleep/wake and high/low sleep quality are 92.2% and 61.51%, which are significantly higher than baseline classifiers.

Bridging Community, History, and Culture in Personal Informatics Tools

A healthy diet and increased physical activity are essential for reducing the prevalence of cardiovascular disease and related deaths, a worldwide public health concern that disproportionately affects Black American communities. Still, Black Americans can face unique challenges meeting dietary and physical activity requirements due to inequities in access and quality of care, environmental and local factors, and difficulties in changing individual health behaviors. Personal informatics and self-tracking tools are one way of increasing awareness of health behaviors to motivate behavior change. However, there are still gaps in knowledge about what encourages different users to engage with personal informatics tools over time, particularly when used in collaborative, community-health settings. This paper contributes a nuanced understanding of fifteen participants' reasons for engaging in an existing community-based health education and behavior change program that combines collaborative self-tracking with culturally relevant content and social engagement to motivate heart-healthy behaviors. We illustrate participants' positive and negative experiences engaging in self-tracking and collaborative tasks during the program. We also discuss how participants envision that integrating technology might support or hinder participant engagement and the work of deploying community-based public health interventions. Finally, we discuss design implications for culturally informed, community-based personal informatics tools that engage Black American's in heart-healthy activities.

Exploring the Personal Informatics Analysis Gap: "There's a Lot of Bacon".

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.

Deliberate news consumption through the quantified self and the self-regulatory process

Many internet news sites have introduced recommendation systems to help users mitigate information overload. However, these systems may exacerbate mindless information consumption by reducing opportunities for people to voluntarily select news. We propose prototypes of personal informatics tools based on a self-regulatory process and quantified self-theories, which can be used to help people fulfill a eudaimonic motivation through self-observation and self-control and thus lead to deliberate news consumption. A longitudinal field experiment demonstrates that self-control and self-observation tools promote deliberate news consumption and reveals synergistic effects between the two tools. Our results indicate that the effect of the self-observation tool persists longer.