177 research outputs found
New Frontiers of Quantified Self 3: Exploring Understudied Categories of Users
Quantified Self (QS) field needs to start thinking of how situated needs may affect the use of self-tracking technologies. In this workshop we will focus on the idiosyncrasies of specific categories of users
FITtogether: an 'average' activity tracker
In this paper we discuss an app we have implemented
for iOS and Android called FITtogether. The app counts
usersâ steps and enables them to compare these with
the average steps of all other users. We have trialed
the app over a two week period in the wild on usersâ
own devices. Our findings suggest that comparison with
an average leads to users feeling that they are
successful if they are above average, and that by
making a personal step count available to others only
as part of an average does not lead to anonymity and
identity concern
New Frontiers of Quantified Self: Finding New Ways for Engaging Users in Collecting and Using Personal Data
In spite of the fast growth in the market of devices and applications that allow people to collect personal information, Quantified Self (QS) tools still present a variety of issues when they are used in everyday lives of common people. In this workshop we aim at exploring new ways for designing QS systems, by gathering different researchers in a unique place for imagining how the tracking, management, interpretation and visualization of personal data could be addressed in the future
Insights from Machine-Learned Diet Success Prediction
To support people trying to lose weight and stay healthy, more and more
fitness apps have sprung up including the ability to track both calories intake
and expenditure. Users of such apps are part of a wider ``quantified self''
movement and many opt-in to publicly share their logged data. In this paper, we
use public food diaries of more than 4,000 long-term active MyFitnessPal users
to study the characteristics of a (un-)successful diet. Concretely, we train a
machine learning model to predict repeatedly being over or under self-set daily
calories goals and then look at which features contribute to the model's
prediction. Our findings include both expected results, such as the token
``mcdonalds'' or the category ``dessert'' being indicative for being over the
calories goal, but also less obvious ones such as the difference between pork
and poultry concerning dieting success, or the use of the ``quick added
calories'' functionality being indicative of over-shooting calorie-wise. This
study also hints at the feasibility of using such data for more in-depth data
mining, e.g., looking at the interaction between consumed foods such as mixing
protein- and carbohydrate-rich foods. To the best of our knowledge, this is the
first systematic study of public food diaries.Comment: Preprint of an article appearing at the Pacific Symposium on
Biocomputing (PSB) 2016 in the Social Media Mining for Public Health
Monitoring and Surveillance trac
Supporting Accurate Interpretation of Self-Administered Medical Test Results for Mobile Health: Assessment of Design, Demographics, and Health Condition
Background: Technological advances in personal informatics allow people to track their own health in a variety of ways, representing a dramatic change in individualsâ control of their own wellness. However, research regarding patient interpretation of traditional medical tests highlights the risks in making complex medical data available to a general audience.
Objective: This study aimed to explore how people interpret medical test results, examined in the context of a mobile blood testing system developed to enable self-care and health management.
Methods: In a preliminary investigation and main study, we presented 27 and 303 adults, respectively, with hypothetical results from several blood tests via one of the several mobile interface designs: a number representing the raw measurement of the tested biomarker, natural language text indicating whether the biomarkerâs level was low or high, or a one-dimensional chart illustrating this level along a low-healthy axis. We measured respondentsâ correctness in evaluating these results and their confidence in their interpretations. Participants also told us about any follow-up actions they would take based on the result and how they envisioned, generally, using our proposed personal health system.
Results: We find that a majority of participants (242/328, 73.8%) were accurate in their interpretations of their diagnostic results. However, 135 of 328 participants (41.1%) expressed uncertainty and confusion about their ability to correctly interpret these results. We also find that demographics and interface design can impact interpretation accuracy, including false confidence, which we define as a respondent having above average confidence despite interpreting a result inaccurately. Specifically, participants who saw a natural language design were the least likely (421.47 times, P=.02) to exhibit false confidence, and women who saw a graph design were less likely (8.67 times, P=.04) to have false confidence. On the other hand, false confidence was more likely among participants who self-identified as Asian (25.30 times, P=.02), white (13.99 times, P=.01), and Hispanic (6.19 times, P=.04). Finally, with the natural language design, participants who were more educated were, for each one-unit increase in education level, more likely (3.06 times, P=.02) to have false confidence.
Conclusions: Our findings illustrate both promises and challenges of interpreting medical data outside of a clinical setting and suggest instances where personal informatics may be inappropriate. In surfacing these tensions, we outline concrete interface design strategies that are more sensitive to usersâ capabilities and conditions
Self-monitoring Practices, Attitudes, and Needs of Individuals with Bipolar Disorder: Implications for the Design of Technologies to Manage Mental Health
Objective To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management.
Materials and Methods Participants with BD (Nâ=â552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses.
Results Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data.
Discussion Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor.
Conclusion This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management
Self-Experimentation and the Value of Uncertainty
Self-tracking technologies have a great potential to transform the ways people understand and manage their personal health and wellbeing. However, studies on self-tracking suggest that people face challenges in gaining self-knowledge because of a lack of scientifically robust self-experimentation systems. In this position paper we focus attention on the value of uncertainty in self-experimentation by drawing on diagnostic tracking practices in multiple sclerosis (MS). In doing so, we illustrate the role of uncertainty and scientific thinking in self-tracking and managing the complex and unpredictable nature of the disease. Based on this understanding, we propose a set of design considerations to motivate discussion of the ways in which the design of future self-experimentation tools could spark scientific thinking and acknowledge uncertainty in everyday life
Problematising upstream technology through speculative design: the case of quantified cats and dogs
There is growing interest in technology that quantifies aspects of our lives. This paper draws on critical practice and speculative design to explore, question and problematise the ultimate consequences of such technology using the quantification of companion animals (pets) as a case study. We apply the concept of âmoving upstreamâ to study such technology and use a qualitative research approach in which both pet owners, and animal behavioural experts, were presented with, and asked to discuss, speculative designs for pet quantification applications, the design of which were extrapolated from contemporary trends. Our findings indicate a strong desire among pet owners for technology that has little scientific justification, whilst our experts caution that the use of technology to augment human-animal communication has the potential to disimprove animal welfare, undermine human-animal bonds, and create human-human conflicts. Our discussion informs wider debates regarding quantification technology
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