Information Visualisation Practices for Improving Patient Readability of Blood Pressure, Health Data, and Health Literacy

Personal health data obtained through self-monitoring is often presented through standardised representations with little intrinsic meaning for those who may need it the most since low health literacy is associated with poor health. By failing to inform users about their health status, these representations can be dangerous, leaving patients feeling lost, confused, anxious, or even depressed. Information Visualisation can play an important role in aiding patients making sense of their health data and health status, as long as it's aligned with their needs, motivations, and goals. Following Human Centred Design practices, user research methods were applied in order to understand the context of self-monitorisation, as well as identifying which metrics differed the most from participants' mental models. Thanks to quantitative data obtained from a survey, Blood Pressure was identified as the most problematic health variable. A series of interviews allowed patients of chronic conditions to vocalize the challenges they faced in the management of their conditions. Taking into account information obtained from previous steps, multiple ways to map blood pressure data onto design elements were explored and different visualisations were designed. Finally, said visualisations were tested through guided interviews with patients with blood pressure problems. Results showed that participants prefered different visualisations for different goals, and enjoyed being able to choose freely from them; participants with lower literacy but who were deeply invested in monitoring their health found tables to be the most informative visualizations; finally, participants identified colour scales as the most intuitive method to represent health status and health risk

QuantifyMe: An Open-Source Automated Single-Case Experimental Design Platform

Smartphones and wearable sensors have enabled unprecedented data collection, with many products now providing feedback to users about recommended step counts or sleep durations. However, these recommendations do not provide personalized insights that have been shown to be best suited for a specific individual. A scientific way to find individualized recommendations and causal links is to conduct experi ments using single-case experimental design; however, properly designed single-case experiments are not easy to conduct on oneself. We designed, developed, and evaluated a novel platform, QuantifyMe, for novice self-experimenters to conduct proper-methodology single-case self-experiments in an automated and scientific manner using their smartphones. We provide software for the platform that we used (available for free on GitHub), which provides the methodological elements to run many kinds of customized studies. In this work, we evaluate its use with four different kinds of personalized investigations, examining how variables such as sleep duration and regularity, activity, and leisure time affect personal happiness, stress, productivity, and sleep efficiency. We conducted a six-week pilot study (N = 13) to evaluate QuantifyMe. We describe the lessons learned developing the platform and recommendations for its improvement, as well as its potential for enabling personalized insights to be scientifically evaluated in many individuals, reducing the high administrative cost for advancing human health and wellbeing. Keywords: single-case experimental design; mobile health; wearable sensors; self-experiment; self-trackin

Motivation and User Engagement in Fitness Tracking: Heuristics for Mobile Healthcare Wearables

Wearable fitness trackers have gained a new level of popularity due to their ambient data gathering and analysis. This has signalled a trend toward self-efficacy and increased motivation among users of these devices. For consumers looking to improve their health, fitness trackers offer a way to more readily gain motivation via the personal data-based insights the devices offer. However, the user experience (UX) that accompanies wearables is critical to helping users interpret, understand, gain motivation and act on their data. Despite this, there is little evidence as to specific aspects of fitness tracker user engagement and long-term motivation. We report on a 4-week situated diary study and Healthcare Technology Self-efficacy (HTSE) questionnaire assessment of 34 users of two popular American fitness trackers: JawBone and FitBit. The study results illustrate design implications and requirements for fitness trackers and other self-efficacy mobile healthcare applications.We would like to thank all users who participated in this research as well as Experience Dynamics, Inc. for providing the necessary resources and coordinating the user diary study. This study has been supported by financial aid from the Spanish Ministry of Economy and Competitiveness under the project ECO2012-36160; Spanish Ministry of Economy, Industry and Competitiveness (MINECO) and the Fondo Europeo de Desarollo Regional (FEDER) under the project ECO2015-67296-R and, Communidad de Madrid and Fondo Social Europeo under the project INNCOMCON-CM S2015/HUM-3417

Reflections on Visualization in Motion for Fitness Trackers

International audienceIn this paper, we reflect on our past work towards understanding how to design visualizations for fitness trackers that are used in motion. We have coined the term "visualization in motion" for visualizations that are used in the presence of relative motion between a viewer and the visualization. Here, we describe how visualization in motion is relevant to sports scenarios. We also provide new data on current smartwatch visualizations for sports and discuss future challenges for visualizations in motion for fitness trackers

Comparing and Improving the Design of Physical Activity Data Visualizations

Heart disease is a leading cause of death in the United States, and older adults are at highest risk of being diagnosed with heart disease. Consistent physical exercise is an effective means of deterring onset of heart disease, and physical activity tracking devices can inspire greater activity in older adults. However, physical activity tracking device abandonment is quite common due to limitations on what can be learned from the activity data that is collected. Better data visualization of physical data presents an opportunity to surpass these limitations. In this thesis, a task-based human subject study was performed with three different data visualizations to gain insight into how the format of physical activity data visualizations impact older adults’ abilities to infer meaning from physical activity data. Participants (n = 30) interacted with a prototype data visualization as well as two data visualizations from popular fitness tracking applications (Fitbit and Strava) and used these visualizations to complete 11 tasks. Results from these tasks show each visualization was able to facilitate users answer some task questions effectively, though no visualizations exhibited strong performance across all tasks. From the successes and shortcomings of each visualization, three key design recommendations for the design of data visualizations for physical activity data were made: 1) make exact values available, 2) summarize data at multiple timescales, and 3) ensure accessibility for the entire population of users

Design Fiction Diegetic Prototyping: A Research Framework for Visualizing Service Innovations

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose: This paper presents a design fiction diegetic prototyping methodology and research framework for investigating service innovations that reflect future uses of new and emerging technologies. Design/methodology/approach: Drawing on speculative fiction, we propose a methodology that positions service innovations within a six-stage research development framework. We begin by reviewing and critiquing designerly approaches that have traditionally been associated with service innovations and futures literature. In presenting our framework, we provide an example of its application to the Internet of Things (IoT), illustrating the central tenets proposed and key issues identified. Findings: The research framework advances a methodology for visualizing future experiential service innovations, considering how realism may be integrated into a designerly approach. Research limitations/implications: Design fiction diegetic prototyping enables researchers to express a range of ‘what if’ or ‘what can it be’ research questions within service innovation contexts. However, the process encompasses degrees of subjectivity and relies on knowledge, judgment and projection. Practical implications: The paper presents an approach to devising future service scenarios incorporating new and emergent technologies in service contexts. The proposed framework may be used as part of a range of research designs, including qualitative, quantitative and mixed method investigations. Originality: Operationalizing an approach that generates and visualizes service futures from an experiential perspective contributes to the advancement of techniques that enables the exploration of new possibilities for service innovation research

현장 데이터 수집 능력을 확장하기 위한 자유도 높은 셀프 트래킹 기술의 디자인

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 서진욱.Collecting and tracking data in everyday contexts is a common practice for both individual self-trackers and researchers. The increase in wearable and mobile technologies for self-tracking encourages people to gain personal insights from the data about themselves. Also, researchers exploit self-tracking to gather data in situ or to foster behavioral change. Despite a diverse set of available tracking tools, however, it is still challenging to find ones that suit unique tracking needs, preferences, and commitments. Individual self-tracking practices are constrained by the tracking tools' initial design, because it is difficult to modify, extend, or mash up existing tools. Limited tool support also impedes researchers' efforts to conduct in situ data collection studies. Many researchers still build their own study instruments due to the mismatch between their research goals and the capabilities of existing toolkits. The goal of this dissertation is to design flexible self-tracking technologies that are generative and adaptive to cover diverse tracking contexts, ranging from personal tracking to research contexts. Specifically, this dissertation proposes OmniTrack, a flexible self-tracking approach leveraging a semi-automated tracking concept that combines manual and automated tracking methods to generate an arbitrary tracker design. OmniTrack was implemented as a mobile app for individuals. The OmniTrack app enables self-trackers to construct their own trackers and customize tracking items to meet their individual needs. A usability study and a field development study were conducted with the goal of assessing how people adopt and adapt OmniTrack to fulfill their needs. The studies revealed that participants actively used OmniTrack to create, revise, and appropriate trackers, ranging from a simple mood tracker to a sophisticated daily activity tracker with multiple fields. Furthermore, OmniTrack was extended to cover research contexts that enclose manifold personal tracking contexts. As part of the research, this dissertation presents OmniTrack Research Kit, a research platform that allows researchers without programming expertise to configure and conduct in situ data collection studies by deploying the OmniTrack app on participants' smartphones. A case study in deploying the research kit for conducting a diary study demonstrated how OmniTrack Research Kit could support researchers who manage study participants' self-tracking process. This work makes artifacts contributions to the fields of human-computer interaction and ubiquitous computing, as well as expanding empirical understanding of how flexible self-tracking tools can enhance the practices of individual self-trackers and researchers. Moreover, this dissertation discusses design challenges for flexible self-tracking technologies, opportunities for further improving the proposed systems, and future research agenda for reaching the audiences not covered in this research.일상의 맥락에서 데이터를 모으는 활동인 셀프 트래킹(self-tracking)은 개인과 연구의 영역에서 활발히 활용되고 있다. 웨어러블 디바이스와 모바일 기술의 발달로 인해 사람들은 각자의 삶에 대해 말해주는 데이터를 더 쉽게 수집하고, 통찰할 수 있게 되었다. 또한, 연구자들은 현장(in situ) 데이터를 수집하거나 사람들에게 행동 변화를 일으키는 데에 셀프 트래킹을 활용한다. 비록 셀프 트래킹을 위한 다양한 도구들이 존재하지만, 트래킹에 대해 다양화된 요구와 취향을 완벽히 충족하는 것들을 찾는 것은 쉽지 않다. 대부분의 셀프 트래킹 도구는 이미 설계된 부분을 수정하거나 확장하기에 제한적이다. 그렇기 때문에 사람들의 셀프 트래킹에 대한 자유도는 기존 도구들의 디자인 공간에 의해 제약을 받을 수밖에 없다. 마찬가지로, 현장 데이터를 수집하는 연구자들도 이러한 도구의 한계로 인해 여러 문제에 봉착한다. 연구자들이 데이터를 통해 답하고자 하는 연구 질문(research question)은 분야가 발전할수록 세분되고, 치밀해지기 때문에 이를 위해서는 복잡하고 고유한 실험 설계가 필요하다. 하지만 현존하는 연구용 셀프 트래킹 플랫폼들은 이에 부합하는 자유도를 발휘하지 못한다. 이러한 간극으로 인해 많은 연구자들이 각자의 현장 데이터 수집 연구에 필요한 디지털 도구들을 직접 구현하고 있다. 본 연구의 목표는 자유도 높은---연구적 맥락과 개인적 맥락을 아우르는 다양한 상황에 활용할 수 있는---셀프 트래킹 기술을 디자인하는 것이다. 이를 위해 본고에서는 옴니트랙(OmniTrack)이라는 디자인 접근법을 제안한다. 옴니트랙은 자유도 높은 셀프 트래킹을 위한 방법론이며, 반자동 트래킹(semi-automated tracking)이라는 컨셉을 바탕으로 수동 방식과 자동 방식의 조합을 통해 임의의 트래커를 표현할 수 있다. 먼저 옴니트랙을 개인을 위한 모바일 앱 형태로 구현하였다. 옴니트랙 앱은 개개인이 자신의 트래킹 니즈에 맞는 트래커를 커스터마이징하여 활용할 수 있도록 구성되어 있다. 본고에서는 사람들이 어떻게 옴니트랙을 자신의 니즈에 맞게 활용하는지 알아보고자 사용성 테스트(usability testing)와 필드 배포 연구(field deployment study)를 수행하였다. 참가자들은 옴니트랙을 활발히 이용해 다양한 디자인의 트래커—아주 단순한 감정 트래커부터 여러 개의 필드를 가진 복잡한 일일 활동 트래커까지—들을 생성하고, 수정하고, 활용하였다. 다음으로, 옴니트랙을 현장 데이터 수집 연구에 활용할 수 있도록 연구 플랫폼 형태의 '옴니트랙 리서치 킷(OmniTrack Research Kit)'으로 확장하였다. 옴니트랙 리서치 킷은 연구자들이 프로그래밍 언어 없이 원하는 실험을 설계하고 옴니트랙 앱을 참가자들의 스마트폰으로 배포할 수 있도록 디자인되었다. 그리고 옴니트랙 리서치 킷을 이용해 일지기록 연구(diary study)를 직접 수행하였고, 이를 통해 옴니트랙 접근법이 어떻게 연구자들의 연구 목적을 이루는 데에 도움을 줄 수 있는지 직접 확인하였다. 본 연구는 휴먼-컴퓨터 인터랙션(Human-Computer Interaction) 및 유비쿼터스 컴퓨팅(Ubiquitous Computing) 분야에 기술적 산출물로써 기여하며, 자유도 높은 셀프 트래킹 도구가 어떻게 개인과 연구자들을 도울 수 있는지 실증적인 이해를 증진한다. 또한, 자유도 높은 셀프트래킹 기술에 대한 디자인적 난제, 연구에서 제시한 시스템에 대한 개선방안, 마지막으로 본 연구에서 다루지 못한 다른 집단을 지원하기 위한 향후 연구 논제에 대하여 논의한다.Abstract CHAPTER 1. Introduction 1.1 Background and Motivation 1.2 Research Questions and Approaches 1.2.1 Designing a Flexible Self-Tracking Approach Leveraging Semiautomated Tracking 1.2.2 Design and Evaluation of OmniTrack in Individual Tracking Contexts 1.2.3 Designing a Research Platform for In Situ Data Collection Studies Leveraging OmniTrack 1.2.4 A Case Study of Conducting an In Situ Data Collection Study using the Research Platform 1.3 Contributions 1.4 Structure of this Dissertation CHAPTER 2. Related Work 2.1 Background on Self-Tracking 2.1.1 Self-Tracking in Personal Tracking Contexts 2.1.2 Utilization of Self-Tracking in Other Contexts 2.2 Barriers Caused by Limited Tool Support 2.2.1 Limited Tools and Siloed Data in Personal Tracking 2.2.2 Challenges of the Instrumentation for In Situ Data Collection 2.3 Flexible Self-Tracking Approaches 2.3.1 Appropriation of Generic Tools 2.3.2 Universal Tracking Systems for Individuals 2.3.3 Research Frameworks for In Situ Data Collection 2.4 Grounding Design Approach: Semi-Automated Tracking 2.5 Summary of Related Work CHAPTER3 DesigningOmniTrack: a Flexible Self-Tracking Approach 3.1 Design Goals and Rationales 3.2 System Design and User Interfaces 3.2.1 Trackers: Enabling Flexible Data Inputs 3.2.2 Services: Integrating External Trackers and Other Services 3.2.3 Triggers: Retrieving Values Automatically 3.2.4 Streamlining Tracking and Lowering the User Burden 3.2.5 Visualization and Feedback 3.3 OmniTrack Use Cases 3.3.1 Tracker 1: Beer Tracker 3.3.2 Tracker 2: SleepTight++ 3.3.3 Tracker 3: Comparison of Automated Trackers 3.4 Summary CHAPTER 4. Understanding HowIndividuals Adopt and Adapt OmniTrack 4.1 Usability Study 4.1.1 Participants 4.1.2 Procedure and Study Setup 4.1.3 Tasks 4.1.4 Results and Discussion 4.1.5 Improvements A_er the Usability Study 4.2 Field Deployment Study 4.2.1 Study Setup 4.2.2 Participants 4.2.3 Data Analysis and Results 4.2.4 Reflections on the Deployment Study 4.3 Discussion 4.3.1 Expanding the Design Space for Self-Tracking 4.3.2 Leveraging Other Building Blocks of Self-Tracking 4.3.3 Sharing Trackers with Other People 4.3.4 Studying with a Broader Audience 4.4 Summary CHAPTER 5. Extending OmniTrack for Supporting In Situ Data Collection Studies 5.1 Design Space of Study Instrumentation for In-Situ Data Collection 5.1.1 Experiment-Level Dimensions 5.1.2 Condition-Level Dimensions 5.1.3 Tracker-Level Dimensions 5.1.4 Reminder/Trigger-Level Dimensions 5.1.5 Extending OmniTrack to Cover the Design Space 5.2 Design Goals and Rationales 5.3 System Design and User Interfaces 5.3.1 Experiment Management and Collaboration 5.3.2 Experiment-level Configurations 5.3.3 A Participants Protocol for Joining the Experiment 5.3.4 Implementation 5.4 Replicated Study Examples 5.4.1 Example A: Revisiting the Deployment Study of OmniTrack 5.4.2 Example B: Exploring the Clinical Applicability of a Mobile Food Logger 5.4.3 Example C: Understanding the Effect of Cues and Positive Reinforcement on Habit Formation 5.4.4 Example D: Collecting Stress and Activity Data for Building a Prediction Model 5.5 Discussion 5.5.1 Supporting Multiphase Experimental Design 5.5.2 Serving as Testbeds for Self-Tracking Interventions 5.5.3 Exploiting the Interaction Logs 5.6 Summary CHAPTER 6. Using the OmniTrack Research Kit: A Case Study 6.1 Study Background and Motivation 6.2 OmniTrack Configuration for Study Instruments 6.3 Participants 6.4 Study Procedure 6.5 Dataset and Analysis 6.6 Study Result 6.6.1 Diary Entries 6.6.2 Aspects of Productivity Evaluation 6.6.3 Productive Activities 6.7 Experimenter Experience of OmniTrack 6.8 Participant Experience of OmniTrack 6.9 Implications 6.9.1 Visualization Support for Progressive, Preliminary Analysis of Collected Data 6.9.2 Inspection to Prevent Misconfiguration 6.9.3 Providing More Alternative Methods to Capture Data 6.10 Summary CHAPTER 7. Discussion 7.1 Lessons Learned 7.2 Design Challenges and Implications 7.2.1 Making the Flexibility Learnable 7.2.2 Additive vs. Subtractive Design for Flexibility 7.3 Future Opportunities for Improvement 7.3.1 Utilizing External Information and Contexts 7.3.2 Providing Flexible Visual Feedback 7.4 Expanding Audiences of OmniTrack 7.4.1 Supporting Clinical Contexts 7.4.2 Supporting Self-Experimenters 7.5 Limitations CHAPTER 8. Conclusion 8.1 Summary of the Approaches 8.2 Summary of Contributions 8.2.1 Artifact Contributions 8.2.2 Empirical Research Contributions 8.3 Future Work 8.3.1 Understanding the Long-term E_ect of OmniTrack 8.3.2 Utilizing External Information and Contexts 8.3.3 Extending the Input Modality to Lower the Capture Burden 8.3.4 Customizable Visual Feedback 8.3.5 Community-Driven Tracker Sharing 8.3.6 Supporting Multiphase Study Design 8.4 Final Remarks APPENDIX A. Study Material for Evaluations of the OmniTrack App A.1 Task Instructions for Usability Study A.2 The SUS (System Usability Scale) Questionnaire A.3 Screening Questionnaire for Deployment Study A.4 Exit Interview Guide for Deployment Study A.5 Deployment Participant Information APPENDIX B Study Material for Productivity Diary Study B.1 Recruitment Screening Questionnaire B.2 Exit Interview Guide Abstract (Korean)Docto

Fitness trackers : user experience and motivation

Fitness trackers have become a popular technology for the fitness industry within the wearable market. Fitness trackers provide a unique user experience in that they are ubiquitous and provide instantaneous biometric data to users. However, the market is oversaturated with many devices ranging from fitness trackers strictly used for tracking biometric data, and smartwatches that are an extension of a mobile device. Furthermore, despite their popularity, users are quick to abandon fitness trackers. There is little research that understands how the user experience of fitness trackers assists in creating positive exercise routines for users and why users adopt these devices for long term. The goal of this thesis is to understand how the usability, design, and social features provided by fitness trackers affects the user experience, which of these factors drive users toward long-term adoption or abandonment of their devices, and provide recommendations for fitness tracker designers and product developers to improve the user experience.Thesis (M.A.)Department of Journalis

Tracking in the wild: exploring the everyday use of physical activity trackers

As the rates of chronical diseases, such as obesity, cardiovascular disease and diabetes continue to increase, the development of tools that support people in achieving healthier habits is becoming ever more important. Personal tracking systems, such as activity trackers, have emerged as a promising class of tools to support people in managing their everyday health. However, for this promise to be fulfilled, these systems need to be well designed, not only in terms of how they implement specific behavior change techniques, but also in how they integrate into people’s daily lives and address their daily needs. My dissertations provides evidence that accounting for people’s daily practices and needs can help to design activity tracking systems that help people get more value from their tracking practices. To understand how people derive value from their activity tracking practices, I have conducted two inquiries into people’s daily uses of activity tracking systems. In a fist attempt, I led a 10-month study of the adoption of Habito, our own activity tracking mobile app. Habito logged not only users’ physical activity, but also their interactions with the app. This data was used to acquire an estimate of the adoption rate of Habito, and understanding of how adoption is affected by users’ ‘readiness’, i.e., their attitude towards behavior change. In a follow-up study, I turned to the use of video methods and direct, in-situ observations of users’ interactions to understand what motivates people to engage with these tools in their everyday life, and how the surrounding environment shapes their use. These studies revealed some of the complexities of tracking, while extending some of the underlying ideas of behavior change. Among key results: (1) people’s use of activity trackers was found to be predominantly impulsive, where they simultaneously reflect, learn and change their behaviors as they collect data; (2) people’s use of trackers is deeply entangled with their daily routines and practices, and; (3) people use of trackers often is not in line with the traditional vision of these tools as mediators of change – trackers are also commonly used to simply learn about behaviors and engage in moments of self-discovery. Examining how to design activity tracking interfaces that best support people’s different needs , my dissertation further describes an inquiry into the design space of behavioral feedback interfaces. Through a iterative process of synthesis and analysis of research on activity tracking, I devise six design qualities for creating feedback that supports people in their interactions with physical activity data. Through the development and field deployment of four concepts in a field study, I show the potential of these displays for highlighting opportunities for action and learning.À medida que a prevalência de doenças crónicas como a obesidade, doenças cardiovasculares e diabetes continua a aumentar, o desenvolvimento de ferramentas que suportam pessoas a atingir mudanças de comportamento tem-se tornado essencial. Ferramentas de monitorização de comportamentos, tais como monitores de atividade física, têm surgido com a promessa de encorajar um dia a dia mais saudável. Contudo, para que essa promessa seja cumprida, torna-se essencial que estas ferramentas sejam bem concebidas, não só na forma como implementam determinadas estratégias de mudança de comportamento, mas também na forma como são integradas no dia-a-dia das pessoas. A minha dissertação demonstra a importância de considerar as necessidades e práticas diárias dos utilizadores destas ferramentas, de forma a ajudá-las a tirar melhor proveito da sua monitorização de atividade física. De modo a entender como é que os utilizadores destas ferramentas derivam valor das suas práticas de monitorização, a minha dissertação começa por explorar as práticas diárias associadas ao uso de monitores de atividade física. A minha dissertação contribui com duas investigações ao uso diário destas ferramentas. Primeiro, é apresentada uma investigação da adoção de Habito, uma aplicação para monitorização de atividade física. Habito não só registou as instâncias de atividade física dos seus utilizadores, mas também as suas interações com a própria aplicação. Estes dados foram utilizados para adquirir uma taxa de adopção de Habito e entender como é que essa adopção é afetada pela “prontidão” dos utilizadores, i.e., a sua atitude em relação à mudança de comportamento. Num segundo estudo, recorrendo a métodos de vídeo e observações diretas e in-situ da utilização de monitores de atividade física, explorei as motivações associadas ao uso diário destas ferramentas. Estes estudos expandiram algumas das ideias subjacentes ao uso das ferramentas para mudanças de comportamento. Entre resultados principais: (1) o uso de monitores de atividade física é predominantemente impulsivo, onde pessoas refletem, aprendem e alteram os seus comportamentos à medida que recolhem dados sobe estes mesmos comportamentos; (2) o uso de monitores de atividade física está profundamente interligado com as rotinas e práticas dos seus utilizadores, e; (3) o uso de monitores de atividade física nem sempre está ligado a mudanças de comportamento – estas ferramentas também são utilizadas para divertimento e aprendizagem. A minha dissertação contribui ainda com uma exploração do design de interfaces para a monitorização de atividade física. Através de um processo iterativo de síntese e análise de literatura, seis qualidades para a criação de interfaces são derivadas. Através de um estudo de campo, a minha dissertação demonstro o potencial dessas interfaces para ajudar pessoas a aprender e gerir a sua saúde diária