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

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

Impact of experience sampling methods on tap pattern based emotion recognition

Smartphone based emotion recognition uses predictive modeling to recognize user's mental states. In predictive modeling, determining ground truth plays a crucial role in labeling and training the model. Experience Sampling Method (ESM) is widely used in behavioral science to gather user responses about mental states. Smartphones equipped with sensors provide new avenues to design Experience Sampling Methods. Sensors provide multiple contexts that can be used to trigger collection of user responses. However, subsampling of sensor data can bias the inference drawn from trigger based ESM. We investigate whether continuous sensor data simplify the design of ESM. We use the typing pattern of users on smartphone as the context that can trigger response collection. We compare the context based and time based ESM designs to determine the impact of ESM strategies on emotion modeling. The results indicate how different ESM designs compare against each other

Wild by Design: Workshop on Designing Ubiquitous Health Monitoring Technologies for Challenging Environments

Recent years have shown an emergence of ubiquitous technologies that aim to monitor a person’s health in their day to day. However, albeit focused at a real world setting and technically able, most research is still limited in its real-world coverage, suitability, and adoption. In this workshop, we will focus on the challenges of real world health monitoring deployments to produce forward-looking insights that can shape the way researchers and practitioners think about health monitoring, in platforms and systems that account for the complex environments where they are bound to be used

Evaluating the impact of physical activity apps and wearables: interdisciplinary review

Background: Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. Objectives: This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. Method: An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. Results: A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). Conclusions: The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines

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

Complementing human behavior assessment by leveraging personal ubiquitous devices and social links:An evaluation of the peer-ceived momentary assessment method

Background: Ecological momentary assessment (EMA) enables individuals to self-report their subjective momentary physical and emotional states. However, certain conditions, including routine observable behaviors (e.g., moods, medication adherence) as well as behaviors that may suggest declines in physical or mental health (e.g., memory losses, compulsive disorders) cannot be easily and reliably measured via self-reports. Objective: This study aims to examine a method complementary to EMA, denoted as peer-ceived momentary assessment (PeerMA), which enables the involvement of peers (e.g., family members, friends) to report their perception of the individual's subjective physical and emotional states. In this paper, we aim to report the feasibility results and identified human factors influencing the acceptance and reliability of the PeerMA. Methods: We conducted two studies of 4 weeks each, collecting self-reports from 20 participants about their stress, fatigue, anxiety, and well-being, in addition to collecting peer-reported perceptions from 27 of their peers. Results: Preliminary results showed that some of the peers reported daily assessments for stress, fatigue, anxiety, and well-being statistically equal to those reported by the participant. We also showed how pairing assessments of participants and peers in time enables a qualitative and quantitative exploration of unique research questions not possible with EMA-only based assessments. We reported on the usability and implementation aspects based on the participants' experience to guide the use of the PeerMA to complement the information obtained via self-reports for observable behaviors and physical and emotional states among healthy individuals. Conclusions: It is possible to leverage the PeerMA method as a complement to EMA to assess constructs that fall in the realm of observable behaviors and states in healthy individuals