3,984 research outputs found
A Trip to the Moon: Personalized Animated Movies for Self-reflection
Self-tracking physiological and psychological data poses the challenge of
presentation and interpretation. Insightful narratives for self-tracking data
can motivate the user towards constructive self-reflection. One powerful form
of narrative that engages audience across various culture and age groups is
animated movies. We collected a week of self-reported mood and behavior data
from each user and created in Unity a personalized animation based on their
data. We evaluated the impact of their video in a randomized control trial with
a non-personalized animated video as control. We found that personalized videos
tend to be more emotionally engaging, encouraging greater and lengthier writing
that indicated self-reflection about moods and behaviors, compared to
non-personalized control videos
νμ₯ λ°μ΄ν° μμ§ λ₯λ ₯μ νμ₯νκΈ° μν μμ λ λμ μ ν νΈλνΉ κΈ°μ μ λμμΈ
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : 곡과λν μ»΄ν¨ν°κ³΅νλΆ, 2019. 2. μμ§μ±.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.μΌμμ λ§₯λ½μμ λ°μ΄ν°λ₯Ό λͺ¨μΌλ νλμΈ μ
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ν νΈλνΉ κΈ°μ μ λμμΈνλ κ²μ΄λ€. μ΄λ₯Ό μν΄ λ³Έκ³ μμλ μ΄λνΈλ(OmniTrack)μ΄λΌλ λμμΈ μ κ·Όλ²μ μ μνλ€. μ΄λνΈλμ μμ λ λμ μ
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λ€μμΌλ‘, μ΄λνΈλμ νμ₯ λ°μ΄ν° μμ§ μ°κ΅¬μ νμ©ν μ μλλ‘ μ°κ΅¬ νλ«νΌ ννμ 'μ΄λνΈλ 리μμΉ ν·(OmniTrack Research Kit)'μΌλ‘ νμ₯νμλ€. μ΄λνΈλ 리μμΉ ν·μ μ°κ΅¬μλ€μ΄ νλ‘κ·Έλλ° μΈμ΄ μμ΄ μνλ μ€νμ μ€κ³νκ³ μ΄λνΈλ μ±μ μ°Έκ°μλ€μ μ€λ§νΈν°μΌλ‘ λ°°ν¬ν μ μλλ‘ λμμΈλμλ€. κ·Έλ¦¬κ³ μ΄λνΈλ 리μμΉ ν·μ μ΄μ©ν΄ μΌμ§κΈ°λ‘ μ°κ΅¬(diary study)λ₯Ό μ§μ μννμκ³ , μ΄λ₯Ό ν΅ν΄ μ΄λνΈλ μ κ·Όλ²μ΄ μ΄λ»κ² μ°κ΅¬μλ€μ μ°κ΅¬ λͺ©μ μ μ΄λ£¨λ λ°μ λμμ μ€ μ μλμ§ μ§μ νμΈνμλ€.
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(Human-Computer Interaction) λ° μ λΉμΏΌν°μ€ μ»΄ν¨ν
(Ubiquitous Computing) λΆμΌμ κΈ°μ μ μ°μΆλ¬Όλ‘μ¨ κΈ°μ¬νλ©°, μμ λ λμ μ
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Όμνλ€.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
COUNTING ON: Humanizing self-tracked data in a connected world
This thesis explores the evolving role of the Quantified Self and self-tracking culture within personalized healthcare. Health and fitness wearables are proliferating globally. However, wearable device abandonment rates are also surging. Wearables can sometimes be authoritative or punitive when presenting wearers with their biological data. In the past, some devices have even triggered adverse health-related conditions. This thesis proposes an approach to visualizing biological data from wearables, in ways that are coherent, contextual, and humane. It critiques normative data visualizations in commercial wearables and speculates alternate futures for self-tracking to empower individuals to manage their health and well-being autonomously. Through an iterative development process to prototype creation, the author gathers biological data using a consumer wearable device and uses it to propose an information architecture that categorizes the data coherently. The architecture is applied in hand-drawn, domestic, embedded visualization prototypes that present the authorβs biological data. Lastly, user interviews are conducted to acquire responses to the prototypes and plan possibilities for future iterations. The purpose of this research is to advocate empathy and compassion in the emerging culture of living with data while considering the intricacies of everyday life, the imperfections of being human, and the need for autonomy in personal data management
The Value of Tracking Data on the Behavior of Patients Who Have Undergone Bariatric Surgery:Explorative Study
Background: To maintain the benefits of a bariatric procedure, patients have to change their lifestyle permanently. This happens within a context of coresponsibilities of health care professionals and their social support system. However, most interventions are focused on the patient as an individual. In this explorative pilot study, behavioral, contextual, and experiential data were gathered to obtain insight on coresponsibility. Objective: The aim of this study is to explore the use of trackers by patients who have undergone bariatric surgery in a data-enabled design approach. Methods: Behavioral and contextual data on the households of patients who have undergone bariatric surgery were explored using a smartphone with an interactive user interface (UI), weight scale, activity bracelet, smart socket, accelerometer motion sensor, and event button to find examples of opportunities for future interventions. Results: A total of 6 households were monitored. Approximately 483,000 data points were collected, and the participants engaged in 1483 conversations with the system. Examples were found using different combinations of data types, which provided the obesity team a better understanding of patient behaviors and their support system, such as a referral to a family coach instead of a dietician. Another finding regarding the partners was, for example, that the conversational UI system facilitated discussion about the support structure by asking for awareness. Conclusions: An intelligent system using a combination of quantitative data gathered by data tracking products in the home environment and qualitative data gathered by app-enhanced short conversations, as well as face-to-face interviews, is useful for an improved understanding of coresponsibilities in the households of patients who have undergone bariatric surgery. The examples found in this explorative study so far encourage research in this field.</p
Recommended from our members
Supporting Diabetes Self-Management with Ubiquitous Computing Technologies: A User-Centered Inquiry
Ubiquitous computing technologies offer opportunities to improve treatments for chronic health conditions. Type 1 diabetes is a compelling use-case for such approaches, given its severity, and need for individuals to make frequent care decisions, informed by complex data. However, current apps, typically based on effortful reflection on collected data, generally show poor adoption, lack vital cognitive and emotional support, and are poorly tailored to usersβ actual diabetes decision making processes. This thesis investigates how diabetes apps can be improved from a user-centered perspective. An initial questionnaire-based study investigated how well existing diabetes apps meet user needs. Perceived benefits, limitations, and reasons for low adoption rates were identified. A talk-aloud study of detailed user interactions with diabetes logging apps was conducted to characterize the benefits and limitations of diverse UI elements for T1 diabetes management, and to more precisely identify wider problems with current interaction designs. This led to positing a refined version of Mamykina et al.βs model for diabetes self-management, to account for observed practices, whereby the previously accepted habitual and sensemaking cognitive states are augmented by a posited βfluid contextual reasoningβ (FCR) mode, which allows multiple contextual factors to be balanced for dynamic course correction when navigating complex situations, using previously learned knowledge. To investigate user perceptions of the levels and kinds of monitoring anticipated in next generation diabetes decision support systems, a 4-week technology probe, in which participants used multiple networked devices and external data aggregation, was used to frame requirements for user-centered development of such future systems. Integrating all of the above work, an iterative design process was undertaken to create DUETS, a card-based system to facilitate reflection by designers, users, and other stakeholders on diabetes support management systems. The resulting tool and method were then implemented and evaluated through structured sessions with stakeholder focus groups
The Value of Tracking Data on the Behavior of Patients Who Have Undergone Bariatric Surgery:Explorative Study
Background: To maintain the benefits of a bariatric procedure, patients have to change their lifestyle permanently. This happens within a context of coresponsibilities of health care professionals and their social support system. However, most interventions are focused on the patient as an individual. In this explorative pilot study, behavioral, contextual, and experiential data were gathered to obtain insight on coresponsibility. Objective: The aim of this study is to explore the use of trackers by patients who have undergone bariatric surgery in a data-enabled design approach. Methods: Behavioral and contextual data on the households of patients who have undergone bariatric surgery were explored using a smartphone with an interactive user interface (UI), weight scale, activity bracelet, smart socket, accelerometer motion sensor, and event button to find examples of opportunities for future interventions. Results: A total of 6 households were monitored. Approximately 483,000 data points were collected, and the participants engaged in 1483 conversations with the system. Examples were found using different combinations of data types, which provided the obesity team a better understanding of patient behaviors and their support system, such as a referral to a family coach instead of a dietician. Another finding regarding the partners was, for example, that the conversational UI system facilitated discussion about the support structure by asking for awareness. Conclusions: An intelligent system using a combination of quantitative data gathered by data tracking products in the home environment and qualitative data gathered by app-enhanced short conversations, as well as face-to-face interviews, is useful for an improved understanding of coresponsibilities in the households of patients who have undergone bariatric surgery. The examples found in this explorative study so far encourage research in this field.</p
Aerospace medicine and biology: A continuing bibliography with indexes (supplement 333)
This bibliography lists 122 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during January, 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance
Contextual and design factors that influence the use of consumer technologies for self-management of stress by teachers
Persistent psychosocial stress is endemic in the modern workplace, including amongst secondary school teachers in England. There is intense interest in the potential role of digital technology such as apps, wearables and online programmes to support stress management but insufficient understanding of how the contexts of teachersβ work influence their use.
Using a constructivist paradigm, a series of qualitative studies was conducted to understand the influence of these contextual factors. First semi-structured qualitative interviews with teachers were thematically analysed to reveal the physical, social and cultural contextual constraints on teachersβ stress management.
Then to enable teachersβ choice of consumer technology for the longitudinal study, an analytical study generated a populated taxonomy of self-management strategies for stress with digital support options. This was presented in workshops to enable some informed choice.
Finally, the qualitative longitudinal summer term study explored eight teachersβ experiences of using their chosen technology in their daily lives. The pandemic meant interviews were online and teachers were mainly working from home. The study was extended with six participants into the autumn term when all teachers had returned to school premises. Cross-case analysis revealed the teachersβ experiences of using technology for stress management included the explanatory power of contextually mediated data, generating awareness, permission to self-care and empathy. The findings suggest implications for self-determination theory (SDT). Thematic analysis revealed facilitators and barriers to using the technology in the school context. There are associated implications for school wellbeing support and designers, and considerations for the Unified Theory of Acceptance and Use of Technology (UTAUT).
This thesisβ main contributions include unique insight into teachersβ experiences of consumer technologies for workplace stress management and the technology features that facilitate self-care. Stress awareness derived from interaction with the technology and personal data gave teachers permission to self-care. Facilitators included brief, discreet interactions and contextually relevant prompts and information. Barriers to use included insufficient technology instructions, and contextual constraints of the relentless work culture, social stigma and lack of privacy. This thesis also documents an innovative process for developing and populating a taxonomy to facilitate technology selection, including specifically for teachers managing stress. Finally, it makes recommendations of interest to designers, school leaders and policy makers seeking to improve teachersβ ability to digitally support their stress self-management
Determinants of Longitudinal Adherence in Smartphone-Based Self-Tracking for Chronic Health Conditions: Evidence from Axial Spondyloarthritis
The use of interactive mobile and wearable technologies for understanding and managing health conditions is a growing area of interest for patients, health professionals and researchers. Self-tracking technologies such as smartphone apps and wearabledevices for measuring symptoms and behaviours generate a wealth of patient-centric data with the potential to support clinical decision making. However, the utility of self-tracking technologies for providing insight into patientsβ conditions is impacted by poor adherence with data logging. This paper explores factors associated with adherence in smartphone-based tracking, drawing on two studies of patients living with axial spondyloarthritis (axSpA), a chronic rheumatological condition. In Study1, 184 axSpA patients used the uMotif health tracking smartphone app for a period of up to 593 days. In Study 2, 108 axSpA patients completed a survey about their experience of using self-tracking technologies. We identify six significant correlates of self-tracking adherence, providing insight into the determinants of tracking behaviour. Specifically, our data provides evidence that adherence correlates with the age of the user, the types of tracking devices that are being used (smartphone OS and physical activity tracker), preferences for types of data to record, the timing of interactions with a self-tracking app, and the reported symptom severity of the user. We discuss how these factors may have implications for those designing, deploying or using mobile and wearable tracking technologies to support monitoring and management of chronic diseases
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