Flexible and Mindful Self-Tracking: Design Implications from Paper Bullet Journals

Digital self-tracking technologies offer many potential benefits over self-tracking with paper notebooks. However, they are often too rigid to support people’s practical and emotional needs in everyday settings. To inform the design of more flexible self-tracking tools, we examine bullet journaling: an analogue and customisable approach for logging and reflecting on everyday life. Analysing a corpus of paper bullet journal photos and related conversations on Instagram, we found that individuals extended and adapted bullet journaling systems to their changing practical and emotional needs through: (1) creating and combining personally meaningful visualisations of different types of trackers, such as habit, mood, and symptom trackers; (2) engaging in mindful reflective thinking through design practices and self-reflective strategies; and (3) posting photos of paper journals online to become part of a selftracking culture of sharing and learning. We outline two interrelated design directions for flexible and mindful selftracking: digitally extending analogue self-tracking and supporting digital self-tracking as a mindful design practice

Inferring Meal Eating Activities in Real World Settings from Ambient Sounds: A Feasibility Study

Copyright ©2015 ACMDOI: 10.1145/2678025.2701405Dietary self-monitoring has been shown to be an effective method for weight-loss, but it remains an onerous task despite recent advances in food journaling systems. Semi-automated food journaling can reduce the effort of logging, but often requires that eating activities be detected automatically. In this work we describe results from a feasibility study conducted in-the-wild where eating activities were inferred from ambient sounds captured with a wrist-mounted device; twenty participants wore the device during one day for an average of 5 hours while performing normal everyday activities. Our system was able to identify meal eating with an F-score of 79.8% in a person-dependent evaluation, and with 86.6% accuracy in a person-independent evaluation. Our approach is intended to be practical, leveraging off-the-shelf devices with audio sensing capabilities in contrast to systems for automated dietary assessment based on specialized sensors

Eat4Thought: A Design of Food Journaling

Food journaling is an effective method to help people identify their eating patterns and encourage healthy eating habits as it requires self-reflection on eating behaviors. Current tools have predominately focused on tracking food intake, such as carbohydrates, proteins, fats, and calories. Other factors, such as contextual information and momentary thoughts and feelings that are internal to an individual, are also essential to help people reflect upon and change attitudes about eating behaviors. However, current dietary tracking tools rarely support capturing these elements as a way to foster deep reflection. In this work, we present Eat4Thought -- a food journaling application that allows users to track their emotional, sensory, and spatio-temporal elements of meals as a means of supporting self-reflection. The application enables vivid documentation of experiences and self-reflection on the past through video recording. We describe our design process and an initial evaluation of the application. We also provide design recommendations for future work on food journaling.Comment: 8 page

헬스케어 서비스에서 데이터 기반 커뮤니케이션을 위한 디자인 연구

학위논문(박사)--서울대학교 대학원 :융합과학기술대학원 융합과학부(디지털정보융합전공),2020. 2. 이중식.스마트폰과 웨어러블 기기의 보급으로 인해 환자 생성 건강 데이터(Patient-Generated Health Data; PGHD)가 크게 증가하였고, 이는 의사-환자 의사 소통을 개선하여 데이터 중심으로 발전 할 수있는 새로운 기회를 제공했다. PGHD를 사용한 데이터 중심 커뮤니케이션을 통해 환자와 의사는 기존 임상 데이터를 보완하여 이해의 차이를 메울 수 있으며, 환자 건강에 대한 포괄적인 관점도 획득할 수 있다. 그러나, 이러한 새로운 유형의 데이터와 기술을 기존 의료 커뮤니케이션에 통합하는 데에는 여전히 어려움이 남아 있다. 환자는 종종 데이터 수집에 대한 참여와 동기를 잃어버리며, 이에 따라 수집한 데이터는 불완전해지는 문제가 발생한다. 또한 PGHD가 온전하게 수집 되더라도 의사와 환자는 의료 관행에서 이러한 데이터를 활용하는 데 어려움을 겪게 된다. 또한, 시간과 정보의 부족으로 인해 현재 워크 플로우에서 환자와 의사 모두가 PGHD를 통해 협업하는 것은 매우 어려운 일로 알려져 있다. HCI 연구 관점에서, PGHD를 활용 한 데이터 중심 통신을 지원하는 시스템을 설계하면 이러한 과제를 해결할 수 있는 잠재력이 있으며, 이는 데이터 수집(collection), 표현(representation), 해석(interpretation) 및 협업(collaboration)의 네 가지 설계 공간(design space)에서 추가적인 탐색을 요구한다. 따라서, 이 논문에서는 시스템 설계 및 현장 배포 연구를 수행하여, 각 설계 공간에서 해결되지 않은 질문을 탐색하고 경험적 연구 결과 및 설계 지침을 제공하는 것을 목표로 한다. 먼저, 데이터 수집에 대한 설계 공간의 연구로서, 접근성 높은 데이터 추적 도구가 환자가 다양한 유형의 PGHD, 특히 식사 데이터를 수집하는 데 어떤 도움을 줄 수 있는지에 대해 연구하고자 하였다. 이를 위해, 접근성 높은 데이터 추적 도구인 mFood Logger을 디자인한 후, 20 명의 환자와 6 명의 임상의를 대상으로 실증적 연구를 수행했다. 그 결과, 환자와 임상의가 데이터 기반 커뮤니케이션을 위해 원하는 데이터 유형이 무엇인지 파악할 수 있었고, 임상적 맥락에서 데이터를 수집 할 때의 난점과 기회를 발견했다. 둘째, 임상의를 위한 데이터 표현을 파악하기 위해, 18명의 다양한 이해 관계자(e.g., 임상의, EMR 개발자)와 참여적 디자인(participatory design) 프로세스를 통해 PGHD를 표시하는 DataMD를 설계하고 구현했다. 참여적 디자인 워크샵을 통해 알아낸 것은, 의료적 상황의 제약 때문에 임상의가 원하는 데이터 표현 방식이 효율성과 친숙함으로 수렴된다는 점이었다. 임상의는 학습에 걸리는 시간 문제로 인해 새로운 시각화 방법을 사용하지 않았고, 한 번에 많은 양의 데이터를 보고 싶어했다. 이러한 요구 사항을 고려하여, 다양한 유형의 PGHD가 한 눈에 보여지며, 여러 가지 임상 상황을 고려한, DataMD를 설계하고 구현했다. 셋째, 데이터 기반 커뮤니케이션의 중요한 측면으로서, 환자를 위한 데이터 해석 전략을 제시하여 효과적인 데이터 해석을 돕는 설계 지침을 제공합니다. 20명의 만성 질환 환자와의 인터뷰를 통해, 환자들이 PGHD를 해석할 때, 논리적 증거가 아닌 자신의 과거 경험에 강하게 의존한다는 점을 밝혀냈다. 환자들은 자신의 신념과 경험에 따라 여러 데이터 사이의 관계를 가정하며, 이를 확인하기 위해 네 가지의 데이터 해석 전략을 구사했다. 이러한 이해는 설계자와 연구원이 데이터 해석을 지원하는 시스템 설계를 발전시키는 데 도움이 될 수 있다. 마지막으로, 데이터를 통한 협업을 지원하기 위해 앞선 연구에서 디자인한 시스템을 기반으로 PGHD를 공유하고 활용함으로써, 임상의와 환자가 어떻게 협업하는지를 조사하고자 했다. 환자의 데이터 수집 및 해석을 돕는 앱인 MyHealthKeeper와 임상의를 위한 인터페이스인 DataMD로 구성된 통합 시스템을 임상 현장에 배포했다. 80명의 외래환자와의 임상시험 결과에 따르면 PGHD를 통한 협력으로 환자가 행동 변화에 성공할 수있었다. 또한, 앱 사용 로그에 따르면 환자는 직접적인 상호 작용 없이도 임상의와 원격으로 협업 할 수도 있는 것으로 나타났다. 이러한 결과를 바탕으로, 이 연구에서는 임상의와 환자 사이의 협력을 지원할 수있는 주요 기회가 기존 임상 워크플로우에 PGHD 사용을 통합하는 것에 있음을 제시한다. 앞선 연구들을 통해, 데이터 기반 커뮤니케이션을 위한 디자인이 환자와 의사가 PGHD를 통해 협업하는 데 도움이 될 수 있음을 발견했다. PGHD가 네 개의 설계 공간 내에서 기존 의사-환자 통신을 데이터 중심 통신으로 개선 할 수있는 방법을 개념화함으로써, 이 연구는 환자와 의사 간의 데이터 기반 커뮤니케이션을 위한 디자인이 어떻게 도출되어야 하는지에 대한 새로운 시각을 제공할 것으로 기대한다. 이 작업은 HCI, CSCW과 건강 정보학 커뮤니티의 경험적 이해를 높이고, 실용적인 설계 지침을 제공하며, 이론적 확장에 기여한다. 또한, 이 연구는 향후 다른 분야에서 데이터 기반 커뮤니케이션을 지원하는 시스템의 설계가 어떻게 이뤄져야 하는지에 대한 기초를 제공한다.The prevalence of smartphones and wearable devices has led to a dramatic increase in patient-generated health data (PGHD). The growing interest in PGHD has offered new opportunities to improve doctor-patient communication to become more data-driven. Data-driven communication using PGHD enables patients and physicians to fill in gaps between understandings by supplementing existing clinical data, as well as providing a more comprehensive picture of ongoing patient health. However, challenges in integrating such new types of data and technologies into existing healthcare communications remain. Patients often lose their engagement and motivation in data collection, resulting in incomplete data. Even if PGHD is wholly collected, physicians and patients encounter challenges in utilizing such data--representation and interpretation--in healthcare practices. Furthermore, it is challenging for both patients and physicians to collaborate through PGHD in the current workflow due to the lack of time and information overload. From the HCI research perspective, designing a system supporting data-driven communication utilizing PGHD has the potential to address such challenges, which calls for further exploration in four design spaces: data collection, representation, interpretation, and collaboration. Therefore, in this dissertation work, I aim to explore unsolved questions in each design space by conducting a series of design and deployment studies and provide empirical findings and design guidelines. In the design space of data collection, I investigated how the semi-automated tracking tool can support patients to track various types of PGHD, especially food journaling. With the design of mFood Logger, a semi-automated data tracking tool, I conducted an empirical study with 20 patients and 6 clinicians. I identified desired data types for data-driven communication from the patients' and clinicians' sides and uncovered the challenges and opportunities in collecting data within clinical contexts. I was able to understand the feasibility and acceptability of PGHD in clinical practices, as well as clinicians' presence--either remotely or in-person--as an enabler that encourages patients to keep tracking PGHD in the longer-term. Incorporating critical topics regarding data collection from the literature and findings from my work, I discuss the applicability of PGHD and data tracking modes. To support data representation for clinicians, I designed and implemented DataMD that displays PGHD, considering situational constraints through a participatory design process with 18 various stakeholders (e.g., clinicians, EMR developers). Through the participatory design workshop, I found that the ways of data representation that clinicians desired converged to efficiency and familiarity due to the situational constraints. Clinicians wanted to see a large amount of data at once, avoiding using novel visualization methods due to the issue of learnability. Considering those requirements, I designed and implemented DataMD, in which various types of PGHD are represented with considerations of clinical contexts. I discussed the role of data representation in data-driven communication. As the critical aspect of data-driven communication, I present different data-interpretation strategies from patients, providing design guidelines to help effective data-interpretation. By conducting interviews with 20 chronic disease patients, I found that they shaped their interests and assumptions by incorporating prior experiences rather than logical evidence. I also identified four data-interpretation strategies: finding evidence to confirm assumptions, discrediting data to preserve initial assumptions, discovering new insights, and deferring drawing hasty conclusions from data. These understandings help designers and researchers advance the design of systems to support data-interpretation. Lastly, to support collaboration via data, I demonstrate how clinicians and patients collaborate by sharing and utilizing PGHD based on the system I designed. I deployed the integrated system consisting of a patient app, MyHealthKeeper, and a clinician interface, DataMD. I investigated how the system could support collaboration via data. Clinical outcomes revealed that collaboration via PGHD led patients to succeed in behavior change. App usage log also showed that patients could even remotely collaborate with clinicians without direct interactions. Findings from these studies indicate that the key opportunities to facilitate collaboration between clinicians and patients are the integration of data prescriptions into the clinician's workflow and intervention based on natural language feedback generated within clinical contexts. Across these studies, I found that the design for data-driven communication can support patients and physicians to collaborate through PGHD. By conceptualizing how PGHD could improve the existing doctor-patient communication to data-driven communication within four design spaces, I expect that this work will shed new light on how the design should be derived for data-driven communication between patients and physicians in the real world. Taken together, I believe this work contributes to empirical understandings, design guidelines, theoretical extensions, and artifacts in human-computer interaction, computer-supported cooperative work, and health informatics communities. This work also provides a foundation for future researchers to study how the design of the system supporting data-driven communication can empower various users situated in different contexts to communicate through data in other domains, such as learning, beyond the context of healthcare services.1 Introduction 1 1.1 Background 1 1.2 Motivation 4 1.3 Topics of Interest 5 1.3.1 Design Spaces 5 1.3.2 Research Scope 11 1.4 Thesis Statements and Research Questions 13 1.5 Thesis Overview 15 1.6 Contribution 18 1.6.1 Empirical research contributions 18 1.6.2 Artifacts contributions 18 1.6.3 Theoretical contributions 19 2 Conceptual Background & Related Work 20 2.1 Data-driven Communication in Healthcare Services 20 2.1.1 Concept of Doctor-Patient Communication 21 2.1.2 Brief History of Patient-Centered Approach 25 2.1.3 Emergence of Patient-Generated Health Data 27 2.2 Four Design Spaces for Data-Driven Communication 30 2.2.1 Data collection 34 2.2.2 Data Representation 41 2.2.3 Data Interpretation 47 2.2.4 Collaboration via Data 50 3 Data Collection: Study of mFood Logger 54 3.1 Motivation 55 3.2 Preliminary Work & Tool Design 57 3.2.1 Clinical Requirements for Data Collection 57 3.2.2 Design of Data Collection Tool: mFood Logger 60 3.3 Study Design 63 3.3.1 Participants 63 3.3.2 StudyProcedure 64 3.4 Results 69 3.4.1 PatientSide 69 3.4.2 ClinicianSide 76 3.5 Limitations & Conclusion 80 3.6 Chapter 3 Summary 81 4 Data Representation: Design of DataMD 83 4.1 Motivation 84 4.2 Preliminary Work 86 4.2.1 Workflow Journey Maps 87 4.2.2 DesignGoals 89 4.3 Study Design 90 4.3.1 Participants 91 4.3.2 ParticipatoryDesignworkshop 91 4.4 Results 92 4.4.1 DesignRequirements 92 4.4.2 Implementation: DataMD 98 4.5 Limitations & Conclusion 102 4.6 Summary of Chapter4 102 5 Data Interpretation: Data-Interpretation Strategies 103 5.1 Motivation 103 5.2 Study Design 106 5.2.1 Participants 106 5.2.2 Study Procedure 108 5.2.3 Data Analysis 110 5.3 Results 111 5.3.1 Change of Interest in Data 111 5.3.2 Assumptions on Relationships between Data Types 113 5.3.3 Data-InterpretationStrategy 117 5.4 Limitations & Conclusion 124 5.5 Summary of Chapter5 125 6 Collaboration via Data: Deployment Study 126 6.1 Motivation 127 6.2 System Design 128 6.2.1 MyHealthKeeper: Patient App 128 6.2.2 DataMD: Clinician Interface 132 6.3 Study Design 133 6.3.1 Participants 134 6.3.2 Procedure 135 6.4 Data Analysis 138 6.4.1 Statistical Analysis of Clinical Outcomes 139 6.4.2 App Usage Log 139 6.4.3 Observation Data Analysis 139 6.5 Results 140 6.5.1 Behavior Change 140 6.5.2 Data-Collection & Journaling Rate 144 6.5.3 Workflow Integration & Communication Support 146 6.6 Limitations & Conclusion 150 6.7 Summary of Chapter6 151 7 Discussion 152 7.1 Towards a Design for Data-Driven Communication 152 7.1.1 Improve Data Quality for Clinical Applicability 153 7.1.2 Support Accessibility of Data Collection 154 7.1.3 Understand Clinicians Preference for Familiar Data Representation. 157 7.1.4 Embrace Lived Experience for Rich Data Interpretation 158 7.1.5 Prioritize Workflow Integration for Successful Data-Driven Communication 163 7.1.6 Consider Risks of Using Patient-Generated Health Data in Clinical Settings 165 7.2 Opportunities for Future Work 166 7.2.1 Leverage Ubiquitous Technology to Design Data CollectionTools 166 7.2.2 Provide Data-Interpretation Guidelines for People with Different Levels of Literacy and Goals 169 7.2.3 Consider Cultural Differences in Data-Driven Communication 170 8 Conclusion 173 8.1 Summary of Contributions 173 8.2 Future Directions 175 8.3 Final Remarks 176Docto

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A Behavioral Weight Loss Program for Low-Income Adults with Obesity and HIV

Problem: Obesity, having a body mass index (BMI) of 30 kg/m2 or greater, increases the risk for cardiovascular disease. For people living with HIV (PLWH), this increases their risk for myocardial infarctions by 50% and causes this life-threatening event to occur at a younger age compared to those who are not diagnosed with HIV. Methods: With obesity disproportionately affecting people with low income, this study aims to evaluate the effects of a 12-week behavioral weight loss program that utilizes journaling as a self-monitoring tool, weekly educational classes, and individualized goal-setting on PLWH with obesity who live in a residential facility for the low-income, formerly homeless. Outcome measures include average weight, BMI, waist circumference (WC), intake of the five food groups per MyPlate recommendations, water intake, sugar-sweetened beverage (SSB) intake, minutes of physical activity (PA), and quality of life (QoL). A convenience sample (n=2) were recruited. Data: Findings from this cohort study showed the following: average weight increased, BMI increased, WC decreased, protein intake increased, dairy intake decreased, vegetable intake decreased, grain intake increased, fruit intake decreased, water intake increased, SSB intake decreased, minutes of PA increased, and QoL decreased. Implication: Providing a behavioral change strategy and health education encouraged participants to desire choosing healthy behaviors; however, without access to fresh produce, significant changes in BMI may continue to be difficult to achieve