Advancement in Dietary Assessment and Self-Monitoring Using Technology

Although methods to assess or self-monitor intake may be considered similar, the intended function of each is quite distinct. For the assessment of dietary intake, methods aim to measure food and nutrient intake and/or to derive dietary patterns for determining diet-disease relationships, population surveillance or the effectiveness of interventions. In comparison, dietary self-monitoring primarily aims to create awareness of and reinforce individual eating behaviours, in addition to tracking foods consumed. Advancements in the capabilities of technologies, such as smartphones and wearable devices, have enhanced the collection, analysis and interpretation of dietary intake data in both contexts. This Special Issue invites submissions on the use of novel technology-based approaches for the assessment of food and/or nutrient intake and for self-monitoring eating behaviours. Submissions may document any part of the development and evaluation of the technology-based approaches. Examples may include: web adaption of existing dietary assessment or self-monitoring tools (e.g., food frequency questionnaires, screeners) image-based or image-assisted methods mobile/smartphone applications for capturing intake for assessment or self-monitoring wearable cameras to record dietary intake or eating behaviours body sensors to measure eating behaviours and/or dietary intake use of technology-based methods to complement aspects of traditional dietary assessment or self-monitoring, such as portion size estimation

Inferring Mood-While-Eating with Smartphone Sensing and Community-Based Model Personalization

The interplay between mood and eating has been the subject of extensive research within the fields of nutrition and behavioral science, indicating a strong connection between the two. Further, phone sensor data have been used to characterize both eating behavior and mood, independently, in the context of mobile food diaries and mobile health applications. However, limitations within the current body of literature include: i) the lack of investigation around the generalization of mood inference models trained with passive sensor data from a range of everyday life situations, to specific contexts such as eating, ii) no prior studies that use sensor data to study the intersection of mood and eating, and iii) the inadequate examination of model personalization techniques within limited label settings, as we commonly experience in mood inference. In this study, we sought to examine everyday eating behavior and mood using two datasets of college students in Mexico (N_mex = 84, 1843 mood-while-eating reports) and eight countries (N_mul = 678, 329K mood reports incl. 24K mood-while-eating reports), containing both passive smartphone sensing and self-report data. Our results indicate that generic mood inference models decline in performance in certain contexts, such as when eating. Additionally, we found that population-level (non-personalized) and hybrid (partially personalized) modeling techniques were inadequate for the commonly used three-class mood inference task (positive, neutral, negative). Furthermore, we found that user-level modeling was challenging for the majority of participants due to a lack of sufficient labels and data from the negative class. To address these limitations, we employed a novel community-based approach for personalization by building models with data from a set of similar users to a target user

Tackling food marketing to children in a digital world: trans-disciplinary perspectives. Children’s rights, evidence of impact, methodological challenges, regulatory options and policy implications for the WHO European Region

There is unequivocal evidence that childhood obesity is influenced by marketing of foods and non-alcoholic beverages high in saturated fat, salt and/or free sugars (HFSS), and a core recommendation of the WHO Commission on Ending Childhood Obesity is to reduce children’s exposure to all such marketing. As a result, WHO has called on Member States to introduce restrictions on marketing of HFSS foods to children, covering all media, including digital, and to close any regulatory loopholes. This publication provides up-to-date information on the marketing of foods and non-alcoholic beverages to children and the changes that have occurred in recent years, focusing in particular on the major shift to digital marketing. It examines trends in media use among children, marketing methods in the new digital media landscape and children’s engagement with such marketing. It also considers the impact on children and their ability to counter marketing as well as the implications for children’s rights and digital privacy. Finally the report discusses the policy implications and some of the recent policy action by WHO European Member States

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

헬스케어를 위한 대화형 인공지능의 모사된 페르소나 디자인 및 사용자 경험 연구

학위논문(박사) -- 서울대학교대학원 : 인문대학 협동과정 인지과학전공, 2021.8. 이준환.디지털 헬스케어(Digital Healthcare) 기술의 발전은 일상 헬스케어 영역에서의 혁신을 주도 하고 있다. 이는 의학 전문가들의 정확한 진단, 질병의 치료를 도울 뿐만 아니라 사용자가 스스로 일상에서 자기관리를 할 수 있도록 돕는다. 디지털 헬스케어 기술의 대표적인 목표 중 하나는 효과적으로 헬스케어 서비스를 개인화 시키는 것인데, 이러한 측면에서 대화형 인공지능(Conversational AI)은 사용하기 쉽고 효율적인 비용으로 개인화된 서비스를 제공할 수 있기에 주목받고 있다. 기존의 개인화된 케어 서비스들의 경우는 대부분 의료기관의 질병치료 서비스들에 포함되었는데, 대화형 인공지능은 이러한 개인화된 케어 서비스의 영역을 일상에서의 질병 예방을 위한 관리로 확장하는데 기여한다. 일대일 대화를 통해 맞춤형 교육, 테라피, 그외의 관련 정보 등을 제공할 수 있다는 측면에서 일상 헬스케어에 적합한 디지털 헬스케어 기술로의 활용도가 높다. 이러한 이점으로 인해 다양한 역할을 가진 대화형 인공지능들의 개발이 이루어지고 있다. 그러나, 이러한 대화형 인공지능들에게 사용자의 선호도에 적합한 페르소나를 부여하는 연구는 드물게 이루어 지고 있다. 대화형 인공지능의 주요 기능인 자연어 기반 상호작용은 CASA 패러다임(CASA Paradigm)에서 제기하는 사용자가 시스템을 의인화하는 경향을 높인다. 때문에 페르소나에 대한 사용자의 선호도가 지속적인 대화형 인공지능의 사용과 몰입에 영향을 미친다. 또한 대화형 인공지능의 장기적인 사용을 위해서 적절한 사용자와의 사회적, 감정적 상호작용을 디자인 해 주어야 하는데, 인지된 페르소나에 대한 사용자의 선호도가 이 과정에도 유의미한 영향을 미친다. 때문에 지속적인 참여가 결과에 큰 영향을 미치는 일상 헬스케어 영역에서 대화형 인공지능을 활용하는데 개인화된 페르소나 디자인이 긍정적인 사용자 경험 및 사용자 건강 증진의 가능성을 높일 것으로 본 연구는 가정한다. 개인화된 페르소나 디자인을 위해 사용자와 현실에서 친밀한 관계에 있는 실존인물(호스트)의 페르소나를 대화형 인공지능에 적용하고 평가하는 것이 본 연구의 핵심적인 아아디어이다. 이를 검증하기 위해서 해당 학위 논문은 총 세 가지의 세부 연구를 포함한다. 첫째는 실존인물의 페르소나 중에서도 일상 건강관리에 적합한 호스트의 페르소나를 탐색하는 연구이다. 둘째는 호스트의 페르소나를 대화형 인공지능에 적용하기 위해 고려해야 할 언어적 요소들을 정의하는 연구이다. 마지막으로는 위의 과정을 통해 개발된 실존하는 인물의 페르소나를 가진 대화형 인공지능이 일상 헬스케어 영역에서 실제 효과를 보이는지를 평가하는 연구이다. 또한 해당 학위논문은 일련의 연구들에서 발견한 결과들을 바탕으로 사용자와 친밀한 관계에 있는 페르소나를 일상 헬스케어를 위한 대화형 인공지능에 적용할 때 고려해야할 디자인 함의점들을 도출하고 가이드라인을 제시한다.Advance in digital healthcare technologies has been leading a revolution in healthcare. It has been showing the enormous potential to improve medical professionals’ ability for accurate diagnosis, disease treatment, and the users’ daily self-care. Since the recent transformation of digital healthcare aims to provide effective personalized health services, Conversational AI (CA) is being highlighted as an easy-to-use and cost-effective means to deliver personalized services. Particularly, CA is gaining attention as a mean for personalized care by ingraining positive self-care behavior in a daily manner while previous methods for personalized care are focusing on the medical context. CA expands the boundary of personalized care by enabling one-to-one tailored conversation to deliver health education and healthcare therapies. Due to CA's opportunities as a method for personalized care, it has been implemented with various types of roles including CA for diagnosis, CA for prevention, and CA for therapy. However, there lacks study on the personalization of healthcare CA to meet user's preferences on the CA's persona. Even though the CASA paradigm has been applied to previous studies designing and evaluating the human-likeness of CA, few healthcare CAs personalize its human-like persona except some CAs for mental health therapy. Moreover, there exists the need to improve user experience by increasing social and emotional interaction between the user and the CA. Therefore, designing an acceptable and personalized persona of CA should be also considered to make users to be engaged in the healthcare task with the CA. In this manner, the thesis suggests an idea of applying the persona of the person who is in a close relationship with the user to the conversational CA for daily healthcare as a strategy for persona personalization. The main hypothesis is the idea of applying a close person's persona would improve user engagement. To investigate the hypothesis, the thesis explores if dynamics derived from the social relationship in the real world can be implemented to the relationship between the user and the CA with the persona of a close person. To explore opportunities and challenges of the research idea, series of studies were conducted to (1) explore appropriate host whose persona would be implemented to healthcare CA, (2) define linguistic characteristics to consider when applying the persona of a close person to the CA, and (3)implement CA with the persona of a close person to major lifestyle domains. Based on findings, the thesis provides design guidelines for healthcare CA with the persona of the real person who is in a close relationship with the user.Abstract 1 1 Introduction 12 2 Literature Review 18 2.1 Roles of CA in Healthcare 18 2.2 Personalization in Healthcare CA 23 2.3 Persona Design CA 25 2.4 Methods for Designing Chatbot’s Dialogue Style 30 2.4.1 Wizard of Oz Method 32 2.4.2 Analyzing Dialogue Data with NLP 33 2.4.3 Participatory Design 35 2.4.4 Crowdsourcing 37 3 Goal of the Study 39 4 Study 1. Exploring Candidate Persona for CA 43 4.1 Related Work 44 4.1.1 Need for Support in Daily Healthcare 44 4.1.2 Applying Persona to Text-based CA 45 4.2 Research Questions 47 4.3 Method 48 4.3.1 Wizard of Oz Study 49 4.3.2 Survey Measurement 52 4.3.3 Post Interview 54 4.3.4 Analysis 54 4.4 Results 55 4.4.1 System Acceptance 56 4.4.2 Perceived Trustfulness and Perceived Intimacy 57 4.4.3 Predictive Power of Corresponding Variables 58 4.4.4 Linguistic Factors Affecting User Perception 58 4.5 Implications 60 5 Study 2. Linguistic Characteristics to Consider When Applying Close Person’s Persona to a Text-based Agent 63 5.1 Related Work 64 5.1.1 Linguistic Characteristics and Persona Perception 64 5.1.2 Language Component 66 5.2 Research Questions 68 5.3 Method 69 5.3.1 Modified Wizard of Oz Study 69 5.3.2 Survey 72 5.4 Results 73 5.4.1 Linguistic Characteristics 73 5.4.2 Priority of Linguistic Characteristics 80 5.4.3 Differences between language Component 82 5.5 Implications 82 6 Study3.Implementation on Lifestyle Domains 85 6.1 Related Work 86 6.1.1 Family as Effective Healthcare Provider 86 6.1.2 Chatbots Promoting Healthy Lifestyle 87 6.2 Research questions 94 6.3 Implementing Persona of Family Member 95 6.3.1 Domains of Implementation 96 6.3.2 Measurements Used in the Study 97 6.4 Experiment 1: Food Journaling Chatbot 100 6.4.1 Method 100 6.4.2 Results 111 6.5 Experiment 2: Physical Activity Intervention 128 6.5.1 Method 131 6.5.2 Results 140 6.6 Experiment 3: Chatbot for Coping Stress 149 6.6.1 Method 151 6.6.2 Results 158 6.7 Implications from Domain Experiments 169 6.7.1 Comparing User Experience 170 6.7.2 Comparing User Perception 174 6.7.3 Implications from Study 3 183 7 Discussion 192 7.1 Design Guidelines 193 7.2 Ethical Considerations 200 7.3 Limitations 206 8 Conclusion 210 References 212 Appendix 252 국문초록 262박