a randomized controlled trial

학위논문(박사) -- 서울대학교대학원 : 의과대학 의과학과, 2021.8. 최형진.Background - Since lifestyle modification is the cornerstone of the obesity treatment, digital therapeutics (DTx) became one of the compelling and easily accessible treatment modalities. Objective - This research proposes to validate the treatment efficacy, understand behavioral changes by eating behavioral analysis, identify the predictive digital phenotypes for engagement and clinical outcomes, and examine genetic precision medicine of a novel digital therapeutic for obesity (dCBT-O). Method – This was an open-label, active-comparator, randomized controlled trial. Seventy female participants with body mass index (BMI) scores above 24kg/m² and no clinical problems besides obesity were randomized into experimental and control groups. The experimental group (dCBT-O group; 45 participants) was connected with a therapist intervention using a digital healthcare service that provided daily feedback and assignments for 8 weeks. The control group (25 participants) also used the digital healthcare service but practiced self-care without therapist intervention. Regarding the validating treatment efficacy, the primary outcomes of this study were objectively measured: weight in kg as well as other body compositions at 0, 8, and 24 weeks. Also, several eating behavioral phenotypes were assessed by buffet test-meal and food diary in app to examine the healthy behavioral change. Regarding the predictors for treatment efficacy, multidimensional digital phenotypes within time-series data were analyzed by elastic net regression method and obesity-related SNPs were genotyped from dCBT-O group. Result – Both weight (–3.1%, SD 4.5, vs –0.7%, SD 3.4; p = 0.036) and fat mass (–6.3%, SD 8.8, vs –0.8%, SD 8.1; p = 0.021) reduction at 8 weeks in the dCBT-O group were significantly higher than in the control group. Applying the machine learning approach, sixteen types of digital phenotypes (i.e., lower intake of high calorie food and evening snack, higher interaction frequency with mentors) predicted engagement rates, thirteen different digital phenotypes (i.e., lower intake of high calorie food and carb, higher intake of low calorie food) predicted the short-term weight change, and eight measures of digital phenotypes (i.e., lower intake of carb and evening snack, higher motivation) predicted the long-term weight change. The dCBT-O was also successful in promoting healthy eating behaviors that led to physiological and psychological adjustment for the metabolic mechanisms and consequences of healthy eating behavior. Lastly, CETP and APOA2 SNPs were significantly associated with the change in BMI (p = 0.028 and p = 0.005, respectively) at 24 weeks and eating behavioral phenotypes (p = 0.007 for healthy diet diversity and p = 0.036 for healthy diet proportion, respectively), the clinical efficacy markers of this study. Conclusion – These findings confirm that the multidisciplinary approach via digital modalities enhances the clinical efficacy of digital-based interventions for obesity. Moreover, it contributes to better understand the mechanisms of human eating behavior related to weight control. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics.비만은 대표적인 생활습관 질병으로 알려져 있다. 따라서, 효과적인 비만 치료를 위해서는 다차원적인 치료적 접근이 중요시되는데, 디지털 치료제(Digital Therapeutics; DTx)는 이러한 접근에 최적화 되어있다. 본 연구의 목적은 새로 개발한 비만 디지털 치료제의 효과를 임상적 지표들과 섭식 행동 표현형들의 변화를 기반으로 검증하며, 치료적 순응도와 효과성을 예측할 수 있는 디지털 표현형들과 유전형들을 탐색하는 것이다. 본 연구에서는 BMI 24 이상, 기타 임상적인 증상을 보이지 않는 70명의 2-30대 여성들을 대상으로 대조군 대비 비만 디지털 치료제군(Digital Therapeutic for Obesity; dCBT-O군)에 1:2 비율의 무작위배정 임상시험을 시행하였다. dCBT-O군의 비만 치료는 임상심리학 전공 및 디지털 헬스케어 전문가가 8주 동안 진행하였으며, 24주차에는 치료 후 경과에 대한 평가를 실시하였다. 비만 디지털 치료제 효과 검증의 주요 지표는 체중을 비롯한 다양한 신체 계측 지표들의 변화이다. 이차 지표는 뷔폐실험과 모바일 어플리케이션 내 식단기록에서 수집된 섭식행동 표현형들을 기반으로 건강한 섭식행동 변화이다. 치료 순응도 및 효과 예측 인자들을 발굴하기 위해서는 다차원적인 시계열 디지털 표현형들을 머신러닝 기법으로 분석하였다. 그리고, 치료 반응 수준을 예측하는 유전형들을 찾기 위해 단일염기다형(Single Nucleotide Polymorphisms; SNP) 분석을 시행하였다. 본 연구의 주요 결과로 첫째, 8주간 치료 직후 dCBT-O군의 체중 변화가 대조군의 체중 변화에 비해 유의미하게 감량하였으며, 치료 종료 후 24주차도 체중이 감량 및 유지되었다. 둘째, dCBT-O군의 섭식행동이 대조군의 섭식행동에 비해 유의미하게 건강한 섭식행동으로 증진되었다. 셋째, 머신러닝 분석의 결과 16가지 디지털 표현형들이 치료적 순응도를 예측하고, 13가지 디지털 표현형들이 단기적인 치료효과를 예측하며, 8가지 디지털 표현형들이 장기적인 치료효과를 예측하였다. 마지막으로, CETP와 APOA2 SNP 유전형들이 신체계측 변화와 섭식행동변화와 유의미한 상관을 보였다. 본 연구는 디지털 기술을 활용한 다학제적인 접근이 비만 디지털 치료제의 임상 효과를 향상시킨다는 것을 보여준다. 또한 다차원적인 분석을 통해 체중 조절과 관련된 인간의 섭식 행동의 메커니즘을 더 잘 이해하는 데 기여한다. 본 연구는 첨단 예방의학과 정밀의학을 위한 디지털 치료제 개발에 중요한 패러다임을 제시할 것이다.Chapter 1. Introduction 1 Part I. Validating the treatment efficacy and finding its predictive markers: development of a dCBT-O 6 Part II. Eating behavioral analysis using buffet test-meal and food diary in app: understanding human eating behavior change by dCBT-O 8 Part III. Digital phenotyping using machine-learning analysis: identifying a predictive model for engagement in application and clinical outcomes of dCBT-O 11 Part IV. Genetic analysis for predicting the clinical responses: genetic precision medicine of dCBT-O 14 Chapter 2. Method 19 Chapter 3. Results 40 Chapter 4. Discussion 75 Perspectives A. Main issues related to DTx for obesity and eating behavior problems 91 Perspectives B. Limitations of DTx being applied in the clinics 96 Perspectives C. Future perspectives and recommendations 96 Chapter 5. Conclusion 99 Bibliography 100 Abstract in Korean 118 Acknowledgement 120박

Designing Personalised mHealth solutions: An overview

Introduction Mobile health, or mHealth, is based on mobile information and communication technologies and provides solutions for empowering individuals to participate in healthcare. Personalisation techniques have been used to increase user engagement and adherence to interventions delivered as mHealth solutions. This study aims to explore the current state of personalisation in mHealth, including its current trends and implementation. Materials and Methods We conducted a review following PRISMA guidelines. Four databases (PubMed, ACM Digital Library, IEEE Xplore, and APA PsycInfo) were searched for studies on mHealth solutions that integrate personalisation. The retrieved papers were assessed for eligibility and useful information regarding integrated personalisation techniques. Results Out of the 1,139 retrieved studies, 62 were included in the narrative synthesis. Research interest in the personalisation of mHealth solutions has increased since 2020. mHealth solutions were mainly applied to endocrine, nutritional, and metabolic diseases; mental, behavioural, or neurodevelopmental diseases; or the promotion of healthy lifestyle behaviours. Its main purposes are to support disease self-management and promote healthy lifestyle behaviours. Mobile applications are the most prevalent technological solution. Although several design models, such as user-centred and patient-centred designs, were used, no specific frameworks or models for personalisation were followed. These solutions rely on behaviour change theories, use gamification or motivational messages, and personalise the content rather than functionality. A broad range of data is used for personalisation purposes. There is a lack of studies assessing the efficacy of these solutions; therefore, further evidence is needed. Discussion Personalisation in mHealth has not been well researched. Although several techniques have been integrated, the effects of using a combination of personalisation techniques remain unclear. Although personalisation is considered a persuasive strategy, many mHealth solutions do not employ it. Conclusions Open research questions concern guidelines for successful personalisation techniques in mHealth, design frameworks, and comprehensive studies on the effects and interactions among multiple personalisation techniques

“Now i can see me” designing a multi-user virtual reality remote psychotherapy for body weight and shape concerns

Recent years have seen a growing research interest towards designing computer-assisted health interventions aiming to improve mental health services. Digital technologies are becoming common methods for diagnosis, therapy, and training. With the advent of lower-cost VR head-mounted-displays (HMDs) and high internet data transfer capacity, there is a new opportunity for applying immersive VR tools to augment existing interventions. This study is among the first to explore the use of a Multi-User Virtual Reality (MUVR) system as a therapeutic medium for participants at high-risk for developing Eating Disorders. This paper demonstrates the positive effect of using MUVR remote psychotherapy to enhance traditional therapeutic practices. The study capitalises on the opportunities which are offered by a MUVR remote psychotherapeutic session to enhance the outcome of Acceptance and Commitment Therapy, Play Therapy and Exposure Therapy for sufferers with body shape and weight concerns. Moreover, the study presents the design opportunities and challenges of such technology, while strengths on the feasibility, and the positive user acceptability of introducing MUVR to facilitate remote psychotherapy. Finally, the appeal of using VR for remote psychotherapy and its observed positive impact on both therapists and participants is discussed

Development of “LvL UP”, a smartphone-based, conversational agent-delivered holistic lifestyle intervention for the prevention of non-communicable diseases and common mental disorders

Background: Non-communicable diseases (NCDs) and common mental disorders (CMDs) are the leading causes of death and disability worldwide. Lifestyle interventions via mobile apps and conversational agents present themselves as low-cost, scalable solutions to prevent these conditions. This paper describes the rationale for, and development of, “LvL UP”, a digital lifestyle intervention aimed at preventing NCDs and CMDs.Materials and Methods: A multidisciplinary team led the intervention design process of LvL UP, involving four phases: (i) preliminary research (stakeholder consultations, systematic market reviews), (ii) selecting intervention components and developing the conceptual model, (iii) whiteboarding (prototype development), and (iv) testing and refinement. The Multiphase Optimization Strategy and the UK Medical Research Council framework for developing and evaluating complex interventions were used to guide the intervention development.Results: The first version of LvL UP features a scalable, smartphone-based, and conversational agent-delivered holistic lifestyle intervention built around three pillars: Move More (physical activity), Eat Well (nutrition and healthy eating), and Stress Less (emotional regulation and wellbeing). Intervention components include health literacy and psychoeducational coaching sessions, daily "Life Hacks” (healthy activity suggestions), breathing exercises, and journaling. Engagement components involve motivational interviewing and storytelling to deliver the coaching sessions, as well as progress feedback and gamification. Offline materials are also offered to allow users access to essential intervention content without needing a digital device.Conclusions: The development process of LvL UP led to an evidence-based and user-informed digital health intervention aimed at preventing NCDs and CMDs. LvL UP is designed to be a scalable, engaging, prevention-oriented, holistic intervention for adults at risk of NCDs and CMDs. A feasibility study, and subsequent optimisation and randomised-controlled trials are planned to further refine the intervention and establish effectiveness. The development process described here may prove helpful to other intervention developers

Designing personalised mHealth solutions: An overview

Introduction: Mobile health, or mHealth, is based on mobile information and communication technologies and provides solutions for empowering individuals to participate in healthcare. Personalisation techniques have been used to increase user engagement and adherence to interventions delivered as mHealth solutions. This study aims to explore the current state of personalisation in mHealth, including its current trends and implementation. Materials and Methods: We conducted a review following PRISMA guidelines. Four databases (PubMed, ACM Digital Library, IEEE Xplore, and APA PsycInfo) were searched for studies on mHealth solutions that integrate personalisation. The retrieved papers were assessed for eligibility and useful information regarding integrated personalisation techniques. Results: Out of the 1,139 retrieved studies, 62 were included in the narrative synthesis. Research interest in the personalisation of mHealth solutions has increased since 2020. mHealth solutions were mainly applied to endocrine, nutritional, and metabolic diseases; mental, behavioural, or neurodevelopmental diseases; or the promotion of healthy lifestyle behaviours. Its main purposes are to support disease self- management and promote healthy lifestyle behaviours. Mobile applications are the most prevalent technological solution. Although several design models, such as user-centred and patient-centred designs, were used, no specific frameworks or models for personalisation were followed. These solutions rely on behaviour change theories, use gamification or motivational messages, and personalise the content rather than functionality. A broad range of data is used for personalisation purposes. There is a lack of studies assessing the efficacy of these solutions; therefore, further evidence is needed. Discussion: Personalisation in mHealth has not been well researched. Although several techniques have been integrated, the effects of using a combination of personalisation techniques remain unclear. Although personalisation is considered a persuasive strategy, many mHealth solutions do not employ it. Conclusions: Open research questions concern guidelines for successful personalisation techniques in mHealth, design frameworks, and comprehensive studies on the effects and interactions among multiple personalisation techniques

Annual research review: Digital health interventions for children and young people with mental health problems: a systematic and meta-review

Digital health interventions (DHIs), including computer-assisted therapy, smartphone apps and wearable technologies, are heralded as having enormous potential to improve uptake and accessibility, efficiency, clinical effectiveness and personalisation of mental health interventions. It is generally assumed that DHIs will be preferred by children and young people (CYP) given their ubiquitous digital activity. However, it remains uncertain whether: DHIs for CYP are clinically and cost-effective, CYP prefer DHIs to traditional services, DHIs widen access and how they should be evaluated and adopted by mental health services. This review evaluates the evidence-base for DHIs and considers the key research questions and approaches to evaluation and implementation. We conducted a meta-review of scoping, narrative, systematic or meta-analytical reviews investigating the effectiveness of DHIs for mental health problems in CYP. We also updated a systematic review of randomised controlled trials (RCTs) of DHIs for CYP published in the last 3 years. Twenty-one reviews were included in the meta-review. The findings provide some support for the clinical benefit of DHIs, particularly computerised cognitive behavioural therapy (cCBT), for depression and anxiety in adolescents and young adults. The systematic review identified 30 new RCTs evaluating DHIs for attention deficit/hyperactivity disorder (ADHD), autism, anxiety, depression, psychosis, eating disorders and PTSD. The benefits of DHIs in managing ADHD, autism, psychosis and eating disorders are uncertain, and evidence is lacking regarding the cost-effectiveness of DHIs. Key methodological limitations make it difficult to draw definitive conclusions from existing clinical trials of DHIs. Issues include variable uptake and engagement with DHIs, lack of an agreed typology/taxonomy for DHIs, small sample sizes, lack of blinded outcome assessment, combining different comparators, short-term follow-up and poor specification of the level of human support. Research and practice recommendations are presented that address the key research questions and methodological issues for the evaluation and clinical implementation of DHIs for CYP

A theoretical and practical approach to a persuasive agent model for change behaviour in oral care and hygiene

There is an increased use of the persuasive agent in behaviour change interventions due to the agent‘s features of sociable, reactive, autonomy, and proactive. However, many interventions have been unsuccessful, particularly in the domain of oral care. The psychological reactance has been identified as one of the major reasons for these unsuccessful behaviour change interventions. This study proposes a formal persuasive agent model that leads to psychological reactance reduction in order to achieve an improved behaviour change intervention in oral care and hygiene. Agent-based simulation methodology is adopted for the development of the proposed model. Evaluation of the model was conducted in two phases that include verification and validation. The verification process involves simulation trace and stability analysis. On the other hand, the validation was carried out using user-centred approach by developing an agent-based application based on belief-desire-intention architecture. This study contributes an agent model which is made up of interrelated cognitive and behavioural factors. Furthermore, the simulation traces provide some insights on the interactions among the identified factors in order to comprehend their roles in behaviour change intervention. The simulation result showed that as time increases, the psychological reactance decreases towards zero. Similarly, the model validation result showed that the percentage of respondents‘ who experienced psychological reactance towards behaviour change in oral care and hygiene was reduced from 100 percent to 3 percent. The contribution made in this thesis would enable agent application and behaviour change intervention designers to make scientific reasoning and predictions. Likewise, it provides a guideline for software designers on the development of agent-based applications that may not have psychological reactance

Application of Mobile Health Services to Support Patient Self-Management of Chronic Conditions

Background: Chronic conditions are the leading cause of ill-health, disability and premature death, adding huge health and socioeconomic burden to the healthcare system. Although mobile health (mHealth) services have the potential to provide patients with a timely, ubiquitous, and cost-effective means to access healthcare services, to date, much remains to be revealed for their application in chronic condition management. Aim: This doctoral project aims to comprehensively understand the application of mHealth services to support patient self-management of chronic conditions. This aim is achieved through four objectives: (1) to synthesise research evidence about health outcomes of applying mHealth services to support patient self-management of chronic conditions and the essential components to achieve these outcomes, (2) to determine the mechanism for applying mHealth services to support patient self-management of chronic conditions, (3) to explore critical factors and how these factors influence patients\u27 intention to continuously use mHealth services, and (4) to apply the above findings to guide the design of a prototype mHealth service. Methods: To increase the generalisability of the findings, three chronic conditions that could benefit from mHealth services were purposively studied to address the research objectives within the feasibility of available study sites and resources at different stages of the project. First, two literature review studies were conducted to achieve Objective 1. One was a systematic review to investigate health outcomes of mHealth services to support patient self-management of one chronic condition, unhealthy alcohol use, and the essential components to achieve these outcomes. The other was a rapid review on using behavioural theory to guide the design of mHealth services that support patient self-management of another chronic condition, hypertension. Second, two field studies were conducted to achieve Objectives 2 and 3, respectively. One was an interview study that explored patients\u27 perceptions of a mHealth service to support their self-management of hypertension in China. The other was a questionnaire survey study conducted on the same site that explored critical factors influencing patients\u27 intention to continuously use the mHealth service. Third, a clinician-led, experience-based co-design approach was implemented to apply the above-mentioned learning experience to the development practice of a mHealth service that supports patient self-management of obesity before elective surgery in Australia, achieving Objective 4. Results: Literature reviews identify five structural components - context, theory, content, delivery mode, and implementation procedure - which are essential for mHealth services to achieve three health outcomes - behavioural, physiological, and cognitive outcomes. Inductive synthesis of the interview findings lead to a 6A framework that summarises the mechanisms for mHealth services: access, assessment, assistance, awareness, ability, and activation. Mobile health services provide patients with easy access to health assessment and healthcare assistance to increase their self-management awareness and ability, thereby activating their self-management behaviours. Questionnaire survey study finds that patients\u27 intention to continuously use mHealth services can be influenced by the information quality, system quality and service quality by influencing their perceived usefulness and satisfaction with the mHealth services. Guided by Social Cognitive Theory, the developed prototype mHealth service provide patients with functions of automatic push notifications, online resources, goal setting and monitoring, and interactive health-related exchanges that encourage their physical activity, healthy eating, psychological preparation, and a positive outlook for elective surgery. The patients\u27 requirements in two focus group discussions enabled the research team to improve the mHealth service design. Conclusion: Mobile health services guided by behavioural theories can provide patients with easy access to health assessment and healthcare assistance to increase their self-management awareness and ability, thereby activating their self-management behaviours. The effort for designing mHealth services needs to be placed on crafting content (to improve information quality), developing useful functions and selecting a proper delivery mode (to improve system quality), and establishing effective implementation procedures (to improve service quality). These will ensure patients\u27 perceived usefulness and satisfaction with mHealth services, increase their intention to continuously use such services, thus supporting long-term patient self-management of chronic conditions. As demonstrated by the design case, the findings of this PhD project can be generalised to guide the design of other mHealth services that aim to support patient self-management of chronic conditions