Towards a Holistic Approach to Designing Theory-based Mobile Health Interventions

Increasing evidence has shown that theory-based health behavior change interventions are more effective than non-theory-based ones. However, only a few segments of relevant studies were theory-based, especially the studies conducted by non-psychology researchers. On the other hand, many mobile health interventions, even those based on the behavioral theories, may still fail in the absence of a user-centered design process. The gap between behavioral theories and user-centered design increases the difficulty of designing and implementing mobile health interventions. To bridge this gap, we propose a holistic approach to designing theory-based mobile health interventions built on the existing theories and frameworks of three categories: (1) behavioral theories (e.g., the Social Cognitive Theory, the Theory of Planned Behavior, and the Health Action Process Approach), (2) the technological models and frameworks (e.g., the Behavior Change Techniques, the Persuasive System Design and Behavior Change Support System, and the Just-in-Time Adaptive Interventions), and (3) the user-centered systematic approaches (e.g., the CeHRes Roadmap, the Wendel's Approach, and the IDEAS Model). This holistic approach provides researchers a lens to see the whole picture for developing mobile health interventions

A Conversational Interface to Improve Medication Adherence: Towards AI Support in Patient's Treatment

Medication adherence is of utmost importance for many chronic conditions, regardless of the disease type. Engaging patients in self-tracking their medication is a big challenge. One way to potentially reduce this burden is to use reminders to promote wellness throughout all stages of life and improve medication adherence. Chatbots have proven effectiveness in triggering users to engage in certain activity, such as medication adherence. In this paper, we discuss "Roborto", a chatbot to create an engaging interactive and intelligent environment for patients and assist in positive lifestyle modification. We introduce a way for healthcare providers to track patients adherence and intervene whenever necessary. We describe the health, technical and behavioural approaches to the problem of medication non-adherence and propose a diagnostic and decision support tool. The proposed study will be implemented and validated through a pilot experiment with users to measure the efficacy of the proposed approach

Conversational Agent for Health Coaching

Poor diet and physical inactivity are two of the biggest healthcare challenges we are facing, and are related to individuals lifestyle. In fact, a poor lifestyle is strongly correlated to chronic diseases, the leading causes for morbidity and mortality. Adhering to a healthy diet and following an active lifestyle are thus necessary to promote the overall health. However, maintaining a healthy diet and physically active lifestyle is hard. This is due to poor health literacy, lack of awareness, motivation and effective intervention support. Recent years have seen a blast of mHealth apps for health promotion, targeting in particular dietary behavior change. However, reviews showed difficulties in effective adoption and use of these applications in long-term health promotion. Contemporary approaches have focused on tracking user condition and few have analyzed aspects of user interaction with the system. To promote individuals health, users can benefit from some form of tailored guidance or coaching. That said, to ensure adequate users support, personalized care with a human agent in the loop can enhance the care delivery. Due to the increasing demand for continuous care by users and the shortage of caregiver resources, current health services are inefficient relative to user support and decreasing caregivers workload. Digital health devices can act as a key player in providing interactive health activities (via mobile and telemedicine systems), enhancing self-monitoring (through wearable tracker) and tailored coaching (using either automated or manual coaching systems). However, they’re ineffective in providing continuous health services and creating a balance between users support and caregivers workload. In addition, even with the technology existence, there is low motivation to maintain a healthy diet or exercise routines. Individuals use messaging applications as part of their regular daily routines. We harness the power of messaging chatbot systems to provide behavior change interventions for healthy lifestyle promotion. We particularly introduce the role of chatbot in task automation and adhering users to a health plan. Thus, in this thesis we present the concept of "Conversational User Interface in Health Coaching Interventions" that consists of a just-in-time health services to users and caregivers. We discuss ways to integrate the chatbot to assist caregivers with their tasks and support users with their condition. We get users to cue themselves to action by attaching the chatbot with users’ daily messaging routines. The service will eliminate the technology barrier and impairment for the users i.e., elderly. The chatbot accesses reliable user compliance data, sets adherence reminders by condition, and reports daily individuals adherence. The chatbot alerts the coach through a web application in critical cases. The approach facilitates adherence to health interventions by investigating a human-virtual agent mediated coaching approach on user motivation to adhere to the health promotion plan. The approach was validated with different experimentation phases. Using multiple research methods, this dissertation has made several contributions to the understanding of user motivation and the role of a semi-automated system with a human and virtual agent in tracking individuals with poor lifestyle. We will discuss the main contributions and experimentation results throughout the thesis

The Good, The Bad & The Ugly Features: A Meta-analysis on User Review About Food Journaling Apps

Users review about an app is a crucial component for open mobile application market, such as the AppStore and the Google play. Analyzing these reviews can reveal user's sentiment towards a feature in the app. There exist several analytical tools to summarize user reviews and extract meaningful sense out of them. However, these tools are still limited in terms of expressiveness and accurately classifying the reviews into more than a positive and a negative review. There is a need to get more insights from user app reviews and direct it to future app development. In this paper, we present our result of analyzing user reviews of 20 food journaling and health tracking apps. We gathered and analyzed reviews per app and classified them into three distinct categories using the sentiment treebank with recursive neural tensor network. We then analyzed the vocabulary frequency per category using the Gensim implementation of Word2Vec model. The analysis result clustered the reviews into good, bad and ugly feature reviews. Different usage patterns were detected from users review. We identified major reasons why users express a certain sentiment towards an app and learned how users' satisfaction or complaints was related to a specific feature. This research could be a guideline for app developers to follow when developing an app to refrain from adopting techniques that might demotivate (hinder) the application use or adopt those perceived positively by the users

A Review of Empirical Applications on Food Waste Prevention & Management

Food waste has a significant detrimental economic, environmental and social impact. Recent efforts in HCI re-search have examined ways of influencing surplus food waste management. In this paper, we conduct a research survey to investigate and compare the effectiveness of existing approaches in food waste management throughout its lifecycle from agricultural production, post-harvest handling and storage, processing, distribution and consumption. The objectives of the survey are 1) to identify methods in food waste management, 2) their area of focus, 3) the ICT techniques they apply, 4) and the food waste lifecycle they target. In addition, we analyse if 5) they provide an open access API for food waste data analysis. Based on the literature analysis, we then highlight their pros and cons with respect to applications in food waste management. The implications of this research could present a new opportunity for interested stack-holders and future technologies to play a key role in reducing domestic and national food waste

Towards Automatic & Personalised Mobile Health Interventions: An Interactive Machine Learning Perspective

Machine learning (ML) is the fastest growing field in computer science and healthcare, providing future benefits in improved medical diagnoses, disease analyses and prevention. In this paper, we introduce an application of interactive machine learning (iML) in a telemedicine system, to enable automatic and personalised interventions for lifestyle promotion. We first present the high level architecture of the system and the components forming the overall architecture. We then illustrate the interactive machine learning process design. Prediction models are expected to be trained through the participants' profiles, activity performance, and feedback from the caregiver. Finally, we show some preliminary results during the system implementation and discuss future directions. We envisage the proposed system to be digitally implemented, and behaviourally designed to promote healthy lifestyle and activities, and hence prevent users from the risk of chronic diseases

Can a Chatbot Determine My Diet?: Addressing Challenges of Chatbot Application for Meal Recommendation

Poor nutrition can lead to reduced immunity, increased susceptibility to disease, impaired physical and mental development, and reduced productivity. A conversational agent can support people as a virtual coach, however building such systems still have its associated challenges and limitations. This paper describes the background and motivation for chatbot systems in the context of healthy nutrition recommendation. We discuss current challenges associated with chatbot application, we tackled technical, theoretical, behavioural, and social aspects of the challenges. We then propose a pipeline to be used as guidelines by developers to implement theoretically and technically robust chatbot systems

Beyond Patient Monitoring: Conversational Agents Role in Telemedicine & Healthcare Support For Home-Living Elderly Individuals

There is a need for systems to dynamically interact with ageing populations to gather information, monitor health condition and provide support, especially after hospital discharge or at-home settings. Several smart devices have been delivered by digital health, bundled with telemedicine systems, smartphone and other digital services. While such solutions offer personalised data and suggestions, the real disruptive step comes from the interaction of new digital ecosystem, represented by chatbots. Chatbots will play a leading role by embodying the function of a virtual assistant and bridging the gap between patients and clinicians. Powered by AI and machine learning algorithms, chatbots are forecasted to save healthcare costs when used in place of a human or assist them as a preliminary step of helping to assess a condition and providing self-care recommendations. This paper describes integrating chatbots into telemedicine systems intended for elderly patient after their hospital discharge. The paper discusses possible ways to utilise chatbots to assist healthcare providers and support patients with their condition

Towards a Holistic Approach to Designing Theory-based Mobile Health Interventions

Increasing evidence has shown that theory-based health behavior change interventions are more effective than non-theory-based ones. However, only a few segments of relevant studies were theory-based, especially the studies conducted by non-psychology researchers. On the other hand, many mobile health interventions, even those based on the behavioral theories, may still fail in the absence of a user-centered design process. The gap between behavioral theories and user-centered design increases the difficulty of designing and implementing mobile health interventions. To bridge this gap, we propose a holistic approach to designing theory-based mobile health interventions built on the existing theories and frameworks of three categories: (1) behavioral theories (e.g., the Social Cognitive Theory, the Theory of Planned Behavior, and the Health Action Process Approach), (2) the technological models and frameworks (e.g., the Behavior Change Techniques, the Persuasive System Design and Behavior Change Support System, and the Just-in-Time Adaptive Interventions), and (3) the user-centered systematic approaches (e.g., the CeHRes Roadmap, the Wendel's Approach, and the IDEAS Model). This holistic approach provides researchers a lens to see the whole picture for developing mobile health interventions

Persuasive technology in reducing prolonged sedentary behavior at work: A systematic review

Prolonged sedentary behavior is prevalent among office workers and has been found to be detrimental to health. Preventing and reducing prolonged sedentary behavior require interventions, and persuasive technology is expected to make a contribution in this domain. In this paper, we use the framework of persuasive system design (PSD) principles to investigate the utilization and effectiveness of persuasive technology in intervention studies at reducing sedentary behavior at work. This systematic review reveals that reminders are the most frequently used PSD principle. The analysis on reminders shows that hourly PC reminders alone have no significant effect on reducing sedentary behavior at work, while coupling with education or other informative session seems to be promising. Details of deployed persuasive technology with behavioral theories and user experience evaluation are lacking and expected to be reported explicitly in the future intervention studies