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.Comment: 7 page

Comparison of Self-monitoring Feedback Data from Electronic Food and Nutrition Tracking Tools

Changing dietary habits and keeping food diary encourages fewer calorie consumption, and thus weight loss. Studies have shown that people who keep food diary are more successful in losing weight and keeping it off. However, no study has investigated the nutritional values produced by food journaling applications. This is crucial since keeping food diaries helps identify areas where changes needed to help user's loss weight, based on the application feedback. To achieve this, the provided data should be consistent among all applications. Otherwise, this will question the effectiveness and reliability of such tools in tracking diet and weight loss, and hence question user trust in these applications. This study characterizes the use of 4 food journaling applications to track user diet for 10 days (namely, MyFitnessPal, Lose It, FatSecret, CRONOMeter). We measured variations between the output of each application. The findings revealed an inconsistent and a variation in the output feedback given by all the 4 tools. Although some tools provided closer values, still their data were different and inconsistent. Moreover, some tools were missing essential nutritional fact data, such as sugar and fiber. We additionally compared a sample of food items common among all tools with the Swiss Food Composition Database and checked for their consistency with the same items in the database. The evaluation of the applications showed a gap in the data consistency among applications and the FCD, and questions how reliable they are for food logging and diet tracking. This study contributes to current research in health and wellbeing and can be referenced by researchers to provide deeper insights into the data consistency. Future work should examine ways to provide precise output that is common among all applications, so to guarantee the effect on weight loss

Different Stages of Wearable Health Tracking Adoption & Abandonment: A Survey Study and Analysis

Health trackers are widely adopted to support users with daily health and wellness tracking. They can help increase steps taken, enhance sleeping pattern, improve healthy diet, and promote overall health. Despite the growth in the adoption of such technology, their reallife use is still questionable. While some users derive longterm value from their trackers, others face barriers to integrate it into their daily routine. Studies have analysed technical aspects of these barriers. In this study, we analyse the behavioural factors of discouragement and wearable abandonment strictly tied to user habits and living circumstances. A data analysis was conducted in two different studies, one with users posts about wearable sales and the other one was a survey analysis. The two studies were used to analyse the stages of wearable adoption, use and abandonment. Therefore, we mainly focused on users motives to get a wearable tracker and to post it for sale. We extracted insights about user motives, highlighted technology condition and limitations, and timeframe before abandonment. The findings revealed certain user behavioural pattern throughout the wearable use and abandonment.Comment: arXiv admin note: substantial text overlap with arXiv:1904.0798

Beyond Technical Motives: Perceived User Behavior in Abandoning Wearable Health & Wellness Trackers

Health trackers are widely adopted to support individuals with daily health and wellness activity tracking. They can help increase steps taken, enhance sleeping pattern, improve healthy diet, and promote the overall health. Despite the growth in wearable adoption, their real-life use is still questionable. While some users derive long-term values from their trackers, others face barriers to integrate it into their daily routine. Studies have analysed technical aspects of these barriers. In this paper, we analyse the behavioural factors of discouragement and wearable abandonment strictly tied to user habits and lifestyle circumstances. A data analysis was conducted on 8 of the highly rated wearables for 2017. The analysis collected sale posts on Kijiji and Gumtree, the second sales online retailers for both the Italian and UK market, respectively. We extracted insights from the posts about user motives, highlighted technology condition and limitations, and timeframe before the abandonment. The findings revealed certain user behavioural patterns when abandoning their wearables. In addition, analysing the posts showed other motives for the posts and not strictly related to wearable abandonment

Domain Specific Design Patterns: Designing For Conversational User Interfaces

Designing conversational user interface experience is complicated because conversation comes with many expectations. When these expectations are met, we feel the interface is natural, but once violated, we feel something is amiss. The last decade witnessed human language technologies and behaviours to enable humans converse with software using spoken dialogue to access, create and process information. Less is known about the practicalities of designing chatbot interactions. In this paper, we introduce the nature of conversational user interfaces (CUIs) and describe the underlying technologies they are based on. Moreover, we define guidelines for designing conversational interfaces in various domains. This paper particularly focuses on classifying the elements and techniques used in CUI design patterns. After concluding certain challenges with CUI, we discuss important features and chatbot states to be considered in CUI design for specific domain. We envisage this study to support CUI researchers to design tailored chatbots applicable into certain domain and improve the current state of research challenges in the field of Artificial Intelligence and conversational agents.Comment: 7 page

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.Comment: 7 page

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.Comment: 13 page

Assistive System in Conversational Agent for Health Coaching: The CoachAI Approach

With increasing physicians' workload and patients' needs for care, there is a need for technology that facilitates physicians work and performs continues follow-up with patients. Existing approaches focus merely on improving patient's condition, and none have considered managing physician's workload. This paper presents an initial evaluation of a conversational agent assisted coaching platform intended to manage physicians' fatigue and provide continuous follow-up to patients. We highlight the approach adapted to build the chatbot dialogue and the coaching platform. We will particularly discuss the activity recommender algorithms used to suggest insights about patients' condition and activities based on previously collected data. The paper makes three contributions: (1) present the conversational agent as an assistive virtual coach, (2) decrease physicians workload and continuous follow up with patients, all by handling some repetitive physician tasks and performing initial follow up with the patient, (3) present the activity recommender that tracks previous activities and patient information and provides useful insights about possible activity and patient match to the coach. Future work focuses on integrating the recommender model with the CoachAI platform and test the prototype with patient's in collaboration with an ambulatory clinic

Health Behaviour Change Techniques in Diabetes Management Applications: A Systematic Review

The rapid growth in mobile healthcare technology could significantly help control chronic diseases, such as diabetes. This paper presents a systematic review to characterise type 1 & type 2 diabetes management applications available in Apple's iTunes store. We investigated "Health & Fitness" and "Medical" apps following a two-step filtering process (Selection and Analysis phases). We firstly investigated the apps compliance to the persuasive system design (PSD) model. We then characterised the behaviour change techniques (BCTs) of top-ranked apps for diabetes management. Finally, we checked the apps regarding the stages of disease continuum. The findings revealed apps incorporation some PSD principles based on their configuration and behaviour change techniques. Most apps miss the element of BCT and focus on measuring exercise and caloric intake. Few apps consider managing specific diabetes type, which raises doubts about the effectiveness of those apps in providing sustainable diabetes management. Moreover, people may need multiple apps to initiate and maintain a healthy behaviour

Designing for Health Chatbots

Building conversational agents have many technical, design and linguistic challenges. Other more complex elements include using emotionally intelligent conversational agent to build trust with the individuals. In this chapter, we introduce the nature of conversational user interfaces (CUIs) for health and describe UX design principles informed by a systematic literature review of relevant research works. We analyze scientific literature in conversational interfaces and chatterbots, providing a survey of major studies and describing UX design principles and interaction patterns