FeelFit – Design and Evaluation of a Conversational Agent to Enhance Health Awareness

In the course of digitalisation, healthcare systems are undergoing a major transformation. The generation and processing of health-related data are intended to improve health concerns. However, individual health awareness remains inadequate. To counteract this problem, issues in the fields of health awareness, wearable health monitoring systems, conversational agents, and user interface design were identified. Meta-requirements were derived from these issues and then converted into design principles. We developed the FeelFit conversational agent under consideration of those design principles. FeelFit measures vital parameters with various wearable sensors and presents them, enriched with personalised health information, to the user in the form of a conversation via individually configurable input and output devices. The conversational agent was evaluated by two experiments with 90 participants and a workshop. The results confirm a positive usability and task fulfilment of our conversational agent. Compared to known applications, the participants highlighted the more natural interaction and seamless integration of various sensors as strengths of FeelFit

Usability Assessment of Conversational Agents in Healthcare: A Literature Review

Conversational agents (CA) are chatbot-based systems supporting the interaction with users through text, speech, or other modalities. They are used in an increasing number of medical use cases. Even though usability is considered a prerequisite for the success of mHealth apps using CA, there is still no standard procedure to study usability of health CA. In this paper, we report the results from a systematic literature review aiming at identifying study designs, tools, and metrics used to assess usability in health CA. We searched three bibliographic databases (PubMed, Scopus, IEEE Xplore) for papers reporting on CA in healthcare to extract information on the usability assessment of those CA. From 273 retrieved results, we included 66 papers for full text review. 34 of them reported on usability assessments. A broad range of tools is used (e.g. SUS, UEQ), but also individual questionnaires are exploited. The examined studies use scenario-based setups but assess also real-world usage. Exploratory setups are rarely reported. Due to the differences in the study designs and assessment tools, it is impossible to compare usability among CA. Thus, we recommend to develop a standardised procedure that can be always applied and which can be enriched by assessments needed for evaluating usability of CA-specific features

Be a Miracle - Designing Conversational Agents to Influence Users’ Intention Regarding Organ Donation

The increasing need for organ donations remains a worldwide challenge as transplant waiting lists grow and donation rates persist at constant levels. The increasing popularity of conversational agents (CAs) has prompted new strategies for educating and persuading individuals to adjust their cognitive and behavioral beliefs and become donors. However, how CAs should be designed to modify uninformed users’ intention to donate remains unclear. Against this background, we conducted an online experiment (N=134) to examine the impact of a human-like CA design on users\u27 intention to become organ donors. Based on the three-factor theory of anthropomorphism and the elaboration likelihood model, we derive three theoretical mechanisms to understand the influence of a CAs human-like design on users’ intention to donate. The findings show that perceived anthropomorphism does not directly impact persuasion and empathy but is mediated via perceived usefulness to influence the intention to donate

Artificial Intelligence Agents and Knowledge Acquisition in Health Information System

This research work highlights the need for AI-powered applications and their usages for theoptimization of information flow processes in the medical sector, from the perspective of howAI-agents can impact human-machine interaction (HCI) for acquiring relevant and necessaryinformation in emergency department (ED). This study investigates how AI-agents can be applied to manage situations of patient related unexpected experiences, such as long waiting times,overcrowding issues, and high number of patients leaving without being diagnosed. For knowledge acquisition, we incorporated modelling workshop techniques for gathering domain information from the domain experts in the context of emergency department in Karolinska Hospi-tal, Solna, Stockholm, Sweden, and for designing the AI-agent utilizing NLP techniques. We dis-cuss how the proposed solution can be used as an assistant to healthcare practitioners and workers to improve medical assistance in various medical procedures to increase flow and to reduce workloads and anxiety levels. The implementation part of this work is based on the natural language processing (NLP) techniques that help to develop the intelligent behavior for information acquisition and itsretriev-al in a natural way to support patients/relatives’ communication with the healthcare organization efficiently and in a natural way

Exploring the Abstraction Levels of Design Principles: The Case of Chatbots

Formulating design principles is the primary mechanism to codify design knowledge which elevates its meaning to a general level and applicability. Although we can observe a great variety of abstraction levels in available design principles, spanning from more situated to more generic levels, there is only limited knowledge about the corresponding (dis-)advantages of using a certain level of abstraction. That is problematic because it hinders researchers in making informed decisions regarding the (intended) level of abstraction and practitioners in being oriented whether the principles are already contextualized or still require effort to apply them within their situation. Against this backdrop, this paper (1) explores different abstraction levels of design principles based on a sample of 69 principles from the chatbot domain, as well as (2) provides a preliminary positioning framework and lessons learned. We aim to complement methodological guidance and strengthen the principles\u27 applicability, ultimately leading to knowledge reuse

Aging between Participation and Simulation

This publication aims to initiate an interdisciplinary discourse on the ethical, legal, and social implications of socially assistive technologies in healthcare. It combines practically relevant insights and examples from current research and development with ethical analysis to uncover moral pitfalls at the intersection between the promotion of social participation and well-being, and risks that may diminish the achievement of these ends

“May I Help You?”: Exploring the Effect of Individuals’ Self-Efficacy on the Use of Conversational Agents

Conversational agents (CAs) increasingly permeate our lives and offer us assistance for a myriad of tasks. Despite promising measurable benefits, CA use remains below expectations. To complement prior technology-focused research, this study takes a user-centric perspective and explores an individual’s characteristics and dispositions as a factor influencing CA use. In particular, we investigate how individuals’ self-efficacy, i.e., their belief in their own skills and abilities, affects their decision to seek assistance from a CA. We present the research model and study design for a laboratory experiment. In the experiment, participants complete two tasks embedded in realistic scenarios including websites with integrated CAs – that they might use for assistance. Initial results confirm the influence of individuals’ self-efficacy beliefs on their decision to use CAs. By taking a human-centric perspective and observing actual behavior, we expect to contribute to CA research by exploring a factor likely to drive CA use

Trust Recipes for Enhancing the Intention to Adopt Conversational Agents for Disease Diagnosis: An fsQCA Approach

In this study, we examine the configurations of trust-enhancing factors that determine the intention to adopt conversational agents (CAs) for disease diagnosis. After identifying trust factors influencing the behavioral intent to adopt CAs based on the information systems acceptance research field, we assigned 201 participants to use the mobile Ada application and surveyed them about their experience. Ada is a medical diagnostic CA that combines patients’ symptoms with their medical history and provides diagnostic suggestions. The collected data was analyzed using a fuzzy set qualitative comparative analysis to capture the causal complexity of trust. We identified several configurations of trust-enhancing factors affecting the intention to adopt the CA. In particular, our results show that the adoption intentions are strongly determined by trust factors associated with the performance dimension. Furthermore, we derived two propositions for the development of CAs for healthcare purposes and elaborated implications for research and practice

Towards Functionalities of Self-Tracking Wearables, their Effects on Humans and their Application Areas: Where can We Improve?

Self-Tracking wearables bear valuable opportunities, which unfold when the frame conditions invite users to keep track. In this work, we present the following six crucial functionalities of self-tracking devices: feedback, socializing, goal setting, self-monitoring, gamification and measurement itself. We describe effects that result from functionalities. Subsequently, we derive potential relations between functionalities and their main effects mentioned in literature. We identified sets of functionalities that are combined by the manufacturer so that a certain effect can be enhanced or attained. Furthermore, we put the functionalities of self-tracking devices in connection with lifestyle areas and show in which areas the functionalities are already applied and can be used in future. These findings are summarized in the result artifact and are based on a structured literature review, carried out with five prevalent databases. From the findings, we derived three scientific implications as well as three practical implications for wearable manufacturers and physicians

Aging between Participation and Simulation

This publication aims to initiate an interdisciplinary discourse on the ethical, legal, and social implications of socially assistive technologies in healthcare. It combines practically relevant insights and examples from current research and development with ethical analysis to uncover moral pitfalls at the intersection between the promotion of social participation and well-being, and risks that may diminish the achievement of these ends