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

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

Process-driven Innovation: An Analysis of Digital Health Technologies

Healthcare is facing a major transformation driven by digitalization and the shift of responsibility to the individual patient level. Digital health enables significant improvements in terms of efficiency, effectiveness and quality of healthcare. This dissertation provides a framework, which underlines the relevance of combining innovation and process management in the healthcare system. The current and future state of research and practice of promising technologies, their benefits and their challenges were elaborated. Moreover, approaches for integrating suitable and emerging digital health technologies in existing healthcare infrastructure were investigated, in particular the motivation and acceptance of different stakeholders and users. The dissertation contributes recommendations for science, government, and healthcare actors by elaborating the concept of patient-centricity and process improvement

THE HUMANS BEHIND ARTIFICIAL INTELLIGENCE – AN OPERATIONALISATION OF AI COMPETENCIES

Despite the importance of artificial intelligence (AI) proficiency as a determinant for AI adoption, there remains a lack of empirical research studying competencies needed to leverage AI effectively. This paper addresses this research gap with a mixed methods approach. First, we conduct a qualitative content analysis of the practical and scientific literature to derive and structure the existing body of knowledge. We subsequently perform a quantitative content analysis of 9,247 job advertisements. We merge the results using a triangulation approach and a) present a comprehensive overview of key technical and managerial competencies essential for implementing and utilising AI on an individual level, b) highlight the demand for AI-related competencies in the three occupational fields Data Science and Engineering, Software Engineering and Development, and Business Development and Sales, and c) underline the need to adapt workforce competencies to a labour market transformation induced by AI

Knowledge Behavior Gap Model: An Application for Technology Acceptance

International audienc

How to Overcome the Barriers of AI Adoption in Healthcare: A Multi-Stakeholder Analysis

We present barriers of AI adoption in healthcare on macro to micro level and respective actions to overcome these challenges for each stakeholder group. The findings are verified with results from literature. We used two qualitative methods:(1) a systematic literature review and (2) expert interviews with seven AI experts and nine physicians. We applied a deductive coding scheme. The barriers can be classified in social, ethical, political, economic, technological, educational and organizational barriers. The findings provide that the most hindering barriers are of technological, political and organizational nature. Social and economic barriers are less difficult to overcome, in particular when the benefits of AI application become apparent in practice. From our results, we infer the following four actions: enlightment, regulation, incentives and collaboration. We linked all derived actions with the identified barriers and stakeholders. Thus, we provide a guidance to overcome the adoption barriers of AI in healthcare

Understanding the Role of Predictive and Prescriptive Analytics in Healthcare: A Multi-Stakeholder Approach

The volume, velocity and variety of data is continuously rising. While many industry sectors are already applying big data analytics for various purposes, the use of big data in healthcare remains limited. A major reason for this development lies in the fragmented structure and conflicts of interests among the various stakeholders in the sector. To date, there is a lack of a comprehensive study that integrates insights from both practical and academic literature with expert knowledge to create a holistic picture of the main use cases, challenges and benefits of predictive and prescriptive analytics (PPA) in healthcare. To fill this gap, we investigated the role of PPA in healthcare from different stakeholder perspectives. We conducted a systematic literature review and applied content analysis to identify the main patterns extracted from the literature. The findings were triangulated with insights gained from 9 interviews with healthcare experts. Overall, we identified 8 use case clusters, 18 key benefits and 10 key challenges for the stakeholders involved. Furthermore, the role of PPA in healthcare is discussed from different stakeholders’ perspectives. Our findings reveal that the stakeholders pursue contrasting interests, which require legal regulation such that PPA can diffuse on a wider scale

Learnings from the design and acceptance of the German COVID-19 tracing app for IS-driven crisis management: a design science research

Abstract Background This article investigates the research problem of digital solutions to overcome the pandemic, more closely examining the limited effectiveness and scope of the governmental COVID-19 tracing apps, using the German COVID-19 tracing app (Corona-Warn-App) as an example. A well-designed and effective instrument in the technological toolbox is of utmost importance to overcome the pandemic. Method A multi-methodological design science research approach was applied. In three development and evaluation cycles, we presented, prototyped, and tested user-centered ideas of functional and design improvement. The applied procedure contains (1) a survey featuring 1993 participants from Germany for evaluating the current app, (2) a gathering of recommendations from epidemiologists and from a focus group discussion with IT and health experts identifying relevant functional requirements, and (3) an online survey combined with testing our prototype with 53 participants to evaluate the enhanced tracing app. Results This contribution presents 14 identified issues of the German COVID-19 tracing app, six meta-requirements, and three design principles for COVID-19 tracing apps and future pandemic apps (e.g., more user involvement and transparency). Using an interactive prototype, this study presents an extended pandemic app, containing 13 potential front-end (i.e., information on the regional infection situation, education and health literacy, crowd and event notification) and six potential back-end functional requirements (i.e., ongoing modification of risk score calculation, indoor versus outdoor). In addition, a user story approach for the COVID-19 tracing app was derived from the findings, supporting a holistic development approach. Conclusion Throughout this study, practical relevant findings can be directly transferred to the German and other international COVID-19 tracing applications. Moreover, we apply our findings to crisis management theory—particularly pandemic-related apps—and derive interdisciplinary learnings. It might be recommendable for the involved decision-makers and stakeholders to forego classic application management and switch to using an agile setup, which allows for a more flexible reaction to upcoming changes. It is even more important for governments to have a well-established, flexible, design-oriented process for creating and adapting technology to handle a crisis, as this pandemic will not be the last one

Exploring User Acceptance Determinants of COVID-19-Tracing Apps to Manage the Pandemic

International audienceTracing infectious individuals and clusters is a major tactic for mitigating the pandemic. This paper explores the factors impacting the intentions and actual use of COVID-19 contact tracing apps based on a technology acceptance model. A partial least squares structural equation model has been applied to understand determinants for the usage of tracing apps based on a large sample (N = 2,398) from more than 30 countries (mainly from Germany and USA). Further, the paper presents a classification of COVID-19 apps and users. Through that, the study provides insights for technologists and designers of tracing apps as well as policy makers and practitioners to work toward enhancing user acceptance. Moreover, the results are abstracted to general social participation with apps in order to manage future strategies. The theoretical contribution of this work includes the results of our acceptance model and a classification of COVID-19 tracing and tracking apps