Supporting Diabetes Self-Management with Ubiquitous Computing Technologies: A User-Centered Inquiry

Ubiquitous computing technologies offer opportunities to improve treatments for chronic health conditions. Type 1 diabetes is a compelling use-case for such approaches, given its severity, and need for individuals to make frequent care decisions, informed by complex data. However, current apps, typically based on effortful reflection on collected data, generally show poor adoption, lack vital cognitive and emotional support, and are poorly tailored to users’ actual diabetes decision making processes. This thesis investigates how diabetes apps can be improved from a user-centered perspective. An initial questionnaire-based study investigated how well existing diabetes apps meet user needs. Perceived benefits, limitations, and reasons for low adoption rates were identified. A talk-aloud study of detailed user interactions with diabetes logging apps was conducted to characterize the benefits and limitations of diverse UI elements for T1 diabetes management, and to more precisely identify wider problems with current interaction designs. This led to positing a refined version of Mamykina et al.’s model for diabetes self-management, to account for observed practices, whereby the previously accepted habitual and sensemaking cognitive states are augmented by a posited ‘fluid contextual reasoning’ (FCR) mode, which allows multiple contextual factors to be balanced for dynamic course correction when navigating complex situations, using previously learned knowledge. To investigate user perceptions of the levels and kinds of monitoring anticipated in next generation diabetes decision support systems, a 4-week technology probe, in which participants used multiple networked devices and external data aggregation, was used to frame requirements for user-centered development of such future systems. Integrating all of the above work, an iterative design process was undertaken to create DUETS, a card-based system to facilitate reflection by designers, users, and other stakeholders on diabetes support management systems. The resulting tool and method were then implemented and evaluated through structured sessions with stakeholder focus groups

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

Implementing data-driven systems for work and health: The role of incentives in the use of physiolytics

Following the recent success of health wearable devices (smartwatches, activity trackers) for personal and leisure activities, organizations have started to build digital occupational health programs and data-driven health insurance around these systems. In this way, firms or health insurance companies seek to both support a new form of health promotion for their workforce/clients and to take advantage of large amounts of collected data for organizational purposes. Still, the success in the implementation of wearable health devices (also known as physiolytics) in organizational settings is entirely dependent on the individual motivation to adopt and use physiolytics over time (since organizations cannot establish a mandated use). Therefore, organizations often use incentives to encourage individuals to participate in such data-driven programs. Yet, little is known about these mechanisms that serve to align the interests of an organization with the interests of a group of individuals. This is an important challenge because these incentives may blunder the frontiers between what is voluntary and what is not. Against this background, this thesis aims, from a critical realist perspective, to build general knowledge regarding incentives in physiolytics-centered organizational programs. By doing so, individuals may be able to recognize challenges linked to participation in such programs; organizations may create sensible incentives; policymakers may identify new social issues that appear with this form of digitalization in organizations; and, finally, researchers may investigate new practical and social challenges regarding digitalization in organizations. In concrete terms, the first explorative phase of the thesis shows that feedback, gamification features and financial incentives are the most implemented incentives in physiolytics-centered organizational programs. There is also an overrepresentation of financial incentives for data-health plans, indicating that health insurance companies are building their strategy on external motivators. A second, more explanatory phase serves to further explore these types of incentives and specify recommendations by taking a higher perspective than normative views, so that it is possible to create more alternative managerial strategies or develop other policy perspectives. This part principally shows that the most influential incentives on user behavior are the ones that are transparent, that stimulate individual empowerment, and that propose defined benefits. In terms of contributions, this thesis allows individuals to evaluate how their autonomy and integrity is impacted by incentives in such data-driven programs. This thesis also outlines the necessity for organizations to invest time and resources to know their audience. Organizations additionally need to develop several strategies, by mixing incentives or gradually introducing them. Policymakers must ensure that regulations permit the clear consent of participants; guarantee a proportionality of incentives, and involve entities that can guide individuals through data-sharing. Finally, this thesis enables researchers to further investigate how organizations can develop appropriate and desirable environments regarding data-driven technology, so that individuals may enhance their decision-making processes and organizations may succeed in their implementation

Validation of design artefacts for blockchain-enabled precision healthcare as a service.

Healthcare systems around the globe are currently experiencing a rapid wave of digital disruption. Current research in applying emerging technologies such as Big Data (BD), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Augmented Reality (AR), Virtual Reality (VR), Digital Twin (DT), Wearable Sensor (WS), Blockchain (BC) and Smart Contracts (SC) in contact tracing, tracking, drug discovery, care support and delivery, vaccine distribution, management, and delivery. These disruptive innovations have made it feasible for the healthcare industry to provide personalised digital health solutions and services to the people and ensure sustainability in healthcare. Precision Healthcare (PHC) is a new inclusion in digital healthcare that can support personalised needs. It focuses on supporting and providing precise healthcare delivery. Despite such potential, recent studies show that PHC is ineffectual due to the lower patient adoption in the system. Anecdotal evidence shows that people are refraining from adopting PHC due to distrust. This thesis presents a BC-enabled PHC ecosystem that addresses ongoing issues and challenges regarding low opt-in. The designed ecosystem also incorporates emerging information technologies that are potential to address the need for user-centricity, data privacy and security, accountability, transparency, interoperability, and scalability for a sustainable PHC ecosystem. The research adopts Soft System Methodology (SSM) to construct and validate the design artefact and sub-artefacts of the proposed PHC ecosystem that addresses the low opt-in problem. Following a comprehensive view of the scholarly literature, which resulted in a draft set of design principles and rules, eighteen design refinement interviews were conducted to develop the artefact and sub-artefacts for design specifications. The artefact and sub-artefacts were validated through a design validation workshop, where the designed ecosystem was presented to a Delphi panel of twenty-two health industry actors. The key research finding was that there is a need for data-driven, secure, transparent, scalable, individualised healthcare services to achieve sustainability in healthcare. It includes explainable AI, data standards for biosensor devices, affordable BC solutions for storage, privacy and security policy, interoperability, and usercentricity, which prompts further research and industry application. The proposed ecosystem is potentially effective in growing trust, influencing patients in active engagement with real-world implementation, and contributing to sustainability in healthcare

Practical, appropriate, empirically-validated guidelines for designing educational games

There has recently been a great deal of interest in the potential of computer games to function as innovative educational tools. However, there is very little evidence of games fulfilling that potential. Indeed, the process of merging the disparate goals of education and games design appears problematic, and there are currently no practical guidelines for how to do so in a coherent manner. In this paper, we describe the successful, empirically validated teaching methods developed by behavioural psychologists and point out how they are uniquely suited to take advantage of the benefits that games offer to education. We conclude by proposing some practical steps for designing educational games, based on the techniques of Applied Behaviour Analysis. It is intended that this paper can both focus educational games designers on the features of games that are genuinely useful for education, and also introduce a successful form of teaching that this audience may not yet be familiar with

A Framework for Evaluating Technology-Mediated Collaborative Workflow

The adoption of new technology into collaborative workflows has permeated every aspect of our personal and professional lives with the promise of performing work processes more efficiently and with greater capability. The continued rise of ubiquitous computing and heightened need for collaborative features suggest that a view of enabling technologies in a workflow should include the physical computing infrastructure, the collaborative interaction between humans and computers, and the informatics (i.e., collection and representation of data within the workflow). The development and integration of technology for collaborative workflows introduces many variables that are of great concern to companies, organization, and individuals. These variables include the costs of development, the switching cost associated with migrating from the current workflow to the technology-enhanced workflow, and details of how the technology-mediated workflow functions compare to the current workflow functions. There is, however, no consistent, generalizable approach to evaluate and compare an existing workflow with the enhanced technology-mediated workflow in a manner that identifies improvements and barriers in replicable qualitative and quantitative measures. In order to develop such a consistent, generalizable approach, this research investigates what necessary set of cross-disciplinary metrics and methodology is required to effectively evaluate technology-mediated collaborative workflow through an analysis of related works from four disciplines (Social Sciences, Organization and Behavioral Management, Industrial Engineering, and Human-Computer Interaction). The research introduces the Collaborative Space – Analysis Framework (CS-AF), a cross-disciplinary model and methodology designed to evaluate and compare collaborative workflows. The research includes testing the CS-AF model using two diverse empirical studies designed to evaluate a current-state workflow, compared to a technology-mediated workflow on five key collaborative areas (Context, Technology, Process, Attitude and Behavior, and Outcomes). The research incorporates the CS-AF model and methodology to test the effectiveness of the approach for capturing and analyzing essential quantitative and qualitative parameters of the collaborative workflows. The second empirical study tested hypertensive patients currently involved in clinical maintenance with regular outpatient monitoring. The test included 50 hypertension patients, selected based on matched-pairs for age and gender to test the workflow model in a 3-week trial. All participants were tested on an existing workflow (current-state), then the population was randomly split within pairs. The matched-pairs were assigned to one of two alternative workflows: 25 patients were introduced to a manual hypertension self-exam workflow (control group), and their matched-pair counterparts were introduced to technology-mediated hypertension self-exam workflow. All participants were tested on the existing workflow (current-state), followed by the introduction of an alternate workflow, and then tested a second time (pre-/ post-) with the same CS-AF procedure. The study incorporated the research findings from these two tests and a comparison between the workflows introduced using the CS-AF metrics. Findings from the two diverse empirical studies using the CS-AF (Graphic Communications sales order process, and Health Information Technology hypertension exam workflow) indicate that technology-mediated workflows do improve collaborative performance; however, adoption is not as pronounced as hypothesized. The research findings indicate that the lack of acceptance is due to non-technology factors, such as attitude and behavior, which play a significant role in adoption and need similar attention as technology innovation to drive true adoption and ultimately better collaborative performance. The research findings also indicate that the effectiveness of the CS-AF may have potential as a generalizable approach for evaluating technology-mediated collaborative workflow in a variety of unique domains

Analysis and design of individual information systems to support health behavior change

As a wide-ranging socio-technical transformation, the digitalization has significantly influenced the world, bringing opportunities and challenges to our lives. Despite numerous benefits like the possibility to stay connected with people around the world, the increasing dispersion and use of digital technologies and media (DTM) pose risks to individuals’ well-being and health. Rising demands emerging from the digital world have been linked to digital stress, that is, stress directly or indirectly resulting from DTM (Ayyagari et al. 2011; Ragu-Nathan et al. 2008; Tarafdar et al. 2019; Weil and Rosen 1997), potentially intensifying individuals’ overall exposure to stress. Individuals experiencing this adverse consequence of digitalization are at elevated risk of developing severe mental health impairments (Alhassan et al. 2018; Haidt and Allen 2020; Scott et al. 2017), which is why various scholars emphasize that research should place a stronger focus on analyzing and shaping the role of the individual in a digital world, pursuing instrumental as well as humanistic objectives (Ameen et al. 2021; Baskerville 2011b). Information Systems (IS) research has long placed emphasis on the use of information and communication technology (ICT) in organizations, viewing an information system as the socio-technical system that emerges from individuals’ interaction with DTM in organizations. However, socio-technical information systems, as the essence of the IS discipline (Lee 2004; Sarker et al. 2019), are also present in different social contexts from private life. Acknowledging the increasing private use of DTM, such as smartphones and social networks, IS scholars have recently intensified their efforts to understand the human factor of IS (Avison and Fitzgerald 1991; Turel et al. 2021). A framework recently proposed by Matt et al. (2019) suggests three research angles: analyzing individuals’ behavior associated with their DTM use, analyzing what consequences arise from their DTM use behavior, and designing new technologies that promote positive or mitigate negative effects of individuals’ DTM use. Various recent studies suggest that individuals’ behavior seems to be an important lever influencing the outcomes of their DTM use (Salo et al. 2017; Salo et al. 2020; Weinstein et al. 2016). Therefore, this dissertation aims to contribute to IS research targeting the facilitation of a healthy DTM use behavior. It explores the use behavior, consequences, and design of DTM for individuals' use with the objective to deliver humanistic value by increasing individuals' health through supporting a behavior change related to their DTM use. The dissertation combines behavioral science and design science perspectives and applies pluralistic methodological approaches from qualitative (e.g., interviews, prototyping) and quantitative research (e.g., survey research, field studies), including mixed-methods approaches mixing both. Following the framework from Matt et al. (2019), the dissertation takes three perspectives therein: analyzing individuals’ behavior, analyzing individuals’ responses to consequences of DTM use, and designing information systems assisting DTM users. First, the dissertation presents new descriptive knowledge on individuals’ behavior related to their use of DTM. Specifically, it investigates how individuals behave when interacting with DTM, why they behave the way they do, and how their behavior can be influenced. Today, a variety of digital workplace technologies offer employees different ways of pursuing their goals or performing their tasks (Köffer 2015). As a result, individuals exhibit different behaviors when interacting with these technologies. The dissertation analyzes what interactional roles DTM users can take at the digital workplace and what may influence their behavior. It uses a mixed-methods approach and combines a quantitative study building on trace data from a popular digital workplace suite and qualitative interviews with users of this digital workplace suite. The empirical analysis yields eight user roles that advance the understanding of users’ behavior at the digital workplace and first insights into what factors may influence this behavior. A second study adds another perspective and investigates how habitual behavior can be changed by means of DTM design elements. Real-time feedback has been discussed as a promising way to do so (Schibuola et al. 2016; Weinmann et al. 2016). In a field experiment, employees working at the digital workplace are provided with an external display that presents real-time feedback on their office’s indoor environmental quality. The experiment examines if and to what extent the feedback influences their ventilation behavior to understand the effect of feedback as a means of influencing individuals’ behavior. The results suggest that real-time feedback can effectively alter individuals’ behavior, yet the feedback’s effectiveness reduces over time, possibly as a result of habituation to the feedback. Second, the dissertation presents new descriptive and prescriptive knowledge on individuals’ ways to mitigate adverse consequences arising from the digitalization of individuals. A frequently discussed consequence that digitalization has on individuals is digital stress. Although research efforts strive to determine what measures individuals can take to effectively cope with digital stress (Salo et al. 2017; Salo et al. 2020; Weinert 2018), further understanding of individuals’ coping behavior is needed (Weinert 2018). A group at high risk of suffering from the adverse effects of digital stress is adolescents because they grow up using DTM daily and are still developing their identity, acquiring mental strength, and adopting essential social skills. To facilitate a healthy DTM use, the dissertation explores what strategies adolescents use to cope with the demands of their DTM use. Combining a qualitative and a quantitative study, it presents 30 coping responses used by adolescents, develops five factors underlying adolescents’ activation of coping responses, and identifies gender- and age-related differences in their coping behavior. Third, the dissertation presents new prescriptive knowledge on the design of individual information systems supporting individuals in understanding and mitigating their perceived stress. Facilitated by the sensing capabilities of modern mobile devices, it explores the design and development of mobile systems that assess stress and support individuals in coping with stress by initiating a change of stress-related behavior. Since there is currently limited understanding of how to develop such systems, this dissertation explores various facets of their design and development. As a first step, it presents the development of a prototype aiming for life-integrated stress assessment, that is, the mobile sensor-based assessment of an individual’s stress without interfering with their daily routines. Data collected with the prototype yields a stress model relating sensor data to individuals’ perception of stress. To deliver a more generalized perspective on mobile stress assessment, the dissertation further presents a literature- and experience-based design theory comprising a design blueprint, design requirements, design principles, design features, and a discussion of potentially required trade-offs. Mobile stress assessment may be used for the development of mobile coping assistants. Aiming to assist individuals in effectively coping with stress and preventing future stress, a mobile coping assistant should recommend adequate coping strategies to the stressed individual in real-time or execute targeted actions within a defined scope of action automatically. While the implementation of a mobile coping assistant is yet up to future research, the dissertation presents an abstract design and algorithm for selecting appropriate coping strategies. To sum up, this dissertation contributes new knowledge on the digitalization of individuals to the IS knowledge bases, expanding both descriptive and prescriptive knowledge. Through the combination of diverse methodological approaches, it delivers knowledge on individuals’ behavior when using DTM, on the mitigation of consequences that may arise from individuals’ use of DTM, and on the design of individual information systems with the goal of facilitating a behavior change, specifically, regarding individuals’ coping with stress. Overall, the research contained in this dissertation may promote the development of digital assistants that support individuals’ in adopting a healthy DTM use behavior and thereby contribute to shaping a socio-technical environment that creates more benefit than harm for all individuals