This chapter discusses main opportunities and challenges of assessing and utilizing personality traits in personalized interactive systems and services. This unique perspective arises from our long-term collaboration on research projects involving three groups on human-computer interaction (HCI), psychology, and statistics. Currently, personalization in HCI is often based on past user behavior, preferences, and interaction context. We argue that personality traits provide a promising additional source of information for personalization, which goes beyond context- and device-specific behavior and preferences. We first give an overview of the well-established Big Five personality trait model from psychology. We then present previous findings on the influence of personality in HCI associated with the benefits and challenges of personalization. These findings include the preference for interactive systems, filtering of information to increase personal relevance, communication behavior, and the impact on trust and acceptance. Moreover, we present first approaches of personality-based recommender systems. We then identify several opportunities and use cases for personality-aware personalization: (i) personal communication between users, (ii) recommendations upon first use, (iii) persuasive technology, (iv) trust and comfort in autonomous vehicles, and (v) empathic intelligent systems. Furthermore, we highlight main challenges. First, we point out technological challenges of personality computing. To benefit from personality awareness, systems need to automatically assess the user’s personality. To create empathic intelligent agents (e. g., voice assistants), a consistent personality has to be synthesized. Second, personality-aware personalization raises questions about user concerns and views, particularly privacy and data control. Another challenge is acceptance and trust in personality-aware systems due to the sensitivity of the data. Moreover, the importance of an accurate mental model for users’ trust in a system was recently underlined by the right for explanations in the EU’s General Data Protection Regulation. Such considerations seem particularly relevant for systems that assess and utilize personality. Finally, we examine methodological requirements such as the need for large sample sizes and appropriate measurements. We conclude with a summary of opportunities and challenges of personality-aware personalization and discuss future research questions.

Modern systems that deal with inference in texts need automatized methods to extract meaning representations (MRs) from texts at scale. Open Information Extraction (IE) is a prominent way of extracting all potential relations from a given text in a comprehensive manner. Previous work in this area has mainly focused on the extraction of isolated relational tuples. Ignoring the cohesive nature of texts where important contextual information is spread across clauses or sentences, state-of-the- art Open IE approaches are thus prone to generating a loose arrangement of tuples that lack the expressiveness needed to infer the true meaning of complex assertions. To overcome this limitation, we present a method that allows existing Open IE systems to enrich their output with additional meta information. By leveraging the semantic hierarchy of minimal propositions generated by the discourse-aware Text Simplification (TS) approach presented in Niklaus et al. (2019), we propose a mechanism to extract semantically typed relational tuples from complex source sentences. Based on this novel type of output, we introduce a lightweight semantic representation for Open IE in the form of normalized and context-preserving relational tuples. It extends the shallow semantic representation of state-of-the-art approaches in the form of predicate-argument structures by capturing intra-sentential rhetorical structures and hierarchical relationships between the relational tuples. In that way, the semantic context of the extracted tuples is preserved, resulting in more informative and coherent predicate-argument structures which are easier to interpret. In addition, in a comparative analysis, we show that the semantic hierarchy of minimal propositions benefits Open IE approaches in a second dimension: the canonical structure of the simplified sentences is easier to process and analyze, and thus facilitates the extraction of relational tuples, resulting in an improved precision (up to 32%) and recall (up to 30%) of the extracted relations on a large benchmark corpus.

Errors involving medication administration can be costly, both in financial and in human terms. Indeed, there is much potential for errors due to the complexity of the medication administration process. Nurses are often singled out as the only responsible of these errors because they are in charge of drug administration. Nevertheless, the interventions of every actor involved in the process and the system design itself contribute to errors (Wakefield et al. (1998) [23]). Proper inpatient medication safety systems can help to reduce such errors in hospitals. In this paper, we review in depth two recent proposals (Chien et al. (2010) [7]; Huang and Ku (2009) [12]) that pursue the aforementioned objective. Unfortunately, they fail in their attempt mainly due to their security faults but interesting ideas can be drawn from both. These security faults refer to impersonation and replay attacks that could produce the generation of a forged proof stating that certain medication was administered to an inpatient when it was not. We propose a leading-edge solution to enhance inpatient medication safety based on RFID technology that overcomes these weaknesses. Our solution, named Inpatient Safety RFID system (IS-RFID), takes into account the Information Technology (IT) infrastructure of a hospital and covers every phase of the drug administration process. From a practical perspective, our system can be easily integrated within hospital IT infrastructures, has a moderate cost, is very ease to use and deals with security aspects as a key point.

In online learning settings interactive and meaningful feedback is becoming increasingly important. However, feedback from teachers is oftentimes missing in online learning settings. To overcome challenges that arise from the missing representation of teachers, our study analyzes the relevance of avatar designs in learning settings. We therefore rely on avatars as game design elements and analyze how their design can influence emotional attachment, learning process satisfaction, and extraneous cognitive load in learning. To achieve our goal, we conduct a qualitative comparative analysis with 998 datasets that were collected in a 2x2x2 pre-post online experiment that was developed to train participants in learning functions in Excel. Our results indicate that interaction, familiarity, motivation, and aesthetic experiences are important configurations for avatars that are used in learning. We contribute to different streams of theory such as self-expansion and guide practitioners by providing implications about how to create meaningful avatar designs for learning applications.

The World Wide Web has evolved drastically over the past decade -- and the proliferation of Web APIs has turned it into the middleware of choice for most distributed systems. The recent focus on hypermedia-driven APIs together with initiatives such as the Web of Things and Linked Data are now promoting and advancing the development of a new generation of dynamic, open, and long-lived systems on the Web. These systems require agent-based solutions to the point that Web researchers have started to build autonomous systems on their own. It is thus both timely and necessary to investigate and align the latest developments in Web research and multi-agent systems (MAS) research. In this paper, we analyze in hindsight the factors that hindered the widespread acceptance of early Web-based MAS. We argue that the answer lies equally in a lack of practical use cases as well as the premature development and alignment of Web and agent technologies. We then present our vision for a new generation of autonomous systems on the Web, which we call hypermedia MAS, together with the research opportunities and challenges they bring.

Recently, work with Distributed Ledger Technologies (DLTs) has focussed on leveraging the decentralised, immutable ledger for use outside of cryptocurrency. One industry poised to benefit from DLTs is the Industrial Internet of Things (IIoT); as the inherent cryptographic mechanisms and alternative trust model make DLTs an attractive solution for distributed networks. Existing DLTs are unsuitable for the IIoT, owing to the large computational and energy requirements for consensus operations and the slow throughput of validated blocks. With limited processing, energy and storage resources and a deadline sensitive operational environment, DLTs in their current state could serve to introduce intolerable latency into IIoT processes and deplete constrained, device resources. Designed for the IIoT context, and based off Delegated Proof of Stake, this work serves to introduce a new consensus mechanism called Delegated Proof of Proximity (DPoP). Using existing location discovery processes, nodes in close proximity to a sensor event are elected as delegates; whose role is to handle consensus and block generation. In using information already known to IIoT devices, DPoP aims to reduce wasted effort, improve throughput by limiting the number of nodes required for consensus operations and improve scalability and flexibility of DLT solutions as the IIoT network continues to grow.

Digital health interventions (DHIs) have the potential to help the growing number of chronic disease patients better manage their everyday lives. However, guidelines for the systematic development of DHIs are still scarce. The current work has, therefore, the objective to propose a framework for the design and evaluation of DHIs (DEDHI). The DEDHI framework is meant to support both researchers and practitioners alike from early conceptual DHI models to large-scale implementations of DHIs in the healthcare market.

Background: Asthma is one of the most common chronic conditions worldwide. Successful asthma management requires knowledge about the condition, treatment adherence, and behavioral skills. In addition, when treating children with asthma, a trustful and empathetic collaboration between healthcare providers, patients and their family is necessary for successful asthma management. However, resources of healthcare providers are limited to few face-to-face consultations, and personal support in the everyday life of chronic patients is not feasible. Digital assistants may overcome this challenge, because they are computer programs that imitate human interactions and can be designed to support healthcare providers in reaching out to patients in their everyday lives. Until now, however, it has not been clear whether digital assistants would be adopted by healthcare providers, patients or supportive family members and whether they could have a positive impact on the management of asthma in children. Objective: The goal of this project was to develop and test an empathetic digital assistant for healthcare providers that targets 10 to 15-year-olds with asthma and a supportive family member. Method: The digital assistant MAX was collaboratively designed by healthcare providers, young patients, a media didactician, a clinical psychologist and computer scientists. MAX communicated with all relevant stakeholders along a pre-defined intervention schedule, i.e., with healthcare providers via email, with patients via a mobile chat app and with a family member via SMS. The 14 lessons focused on asthma knowledge (e.g., what to do in case of an asthma attack), treatment adherence (i.e., discussion of medication plans), and behavioral skills (i.e., inhalation and breathing techniques). A family member was requested to actively participate in seven lessons. Healthcare providers were requested to assess patients’ inhalation technique based on video clips recorded by a family member. A pilot study was carried out to assess reach, impact, therapeutic alliance, technical feasibility and acceptability of MAX. Reach was measured by the proportion of patients approached and those who started to interact with MAX. Impact was assessed via a pre-post asthma knowledge test of a validated asthma quiz and by the number of inhalation mistakes made after healthcare providers gave their feedback. Therapeutic alliance between MAX and patients was assessed by the Session Alliance Inventory. Finally, technical feasibility and acceptance of MAX were evaluated by patients’ adherence, the number of technical shortcomings, and qualitative feedback gathered from healthcare providers, patients and family members. The study was approved by the first author’s institutional review board. Results: Overall, 99 children with asthma were screened by healthcare providers at 6 study sites (4 hospitals and 2 local Swiss Lung Association sites, a home care provider for integrated care in Switzerland) between January and April 2019. Overall, 49 (49.5% of those screened) young patients (33 male, 27 iOS and 22 Android users) with an average of 12 years (SD=1.54) fulfilled all inclusion criteria (i.e., asthma diagnosis, 10 to 15 years old, German-speaking, smartphone available, interested in investing ca. 4h of their time, supportive family member with smartphone access), and started to interact with MAX. Thirty-nine (79.6%) patients who completed Lesson 2 indicated that they had lived with asthma for 5.61 years (SD=4.17) and 13 (30%) reported that they were uncertain about how to manage their asthma. The average completion rate of the 49 participants regarding the 14 MAX lessons was 80.4%, and 37 (75.5%) patients completed all lessons in 3 weeks. A paired t-test with the baseline observation carried forward showed that asthma knowledge had increased significantly from the first lesson until the last lesson with a large effect (d=0.91). Out of 192 random lesson assessments, patients indicated 86 times (44.8%) that they learned a lot, 73 times (38.0%) that they learned some new aspects and 33 times (17.2%) that they already knew everything about asthma. The technical quality of all 42 inhalation video clips was good, and it took healthcare providers ca. 118s to assess each video clip. Patients received feedback on their inhalation technique within 1.9 days through a second chat channel of the MAX app dedicated to communication with their healthcare provider. On average, healthcare providers identified 1 inhalation mistake in each video clip, and 3 serious inhalation mistakes were identified and corrected in a second video clip. Out of 275 lessons, patients indicated 269 times (97.8%) that they were supported by family members in collaborative exercises. Only 74 (0.5%) of all chat interactions took place in the chat channel dedicated to healthcare provider communication whereas 15’087 (99.5%) interactions took place in the scalable chat channel with MAX. Therapeutic alliance was rated very high by the patients, who also enjoyed using the mobile app and stated they wanted to continue working with MAX. The young patients also found the MAX app easy to use and reported that it offered clear benefits. Overall, MAX was assessed very positively by all relevant stakeholders, and several suggestions for improvement and technical barriers, particularly related to the technical infrastructure in the participating hospitals, were provided (e.g., lack of easy WIFI access to patients or access to state-of-the-art browser technology for healthcare providers). Conclusion: Digital assistants for healthcare providers targeting 10 to 15-year-old patients with asthma and a family member have the potential to improve asthma knowledge, treatment adherence, and behavioral skills. The reach of such interventions is limited by the technical infrastructure of healthcare providers. Future work should assess the impact of digital assistants on asthma outcomes.

Background: Many young adults with type 1 diabetes (T1D) struggle with the complex daily demands of adherence to their medical regimen and fail to achieve target range glycemic control. Few interventions, however, have been developed specifically for this age group. Objective: In this randomized trial, we will provide a mobile app (SweetGoals) to all participants as a “core” intervention. The app prompts participants to upload data from their diabetes devices weekly to a device-agnostic uploader (Glooko), automatically retrieves uploaded data, assesses daily and weekly self-management goals, and generates feedback messages about goal attainment. Further, the trial will test two unique intervention components: (1) incentives to promote consistent daily adherence to goals, and (2) web health coaching to teach effective problem solving focused on personalized barriers to self-management. We will use a novel digital direct-to-patient recruitment method and intervention delivery model that transcends the clinic. Methods: A 2x2 factorial randomized trial will be conducted with 300 young adults ages 19-25 with type 1 diabetes and (Hb)A1c ≥ 8.0%. All participants will receive the SweetGoals app that tracks and provides feedback about two adherence targets: (a) daily glucose monitoring; and (b) mealtime behaviors. Participants will be randomized to the factorial combination of incentives and health coaching. The intervention will last 6 months. The primary outcome will be reduction in A1c. Secondary outcomes include self-regulation mechanisms in longitudinal mediation models and engagement metrics as a predictor of outcomes. Participants will complete 6- and 12-month follow-up assessments. We hypothesize greater sustained A1c improvements in participants who receive coaching and who receive incentives compared to those who do not receive those components. Results: Data collection is expected to be complete by February 2025. Analyses of primary and secondary outcomes are expected by December 2025. Conclusions: Successful completion of these aims will support dissemination and effectiveness studies of this intervention that seeks to improve glycemic control in this high-risk and understudied population of young adults with T1D.

Information systems (IS) research has long been promoting the necessity of aligning local IS investments in organizations with their enterprise-wide objectives. One of the prominent means to realize such an alignment are mechanisms that coordinate various stakeholders in different organizational entities. Despite its prominent origins and manifold translations from organization science (OS), there is no coherent body of coordination theory. The research at hand con-ducts a literature review of coordination mechanisms to offer a more coherent understanding of coordination for prospective IS research. To this end and structured in eight categories of mechanisms, we contrast a reflection of coordination in OS and IS research. We also discuss how IS studies follow and complement OS research, outlining implications for future research.

Satellites allow spatially precise monitoring of the Earth, but provide only limited information on events of societal impact. Subjective societal impact, however, may be quantified at a high frequency by monitoring social media data. In this work, we propose a multi-modal data fusion framework to accurately identify periods of COVID-19-related lockdown in the United Kingdom using satellite observations (NO2 measurements from Sentinel-5P) and social media (textual content of tweets from Twitter) data. We show that the data fusion of the two modalities improves the event detection accuracy on a national level and for large cities such as London.