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A majority of employees over the age of 40 have hypertension, impacting their health and performance. A two-week self-management support (SMS) intervention was tested, with daily feedback and microlearning cycles to improve health self-management competences. On average, participants (n= 8) reduced their blood pressure from 145/92 mmHg to 126/86 mmHg. User evaluation confirmed the importance of core SMS aspects: information transfer, daily monitoring, enhancing problem solving/decision making, self-treatment using a tailored action plan, coping skills, and skilful coach follow-up. Several lessons are drawn on microlearning, peer coaching, health results, intrinsic motivation, and social learning, which appear useful for other health improvement initiatives.

Globally, the burden of disease is rising. High performance employees and organisations need to improve their health self-management options and skills. Unfortunately, there are an overwhelming number (> 500,000) of new health publications every year. We aim to design a health AI on top of Scholar Google, to support rapid employee do-it-yourself (DIY) health improvement. Thus, we analysed user requirements, based on design analyses for two cases: hypertension and type-2 diabetes (T2D), two major diseases of affluence in our society, which are reversible with healthy living. We show how a hybrid AI may empower employees instead of medicalising them. To conclude, we propose a next level of quantified self for worker health self-management.

Post-traumatic stress affects millions of people worldwide. Appraisal training is an intervention that has been used to decrease the negative effects of a traumatic event. In two studies, the acceptance and effects of technology in supporting appraisal was studied. Study 1, a descriptive study, examined the response to and acceptance of a workshop on perspective broadening with technological support among soldiers and firefighters. Results revealed that both groups evaluated the training as useful and feasible, and both favoured the full version of the tool. Study 2 investigated the effect of the support tool among a student sample in comparison to the paper-based training. Participants followed the training individually. Comparisons between the two groups revealed no significant differences on multiple outcome measures. Behaviour observed during the training suggests that shorter sessions might prove more effective. The findings indicate acceptance of the technology supported training but gives no indication that the effects of the training are improved by technological support.

The eight papers in this special issue focus on agent and system transparency in human-machine systems. The concept of transparency is investigated in a variety of contexts of human agent interaction—from a single robot to multiple heterogeneous agents and swarms. Two studies examine the effects of individual and cultural differences and, based on the compelling results, provide design recommendations related to transparent interfaces. One of the papers provides a thorough review on theoretical aspects of agent transparency and empirical findings on operator performance, situation awareness, trust and workload, among other outcomes. These measures are also among some of the most common metrics reported in studies in this special issue.

Mobile location-sharing technology is increasingly being used by parents to locate their children. Research shows that these technologies may pose risks to important user values such as privacy and responsibility, while they aim to promote others such as family security. As a solution, we proposed the use of Social Commitment (SC) models for governing the sharing and receiving of data. A social commitment represents an agreement between two people about which data should (not) be shared and received in which situation. We hypothesize that the use of SCs in mobile location sharing applications provides improved support for user values since it allows for a more flexible, context-aware location sharing. In this paper, we present a user study to test this hypothesis. The study focuses on primary school children (n=34) as the main target group, who’s values may be demoted through the use of location-sharing technology. Children were provided with two versions of a mobile location sharing app: one with basic check-in functionality –the basic app –and one augmented with an SC model, which we call a Socially Adaptive Electronic Partner( saep). Our findings suggest, among other things that the saepwould provide improved support for children’s values compared to the basic app.

Personalization of eHealth systems is a promising technique for improving patients’ adherence. This paper explores the possibility of personalisation based on the patients’ medical health situation and on their health literacy. The study is set within the context of a self-management support system (SMSS) for renal transplant patients. A SMSS is designed with layering, nudging, emphaticizing, and focusing principles. It has two communication styles: (1) a guided style that provided more interpretation support and addressed emotional needs; and (2) a factual style that showed only measurement history, medical information, and recommendations. To evaluate the design, 49 renal transplant patients with three different experience levels participated in a lab study, in which they used the system in imaginary scenarios to deal with three medical health situations (alright, mild concern, and concern). A 96% understanding and 87% adherence rate was observed, with a significant interaction effect on adherence between patient group and health situation. Furthermore, compared to recently transplanted patients, not recently transplanted patients were relatively more positive towards the factual than the guided communication style in the “alright” condition. Furthermore, additional medical information was searched more often in health situations that causes mild concern and a majority of patients did not change the communication style to their preferred styles. By attuning the communication style to patient’s experience and medical health situation according to the applied principles and acquired insights, SMSSs are expected to be better used.

Mobile location-sharing technology is used increasingly by parents to know where their children are. It is our aim to make such technology more flexible in adapting to the particular social context in which it operates. We propose to realise this by allowing users to specify norms that govern the respective social contexts, to which the application should adapt at run-time to provide tailored support. The challenge we address in this paper is the development of a normative model tailored for mobile applications that support location sharing in family life. The novelty of our work lies in the fact that we employ empirical user-centred design methods and techniques for developing the model in an iterative and 'bottom-up' way. This results in two main contributions: 1) a normative model, specifically a social commitment model, for family life location sharing applications shown to be useful and usable, and 2) a demonstration of how user-centred design can be employed to develop a normative model for social applications.

The experiment presented in this paper investigated the effects of different kinds of reminders on adherence to automated parts of a cognitive behavioural therapy for insomnia (CBT-I) delivered via a mobile device. Previous studies report that computerized health interventions can be effective. However, treatment adherence is still an issue. Reminders are a simple technique that could improve adherence. A minimal intervention prototype in the realm of sleep treatment was developed to test the effects of reminders on adherence. Two prominent ways to determine the reminder-time are: a) ask users when they want to be reminded, and b) let an algorithm decide when to remind users. The prototype consisted of a sleep diary, a relaxation exercise and reminders. A within subject design was used in which the effect of reminders and two underlying principles were tested by 45 participants that all received the following three different conditions (in random order): a) event-based reminders b) time-based reminders c) no reminders. Both types of reminders improved adherence compared to no reminders. No differences were found between the two types of reminders. Opportunity and self-empowerment could partly mediate adherence to filling out the sleep diary, but not to the number of relaxation exercises conducted. Although the study focussed on CBT-I, we expect that designers of other computerized health interventions benefit from the tested opportunity and self-empowerment principles for reminders to improve adherence, as well.

Manned space missions are typically performed by teams composed of humans as well as technical systems and are situated in complex, dynamic and safety-critical domains. Intelligent electronic partners (ePartners) can play an important role here to support human-robot teams in their collaborative problem solving process when things do not go as planned. To engage in effective team collaboration, ePartners, humans and robots must align their communication at the right level of abstraction. In this paper, an approach is put forward to represent the functionality of human-robot teams in a formal manner using abstraction hierarchies. In this way, formal relations between domain knowledge at the system, functional and mission-oriented levels are established and reasoning rules are used to navigate through these relations. As a consequence, ePartners are equipped with the ability to reason about the status of a mission, propose solutions in non-nominal situations and provide explanations for the proposed solutions. The approach has been implemented within a mobile application on a tablet that can be used to support astronaut-robot teams during space missions. The application has been evaluated during an experiment at the European Space Research and Technology Centre (ESTEC) in the context of a Mars mission.

An intelligent system for automated scenario–based training (SBT) needs knowledge about the training domain, events taking place in the simulated environment, the behaviour of the participating characters, and teaching strategies for effective learning. This knowledge base should be theoretically sound and should represent the information in a generic, consistent, and unambiguous manner. Currently, there is no such knowledge base. This paper investigates the declarative knowledge needed for a system to reason about training and to make intelligent teaching decisions. A frame–based approach was used to model the identified knowledge in an ontology. The ontology specifies the core concepts of SBT and their relationships, and is applicable across training domains and applications. The ontology was used to develop a critical component of SBT: the scenario generator. It was found that the ontology enabled the scenario generator to develop scenarios that fitted the learning needs and skill level of the trainee. The presented work is an important step towards automated scenario–based training systems.

Staffing reduction initiatives and more complicated military operations lead to a higher cognitive workload in command and control (C2) environments. Extending automation with adaptive capabilities can aid the human in overcoming cognitive workload challenges. At present, most adaptive automation research has focused on laboratory experiments and only limited research has aimed to implement and validate adaptive automation in a real-world setting. The objective of the present study was to investigate the effects of adaptive automation in precisely such a setting, extending the scientific knowledge base of adaptive systems with an evaluation of a real-world adaptive task. Implementing adaptive automation in a real-world C2 setting required extending current adaptive automation theories with an object-oriented task model and a hybrid triggering mechanism. The extended model was evaluated with eight naval officers using a high-fidelity C2 environment and showed an overall efficiency effect of 60%. Furthermore, no negative side effects of adaptive automation have been found, and the data show that the scenarios were manipulated correctly. In addition, the positive efficiency effects appear most strongly in the more complicated asymmetrical scenarios (65%). This latter conclusion shows that adaptive automation can be a valuable contribution to future C2 systems.

Team awareness is important when asking team members for assistance, for example in the police domain. This paper investigates how presentation modality (visual or auditory) of relevant team information and communication influences team awareness and choice accuracy in a collaborative team task. An experimental prototype was created and implemented, which provided a visual overview of location, availability, means of transportation and expertise of team members as well as ongoing team communication. In a simulated police task in a virtual environment, this prototype was compared to current auditory support, common in police practice. Results show that a visual overview of team information improves choice accuracy, while auditory information presentation improves team awareness. In designing support systems for the professional domain, this trade-off should be carefully considered.

This paper presents the development of a cognitive task analysis that assesses the task load of jobs and provides indicators for the redesign of jobs. General principles of human task performance were selected and, subsequently, integrated into current task modelling techniques. The resulting cognitive task analysis centres around four aspects of task load: the number of actions in a period, the ratio between knowledge- and rule-based actions, lengthy uninterrupted actions, and momentary overloading. The method consists of three stages: (1) construction of a hierarchical task model, (2) a time-line analysis and task load assessment, and (3), if necessary, adjustment of the task model. An application of the cognitive task analysis in railway traffic control showed its benefits over the 'old' task load analysis of the Netherlands Railways. It provided a provisional standard for traffic control jobs, conveyed two load risks—momentary overloading and underloading—and resulted in proposals to satisfy the standard and to diminish the two load risk.

ABSTRACTBackground: First responders are a prime example of professionals that are at a high risk of being exposed to traumatic experiences. Reappraisal as a coping strategy might help first responders to better cope with their emotional responses to traumatic events.Objective: This study investigated the effects of repeated sessions of a digital reappraisal training among seven firefighters. The training consisted of four sessions supported by a virtual agent, conducted at home or at work, over a two-week period in a single case series.Method: Sixteen data points were collected from each participant in the eight days pre- and post-training.Results: Significantly more themes were used at post-training than at pre-training, implying more flexibility and confirming the main hypothesis of the study. Negative side effects were not reported during or in the week after the training.Conclusions: More controlled studies into the short- and long-term effects of a training of this nature are needed. Furthermore, it provides a reference for developers in this field.

Conduct a pilot study on the effect of personalized robot behaviors on the enjoyment of children (8-12) with diabetes and their health knowledge, at the clinic.