Data visualization and analytics research has great potential to empower people to improve their lives by leveraging their own personal data. However, most quantified selfers (Q-Selfers) are neither visualization experts nor data scientists. Consequently, visualizations Q-Selfers created with their data are often not ideal for conveying insights. Aiming to design a visualization system to help nonexperts gain and communicate personal data insights, the authors conducted a predesign empirical study. Through the lens of Q-Selfers, they examined what insights people gain specifically from their personal data and how they use visualizations to communicate their insights. Based on their analysis of 30 quantified self-presentations, they characterized eight insight types (detail, self-reflection, trend, comparison, correlation, data summary, distribution, and outlier) and mapped the visual annotations used to communicate them. They further discussed four areas for the design of personal visualization systems, including support for encouraging self-reflection, gaining valid insight, communicating insight, and using visual annotations.

The Internet of Vehicles aims to fundamentally improve transportation by connecting vehicles, drivers, passengers, and service providers together. Several new services such as parking space identification, platooning and intersection control—to name just a few—are expected to improve traffic congestion, reduce pollution, and improve the efficiency, safety and logistics of transportation. Proposed end-user services, however, make extensive use of private information with little consideration for the impact on users and third parties (those individuals whose information is indirectly involved). This article provides the first comprehensive overview of privacy and trust issues in the Internet of Vehicles at the service level. Various concerns over privacy are formalised into four basic categories: personal information privacy, multi-party privacy, trust, and consent to share information. To help analyse services and to facilitate future research, the main relevant end-user services are taxonomised according to voluntary and involuntary information they require and produce. Finally, this work identifies several open research problems and highlights general approaches to address them. These especially relate to measuring the trade-off between privacy and service functionality, automated consent negotiation, trust towards the IoV and its individual services, and identifying and resolving multi-party privacy conflicts.

One goal of eScience is to enable the end-to-end publication of experiments and results. In the Combechem project we have developed an innovative human-centred system which captures the process of a chemistry experiment from plan to execution. The system comprises an electronic lab book replacement, which has been successfully trialled in a synthetic organic chemistry laboratory, and a flexible back-end storage system. Working closely with the users, we found that a light touch and a high degree of flexibility was required in the user interface. In this paper, we concentrate on the representation and storage of human-scale experiment metadata, introducing an ontology to describe the record of an experiment, and a storage system for the data from our lab book software. Just as the interfaces need to be flexible to cope with whatever a chemist wishes to record, so the back end solutions need to be similarly flexible to store any metadata that may be created. The storage system is based on Semantic Web technologies, such as RDF, and Web Services. It gives a much higher degree of flexibility to the type of metadata it can store, compared to the use of rigid relational databases.

People with chronic musculoskeletal pain often experience pain-related fear of movement and avoidance behavior. The Fear-Avoidance model proposes a possible mechanism at least partly explaining the development and maintenance of chronic pain. People who interpret pain during movement as being potentially harmful to the organism may initiate a vicious behavioral cycle by generating pain-related fear of movement accompanied by avoidance behavior and hyper-vigilance. This study investigates whether an individually adapted multifactorial approach comprised of biopsychosocial elements, with a focus on physical exercise, mindfulness, and education on pain and behavior, can decrease work-related fear-avoidance beliefs. As part of a large scale 10-week worksite randomized controlled intervention trial focusing on company initiatives to combat work-related musculoskeletal pain and stress, we evaluated fear-avoidance behavior in 112 female laboratory technicians with chronic neck, shoulder, upper back, lower back, elbow, and hand/wrist pain using the Fear-Avoidance Beliefs Questionnaire at baseline, before group allocation, and again at the post intervention follow-up 10 weeks later. A significant group by time interaction was observed (P < 0.05) for work-related fear-avoidance beliefs. The between-group difference at follow-up was –2.2 (–4.0 to –0.5), corresponding to a small to medium effect size (Cohen's d = 0.30). Our study shows that work-related, but not leisure time activity-related, fear-avoidance beliefs, as assessed by the Fear-avoidance Beliefs Questionnaire, can be significantly reduced by 10 weeks of physical-cognitive-mindfulness training in female laboratory technicians with chronic pain.

Many service providers require permissions to access privacy-sensitive data that are not necessary for their core functionality. To support users’ privacy management, we propose a novel agent-based negotiation framework to negotiate privacy permissions between users and service providers using a new multi-issue alternating-offer protocol based on exchanges of partial and complete offers. Additionally, we introduce a novel approach to learning users’ preferences in negotiation and present two variants of this approach: one variant personalised to each individual user, and one personalised depending on the user’s privacy type. To evaluate them, we perform a user study with participants, using an experimental tool installed on the participants’ mobile devices. We compare the take-it-or-leave-it approach, in which users are required to accept all permissions requested by a service, to negotiation, which respects their preferences. Our results show that users share personal data 2.5 times more often when they are able to negotiate while maintaining the same level of decision regret. Moreover, negotiation can be less mentally demanding than the take-it-or-leave-it approach and it allows users to align their privacy choices with their preferences. Finally, our findings provide insight into users’ data sharing strategies to guide the future of automated and negotiable privacy management mechanisms.