Background: Being able to care for and cope with one’s stoma adequately may significantly impact patient’s wellbeing. A well-designed mobile application (app) may improve and solve some of the difficulties patients encounter. This study aims to gain a better understanding of the problems patients face in ostomy care and to determine how to improve these problems by an app. Method: A qualitative study using six focus group interviews was conducted between March and April 2020. Patients with a stoma, representatives of patient associations and stoma-related healthcare providers participated to provide insights. A thematic content analysis method was used to analyse the transcripts. Results: Participants indicated that perioperative information could be improved, information should be applicable for all patients and the amount of stoma materials to be overwhelming. Moreover, the contact with fellow peers could be utilised more and it was unclear which healthcare provider should be contacted. All participants expected an app would be beneficial. The app should provide reliable and up-to-date information which is presented in a visually attractive manner, and facilitate peer contact in which patients can support each other. Conclusion: Adequate self-care and coping is essential for patients’ quality of life. A personalised, mobile app may be promising to overcome some of the problems related to adequate self-provision of stoma care at home, improving self-efficacy and overall well-being.
The Netherlands Research School for Astronomy (NOVA) has operated a Mobile Planetarium for over 14 years. Between 2009-2023, the project reached more than 400,000 learners and their teachers across the Netherlands. The project has been popular with schools since the beginning but continues to grow and reach increasing numbers of learners and schools each year. A project like the Mobile Planetarium does not continue growing this way without developing key ingredients or best practices. In this article, we describe the NOVA Mobile Planetarium project in detail and the challenges faced over the last 14 years. Reflection on the different aspects of the project has led to 10 best practices which have been critical to the continued success of this project. In this article, we aim to share our experiences to help other mobile planetarium projects around the world.
MULTIFILE
Information and communications technologies (ICTs) in human services are on the rise and raise concerns about their place and impact on the daily activities of professionals and clients. This article describes a study in which a social mobile application was developed for job coaches and employees and implemented in a pilot phase. The aim of the mobile application was to provide a better communication between employees and their job coaches and to provide more up-to-date information about the organization. The application consisted of a personal web environment and app with vacancies, personal news, events, tips, and promotions. A qualitative methodology was used in the form of focus groups and in-depth interviews. The results of this study show that the participants are partly positive about the social mobile application. It can be concluded that the use of mobile technologies can be beneficial in a range of human services practice settings for both professionals and clients and, therefore, requires more attention from the academic field to focus on this relatively new but promising theme.
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
Automation is a key enabler for the required productivity improvement in the agrifood sector. After years of GPS-steering systems in tractors, mobile robots start to enter the market. Localization is one of the core functions for these robots to operate properly on fields and in orchards. GNSS (Global Navigation Satellite System) solutions like GPS provide cm-precision performance in open sky, but buildings, poles and biomaterial may reduce system performance. On top, certain areas do not provide a dependable grid communication link for the necessary GPS corrections and geopolitics lead to jamming activities. Other means for localization are required for robust operation. VSLAM (Visual Simultaneous Localization And Mapping) is a complex software approach that imitates the way we as humans learn to find our ways in unknown environments. VSLAM technology uses camera input to detect features in the environment, position itself in that 3D environment while concurrently creating a map that is stored and compared for future encounters, allowing the robot to recognize known environments and continue building a complete, consistent map of the environment covered by its movement. The technology also allows continuous updating of the map in environments that evolve over time, which is a specific advantage for agrifood use cases with growing crops and trees. The technology is however relatively new, as required computational power only recently became available in tolerable cost range and it is not well-explored for industrialized applications in fields and orchards. Orientate investigates the merits of open-source SLAM algorithms on fields - with Pixelfarming Robotics and RapAgra - and in an orchard - with Hillbird - preceded by simulations and initial application on a HAN test vehicle driving in different terrains. The project learnings will be captured in educational material elaborating on VSLAM technology and its application potential in agrifood.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
