Background: A large number of people participate in individual or unorganized sports on a recreational level. Furthermore, many participants drop out because of injury or lowered motivation. Potentially, physical activity–related apps could motivate people during sport participation and help them to follow and maintain a healthy active lifestyle. It remains unclear what the quality of running, cycling, and walking apps is and how it can be assessed. Quality of these apps was defined as having a positive influence on participation in recreational sports. This information will show which features need to be assessed when rating physical activity–related app quality. Objective: The aim of this study was to identify expert perception on which features are important for the effectiveness of physical activity–related apps for participation in individual, recreational sports. Methods: Data were gathered via an expert panel approach using the nominal group technique. Two expert panels were organized to identify and rank app features relevant for sport participation. Experts were researchers or professionals in the field of industrial design and information technology (technology expert panel) and in the field of behavior change, health, and human movement sciences who had affinity with physical activity–related apps (health science expert panel). Of the 24 experts who were approached, 11 (46%) agreed to participate. Each panel session consisted of three consultation rounds. The 10 most important features per expert were collected. We calculated the frequency of the top 10 features and the mean importance score per feature (0-100). The sessions were taped and transcribed verbatim; a thematic analysis was conducted on the qualitative data. Results: In the technology expert panel, applied feedback and feedforward (91.3) and fun (91.3) were found most important (scale 0-100). Together with flexibility and look and feel, these features were mentioned most often (all n=4 [number of experts]; importance scores=41.3 and 43.8, respectively). The experts in the health science expert panels a and b found instructional feedback (95.0), motivating or challenging (95.0), peer rating and use (92.0), motivating feedback (91.3), and monitoring or statistics (91.0) most important. Most often ranked features were monitoring or statistics, motivating feedback, works good technically, tailoring starting point, fun, usability anticipating or context awareness, and privacy (all n=3-4 [number of experts]; importance scores=16.7-95.0). The qualitative analysis resulted in four overarching themes: (1) combination behavior change, technical, and design features needed; (2) extended feedback and tailoring is advised; (3) theoretical or evidence base as standard; and (4) entry requirements related to app use. Conclusions: The results show that a variety of features, including design, technical, and behavior change, are considered important for the effectiveness of physical activity–related apps by experts from different fields of expertise. These insights may assist in the development of an improved app rating scale.
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In this presentation we presented the results of expert meetings. The aim was to identify which features in sport- and health-related apps contribute to effectiveness of apps. A nominal group technique was used.
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In recent years, there has been an exponential increase in the use of health and sports-related smartphone applications (apps). This is also reflected in App-stores, which are stacked with thousands of health- and sports-apps, with new apps launched each day. These apps have great potential to monitor and support people’s physical activity and health. For users, however, it is difficult to know which app suits their needs. In this paper, we present an online tool that supports the decision-making process for choosing an appropriate app. We constructed and validated a screening instrument to assess app content quality, together with the assessment of users’ needs. Both served as input for building the tool through various iterations with prototypes and user tests. This resulted in an online tool which relies on app content quality scores to match the users’ needs with apps that score high in the screening instrument on those particular needs. Users can add new apps to the database via the screening instrument, making the tool self-supportive and future proof. A feedback loop allows users to give feedback on the recommended app and how well it meets their needs. This feedback is added to the database and used in future filtering and recommendations. The principles used can be applied to other areas of sports, physical activity and health to help users to select an app that suits their needs. Potentially increasing the long-term use of apps to monitor and to support physical activity and health.
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Drones have been verified as the camera of 2024 due to the enormous exponential growth in terms of the relevant technologies and applications such as smart agriculture, transportation, inspection, logistics, surveillance and interaction. Therefore, the commercial solutions to deploy drones in different working places have become a crucial demand for companies. Warehouses are one of the most promising industrial domains to utilize drones to automate different operations such as inventory scanning, goods transportation to the delivery lines, area monitoring on demand and so on. On the other hands, deploying drones (or even mobile robots) in such challenging environment needs to enable accurate state estimation in terms of position and orientation to allow autonomous navigation. This is because GPS signals are not available in warehouses due to the obstruction by the closed-sky areas and the signal deflection by structures. Vision-based positioning systems are the most promising techniques to achieve reliable position estimation in indoor environments. This is because of using low-cost sensors (cameras), the utilization of dense environmental features and the possibilities to operate in indoor/outdoor areas. Therefore, this proposal aims to address a crucial question for industrial applications with our industrial partners to explore limitations and develop solutions towards robust state estimation of drones in challenging environments such as warehouses and greenhouses. The results of this project will be used as the baseline to develop other navigation technologies towards full autonomous deployment of drones such as mapping, localization, docking and maneuvering to safely deploy drones in GPS-denied areas.
The focus of the research is 'Automated Analysis of Human Performance Data'. The three interconnected main components are (i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis . Human Performance is both the process and result of the person interacting with context to engage in tasks, whereas the performance range is determined by the interaction between the person and the context. Cheap and reliable wearable sensors allow for gathering large amounts of data, which is very useful for understanding, and possibly predicting, the performance of the user. Given the amount of data generated by such sensors, manual analysis becomes infeasible; tools should be devised for performing automated analysis looking for patterns, features, and anomalies. Such tools can help transform wearable sensors into reliable high resolution devices and help experts analyse wearable sensor data in the context of human performance, and use it for diagnosis and intervention purposes. Shyr and Spisic describe Automated Data Analysis as follows: Automated data analysis provides a systematic process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions and supporting decision making for further analysis. Their philosophy is to do the tedious part of the work automatically, and allow experts to focus on performing their research and applying their domain knowledge. However, automated data analysis means that the system has to teach itself to interpret interim results and do iterations. Knuth stated: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it.[Knuth, 1974]. The knowledge on Human Performance and its Monitoring is to be 'taught' to the system. To be able to construct automated analysis systems, an overview of the essential processes and components of these systems is needed.Knuth Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.
Coastal nourishments, where sand from offshore is placed near or at the beach, are nowadays a key coastal protection method for narrow beaches and hinterlands worldwide. Recent sea level rise projections and the increasing involvement of multiple stakeholders in adaptation strategies have resulted in a desire for nourishment solutions that fit a larger geographical scale (O 10 km) and a longer time horizon (O decades). Dutch frontrunner pilot experiments such as the Sandmotor and Ameland inlet nourishment, as well as the Hondsbossche Dunes coastal reinforcement project have all been implemented from this perspective, with the specific aim to encompass solutions that fit in a renewed climate-resilient coastal protection strategy. By capitalizing on recent large-scale nourishments, the proposed Coastal landSCAPE project C-SCAPE will employ and advance the newly developed Dynamic Adaptive Policy Pathways (DAPP) approach to construct a sustainable long-term nourishment strategy in the face of an uncertain future, linking climate and landscape scales to benefits for nature and society. Novel long-term sandy solutions will be examined using this pathways method, identifying tipping points that may exist if distinct strategies are being continued. Crucial elements for the construction of adaptive pathways are 1) a clear view on the long-term feasibility of different nourishment alternatives, and 2) solid, science-based quantification methods for integral evaluation of the social, economic, morphological and ecological outcomes of various pathways. As currently both elements are lacking, we propose to erect a Living Lab for Climate Adaptation within the C-SCAPE project. In this Living Lab, specific attention is paid to the socio-economic implications of the nourished landscape, as we examine how morphological and ecological development of the large-scale nourishment strategies and their design choices (e.g. concentrated vs alongshore uniform, subaqueous vs subaerial, geomorphological features like artificial lagoons) translate to social acceptance.