This paper presents a gamified motor imagery brain-computer interface (MI-BCI) training in immersive virtual reality. The aim of the proposed training method is to increase engagement, attention, and motivation in co-adaptive event-driven MI-BCI training. This was achieved using gamification, progressive increase of the training pace, and virtual reality design reinforcing body ownership transfer (embodiment) into the avatar. From the 20 healthy participants performing 6 runs of 2-class MI-BCI training (left/right hand), 19 were trained for a basic level of MI-BCI operation, with average peak accuracy in the session = 75.84%. This confirms the proposed training method succeeded in improvement of the MI-BCI skills; moreover, participants were leaving the session in high positive affect. Although the performance was not directly correlated to the degree of embodiment, subjective magnitude of the body ownership transfer illusion correlated with the ability to modulate the sensorimotor rhythm.

• Klíčová slova
• body ownership transfer, brain-computer interface, embodiment, gamification, motor imagery,
• Publikační typ
• časopisecké články MeSH

Gamification is known to enhance users' participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process.

• Klíčová slova
• CREDO, citizen science, convolutional neural networks, deep learning, gamification, global sensor network, image classification, image sensors,
• MeSH
• artefakty MeSH
• lidé MeSH
• neuronové sítě (počítačové) * MeSH
• počítačové zpracování obrazu * MeSH
• strojové učení MeSH
• vlnková analýza MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

The research on using process mining in learning analytics of cybersecurity exercises relies on datasets that reflect the real behavior of trainees. Although modern cyber ranges, in which training sessions are organized, can collect behavioral data in the form of event logs, the organization of such exercises is laborious. Moreover, the collected raw data has to be processed and transformed into a specific format required by process mining techniques. We present two datasets with slightly different characteristics. While the first exercise with 52 participants was not limited in time, the second supervised exercise with 42 trainees lasted two hours. Also, the cybersecurity tasks were slightly different. A total of 11757 events were collected. Of these, 3597 were training progress events, 5669 were Bash commands, and 2491 were Metasploit commands. Joint CSV files distilled from the raw event data can be used as input for existing process mining tools.

• Klíčová slova
• Education, Host-based data collection, Learning analytics, Puzzle-based gamification,
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Genetic testing rapidly penetrates into all medical specialties and medical students must acquire skills in this area. However, many of them consider it difficult. Furthermore, many find these topics less appealing and not connected to their future specialization in different fields of clinical medicine. Student-centred strategies such as problem-based learning, gamification and the use of real data can increase the appeal of a difficult topic such as genetic testing, a field at the crossroads of genetics, molecular biology and bioinformatics. METHODS: We designed an electronic teaching application which students registered in the undergraduate Medical Biology course can access online. A study was carried out to assess the influence of implementation of the new method. We performed pretest/posttest evaluation and analyzed the results using the sign test with median values. We also collected students' personal comments. RESULTS: The newly developed interactive application simulates the process of molecular genetic diagnostics of a hereditary disorder in a family. Thirteen tasks guide students through clinical and laboratory steps needed to reach the final diagnosis. Genetics and genomics are fields strongly dependent on electronic databases and computer-based data analysis tools. The tasks employ publicly available internet bioinformatic resources used routinely in medical genetics departments worldwide. Authenticity is assured by the use of modified and de-identified clinical and laboratory data from real families analyzed in our previous research projects. Each task contains links to databases and data processing tools needed to solve the task, and an answer box. If the entered answer is correct, the system allows the user to proceed to the next task. The solving of consecutive tasks arranged into a single narrative resembles a computer game, making the concept appealing. There was a statistically significant improvement of knowledge and skills after the practical class, and most comments on the application were positive. A demo version is available at https://medbio.lf2.cuni.cz/demo_m/ . Full version is available on request from the authors. CONCLUSIONS: Our concept proved to be appealing to the students and effective in teaching medical molecular genetics. It can be modified for training in the use of electronic information resources in other medical specialties.

• Klíčová slova
• Bioinformatics, E-learning, Gamification, Genomics, Interactive teaching application, Medical databases, Medical genetics, Problem-based learning,
• MeSH
• genetické nemoci vrozené diagnóza   MeSH
• genetické testování * MeSH
• lékařská genetika výchova   MeSH
• lidé MeSH
• molekulární medicína výchova   MeSH
• počítačem řízená výuka * MeSH
• problémově orientovaná výuka MeSH
• studium lékařství pregraduální metody   MeSH
• uživatelské rozhraní počítače MeSH
• videohry MeSH
• výpočetní biologie výchova   MeSH
• vyučování MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH

During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes. Novel and instigating teaching methods were required to improve the learning experience and so recent implementations of the annual PhysioNet/Computing in Cardiology (CinC) Challenges posed as a reference. For over 20 years, the challenges have proven repeatedly to be of immense educational value, besides leading to technological advances for specific problems. In this paper, we report results from the class 'Artificial Intelligence in Medicine Challenge', which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 'AF Classification from a Short Single Lead ECG Recording'. Atrial fibrillation is a common cardiac disease and often remains undetected. Therefore, we selected the two most promising models of the course and give an insight into the Transformer-based DualNet architecture as well as into the CNN-LSTM-based model and finally a detailed analysis for both. In particular, we show the model performance results of our internal scoring process for all submitted models and the near state-of-the-art model performance for the two named models on the official 2017 challenge test set. Several teams were able to achieve F1scores above/close to 90% on a hidden test-set of Holter recordings. We highlight themes commonly observed among participants, and report the results from the self-assessed student evaluation. Finally, the self-assessment of the students reported a notable increase in machine learning knowledge.

• Klíčová slova
• atrial fibrillation, deep learning, electrocardiogram, gamification,
• MeSH
• algoritmy MeSH
• COVID-19 * diagnóza   MeSH
• elektrokardiografie metody   MeSH
• fibrilace síní * diagnóza   MeSH
• kontrola infekčních nemocí MeSH
• lidé MeSH
• strojové učení MeSH
• umělá inteligence MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Current mobility patterns over-rely on transport modes that do not benefit sustainable and healthy lifestyles. To explore the potential for active mobility, we conducted a randomized experiment aimed at increasing regular commuter cycling in cities. In designing the experiment, we teamed up with developers of the "Cyclers" smartphone app to improve the effectiveness of the app by evaluating financial and non-financial motivational features. Participants in the experiment were recruited among new users of the app, and were randomly assigned to one of four different motivational treatments (smart gamification, two variants of a financial reward, and a combination of smart gamification and a financial reward) or a control group (no specific motivation). Our analysis suggests that people can be effectively motivated to engage in more frequent commuter cycling with incentives via a smartphone app. Offering small financial rewards seems to be more effective than smart gamification. A combination of both motivational treatments-smart gamification and financial rewards-may work the same or slightly better than financial rewards alone. We demonstrate that small financial rewards embedded in smartphone apps such as "Cyclers" can be effective in nudging people to commute by bike more often.

• Klíčová slova
• active mobility, behavioral change, incentives, randomized experiment, smartphone app,
• MeSH
• chytrý telefon * MeSH
• cyklistika * MeSH
• dospělí MeSH
• financování osobní MeSH
• lidé středního věku MeSH
• lidé MeSH
• mobilní aplikace * MeSH
• motivace * MeSH
• odměna MeSH
• zdravé chování * MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• randomizované kontrolované studie MeSH

Generation Z is expected to officially surpass the Baby Boomers in the labor market by 2024 and to represent 30% of the global workforce by 2030. In the work environment, they are referred to oxymoronically as competitively ambivalent. Therefore, it is necessary to investigate the reasons for this behavior and to identify initiatives that would facilitate understanding between Generation Z and other generations. The aim of the present study was to find out whether Generation Z in the Czech Republic and Slovakia, which lives in conditions of deepening polarization of society and differentiated opportunities (e.g., in access to education, consumption of goods and services, work and entertainment), exhibits compatible value orientation or whether significant antagonisms exist in the value system. The study utilized the referential Schwartz's theory of values, which handles universal values dynamically. This theoretical framework was extended to include the dimension of instrumental values that were contextualized in the labor market environment. The results show that the representatives of Generation Z in the Czech Republic and Slovakia prefer collective values (Benevolence and Universalism) in the first two places. However, they subsequently lean toward two individual values (Hedonism and Self-Direction). The comparison of the results in the European context showed the same values being shared by the representatives of Generation Z with preference nuances. The comparison of Generation Z representatives with members of other generations in the European context showed consistency of sharing collective values (Benevolence and Universalism). Discussion: Intergenerational value congruence, as well as knowledge of the difference in preferred values across generations (the collectivism value of Tradition shared by Baby Boomers and Generation X, and Hedonism as an individualism value shared by Generation Y and Generation Z) can help the successful integration of Generation Z representatives in the labor market. A way toward intergenerational synergy can be the recommended strategies for managing Generation Z in the context of career paths: Flexibility of development; Gamification; Mentoring.

• Klíčová slova
• collectivism, generation Z, individualism, instrumental, motivating factors, universal, values,
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Managing type 1 diabetes in children and adolescents can be difficult for parents, health care professionals, and even patients. However, over the last decades, the quality of services provided to patients with diabetes has increased due to advances in IT. OBJECTIVE: This study aims to comprehensively document the range of IT tools used in the management of diabetes among children and adolescents, with a focus on identifying the technologies most commonly used based on their frequency. In addition, the study aims to explore relevant methodologies for developing diabetes technology and provide valuable information to developers by delineating essential phases of the design process. METHODS: The literature search was focused on MEDLINE (PubMed), Web of Science, and Google Scholar for relevant studies. Keywords such as "type 1 diabetes," "adolescents," "kids," "mHealth," "children," and "coaching" were combined using Boolean operators. The inclusion criteria were open access, English-language papers published between 2012 and 2023 focusing on patients younger than 18 years and aligned with our research goal. The exclusion criteria included irrelevant topics and papers older than 18 years. By applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, 2080 studies were recognized, and after selection, 33 papers were agreed upon between the researchers. RESULTS: Four primary categories were defined: types of IT, methodology identification, purpose identification, and feature determination. Among these, mobile health (mHealth) apps emerged as the predominant type of information, garnering 27 mentions. In particular, user-centered design was identified as the most prevalent methodology, cited 22 times. The primary purpose of self-monitoring blood glucose values was mentioned 20 times, while patient education was the highest among common characteristics, with 23 mentions. CONCLUSIONS: Based on our research, we advocate for developers to focus on creating an mHealth app that integrates gamification techniques to develop innovative diabetes management solutions. This app should include vital functionalities such as blood glucose monitoring, strategies to improve hemoglobin A1c levels, carbohydrate tracking, and comprehensive educational materials for patients and caregivers. By prioritizing these features, developers can enhance the usability and effectiveness of the technology, thereby better supporting children or adolescents with diabetes in their daily management endeavors.

• Klíčová slova
• PRISMA, adolescents, children, information technology, mHealth, parents, type 1 diabetes,
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH