User Goals Research Articles

Fitness and Sports Academy Integration Web Application

In the modern health and fitness landscape, gyms and athletes face unique challenges in managing schedules, tracking workouts, and fostering engagement. Existing systems often lack comprehensive tools to connect gym owners and customers effectively, impeding workout management and client retention. This paper presents a gym management and athlete engagement web application, developed with the MERN stack and leveraging Firebase for real-time data updates. By incorporating features such as personalized workout tracking, feedback systems, and integrated payment processing, this application aims to enhance gym operations and user experience. The research also explores the application of machine learning algorithms for injury prevention and personalized recommendations. Evaluation of the platform demonstrates significant improvements in user engagement, workout tracking, and gym management. This study highlights the potential of technology-driven solutions to address fitness industry challenges, creating scalable systems for gym management, athlete engagement, and personalized fitness plans. In the era of digital fitness, gym users, trainers, and gym owners increasingly rely on technology to track progress, foster community, and manage services. FitConnect aims to bridge this gap by providing a comprehensive fitness networking platform with real-time workout tracking, role-based functionalities, and payment integration. Unlike conventional fitness apps, FitConnect introduces a collaborative ecosystem where users can connect with trainers, join gym sessions, and manage subscriptions efficiently. Leveraging scalable architecture and user-friendly interfaces, this project combines essential fitness tracking with networking capabilities to create an integrated solution for fitness enthusiasts and professionals alike. This paper explores the design, development, and deployment strategies used to create this platform and assesses its impact on improving user engagement, goal tracking, and service management..

The Role of Technophilia and User Goals in the Intention to Use a Mobility Management Travel App

The ubiquitous use of mobile devices along with the amount of traffic, transportation services, and travel pattern data available has led to the emergence and deployment of smartphone applications for providing information about personal travel management. Several of these travel apps are aimed at voluntary travel behavior change (VTBC) to support and increase sustainable mobility, and have led to the development of research to investigate their influence on travel behavior. Here, the aim is to study the role of technophilia and goal-framing theory in the intention to adopt and situationally use a prospective VTBC travel app. A Structural Equation Model is developed with the aim of empirically testing a sample of 971 respondents collected in two suburban corridors in the Lisbon Metropolitan Area. The results support that goal-framing theory is important for explaining the adoption of VTBC travel apps. Gain and normative motives are more relevant than hedonic motives, pointing to the importance of their tangible benefits. Frequent car users may benefit from VTBC travel apps in terms of encouraging behavioral changes, supporting sustainable mobility management solutions. The results also outline the importance of technophilia and the current use of travel apps in influencing the intention to use VTBC apps.

Zooming In: A Review of Designing for Photo Taking in Human-Computer Interaction and Future Prospects

Photography has been pivotal in culture for decades and its importance has increased with the rise of digital technology. However, the exploration of picture taking within the Human-Computer Interaction (HCI) community does not seem to match its cultural and technological significance. Recognizing this discrepancy, we sought to understand areas of interest in photography as a conscious creative process. To address this issue, we performed a systematic literature review using the PRISMA methodology. From our research, we identified 62 pertinent papers spanning from 2005 to 2022. Our examination revealed six primary dimensions, further classified into study type, design goal, photo-taking style, device type, interaction style, and number of users. In-depth analysis showed the dominant role of exploratory and functional research, the balance between qualitative and quantitative methods, and a strong focus on smartphone cameras. Our review has highlighted significant gaps in the existing literature, offering valuable insights for future research on photo taking.

Human factors considerations of Interaction between wearers and intelligent lower-limb prostheses: a prospective discussion.

Compared to traditional lower-limb prostheses (LLPs), intelligent LLPs are more versatile devices with emerging technologies, such as microcontrollers and user-controlled interfaces (UCIs). As emerging technologies allow a higher level of automation and more involvement from wearers in the LLP setting adjustments, the previous framework established to study human factors elements that affect wearer-LLP interaction may not be sufficient to understand the new elements (e.g., transparency) and dynamics in this interaction. In addition, the increased complexity of interaction amplifies the limitations of the traditional evaluation approaches of wearer-LLP interaction. Therefore, to ensure wearer acceptance and adoption, from a human factors perspective, we propose a new framework to introduce elements and usability requirements for the wearer-LLP interaction. This paper organizes human factors elements that appear with the development of intelligent LLP technologies into three aspects: wearer, device, and task by using a classic model of the human-machine systems. By adopting Nielsen's five usability requirements, we introduce learnability, efficiency, memorability, use error, and satisfaction into the evaluation of wearer-LLP interaction. We identify two types of wearer-LLP interaction. The first type, direct interaction, occurs when the wearer continuously interacts with the intelligent LLP (primarily when the LLP is in action); the second type, indirect interaction, occurs when the wearer initiates communication with the LLP usually through a UCI to address the current or foreseeable challenges. For each type of interaction, we highlight new elements, such as device transparency and prior knowledge of the wearer with the UCI. In addition, we redefine the usability goals of two types of wearer-LLP interaction with Nelson's five usability requirements and review methods to evaluate the interaction. Researchers and designers for intelligent LLPs should consider the new device elements that may additionally influence wearers' acceptance and the need to interpret findings within the constraints of the specific wearer and task characteristics. The proposed framework can also be used to organize literature and identify gaps for future directions. By adopting the holistic usability requirements, findings across empirical studies can be more comparable. At the end of this paper, we discuss research trends and future directions in the human factors design of intelligent LLPs.

The Development and Piloting of an Early Youth-Engagement (EYE) Model to Improve Engagement of Young People in First Episode Psychosis Services: A Mixed Methods Study.

Psychosis is associated with significant health and societal costs. Early intervention in psychosis services (EIP) are highly effective in promoting recovery, yet substantial proportions of young people disengage. The current study aimed to develop and evaluate a novel engagement intervention in EIP services. A qualitative investigation of facilitators and barriers to engagement in 68 first episode psychosis patients, family members and young people, and a Delphi consultation with 27 regional and national youth and psychosis service leads informed the development of the intervention. A mixed-methods feasibility-pilot study then compared engagement outcomes in 298 EIP service users in two cohorts: standard EIP versus standard EIP plus the novel early youth-engagement (EYE) intervention. A qualitative study explored intervention experiences in 22 randomly selected service users, carers and clinicians. A process evaluation explored delivery. Disengagement was 24% in the standard EIP cohort compared to 14.5% in the standard EIP plus EYE intervention cohort. A 95% Bayesian credibility interval revealed a 95% probability that the true reduction in disengagement lay somewhere between 0% and 18%. The number needed to treat was 11, 95% CI [5, 242]. Use of the EYE resources was associated with engagement. Qualitiative feedback supported effects on communication, social network engagement, service user goals, mental health and well-being outcomes. The EYE intervention was designed from a service user, young person and carer perspective. Both qualitative and quantitative data support impacts on engagement. We now need to evaluate effectiveness, cost-effectiveness and implementation in a multi-site randomised controlled trial.

Considering User Experience and Behavioral Approaches in the Design of mHealth Interventions for Atrial Fibrillation: Systematic Review.

Atrial fibrillation (AF) is a leading chronic cardiac disease associated with an increased risk of stroke, cardiac complications, and general mortality. Mobile health (mHealth) interventions, including wearable devices and apps, can aid in the detection, screening, and management of AF to improve patient outcomes. The inclusion of approaches that consider user experiences and behavior in the design of health care interventions can increase the usability of mHealth interventions, and hence, hopefully, yield an increase in positive outcomes in the lives of users. This study aims to show how research has considered user experiences and behavioral approaches in designing mHealth interventions for AF detection, screening, and management; the phases of designing complex interventions from the UK Medical Research Council (MRC) were referenced: namely, identification, development, feasibility, evaluation, and implementation. Studies published until September 7, 2022, that examined user experiences and behavioral approaches associated with mHealth interventions in the context of AF were extracted from multiple databases. The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines were used. A total of 2219 records were extracted, with only 55 records reporting on usability, user experiences, or behavioral approaches more widely for designing mHealth interventions in the context of AF. When mapping the studies onto the phases of the UK MRC's guidance for developing and evaluating complex interventions, the following was found: in the identification phase, there were significant differences between the needs of patients and health care workers. In the development phase, user perspectives guided the iterative development of apps, interfaces, and intervention protocols in 4 studies. Most studies (43/55, 78%) assessed the usability of interventions in the feasibility phase as an outcome, although the data collection tools were not designed together with users and stakeholders. Studies that examined the evaluation and implementation phase entailed reporting on challenges in user participation, acceptance, and workflows that could not be captured by studies in the previous phases. To realize the envisaged human behavior intended through treatment, review results highlight the scant inclusion of behavior change approaches for mHealth interventions across multiple levels of sociotechnical health care systems. While interventions at the level of the individual (micro) and the level of communities (meso) were found in the studies reviewed, no studies were found intervening at societal levels (macro). Studies also failed to consider the temporal variation of user goals and feedback in the design of long-term behavioral interventions. In this systematic review, we proposed 2 contributions: first, mapping studies to different phases of the MRC framework for developing and evaluating complex interventions, and second, mapping behavioral approaches to different levels of health care systems. Finally, we discuss the wider implications of our results in guiding future mHealth research.

Cross-Subject EEG Feedback for Implicit Image Generation.

Generative models are powerful tools for producing novel information by learning from example data. However, the current approaches require explicit manual input to steer generative models to match human goals. Furthermore, how these models would integrate implicit, diverse feedback and goals of multiple users remains largely unexplored. Here, we present a first-of-its-kind system that produces novel images of faces by inferring human goals directly from cross-subject brain signals while study subjects are looking at example images. We report on an experiment where brain responses to images of faces were recorded using electroencephalography in 30 subjects, focusing on specific salient visual features (VFs). Preferences toward VFs were decoded from subjects' brain responses and used as implicit feedback for a generative adversarial network (GAN), which generated new images of faces. The results from a follow-up user study evaluating the presence of the target salient VFs show that the images generated from brain feedback represent the goal of the study subjects and are comparable to images generated with manual feedback. The methodology provides a stepping stone toward humans-in-the-loop image generation.

Developing a Machine Learning 'Smart' Polymerase Chain Reaction Thermocycler Part 2: Putting the Theoretical Framework into Practice.

The introduction of PCR into forensic science and the rapid increases in the sensitivity, specificity and discrimination power of DNA profiling that followed have been fundamental in shaping the field of forensic biology. Despite these developments, the challenges associated with the DNA profiling of trace, inhibited and degraded samples remain. Thus, any improvement to the performance of sub-optimal samples in DNA profiling would be of great value to the forensic community. The potential exists to optimise the PCR performance of samples by altering the cycling conditions used. If the effects of changing cycling conditions upon the quality of a DNA profile can be well understood, then the PCR process can be manipulated to achieve a specific goal. This work is a proof-of-concept study for the development of a smart PCR system, the theoretical foundations of which are outlined in part 1 of this publication. The first steps needed to demonstrate the performance of our smart PCR goal involved the manual alteration of cycling conditions and assessment of the DNA profiles produced. In this study, the timing and temperature of the denaturation and annealing stages of the PCR were manually altered to achieve the goal of reducing PCR runtime while maintaining an acceptable quality and quantity of DNA product. A real-time feedback system was also trialled using an STR PCR and qPCR reaction mix, and the DNA profiles generated were compared to profiles produced using the standard STR PCR kits. The aim of this work was to leverage machine learning to enable real-time adjustments during a PCR, allowing optimisation of cycling conditions towards predefined user goals. A set of parameters was found that yielded similar results to the standard endpoint PCR methodology but was completed 30 min faster. The development of an intelligent system would have significant implications for the various biological disciplines that are reliant on PCR technology.

Effectiveness of an mHealth App That Uses Financial Incentives and Gamification to Promote Health Behavior Change in Adolescents and Caregivers: Protocol for a Clinic-Based Randomized Controlled Trial.

Adolescent and adult obesity continues to be a public health epidemic in the United States. Despite the popularity of mHealth apps with gamification among adolescents, there are insufficient studies to evaluate the efficacy of gamified mHealth apps and financial incentives to motivate sustained health behavior change in adolescents or their adult caregivers. This study aims to evaluate the effectiveness of gamification techniques and financial incentives used in the novel "CommitFit" mHealth app to motivate health behavior change and improve various mental and physical health metrics in adolescents and their caregivers. This study is a 3-month randomized controlled trial (RCT) with 30 adolescents (aged 13-15 years) and their adult caregivers (N=60). It evaluates "CommitFit," which uses gamification including points and leaderboards to motivate logging and achievement of self-selected health behavior goals (eg, more water, sleep, physical activity, fruits, or vegetables or fewer sugary beverages). The RCT had three arms, each with 10 dyads: (1) CommitFit-only users; (2) CommitFit$, where adolescents were paid US $0.05 for each point they earned; and (3) waitlist control. Intervention dyads used the app for 3 months and had the option to use it for the fourth month without prompts or extra financial incentives. User analytic software was used to evaluate the frequency of user logs and goal achievement. Monthly surveys evaluated self-reported change in the 5 CommitFit health behaviors. Changes in BMI and blood pressure were evaluated for all participants at 3 clinical visits. Mental health, gamification, and behavior economics surveys were completed during the clinical visits. Recruitment began in August 2023 and was completed in 10 weeks. The research team successfully recruited and enrolled 30 dyads. Researchers emailed and called 89 caregivers on a physician-approved adolescent patient list, a 33% recruitment rate. Data collection and analysis will be conducted in the spring and summer of 2024. The results of this study are anticipated to be published between late 2024 and early 2025. This RCT will expand knowledge of the effectiveness of gamification techniques, financial incentives, and mHealth apps to motivate sustained health behavior change among adolescents and caregivers. These results may offer new opportunities to caregivers, health insurers, health care systems, and clinicians to motivate health behavior change in adolescents and caregivers, with the ultimate goal of preventing or reducing obesity and obesity-related diseases. Additional gamification, mental health surveys, and app user analytics included in the study may provide further insight into the characteristics of adolescents or caregivers who would benefit the most from using a gamified mHealth app like CommitFit. DERR1-10.2196/63505.

Design and evaluation of Enthralled: A higher education conversation game based on Euripides’ Bacchae

This article presents the design, goals, and evaluation of Enthralled following the 2022 playtesting in three undergraduate Liberal Arts core courses. Enthralled draws on ancient Greek myths and the classical tragedy Bacchae by Euripides. As an immersive pedagogical intervention, Enthralled promotes group collaboration and rewards consideration of diverse viewpoints and cultural values. Participants vote on the motivations of dramatic characters, which allows for a deeper understanding of their preconceptions and implicit biases and offers the opportunity for self-reflection and dialogue. The game toolkit includes an original deck of cards, a role-playing script, a character sheet, ballots, a hints-and-clues deck, instructions for players and moderators, and scoring materials. The third version of Enthralled exceeded our entertainment efficacy, playability, and usability goals and contributed positively to the learning outcomes of the courses. Minor game improvements and a course-tailored implementation toolkit are the next steps for scaling this project.

The relationship of hip strength to walking and balance performance in unilateral lower limb prosthesis users differs by amputation level.

Safe and efficient locomotion is a frequently stated goal of lower limb prosthesis users, for which hip strength may play a central yet poorly understood role. Additional research to identify associations between hip strength, balance, and mobility among transtibial and transfemoral prosthesis users is required. To test whether residual and/or intact limb isometric hip strength was associated with lower limb prosthesis users' walking speed, endurance, and balance. Cross-sectional study. Research laboratory. Convivence sample of 14 transtibial and 14 transfemoral prosthesis users. Multiple linear regression was used to evaluate the relationship between isometric measures of residual and intact limb hip strength and walking and balance performance. Measures of isometric hip muscle strength, including peak torque, average torque, torque impulse, and torque steadiness (i.e. consistency with which an isometric torque can be sustained) were derived from maximum voluntary hip flexion, extension, abduction and adduction torque signals collected with a motor-driven dynamometer. Walking speed, endurance, and balance were assessed by administering the 10-meter walk test, 2-minute walk test, Four Square Step Test, and Narrowing Beam Walking Test, respectively. Residual limb hip extensor max torque and abductor torque steadiness explained between 51% and 69% of the variance in transtibial prosthesis users' walking speed, endurance, and balance. In contrast, intact limb hip abductor torque impulse explained between 33% and 48% of the variance in transfemoral prosthesis users' walking speed, endurance, and balance. Our results suggest that unilateral transtibial and transfemoral prosthesis users' walking and balance performance may depend on different hip muscles, and different facets of hip strength. Amputation level-specific hip strength interventions may therefore be required to improve walking and balance performance in unilateral transtibial and transfemoral prosthesis users. The "intact leg strategy" adopted by transfemoral prosthesis users may be due to a variety of prosthesis and biomechanical factors that limit the efficiency with which transfemoral prosthesis users can exploit the strength of their residual limb hip muscles while walking.

Application of entertainment and fitness robots based on game interaction in sports training data analysis

This study aims to explore the application of sports data analysis of recreational fitness robots in sports training. By introducing interactive elements of games, recreational fitness robots can provide more interesting and challenging fitness experience, thus attracting more people to participate in sports training. Research and design and develop a game interactive entertainment fitness robot, with action recognition and user interaction functions, can perceive the user’s actions and make corresponding responses. When the user interacts with the robot, the sensor continuously collects and records data, establishes a data acquisition and storage system, analyzes and processes the collected interactive data, identifies the user’s movement pattern, assesses the user’s physical fitness level, and identifies potential improvement points. According to the user’s physical condition and training objectives, the appropriate exercise plan and game interaction mode are designed to provide better training results and entertainment experience. It is found that the game-based interactive entertainment fitness robot can effectively improve the user’s exercise motivation and participation in sports training. Through the analysis of sports data, we can develop personalized training plans according to the specific needs and goals of users, so as to achieve better training results.

From Invisible to Visible: Impacts of Metadata in Communicative Data Visualization.

Leaving the context of visualizations invisible can have negative impacts on understanding and transparency. While common wisdom suggests that recontextualizing visualizations with metadata (e.g., disclosing the data source or instructions for decoding the visualizations' encoding) may counter these effects, the impact remains largely unknown. To fill this gap, we conducted two experiments. In Experiment 1, we explored how chart type, topic, and user goal impacted which categories of metadata participants deemed most relevant. We presented 64 participants with four real-world visualizations. For each visualization, participants were given four goals and selected the type of metadata they most wanted from a set of 18 types. Our results indicated that participants were most interested in metadata which explained the visualization's encoding for goals related to understanding and metadata about the source of the data for assessing trustworthiness. In Experiment 2, we explored how these two types of metadata impact transparency, trustworthiness and persuasiveness, information relevance, and understanding. We asked 144 participants to explain the main message of two pairs of visualizations (one with metadata and one without); rate them on scales of transparency and relevance; and then predict the likelihood that they were selected for a presentation to policymakers. Our results suggested that visualizations with metadata were perceived as more thorough than those without metadata, but similarly relevant, accurate, clear, and complete. Additionally, we found that metadata did not impact the accuracy of the information extracted from visualizations, but may have influenced which information participants remembered as important or interesting.

A Multi-Objective Genetic Algorithm-Based Predictive Model and Parameter Optimization for Forming Quality of SLM Aluminum Anodes

Aluminum–air batteries are characterized as “green energy for the 21st century” due to their clear advantages in terms of high current discharge, high specific energy, low cost, and easy-to-obtain electrode materials. This study develops the SLM aluminum anode quality prediction model and evaluates its learning and training results using the BP neural network architecture. By altering the network topology of the SLM aluminum anode quality prediction model, we create a process parameter backpropagation model that takes advantage of the extremely adaptable capabilities of artificial neural networks. The quick and exact selection of process parameters meets the goals of density, self-corrosion current, and anode usage, hence improving the forming quality and processing efficiency of SLM aluminum anodes. The experimental results show that the process parameter backpropagation model’s parameter configurations match to the real densities and self-corrosion currents, which are somewhat higher than the specified target values. The maximum error rate for the aluminum anode forming quality prediction model is 8.23%. Furthermore, the actual anode utilization rate is somewhat lower than the projected target value, indicating that the backpropagation model can satisfy actual production needs.

AI Enabled Personalized Learning Platform

The AI-powered individualized learning environment designed specifically for teaching programming. The platform makes it easier for users to log in, choose a domain, take interactive tests, assess their performance, and receive personalized video recommendations based on how well students perform. Understanding the value of individualized instruction in programming, the platform dynamically adjusts to each user's skill level and learning goals. Front-end and back-end technologies are seamlessly integrated to allow users to take quizzes specific to the domain they have chosen, get instant response, and get performance reviews. The website makes recommendations for videos based on users' skill levels. The architecture, functionality, user experience, implementation specifics, evaluation outcomes, and future directions of the platform are all made clear in this document. Through providing insights into AI-powered tailored learning, this study advances teaching methodologies and gives students more confidence when it comes to programming.

ViRgilites: Multilevel Feedforward for Multimodal Interaction in VR

Navigating the interaction landscape of Virtual Reality (VR) and Augmented Reality (AR) presents significant complexities due to the plethora of available input hardware and interaction modalities, compounded by spatially diverse visual interfaces. Such complexities elevate the likelihood of user errors, necessitating frequent backtracking. To address this, we introduce ViRgilites, a virtual guidance framework that delivers multi-level feedforward information covering the available interaction techniques as well as the future possibilities to interact with virtual objects, anticipating the interaction effects and how they fit with the overall user's goal. ViRgilites is engineered to facilitate task execution, empowering users to make informed decisions about action methodologies and alternative courses of action. This paper presents the architecture and functionality of ViRgilites and demonstrates its efficacy through evaluation with a formative user study.

A Visually Meaningful Color-Video Encryption Scheme That Combines Frame Channel Fusion and a Chaotic System

Compared to text and images, video can show information more vividly and intuitively via a moving picture; therefore, video is widely used in all walks of life. However, videos uploaded or stored in various video applications have not been treated with any protection, and these videos contain a lot of sensitive information that is more likely to be compromised. To solve this problem, video encryption schemes have been proposed. However, the main concern with existing video encryption schemes is that the private information in the encrypted video should be effectively protected, and, thus, the pixel distribution of the original video can be greatly damaged in the process of encryption, resulting in no or poor visual usability of the encrypted video. To this end, a novel color-video encryption scheme is proposed, which can effectively protect video privacy information while retaining certain visual information, thus enhancing the usability of encrypted videos. Firstly, the R, G, and B channels of the original color video are viewed as a whole for splitting. The dimensions of the blocks are three-dimensional, and permutation encryption is performed in three-dimensional blocks, which eliminates the redundancy of information between the video frame channels. Secondly, after permutation encryption, the channels of the video frame are separated, and then each channel is divided into blocks. The shape of the blocks is a square, and substitution encryption and permutation encryption operations are performed in turn. The whole encryption process is combined with the 2D-LSM chaotic system to improve the security of the scheme, as well as to reduce the time. Extensive experiments have been carried out, and the results show that the proposed scheme allows the encrypted video to retain rough visual information and, at the same time, effectively protects privacy, achieving the goal of encrypted video security and usability.

Simulation of E-learning in English personalized learning recommendation system based on Markov chain algorithm and adaptive learning algorithm

The research topic of this article is the English personalized learning recommendation module based on Markov chain algorithm and adaptive learning algorithm. Personalized learning recommendation systems have been widely applied in the field of education. Firstly, this article uses Markov chain algorithm to analyze user learning behavior data, abstract user learning behavior as a process of state transition, predict their future behavior based on their past behavior, better understand their learning needs and interests, and provide more personalized learning recommendations for them. Subsequently, combined with adaptive learning algorithms, recommendation strategies and content are dynamically adjusted based on the user's learning goals and level, providing more accurate and effective learning recommendations according to their personalized needs and learning progress. By comparing with traditional recommendation systems, evaluate the accuracy and personalization of the recommendation module to better meet the learning needs of users.

The role of generation Z media literacy in confronting the destructive value messages of digital media

Introduction. Today, the Internet environment has become a usual place of living and working for Generation Z. Digital media have created a unique digital environment adapted to the most diverse requests and needs of the new generation. However, in addition to its constructive capacity and boundless opportunities for education, the digital environment has a negative impact on the value orientations and priorities of the younger generation. The aim of this article is to review the studies on the role of media literacy in countering the negative effects and destructive value messages in digital media, as well as to summarize current trends and educational solutions in teaching media literacy. Materials and methods. The methodological basis of the research includes general scientific methods such as analysis, synthesis, induction, deduction, description, philosophical methods (hermeneutics, dialectics), social and pedagogical methods. Materials of the research are articles from Russian and foreign periodicals published mainly for the last 5 years. The main source of data was the analysis of publications in the search engine for world scientific publications Google Scholar. The search for publications was additionally carried out through electronic scientific libraries Elibrary. ru, CyberLeninka, and ResearchGate. Research results. The increasing complexity and growth of the destructive influence of digital media on the young generation determines the principles of developing and shaping the content and focus of modern media education. The development of media literacy should be carried out according to the age, value, and motivational features of personal development at all levels of education, combining technological, cognitive, social, and ethical components of media competence. Conclusion. The development and execution of media literacy projects and educational programs aimed at developing media literacy and increasing resistance to manipulative and destructive content of digital media is an urgent research and practical task, especially in the context of the experimental-evidence-based paradigm. Today, under conditions of increasing complexity and improvement of digital media, literacy and information culture become the key to the safe psychological and physical growth of a person in the digital world, including in the context of increasing civil involvement of young people. Media literacy is a sophisticated set of abilities, including the ability to think critically about a diverse range of digital content (news, entertainment, commercial, educational, viral) and to understand its consequences. It also includes the ability to use digital tools and technologies to create content, the ability to reflect on one's state and behavior and to manage emotional reactions, and the ability to build socially acceptable and ethically correct communication and interaction with other users. There is a shortage of Russian studies aimed at investigating the educational effects related to literacy education of different age groups in accordance with the user's goals and motivations for digital content consumption.

Hubungan Aplikasi Metode EUCS (End User Computing Satisfaction) dengan Kepuasan Kerja Perawat dalam Penggunaan SIMRS di RSUD Dr. Doris Sylvanus Palangka Raya

Hospitals, as institutions providing healthcare services, must maximize technological advancements. One such advancement is the use of SIMRS (Hospital Management Information System) in accordance with the Indonesian Minister of Health Regulation No. 82 of 2013. The use of SIMRS is predicted to enhance the hospital's efficiency, effectiveness, professionalism, performance, as well as access and services. Nurses' satisfaction in using SIMRS will impact the improvement of patient care quality. One method to evaluate user satisfaction with SIMRS is EUCS (End User Computing Satisfaction), developed by Doll and Torkzadeh, which consists of five dimensions: content, format, accuracy, ease of use, and timeliness. This study aims to determine the relationship between the application of the EUCS method and nurses' job satisfaction in the use of SIMRS at RSUD dr. Doris Sylvanus Palangka Raya. Methods: This research is correlational, employing a cross-sectional design. Respondents were selected using purposive sampling and the chi-square statistical test was used. The sample consisted of 175 inpatient nurses at RSUD dr. Doris Sylvanus Palangka Raya. Results: The chi-square statistical test showed a p-value = 0.000, or a significance level of p < 0.05, indicating that there is a relationship between the application of the EUCS method and nurses' job satisfaction in the use of SIMRS at RSUD dr. Doris Sylvanus Palangka Raya. Conclusion: Effectiveness and efficiency are the primary goals of SIMRS usage, which creates convenience and transparency, ultimately expected to enhance nurses' job satisfaction.