Adult obesity is a significant health problem, with nearly a quarter of Hungarian citizens aged 15 years and older being obese in 2019 (KSH, 2019a). The use of mobile devices for health purposes is increasing, and many m-health apps target weight-related behaviours. This study uniquely examines the effectiveness and user satisfaction of health-oriented apps among Hungarian adults, with a focus on health improvement. Using a mixed-methods approach, the study identifies six key determinants of health improvement and refines measurement tools by modifying existing parameters and introducing new constructs. The principal objective was to develop a measurement instrument for the usability of nutrition, relaxation and health promotion applications. The research comprised three phases: (1) qualitative content analysis of 13 app reviews conducted in June 2022; (2) focus group interviews involving 32 students from the fields of business, economics and health management; and (3) an online survey (n = 348 users) conducted in December 2023 that included Strava (105 users), Yazio (109 users) and Calm (134 users). Six factors were identified as determinants of health improvement: physical activity, diet, weight loss, general well-being, progress, and body knowledge. The LAUQ (Lifestyle Application Usability Questionnaire) scale was validated, including 'ease of use' (5 items), 'interface and satisfaction' (7 items) and 'modified usefulness and effectiveness' (9 items), with modifications based on qualitative findings. This research offers valuable insights into the factors influencing health improvement and user satisfaction with healthy lifestyle-oriented applications. It also contributes to the refinement of measurement tools such as the LAUQ, which will inform future studies in health psychology, digital health, and behavioural economics.

This paper presents an effective method for performing audio steganography, which would help in improving the security of information transmission. Audio steganography is one of the techniques for hiding secret messages within an audio file to maintain communication secrecy from unwanted listeners. Most of these conventional methods have several drawbacks related to distortion, detectability, and inefficiency. To mitigate these issues, a new scheme is presented which combines the techniques of image interpolation with LSB encoding. It selects non-seed pixels to allow reversibility and diminish distortion in medical images. Our technique also embeds a fragile watermarking scheme to identify any breach during transmission to recover data securely and reliably. A magic rectangle has also been used for encryption to enhance data security. This paper proposes a robust, low-distortion audio steganography technique that provides high data integrity with undetectability and will have wide applications in sectors like e-healthcare, corporate data security, and forensic applications. In the future, this approach will be refined for broader audio formats and overall system robustness.

Technological advancements in genetic research are crucial for nations aiming to uplift their population’s quality of life and ensure a sustainable economy. Genomic information and biotechnology can enhance healthcare quality, outcomes, and affordability. The “P4 medicine approach”—predictive, preventive, personalized, and participatory—aligns with objectives like promoting long-term well-being, optimizing resources, and reducing environmental impacts, all vital for sustainable healthcare. This paper highlights the importance of adopting the P4 approach extensively. It emphasizes the need to enhance healthcare operations in real-time and integrate cutting-edge genomic technologies. Eco-friendly designs can significantly reduce the environmental impact of healthcare. Additionally, addressing health disparities is crucial for successful healthcare reforms.

In the wake of the COVID-19 pandemic, the prevalence of online education in primary education has exhibited an upward trajectory. Relative to traditional learning environments, online instruction has evolved into a pivotal pedagogical modality for contemporary students. Thus, to comprehensively comprehend the repercussions of environmental changes on students’ psychological well-being in the backdrop of prolonged online education, this study employs an innovative methodology. Founded upon three elemental feature sequences—images, acoustics, and text extracted from online learning data—the model ingeniously amalgamates these facets. The fusion methodology aims to synergistically harness information from diverse perceptual channels to capture the students’ psychological states more comprehensively and accurately. To discern emotional features, the model leverages support vector machines (SVM), exhibiting commendable proficiency in handling emotional information. Moreover, to enhance the efficacy of psychological well-being prediction, this study incorporates an attention mechanism into the traditional Convolutional Neural Network (CNN) architecture. By innovatively introducing this attention mechanism in CNN, the study observes a significant improvement in accuracy in identifying six psychological features, demonstrating the effectiveness of attention mechanisms in deep learning models. Finally, beyond model performance validation, this study delves into a profound analysis of the impact of environmental changes on students’ psychological well-being. This analysis furnishes valuable insights for formulating pertinent instructional strategies in the protracted context of online education, aiding educational institutions in better addressing the challenges posed to students’ psychological well-being in novel learning environments.