ADM - Žežula I., Klein D., Roy A.: Testing of multivariate data with exchangeable block covariance structure, Test 27:2 (2018), pp. 360–378. Citations 4.

ADC - Škorvánek M., Martinez-Martin P., Kovacs N., Žežula I., Rodriguez-Violante M., Corvol J.-C., Taba P., Seppi K., Levin O., Schrag A. et al.: Relationship between the MDS-UPDRS and Quality of Life: A large multicenter study of 3206 patients, Parkinsonism & Related Disorders 52 (2018), pp. 83-89. Citations 54.

ADM - Kopčová V., Žežula I.: On intraclass structure estimation in the growth curve model, Statistical Papers 61:3 (2020), pp. 1085-1106. Citations 3.

ADC - Tonelli Gombalová Z., Košuth J., Alexovič Matiašová A., Zrubáková J., Žežula I., Giallongo T., Di Giulio A.M., Carelli S., Tomašková L., Daxnerová Z., Ševc J.: Majority of cerebrospinal fluid-contacting neurons in the spinal cord of C57Bl/6N mice is present in ectopic position unlike in other studied experimental mice strains and mammalian species, Journal of Comparative Neurology 528:15 (2020), pp. 2523-2550. Citations 12.

ADM - Jurková V., Žežula I., Klein D., Hutník O.: Unbiased estimator of correlation coefficient, Communications in Statistics - Theory and Methods 51:1 (2022), pp. 95-115. Citations 3.

VEGA 1/0585/24: Effective methods for analysis and modelling of economic and medical data with complex structures, deputy principal investigator, 2024-2027.

Summary: Project deals with the research of effective mathematical-statistical methods in analysis and modelling of complex data structures of real systems in different fields of human activity (economy, medicine, natural and social sciences, technical sciences and engeneering) with link to modern technologies and digital tools of statistics and data science. Basic areas of research are:

1. analysis of multivariate and highdimensional data structures with focus on complex dependence structures;

2. analysis of models and study of computational complexity of problems motivated by allocation of scarce resources;

3. study of problems of collective decisions, computational and optimization methods for modelling, prediction and measurement of time series and longitudinal data using simulation experiments and high-performance computing for the evaluation of their effectiveness;

4. developement of software support for these analyses.

VEGA 1/0097/24: Digital health literacy of older adults as a tool for supporting active aging,scientific co-worker, 2024-2026.

Summary: Active aging is the process of optimizing opportunities for health, participation and safety to support people's quality of life as they age. Older adults may already have health problems that require ongoing management. To ensure their access to health knowledge in the digital era, it is necessary to support their digital health literacy (DHL). DHL represents the ability of individuals to acquire, process, communicate and understand health information and make effective decisions to promote and improve health in the context of the use of digital technologies. The aim of the project is to improve the quality of life and support the active aging of older adults through the development of their DHL. Based on identifying the level, educational needs, facilitators, and barriers, a pilot intervention will be designed and implemented. After verification of its effectiveness, it will be recommended as an example of good practice in the field of policies and strategies to support active aging in Slovakia.

APVV-22-0587: Behavioural innovations in chronic condition management, scientific co-worker, 2023-2027.

Summary: Many of today’s most pressing public health challenges have a strong behavioural component. More people than ever are living longer with chronic conditions such as obesity, type 2 diabetes, heart disease, neurological and musculoskeletal diseases, and mental health problems. Behavioural, psychosocial, and environmental factors play a major role in the development and progression of chronic diseases. Behavior change for effective disease prevention and self-management support can improve treatment adherence, health outcomes, adjustment to diseases, and quality of life in people living with a chronic disease. The science of developing behavior change interventions, hich have an impact for patients, strives to maximize the reach, effectiveness, adoption, implementation, and maintenance of these interventions. The expansion of new services and technologies, including mHealth technologies, offers opportunities to enhance the scope of delivery of multisectoral low-cost interventions to support behavior change and self-management at scale. It also enables to test novel emerging methods for rapid and agile intervention development. The main goal of this transdisciplinary project is to deepen our understanding and enhance competency to deliver innovative, effective, and evidence-based health behaviourchange intervention programmes in mainstream clinical practice. The potential benefits of behavioural interventions are multiple as they are often very low cost, scalable, and light touch. This is especially relevant within the context of underfunded and overstretched healthcare systems.

APVV-17-0568: Applicatons of mathematical methods in economic and medical decision-making, principal investigator, 2018-2022.

Summary: Project focuses on development and application of mathematical methods in economic and medical decisionmaking. Partial goals:

1. Application and research of linear modelling methods and optimal empirical predictions in economic and medical data.

2. Analysis of uni- and multivariate medical data with special regard to linear and generalized linear models.

3. Decision-making on optimal allocation in financial portfolios with uncertainty in parameters.

4. Research and comparison of models of search and allocation of kidneys for transplantations, and related models.

5. Development of software for corresponding analyses.

APVV-21-0369: Optimal decision-making and control methods in complex data structures, principal investigator, 2022-2026.

Summary: Project focuses on development and application of mathematical methods for optimal decision-making and process

control in framework of complex data structures. Partial goals:

1. Application and research of linear modelling methods and optimal empirical predictions in economic, medical, and social science data.

2. Analysis of multivariate and high-dimensional medical data with special regard tocomplex data structures.

3. Study of measures of uncertainty and risk in financial markets and the impact of negative interest rates and key factors in investment decision-making problems.

4. Development of software for corresponding analyses.