Прогнозування фізичної активності та якості життя працездатного населення

Svitlana Indyka; Natalia Bielikova; Аnatolіi Tsos; Iaroslav Pasternak

doi:10.29038/2220-7481-2025-01-26-35

Authors

Svitlana Indyka Lesya Ukrainka Volyn National University
Natalia Bielikova Lesya Ukrainka Volyn National University
Аnatolіi Tsos Lesya Ukrainka Volyn National University
Iaroslav Pasternak Lesya Ukrainka Volyn National University

DOI:

https://doi.org/10.29038/2220-7481-2025-01-26-35

Keywords:

working-age population, quality of life, physical activity, machine learning methods

Abstract

Relevance. Researching the quality of life of the working-age population is critically important for ensuring health, well-being, and effective state reconstruction in the post-war period. The use of machine learning algorithms alongside traditional statistical methods for creating predictive models is relevant, as these methods can identify complex patterns in large datasets and predict possible changes influenced by various factors. This is crucial for developing individualized or group recommendations. The Aim of the Study was to characterize the process of forecasting physical activity and quality of life of the working-age population using machine learning methods and to create a corresponding computer program. Methods. The study involved 900 individuals from various social groups of working age (18 to 64 years), including 556 women and 334 men. The research methodology included several stages of data collection and processing to assess the physical and psychological components of quality of life. Participants were surveyed with social-demographic data; international standardized questionnaires such as IPAQ, SF-36 scale, and Ron Kessler’s questionnaire were used. The data were standardized for further use in machine learning algorithms. Research Results and Conclusions. The implementation of procedural actions for modeling and forecasting the quality of life of the working-age population enabled the creation of the computer program “Forecasting the Quality of Life of the Working-Age Population”. The use of this program allowed for the prediction of indicators of the general physical and mental components of the quality of life for this group. Special attention was given to identifying key factors that influenced the indicators of the general physical component of the quality of life, including age, education level, marital status, and distress level. Additionally, monitoring of the physical and psychological state of the working-age population was conducted under martial law conditions to develop effective strategies for improving the quality of life of this contingent.

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