@misc{Małowiecki_Andrzej_Predicting_2023,
 author={Małowiecki, Andrzej and Jaśkiewicz, Julia},
 identifier={DOI: 10.15611/ie.2023.1-2.04},
 year={2023},
 rights={Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy},
 description={Business Informatics = Informatyka Ekonomiczna, 2023, Nr 1-2 (65-66), s. 33-41},
 publisher={Publishing House of Wroclaw University of Economics and Business},
 language={eng},
 abstract={Aim: The aim of this paper is to verify whether a machine learning model can be effectively used to predict people’s physical activity levels for personalising employee well-being programmes in companies. Methodology: The following research methodologies were used in this paper: literature analysis and experiment, in the form of verification of the predictions made by the created machine learning model. Results: The results obtained from the evaluation of the model showed that the use of human characteristics data to predict physical activity levels for the personalisation of well-being programmes does not guarantee good enough results. Implications and recommendations: By effectively predicting physical activity levels, well-being programmes can be more effectively personalised to the individual needs of employees, which can contribute to improving their health. Originality/value: The literature review found that the use of machine learning to predict physical activity levels has not been described in detail in the literature.},
 type={artykuł},
 title={Predicting Physical Activity Levels for the Personalization of Well-Being Programmes},
 keywords={GPAQ, MET, well-being, machine learning},
}