Title:

Predicting Physical Activity Levels for the Personalization of Well-Being Programmes

Group publication title:

Informatyka Ekonomiczna = Business Informatics

Title in english:

Przewidywanie poziomu aktywności fizycznej na potrzeby personalizacji programów well-being

Creator:

Małowiecki, Andrzej ; Jaśkiewicz, Julia

Subject and Keywords:

GPAQ ; MET ; well-being ; machine learning

Description:

Business Informatics = Informatyka Ekonomiczna, 2023, Nr 1-2 (65-66), s. 33-41

Abstrakt:

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.

Publisher:

Publishing House of Wroclaw University of Economics and Business

Place of publication:

Wroclaw

Date:

2023

Resource Type:

artykuł

Resource Identifier:

doi:10.15611/ie.2023.1-2.04

Language:

eng

Relation:

Business Informatics = Informatyka Ekonomiczna, 2023, Nr 1-2 (65-66)

Rights:

Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy

Access Rights:

Dla wszystkich zgodnie z licencją

License:

CC BY-SA 4.0

Location:

Uniwersytet Ekonomiczny we Wrocławiu