# Measuring the physical activity in the EU using a fuzzy hybrid synthetic index and an ordered probit model

## TL;DR

This study measures physical activity across the EU using a fuzzy synthetic index and statistical model to identify factors influencing activity levels.

## Contribution

A novel fuzzy hybrid synthetic index and ordered probit model are applied to analyze physical activity determinants in the EU.

## Key findings

- A synthetic index of physical activity was developed using fuzzy logic and survey data from 26,578 EU respondents.
- Key factors like gender, age, education, and social class significantly influence physical activity levels.
- The method provides insights for creating targeted programs to reduce physical inactivity in the EU.

## Abstract

Physical activity can be measured by different attributes, such as sports activities, moderate exercise, or even walking time. The most recent Eurobarometer on Sport and Physical Activity included nine questions that permit physical activity measurement at the EU.

The study uses a Fuzzy Hybrid Analysis approach to calculate a synthetic index that measures the physical activity of EU citizens. The method is applied to the dataset obtained from a survey administered to a total of 26,578 respondents who represent the EU. Nine items measure the physical activity latent variable with an answer format based on three different semantic ordinal point scales.

The method provides a synthetic indicator at aggregated and individual levels. Seventeen covariates were used to analyze the main determinants of physical activity, particularly gender, age, education, social class, and political orientation.

The results reveal that certain covariates influence the latent variable under study, providing interesting insights to inform the development of targeted programs that reduce physical inactivity in the EU.