Title: A clustering approach for identification of profiles given barriers and facilitators for the practice of exercise during pandemic

Authors: Maria Luiza C. Wuillaume; Fernanda Castro Monteiro; Karla Figueiredo; Arthur Santana; Carlos Linhares Veloso Filho; Manoel Carlos Pego Saísse; Andrea Deslandes; Andrea Nunes Carvalho

Addresses: Instituto Nacional de Tecnologia (INT), Av. Venezuela, 82 – Saúde, Rio de Janeiro – RJ, 20081-312, Brazil; Department of Electronic and Computer Engineering, Polytechnic School, Federal University of Rio de Janeiro, Av. Athos da Silveira Ramos, 149 – Centro de Tecnologia, Bloco H, Sala 217, Cidade Universitária, Rio de Janeiro – RJ, 21941-914, Brazil ' Institute of Psychiatry, Federal University of Rio de Janeiro, Av. Venceslau Brás, 71 – Praia Vermelha Campus, Botafogo, Rio de Janeiro, Brazil ' Rio de Janeiro State University, Rua São Francisco Xavier, 524 – Maracanã, Rio de Janeiro – RJ, 20550-900, Brazil ' Instituto Nacional de Tecnologia (INT), Av. Venezuela, 82 – Saúde, Rio de Janeiro – RJ, 20081-312, Brazil; COPPE, Federal University of Rio de Janeiro, Av. Horácio Macedo, 2030 – Centro de Tecnologia, Bloco H, Sala 319, Cidade Universitária, Rio de Janeiro – RJ, 21941-914, Brazil ' Institute of Psychiatry, Federal University of Rio de Janeiro, Av. Venceslau Brás, 71 – Praia Vermelha Campus, Botafogo, Rio de Janeiro, Brazil ' Instituto Nacional de Tecnologia (INT), Av. Venezuela, 82 – Saúde, Rio de Janeiro – RJ, 20081-312, Brazil ' Institute of Psychiatry, Federal University of Rio de Janeiro, Av. Venceslau Brás, 71 – Praia Vermelha Campus, Botafogo, Rio de Janeiro, Brazil ' Instituto Nacional de Tecnologia (INT), Av. Venezuela, 82 – Saúde, Rio de Janeiro – RJ, 20081-312, Brazil

Abstract: Practising physical activity and avoiding sedentary lifestyle are essential for maintaining physical and mental well-being. Understanding the distribution of facilitators and barriers to physical activity among the population is essential for developing public policies that promote a more active lifestyle. Using data from a questionnaire applied during the COVID-19 pandemic, a clustering method was developed to identify individuals' profiles based on their barriers and facilitators to physical activity. The questionnaire comprised exclusively closed-ended questions, therefore, the proposed method operates with categorical datasets, applying Kmodes and ROCK clustering techniques, along with adaptations of silhouette score and Calinski-Harabasz evaluation techniques. The results obtained were validated and endorsed by health experts. The study revealed that barriers played a more significant role in determining consistent profiles. This research addresses a problem often overlooked in the machine learning literature: applying clustering algorithms to categorical data.

Keywords: unsupervised learning; clustering; categorical variables; physical activity; sedentary behaviour; Kmodes; ROCK; questionnaire.

DOI: 10.1504/IJCSE.2025.149768

International Journal of Computational Science and Engineering, 2025 Vol.28 No.6, pp.607 - 618

Received: 22 Nov 2023
Accepted: 03 Jul 2024
Published online: 12 Nov 2025 *

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