Lifestyle Behavior Patterns and Their Association with Active Commuting to School Among Spanish Adolescents: A Cluster Analysis
Metadatos
Mostrar el registro completo del ítemAutor
Campos-Garzón, Pablo; Saucedo-Araujo, Romina Gisele; Rodrigo-Sanjoaquín, Javier; Palma-Leal, Ximena; Huertas-Delgado, Francisco Javier; Chillón-Garzón, PalmaEditorial
MDPI
Materia
Physical activity Sedentary Time Screen time Sleep duration Breakfast Transport
Fecha
2025-07-10Referencia bibliográfica
Campos-Garzón, P.; Saucedo-Araujo, R.G.; RodrigoSanjoaquín, J.; Palma-Leal, X.; Huertas-Delgado, F.J.; Chillón, P. Lifestyle Behavior Patterns and Their Association with Active Commuting to School Among Spanish Adolescents: A Cluster Analysis. Healthcare 2025, 13, 1662. https://doi.org/10.3390/healthcare13141662
Patrocinador
MINECO / FEDER, EU (PACO project; Reference DEP2016-75598-R); Unit of Excellence on Exercise and Health (UCEES) - Junta de Andalucía, Consejería de Conocimiento, Investigación y Universidades, European Regional Development Fund (Reference SOMM17/6107/UGR)Resumen
Objectives: We aimed to identify clustering patterns of the device-measured physical
activity (PA) levels (i.e., light PA and moderate-to-vigorous PA) and sedentary time (ST),
screen time, sleep duration, and breakfast consumption of Spanish adolescents and their
associations with the mode of commuting to and from schools (i.e., active and passive).
Methods: A total of 151 adolescents aged 14.4 ± 0.6 years (53.64% girls) were included
in this study. Participants wore an accelerometer device during seven consecutive days
to measure PA levels and ST levels. Screen time, sleep duration, breakfast consumption,
and the mode of commuting to and from school were self-reported by the participants. A
two-step cluster analysis was performed to examine the different lifestyle behavior patterns
(defined as data-driven groupings of daily behaviors identified through cluster analysis).
Logistic regression models were used to determine the associations among the lifestyle
behavior patterns and the mode of commuting to and from school. Results: The main
characteristics of the three identified clusters were as follows: (active) high PA levels and
low ST (38.4%); (inactive) high sleep duration and daily breakfast consumption, but low
PA levels and high ST and screen time (37.2%); and (unhealthy) low PA levels and sleep
duration, high ST and screen time, and usually skip breakfast (24.4%). No associations were
found between these clusters and the mode of commuting to and from school (all, p > 0.05).
Conclusions: Three different lifestyle behavior patterns were identified among Spanish
adolescents, but no associations were found between these patterns and their mode of
commuting to and from school.





