Clustered Lifestyle Profiles of Adolescent Football Players: Examining the Relationship Between Off-Training Lifestyle and Training Performance

• Published in: Research quarterly for exercise and sport pp. 1 - 13
• Main Authors: Mateus, Nuno, Coutinho, Diogo, Santos, Sara, Gonçalves, Bruno, Abrantes, Catarina, Sampaio, Jaime
• Format: Journal Article
• Language: English
• Published: United States 12.05.2025
• Subjects: small-sided games; Physical activity; sedentary behavior; young football
• ISSN: 0270-1367; 2168-3824; 2168-3824
• DOI: 10.1080/02701367.2025.2496268

This study clustered young male football players based on off-training physical activity (PA) and sedentary behavior (SB) to examine their impact on physical and technical-tactical performance in small-sided-games (SSG). In Stage 1, thirty-four adolescent male football players (mean age 14.2   ±   0.8 years) were monitored for one week using tri-axial accelerometers to quantify PA and SB levels. Participants were classified into two groups: Active (higher moderate-to-vigorous PA and lower SB) and Sedentary (higher SB and lower PA). In Stage 2, sixteen players, divided into four teams, participated in SSG. Two teams comprised players from the Active group (ACT) and two from the Sedentary group (SED). They competed under three conditions: I ; II ; and III . Cumming estimation plots illustrated that ACT players in I covered greater total distance (d = -1.43), distances in jogging (d = -0.69), running (d = -1.33), and sprinting (d = -1.26), with higher player load (d = -1.28) and decelerations (d = -0.58), but lower walking distances (d = 0.79), successful shots (d = 0.87), passes (d = 1.17), and dribbles (d = 0.89) compared to other conditions.. SED players in III showed higher running distances (d = 0.76) and more decelerations (d = 0.7) but fewer passes. Bayesian ANOVA confirmed longer distances and more sprints in I versus III , with Bayes factors of 6.50 and 9.48, respectively. Clustering young players based on off-training PA and SB profiles affects SSG performance, with teams having lower PA and higher SB showing compromised physical and technical-tactical outcomes. Coaches can use this information to tailor training, enhance learning environments, and optimize player development.