Physical Activity-Sedentary Behaviour Profiles and Co-Occurring Lifestyle Behaviours Among Adolescents: A Person-Oriented Analysis.

Background/Objectives: Physical activity and sedentary behaviour are important components of adolescent lifestyle, but favourable movement behaviour may not correspond to favourable behaviours across other health-related domains. This study aimed to identify empirical physical activity-sedentary behaviour profiles among adolescents and examine their co-occurring dietary, sleep, digital, and general health behaviours. Methods: This cross-sectional, school-based study used data from 3307 Polish adolescents aged 15-17 years. Following final processing of the International Physical Activity Questionnaire-Long Form, 1932 participants had complete and analytically eligible data for sedentary time, walking, moderate-intensity physical activity, and vigorous-intensity physical activity and were included in profile derivation. The four variables were Z-standardised before k-means clustering. Solutions from k = 2 to k = 6 were evaluated using the silhouette coefficient, Calinski-Harabasz index, Davies-Bouldin index, and Adjusted Rand Index (ARI). Co-occurring behaviours were compared using chi-square and Mann-Whitney U tests with effect-size estimates. Results: Cluster assignments were almost identical across repeated k-means runs initiated from different random starting centroids (ARI = 0.999). Because all runs were performed on the same set of participants, this result reflects low dependence on the initial centroid configuration rather than robustness to changes in sample composition. Separation between the two groups was limited (silhouette coefficient = 0.294), while the correspondence between classifications obtained from unstandardised and Z-standardised variables was minimal (ARI = 0.008), demonstrating a strong influence of scaling on cluster membership. The final classification included 1070 adolescents (55.4%) in the Higher Physical Activity profile and 862 (44.6%) in the Lower Physical Activity profile. The profiles showed no clear differences in school-day sleep duration, television/computer use, or internet-related risk. Weekend sleep duration differed between profiles (V = 0.120, p = 0.001). The Higher Physical Activity profile had higher Pro-Healthy Diet Index (r = 0.100, p < 0.001) and Non-Healthy Diet Index scores (r = 0.090, p < 0.001), slightly higher energy-drink intake (r = 0.080, p = 0.001), and more frequent out-of-home meal consumption (V = 0.079, p = 0.002). Effect sizes for co-occurring behaviours were generally small. Conclusions: Empirically distinct movement-behaviour profiles showed only partial correspondence with broader lifestyle characteristics. Higher physical activity coexisted with both more and less favourable dietary characteristics, whereas the Lower Physical Activity profile did not show a consistently adverse sleep or digital-behaviour pattern. These findings indicate that physical activity level alone should not be treated as a proxy for overall adolescent lifestyle quality. Given the exploratory, cross-sectional design and method-dependent nature of the profiles, the findings should be interpreted as hypothesis-generating rather than as evidence for fixed lifestyle types or profile-specific interventions.