Physical activity from adolescence to young adulthood: patterns of change, and their associations with activity domains and sedentary time

Abstract

Background: Longitudinal studies demonstrate an average decline in physical activity (PA) from adolescence to young adulthood. However, while some subgroups of adolescents decrease activity, others increase or maintain high or low activity. Activity domains may differ between subgroups (exhibiting different PA patterns), and they offer valuable information for targeted health promotion. Hence, the aim of this study was to identify PA patterns from adolescence to young adulthood; also to explore the associations of (i) changes in PA domains and in sedentary time, (ii) sociodemographic factors, and (iii) self-rated health with diverging PA patterns.

Methods: The observational cohort study data encompassed 254 adolescents at age 15 and age 19. K-means cluster analysis for longitudinal data was performed to identify participant clusters (patterns) based on their accelerometry-measured moderate-to-vigorous PA (MVPA). Logistic regressions were applied in further analysis.

Results: Five PA patterns were identified: inactivity maintainers (n = 71), activity maintainers (n = 70), decreasers from moderate (to low) PA (n = 61), decreasers from high (to moderate) PA (n = 32), and increasers (n = 20). At age 15, participation in sports clubs (SC, 41-97%) and active commuting (AC, 47-75%) was common in all the patterns. By age 19, clear dropout from these activities was prevalent (SC participation mean 32%, AC 31-63%). Inactivity maintainers reported the lowest amount of weekly school physical education. Dropout from SC - in contrast to non-participation in SC - was associated with higher odds of being a decreaser from high PA, and with lower odds of being an inactivity maintainer. Maintained SC participation was associated with higher odds of belonging to the decreasers from high PA, and to the combined group of activity maintainers and increasers; also with lower odds of being an inactivity maintainer. Maintenance/adoption of AC was associated with decreased odds of being an inactivity maintainer. Self-reported health at age 19 was associated with the patterns of maintained activity and inactivity.

Conclusions: PA patterns diverge over the transition to adulthood. Changes in SC participation and AC show different associations with diverging PA patterns. Hence, tailored PA promotion is recommended.

Keywords: Accelerometer; Adolescence; Longitudinal studies; Physical activity; Sedentary behaviour; Sports; Young adults.

Fig. 1

Measured PA patterns (formed by the KmL data-driven clustering method [23]) (n = 254)

Fig. 2

Sedentary behaviour and physical activity (PA) as proportions of device wear-time by PA pattern. Sedentary time 15-y.: A > B**, C-E***, D < B**; 19-y.: E < D*, C, A***, B < C**, A***, change over time: B***, C**, D, E*. Standing still 15-y.: D < C*. Light PA 15-y.: A < B, D*, C**; change over time: A***, B**, E*. Moderate PA 15-y: A < B-E***, D > B, C***; 19-y.: A < B, D, E***, C < D*, C < B, E***, D < B**, E***; change over time: A, E*, C, D***. High PA 15-y: A < B-***, B < E**, D***, C < D**; 19-y.: A < B-E***, C < B, E***, D**; change over time: A*, C, D***. p-values determined from the Kruskal-Wallis test for differences in mean values between PA patterns cross-sectionally (post hoc Dunn’s test adjusted by the Bonferroni correction for multiple tests) (only significant differences presented) and the significance of the changes over time in mean values by the Wilcoxon Signed rank test. *p < 0.05, **p < 0.01, ***p < 0.001