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Background: Higher levels of sedentary behaviour (SB) and screen-time are associated with greater symptoms of depression in adolescents, but the effect of the type and context of SB and screen-time remains underexplored. As part of a nationally-representative observational study, the current cross-sectional study examined associations between SB, screen-time and depressive symptoms among 422 adolescents (13.5 ± 0.92 years; 125 female) in the Republic of Ireland. Method: Participants completed the Quick Inventory of Depressive Symptomatology and self-reported weekly SB, categorised into mentally-active screen-time (e.g., computer use for fun), mentally-passive screen-time (e.g., television viewing) and mentally-active non-screen-based SB (e.g., reading). Mann–Whitney U tests and Kruskal–Wallis H tests examined differences in screen-time and depressive symptoms by relevant covariates. Linear regression quantified crude and adjusted associations between total SB and mentally-active and mentally-passive screen-time and SB, and depressive symptoms. Results: Crude and adjusted linear regressions showed total SB was significantly, positively associated with depressive symptoms (unadjusted: β = 0.27, p = 0.002, adjusted: β = 0.27, p = 0.002). When type and context were examined in the same model, only mentally-active screen-time was positively associated with depressive symptoms (unadjusted: β = 0.37, p = 0.009, adjusted: β = 0.39, p = 0.007). Conclusion: Differential associations between total SB and mentally-active screen-time and SB, versus mentally-passive screen-time, and depressive symptoms among Irish adolescents were observed. Findings highlight the importance of investigating the context and type of SB and screen-time in adolescents. [ABSTRACT FROM AUTHOR]

Background: All physical activity (PA) behaviours undertaken over the day, including sleep, sedentary time, standing time, light-intensity PA (LIPA) and moderate-to-vigorous PA (MVPA) have the potential to influence cardiometabolic health. Since these behaviours are mutually exclusive, standard statistical approaches are unable to account for the impact on time spent in other behaviours. Objective: By employing a compositional data analysis (CoDA) approach, this study examined the associations of objectively measured time spent in sleep, sedentary time, standing time, LIPA and MVPA over a 24-h day on markers of cardiometabolic health in older adults. Methods: Participants (n =366; 64.6 years [5.3]; 46% female) from the Mitchelstown Cohort Rescreen Study provided measures of body composition, blood lipid and markers of glucose control. An activPAL3 Micro was used to obtain objective measures of sleep, sedentary time, standing time, LIPA and MVPA, using a 7-day continuous wear protocol. Regression analysis, using geometric means derived from CoDA (based on isometric log-ratio transformed data), was used to examine the relationship between the aforementioned behaviours and markers of cardiometabolic health. Results: Standing time and LIPA showed diverging associations with markers of body composition. Body mass index (BMI), body mass and fat mass were negatively associated with LIPA (all p <0.05) and positively associated with standing time (all p <0.05). Sedentary time was also associated with higher BMI (p <0.05). No associations between blood markers and any PA behaviours were observed, except for triglycerides, which were negatively associated with standing time (p < 0.05). Reallocating 30 min from sleep, sedentary time or standing time, to LIPA, was associated with significant decreases in BMI, body fat and fat mass. Conclusion: This is the first study to employ CoDA in older adults that has accounted for sleep, sedentary time, standing time, LIPA and MVPA in a 24-h cycle. The findings support engagement in LIPA to improve body composition in older adults. Increased standing time was associated with higher levels of adiposity, with increased LIPA associated with reduced adiposity; therefore, these findings indicate that replacing standing time with LIPA is a strategy to lower adiposity. [ABSTRACT FROM AUTHOR]