Breaks in Sedentary Time and Cardiometabolic Markers in Adolescents

Sedentary Behavior and Cardiometabolic Markers in Adolescents: A 4-Year Longitudinal Study

PURPOSE

Analyzed the associations of sedentary behavior (SB) measured by questionnaire and accelerometer, with cardiometabolic markers in adolescents.

METHODS

Longitudinal study with 4 years of follow-up with adolescents from João Pessoa, Brazil. SB was measured using a questionnaire (305 adolescents: 54.5% females; age 11.7 [SD = 0.7]) and use of accelerometer (136 adolescents: 54.8% females; age 11.5 [SD = 0.7]). The cardiometabolic markers were body mass index, waist circumference, systolic and diastolic blood pressure, fasting glucose, total cholesterol, triglycerides, low-density lipoproteins and high-density lipoproteins (HDL-C), total cholesterol/HDL ratio, triglycerides/HDL ratio, and non-HDL-C. Generalized Estimating Equation analysis was used to for analyses.

RESULTS

The average time in SB by the accelerometer was greater (average 8.3 [SD = 1.5], 8.8 [SD = 1.6], and 8.4 [SD = 1.9] h/d/wk) than observed in the questionnaire (on average 6.0 [SD = 4.1], 7.2 [SD = 4.9], and 6.6 [SD = 5.4] h/d/wk), in all years of the study, but without a significant increasing trend (P > .05) over time for both measures. There was a significant and positive association between SB measured by the questionnaire and SBP (β = 0.148; 95% CI, 0.021-0.274).

CONCLUSIONS

The SB generally does not seem to contribute to significant changes in cardiometabolic markers in adolescents, despite it being associated with increased systolic blood pressure levels.

Association of different domains of sedentary behavior and cardiovascular risk factors in adolescents: Cross-sectional study

BACKGROUND

Sedentary behavior (SB) has been related to cardiovascular risk factors (CVRF) such as high BMI, waist circumference (WC) and blood pressure (BP), including pediatric populations. However, it is still unclear whether the association between SB and CVRF could be domain dependent. Therefore, this study aimed to analyze the relationship between sedentary at different domains (time spent in TV, videogames, computer, smartphone) with CVRF in adolescents.

METHODS

A sample of 1011 adolescents (10-17 years old; 55.1% girls) was assessed. The different BS domains were obtained through a questionnaire, as well as socioeconomic status, habitual physical activity, smoking and alcohol consumption. BMI was calculated by objectively measured height and body mass, WC was assessed at middle point between the last rib and iliac crest, and BP was assessed by a digital oscillometric device. The relationship between SB and CVRF was determined by quantile regression, adjusted for sex, age, socioeconomic status, physical activity, smoking and alcohol consumption.

RESULTS

Elevated time in smartphone use was associated with higher median values of WC (β=1.88; 95%CI: (0.27; 3.49) and SBP (β=2.70; 95%CI: 0.35; 5.05). High total time spent in SB was associated with higher median values of BMI (β=0.68; 95%CI=(0.02; 1.35), WC (β=1.95; 95%=0.47; 3.42) and SBP (β=2.52; 95%CI: 0.37; 4.68).

CONCLUSIONS

Smartphone use and total SB time were related to higher CVRF in adolescents. Cardiovascular health promotion strategies should focus on reducing SB in pediatric populations, especially smartphone use.

Longitudinal association between moderate to vigorous physical activity and lipid profile indicators in adolescents

The objective of this study was to examine the association between time engaged in moderate (MPA), vigorous (VPA), and moderate-to-vigorous (MVPA) physical activity (PA) and indicators of lipid profile in adolescents. This longitudinal study with a four-year follow-up (2014 to 2017), and three collection points (2014, 2015 and 2017) analyzed the data of 136 adolescents aged between 10 and 13 years (53.7% girls), in João Pessoa, Paraíba state, Brazil. The time in MPA, VPA and MVPA times was measured by Actigraph GT3X+ accelerometers. The lipid profile indicators analyzed were total cholesterol (TC), low-density (LDL-C) and high-density lipoproteins cholesterol (HDL-C), triglycerides (TG), non-HDL-C, TC/HDL-C and TG/HDL-c ratios. A generalized estimating equation (GEE) model was used to analyze the association between PA and lipid profile indicators. There was an inverse association between MPA time and TC values (ß = -0.560; 95%CI: -1.116; -0.004); VPA and LDL-C (ß = -0.962; 95%CI: -1.678; -0.246) and non-HDL-C (ß = -0.955; 95%CI: -1.708; -0.201); and MVPA and TC (ß = -0.436; 95%CI: -0.816; -0.055), TG (ß = -0.415; 95%CI: -0.712; -0.118), LDL-C (ß = -0.460; 95%CI: -0.823; -0.096), non-HDL-C (ß = -0.522; 95%CI: -0.908; -0.136) and TC/HDL-C (ß = -0.472; 95%CI: -0.889; -0.055). We conclude that over 4 years, adolescents more engaged in PA, especially in MVPA, exhibited better levels of TC, TG, LDL-C, non-HDL-C and TC/HDL-C; and AFV exerted a greater influence on LDL-c and non-HDL-c levels.