Sedentary behavior is more related with cardiovascular parameters in normal weight than overweight adolescents

Prolonged screen watching behavior is associated with high blood pressure among children and adolescents: a systematic review and dose-response meta-analysis

BACKGROUND

Numerous cardio-metabolic risk factors influence screen-related behaviors in children and adolescents. Numerous studies with inconsistent results revealed a relationship between blood pressure and screen time in the children and adolescents. This systematic review and meta-analysis summarized the data regarding the relationship between screen time and hypertension (HTN) in children and adolescents.

METHODS

We examined three electronic databases, including Scopus, PubMed, and Embase to find the recent research on the relationship between screen time and HTN up to 19 July 2022. Twenty papers were included in the final two-class and dose-response meta-analysis. We conducted subgrouping to identify the source of heterogeneity.

RESULTS

The highest category of screen time increased the odds of HTN by 8% [odds ratio (OR): 1.15; 95% confidence interval (CI): 1.08, 1.23; P < 0.001; I2 = 83.20%] and 1.9 mmHg increase in systolic blood pressure [weighted mean difference (WMD): 1.89; 95% CI: 0.18-3.62; P = 0.030; I2 = 83.4]. However, there was no significant difference in diastolic blood pressure. Moreover, screen time in hypertensive children and adolescents was 0.79 h (47.4 min) higher than normotensive subjects (WMD: 0.79; 95% CI: 0.02, 1.56; P = 0.046; I2 = 92.8). A departure from linearity was observed between increased screen time [digital video discs, personal computers, and video games and HTN (Pnonlinearity = 0.049).

CONCLUSION

This systematic meta-analysis review is the first to demonstrate a positive correlation between screen time and HTN in children and adolescents.

Behavioural activity pattern, genetic factors, and the risk of nonalcoholic fatty liver disease: A prospective study in the UK Biobank

BACKGROUND & AIMS

Physical activity, sedentary behaviour, and genetic variants have been associated with the nonalcoholic fatty liver disease (NAFLD). However, whether and how the degree of healthy activity patterns may modify the impact of genetic susceptibility on NAFLD remains unknown.

METHODS

Behaviour activity factors were determined according to total physical activity (TPA) and sedentary time. The polygenic risk score (PRS) was calculated by variants in PNPLA3, TM6SF2, MBOAT7, and GCKR. Cox regression was used to analyse the associations of genetic and behaviour activity factors with incident NAFLD in the UK Biobank (N = 338 087).

RESULTS

During a median follow-up of 12.4 years, 3201 incident NAFLD cases were ascertained. Analyses of TPA and sedentary time simultaneously showed a dose-response association with the risk of NAFLD (ptrend  < .001). The association of behaviour activity patterns with NAFLD varied by genetic variants. Of the subjects with high genetic risk, we observed a null protective effect of moderate or high TPA on NAFLD risk, while sitting less than three hours a day significantly decreased the risk of NAFLD (p = 3.50 × 10-4 ). The high genetic risk of NAFLD can also be offset by the combination of moderate physical activity and shorter sedentary time. Moreover, the high genetic risk group has the greatest reduction of 10-year absolute risk (6.95 per 1000 person-years) if reaching both healthy activities.

CONCLUSIONS

Moderate-to-high physical activity and favourable sedentary behaviour may be lifestyle modifications in preventing NAFLD, which could offset the harmful effect of predisposing genetic factors.

Comparison of bone mineral density according to domains of sedentary behavior in children and adolescents

BACKGROUND

Somatic maturation and the age at onset of puberty are closely related to bone mineral density (BMD), and are potential confounders of the associations between physical activity, sedentary behavior (SB) and BMD in adolescents. Thus the aim was compare BMD at different anatomical sites according to different domains of SB.

METHODS

The sample consisted of 88 young people (54 boys and 34 girls; 9.5 ± 1.5 years). The self-reported SB was measured by the time spent on TV, computer, video game and smartphone. BMD at each location and throughout the body was assessed by DEXA. Physical activity was assessed by a questionnaire. The comparison of the different types of BMD sites according to the SB levels for each screen device and the total SB were analyzed by Covariance Analysis (ANCOVA).

RESULTS

Whole-body BMD was higher in young people with low total SB (Total BMD = 0.957 ± 0.042) than in those with moderate (Total BMD = 0.921 ± 0.053) and high SB (Total BMD = 0.929 ± 0.051) (p-value = 0.011). Children and adolescents with low total SB had higher BMD legs (0.965 ± 0.056) than young people with high total SB (BMD legs = 0.877 ± 0.209), but this relationship was attenuated when the analyzes were adjusted for physical activity (p-value = 0.068).

CONCLUSION

Adolescents with high sedentary behavior tend to have lower whole body bone mineral density than those with low sedentary behavior.

Sedentary behavior is associated with musculoskeletal pain in adolescents: A cross sectional study

BACKGROUND

High prevalence of back pain has been observed in adolescents. Sedentary behavior (SB) is considered a risk factor for musculoskeletal pain. The association between back pain and SB in the pediatric/adolescent population is not well established.

OBJECTIVE

To investigate the association between SB and low back and neck pain in adolescents according to sex.

METHODS

This is a cross-sectional study with children and adolescents aged 10-17 years, randomly recruited from public and private schools in Presidente Prudente, Brazil. All students enrolled in the selected schools were eligible to participate. SB was evaluated by adding the number of hours of use of screen devices, such as television, computer, video game, and smartphone/tablet. To assess neck and low back pain, the Nordic Musculoskeletal Questionnaire was used. Physical activity and socioeconomic status were assessed by the Baecke Questionnaire and the Brazilian Criteria for Economic Classification (ABEP), respectively. Odds ratio (OR) from Binary Logistic Regression in the unadjusted and adjusted model (physical activity, abdominal obesity, and socioeconomic status) showed the relationship between musculoskeletal pain and SB.

RESULTS

A total of 1011 adolescents (557 girls) with a mean ± standard deviation age of 13.2±2.4 years were included. Moderate (OR = 1.80; 95%CI: 1.00, 3.23) and high (OR = 1.91; 95%CI: 1.02, 3.53) SB were associated with neck pain in girls. In boys, moderate SB [OR = 2.75; 95%CI: 1.31, 5.78) were associated with neck pain. Moderate (OR = 2.73; 95%CI: 1.45, 5.02) and high (OR = 2.49; 95%CI: 1.30, 4.76) SB were associated with low back pain only in girls.

CONCLUSION

Moderate and high SB were associated with neck pain in girls and boys, while moderate and high SB were associated with low back pain only in girls.

The Promotion of Physical Activity from Digital Services: Influence of E-Lifestyles on Intention to Use Fitness Apps

E-Lifestyles are individual forms of behavior in the digital environment that reflect the values, activities, interests, and opinions of consumers. Likewise, fitness Apps are considered technological tools for promoting physical activity online. Although there are studies related to sports lifestyles, it has not been analyzed yet how e-lifestyles are related to the use of fitness Apps. Based on this, this study represents a step to clarify how e-lifestyles influence different relationships with perceived ease of use, perceived usefulness, attitude, and intentions to use Fitness Apps. Therefore, the objective of the study was to analyze the relationship between the e-lifestyles of consumers of Boutique fitness centers and their relationship with the perceived ease of use, the perceived usefulness, the attitude, and the intention to use Fitness Apps. The sample was 591 customers (378 women and 213 men) of 25 Boutique fitness centers. An online questionnaire was used for data collection. Data was analyzed with confirmatory factor analysis and structural equation model. Findings provide an insight into the importance of e-lifestyles in the intention of using fitness Apps and therefore in promoting physical activity through online fitness services. The results showed positive relationships between e-lifestyles, perceived ease of use, perceived usefulness and attitude toward fitness Apps. Finally, the attitude toward fitness Apps offered a very high predictive value on use intention. This study provides a better understanding of consumer´s intention to use fitness Apps. The conclusions and recommendations for sports managers of fitness centers highlight the importance of e-lifestyles as a predecessor for the use of fitness Apps.

Associations of Objectively-Assessed Smartphone Use with Physical Activity, Sedentary Behavior, Mood, and Sleep Quality in Young Adults: A Cross-Sectional Study

This study assesses the associations of objectively-measured smartphone time with physical activity, sedentary behavior, mood, and sleep patterns among young adults by collecting real-time data of the smartphone screen-state. The sample consisted of 306 college-aged students (mean age ± SD: 20.7 ± 1.4 years; 60% males). Over seven days of time, the following variables were measured in the participants: objectively-measured smartphone use (Your Hour and Screen Time applications), objective and subjective physical activity (GoogleFit and Apple Health applications, and the International Physical Activity Questionnaire (IPAQ), respectively), the number of hours sitting (IPAQ), mood (The Profile of Mood State (POMS)), and sleep (The Pittsburgh Sleep Quality Index (PSQI)). Multiple regressions analyses showed that the number of hours sitting per day, physical activity, and the POMS Global Score significantly predicted smartphone use (adj.R2 = 0.15). Further, participants with low levels of physical activity were more likely to increase the use of smartphones (OR = 2.981). Moreover, mood state (β = 0.185; 95% CI = 0.05, 0.32) and sleep quality (β = 0.076; 95% CI = -0.06, 0.21) predicted smartphone use, with those reporting poor quality of sleep (PSQI index >5) being more likely to use the smartphone (OR = 2.679). In conclusion, there is an association between objectively-measured smartphone use and physical activity, sedentary behavior, mood, and sleep patterns. Those participants with low levels of physical activity, high levels of sedentary behavior, poor mood state, and poor sleep quality were more likely to spend more time using their smartphones.