Determinants of Physical Activity and Screen Time Trajectories in 7th to 9th Grade Adolescents-A Longitudinal Study

Associations between screen-time, physical activity and depressive symptoms differ based on gender and screen-time mode

Higher levels of physical activity (PA) and screen time (ST) are associated with positive and negative mental health outcomes among adolescents, respectively. Research is needed to determine the interrelationship between ST and PA with depressive symptoms and the influence of ST modes. This study examines the associations between ST and PA level with depressive symptoms among 1756 adolescents (15.2 ± 1.6y; 995 female) in Ireland. Participants completed the Quick Inventory of Depressive Symptomatology and self-reported weekly ST (TV, computer, and phone use) and PA level (low, moderate, and high). Linear regressions examined associations between ST mode, PA level and relevant covariates with depressive symptoms in the full sample by gender, and in a sample stratified by PA level. Chi-square tests and one-way ANOVA examined gender differences in hours of ST, PA levels, and depressive symptoms. Differential associations were observed depending on the gender and ST mode. Higher levels of computer (β  =  0.106, p  ≤  0.000) and phone use (β  =  0.138, p  ≤  0.000) showed the strongest associations with depressive symptoms. PA level was inversely associated with depressive symptoms (β  =  - 0.175, p  ≤  0.000). When the sample was stratified by PA level, only associations between phone use and depressive symptoms were moderated by PA level for males and females. These findings highlight the complex interrelationships between ST, PA, and depressive symptoms, and that associations may vary based on gender and ST mode. This may have implications for future interventions. Increasing PA and reducing ST should be targeted concurrently with consideration given to different media and genders. Future research should explore longitudinal and prospective associations.

Screen time among school-aged children of aged 6-14: a systematic review

BACKGROUND

Screen time refers to the time an individual spends using electronic or digital media devices such as televisions, smart phones, tablets or computers. The purpose of this study was to conduct systematic review to analyze the relevant studies on the length and use of screen time of school-aged children, in order to provide scientific basis for designing screen time interventions and perfecting the screen use guidelines for school-aged children.

METHODS

Screen time related studies were searched on PubMed, EMBASE, Clinical Trials, Controlled Trials, The WHO International Clinical Trials Registry Platform, the Cochrane Central Register of Controlled Trials, CNKI, and Whipple Journal databases from January 1, 2016 to October 31, 2021. Two researchers independently screened the literature and extracted the data, and adopted a qualitative analysis method to evaluate the research status of the length and usage of screen time of school-aged students.

RESULTS

Fifty-three articles were included. Sixteen articles studied screen time length in the form of continuous variables. Thirty-seven articles studied screen time in the form of grouped variables. The average screen time of schoolchildren aged 6 to 14 was 2.77 h per day, and 46.4% of them had an average screen time ≥ 2 h per day. A growth trend could be roughly seen by comparing studies in the same countries and regions before and after the COVID-19 outbreak. The average rates of school-aged children who had screen time within the range of ≥ 2 h per day, were 41.3% and 59.4% respectively before and after January 2020. The main types of screen time before January 2020 were watching TV (20 literatures), using computers (16 literature), using mobile phones/tablets (4 literatures). The mainly uses of screens before January 2020 were entertainment (15 literatures), learning (5 literatures) and socializing (3 literatures). The types and mainly uses of screen time after January 2020 remained the same as the results before January 2020.

CONCLUSIONS

Excessive screen time has become a common behavior among children and adolescents around the world. Intervention measures to control children's screen use should be explored in combination with different uses to reduce the proportion of non-essential uses.

COVID-19: Effects of Pandemic Related Restrictions on Physical Activity, Screen Time, and Mental Well-being in German adolescents

Objective: To describe the impact of the COVID-19 pandemic-related restrictions (PR) in April and May 2020 on physical activity (PA), sedentary screen time (SST), and mental well-being (MWB) in German adolescents, and to analyze associations between these variables. Methods: The Münster District Government invited all secondary school students (aged 11-17) in the region to take part in the online survey that assessed PA, SST, and MWB. For data analysis, we calculated descriptive statistics and ran linear regression analysis. Results: 1,038 students (627 [60.4%] female; 14.18 [± 1.97] years) were included in the analysis. During the PR, a marked decline in overall PA (p < .001) and a significant increase (p < .001) in SST were observed. One-third of the students reported worrying more and being less satisfied with their lives since PR. A decrease in life satisfaction (ß = -.524, p < .001) as well as an increase in general worrying (ß = -.336, p = .015) were associated with a decrease in PA during PR. Conclusion: The results show that the restrictions led to a decrease in physical activity, which may have detrimental effects on the students' mental and physical health.

Effectiveness of a school-based randomized controlled trial aimed at increasing physical activity time in adolescents

BACKGROUND

This study aimed to evaluate the effectiveness of obesity prevention educational activities, isolated or combined with the modification of the school environment on total physical activity time.

METHODS

This is a school-based randomized controlled trial, conducted with 2511 students from fifth and sixth-grade in Brazil, that employed a parallel, three-group experimental arms: control group (CG), PAAPPAS group (PG) and PAAPPAS-environment group (PEG). During the 2016 school year, the PG received educational activities in the classroom, providing a general basis for a healthy lifestyle. The PEG received the same educational activities and also modifications in the school environment to stimulate physical activity practice during one month. Physical activity was evaluated through a validated physical activity questionnaire. Intention-to-treat analysis was performed to evaluate the rate of change of physical activity time between groups using PROC GENMOD procedure. All analyses were performed using SAS.

RESULTS

Total physical activity time increased by 22% in PEG compared with CG (P = 0.003). No difference was observed between PEG and PG (Δ = 60.20 vs. 36.37, respectively; P = 0.27) and between PG and CG (Δ = 36.37 vs. 9.70, respectively; P = 0.23). The proportion of individuals who attended at least 150 min week-1 of physical activity increased in PEG compared with PG (P = 0.04); however, no difference was observed between PEG and CG (P = 0.19) and between PG and CG (P = 0.26). For 300 min week-1, no difference was observed between groups.

CONCLUSION

A school-based multi-component intervention including modification of the school environment was effective for increasing physical activity time among adolescents.

Contemporaneous trajectories of physical activity and screen time in adolescents

Adolescents often report low moderate-to-vigorous physical activity (MVPA) and high screen time. We modeled sex-specific MVPA and screen time trajectories during adolescence and identified contemporaneous patterns of evolution. Data were drawn from 2 longitudinal investigations. The Nicotine Dependence in Teens (NDIT) study included 1294 adolescents recruited at age 12-13 years who completed questionnaires every 3 months for 5 years. The Monitoring Activities of Teenagers to Comprehend their Habits (MATCH) study included 937 participants recruited at age 9-12 years who completed questionnaires every 4 months for 7 years. MVPA was measured as the number of days per week of being active for at least 5 min (NDIT) or 60 min (MATCH). In both studies, screen time was measured as the number of hours spent weekly in screen activities. In each study, sex-specific group-based trajectories were modeled separately for MVPA and screen time from grade 7 to 11. Contemporaneous patterns of evolution were examined in mosaic plots. In both studies, 5 MVPA trajectories were identified in both sexes, and 4 and 5 screen time trajectories were identified in boys and girls, respectively. All combinations of MVPA and screen time trajectories were observed. However, the contemporaneous patterns of evolution were favourable in 14%-31% of participants (i.e., they were members of the stable high MVPA and the lower screen time trajectories). Novelty: MVPA and screen time trajectories during adolescence and their combinations showed wide variability in 2 Canadian studies. Up to 31% of participants showed favourable contemporaneous patterns of evolution in MVPA and screen time. Using uniform methods for trajectory modeling may increase the potential for replication across studies.

The Relationship between Technology Use and Physical Activity among Typically-Developing Children

Objectives: This study aimed to investigate the relationship between technology use and physical activity level and to measure the association between sociodemographic characteristics of the participants, technology use, and physical activity level among Saudi children. Materials and Methods: This cross-sectional study was conducted among 458 parents of typically-developing Saudi children (6–12 years). A translated validated questionnaire used for data collection consisted of three parts: Children’s Physical Activity Questionnaire (CPAQ), Questionnaire on the Impact of Technology on Children (used to investigate the impact of technology on children’s physical activity) and sociodemographic questions (e.g, children’s age and sex, age, educational level, marital status of parents and monthly income). Data were analyzed using Pearson correlation and Mann-Whitney U test to assess the relationship between technology use and physical activity level. A chi-squared test was used to assess the relationship between technology use and sociodemographic variables. Statistical significance was set at p < 0.05. Results: Mean age of the sampled children was (8.44 ± 2.07). Data analysis revealed that high use of technology was significantly associated with low level of activity. Pearson’s correlation analysis showed a negative relationship between a high level of activity and technology use (r = −0.138, p = 0.047). Ownership of a device was significantly associated with higher technology time consumption. Regression analysis revealed that age of the child, educational level of the parents, screen time use, and owning electrical devices significantly predicted the level of practicing physical activity among children of sampled parents (p < 0.05). Conclusions: practicing inadequate physical activity among children could be influenced by educational level of parents, screen time use, and owning electrical devices. Therefore, parental involvement is required to reduce time of exposure to technology screens among children.