The Relationships between Physical Activity, Screen Time and Sleep Time According to the Adolescents' Sex and the Day of the Week

Association between Screen Time and Sociodemographic Factors, Physical Activity, and BMI among Children in Six European Countries (Feel4Diabetes): A Cross-Sectional Study

Screen time among children in most European countries is notably high and is influenced by various sociodemographic and other factors. Our study aimed to explore the associations between parents' sociodemographic characteristics, socioeconomic status, body mass index (BMI), physical activity, risk status for type 2 diabetes, and their children's BMI, physical activity, and screen time. The data were sourced from the 2016 Feel4Diabetes study, involving 12,280 parents and 12,211 children aged 6-9 years (average age 8.21 years) in a cross-sectional study design. We used a logistic regression model to identify potential factors associated with children's screen time. The results showed that mothers with tertiary education (OR = 0.64; 95%CI = 0.49-0.82; p < 0.001), the middle age group (45-54 years) (OR = 0.81 95%CI = 0.66-0.98; p = 0.033), and families with higher incomes (middle-OR = 0.85; 95%CI = 0.75-0.97; p = 0.014; high-OR = 0.8; 95%CI = 0.69-0.93; p = 0.003) were associated with a decreased chance of children spending more than 2 h/day in front of the screen. In contrast, maternal overweight/obesity (OR = 1.15; 95%CI = 1.03-1.29; p = 0.013) and lower physical activity in children were linked to an increased likelihood of more than 2 h of screen time per day. Our findings suggest that targeted interventions should be developed to mitigate excessive screen time, particularly focusing on low-income families and mothers with low educational levels.

Predictive Role of Physical Activity and Health-Related Quality of Life in Police Officers' Work Assessment

Police officers (POs) frequently encounter high stress and burnout risks in their demanding professional environment. This study delves into the relationship between physical activity (PA), health-related quality of life (HRQoL), and job performance among POs. A cross-sectional survey was conducted involving 1175 POs, with 691 providing complete responses. The survey included questions on biosocial and professional characteristics; the International Physical Activity Questionnaire-short form; the Short Form Health Survey version 2.0; and a qualitative job performance evaluation. The key findings highlight that vigorous PA significantly enhances job performance. About 46.2% of POs engage in vigorous PA, with a notable 73.7% participating in some form of PA weekly. This study also found that age and gender considerably impact the HRQoL, especially in mental health aspects like vitality and social functioning. Vigorous PA is linked to higher job performance ratings, especially when practised consistently. In conclusion, this research underscores the importance of vigorous PA in improving job performance among POs. We suggest that institutions prioritise facilitating environments that encourage regular PA, recognising its substantial benefits in both professional effectiveness and the overall health of POs. This study contributes to understanding the critical role of physical fitness in enhancing the occupational well-being of law enforcement personnel.

Screen time is negatively associated with sleep quality and duration only in insufficiently active adolescents: A Brazilian cross-sectional school-based study

Background

Time spent on screen devices affects sleep quality and duration leading to several health impairments in youth. Although physical activity (PA) benefits sleep patterns and decreases screen time in adolescents, it is unclear whether the relationship between sleep quality/duration and screen time could be influenced by PA levels.

Objective

To analyze the association between sleep quality and duration with screen time in Brazilian adolescents according PA levels.

Methods

The sample included 1010 adolescents aged 13.2 ± 2.4 years (n = 556 females - 55 % of the sample). Sleep quality and sleep duration, and PA were assessed by Mini Sleep and Baecke questionnaires, respectively. Participants in the highest quartile were classified as physically active. Screen time was analyzed by the self-reported number of hours spent on different screen devices (i.e., television, computer, videogame, and cellphone/tablet). Participants in the highest tertile were classified as having high screen time. Sex, age, and body mass index were considered covariates in binary logistic regression models.

Results

Poor sleep quality was observed in 52.3 % of the sample, whereas 46.6 % reported sleeping less than eight hours/day. High screen time was associated with poor sleep quality (OR = 1.45; 95 %CI = 1.01-2.12) and insufficient sleep duration (OR = 1.52; 95 %CI = 1.01-2.03) in adolescents insufficiently active. There were no associations between screen time and sleep parameters in active adolescents.

Conclusion

High screen time was associated with poor sleep quality and insufficient sleep duration only in insufficiently active adolescents. These results suggest that high PA levels may contribute to improving sleep patterns in pediatric population.

Why Do Iranian Preschool-Aged Children Spend too Much Time in Front of Screens? A Preliminary Qualitative Study

There is evidence that Iranian preschool children are increasingly spending their time in front of screens (screen time: ST; time spent with any screen such as TVs, computers, tablets, smartphones, game consoles, or video games), but few studies have explored the possible causes of such an increase. Given this, the present study aimed to qualitatively explore determinants of excessive ST in Iranian children. To this end, parents of preschool children were interviewed, and their answers were qualitatively clustered to identify additional important factors. Key informant interviews were conducted with parents of preschool children in Tehran (Iran). A semi-structured interview was developed to assess child and family life, daily routine, family rules, family interactions, and home climate as possible contributing factors to ST. Parents' audiotaped statements were transcripted verbatim, coded, and clustered into main themes using thematic analysis with the MaxQda^® software. A total of 20 parents of children aged 2 to 7 were interviewed, and a total of 6 key themes and 28 subthemes were extracted from their interviews. The results of the analysis identified a broad range of both independent and interrelated factors leading to the development and maintenance of ST behaviors among preschool children. Our findings indicate that the central concept is the family. Considering screen-related behaviors, family life encompasses parental health literacy (e.g., parenting pattern, monitoring standards, thoughtful parenting), family psychological atmosphere (e.g., presence of parents, family norms, parent-parent and parent-child interaction, congruency/incongruency of parents with each other) and the digital structure of the home. The child's and parents' actions and characteristics can influence family interactions. A child's and parent's behavior is also influenced by social/cultural factors. Parents' behaviors and attitudes, family communications, and interactions contribute to healthy ST habits in children. It is not possible to examine the child's behavior without considering the family and the dominant environment, since the behavior of family members as a whole affects each family member. Given this, interventions should make parents aware of their role and responsibilities in reducing children's ST and consider the family system as a whole, and interventions also can benefit from considering the parental perceptions of children's behaviors.

Social dimensions impact individual sleep quantity and quality

While sleep positively impacts well-being, health, and productivity, the effects of societal factors on sleep remain underexplored. Here we analyze the sleep of 30,082 individuals across 11 countries using 52 million activity records from wearable devices. Our data are consistent with past studies of gender and age-associated sleep characteristics. However, our analysis of wearable device data uncovers differences in recorded vs. self-reported bedtime and sleep duration. The dataset allowed us to study how country-specific metrics such as GDP and cultural indices relate to sleep in groups and individuals. Our analysis indicates that diverse sleep metrics can be represented by two dimensions: sleep quantity and quality. We find that 55% of the variation in sleep quality, and 63% in sleep quantity, are explained by societal factors. Within a societal boundary, individual sleep experience was modified by factors like exercise. Increased exercise or daily steps were associated with better sleep quality (for example, faster sleep onset and less time awake in bed), especially in countries like the U.S. and Finland. Understanding how social norms relate to sleep will help create strategies and policies that enhance the positive impacts of sleep on health, such as productivity and well-being.

Effects of Socioeconomic Environment on Physical Activity Levels and Sleep Quality in Basque Schoolchildren

The socioeconomic and built environment of an area are interrelated with health data and have a direct influence on children’s development. There are facilitators and barriers for schools to promote physical activity depending on the socioeconomic status of the school. The aim of this study was to analyse the relationship between physical activity and sleep and the socioeconomic level of children in the Basque Country. The sample consisted of 1139 schoolchildren between the ages of six and seventeen (566 boys and 573 girls) from 75 schools (43 public and 32 private). Differences between groups were compared using the Mann–Whitney U test (two samples), Kruskal–Wallis one-factor ANOVA (k samples), and Spearman’s Rho correlation. There are sex differences in light (200.8 ± 62.5 vs. 215.9 ± 54.7) and moderate (69.0 ± 34.3 vs. 79.9 ± 32.1) physical activity in favour of the female group of higher socioeconomic status compared to male group of higher socioeconomic status. In the case of vigorous physical activity, the female group performed less than the male group across all socioeconomic statuses, which was statistically significant in the groups of high socioeconomic status (11.6 ± 9.3 vs. 6.9 ± 5.7) in group 2 and medium socioeconomic status (11.1 ± 9.3 vs. 7.7 ± 6.1) in group 3. There is an inverse relationship between sedentary behaviour and BMI, total bed time, total sleep time, and night-time awakenings. There is also an inverse relationship between all levels of physical activity performed with respect to BMI and total sleep efficiency. These data point towards notable inequalities in physical activity and daily sleep in Basque schoolchildren, which in turn may be marginalised in our current school system due to the effects of the socioeconomic environment.