Clustering of screen time behaviours in adolescents and its association with waist circumference and cardiorespiratory fitness

School and non-school day screen time profiles and their differences in health and educational indicators in adolescents

Sedentary behavior and screen-based devices in particular have been negatively associated with a wide range of health and educational indicators. However, few have examined these relationships separately for school days and non-school days, and none have used a person-centered approach. This study aimed to identify school and non-school day screen time profiles, as well as examine possible differences in health indicators (physical fitness, fatness, physical activity, sleep duration, and Mediterranean diet) and academic performance. This study involved the participation of 1573 Spanish adolescents aged 12-16 years (54.73% girls). Academic performance was measured through grades in Mathematics, Language, English, and Physical Education. Physical fitness was measured through a battery of tests (cardiorespiratory fitness was measured using the 20 m shuttle run test, and muscular strength with both handgrip and standing long jump tests), while fatness (skinfold thicknesses) was assessed with calipers. Finally, physical activity, screen time, sleep duration, and adherence to the Mediterranean diet were measured using self-reported questionnaires. Hierarchical cluster analyses based on square Euclidian distances and Ward's method were performed based on daily minutes of screen time recorded on school and non-school days. We identified four clusters labeled and described as: (1) "High-high": highest screen time on school and non-school days; (2) "High-low": high screen time on school days and low screen time on non-school days; (3) "Low-high": low screen time on school days and high screen time on non-school days; (4) "Low-low": lowest screen time on school and non-school days. Adolescents who belonged to the "High-high" profile had worse health-related behaviors (i.e., physical activity, sleep duration, and adherence to Mediterranean diet) and academic performance than most other profiles, while adolescents who belonged to "Low-low" profile showed the opposite pattern. Adolescents in the "Low-high" profile had a higher sleep duration on school days and better academic performance than those in the "High-low" profile. No differences in body fat, cardiorespiratory fitness, and muscular strength were found between the four different profiles. The results suggest that adolescents who accumulated a large amount of screen time on school and non-school days reported worse health-related behaviors and academic performance. Moreover, adolescents who had high screen time on school days reported only a short sleep duration on school days and worse academic performance than on non-school days. Conducting interventions to reduce screen time in these four profiles, particularly in the groups of students with more screen time on school days, becomes essential to improving adolescents' healthy lifestyles and academic performance.

Association of Night-Time Screen-Viewing with Adolescents' Diet, Sleep, Weight Status, and Adiposity

Night-time screen-viewing (SV) contributes to inadequate sleep and poor diet, and subsequently excess weight. Adolescents may use many devices at night, which can provide additional night-time SV. Purpose: To identify night-time SV patterns, and describe differences in diet, sleep, weight status, and adiposity between patterns in a cross-sectional and longitudinal manner. Methods: Adolescents (10–16 y) reported devices they viewed at night and completed food recalls. Accelerometry, anthropometrics, and imaging were conducted to measure sleep, weight status, and adiposity, respectively. Latent class analysis was performed to identify night-time SV clusters. Linear regression analysis was used to examine associations between clusters with diet, sleep, weight status, and adiposity. Results: Amongst 273 adolescents (12.5 ± 1.9 y, 54% female, 59% White), four clusters were identified: no SV (36%), primarily cellphone (32%), TV and portable devices (TV+PDs, 17%), and multiple PDs (17%). Most differences in sleep and adiposity were attenuated after adjustment for covariates. The TV+PDs cluster had a higher waist circumference than the no SV cluster in cross-sectional analysis. In longitudinal analysis, the primarily cellphone cluster had less change in waist circumference compared to the no SV cluster. Conclusions: Directing efforts towards reducing night-time SV, especially TV and PDs, may promote healthy development.

Clustering of Physical Activity, Sleep, Diet, and Screen-Based Device Use Associated with Self-Rated Health in Adolescents

BACKGROUND

Little is known about how the interplay among health-related behaviors impacts self-rated health (SRH). We examined the clustering of physical activity (PA), sleep, diet, and specific screen-based device use, and the associations between the emergent clusters and SRH among Brazilian adolescents.

METHOD

The data used in this cross-sectional study were from the baseline of the Movimente Program. Self-reported data were analyzed. SRH was recorded as a 5-point scale (from poor to excellent). Daily duration of exposure to the computer, the television, the cell phone, and games; PA; sleep; and weekly consumption of fruits and vegetables and ultra-processed foods were included in a Two-Step cluster analysis. Multilevel ordered logistic regressions assessed the associations between the clusters and SRH.

RESULTS

The data of 750 students (girls: 52.8%, 13.1 ± 1.0 years) were analyzed. Good SRH was more prevalent (52.8%). Three clusters were identified: the Phubbers (50.53%; characterized by the longest cell phone use duration, shortest gaming and computer use, lowest PA levels, and low consumption of fruits and vegetables), the Gamers (22.80%; longest gaming and computer use duration, PA < sample average, highest intake of ultra-processed foods), and a Healthier cluster (26.67%; physically active, use of all screen-based devices < sample average, and healthier dietary patterns). For both Gamers (-0.85; 95% CI -1.24, -0.46) and Phubbers (-0.71; 95% CI -1.04, -0.38), it was found a decrease in the log-odds of being in a higher SRH category compared with the Healthier cluster.

CONCLUSION

Specific clusters represent increased health-related risk. Assuming the interdependence of health-related behaviors is indispensable for accurately managing health promotion actions for distinguishable groups.

Association between screen time and accelerometer-measured 24-h movement behaviors in a sample of Brazilian adolescents

OBJECTIVES

Different screen time activities may be related to sleep, physical activity, and sedentary behavior. The objective was to examine the association between self-reported screen time activities and accelerometer-measured 24-h movement behaviors.

STUDY DESIGN

This was a cross-sectional study.

METHODS

Adolescents' (n = 718, 50.4% girls, 16 years) sleep duration, sedentary behavior, light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) were estimated with wrist-worn accelerometry. Time spent on screen time activities related to studying, working, watching videos, playing video games, and using social media was self-reported. Multilevel linear regressions were used to test the association between screen time with sleep, sedentary behavior, and physical activity.

RESULTS

Boys and girls slept 6.4 and 6.7 h per night, spent 10.4 and 10.1 h/d in sedentary behavior, spent 4.0 and 4.4 h/d in LPA, and spent 34.7 and 29.2 min/d in MVPA, respectively. Studying was inversely related to LPA and MVPA. Working was inversely related to sleep and positively related to LPA. Watching videos was associated with lower LPA and MVPA. For boys, videogames were associated with increased sedentary behavior and lower LPA and MVPA. For girls, studying and/or using social media were associated with lower LPA and MVPA.

CONCLUSIONS

Indicators of screen time were associated with different accelerometer-measured 24-h movement behaviors in this sample of Brazilian adolescents.

Identifying typologies of diurnal patterns in desk-based workers' sedentary time

The purpose of this study was to identify typologies of diurnal sedentary behavior patterns and sociodemographic characteristics of desk-based workers. The sedentary time of 229 desk-based workers was measured using accelerometer devices. The within individual diurnal variations in sedentary time was calculated for both workdays and non-workdays. Diurnal variations in sedentary time during each time period (morning, afternoon, and evening) was calculated as the percentage of sedentary time during each time period divided by the percentage of the total sedentary time. A hierarchical cluster analysis (Ward’s method) was used to identify the optimal number of clusters. To refine the initial clusters, a non-hierarchical cluster analysis (k-means method) was performed. Four clusters were identified: stable sedentary cluster (46.7%), off-morning break cluster (26.6%), off-afternoon break cluster (8.3%), and evening sedentary cluster (18.3%). The stable sedentary cluster had the lowest variations in sedentary time throughout the day and the highest amount of total sedentary time. Participants in the off-morning and off-afternoon break clusters had nearly the same sedentary patterns but took short-term breaks during non-workday mornings or afternoons. The evening sedentary cluster had a completely different pattern, with a longer sedentary time during the evening both on workdays and non-workdays. Sociodemographic attributes such as sex, household income, educational attainment, employment status, sleep duration, and residential area, differed significantly between groups. Initiatives to address desk-based workers’ sedentary behavior need to focus not only on the workplace but also on the appropriate timing for reducing excessive sedentary time in non-work contexts depending on the characteristics and diurnal patterns of target subgroups.

Seasonal variability of the vitamin D effect on physical fitness in adolescents

Studies investigating the relationship between vitamin D and physical fitness in youth have provided inconsistent findings. Recent evidence indicates that the expression of receptors and vitamin D-modulated genes in young subjects has a seasonal profile. Therefore, we investigated the role of vitamin D on physical fitness across seasons in a total of 977 male adolescents. Anthropometrics, lifestyle, dietary habits, biochemical profiles and physical fitness were studied. Multiple linear regression models, including pairwise interaction terms involving total 25-OH-vitamin D, were fitted. The interacting effect of season and total 25-OH-vitamin D had a significant influence on physical fitness performance (spring and total 25-OH-vitamin D: ß 0.19, SE 0.07, p = 0.007; summer and total 25-OH-vitamin D: ß 0.10, SE 0.06, p = 0.11; autumn and total 25-OH-vitamin D: ß 0.18, SE 0.07, p = 0.01), whereas the main effect of total 25-OH-vitamin D alone was not significant (p = 0.30). Body fat percentage, recreational physical activity level, time spent per day gaming/TV-watching, smoking, and hemoglobin levels were also related to the physical fitness performance score. Future studies should further explore the role of seasonal-dependent effects of vitamin D on health.

Clustering of 24-h movement behaviors associated with cardiorespiratory fitness among adolescents: a latent class analysis

This study aimed to examine the clustering of 24-h movement behaviors (moderate to vigorous physical activity, screen time, and sleep duration) and their association with cardiorespiratory fitness among adolescents. We evaluated 561 adolescents (52.1% girls; mean age, 13.0 ± 1.0 years) from Florianópolis, Brazil. A 20-m shuttle run was used to assess cardiorespiratory fitness, while a questionnaire was used to measure 24-h movement behaviors. A latent class analysis was performed to identify the clustering of 24-h movement behaviors, while linear Bayesian mixed-effect regression models were applied to identify their association with cardiorespiratory fitness. Two classes were identified: unhealthy (10.4%), characterized as a high probability of practicing less than 300 min/week of moderate to vigorous physical activity, spending more than 4 h/day in front of screens, and sleeping less than 8 h/day; and healthy (89.6%), characterized by a high probability of practicing more than 420 min/week of moderate to vigorous physical activity, spending less than 2 h/day in front of screens, and sleeping 8-10 h/day. Adolescents in the healthy class had a higher cardiorespiratory fitness level than those in the unhealthy class. Most adolescents were grouped in the healthy class and had higher cardiorespiratory fitness levels than those in the unhealthy class. These results suggest that families and professionals should work toward creating healthier lifestyles for adolescents by increasing opportunities to practice moderate to vigorous physical activity, reduce screen time, and favor healthy sleep to increase their cardiorespiratory fitness levels. What is Known: • Moderate to vigorous physical activity, screen time, and sleep duration are positively, negatively, and inconsistently associated with cardiorespiratory fitness, respectively, when analyzed separately. • Little is known about the clustering of 24-h movement behaviors and how they are associated with cardiorespiratory fitness levels in adolescents. What is New: • The 24-h movement behaviors clustered into almost opposite classes among adolescents (healthy and unhealthy classes). • Adolescents in the healthy class had greater cardiorespiratory fitness levels than those in the unhealthy class.