Sensor-based physical activity, sedentary time, and reported cell phone screen time: A hierarchy of correlates in youth

Cardiorespiratory fitness and muscular fitness correlates in youth: A hierarchy of behavioral, contextual, and health-related outcomes

OBJECTIVES

Despite the health importance of identifying correlates of physical fitness in youth, no investigation to date has explored the influence of behavioral, health-related, and contextual correlates simultaneously. We investigated the hierarchical relationship of multiple modifiable correlates favoring or diminishing cardiorespiratory and muscular fitness in youth.

DESIGN

Cross-sectional investigation.

METHODS

In a sample of 5174 children and adolescents, 31 correlates were hierarchized according to their impact on cardiorespiratory and muscular fitness assessed using the FITESCOLA® fitness battery. A Chi-squared Automatic Interaction Detection approach was employed and measures of correlation and association were used to investigate the relationship between physical fitness and correlates.

RESULTS

In children, body mass index was the most relevant factor to discriminate between high and low cardiorespiratory and muscular fitness of the upper, middle, and lower body. While body mass index was more important than any other correlate to differentiate levels of upper and lower body muscular fitness during adolescence, specific characteristics of sports participation emerged as key factors to discriminate between high and low cardiorespiratory fitness and middle body muscular fitness. Other correlates, including the self-report of active recess time, active commuting to school, favorable neighborhood conditions, and limited time on screens and cellphones, were demonstrative of favorable physical fitness levels.

CONCLUSIONS

Both body composition and sports-related characteristics emerged as the two most relevant factors of physical fitness in youth. Additional health benefits may be obtained from building supportive environments for sports and healthy exercise habits within the household and at different school education levels.

Environmental Correlates of Physical Activity and Screen-Time in Youth with Autism Spectrum Disorder: A Seven-Country Observational Study

This cross-sectional observational study sought to examine the environmental correlates of physical activity and screen-time among youth with autism spectrum disorder (ASD). Parents of youth with ASD (n = 1,165) from seven countries/regions provided responses to an online survey form measuring environmental correlates (i.e., physical activity neighborhood environment, social network, social trust and cohesion, bedroom media, social home environment) and outcomes (i.e., physical activity, screen-time). Multiple linear regression analyses were conducted to determine environmental predictors of the outcomes. Physical activity neighborhood environment (B = 0.15, p = 0.047), social network (B = 0.16, p = 0.02), and social home environment (B = 1.07, p < 0.001) were significantly associated with physical activity, whereas social trust and cohesion and bedroom media were not. Further, social trust and cohesion (B = -0.14, p = 0.001), bedroom media (B = 0.10, p = 0.001), and social home environment (B = -0.16, p < 0.001) were significantly associated with screen-time while neighborhood environment and social network were not. The identified environmental attributes of physical activity and screen-time behaviors should be targeted for health promotion among youth with ASD.

Sleep Time, Physical Activity, and Screen Time among Montana American Indian Youth

The purpose of this study is to describe sleep, PA, and screen time behaviors among rural American Indian (AI) youth, stratified by sex and grade, to better understand how to address these health behaviors in AI youth. Body composition, a screen time survey, and demographic information were collected from 65 AI youth. Accelerometers were worn for 7 days. Sixty percent were overweight or obese. Sleep did not differ by sex or grade, with an actigraphy-based total sleep time (aTST) of 7.8 h per night. Boys had significantly more light PA (p = 0.002) and vigorous PA (p = 0.01) compared to girls. Screen time did differ by sex but not by grade, with girls in the sixth and seventh grades reporting more screen time than boys, but boys in the eighth grade reporting more screen time than girls. Despite sex differences in screen time, high levels of screen time and obesity and low levels of PA and sleep are a concern in this population.

Association of screen time and physical activity with health-related quality of life in college students

OBJECTIVE

To determine the association between screen time and physical activity with quality of life among Chilean university students.

METHODS

Cross-sectional and analytical study conducted on 726 first-year university students who replied three questionnaires: Youth Risk Behavior Survey, WHO Quality of Life-BREF scale, and the International Physical Activity Questionnaire.

RESULTS

There were differences in overall quality of life (p < .001) and level of satisfaction in health (p < .01) according to screen exposure time. These results follow a linear trend for all the quality of life domains (p < .01), and they indicate that there is an inverse association between screen time and quality of life.

CONCLUSION

There is an inverse association between screen time and quality of life in university students. Students with a longer screen exposure time showed a lower quality of life, specifically in the domains of social relationships and psychological health, regardless of sex, physical activity, or socioeconomic level.

The impact of screen time changes on anxiety during the COVID-19 pandemic: sleep and physical activity as mediators

Under the burden caused by COVID-19 and rapid lifestyle changes, many people increased their screen time due to psychological needs and social requirements. The current study investigated the relationship between screen time changes and anxiety symptoms during the pandemic of COVID-19. Furthermore, we examined whether sleep and physical activity would mediate the association between screen time changes and anxiety. The self-developed questionnaire was delivered online to collect people's changes in anxiety, sleep patterns, and screen time during COVID-19. 970 participants (74.4% female) with an average age of 23 years were involved in this study. After adjusting demographic variables, the ordinal logistic regression analyses revealed that a significant increase in screen time was linked with anxiety. Slightly increased screen time, slightly and significantly decreased screen time did not predict anxiety symptoms during the pandemic. The level of anxiety was significantly higher among respondents who reported decreased sleep quality. Sleep quality directly mediated the association between screen time changes and anxiety, while sleep latency did not. The longer sleep latency caused by increased screen time would amplify anxiety by affecting sleep quality. In addition, the relationship between screen time changes and anxiety was also mediated by physical activity. We concluded that the fluctuation of screen time in a modest range does not affect the anxiety level substantially. The significantly increased screen time would contribute to poor sleep (including longer sleep latency and worse sleep quality) and lack of physical activity, which would lead to higher levels of anxiety.

Are There Common Correlates of Adolescents' Sport Participation and Screen Time?

Objective: This study aimed to identify intrapersonal, interpersonal, and environmental correlates of leisure-time participation in sports and examine whether they are also associated with screen-time among adolescents. Methods: Adolescents from eight schools in Victoria, Australia were recruited for this study. Participants (n = 400) comprised of an even distribution of boys (51%) and girls (49%) aged 12-16 years. Their engagement in leisure-time activities (including sports and screen-time) and 13 potential correlates were self-reported. Multinomial logistic regression analyses examined correlates of sports and screen-time participation categories, accounting for clustering by school and adjusting for significant sociodemographic characteristics. Results: The majority (76%) participated in leisure-time sports and exceeded screen-time recommendations (<2 h/day) on both weekdays (69%) and weekend days (85%). Greater internal motivation for sports was positively associated with sports participation and inversely associated with screen-time on weekend days, respectively. Fewer barriers, positive family and coach support and more neighborhood sporting opportunities were significantly associated with greater sports participation. Neighborhood social norms were inversely associated with sports participation. Preference for sports was associated with less weekday and weekend screen-time. Friend support for sports was inversely associated with weekday screen-time. Access to sports facilities in the local neighborhood was associated with more weekday and weekend screen-time. Conclusion: Internal motivation for sports was associated with both sports participation and less screen-time. Preference for leisure-time sports and friend support for sports were associated with less screen-time. Future research should continue to explore common correlates of multiple leisure-time behaviors to inform the development of effective intervention strategies.

The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities

Physical behaviors (e.g., physical activity and sedentary behavior) have been the focus among many researchers in the biomedical and behavioral science fields. The recent shift from hip- to wrist-worn accelerometers in these fields has signaled the need to develop novel approaches to process raw acceleration data of physical activity and sedentary behavior. However, there is currently no consensus regarding the best practices for analyzing wrist-worn accelerometer data to accurately predict individuals' energy expenditure and the times spent in different intensities of free-living physical activity and sedentary behavior. To this end, accurately analyzing and interpreting wrist-worn accelerometer data has become a major challenge facing many clinicians and researchers. In response, this paper attempts to review different methodologies for analyzing wrist-worn accelerometer data and offer cutting edge, yet appropriate analysis plans for wrist-worn accelerometer data in the assessment of physical behavior. In this paper, we first discuss the fundamentals of wrist-worn accelerometer data, followed by various methods of processing these data (e.g., cut points, steps per minute, machine learning), and then we discuss the opportunities, challenges, and directions for future studies in this area of inquiry. This is the most comprehensive review paper to date regarding the analysis and interpretation of free-living physical activity data derived from wrist-worn accelerometers, aiming to help establish a blueprint for processing wrist-derived accelerometer data.

Identifying risk profiles for excess sedentary behaviour in youth using individual, family and neighbourhood characteristics

There are few known determinants of sedentary behaviour (SB) in children. We generated and compared profiles associated with risk of excess SB among children (n = 294) both at 8-10 and 10-12 years of age (Visits 1 and 2, respectively), using data from the QUebec Adipose and Lifestyle InvesTigation in Youth. Excess SB was measured by accelerometry and defined as >50% of total wear time at <100 counts/minutes. Recursive partitioning analyses were performed with candidate individual-, family-, and neighbourhood-level factors assessed at Visit 1, and distinct groups at varying risk of excess SB were identified for both timepoints. From the ages of 8-10 to 10-12 years, the prevalence of excess SB more than doubled (24.5% to 57.1%). At Visit 1, excess SB was greatest (73%) among children simultaneously not meeting physical activity guidelines, reporting >2 h/day of weekday non-academic screen time, living in low-dwelling density neighbourhoods, having poor park access, and living in neighbourhoods with greater disadvantage. At Visit 2, the high-risk group (70%) was described by children simultaneously not meeting physical activity guidelines, reporting >2 h/day of non-academic screen time on weekends, and living in neighbourhoods with low disadvantage. Risk factors related to individual lifestyle behaviours are generally consistent, and neighbourhood factors generally inconsistent, as children age from late childhood to pre-adolescence. Multiple factors from developmental, behavioural and contextual domains increase risk for excess sedentary behaviour; these warrant consideration to devise effective prevention or management strategies.

Using Multilevel Regression and Poststratification to Estimate Physical Activity Levels from Health Surveys

BACKGROUND

Large-scale health surveys often consider sociodemographic characteristics and several health indicators influencing physical activity that often vary across subpopulations. Data in a survey for some small subpopulations are often not representative of the larger population.

OBJECTIVE

We developed a multilevel regression and poststratification (MRP) model to estimate leisure-time physical activity across Brazilian state capitals and evaluated whether the MRP outperforms single-level regression estimates based on the Brazilian cross-sectional national survey VIGITEL (2018).

METHODS

We used various approaches to compare the MRP and single-level model (complete-pooling) estimates, including cross-validation with various subsample proportions tested.

RESULTS

MRP consistently had predictions closer to the estimation target than single-level regression estimations. The mean absolute errors were smaller for the MRP estimates than single-level regression estimates with smaller sample sizes. MRP presented substantially smaller uncertainty estimates compared to single-level regression estimates. Overall, the MRP was superior to single-level regression estimates, particularly with smaller sample sizes, yielding smaller errors and more accurate estimates.

CONCLUSION

The MRP is a promising strategy to predict subpopulations' physical activity indicators from large surveys. The observations present in this study highlight the need for further research, which could, potentially, incorporate more information in the models to better interpret interactions and types of activities across target populations.

Association Between Screen Time, Fast Foods, Sugar-Sweetened Beverages and Depressive Symptoms in Chinese Adolescents

OBJECTIVE

Although previous studies have shown that screen time (ST), fast foods (FFs) and sugar-sweetened beverages (SSBs) consumption are associated with depressive symptoms in adolescents, research on these associations in Chinese adolescents is scarce. This study aimed to examine the association between ST, FFs, SSBs and depressive symptoms in Chinese adolescents, and explore the mediating effects of FFs and SSBs in the association between ST and depressive symptoms.

METHODS

This school-based nationwide survey was carried out among 14,500 students in four provinces of China. The Children's Depression Inventory was used to assess the participants' depressive symptoms. ST, FFs and SSBs consumption was measured by a self-reported questionnaire. The Bayesian multiple mediation model was used to analyze the mediation effect.

RESULTS

ST, FFs and SSBs, were more likely to be associated with depressive symptoms, and ORs (95%CI) was 1.075 (1.036-1.116), 1.062 (1.046-1.078) and 1.140 (1.115-1.166), after we adjusted for sociodemographic variables. Additionally, in Bayesian multiple mediation model, direct effect, mediating effect, total effect, the ratio of mediating effect to total effect was 0.125, 0.034, 0.159, and 0.214, respectively. All path coefficients of the three mediation paths are statistically significant (p < 0.05).

CONCLUSIONS

Our study demonstrates that ST, FFs and SSBs consumption are associated with depressive symptoms in Chinese adolescents. It is likely that FFs and SSBs partially mediate the association between ST and depressive symptoms by chain-mediating effects.