Screen time (including TV viewing/computer use) may be adversely associated with metabolic and mental health in children.
To describe the prevalence and sociodemographic correlates of screen time in ...an international sample of children aged 4-17 years.
Data from the International Children's Accelerometry Database were collected between 1997-2009 and analyzed in 2013. Participants were 11,434 children (48.9% boys; mean SD age at first assessment, 11.7 3.2 years). Exposures were sex, age, weight status, maternal education, and ethnicity. The outcome was self- or proxy-reported screen time <2 or >2 hours/day. Analyses were conducted initially at study level and then combined using random-effects meta-analysis.
Within each contributing study, at least two thirds of participants exceeded 2 hours/day of screen time. In meta-analytic models, overweight or obese children were more likely to exceed 2 hours/day of screen time than those who were non-overweight (OR=1.58, 95% CI=1.33,1.88). Girls (vs boys: 0.65; 0.54, 0.78) and participants with more highly educated mothers (vs <university level: 0.53; 0.42, 0.68) were less likely to exceed 2 hours/day of screen time. Associations of age and ethnicity with screen time were inconsistent at study level and non-significant in pooled analyses.
Screen time in excess of public health guidelines was highly prevalent, particularly among boys, those who were overweight or obese, and those with mothers of lower educational attainment. The population-attributable risk associated with this exposure is potentially high; further efforts to understand the determinants of within- and between-country variation in these behaviors and inform the development of effective behavior change intervention programs is warranted.
To gain more understanding of the potential health effects of sedentary time, knowledge is required about the accumulation and longitudinal development of young people's sedentary time. This study ...examined tracking of young peoples' total and prolonged sedentary time as well as their day-to-day variation using the International Children's Accelerometry Database.
Longitudinal accelerometer data of 5991 children (aged 4-17y) was used from eight studies in five countries. Children were included if they provided valid (≥8 h/day) accelerometer data on ≥4 days, including ≥1 weekend day, at both baseline and follow-up (average follow-up: 2.7y; range 0.7-8.2). Tracking of total and prolonged (i.e. ≥10-min bouts) sedentary time was examined using multilevel modelling to adjust for clustering of observations, with baseline levels of sedentary time as predictor and follow-up levels as outcome. Standardized regression coefficients were interpreted as tracking coefficients (low: < 0.3; moderate: 0.3-0.6; high: > 0.6).
Average total sedentary time at study level ranged from 246 to 387 min/day at baseline and increased annually by 21.4 min/day (95% confidence interval 19.6-23.0) on average. This increase consisted almost entirely of prolonged sedentary time (20.9 min/day 19.2-22.7). Total (standardized regression coefficient (B) = 0.48 0.45-0.50) and prolonged sedentary time (B = 0.43 0.41-0.45) tracked moderately. Tracking of day-to-day variation in total (B = 0.04 0.02-0.07) and prolonged (B = 0.07 0.04-0.09) sedentary time was low.
Young people with high levels of sedentary time are likely to remain among the people with highest sedentary time as they grow older. Day-to-day variation in total and prolonged sedentary time, however, was rather variable over time.
Sedentary behaviours (SB) are highly prevalent in young people and may be adversely associated with physical and mental health. Understanding of the modifiable determinants of SB is necessary to ...inform the design of behaviour change interventions but much of the existing research is cross-sectional and focussed upon screen-based behaviours.
To examine the social, psychological and environmental determinants of change in children's objectively measured sedentary time from age 11 to 14 years.
Data are from the second (2008) and third (2011) waves of assessment in the Sport, Physical Activity, and Eating Behaviour: Environmental Determinants in Young People (SPEEDY) study, conducted in the county of Norfolk, United Kingdom. Longitudinal data on accelerometer assessed sedentary time were available for 316 (53.5% female, 11.2±0.3 years at baseline) and 264 children after-school and at the weekend respectively. Information on 14 candidate determinants, including school travel mode and electronic media ownership, was self-reported. Change in the proportion of registered time spent sedentary was used as the outcome variable in cross-classified linear regression models, adjusted for age, sex, body mass index and baseline sedentary time. Simple and multiple models were run and interactions with sex explored.
Daily sedentary time increased by 30-40 minutes after-school and at the weekend from baseline to follow-up. Participants who travelled to school by cycle exhibited smaller increases in after-school sedentary time (beta; 95%CI for change in % time spent sedentary: -3.3;-6.7,-0.07). No significant determinants of change in weekend sedentary time were identified.
Time spent sedentary increased during the three-year duration of follow-up but few of the variables examined were significantly associated with changes in sedentary time. Children's mode of school travel may influence changes in their sedentary time over this period and should be examined further, alongside broader efforts to identify modifiable determinants of SB during childhood.
ObjectiveTo examine the association of 24-hour time-use compositions with mental health in a large, geographically diverse sample of UK adolescents.DesignCross-sectional, secondary data ...analysis.SettingMillennium Cohort Study (sixth survey), a UK-based prospective birth cohort.ParticipantsData were available from 4642 adolescents aged 14 years. Analytical samples for weekday and weekend analyses were n=3485 and n=3468, respectively (45% boys, 85% white ethnicity).Primary and secondary outcome measuresPrimary outcome measures were the Strengths and Difficulties Questionnaire (SDQ, socioemotional behaviour), Mood and Feelings Questionnaire (MFQ, depressive symptoms) and Rosenberg Self-Esteem Scale (RSE, self-esteem). Behavioural exposure data were derived from 24-hour time-use diaries.ResultsOn weekdays, participants spent approximately 54% of their time in sleep, 3% in physical activity, 9% in school-related activities, 6% in hobbies, 11% using electronic media and 16% in domestic activities. Predicted differences in SDQ, MFQ and RSE were statistically significant for all models (weekday and weekend) that simulated the addition or removal of 15 min physical activity, with an increase in activity being associated with improved mental health and vice versa. Predicted differences in RSE were also significant for simulated changes in electronic media use; an increase in electronic media use was associated with reduced self-esteem.ConclusionSmall but consistent associations were observed between physical activity, electronic media use and selected markers of mental health. Findings support the delivery of physical activity interventions to promote mental health during adolescence, without the need to specifically target or protect time spent in other activities.
Temporal changes in sedentary behavior patterns reflect the evolving nature of our built and social environments, particularly the expanding availability of electronic media. It is important to ...understand what types of sedentary behavior are assessed in national surveillance to determine whether, and to what extent, they reflect contemporary patterns. The aims of this review were to describe the characteristics of questionnaires used for national surveillance of sedentary behavior and to identify the types of sedentary behaviors being measured.
We reviewed questionnaires from national surveillance systems listed on the Global Observatory for Physical Activity (GoPA!) country cards to locate items on sedentary behavior. Questionnaire characteristics were categorized using the Taxonomy of Self-reported Sedentary Behavior Tools (TASST). The purpose and type of sedentary behaviors captured were classified using the Sedentary Behavior International Taxonomy (SIT).
Overall, 346 surveillance systems were screened for eligibility, of which 93 were included in this review. Most questionnaires used a single-item direct measure of sitting time (n = 78, 84%). Work and domestic were the most frequently captured purposes of sedentary behavior, while television viewing and computer use were the most frequently captured types of behaviors.
National surveillance systems should be periodically reviewed in response to evidence on contemporary behavior patterns in the population and the release of updated public health guidelines.
Abstract This commentary provides a critical discussion of current research investigating the correlates and determinants of physical activity in young people, with specific focus on conceptual, ...theoretical and methodological issues. We draw on current child and adolescent literature and our own collective expertise to illustrate our discussion. We conclude with recommendations that will strengthen future research and help to advance the field.
Accelerometer measures of physical behaviours (physical activity, sedentary behaviour and sleep) in observational studies offer detailed insight into associations with health and disease. Maximising ...recruitment and accelerometer wear, and minimising data loss remain key challenges. How varying methods used to collect accelerometer data influence data collection outcomes is poorly understood. We examined the influence of accelerometer placement and other methodological factors on participant recruitment, adherence and data loss in observational studies of adult physical behaviours.
The review was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Observational studies of adults including accelerometer measurement of physical behaviours were identified using database (MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus and Cumulative Index to Nursing & Allied Health Literature) and supplementary searches to May 2022. Information regarding study design, accelerometer data collection methods and outcomes were extracted for each accelerometer measurement (study wave). Random effects meta-analyses and narrative syntheses were used to examine associations of methodological factors with participant recruitment, adherence and data loss.
123 accelerometer data collection waves were identified from 95 studies (92.5% from high-income countries). In-person distribution of accelerometers was associated with a greater proportion of invited participants consenting to wear an accelerometer (+ 30% 95% CI 18%, 42% compared to postal distribution), and adhering to minimum wear criteria (+ 15% 4%, 25%). The proportion of participants meeting minimum wear criteria was higher when accelerometers were worn at the wrist (+ 14% 5%, 23%) compared to waist. Daily wear-time tended to be higher in studies using wrist-worn accelerometers compared to other wear locations. Reporting of information regarding data collection was inconsistent.
Methodological decisions including accelerometer wear-location and method of distribution may influence important data collection outcomes including recruitment and accelerometer wear-time. Consistent and comprehensive reporting of accelerometer data collection methods and outcomes is needed to support development of future studies and international consortia. Review supported by the British Heart Foundation (SP/F/20/150002) and registered (Prospero CRD42020213465).
ObjectiveTo develop a model to assess the long-term costs and health outcomes of physical activity interventions targeting adolescents.DesignA Markov cohort simulation model was constructed with the ...intention of being capable of estimating long-term costs and health impacts of changes in activity levels during adolescence. The model parameters were informed by published literature and the analysis took a National Health Service perspective over a lifetime horizon. Univariate and probabilistic sensitivity analyses were undertaken.SettingSchool and community.ParticipantsA hypothetical cohort of adolescents aged 16 years at baseline.InterventionsTwo exemplar school-based: a comparatively simple, after-school intervention and a more complex multicomponent intervention compared with usual care.Primary and secondary outcome measuresIncremental cost-effectiveness ratio as measured by cost per quality-adjusted life year gained.ResultsThe model gave plausible estimates of the long-term effect of changes in physical activity. The use of two exemplar interventions suggests that the model could potentially be used to evaluate a number of different physical activity interventions in adolescents. The key model driver was the degree to which intervention effects were maintained over time.ConclusionsThe model developed here has the potential to assess long-term value for money of physical activity interventions in adolescents. The two applications of the model indicate that complex interventions may not necessarily be the ones considered the most cost-effective when longer-term costs and consequences are taken into account.
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