Kate Ridley

Development of a compendium of energy expenditures for youth

BackgroundThis paper presents a Compendium of Energy Expenditures for use in scoring physical activity questionnaires and estimating energy expenditure levels in youth.Method/ResultsModeled after the adult Compendium of Physical Activities, the Compendium of Energy Expenditures for Youth contains a list of over 200 activities commonly performed by youth and their associated MET intensity levels. A review of existing data collected on the energy cost of youth performing activities was undertaken and incorporated into the compendium. About 35% of the activity MET levels were derived from energy cost data measured in youth and the remaining MET levels estimated from the adult compendium.ConclusionThe Compendium of Energy Expenditures for Youth is useful to researchers and practitioners interested in identifying physical activity and energy expenditure values in children and adolescents in a variety of settings.

The Multimedia activity recall for children and adolescents (MARCA): development and evaluation

BackgroundSelf-report recall questionnaires are commonly used to measure physical activity, energy expenditure and time use in children and adolescents. However, self-report questionnaires show low to moderate validity, mainly due to inaccuracies in recalling activity in terms of duration and intensity. Aside from recall errors, inaccuracies in estimating energy expenditure from self-report questionnaires are compounded by a lack of data on the energy cost of everyday activities in children and adolescents. This article describes the development of the Multimedia Activity Recall for Children and Adolescents (MARCA), a computer-delivered use-of-time instrument designed to address both the limitations of self-report recall questionnaires in children, and the lack of energy cost data in children.MethodsThe test-retest reliability of the MARCA was assessed using a sample of 32 children (aged 11.8 ± 0.7 y) who undertook the MARCA twice within 24-h. Criterion validity was assessed by comparing self-reports with accelerometer counts collected on a sample of 66 children (aged 11.6 ± 0.8 y). Content and construct validity were assessed by establishing whether data collected using the MARCA on 1429 children (aged 11.9 ± 0.8 y) exhibited relationships and trends in childrens physical activity consistent with established findings from a number of previous research studies.ResultsTest-retest reliability was high with intra-class coefficients ranging from 0.88 to 0.94. The MARCA demonstrated criterion validity comparable to other self-report instruments with Spearman coefficients ranging from rho = 0.36 to 0.45, and provided evidence of good content and construct validity.ConclusionThe MARCA is a valid and reliable self-report questionnaire, capable of a wide variety of flexible use-of-time analyses related to both physical activity and sedentary behaviour, and offers advantages over existing pen-and-paper questionnaires.

Agreement between activPAL and ActiGraph for assessing children's sedentary time

BackgroundAccelerometers have been used to determine the amount of time that children spend sedentary. However, as time spent sitting may be detrimental to health, research is needed to examine whether accelerometer sedentary cut-points reflect the amount of time children spend sitting. The aim of this study was to: a) examine agreement between ActiGraph (AG) cut-points for sedentary time and objectively-assessed periods of free-living sitting and sitting plus standing time using the activPAL (aP); and b) identify cut-points to determine time spent sitting and sitting plus standing.MethodsForty-eight children (54% boys) aged 8-12 years wore a waist-mounted AG and thigh-mounted aP for two consecutive school days (9-3:30 pm). AG data were analyzed using 17 cut-points between 50-850 counts·min-1 in 50 counts·min-1 increments to determine sedentary time during class-time, break time and school hours. Sitting and sitting plus standing time were obtained from the aP for these periods. Limits of agreement were computed to evaluate bias between AG50 to AG850 sedentary time and sitting and sitting plus standing time. Receiver Operator Characteristic (ROC) analyses identified AG cut-points that maximized sensitivity and specificity for sitting and sitting plus standing time.ResultsThe smallest mean bias between aP sitting time and AG sedentary time was AG150 for class time (3.8 minutes), AG50 for break time (-0.8 minutes), and AG100 for school hours (-5.2 minutes). For sitting plus standing time, the smallest bias was observed for AG850. ROC analyses revealed an optimal cut-point of 96 counts·min-1 (AUC = 0.75) for sitting time, which had acceptable sensitivity (71.7%) and specificity (67.8%). No optimal cut-point was obtained for sitting plus standing (AUC = 0.51).ConclusionsEstimates of free-living sitting time in children during school hours can be obtained using an AG cut-point of 100 counts·min-1. Higher sedentary cut-points may capture both sitting and standing time.

Electronic Media Use and Adolescent Health and Well-Being: Cross-Sectional Community Study

OBJECTIVE To describe time adolescents spend using electronic media (television, computer, video games, and telephone); and to examine associations between self-reported health/well-being and daily time spent using electronic media overall and each type of electronic media. METHODS Design-Cross-sectional data from the third (2005) wave of the Health of Young Victorians Study, an Australian school-based population study. Outcome Measures-Global health, health-related quality of life (HRQoL; KIDSCREEN), health status (Pediatric Quality of Life Inventory 4.0; PedsQL), depression/anxiety (Kessler-10), and behavior problems (Strengths and Difficulties Questionnaire). Exposure Measures-Duration of electronic media use averaged over 1 to 4 days recalled with the Multimedia Activity Recall for Children and Adolescents (MARCA) computerized time-use diary. Analysis-Linear and logistic regression; adjusted for demographic variables and body mass index z score. RESULTS A total of 925 adolescents (mean +/- standard deviation age, 16.1+/-1.2 years) spent, on average, 3 hours 16 minutes per day using electronic media (television, 128 minutes per day; video games, 35; computers, 19; telephone, 13). High overall electronic media use was associated with poorer behavior, health status, and HRQoL. Associations with duration of specific media exposures were mixed; there was a favorable association between computer use (typing/Internet) and psychological distress, whereas high video game use was associated with poorer health status, HRQoL, global health, and depression/anxiety. Television and telephone durations were not associated with any outcome measure. CONCLUSIONS Despite televisions associations with obesity, time spent in other forms of media use appear more strongly related to adolescent health and well-being. This study supports efforts to reduce high video game use and further exploration of the role of computers in health enhancement.

Trends in the duration of school-day sleep among 10- to 15-year-old South Australians between 1985 and 2004.

Aim: To compare self‐reported school‐day sleep duration in 10‐ to 15‐year‐old South Australians between 1985 and 2004.

Assigning Energy Costs to Activities in Children: A Review and Synthesis

PURPOSE Compendia of energy costs are often used to assign energy expenditures (EE) to self-reported and observed activity. As there is a lack of data on the energy cost of childrens everyday activities, adult values are often used as surrogates. However, the best way to adjust adult values for use with children remains unclear. Various strategies have been used to estimate rates of EE in children. METHODS To evaluate these existing methods for assigning EE to children, a literature search reviewed all English-language studies that measured energy costs in healthy 6.0-17.9 yr olds using criterion EE measures. Data were combined using the Monte Carlo simulation procedure, with walking and running forming separate data sets. RESULTS The resultant data set (excluding walking and running) contained 5592 data points encompassing 51 activities. Analyses revealed using adults METs, combined with child resting metabolic rates, as the best existing technique to assign EE to children when measured values are not available. Prediction equations for the energy cost of walking and running were calculated using multiple regression. CONCLUSION This study has provided a literature base and analytical support for a compendium of energy costs for use with children with energy costs expressed as METs.

How Do School-Day Activity Patterns Differ with Age and Gender across Adolescence?

PURPOSE A knowledge of how young people use their time could be instrumental in informing health interventions, modeling consumer behaviors, and planning service delivery. The aim of the present study was to describe age- and gender-related patterns in the self-reported use of time on school days in a large sample of Australian children and adolescents aged between 10 and 18 years. METHODS A single, detailed use-of-time diary for a school day was collected from 6024 Australians aged 10-18 from several state and regional surveys conducted in the states of South Australia (SA) and Victoria between 2001 and 2006. Time-use profiles were analyzed for a range of active and sedentary state behaviors. RESULTS Boys reported higher physical activity levels (PALs), moderate-to-vigorous physical activity (MVPA), and sports than girls. There were no differences in free play, and girls used more active transport. All activity-related variables decreased with age, except active transport, which peaked at 14-15 years. Boys exhibited higher levels of screen time, whereas girls had higher levels of passive transport. Screen time and its components (television, videogames, and computer use) peaked in the peripubertal years. CONCLUSION Age- and gender-related patterns of time use vary greatly within adolescence. This may reflect a mix of biological and social factors.

Screenieboppers and extreme screenies: the place of screen time in the time budgets of 10–13 year‐old Australian children

Objectives: Excessive ‘screen time’ has been associated with a range of psychosocial disturbances and increasing pediatric obesity. This study describes the magnitude, distribution, composition and time‐distribution of childrens screen use; examines correlates of screen use; and characterises ‘extreme’ screen users (top quartile).

Correlates of children's time-specific physical activity: A review of the literature

Assessment of correlates of physical activity occurring at different times of the day, locations and contexts, is imperative to understanding children’s physical activity behaviour. The purpose of this review was to identify the correlates of children’s physical activity (aged 8–14 years) occurring during the school break time and after-school periods. A review was conducted of the peer-reviewed literature, published between 1990 and January 2011. A total of 22 studies (12 school break time studies, 10 after-school studies) were included in the review. Across the 22 studies, 17 studies were cross-sectional and five studies were interventions. In the school break time studies, 39 potential correlates were identified, of which gender and age were consistently associated with school break time physical activity in two or more studies, and family affluence, access to a gym, access to four or more physical activity programs and the condition of a playing field were all associated with school break time physical activity in only one study. Access to loose and fixed equipment, playground markings, size of and access to play space and the length of school break time were all positively associated with changes in school break time physical activity in intervention studies. Thirty-six potential correlates of after-school physical activity were identified. Gender (with boys more active), younger age, lower body mass index (for females), lower TV viewing/playing video games, and greater access to facilities were associated with higher levels of after-school physical activity in two or more studies. Parent supervision was negatively associated with females’ after-school physical activity in one study. This review has revealed a relatively small number of studies investigating the school break time and after-school periods in the specified age range and only a few correlates have demonstrated a consistent association with physical activity. This highlights the infancy of this area and a need for further investigation into time-specific physical activity behaviour so that interventions designed for these specific periods can target the important correlates.

Presleep activities and time of sleep onset in children

OBJECTIVE: Presleep activities have been implicated in the declining sleep duration of young people. A use-of-time approach may be used to describe the presleep period. The study aims were to describe the activities undertaken 90 minutes before sleep onset and to examine the association between activities and time of sleep onset in New Zealand young people. METHODS: Participants (N = 2017; 5–18 years) self-reported their time use as part of a national survey. All activities reported in the 90 minutes before sleep were extracted. The top 20 activities were grouped into 3 behavioral sets: screen sedentary time, nonscreen sedentary time, and self-care. An adjusted regression model was used to estimate presleep time spent in each behavioral set for 4 distinct categories of sleep onset (very early, early, late, or very late), and the differences between sleep onset categories were tested. RESULTS: In the entire sample, television watching was the most commonly reported activity, and screen sedentary time accounted for ∼30 minutes of the 90-minute presleep period. Participants with a later sleep onset had significantly greater engagement in screen time than those with an earlier sleep onset. Conversely, those with an earlier sleep onset spent significantly greater time in nonscreen sedentary activities and self-care. CONCLUSIONS: Screen sedentary time dominated the presleep period in this sample and was associated with a later sleep onset. The development of interventions to reduce screen-based behaviors in the presleep period may promote earlier sleep onset and ultimately improved sleep duration in young people.