Jonathan A. Mitchell

Sedentary behaviour in youth

The purpose of this review is to describe the amount of time children spend in sedentary behaviour and to determine if there are specific factors that associate with sedentary behaviour in children. The following search terms were used to identify relevant articles: sedentary behaviour, inactivity, television, computer, video games, small screen, sitting, prevalence, patterns, correlates, factors and determinants. The databases used to conduct the search included PubMed, PsycINFO, ERIC (Education Resources Information Center) and Academic Search Premier. The studies reviewed were limited to those that sampled children (2–18 years), were written in English and used a measure of sedentary behaviour as the dependent variable. Several studies reported the time spent watching television or the proportion of children at or above a threshold for television viewing (eg, ≥3 h/day). Among the accelerometer studies included, the National Health and Nutrition Examination Survey is the largest and reported ∼6.1, 7.5 and 8.0 h/day mean sedentary time in children 6–11, 12–15 and 16–19 years old, respectively. Taken together, the existing literature across the world indicates a slightly higher level of sedentary behaviour in older children. Higher levels of sedentary behaviour were also reported in non-white children, children from lower socioeconomic status background and children from households with more access to televisions/computers. Lower levels of sedentary behaviour were reported in children whose parents have rules/limitations on screen time.

Measurement of Physical Activity in Preschool Children

PURPOSE To provide an overview of the methods that have been developed for measurement of physical activity in children of preschool age. Emphasis will be given to direct observation and accelerometry, but pedometry, HR monitoring, and proxy reports will be reviewed as well. METHOD Research articles detailing the measurement properties of direct observational systems, accelerometry, pedometry, HR monitoring, and proxy reporting specifically in children of preschool age were selected and reviewed. RESULTS Systems for direct observation of physical activity and accelerometry are valid and reliable measures of physical activity in young children. Direct observation, which can provide information on type and context of physical activity, is an excellent complement to accelerometry, which provides detailed information on the intensity and duration of physical activity but no contextual information. CONCLUSIONS Direct observation systems and accelerometry have become well-established measurements of physical activity in young children as well as older groups. Pedometry and HR monitoring have been shown to be applicable, but these methods have been studied less extensively than direct observation and accelerometry. Proxy reports of physical activity are attractive because of low burden, but they have limited validity.

Sleep Duration and Adolescent Obesity

OBJECTIVES: Short sleep has been associated with adolescent obesity. Most studies used a cross-sectional design and modeled BMI categories. We sought to determine if sleep duration was associated with BMI distribution changes from age 14 to 18. METHODS: Adolescents were recruited from suburban high schools in Philadelphia when entering ninth grade (n = 1390) and were followed-up every 6 months through 12th grade. Height and weight were self-reported, and BMIs were calculated (kg/m2). Hours of sleep were self-reported. Quantile regression was used to model the 10th, 25th, 50th, 75th, and 90th BMI percentiles as dependent variables; study wave and sleep were the main predictors. RESULTS: BMI increased from age 14 to 18, with the largest increase observed at the 90th BMI percentile. Each additional hour of sleep was associated with decreases in BMI at the 10th (–0.04; 95% confidence interval [CI]: –0.11, 0.03), 25th (–0.12; 95% CI: –0.20, –0.04), 50th (–0.15; 95% CI: –0.24, –0.06), 75th (–0.25; 95% CI: –0.38, –0.12), and 90th (–0.27; 95% CI: -0.45, -0.09) BMI percentiles. The strength of the association was stronger at the upper tail of the BMI distribution. Increasing sleep from 7.5 to 10.0 hours per day at age 18 predicted a reduction in the proportion of adolescents >25 kg/m2 by 4%. CONCLUSIONS: More sleep was associated with nonuniform changes in BMI distribution from age 14 to 18. Increasing sleep among adolescents, especially those in the upper half of the BMI distribution, may help prevent overweight and obesity.

A Prospective Study of Sedentary Behavior in a Large Cohort of Youth

PURPOSE The studys purpose was to describe longitudinal patterns of objectively measured sedentary behavior from age 12 to 16. METHODS Children participating in the Avon Longitudinal Study of Parents and Children wore accelerometers for 1 wk at ages 12, 14, and 16. Participants included boys (n = 2591) and girls (n = 2845) living in a single geographic location in the United Kingdom (Bristol). Total minutes per day spent in sedentary behavior and time spent in blocks of sedentary behavior lasting 10-19, 20-29, and ≥ 30 min are described. Growth curve models were used to determine the rate of change in sedentary behavior from age 12 to 16. RESULTS At age 12, the boys and girls, on average, were sedentary for 418.0 ± 67.7 and 436.6 ± 64.0 min·d(-1), respectively, and sedentary behavior increased over time to 468.0 ± 74.3 and 495.6 ± 68.9 min·d(-1) at age 14 and to 510.4 ± 76.6 and 525.4 ± 67.4 min·d(-1) at age 16. Growth curve analyses found that total sedentary behavior increased at a rate of 19.5 ± 0.7 and 22.8 ± 0.7 min·d(-1)·yr for the boys and girls, respectively. The absolute mean increase in total sedentary behavior (+92.4 and +88.8 min·d(-1) for the boys and girls, respectively) closely matched the mean decrease in light physical activity (-82.2 and -82.9 min·d(-1) for the boys and girls, respectively) from age 12 to 16. Time spent in continuous sedentary behavior lasting ≥ 30 min increased by 121% from age 12 to 16. CONCLUSIONS Sedentary behavior increased with age, at the expense of light physical activity. The increase in sedentary behavior lasting ≥ 30 min in duration contributed greatly to the increase in total sedentary behavior.

Parental and Environmental Correlates of Physical Activity of Children Attending Preschool

OBJECTIVE To determine, using a social-cognitive framework and structural equation modeling, if parent-reported family physical activity (PA) variables are related to PA of young children. DESIGN Cross-sectional study. SETTING Children attending 23 preschools in and around Columbia, South Carolina. PARTICIPANTS Three hundred sixty-nine children (48.0% male and 50.4% black) and their parents. MAIN EXPOSURES Family variables were reported by parents and included parent PA, parent enjoyment of PA, importance to adults of child playing sports and being active, and family support. MAIN OUTCOME MEASURES Moderate to vigorous physical activity (MVPA) of children was modeled as a latent variable using PA from direct observation, accelerometers, and parents perception of the childs athletic coordination. RESULTS A model of direct and indirect relations of family variables, preschool quality, home PA equipment, and childs enjoyment of PA had acceptable fit (root mean square error of approximation, 0.053; comparative fit index, 0.90). Parent PA, parent enjoyment of PA, and importance of childs PA were significantly related to family support. Family support, quality of preschool attended, home PA equipment, and childs enjoyment of PA were positively related to childs PA. However, there was no direct relationship between parent PA and the childs PA. CONCLUSION Although parent PA was not directly related to childrens MVPA, results showed that parent PA indirectly affects preschool childrens MVPA via its influence on family support for childrens PA.

Greater screen time is associated with adolescent obesity: a longitudinal study of the BMI distribution from Ages 14 to 18.

Previous research has examined the association between screen time and average changes in adolescent body mass index (BMI). Until now, no study has evaluated the longitudinal relationship between screen time and changes in the BMI distribution across mid to late adolescence.

Infant BMI or Weight-for-Length and Obesity Risk in Early Childhood.

BACKGROUND: Weight-for-length (WFL) is currently used to assess adiposity under 2 years. We assessed WFL- versus BMI-based estimates of adiposity in healthy infants in determining risk for early obesity. METHODS: Anthropometrics were extracted from electronic medical records for well-child visits for 73 949 full-term infants from a large pediatric network. World Health Organization WFL and BMI z scores (WFL-z and BMI-z, respectively) were calculated up to age 24 months. Correlation analyses assessed the agreement between WFL-z and BMI-z and within-subject tracking over time. Logistic regression determined odds of obesity at 2 years on the basis of adiposity classification at 2 months. RESULTS: Agreement between WFL-z and BMI-z increased from birth to 6 months and remained high thereafter. BMI-z at 2 months was more consistent with measurements at older ages than WFL-z at 2 months. Infants with high BMI (≥85th percentile) and reference WFL (5th–85th percentiles) at 2 months had greater odds of obesity at 2 years than those with high WFL (≥85th percentile) and reference BMI (5th–85th percentiles; odds ratio, 5.49 vs 1.40; P < .001). At 2 months, BMI had a higher positive predictive value than WFL for obesity at 2 years using cut-points of either the 85th percentile (31% vs 23%) or 97.7th percentile (47% vs 29%). CONCLUSIONS: High BMI in early infancy is more strongly associated with early childhood obesity than high WFL. Forty-seven percent of infants with BMI ≥97.7th percentile at 2 months (versus 29% of infants with WFL ≥97.7th percentile at 2 months) were obese at 2 years. Epidemiologic studies focused on assessing childhood obesity risk should consider using BMI in early infancy.

Moderate‐To‐vigorous physical activity is associated with decreases in body mass index from ages 9 to 15 years

The purpose of this study is to determine whether time spent in objectively measured physical activity is associated with change in body mass index (BMI) from ages 9 to 15.

Sedentary Behavior and Health Outcomes in Children and Adolescents

The purpose of this review was to summarize findings from epidemiological studies that determined if sedentary behavior was associated with obesity, metabolic risk factors, and cardiorespiratory fitness in children and adolescents. We noted if studies adjusted for moderate-to-vigorous physical activity (MVPA), dietary intakes, and/or sleep duration. Articles were identified through PubMed using the search terms: (sedentary OR sitting OR television) AND (adiposity OR blood pressure OR body mass index OR cardiometabolic OR metabolic risk OR waist circumference). The search was limited to ages 6 to 18 years, humans, and published between January 1, 2008 and September 26, 2012. Cross-sectional and longitudinal studies observed associations between more sedentary behavior, especially screen-based sedentary behavior, and measures of obesity; and most associations were independent of MVPA and dietary intake. Cross-sectional and longitudinal studies reported associations between screen-based sedentary behavior and lower cardiorespiratory fitness, and most associations were independent of MVPA and obesity. Cross-sectional studies observed associations between more screen-based and objectively measured sedentary behavior and lower insulin sensitivity; and most associations were independent of MVPA and obesity. There was little-to-no evidence that sedentary behavior was associated with increased blood pressure and increased blood lipids.

Screen-Based Sedentary Behavior and Cardiorespiratory Fitness from Age 11 to 13

PURPOSE The studys purpose was to determine whether time spent in screen-based sedentary behavior is associated with change in cardiorespiratory fitness (CRF) levels in children from age 11 to 13, adjusting for vigorous physical activity (VPA). METHODS Participants were children (n = 2097) enrolled in the control arm of the HEALTHY Study, who performed 20-m shuttle run tests at ages 11 and 13. Self-reported screen time was used as a measure of sedentary behavior. Longitudinal quantile regression was used to model the influence of predictors on changes at the 10th, 25th, 50th, 75th, and 90th shuttle run lap percentiles. Screen time (h·d(-1)) was the main predictor, and adjustment was also made for VPA, body mass index, and household education. RESULTS In boys, more screen time was associated with fewer shuttle run laps completed from age 11 to 13 at the 25th, 50th, and 75th shuttle run lap percentiles; the strongest association was at the 75th shuttle run percentile (-0.57, 95% confidence interval = -0.93 to -0.21). In girls, more screen time was associated with fewer shuttle run laps completed from age 11 to 13 at the 50th, 75th, and 90th shuttle run lap percentiles; the strongest association was at the 90th shuttle run percentile (-0.65, -1.01 to -0.30). Borderline negative associations were found between screen time and shuttle run laps at the 10th shuttle run percentile in boys and girls (-0.28, -0.57 to 0.01, and -0.17, -0.41 to 0.06, respectively). CONCLUSIONS More screen time was associated with lower CRF from age 11 to 13, independent of VPA. However, the association was weakest at the lower tail of the CRF distribution.