Active school transport and weekday physical activity in 9–11-year-old children from 12 countries

Rising rates of overweight children have focused attention on walking and biking to school as a means to increase children's physical activity levels. Despite this attention, there has been little documentation of trends in school travel over the past 30 years or analysis of what has caused the changes in mode choice for school trips. This article analyzes data from the 1969, 1977, 1983, 1990, 1995, and 2001 National Personal Transportation Survey conducted by the U.S. Department of Transportation to document the proportion of students actively commuting to school in aggregate and by subgroups and analyze the relative influence of trip, child, and household characteristics across survey years. All analyses were done in 2006. The National Personal Transportation Survey data show that in 1969, 40.7% (95% confidence interval [CI]=37.9-43.5) of students walked or biked to school; by 2001, the proportion was 12.9% (95% CI=11.8-13.9). Distance to school has increased over time and may account for half of the decline in active transportation to school. It also has the strongest influence on the decision to walk or bike across survey years. Declining rates of active transportation among school travelers represents a worrisome loss of physical activity. Policymakers should continue to support programs designed to encourage children to walk to school such as Safe Routes to School and the Centers for Disease Control and Prevention's KidsWalk. In addition, officials need to design policies that encourage schools to be placed within neighborhoods to ensure that the distance to school is not beyond an acceptable walking distance.

Introduction The prevalence of childhood obesity is increasing in the United Kingdom [1], as it is across Europe [2], and in the United States [3]. This increase has important immediate and long-term health implications [4,5]. Obesity is fundamentally a result of chronic energy imbalance [6,7]. Diet survey data suggest that population levels of obesity have increased in the face of declining energy intake, implying that inactivity may be important in explaining the temporal trends in obesity [6,8]. While studies such as the National Heart Lung and Blood Institute's Growth and Health Study have reported associations between physical activity and obesity [9], the results of studies of the association between physical activity and obesity in children have been inconsistent [10]. This may reflect the fact that most studies have relied on inaccurate measures of physical activity or inaccurate measures of fat mass or both. Physical activity in children is sporadic [11,12], and children are less able than adults to recall or record their physical activity, consequently questionnaires provide a poor measure of physical activity in children. In contrast objective techniques such as heart rate monitors or accelerometers have been shown to provide an accurate measure of physical activity in children [13,14]. Body mass index (BMI) is a measure of weight for height and is widely used to assess population levels of childhood obesity because it is easy to measure and because population standards are available for comparison. It does not, however, distinguish well between fat and lean mass across the normal range [15] unlike methods such as dual energy x-ray absorptiometry (DXA), which produce an estimate of lean mass, fat mass, and regional distribution of body fat [16]. We examined the association between physical activity (measured objectively using accelerometers), and fat and lean mass (measured using total body DXA), and BMI in a large population of contemporary children. Methods Study Population The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective study that has been described in detail elsewhere [17] (http://www.alspac.bris.ac.uk). Briefly, 14,541 pregnant women living in one of three Bristol-based health districts in the former County of Avon with an expected delivery date between April 1991 and December 1992 were enrolled in the study. Detailed information has been collected using self-administered questionnaires, data extraction from medical notes, and linkage to routine information systems and at research clinics. Ethical approval for the study was obtained from the ALSPAC Law and Ethics Committee and Local Research Ethics Committees. Measurement of Physical Activity All children who attended the 11-year clinic were asked to wear an MTI Actigraph AM7164 2.2 accelerometer (Actigraph, http://www.theactigraph.com) for seven days. The Actigraph is an electronic motion sensor comprising a single plane (vertical) accelerometer. The Actigraph is small and light and is worn around the waist. Movement in a vertical plane is detected as a combined function of movement frequency and intensity and recorded as counts. The Actigraph has been validated in both children and adolescents against indirect calorimetry [18] observational techniques [19] and energy expenditure measured by doubly labelled water [20] and shown to be accurate. Actigraphs were initialised for each child using an Actigraph Reader Interface Unit (RIU-41A) with RIU software (version 2.26B, MTI Health Services, http://www.mtifwb.com). Children were asked to wear the Actigraph during waking hours and only to take it off for showering, bathing, or any water sports. Children were asked to record the times when they wore the Actigraph and time spent each day swimming or cycling, as the children did not wear the Actigraph when swimming, and the physical activity of cycling is not accurately recorded by the Actigraph. Actigraphs were returned by post and downloaded onto a PC using the Actigraph Reader Interface Unit and software. Measurement of Body Composition Body composition was measured at the 11-year clinic. Height was measured with shoes and socks removed using a Harpenden stadiometer (Holtain, http://www.fullbore.co.uk/holtain/medical/welcome.html). Weight was measured using a Tanita TBF 305 body fat analyser and weighing scales (Tanita, http://www.tanita.co.uk). BMI was calculated as weight (in kilograms) divided by height squared (in metres). Fat mass and lean mass were measured using a Lunar Prodigy DXA scanner (GE Medical Systems, http://www.gehealthcare.com). Trunk fat mass was estimated using the automatic region of interest that included chest, abdomen, and pelvis. The scans were visually inspected and realigned where necessary. Potential Confounders Age was the age the child attended the 11-year clinic. The 32-week antenatal questionnaire asked the mother to record her highest education level, which was categorised into none/Certificate of Secondary Education (CSE) (national school exams at age 16), vocational, O level (national school exams at age 16, higher than CSE), A level (national school exams at age 18), or degree. She also recorded the occupation of both herself and her partner, which were used to allocate them to social-class groups (classes I to V with III split into nonmanual and manual) using the 1991 Office for Population Censuses and Surveys classification; the lowest class of the mother and her partner was used in analysis. At enrolment, the mother was asked to record her height and prepregnancy weight, which were used to calculate the mother's BMI. The date of the last menstrual period as reported by the mother at enrolment and the actual date of delivery were used to estimate gestation. Infant sex and birthweight were recorded in the delivery room and abstracted from obstetric records and/or birth notifications. In the 18-week antenatal questionnaire, the mother was asked if she smoked in the first three months of pregnancy and in the last two weeks. In the 32-week antenatal questionnaire, the mother was asked how much she was currently smoking. Responses from the three trimesters were combined to create a variable for any smoking during pregnancy. In the 30-month questionnaire, the mother was asked how much time their child spent asleep at night (grouped into 8 h). A puberty questionnaire was filled in by the child's carer (usually the child's mother) when the child was approximately 11 years old, which included questions on pubertal stage [21]. Pubertal stage for boys was based on pubic hair development, and for girls was based on the most advanced stage for pubic hair and breast development. Measures of Physical Activity Data from children who had worn the Actigraph for at least ten hours per day for at least three days were included. Two physical activity variables were used—total physical activity and time spent in moderate and vigorous physical activity (MVPA). Total physical activity was the total volume of physical activity and included activities at different intensities. Total physical activity was measured as the average counts per minute (cpm) over the period of valid recording. Total physical activity was used because this is the summary measure of total physical activity that has been validated against doubly labelled water [20]. MVPA was the average minutes of MVPA per valid day. Minutes of MVPA were used as current physical activity recommendations for children are framed in terms of time spent each day in MVPA [22]. We used a cut point of Actigraph output of greater than 3,600 cpm to define MVPA derived from a calibration study conducted in a subsample of 246 children who were asked to perform a series of everyday activities while wearing an Actigraph and a portable metabolic unit (Cosmed K4b2, Cosmed, http://www.cosmed.it). This estimate corresponded to four times resting metabolic rate that was achieved when children were walking briskly. This cut point was similar to that suggested recently in a study comparing different cut points [23]. Associations with total physical activity were calculated per 100 cpm as this difference is of a similar order to the differences observed between boys and girls. The associations with MVPA were calculated per 15 minutes of MVPA, as current recommendations are that children spend 60 minutes a day in MVPA [22]. Quintiles of MVPA and total activity were also used to look for a dose response by fitting the quintiles in a continuous model. Statistical Methods Means and standard deviations (SDs) were calculated for continuous variables, and proportions were calculated for categorical variables. We used t-tests and Chi2 tests to compare differences between continuous and categorical values between children who provided physical activity data and those who did not. As MVPA, BMI, trunk fat, and fat mass had skewed distributions the median and interquartile range were calculated as summary measures, and logged BMI, trunk fat, and fat mass were used for calculation of the SD scores. Further analysis using continuous variables was based on internally derived SD scores (which are the same as Z-scores) for BMI, fat mass, lean mass, and trunk fat to allow comparison of the regression coefficients across outcome measures. Those in the top decile for fat mass after adjustment for age, height, and height squared were defined as obese. The cut points for the top decile of fat mass (for fat mass that has then been adjusted for age, height, and height squared for the sexes separately) was 17.9 kg in boys and 21.0 kg in girls. The associations with total physical activity and MVPA and the effects of potential confounding factors on the offspring outcomes were assessed using linear regression for continuous outcome variables and logistic regression for obesity. All associations except those with BMI were adjusted for height and height squared to take account of differences in stature (there was evidence of quadratic relationships with height). Previous studies have suggested that the association between physical activity and obesity is different in men and women [24,25]. We therefore formally tested the association between total physical activity and fat mass for an interaction with gender. As there was evidence of interaction (p = 0.005), we have carried out all analyses in boys and girls separately, and quintiles were derived separately for boys and girls. All analyses were performed using Stata version 8 (StataCorp, http://www.stata.com). Data Analysis Strategy We selected possible confounding factors that were available on the whole cohort that have been shown to be independently associated with obesity in previous analyses [26,27]. We used a series of models to explore the possible role of confounders. In model 1 (minimally adjusted) we adjusted for age, height, and height squared (except for BMI) to take account of differences in age and height. In model 2 we adjusted for variables in model 1 plus confounding factors, i.e., factors that might be related to physical activity and obesity or that might be more distal determinants of physical activity—maternal education, social class, birthweight, gestational age, smoking in pregnancy, and obesity of mother in pregnancy. In model 3 we adjusted for the variables in model 2 plus factors that might be more proximal determinants of physical activity or might be proxy indicators of confounding factors – sleep pattern and TV viewing. In model 4 we adjusted for the variables in model 3 and the possible confounding effect of pubertal stage in those children with self-reported pubertal stage available within 16 weeks of their clinical assessment. We repeated the analyses in children who did not report swimming in the week of measurement and in children who did not report cycling in the week of measurement. We used the intraclass correlation coefficient derived from a repeat measures study in a subset of 315 children who wore the Actigraph on up to three subsequent occasions over the course of a year to take account of variation in usual physical activity and to adjust estimates for the effect of regression dilution bias [28]. We used Spearman correlation coefficients to describe the association between MVPA and total activity and fitted both of these variables together in unadjusted and adjusted models to try and examine the independent association of these two measures of activity. Results A total of 11,952 children were invited to attend the research 11-year clinic. Of these, 7,159 (59.9%) came to the clinic, and 6,622 (92.5%) agreed to wear an Actigraph. Of the children who agreed to participate, 5,595 (84.5%) returned Actigraphs that satisfied the validity criteria. Estimates of body composition from the DXA scan were available on 5,500 children with valid physical activity measures. The average age of the children seen in the 11-year clinic was 141 months, so we have referred to them as 12-year-old children. The characteristics of these children are summarised in Tables 1 and 2. Objectively measured physical activity levels were higher in boys than girls, 663 versus 605 cpm (p 1,000) [31,32]. In the first study, 1,292 children, aged nine to ten years, were studied from four distinct regions in Europe (Denmark, Portugal, Norway, and Estonia). Physical activity was measured using the Actigraph with a similar protocol to that employed in our study. There were associations between total physical activity and time spent in MVPA in vigorous activity and obesity, but these associations were considerably weaker than the associations we observed in our population [31]. In the second study, 1,553 ten- to 14-year-old girls from the United States were studied. Physical activity was measured using the Actigraph worn for six days, and the obesity was measured using BMI and triceps skinfold thickness. There were associations between percentage body fat and minutes of MVPA [32]. Both of these studies showed a negative association between physical activity and obesity, but the associations were weaker than those we observed. The measures of physical activity were similar, and the cut points for vigorous physical activity used in the European study and used for MVPA in the United States-based study were similar to those we used for MVPA. Though the associations may vary across populations and at different ages, we think that the fact that we found stronger associations for fat mass than BMI suggests that the accuracy of the measure of obesity used may in part explain the observed differences. Only one study has used an objective measure of physical activity and an accurate measure of obesity [33]. In this study 248 Swedish school children aged eight to 11 wore Actigraphs for up to four days, and percentage body fat was measured using DXA. The odds of obesity (defined as one SD above the mean percentage body fat) in the least activity quartile was 4.0 (95% CI 1.2–13.5). The association with obesity was stronger with vigorous activity (defined as >3,498 cpm) than moderate activity (defined as >1,670 cpm and <3,498 cpm). Our results are thus consistent with these, suggesting that there is a strong cross-sectional association between physical activity and obesity, and that it is stronger for higher intensity physical activity. If causal, the associations we have demonstrated are of potential public health importance. Our data suggest that a modest increase in physical activity of 15 minutes of MVPA is associated with lower odds of obesity of over 50% in boys and nearly 40% in girls. Though total physical activity and MVPA were closely correlated, suggesting that children with high levels of MVPA have high levels of total physical activity, our data provide empirical support for the current physical activity recommendations for children that are framed in terms of MVPA rather than total physical activity [22]. Our finding that the association between physical activity and obesity was stronger in boys than girls was a prespecified analysis based on findings from studies in adults [24,25,34]. We are not aware of any previous reports in children. Our results suggest that though higher levels of physical activity are associated with reduced risk of obesity in both boys and girls, the strength of the association between physical activity level and obesity differs between boys and girls. This may be because physical activity has a stronger effect on appetite and satiety in boys, or it may be that girls use dietary restraint more than boys to regulate their weight. Our study has a number of limitations. First, our study is cross-sectional and we cannot therefore rule out the possibility that these associations represent reverse causality, and that obesity leads to a reduction in physical activity. The fact that these associations were observed across the range of fat mass rather than just in obese children makes this explanation less likely. Even if the associations are due to reverse causality and obesity leads to reduced activity, this is itself an important observation as reduced physical activity in obese people may increase the morbidity and mortality associated with obesity. Second, these data are observational, and it is possible that confounding could explain our results. Though the observed associations could be due to confounding we think this is unlikely as physical activity in this cohort is weakly negatively associated with higher social position (unpublished data), and the associations were largely unaltered by adjustment for a number of confounding factors. More recent measures of possible confounders such as social position were not available, and these could explain these associations. Third, these data are based on a single measure of activity over a three- to seven-day period that didn't necessarily include a weekend day. Though some studies have used longer reporting periods, many studies have included children with three days or fewer [29,30,31,33], and the association between physical activity and obesity was similar in children with different numbers of days of valid recording (unpublished data). Shorter recording periods will measure usual physical activity less accurately and therefore attenuate physical activity–obesity associations; we have used the intraclass correlation coefficient based on repeat measures over the course of a year to quantify the likely effect of such measurement error. Fourth, we used one-minute epochs to define activity level, and we may therefore have underestimated the total amount of MVPA where this is sporadic rather than sustained. It is reassuring, therefore, that our results were similar to those reported in a study using ten-second epochs [33]. Finally, we were not able to collect data on physical activity or body composition on a substantial number of children originally enrolled in the study. These missing data will result in reduced power, which is not a particular problem in a study of this size. Potentially more importantly, missing data can lead to bias if the association between physical activity and obesity is different in the children who did not take part. While we cannot exclude bias due to missing data, the fact that the associations were not altered by adjustment for factors associated with missing data provides some reassurance. Further, although attendance at the 11-year clinic was associated with markers of higher social position, physical activity showed a weak negative association with social position (unpublished data). In conclusion we have shown a strong negative dose-response association between objectively measured physical activity and childhood obesity measured as fat mass and BMI. Our findings, if confirmed, suggest that public health policies that increase physical activity levels and in particular MVPA in children may help to reduce the prevalence of childhood obesity. These associations suggest even a modest increase of 15 minutes MVPA might result in an important reduction in the prevalence of overweight and obesity. Prospective studies are required to confirm these associations and to describe how physical activity-obesity associations vary over time.

Increasing the physical activity of typically sedentary adult populations is at the forefront of the public health agenda. This review addresses the challenges in defining and measuring physical activity in this target group, for a number of purposes, namely, scientific or academic inquiry, surveillance, clinical application and programme evaluation. First, we clarify the conceptual distinctions between the terms sedentarism, physical inactivity, physical activity and energy expenditure. Next, we review and compare the utility of different approaches for quantifying and expressing physical activity in these populations. Physical activity in typically sedentary populations is most likely a simple pattern of behaviour that has been largely obscured by existing measures and its expression as energy expenditure. Existing self-report methods are practical, but suffer from floor effects and recall bias. Walking, the most important activity to assess in this target group, is very difficult to measure through self-report methods. Motion sensors are more appropriate for quantifying physical activity behaviours in typically sedentary populations. Of the 2 types of motion sensors - the accelerometer and the pedometers--the latter is more appealing because it is both an affordable and a 'good enough' measure of physical activity, specifically ambulatory activity. Although a common measurement approach would greatly facilitate our understanding of physical activity behaviour patterns, the selection of an approach ultimately depends on the purpose of the study and to a great extent, its budget. Researchers, clinicians and practitioners interested in accurately capturing the lower end of the continuum of physical activity (that is characteristic of sedentary populations) must thoughtfully consider the relative advantages and disadvantages of the available approaches.