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  • 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. 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. 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. 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 <10.5 or ≥10.5 hours), and in the 38-month questionnaire she was asked how much time they spent watching TV per week (grouped into ≤8 h or >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. 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. 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). 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.
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