PATE, RUSSELL R., MARIA J. ALMEIDA, KERRY L. MCIVER, KARIN A. PFEIFFER, AND MARSHA DOWDA. Validation and calibration of an accelerometer in preschool children. Obesity. 2006;14:2000-2006. Objective: Obesity rates in young children are increasing, and decreased physical activity is likely to be a major contributor to this trend. Studies of physical activity in young children are limited by the lack of valid and acceptable measures. The purpose of this study was to calibrate and validate the ActiGraph accelerometer for use with 3-to 5-year-old children. Research Methods and Procedures:Thirty preschool children wore an ActiGraph accelerometer (ActiGraph, Fort Walton Beach, FL) and a Cosmed portable metabolic system (Cosmed, Rome, Italy) during a period of rest and while performing three structured physical activities in a laboratory setting. Expired respiratory gases were collected, and oxygen consumption was measured on a breath-by-breath basis. Accelerometer data were collected at 15-second intervals. For cross-validation, the same children wore the same instruments while participating in unstructured indoor and outdoor activities for 20 minutes each at their preschool. Results: In calibrating the accelerometer, the correlation between V O 2 (ml/kg per min) and counts was r ϭ 0.82 across all activities. The only significant variable in the prediction equation was accelerometer counts (R 2 ϭ 0.90, standard error of the estimate ϭ 4.70). In the cross-validation, the intraclass correlation coefficient between measured and predicted V O 2 was R ϭ 0.57 and the Spearman correlation coefficient was R ϭ 0.66 (p Ͻ 0.001). Cut-off points for moderate-and vigorous-intensity physical activity were identified at 420 counts/15 s (V O 2 ϭ 20 mL/kg per min) and 842 counts/15 s (V O 2 ϭ 30 mL/kg per min), respectively. When these cutpoints were applied to the cross-validation data, percentage agreement, kappa, and modified kappa for moderate activity were 0.69, 0.36, and 0.38, respectively. For vigorous activity, the same measures were 0.81, 0.13, and 0.62. Discussion: Accelerometer counts were highly correlated with V O 2 in young children. Accelerometers can be appropriately used as a measure of physical activity in this population.

The objective of this study was to determine if moderate to vigorous physical activity (MVPA) of 3-5 year old preschool children varied with differences in policies/practices, and overall quality of preschools. A total of 266 children (47% males, 60% African American) from 9 preschools were observed for 1 hour on 3 different days. PA of children was observed twice per minute and scored as 1-5, with 1 for stationary/motionless and 5 for fast movement. Summary MVPA was calculated over the 3 days as percent of times observed at levels of 4 or 5, and percent of time at levels 1 or 2 as sedentary activity. A structured interview about PA policies was conducted with an administrator at each preschool and overall quality of the preschool was assessed using Early Childhood Environment Rating Scale-Revised Edition (ECERS-R). Preschools were divided into groups according to whether a specific policy/practice that would be logically hypothesized to promote PA was in place at the school. MVPA differences between groups of children was assessed using mixed ANOVA controlling for preschool. When preschools offered more field trips, and more college educated teachers, the children participated in more MVPA. Children who attended preschools with lower quality spent more time in sedentary activity. In conclusion, children in preschools which may have more resources and better quality appear to show both more sedentary behavior and more MVPA.

The Actical accelerometer is a valid tool for measuring PA in young children.

Numerous studies have examined the association between the surrounding neighborhood environment and physical activity levels in adolescents. Many of these studies use a road network buffer or Euclidean distance buffer around an adolescent's home to represent the appropriate geographic area for study (i.e., neighborhood). However, little empirical research has examined the appropriate buffer size to use when defining this area and there is little consistency across published research as to the buffer size used. In this study, 909 12 th grade adolescent girls of diverse racial and geographic backgrounds were asked to report their perceptions of an easy walking distance and a convenient driving distance. These two criterions are often used as the basis for defining one's neighborhood.The mean easy walking distance in minutes reported by adolescent girls was 14.8 minutes (SD = 8.7). The mean convenient driving distance in minutes reported was 17.9 minutes (SD = 10.8). Nested linear multivariate regression models found significant differences in reported 'easy walking distance' across race and BMI. White adolescents reported on average almost 2 minutes longer for an easy walking distance compared to African American adolescents. Adolescents who were not overweight or at risk for overweight reported almost 2 minutes fewer for an easy walking distance relative to those who were overweight or at risk for overweight. Significant differences by urban status were found in the reported 'convenient driving distance'. Those living in non-urban areas reported on average 3.2 minutes more driving time as convenient compared to those living in urban areas. Very little variability in reported walking and driving distances was explained by the predictors used in the models (i.e., age, race, BMI, physical activity levels, urban status and SES).This study suggests the use of a 0.75 mile buffer to represent an older female adolescent's neighborhood, which can be accessed through walking. However, determining the appropriate area inclusive of car travel should be tailored to the geographic location of the adolescent since non-urban adolescents are willing to spend more time driving to destinations. Further research is needed to understand the substantial variability across adolescent perceptions of an easy walking and convenient driving distance.

Background-Physical activity behavior is influenced by a person's physical environment, but few studies have used objective measures to study the influences of the physical environment on physical activity behavior in youth. The purpose of this study was to examine the relationship between selected neighborhood physical activity resources and physical activity levels in high school girls.