The Association Between an Objective Measure ... - Wiley Online Library

The Association Between an Objective Measure of Physical Activity and Weight Status in Preschoolers Elizabeth. S. Metallinos-Katsaras,* Patty S. Freedson,† Janet E. Fulton,‡ and Bettylou Sherry‡

Abstract METALLINOS-KATSARAS, ELIZABETH S., PATTY S. FREEDSON, JANET E. FULTON, AND BETTYLOU SHERRY. The association between an objective measure of physical activity and weight status in preschoolers. Obesity. 2007;15:686 – 694. Objective: Our objective was to determine the association between physical activity and BMI among racially diverse low-income preschoolers. Research Methods and Procedures: This was a crosssectional study of 2- to 5-year-olds (n ⫽ 56) enrolled in Massachusetts Special Supplemental Nutrition Program for Women, Infants & Children (WIC). Physical activity was measured for 7 consecutive days with an accelerometer. Height and weight were obtained from WIC records, and BMI-for-age percentiles were calculated based on the Centers for Disease Control and Prevention’s (CDC) 2000 Growth Charts. At-risk-for-overweight (BMI-for-age of ⱖ85th to ⬍95th percentile) and overweight (BMI-for-age ⱖ95th percentile) groups were combined and referred to as overweight. Final analysis inclusion criteria were: completion of 4.5 days of activity assessment and anthropometric data obtained within 90 and 120 days of the activity assessment for children ages 24 to 35.99 and 36 to 59.99 months, respectively.

Received for review May 8, 2006. Accepted in final form October 4, 2006. The costs of publication of this article were defrayed, in part, by the payment of page charges. This article must, therefore, be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. *Department of Nutrition, School for Health Studies, Simmons College, Massachusetts; †Department of Exercise Science, University of Massachusetts, Amherst, Massachusetts; and ‡Division of Nutrition and Physical Activity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia. Address correspondence to Elizabeth. S. Metallinos-Katsaras, Department of Nutrition, School for Health Studies, Simmons College, MA 02115-5898. E-mail: [email protected] Copyright © 2007 NAASO

686

OBESITY Vol. 15 No. 3 March 2007

Results: Overweight children had significantly lower mean daily very vigorous minutes (VVM) (2.6 mins vs. 4.6 mins, p ⬍ 0.05) and lower very active minutes (VAM) [i.e., sum of vigorous minutes (VM) and VVM] per day (22.9 mins vs. 32.1 mins, p ⬍ 0.05) than children who were not overweight. Daily VVM [odds ratio (OR) ⫽ 0.68; 95% confidence interval (CI), 0.49 to 0.96], VM (OR ⫽ 0.94; CI, 0.88 to 1.00), and VAM (OR ⫽ 0.94; 95% CI, 0.89 to 1.00) were all associated with significantly lower odds of being overweight. Discussion: This study suggests that, in a diverse group of preschoolers, vigorous and very vigorous activity are associated with lower odds of overweight. However, these findings require corroboration in a diverse sample of preschoolers using a longitudinal design. Key words: children, overweight, physical activity

Introduction Because of its magnitude and its concurrent and longterm health consequences, overweight is a primary public health problem among children in the United States (1,2) and worldwide (3). Overweight in childhood places children at increased risk for concurrent health problems, such as undesirable lipid, insulin, and blood pressure levels (4,5); impaired glucose tolerance (6); and type 2 diabetes (7). Moreover, childhood overweight often persists into adulthood (8,9), and those overweight in adolescence are more likely to develop chronic diseases in adulthood and have a higher mortality (10) than their normal-weight counterparts. In the United States, childhood overweight has been steadily rising; the statistics for young children are alarming. Although increases in overweight among preschool children have been noted since the National Health and

Activity and Weight Status in Children, Metallinos-Katsaras et al.

Nutrition Examination Survey (NHANES)1 II (1976 – 1980), recent data indicate no waning in its acceleration. Between the 1988 –1994 and 1999 –2002 NHANES, overweight prevalence among preschoolers (2- to 5-year-olds) has increased from 7.2% to 10.3% (1,2). Moreover, lowincome children may have a higher prevalence of overweight than those in the general population. Based on the Centers for Disease Control and Prevention’s (CDC) Pediatric Nutrition Surveillance System, the prevalence of overweight in low-income preschoolers participating in publicly funded health and nutrition programs in 2002 was 14.3%, representing a relative increase of 34% since 1993 (11). Low-income Hispanic (19.0%) and American Indian/ Alaska Native (17.7%) populations exhibited the highest absolute prevalence of overweight. Pediatric Nutrition Surveillance System data collected from 30 states in 1989 to 2000 provide evidence that the increase in overweight prevalence among low-income preschoolers is accelerating (12). Within the last decade, the substantial increase in overweight and obesity among children and adolescents and in obesity among adults has occurred too quickly to be attributable to a change in genetic expression and instead is likely due to environmental factors. Behaviors such as increased television viewing, increased energy intake, parental restriction of child feeding, or a decline in physical activity may all contribute to the development of overweight among children (10,13–20). Lack of breastfeeding, large portion sizes, consumption of sugar-sweetened beverages, and low consumption of fruits and vegetables may also be important related factors (21). While some studies have examined the association between physical activity as a behavioral risk factor and overweight in older children, few studies have been conducted in young children. One study reported that estimated fat mass and activity time as determined by mothers’ responses to a questionnaire were inversely related after adjusting for fat-free mass, gender, and age (r ⫽ ⫺0.32) (22). Studies among young children are difficult because their cognitive constraints preclude self-reporting of physical activity, and research suggests that parents do not accurately estimate the amount and intensity of unstructured activities (23). To obtain more precise estimates of a youngster’s level of physical activity, motion sensors are a useful measurement tool. Three studies have examined physical activity’s association with adiposity in children younger than 6 years of age; two of these provided evidence that physical activity level as measured by an accelerometer was inversely related to changes in body fatness (24,25), while the third showed no association between physical activity energy expenditure

1 Nonstandard abbreviations: NHANES, National Health and Nutrition Examination Survey; CDC, Centers for Disease Control and Prevention; WIC, Special Supplemental Nutrition Program for Women, Infants & Children; CSA, Computer Science and Applications, Inc.; OR, odds ratio; CI, confidence interval.

and fat mass (26). However, none of the aforementioned study populations was racially diverse or low-income, and as yet it is unclear whether there are subgroup differences in the relationship between physical activity and overweight. The increasing prevalence of overweight and the persistence of childhood overweight underscore the public health importance of identifying underlying correlates of overweight. Therefore, the purpose of this study was to determine the association between physical activity, measured by accelerometry, and BMI among a racially diverse group of low-income preschool children. We hypothesized that an inverse relationship between physical activity level and BMI would be evident.

Research Methods and Procedures Subjects Participants in this study included preschoolers enrolled in the Special Supplemental Nutrition Program for Women, Infants & Children (WIC) and their parents or caregivers in two programs in western Massachusetts. At both locations, between March 1, 2000 and April 28, 2000, a total of 84 parent/caregiver volunteers were recruited in the WIC waiting rooms or by phone if they responded to waiting-room flyers. In one program, participants were also recruited by phone from a WIC staff member. Parents or caregivers who agreed to have their child participate signed an informed consent document approved by the University of Massachusetts Human Subjects Review Panel. Caregivers were provided a stipend for participating in this study based on the number of days that the child wore the accelerometer, with a maximum stipend of $30 if the child wore the accelerometer for 7 days. Inclusion criteria for the analyses were: completion of ⱖ4.5 days of activity assessment (27) and measurement of height and weight within the following time frame of the activity assessment: 1) children 24 to 35.99 months old: within 90 days, 2) children 36 to 59.99 months old: within 120 days. These time frames were based on the magnitude of growth velocity for the aforementioned ages (28,29). Twenty-three children (27.4%) did not complete at least 4.5 days of activity assessment and three (3.6%) did not have height and weight measured within the specified time frame. Two (2.4%) were excluded because they were ⬍24 months old, precluding the calculation of BMI percentile for age. Thus, the final analytic sample included 56 children (66.7%). Physical Activity Physical activity was measured over 7 days with a Computer Science and Applications, Inc. (CSA) model 7164 uniaxial accelerometer (currently the Actigraph 7164; Actigraph, LLC, Fort Walton Beach, FL), which has been validated for use in preschool-age children (30). OBESITY Vol. 15 No. 3 March 2007

687


The CSA uniaxial accelerometer measures 2.0 ⫻ 1.5 ⫻ 0.6 inches and weighs 1.5 oz (31), detects accelerations ranging from 0.05 gravitational acceleration units (g) to 2.0 g, and is band-limited, with a frequency response ranging from 0.25 to 2.5 Hz. These parameters permit the detection of normal body motion and filter out high-frequency movement such as vibrations. Each digitized signal is summed over a user-specified time interval. In the current study, a 60-second sampling interval was used. Each accelerometer was calibrated with the CSA model CAL71265 before each use. All accelerometers used in this study were within ⫾5% of the standard calibration curve at frequencies of 2.5, 1.5, and 0.75 Hz. Data were summarized with a custom software program developed at the University of Massachusetts-Amherst Human Performance Laboratory. The accelerometer was worn at the hip, attached with an adjustable elastic waist belt, on the child’s right side. Caregivers were given both verbal and written instructions on how to use the accelerometer and were instructed to put it on the child each morning of the study when the child woke and to remove it during sleep, bathing, or swimming. Using a log provided by the researchers, parents/caregivers recorded the specific times that the monitor was put on and taken off each day. Caregivers’ logs showing the time the monitor was attached and removed were used to calculate total daily time the monitor was worn each day. Categories of accelerometer-collected physical activity (counts/min) were defined by intensity (light: ⬍615; moderate: 615–2971; vigorous: 2972–5331; very vigorous: ⬎5331) and by frequency and duration (mean counts/d; mean counts/min worn) (32). Using the equation with a low cut-off point to define moderate activity was based on using the value corresponding to 6-year-old children that was determined from a calibration study where metabolic data were used to establish the oxygen consumption corresponding to 3 metabolic equivalents. The accelerometer counts corresponding to this 3 metabolic equivalent value was 615 from the regression analysis. The authors acknowledge that the current literature suggests a higher cut-off point for moderate activity for this age group; however, these data were collected before the establishment of higher cut-off points. Variables were also generated to represent total active minutes (moderate ⫹ vigorous ⫹ very vigorous) and total very active minutes (vigorous ⫹ very vigorous). Data from the log and the monitor were used to estimate time spent in light-intensity activity (⬍615 counts/min) since counts of ⬍615 counts/min could be due to the monitor not being worn. Monitor recordings of ⬍615 counts/min that occurred for the time the monitor was worn (obtained from the log) were classified as light-intensity activity. The logs were incomplete for 5 children. Therefore, in analyses that adjusted for total time worn or examined counts per minute or light activity, the sample size was 51. 688


We additionally tested the hypothesis that physical activity is inversely associated with overweight by creating a variable to represent the “least active” children. This enabled an assessment of the two different variables of physical activity and sedentary behavior, noted as potentially two separate constructs by Must and Tybor (33). We defined those who were least active as having above the median values for time spent in light intensity activity (i.e., ⬎62.6% of the daily time worn) and below the median values for combined time spent in vigorous and very vigorous intensity activity (i.e., ⬍3.5% of the daily time worn). BMI BMI was calculated as weight in kilograms divided by height in meters squared. Height and weight measurements were obtained from WIC clinical records. Measurements were taken by WIC staff using standardized protocols that specify standing height to be measured without shoes on a stadiometer to the nearest eighth inch and weight to be measured without shoes or heavy clothing to the nearest quarter pound using a calibrated digital or beam balance scale (34). At the time of this study, a larger CDC-funded project was underway at nine WIC sites, including the ones used for the current study. One of the specific aims of that CDC-funded project was to improve both the WIC anthropometric data quality by training staff and to provide frequent feedback to WIC sites. An anthropometric trainer conducted training sessions which WIC staff were required to attend (35). This occurred before our conducting this study. In addition, the cut-offs for biologically implausible values set by the CDC, which conform to the World Health Organization’s cut-offs, were applied to all anthropometric data (36). The CDC’s growth chart data files were used to calculate BMI z-scores and percentiles based on each child’s age and sex (28,29). Overweight was defined as a sex-specific BMIfor-age ⱖ95th percentile and at-risk-for-overweight as a sex-specific BMI-for-age of ⱖ85th to ⬍95th percentile (37). Because of the small sample size, we combined these two groups for analysis and refer to them as overweight. The 2- to 5-year age range is a time of slow growth velocity. In fact, BMI values decrease during this period (28,29), so one would not expect an increase in overweight during this period. Statistical Analyses The association between physical activity variables and overweight was assessed in two ways: 1) general linear model regression was used to assess differences in mean levels of physical activity by overweight status and 2) logistic regression was used to estimate the odds of overweight for each category of physical activity. Both the general linear model and logistic regressions were conducted with and without adjustment for race/ethnicity, gen-


Table 1. Sociodemographic characteristics and weight status of low-income preschoolers (n ⫽ 56)* Variable Sex Girls Boys Age (years) 2 to 2.99 3 to 3.99 4 to 5.04 Race African-American Hispanic White non-Hispanic BMI ⬍85th percentile ⱕ85th percentile to ⬍95th percentile ⱖ95th percentile

Sample size % 30 26

53.6 46.4

16 17 23

28.6 33 41.1

17 18 21

30.4 32.2 37.5

35 8 13

62.5 14.3 23.2

* ⬍85th percentile ⫽ normal; ⱖ85th to ⬍95th ⫽ at-risk-foroverweight; ⱖ95th ⫽ overweight.

der, age (i.e., age at which the accelerometer data were collected), and total daily time the accelerometer was worn. Total time the accelerometer was worn was adjusted for in the analyses in two ways. First, total monitor time was

included as a covariate when assessing the association between overweight and minutes spent at each level of activity per day. Second, to define a sedentary pattern, activity variables were expressed as a percentage of total time the monitor was worn. Monitor time-adjusted results were discussed only when the inclusion of total minutes worn substantively altered the associations. A 5% level of significance was used. Data were analyzed using Statistical Analysis Software, version 9.1 (SAS Institute Inc., Cary, NC).

Results Our sample is described in Table 1. Fifty-four percent of participants were girls; there was a greater proportion of 4to 5-year-olds (41% of sample) than younger subjects; race/ ethnicity was relatively equally distributed among AfricanAmerican, non-Hispanic white, and Hispanic children. Thirty-eight percent of the children were classified as overweight or at-risk-for-overweight (CDC criteria). Table 2 summarizes physical activity for the entire sample and for girls and boys. The activity monitor was worn an average of 6.6 days (range, 4.7 to 7.0 days) during awake times, but not while bathing or swimming. On average, girls and boys participated in more light activity and moderate activity than vigorous or very vigorous activity. Indeed, 60.8% of the monitor time was spent in light activity, 35.3% in moderate activity, and 4.0% in either vigorous or very vigorous activity. In addition, boys were significantly more active than girls, as evidenced by significantly (p ⬍ 0.05) higher daily vigorous minutes (29.5 vs. 20.1, respectively),

Table 2. Physical activity variables for the entire sample and differences by sex Physical activity

Total (n ⴝ 56)*

Girls (n ⴝ 30)*

Boys (n ⴝ 26)*

p (for sex difference)

Mean minutes monitored/d Light activity (mins/d)† Moderate activity (mins/d)‡ Vigorous activity (mins/d)§ Very vigorous activity (mins/d)¶ Active time (mins/d)储 Very active time (minutes/d)** Counts/min worn

683.8 ⫾ 72.0 416.2 ⫾ 75.9 243.7 ⫾ 50.1 24.5 ⫾ 13.9 3.9 ⫾ 3.4 272.2 ⫾ 60.1 28.5 ⫾ 16.6 744.0 ⫾ 165.7

682.2 ⫾ 81.0 429.3 ⫾ 82.0 237.2 ⫾ 46.6 20.1 ⫾ 11.3 3.6 ⫾ 3.7 260.9 ⫾ 53.3 23.7 ⫾ 14.3 687.7 ⫾ 120.6

685.5 ⫾ 62.8 402.5 ⫾ 68.1 251.2 ⫾ 53.5 29.5 ⫾ 15.1 4.4 ⫾ 3.1 285.2 ⫾ 65.7 33.9 ⫾ 17.6 802.5 ⫾ 187.3

0.87 0.21 0.30 0.01 0.39 0.13 0.02 0.01

Values are mean ⫾ standard deviation. * The sample size for the counts/minute, light minutes, and total minutes monitored was n ⫽51 (26 girls and 25 boys). † ⬍615 accelerometer counts/min. ‡ 615 to 2971 accelerometer counts/min. § 2971 to 5331 accelerometer counts/min. ¶ ⬎5331 accelerometer counts/min. 储 Sum of moderate, vigorous, and very vigorous minutes per day. ** Sum of vigorous and very vigorous minutes per day.


689

690


426.3 245.2 27.5 4.6 277.3 32.1 760.7

Physical activity measures

Light activity (mins/d)‡ Moderate activity (mins/d)§ Vigorous activity (mins/d)¶ Very vigorous activity (mins/d)储 Active time (mins/d)** Very active time (mins/d)†† Counts per minutes worn

406.1 245.7 20.3 2.6 268.7 22.9 726.0

Overweight (n ⴝ 21) 0.41 0.97 0.05 0.04 0.57 0.04 0.46

p (between-group differences) 0.07 0.32b 0.33b 0.18 0.35b 0.31b 0.26a

R2 412.2 241.2 26.8 4.5 272.5 31.3 768.7

Not overweight (n ⴝ 30) 415.6 246.4 19.9 2.5 268.8 22.4 720.6

Overweight (n ⴝ 21)

a

0.84 0.72 0.06 0.06 0.82 0.05 0.31

p (between-group differences)

0.60c 0.33b 0.37b 0.19 0.36b 0.34b 0.30a

R2

Adjusted for age, sex, race, and monitor time worn†

p ⬍ 0.05; b p ⱕ 0.01; c p ⬍ 0.001. * Adjusted for age group (2, 3, 4 years), sex, and race/ethnicity (African-American, white, Hispanic). † Adjusted for age group (2, 3, 4 years), sex, race/ethnicity (African-American, white, Hispanic), and mean minutes monitor was worn per day. ‡ ⬍615 accelerometer counts/min. § 615 to 2971 accelerometer counts/min. ¶ 2971 to 5331 accelerometer counts/min. 储 ⬎5331 accelerometer counts/min. ** Sum of moderate, vigorous, and very vigorous minutes per day. †† Sum of vigorous and very vigorous minutes per day.

Not overweight (n ⴝ 35)

Adjusted for age, sex, and race*

Table 3. Adjusted means for physical activity variables by overweight status



Table 4. Physical activity and odds of being overweight* Physical activity measures Light activity (mins/d)¶ Moderate activity (mins/d)储 Vigorous activity (mins/d)** Very vigorous activity (mins/d)†† Active time (mins/d)‡‡ Very active time (mins/d)§§ Sedentary pattern¶¶ Counts/mins worn

Unadjusted odds ratio (95% CI) (n ⴝ 56)† 1.00 (0.99 to 1.01) 1.00 (0.99 to 1.01) 0.96 (0.92 to 1.01) 0.75 (0.57 to 0.99) 1.0 (0.99 to 1.01) 0.96 (0.93 to 1.00) 2.99 (0.90 to 9.95) 1.00 (0.99 to 1.00)

p 0.41 0.72 0.11 0.04 0.42 0.08 0.07 0.54

Adjusted odds ratio (95% CI) (n ⴝ 56)‡ 1.00 (0.99 to 1.01) 1.00 (0.99 to 1.02) 0.94 (0.88 to 0.999) 0.68 (0.48 to 0.96) 1.00 (0.98 to 1.01) 0.94 (0.89 to 0.997) 3.60 (0.74 to 17.45) 1.00 (0.99 to 1.00)

p 0.36 0.97 ⬍0.05 0.03 0.54 0.04 0.11 0.43

CI, confidence interval. * Overweight ⫽ BMI (age/sex-adjusted) percentile ⱖ85; 1 ⫽ overweight; 0 ⫽ not overweight. † Except for sedentary pattern in which n ⫽ 51. ‡ Adjusted for age group (2 to 2.99, 3 to 3.99, and 4 to 5), race/ethnicity (African-American, white non-Hispanic, and Hispanic), and sex. ¶ ⬍615 counts/min. 储 615 to 2971 counts/min. ** 2971 to 5331 counts/min. †† ⬎5331 counts/min. ‡‡ Sum of moderate, vigorous, and very vigorous minutes per day. §§ Sum of vigorous and very vigorous minutes per day. ¶¶ Below the sample median for very active minutes per day (i.e., ⬍3.5%) and above the sample median (i.e., ⬎62.6%) for light minutes per day.

very active minutes (33.9 vs. 23.7, respectively), and counts per minute (802.5 vs. 687.7). Mean (standard deviation) monitor time worn was similar for those who were overweight and not overweight: 669 (52) and 694 (82) mins/d, respectively. The proportion of weekdays (0.72 vs. 0.74 for those not overweight and overweight, respectively) and weekend days (0.28 vs. 0.26 for those not overweight and overweight, respectively) did not vary by weight status. Table 3 compares activity variables between children classified as overweight and those not overweight. Using general linear model regression and adjusting for age, sex, and race/ethnicity, those classified as overweight had significantly lower mean daily very vigorous minutes (4.6 mins vs. 2.6 mins, p ⬍ 0.05) and lower mean daily total very active minutes (32.1 min vs. 22.9 mins, p ⫽ 0.04) than normal-weight children. Adjusting for the time that the

monitor was worn per day did not substantially alter the magnitude of the group differences. Logistic regression analysis of the association between physical activity and BMI indicates that, in the age-, sex-, and race-adjusted model, time spent in very vigorous or vigorous activity was significantly (p ⬍ 0.05) inversely associated with overweight (Table 4). For each additional very vigorous minute per day, the odds of being overweight were 32% lower [odds ratio (OR) ⫽ 0.68; 95% confidence interval (CI), 0.49 – 0.96]; for each additional vigorous minute per day, the odds of being overweight were 6% lower (OR ⫽ 0.94; CI, 0.88 to 0.999); and for each additional very active minute per day, the odds of being overweight were 6% lower (OR ⫽ 0.94; 95% CI, 0.89 to 0.997). Adjusting for the total minutes per day the monitor was worn did not substantially alter the magnitude of the assoOBESITY Vol. 15 No. 3 March 2007

691


ciations. Those who were classified as having a sedentary pattern (i.e., low daily very active minutes and high daily light activity minutes) had a tendency to have a higher odds of overweight (approximately three times the odds); however, this was not statistically significant (p ⫽ 0.07 in the unadjusted model).

Discussion The preschool period is an important time to instill habits to prevent childhood and subsequent adult obesity (38). It is, therefore, important to identify behaviors associated with reductions in risk of overweight. This study’s major finding is that, in a racially diverse group of preschool children, there was an inverse association between vigorous and very vigorous activity and overweight. To our knowledge, this is the only study to date to examine the association between overweight and physical activity, as measured by accelerometry, in a preschool sample that includes white, AfricanAmerican, and Hispanic children; underscoring its importance is that these two latter groups are at high risk for overweight. In fact, only two studies have used accelerometers in preschool children, and both involved primarily white non-Hispanic children (24,25). Although we hypothesized that children who were more sedentary would have a higher risk of overweight, we did not find significant associations between light activity or even a sedentary pattern of physical activity and overweight. One explanation may be the small sample size of this study, which limited statistical power. Another possible reason may be that error was introduced in the estimation of sedentary time because caregiver logs in conjunction with the monitor data were used to determine the average daily time during which the child was wearing the monitor but was sedentary. There does, however, seem to be a trend of a progressive reduction in the ORs for overweight as activity levels increase (e.g., adjusted ORs for moderate, vigorous, and very vigorous activities were 1.00, 0.94, and 0.68, respectively). Two of our findings are supported by and one is in contrast to a study on a large sample of 4- to 6-yearolds reported by Janz et al. (25); similar to our findings, daily vigorous activity was inversely associated and the sum of moderate to vigorous activity was not associated with percentage body fat. Conversely, Janz et al. found that a sedentary pattern of behavior (defined as low daily vigorous activity and high television watching) was positively associated with percentage body fat. The difference in results may simply be attributable to the different definitions of sedentary behavior used. Our study defined sedentary behavior using only accelerometer data and did not find a significant association with overweight, although the estimates were in the expected direction (unadjusted OR ⫽ 2.99; 95% CI, 0.90 to 9.95, p ⫽ 0.07). There are several additional explanations for the differences. Our smaller sample size reduced our statistical power to detect an asso692


ciation and precluded our using more extreme values to define sedentary activity. In addition, our sample was racially diverse (30.4% African-American and 37.5% Hispanic), setting it apart from the Janz et al. sample (25), which was 96% white. Moore et al. (24) also did not demonstrate a significant association between low activity level (counts per minutes below the median) and BMI changes in their longitudinal study, although low activity was associated with a higher risk of triceps skinfold thickness increasing over time among those whose baseline triceps skinfold was above the median. In a recent review of longitudinal studies on physical activity and sedentary behavior’s association with weight and adiposity in youth, it was concluded that the research (primarily on older children) suggests a protective effect of increased physical activity and decreased sedentary behavior on gains in relative weight and fatness over childhood and adolescence (33). Although most of the studies reviewed were on older children, the results from the present study are consistent with the conclusions drawn by Must and Tybor (33) regarding the protective effect of increases in physical activity; conclusions regarding sedentary behavior neither confirm nor deny the results of the present study because most of the studies did not use accelerometry to define time spent sedentary. Possible mechanisms by which vigorous or very vigorous activity may reduce overweight risk are by increasing energy expenditure. One study provides limited support for the role that an increase in energy expenditure plays (39). That study in preschool and first-year school-age children found that activity-related energy expenditure was inversely associated with percentage of time spent in “sedentary behavior” (defined as ⬍1100 counts/min) and positively associated with “light activity” (defined as 1100 to 3200 counts/min) but not the percentage of time spent on moderate to vigorous physical activity (i.e., ⬎3200 counts/min) (39). Alternatively, having more daily vigorous activity minutes may simply be a marker for a more active child; in our sample, the correlation coefficients between vigorous minutes or very vigorous minutes per day and counts per minute were 0.86 and 0.49, respectively. Highly statistically significant associations continued to be found between duration of vigorous (and very vigorous) physical activity and counts per minute even after controlling for age, race/ ethnicity, and sex. One limitation of our study is that we did not collect data on dietary intake and, therefore, could not adjust for its effect in the analysis. Nevertheless, controlling for it did not substantially change the associations between inactivity and triceps skinfolds found in one study (24). Clearly, energy in and energy out are both part of the energy balance equation. Growth may influence this balance in children, even though this is a period in which growth velocity is slow and BMI values are decelerating. On the other hand, it is easy to


picture a scenario in which very active children have a higher average energy intake and are less likely to be overweight. This has been found in adults (40). Another limitation is the small sample size, which may have affected our ability to detect associations between overweight and a sedentary pattern of behavior. It also precluded the stratification of the analyses by race, so it was not possible to examine whether the associations between physical activity measures and overweight were similar across race classification. It should also be acknowledged that this study is cross-sectional. It is possible that the associations being attributed to physical activity are indeed attributable to being overweight, which may itself limit activity. Finally, this study was conducted before the publication of cut-off points specifically for this age group (41). It was not possible to redefine the present study’s activity variables using these newer cut-offs. It is possible that using the newer cut-offs may have altered the association between moderate activity and overweight. In summary, this study suggests that, in a diverse group of preschool children, vigorous and very vigorous physical activity were associated with lower odds of being overweight. The implication is that child care programs’ provision of ample opportunities for active play among preschoolers may contribute to overweight prevention. However, these findings require corroboration in a diverse sample of preschool children using a longitudinal design. Until such time, while there are many health reasons to promote vigorous activity among preschool children (42), further research is needed to ascertain whether this would be an effective intervention to prevent overweight.

Acknowledgments The authors thank Mary Kassler, Jan Kallio, Anne Pearson, and Judy Salkeld of the Massachusetts Department of Public Health for their assistance, as well as the program directors, staff, and participants at the WIC sites who participated in this project. Funding was provided by the CDC (Cooperative Agreement U50/CCU115131). References 1. Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the United States, 1999 –2004, JAMA. 2006;295:1549 –55. 2. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM. Prevalence of overweight and obesity among US children, adolescents, and adults, 1999 –2002. JAMA. 2004;291:2847–50. 3. de Onis M, Blo¨ssner M. Prevalence and trends of overweight among preschool children in developing countries. Am J Clin Nutr. 2000;72:1032–9. 4. Freedman DS, Khan LK, Dietz WH, Srinivasan SR, Berenson GS. Relationship of childhood obesity to coronary heart disease risk factors in adulthood: the Bogalusa Heart Study. Pediatrics. 2001;108:712– 8.

5. Sorof J, Daniels S. Obesity hypertension in children: a problem of epidemic proportions. Hypertension. 2002;40:441–7. 6. Sinha R, Fisch G, Teague B, et al. Prevalence of impaired glucose tolerance among children and adolescents with marked obesity. N Engl J Med. 2002;346:802–10. 7. American Diabetes Association. Type 2 diabetes in children and adolescents. Pediatrics. 2000;105:671– 80. 8. Guo SS, Roche AF, Chumlea WC, Gardner JD, Siervogel RM. The predictive value of childhood body mass index values for overweight at age 35 years. Am J Clin Nutr. 1994;59:810 –9. 9. Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? A review of the literature. Prev Med. 1993;22:167–77. 10. Must A, Jacques PF, Dallal GE, Bajema CJ, Dietz WH. Long-term morbidity and mortality of overweight adolescents: a follow-up of the Harvard Growth Study of 1922 to 1935. N Engl J Med. 1992;327:1350 –5. 11. Polhamus B, Dalenius K, Thompson D, et al. Pediatric Nutrition Surveillance 2002 Report. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; 2004. 12. Sherry B, Mei Z, Scanlon KS, Mokdad AH, GrummerStrawn LM. Trends in state-specific prevalence of overweight and underweight in 2- through 4-year old children from low income families from 1989 through 2000. Arch Pediatr Adolesc Med. 2004;158:1116 –24. 13. Dennison BA, Erb TA, Jenkins PL. Television viewing and television in bedroom associated with overweight risk among low-income preschool children. Pediatrics. 2002;109:1028 – 35. 14. Nicklas TA. Eating patterns and obesity in children: the Bogalusa Heart Study. Am J Prev Med. 2003;25:9 –16. 15. Nicklas TA. Eating patterns, dietary quality and obesity. J Am Coll Nutr. 2001;20:599 – 608. 16. Hanley AJ. Overweight among children and adolescents in a Native Canadian community: prevalence and associated factors. Am J Clin Nutr. 2000;71:693–700. 17. Epstein LH. Decreasing sedentary behaviors in treating pediatric obesity. Arch Pediatr Adolesc Med. 2000;154:220 – 6. 18. Takahashi E. Influence factors on the development of obesity in 3-year-old children based on the Toyama study. Prev Med. 1999;28:293– 6. 19. Gortmaker SL. Television viewing as a cause of increasing obesity among children in the United States, 1986 –1990. Arch Pediatr Adolesc Med. 1996;150:356 – 62. 20. Faith MS, Heshka S, Keller KL, et al. Maternal-child feeding patterns and child body weight: findings from a population-based sample. Arch Pediatr Adolesc Med. 2003;157:926 –32. 21. Sherry B. Food behaviors and other strategies to prevent and treat pediatric overweight. Int J Obes. 2005;29(suppl):116 – 26. 22. Goran MI, Hunter G, Nagy TR, Johnson R. Physical activity related energy expenditure and fat mass in young children. Int J Obes. 1997;21:171– 8. 23. Kohl HW, Fulton JE, Caspersen CJ. Assessment of physical activity among children and adolescents: a review and synthesis. Prev Med. 2000;31(suppl):54 –77. OBESITY Vol. 15 No. 3 March 2007

693


24. Moore LL, Nguyen UDT, Rothman KJ, Cupples LA, Ellison RC. Preschool physical activity level and change in body fatness in young children. The Framingham Children’s Study. Am J Epidemiol. 1995;142:982– 8. 25. Janz KF, Levy SM, Burns TL, Torner JC, Willing MC, Warren JJ. Fatness, physical activity, and television viewing in children during the adiposity rebound period: the Iowa Bone Development Study. Prev Med. 2002;35:563–71. 26. Salbe AD, Fontvieille A, Harper IT, Ravussin E. Low levels of physical activity in 5-year-old children. J Pediatr. 1997; 131:423–9. 27. Trost SG, Pate RR, Freedson PS, Sallis JF, Taylor WC. Using objective physical activity measures in youth: how many days of monitoring are needed? Med Sci Sports Exerc. 2000;32:426 –31. 28. Centers for Disease Control and Prevention. Body mass index-for-age percentiles: Boys, 2 to 20 years 5th, 10th, 25th, 50th, 75th, 85th, 90th, 95th percentiles. http://www.cdc.gov/ nchs/data/nhanes/growthcharts/set2/chart%2015.pdf (Accessed February 2004). 29. Centers for Disease Control and Prevention. Body mass index-for-age percentiles: Girls, 2 to 20 years 5th, 10th, 25th, 50th, 75th, 85th, 90th, 95th percentiles http://www.cdc.gov/ nchs/data/nhanes/growthcharts/set2/chart%2016.pdf (Accessed February 2004). 30. Fairweather SC, Reilly JJ, Grant S, Whittaker A, Paton JY. Using the Computer Science and Applications (CSA) activity monitor in preschool children. Pediatr Exerc Sci. 1999;11:413–20. 31. Freedson PS, Sirard J, Debold E, et al. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc. 1997;29(suppl):45. 32. Dowda M, Pate RR, Sallis JF, Freedson PS. Accelerometer (CSA) count cut points for physical activity intensity ranges in youth. Med Sci Sports Exerc. 1997;29(suppl):72. 33. Must A, Tybor DJ. Physical activity and sedentary behavior:

694


34.

35.

36.

37. 38.

39.

40.

41.

42.

a review of longitudinal studies of weight and adiposity in youth. Int J Obes. 2005;29(suppl):84 –96. Commonwealth of Massachusetts, Department of Public Health. WIC Program: Massachusetts Nutrition Assistant (CPA 1) Training Program, Anthropometric Assessment. Boston, MA: Commonwealth of Massachusetts, Department of Public Health; 2001. Metallinos-Katsaras E, Pearson A, Taft Bayerl C, Gallivan P. Massachusetts PNSS and PedNSS Demonstration Sites: Data Quality and Expansion Project [Final Report submitted to CDC], 2000, Boston, MA. World Health Organization Expert Committee. Report of WHO Expert Committee. Physical Status: The Use and Interpretation of Anthropometry [WHO Technical Report Series 854]. Geneva, Switzerland: World Health Organization; 1995. Barlow SE, Dietz WH. Obesity evaluation and treatment: expert committee recommendation. Pediatrics. 1998;102:E29. Berkowitz BI, Stallings VA, Maislin G, Stunkard AJ. Growth of children at high risk of obesity during the first 6 years of life: implications for prevention. Am J Clin Nutr. 2005;81:140 – 6. Montgomery C, Reilly JJ, Jackson DM, et al. Relation between physical activity and energy expenditure in a representative sample of young children. Am J Clin Nutr. 2004;80: 591– 6. Willett W, Stampfer M. Implications of total energy intake for epidemiologic analyses. In: Willett W, ed. Nutritional Epidemiology. 2nd ed. New York: Oxford University Press; 1999, pp. 273–301. Sirard JS, Trost SG, Dowda M, Pate RR. Calibration of the computer science applications (CSA) activity Inc. physical activity monitor in preschool children. Med Sci Sports Exerc. 2001;5(suppl):144. National Association for Sports and Physical Education. Active Start: A Statement of Physical Activity Guidelines for Children Birth to Five Years. Reston, VA: NASPE; 2000, pp. 1–26.