Physical activity and determinants of physical activity in obese and non-obese children

Abstract

OBJECTIVE: To compare the physical activity (PA) patterns and the hypothesized psychosocial and environmental determinants of PA in an ethnically diverse sample of obese and non-obese middle school children.

DESIGN: Cross-sectional study.

SUBJECTS: One-hundred and thirty-three non-obese and 54 obese sixth grade children (mean age of 11.4±0.6). Obesity status determined using the age-, race- and gender-specific 95th percentile for BMI from NHANES-1.

MEASUREMENTS: Objective measurements were collected of PA over a 7-day period using the CSA 7164 accelerometer: total daily counts; daily moderate (3–5.9 METs) physical activity (MPA); daily vigorous physical activity (≥6 METs; VPA); and weekly number of 5, 10 and 20 min bouts of moderate-to-vigorous physical activity (≥3 METs, MVPA). Self-report measures were collected of PA self-efficacy; social influences regarding PA, beliefs about PA outcomes; perceived PA levels of parents and peers, access to sporting and/or fitness equipment at home, involvement in community-based PA organizations; participation in community sports teams; and hours spent watching television or playing video games.

RESULTS: Compared to their non-obese counterparts, obese children exhibited significantly lower daily accumulations of total counts, MPA and VPA as well as significantly fewer 5, 10 and 20 min bouts of MVPA. Obese children reported significantly lower levels of PA self-efficacy, were involved in significantly fewer community organizations promoting PA and were significantly less likely to report their father or male guardian as physically active.

CONCLUSIONS: The results are consistent with the hypothesis that physical inactivity is an important contributing factor in the maintenance of childhood obesity. Interventions to promote PA in obese children should endeavor to boost self-efficacy perceptions regarding exercise, increase awareness of, and access to, community PA outlets, and increase parental modeling of PA.

Introduction

The prevalence of obesity among US children and adolescents is increasing at an alarming rate. In the 10 y between the second (1976–1980) and third (1988–1991) administration of the National Health and Nutrition Examination Survey (NHANES), the prevalence of obesity among children (6–11 y) and adolescents (12–17 y) (based on age- and gender-specific 95th percentile body mass index (BMI) cut-off points) increased from 6 to 10.7% and 4.8 to 10.7%, respectively.1 Recent reports confirm similar increases in the prevalence of obesity among preschoolers and infants.2,3 The rising trend in pediatric obesity represents a critical public health problem. Obese children and adolescents are at increased risk for adult obesity4 and are more likely than their lean counterparts to experience significant short-term health problems such as hyperlipidemia, hypertension, glucose intolerance and orthopedic complications.5,6 Moreover, the adverse social consequences of childhood and adolescent obesity may have long-lasting negative effects on self-esteem, body image and economic mobility.6,7

Lack of physical activity is hypothesized to be an important contributing factor in the development and/or maintenance of childhood obesity.8,9 However, previous studies on the relationship between physical activity and adiposity in youth have produced inconsistent results. While some studies show overweight status or adiposity to be inversely related to physical activity participation, others report no association.10,11,12 Such discrepant findings may, in part, be related to the difficulties associated with obtaining valid and reliable measures of physical activity in children.13,14 To date, a wide range of methods have been used to measure physical activity behavior in lean and obese children. These include self-report questionnaires, direct observation, heart rate monitors, motion sensors such as accelerometers and pedometers, and doubly-labeled water (DLW). However, due to their low cost and ease of administration, self-report methods are the most commonly used method. This is problematic, given that self-reports are subject to considerable recall bias and have limited validity and reliability among children.13,14,15,16

To avoid the limitations of self-report, a relatively small number of investigations have utilized heart rate monitoring (HRM) and doubly-labeled water (DLW) to examine weight-related differences in physical activity and/or energy expenditure. However, these measures have limitations as well. With respect to heart rate monitoring, it is widely recognized that the relationship between heart rate and energy expenditure is influenced by a large number of factors including age, body size, emotional stress, temperature and cardiorespiratory fitness.15,17 Additionally, because heart rate tends to lag behind changes in movement and tends to remain elevated after the cessation of movement, heart rate monitoring may mask the intermittent activity patterns of children.17 The DLW technique provides accurate assessments of total energy expenditure over extended time periods (1–3 weeks), but provides no information on the frequency, intensity and duration of physical activity.18 Moreover, estimates of physical activity energy expenditure are highly dependent on accurate assessments of resting metabolic rate and dietary induced thermogenesis.19

Accelerometers that detect and record the magnitude of movement on a real-time basis have the potential to overcome some of the limitations of HRM and DLW. They provide reliable information about the frequency, duration and intensity of physical activity within a given day or over several days.18,20 Moreover, their small size and robust design features make them suitable for use in field-based studies involving moderate to large numbers of children and adolescents.17,18 To date, however, very few studies have utilized accelerometers to assess obesity-related differences in physical activity, and none have used contemporary models that assess physical activity patterns (ie bouts of activity) over time.

Knowing the ‘determinants’, or the factors that influence physical activity, in obese youth is an important prerequisite to designing effective intervention strategies for this population.21 Presently, however, our knowledge of the psychosocial and environmental factors that influence physical activity behavior in obese children is limited. Previous studies have identified physical activity self-efficacy, beliefs and social norms related to physical activity, involvement in community-based physical activity organizations, access to equipment at home and parental physical activity as factors associated with, or predictive of, physical activity behavior in children and adolescents.14,22 However, to our knowledge, the extent to which these variables differ in obese and non-obese children has not been previously studied. If obesity-related differences in physical activity can be linked to differences in specific determinants of physical activity behavior, these determinants can then be targeted for change in intervention programs designed specifically for the needs of overweight children.

The purpose of this study was to compare the physical activity patterns and the hypothesized psychosocial and environmental determinants of physical activity behavior in an ethnically diverse sample of obese and non-obese middle school children. In contrast to previous studies comparing activity levels or energy expenditure in obese and lean children, we utilized a state-of-the-art accelerometer to objectively measure both the quantity and intensity of physical activity over a 7 day period.

Methods

Subjects

Subjects for this study were 213 sixth grade students from four randomly selected public middle schools in Columbia, South Carolina. The proportion of African-American students attending these schools ranged from 41 to 55%, while the percentage of females ranged from 45.3 to 51.4%. Between 42.9 and 82.7% of the students attending these schools were eligible to receive free or reduced priced lunches. Students participated in the study on a voluntary basis and were recruited from required physical education classes. The initial study group was 51.6% female, 55.9% African-American, with a mean age of 11.4±0.6. After deletions for incomplete physical activity data (n=15) and missing height and weight (n=11), the final sample consisted of 187 students (98 females, 89 males). The descriptive statistics for this group (52.4% female, 55.6% AfricanAmerican, mean age of 11.4±0.6) indicated that the demographic characteristics remained unchanged by the exclusion of these participants. Prior to participation in the study, written informed consent was obtained from each student and his or her primary guardian. The study was approved by the University of South Carolina Institutional Review Board.

Anthropometric measures

Height and weight assessments were conducted in a private setting with students dressed in light clothing. Height was measured to the nearest 1.0 cm using a portable stadiometer. Weight was measured to the nearest 0.2 kg with a standard physician's beam scale (Detecto). Body mass index (BMI) was calculated as body weight in kilograms divided by height in meters squared (kg/m2).

Weight status

Consistent with recent population surveys,23 participants were classified as obese if their BMI was equal to or greater than the sex-, race- and age-specific 95th percentile from the first National Health and Nutrition Examination Survey (NHANES-1).24,25 Using this criterion, the number of participants classified as obese and non-obese was 54 and 133, respectively.

Measurement of physical activity

Instrumentation.

Objective assessments of physical activity behavior were obtained using the Computer Science and Applications Inc. (CSA) 7164 activity monitor (Shalimar, Florida). Briefly, the CSA 7164 is a uniaxial accelerometer designed to detect vertical acceleration ranging in magnitude from 0.05 to 2.00 G with frequency response of 0.25–2.50 Hz. These parameters allow for the detection of normal human motion and will reject high frequency vibrations encountered in activities such as operation of a lawn mower. The filtered acceleration signal is digitized and the magnitude is summed over a user-specified epoch interval. At the end of each epoch, the summed value or activity ‘count’ is stored in memory and the integrator is reset.26 For the current study, a 1 min time interval was used. Trost and co-workers27 recently assessed the validity and inter-instrument reliability of the CSA 7164 activity monitor in children aged 10–14. CSA activity counts were strongly correlated with energy expenditure during treadmill walking and running (Pearson r=0.86). The intraclass correlation for two CSA 7164 monitors worn simultaneously was 0.87, indicating a strong degree of inter-instrument reliability.

Protocol.

Students were outfitted with a single CSA 7164 activity monitor during their regularly scheduled physical education class. Consistent with previous studies, monitors were attached to adjustable elastic belts and worn over the right hip. After receiving detailed instructions regarding the care and use of the monitors, students were instructed to wear the CSA monitor during the waking hours for 7 consecutive days. At the time of distribution, students were given a 7 day log sheet to record the times the monitor was worn and to provide information about participation in non-weight bearing activities such as swimming, cycling and weight training. Upon removal of the activity monitor the following week, stored activity counts were downloaded and saved to a personal computer for subsequent data reduction and analysis.

Data reduction.

Minute-by-minute activity counts were uploaded to a QBASIC data reduction program written by the primary author for determination of time spent in moderate (3–5.9 METs) and vigorous (≥6 METs) physical activity during each 60 min segment of the 7 day monitoring period. The age-specific count ranges corresponding to the above intensity levels were derived from the energy expenditure prediction equation developed by Freedson et al:28

 METs=2.757+(0.0015×counts/min)

−(0.08957×age (y)

−(0.000038×counts/min×age (y)

In an independent sample of 80 children and adolescents aged 6–18 y, this equation accounted for 90% of the variance in observed MET levels and predicted energy expenditure during treadmill running and walking within ±1.1 METs. The correlation between predicted MET level and observed MET level was 0.86.28

Daily totals for participation in moderate physical activity (MPA) and vigorous physical activity (VPA) were calculated by summing the MPA and VPA totals from the 60 min time blocks between 9 am and 9 pm The 12 h time interval was selected to replicate previous monitoring studies29,30,31 and to control for individual differences in time spent wearing the monitors. None of the participants were involved in sports practices or physical education classes prior to 9:00 am. MPA and VPA scores recorded for each day of the monitoring period were averaged to produce an estimate of usual VPA and MPA. Students with less than 7 days of complete monitoring data (≥100 000 counts on each monitoring day) were excluded from the analyses. We have previously shown that 7 days of monitoring provides reliable estimates of daily participation in MVPA among middle school students.32

To evaluate the contribution of ‘non-monitored’ activities to overall activity level, physical activity scores were calculated with and without the inclusion of self-reported participation in swimming, cycling and weight training. Inclusion of these data resulted in no changes to the mean MPA and VPA scores and were not included in the analyses.

To examine patterns of physical activity behavior (ie continuous bouts), an additional program was run to calculate the weekly number of 5, 10 and 20 min bouts with physical activity at an intensity greater than or equal to 3 METs (MVPA) Within the 20 min bouts, students were permitted a ‘break in the action’ or interruption interval of 2 min. That is, during a 20 min bout, counts were permitted to drop below the set cut-off for up to two consecutive minutes. For the 10 min bout, counts were permitted to drop below the set cut-off for 1 min. No interruption interval was permitted in the calculation of 5 min bouts.

Determinants of physical activity

Students completed a questionnaire designed to measure hypothesized demographic, psychosocial and environmental determinants of physical activity. The determinant variables were selected from Social Cognitive Theory33 and the Theory of Reasoned Action/Planned Behavior.34 The questionnaire was administered in a classroom setting by the primary author who read the items to students using a standardized script. At each administration, an assistant moved around the classroom to answer any questions and check for students who had problems. Prior to data collection, the questionnaire was pilot tested to ensure that the reading level and response format was appropriate for sixth grade students.

Psychosocial variables.

Hypothesized psychosocial determinants of physical activity included physical activity self-efficacy, social norms regarding physical activity, and beliefs regarding physical activity outcomes. The physical activity self-efficacy, social influence and belief scales were modeled on the measurement scales developed by Saunders et al.35 Responses to each item were recorded on three-point scales (yes, no and not sure). A brief description of these scales and their associated reliability coefficients (Cronbach's α) is provided in Table 1.

1 Scales used to measure hypothesized psychosocial determinants of physical activity

Environmental variables.

Students completed a series of single items designed to measure hypothesized environ-mental determinants of physical activity behavior. Consistent with Bandura's concept of the physical and social environment,33 these included perceived physical activity of parents and friends, access to sporting and/or fitness equipment at home, involvement in community physical activity organizations, participation in community sports teams, and self-reported hours spent watching television or playing video games. These items were modified from measures used in the National Children and Youth Fitness Study36 and the 1990 Centers for Disease Control and Prevention's Youth Risk Behavior Survey.37 The psycho-metric properties of these measures have been reported elsewhere.38,39

Statistical analysis

All statistical analyses were conducted with SAS (version 6.12). Group differences with respect to age, height, weight and BMI were tested using independent t-tests. Differences in the percentage of females and African American students in the non-obese and obese groups, respectively, were tested using a χ2 test. Group differences on the physical activity and continuous determinant variables were tested using a one-way ANCOVA with sex and race/ethnicity serving as covariates. Differences in the categorical determinants variables were tested using a Mantel–Haenszel χ2 analyses. Statistical significance was set an α level of 0.05.

Results

Descriptive statistics for the obese and non-obese participants are shown in Table 2. Relative to their lean counterparts, obese children were significantly heavier and more likely to be African-American (P<0.05). No significant group differences were observed for age, height, or percentage female.

2 Descriptive statistics for obese (n=54) and non-obese (n=133) sixth grade children

Figure 1 shows the means (±s.e.) for total daily counts, daily minutes of MPA, and daily minutes of VPA. Compared to non-obese children, obese children exhibited significantly lower total counts per day (28.3×104±2.01×104 vs 37.7×104±1.41×104; P=0.003); daily participation in MPA (62.6±4.5 vs 78.2±3.2 min/day; P=0.002); and daily participation in VPA (7.1±1.3 vs 13.5±0.9 min/day; P=0.001).

1
figure1

Movement counts and estimated daily participation in moderate and vigorous physical activity in 133 non-obese and 54 obese children.

Figure 2 shows the mean (±s.e.) number of 5, 10 and 20 min bouts of MVPA per week. Relative to their non-obese counterparts, obese children exhibited significantly fewer 5-min bouts (15.9±1.8 vs 23.4±1.3; P=0.001), 10 min bouts (8.7±1.1 vs 12.6±0.7, P=0.002), and 20 min bouts (3.9±0.6 vs 5.8±0.4, P=0.009) of MVPA over the 7 day monitoring period.

2
figure2

Weekly frequency of objectively measured 5, 10 and 20 min bouts of MVPA in 133 non-obese and 54 obese children.

Group differences with respect to the hypothesized psychosocial and environmental determinants of physical activity are reported in Table 3. Compared to non-obese youth, obese children reported significantly lower levels of physical activity self-efficacy, were involved in significantly fewer community organizations promoting physical activity, and were significantly less likely report their father or male guardian as physically active.

3 Means and percentages for the determinant variables in obese (n=54) and non-obese children (n=133)

Discussion

Our findings are consistent with the hypothesis that physical inactivity is an important contributing factor in the maintenance of childhood obesity. Relative to their non-obese counterparts, obese children exhibited significantly lower daily accumulations of moderate and vigorous physical activity and participated in significantly fewer continuous 5, 10 and 20 min bouts of moderate-to-vigorous physical activity. Consistent with these observations, we found significant obesity-related differences in several key socialcognitive determinants of youth physical activity behavior.

A major strength of this study was the use of an objective monitoring device to quantify both the quantity and intensity of physical activity. This is an important consideration, as young children have difficulty recalling their past behavior accurately.15,16 Using an established algorithm of converting CSA accelerometer counts to units of absolute energy expenditure (METs),22 we were able to assess daily participation in both moderate and vigorous physical activity. Furthermore, because the CSA 7164 samples and stores data on a real-time basis, we were able to evaluate weight-related differences in weekly number of short, medium, and long bouts of moderate-to-vigorous physical activity. The consistency of our findings across all six physical activity variables, coupled with our success in obtaining 7 days of complete monitoring data in a relatively large sample of free-living children, reinforces the view that objective measurement devices such as accelerometers are useful assessment tools in the study of childhood obesity.

Previous studies using motion sensors to assess physical activity have failed to observe significant differences between obese and non-obese children. Romanella et al,40 observed no significant differences between obese and non-obese children after assessing physical activity for two consecutive days with a Caltrac accelerometer. Similarly, Wilkinson et al41 reported no significant differences between obese and non-obese 12-y-old boys after measuring physical activity with a pedometer on a single day. The discrepancy between our findings and those of previous studies may, in part, be attributable to the small sample sizes of earlier studies, differences in the definition of obesity, inferior activity monitoring technology, and the length of the monitoring period. In support of the latter point, Rowlands et al42 reported significant inverse correlations between physical activity and percentage body fat after 6 days of monitoring with the TriTrac-R3D accelerometer. In contrast, correlations based on just one day of monitoring were substantially lower and failed to reach statistical significance.

An important finding of the present study was the significant difference between obese and non-obese children with respect to physical activity self-efficacy. This indicated that, within our sample of sixth grade students, children classified as obese were significantly less confident in their ability to overcome barriers to physical activity, ask parents to provide opportunities for physical activity, and choose physically active pursuits over sedentary ones. According to social-cognitive theory,33 self-efficacy perceptions are derived from four principle sources of information: past performances, vicarious experiences (modeling); verbal persuasion; and physiological state. Therefore, to increase perceptions of physical activity self-efficacy among obese children, physical activity intervention programs should: (1) provide enjoyable, developmentally appropriate activities than enable overweight children to experience success (ie emphasize moderate intensity activities such as walking); (2) create opportunities for obese youth to observe influential others (eg parents and peers) perform physical activity; (3) verbally encourage children to participate in physical activity (ie ‘you can do it’); and (4) reduce any anxiety associated with participation in physical activity by significantly reducing or eliminating competition or grading from planned activities.

Previous studies have demonstrated parental physical activity to be a salient predictor of physical activity in normal-weight children.14,22 Few studies, however, have examined parental physical activity in obese and non-obese children. Fogelholm et al,43 studied the physical activity differences in obese and non-obese children as well as parent–child associations of obesity and physical activity. Consistent with the results the present study, obese children exhibited significantly lower habitual physical activity scores than non-obese children. Parental physical activity was strongly and positively correlated to child physical activity; however, in contrast to our own findings, parental activity level was not associated with child obesity status. This difference is most likely attributable to our use of the child's perception of parental physical activity level rather than actual assessments of parent physical activity behavior. While the significance of perceptions should not be discounted,44 future studies investigating parent–child associations in physical activity and obesity status should employ objective measures of physical activity in both parents and children.

Some limitations of this study warrant consideration. First, the cross-sectional nature of this study design precluded us from inferring a causal relationship between physical activity and obesity status. Thus, the lower activity profile exhibited by the obese children relative to their non-obese counterparts may have been the consequence of obesity and not the cause of it. Second, although we were alarmed to find that approximately 30% of our sixth grade sample could be classified as obese, the modest sample size of our obese group precluded our ability to conduct sex-specific analyses. Third, because of logistic concerns, our study included a relatively small number of determinant variables and did not collect data directly from parents. Therefore, future studies should expand the list of determinants to include physical self-perception variables such as social physique anxiety and body attractiveness; and more comprehensive measures of parental and peer support/ encouragement for physical activity.

In summary, compared to non-obese children, obese children exhibited significantly lower daily accumulations of moderate and vigorous physical activity and participated in significantly fewer 5, 10 and 20 min bouts of moderate-to-vigorous physical activity. Obese children reported lower levels of physical activity self-efficacy, were involved in fewer community organizations promoting physical activity, and were less likely to report their father or male guardian as physically active. These findings suggest that efficacy perceptions regarding exercise, awareness of, and access to, community physical activity outlets, and parental modeling of physical activity are potential targets for physical activity intervention programs involving overweight children.

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Correspondence to SG Trost.

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Trost, S., Kerr, L., Ward, D. et al. Physical activity and determinants of physical activity in obese and non-obese children. Int J Obes 25, 822–829 (2001). https://doi.org/10.1038/sj.ijo.0801621

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Keywords

  • exercise
  • inactivity
  • overweight
  • accelerometry
  • objective monitoring

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