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The aim of this study was to examine whether adiposity level is associated with subsequent physical activity (PA) level in childhood.
Study participants were 326 children participating in the Iowa Bone Development Study. PA and fat mass were measured using accelerometers and dual energy X-ray absorptiometry (DXA) at approximately 5, 8, and 11 years of age. Data for relevant variables such as parents’ education and PA levels and family income were also collected. Gender-specific generalized linear models were fit to examine the association between percent body fat (BF%) at age 8 and intensity-weighted moderate- to vigorous-intensity PA (IW-MVPA) at age 11.
After adjusting for IW-MVPA at age 8, the interval between the age 5 and 8 examinations, residualized change scores of BF% and IW-MVPA from age 5 to 8, and mother’s education level, BF% at age 8 was negatively associated with IW-MVPA at age 11 among boys (P < 0.05). After adjusting for IW-MVPA at age 8, physical maturity, and family income, BF% at age 8 was negatively associated with IW-MVPA at age 11 among girls (P < 0.05). Categorical data analysis also showed that the odds of being in the lowest quartile relative to the highest quartile of IW-MVPA at age 11 for boys and girls with high BF% at age 8 were approximately four times higher than the odds for those with low BF% at age 8 (P < 0.05).
This study suggests that adiposity level may be a determinant of PA behavior. Specific intervention strategies for overweight children may be needed to promote PA.
Physical inactivity is known to increase adiposity level in children. However, the relationship between physical activity (PA) and adiposity may be bi-directional. That is, not only may physical inactivity be a contributor to body fat gain, but adiposity status may influence PA behaviors (this has been referred to as the reverse causation (9, 18) hypothesis). Although cross-sectional studies have demonstrated a negative association between PA and adiposity levels in children (15), a review of prospective studies (28) concluded that low levels of baseline PA were only weakly or not at all associated with body fat gain. It is conceivable that the reported negative cross-sectional relationship may be due to a reduction of PA as a consequence of a high level of adiposity.
A high level of adiposity may negatively influence PA participation by children, presumably through psychological, societal, and physical functioning, such as low self-efficacy, poor body image, fear of being teased by peers, low athletic proficiency, and discomfort from heaviness. There is some evidence that obese youth are likely to be less active because of weight stigma (4). Storch et al. (25) showed that peer victimization among overweight youth is linked to lower levels of PA. Faith et al. (7) also showed that weight criticism during sports and PA is associated with negative attitudes about sports and lower participation in PA among overweight students. Must and Tybor (18) have suggested that, given the fact that PA in children often occurs as part of organized sport, overweight children may be less likely to want to participate in PA, either due to the fear of being teased or because they are “less athletic.”
Five prospective adult population-based cohort studies (3, 6, 17, 20, 29) consistently demonstrated that obesity is negatively associated with PA level later in life. To our knowledge, no study to date has explicitly addressed the reverse causation hypothesis in children. Testing this reverse causation hypothesis may provide evidence to promote PA at an early age before excess body fat is accumulated. If obesity leads to a reduction in PA later in life, specific intervention strategies for overweight children to promote PA would be warranted. The aim of this study was to examine whether adiposity level is associated with subsequent PA level in childhood.
Study participants were a cohort of children participating in the Iowa Bone Development Study which is an ongoing longitudinal study of bone health during childhood and adolescence. The study participants are a sub-set of Midwestern children recruited during 1998 to 2001 from a cohort of 890 families then participating in the Iowa Fluoride Study. Detailed information about the study design and demographic characteristics of participants can be found elsewhere (10, 13, 14). Accelerometer and dual energy X-ray absorptiometry (DXA) measurements were conducted three times per child at approximately 5, 8, and 11 years of age (4.3 to 6.8 years of age at the first examination, 7.6 to 10.8 years at the second examination, and 10.5 to 12.4 years at the third examination). If the time interval between accelerometer measurement and DXA scanning was greater than 1.5 years for any age examination, the data for that examination were excluded. Four hundred thirty-six children completed both accelerometer and DXA examinations at the age 5 examination conducted between February 1998 and November 2000; 502 at the age 8 examination (September 2000 to December 2004); and 454 at the age 11 examination (October 2003 to September 2006). Five hundred seventy-seven children (51% girls) completed at least one examination and 326 (56% girls) completed all three examinations. Those 326 children (95% white) served as the study sample for this report. We examined the association between adiposity at age 8 and PA at age 11, so that data obtained at the age 5 examination could be used to account for the preceding three-year change in PA and adiposity (20). The study was approved by the University of Iowa Institutional Review Board (Human Subjects). Written informed consent was provided by the parents of the children and assent was obtained from the children.
At the age 5 and 8 examinations, whole body scans were conducted using a Hologic QDR 2000 DXA (Hologic, Waltham, MA) with software version 7.20B in the fan-beam mode. At the age 11 examination, the Hologic QDR 4500A DXA (Delphi upgrade) with software version 12.3 and fan-beam mode was used for scan acquisition. Quality control scans were performed daily using the Hologic phantom. To adjust for the difference between the two DXA machines, translational equations from 4500A DXA measures to 2000 DXA measures for age 11 records were used. The translational equations (linear regression equations) were developed specifically for the two scanners in a methodological study where 60 of the children (32 boys, 28 girls) aged 9.9 to12.4 years (mean = 11.4 years, SD = 0.4 years) were scanned on each machine in random order during one clinic visit (13). Fat mass (kg) was derived from the DXA scan images. Percent body fat (BF%) was calculated as fat mass (kg) divided by body weight (kg).
Actigraph uniaxial accelerometers (model number 7164, Pensacola, FL) were used to measure PA level. The procedure for PA measurement has been described elsewhere (11, 12). Accelerometer movement counts were collected in a one-minute interval (one-minute epoch). At the time of the age 5 and 8 examinations, children were asked to wear the monitor during waking hours for four consecutive days, including one weekend day, during the fall season (September through November). At the time of the age 11 examination, they were asked to wear the monitor during waking hours for five consecutive days, including both weekend days during the fall season. In the accelerometer data reduction process, an interval of 20 or more consecutive minutes of zero accelerometer counts was considered as not wearing the monitor and invalid data (5). The two inclusion criteria for accelerometer data were having valid data for more than eight hours per day and wearing the monitor for three or more of the days. Intensity-weighted moderate- to vigorous-intensity PA (IW-MVPA) was defined as the daily sum of accelerometer counts derived during moderate- to vigorous-PA (MVPA) determined by 3,000 or greater accelerometer movement counts per minute (22, 26).
At each DXA visit, research nurses trained in anthropometry measured the child’s height and weight. Sitting height was measured at age 11 to calculate maturity offset (year from peak height velocity) using predictive equations established by Mirwald and colleagues (16). The equations were developed in white Canadian children and adolescents and they have been cross-validated in another Canadian sample and a Flemish sample (16). To estimate physical maturity status, the maturity offset variable was dichotomized as pre-peak height velocity (pre-mature) or post-peak height velocity (mature).
Family income and parental education level data were obtained from a mailed family demographic questionnaire completed by each child’s parents in 2007. Family income level was dichotomized into less than $40,000/year and $40,000/year or more for data analysis. Education levels of mothers and fathers were also dichotomized into some college or lower and college graduate or higher. The modified Baecke Physical Activity Questionnaire (2) was administered to the child’s mother and father at the child’s age 8 examination. The questionnaire was one of the most widely used tools for assessing PA in adults at the time of data collection (19, 21). It was developed to evaluate a person’s PA in three domains: work activity, sports activity, and leisure activity. Sports activity and leisure activity estimated by the questionnaire were used to determine PA levels of parents.
Gender-specific analyses were conducted using SAS version 9.2 (Cary, NC). Descriptive analyses, including frequency distributions and estimation of summary descriptive measures, were conducted. The age 11 IW-MVPA outcome variable was not normally distributed and a Box-Cox power transformation (labeled ‘transformed IW-MVPA’) was performed using the SAS TRANSREG procedure. Bivariate analyses were performed to identify a set of covariates included during the model development process; Pearson correlation coefficients between transformed IW-MVPA at age 11 and continuous covariates were estimated, and two-sample t-tests for IW-MVPA at age 11 and categorical covariates were performed. Potential covariates were considered and if the P-value was less than 0.10, the variable was considered for inclusion in the final model. The BF% and IW-MVPA data from the age 5 examination were used to account for the changes in BF% and IW-MVPA between the first two examinations (ages 5 and 8). Because the error terms of BF% at ages 5 and 8 were not independent (autocorrelated), the BF% residualized change score variable, which was defined as the residual from regressing the yearly change in BF% from age 5 to age 8 on BF% at age 8, was created using the SAS AUTOREG procedure (23). The IW-MVPA residualized change score variable was created in the same manner.
Gender-specific generalized linear models were fit using the SAS GENMOD procedure. The main exposure variable was BF% at age 8 and the main outcome variable was transformed IW-MVPA at age 11. Models included covariates which could possibly confound a relationship between IW-MVPA and BF% based on bivariate analysis results. Several nested models were fit to examine how a parameter estimate for the exposure variable behaved when each additional covariate was included one by one. The likelihood ratio test was performed to compare the fit of the nested models. After model fitting, model diagnostics were conducted. For categorical data analysis, BF% level at age 8 was dichotomized into low BF% (< 25% for boys and < 32% for girls) or high BF% (≥ 25% for boys and ≥ 32% for girls) (24, 30). IW-MVPA at age 11 was divided into tertiles. Odds ratios and their 95% confidence intervals (CIs) were estimated to examine associations between BF% levels at age 8 and IW-MVPA levels at age 11, using logistic regression analysis models. These models included the same set of covariates as final generalized linear models. The significance level was set at 0.05.
Table 1 presents means and 95% CIs of study variables. Fat mass increased a mean of approximately 5.1 kg for boys and 5.3 kg for girls from age 8 to 11. Participants, on average, wore accelerometers for more than 12 hours per day. IW-MVPA was higher in boys than in girls. IW-MVPA tended to increase with age among boys, but not among girls. Table 2 shows results from descriptive analyses of potential categorical covariates and t-tests for the significance of the mean difference of IW-MVPA at age 11 between categories of those potential covariates. Approximately 13% of the study sample reported family income lower than $40,000/year. Approximately two-thirds of parents reported college graduate or higher education levels. At age 11, all boys were classified as pre-peak height velocity (pre-mature), whereas 20% of girls were classified as post-peak height velocity (mature). In t-test results, IW-MVPA at age 11 was positively associated with mother’s education level and father’s education level for boys (P < 0.10). IW-MVPA at age 11 was positively associated with family income and negatively associated with maturity for girls (P < 0.10).
Based on the Box-Cox transformation, the IW-MVPA variable at age 11 was square root-transformed for boys (λ = 0.5) and log-transformed for girls (λ = 0). Pearson correlation coefficients for transformed IW-MVPA at age 11 and BF% at age 8, and transformed IW-MVPA at age 11 and potential covariates are presented in Table 3. A significant negative correlation between transformed IW-MVPA at age 11 and BF% at age 8 was observed in both boys and girls (P < 0.05). IW-MVPA at age 8 was significantly positively correlated with transformed IW-MVPA at age 11 in both boys and girls (P < 0.05). Transformed IW-MVPA at age 11 was positively correlated with the time interval between the age 8 and 11 examinations and negatively correlated with residualized change scores of BF% and IW-MVPA only among boys (P < 0.10). Age, mother’s PA, and father’s PA were not significantly correlated with transformed IW-MVPA at age 11 for either boys or girls. Ultimately, for boys, IW-MVPA at age 8, the interval between the age 8 and 11 examinations, residualized change scores of BF% and IW-MVPA, and mother’s education were selected as covariates in the final model. Because of a modest association between mother’s education level and father’s education level (kappa = 0.37, P for chi-square test < 0.0001), only mother’s education level was included. For girls, IW-MVPA at age 8, family income, and physical maturity were selected as covariates in the final model.
Several nested models were fit to predict transformed IW-MVPA at age 11 based on BF% at age 8. Because full models provided a better fit to the data than reduced models in both boys and girls based on the likelihood ratio test, the full models are presented in Table 4. Two boys and two girls identified as outliers in model diagnostics were excluded from the final model. After adjusting for covariates, BF% at age 8 was significantly negatively associated with IW-MVPA at age 11 among boys (P < 0.05). After adjusting for covariates, BF% at age 8 was significantly negatively associated with IW-MVPA at age 11 among girls (P < 0.05).
In categorical data analysis, 23% of boys and 26% of girls were identified as having high BF% (≥ 25% BF for boys and ≥ 32% BF for girls) at age 8. In a fully-adjusted logistic regression model (Table 5), boys and girls with high BF% at age 8 were more likely to be in the lowest tertile of IW-MVPA at age 11 than their counterparts with low BF% at age 8 (OR: 4.38, 95% CI: 1.05, 18.24 for boys; OR: 4.48, 95% CI: 1.35, 14.85 for girls, reference group: the highest tertile of IW-MVPA at age 11).
The aim of this study was to examine whether adiposity level is associated with subsequent PA level in childhood (the reverse causation hypothesis). This study found that, in continuous data analysis, BF% at age 8 was negatively associated with IW-MVPA at age 11 in both boys and girls. Categorical data analysis also showed that boys and girls with high BF% at age 8 were more likely to have low PA levels at age 11 than those with lower BF% age 8. These findings are consistent with those of five adult cohort studies (3, 6, 17, 20, 29), where obesity was a significant predictor of PA level later in life. However, the current study results are inconsistent with Sallis et al. (23), who reported no association between skinfold thickness category at baseline and total activity accelerometer counts measured for one day at a 20-month follow-up among 732 children who were fourth graders at baseline.
In additional analysis, we examined the association between early adiposity and later PA using other PA indicators. BF% at age 8 was significantly negatively associated with total activity (the daily sum of ≥ 100 accelerometer counts per minutes) at age 11 in both boys and girls (P < 0.05). When time spent in MVPA (Time MVPA) was used as a PA indicator, the negative association between Time MVPA at age 11 and BF% at age 8 was significant among girls (P < 0.05) and suggestive among boys (P < 0.10). Considering that there was a significant negative association between BF% at age 8 and IW-MVPA at age 11 among boys, these results may imply that boys with low BF% are more likely to engage in higher intensity PA than those with high BF%. From an energy expenditure perspective, IW-MVPA was expected to better represent PA level than Time MVPA in establishing a relationship between adiposity and PA. PA intensity may be critical particularly for boys. However, researchers should be cautious because the use of IW-MVPA may amplify measurement error, which is derived from the difference of relative intensities between individuals when absolute intensity is given.
The current study findings indicate that adiposity status may be a determinant of PA behavior in childhood. Godin et al. (8) suggested a theoretical model of the reverse causation hypothesis based on Ajzen’s Theory of Planned Behavior (TPB) (1). According to this model, adiposity may impact PA behaviors by influencing cognition such as intention (motivation) and perceived behavioral control (ease or difficulty in engaging in the behavior, e.g., social barriers). Although we assumed that obesity-related psychological, societal, and physical functioning may negatively influence PA participation, we were not able to examine whether these are mediating factors in an association between early adiposity and subsequent PA because those potentially mediating variables were not assessed in the Iowa Bone Development Study.
The study results suggest at least two significant points in terms of public health implications. First, this study suggests that a new perspective is needed to best develop intervention strategies to promote PA and to prevent obesity in children. This study suggests weight status-specific intervention strategies for PA promotion, since overweight children may have different barriers to PA participation. Given the childhood obesity epidemic, it would be critical to identify specific barriers for overweight children and develop strategies to overcome these barriers. Second, the study results support PA promotion interventions from an early age, before excess fat is accumulated. Once excess fat is accumulated in early childhood possibly due to a low level of PA, it may lead to low PA participation. In turn, lack of PA may exacerbate fat accumulation. This assumption is supported by Valerio’s study (27) showing that overweight and obese children 7 years of age had a higher BMI increase at three-year follow-up than normal-weight children. PA interventions from an early age are recommended to prevent excess fat accumulation throughout childhood and later in life.
Several limitations of this study should be acknowledged. We only included data from those who completed all three examinations. Loss to follow-up may have caused selection bias; but, IW-MVPA and BF% levels at age 8 were not significantly different between those who completed all three examinations and those who did not. The study sample was not randomly selected from Iowa Fluoride Study participants; which could also have led to selection bias. Therefore, an association between early adiposity and subsequent PA observed in this sample may not represent that in the general child population, and caution should be taken in generalizing the results. In the participant cohort, approximately 95% were white, which is a lower risk population for childhood obesity than the Hispanic or African American population. However, homogeneity of ethnicity and living-environment can be an advantage because unknown confounders are less likely to exist. Genetic predisposition was not considered. This observational study cannot eliminate error introduced by residual and unmeasured confounding factors.
Nonetheless, to our knowledge, this study is the first prospective cohort study in a fairly large childhood sample to explicitly examine the reverse causation hypothesis. The use of objective and accurate measures for both PA and adiposity helped reduce measurement error and increase internal validity. Examinations at three time points allowed accounting for the preceding changes in adiposity and PA between the first two examinations.
In conclusion, this study showed that children with low adiposity were more likely to be active at three-year follow-up than their counterparts with high adiposity. Adiposity may be a determinant of PA behavior in childhood. Regarding future research, more evidence should be accumulated to support the reverse causation hypothesis in childhood. Research is required to understand the mechanism underlying the effect of adiposity status on PA behaviors. It would be valuable to test the hypothesis that obesity-related psychological, societal, and physical functioning are mediating factors in an association between early adiposity and subsequent PA, using existing datasets containing PA, adiposity, and related psycho-societal measurement data.
This study was supported by the National Institute of Dental and Craniofacial Research R01-DE12101 and R01-DE09551, and the General Clinical Research Centers Program from the National Center for Research Resources, M01-RR00059. We gratefully acknowledge and thank the children, parents, and staff of the Iowa Fluoride Study and the Iowa Bone Development Study, because without their contributions, this work would not have been possible. Results of the present study do not constitute endorsement by ACSM.
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