INTRODUCTION AND PURPOSE
Over the past several decades, there has been an alarming increase in the prevalence of childhood obesity.1–4 The National Health and Nutrition Examination Survey of 2007 to 2008 revealed that 21.2% of young children aged 2 to 5 years are overweight or obese.4 Short-term health consequences in children who are obese include asthma, chronic low-grade systemic inflammation, diabetes, cardiovascular risk factors, and psychological issues such as low self-esteem.5 Long-term consequences of childhood obesity include adverse effects on socioeconomic status, persistence of obesity into adulthood, premature morbidity, and cardiovascular disease.6 In addition, numerous orthopedic complications of childhood obesity, including Blount disease, spinal dysfunction, and an increased risk of fracture, have been reported.7 To the best of our knowledge, few studies have reported difficulties with gross motor development as a health consequence of obesity.
A few authors examined the relationship obesity has with gross motor skill,8–16 yet only one group examined this connection in the 3- to 5-year-old age range.8–16 These studies provide preliminary indication that a negative correlation between body mass index (BMI) and gross motor development may exist; however, future studies are necessary to further clarify the relationship between BMI and gross motor development in children 3 to 5 years of age. The purpose of this study was to (1) investigate the correlation between BMI and gross motor skills in children and (2) determine whether BMI could be used to predict gross motor difficulties.
A sample of convenience of children 3 to 5 years old from a community daycare center in southern New Hampshire was recruited for participation in this study. Recruitment flyers describing the study and procedures involved were sent home with all children enrolled in the daycare center. Inclusion criteria included 3- to 5-year-old children who were healthy, developing typically, and enrolled in day care, while exclusion criteria included history of medical, orthopedic, or neurological conditions that could affect performance in gross motor skills. Each child's parents provided informed consent before data collection. This study was approved by the University of Indianapolis Institutional Review Board.
Data collection occurred during normal school hours at the center in an area designated for physical therapy. Each child was tested individually, and testing lasted for 30 minutes.
To measure height, the children removed their shoes and were instructed to “stand up tall and look straight ahead” while they were measured to the nearest millimeter using a portable stadiometer. Weight was measured in kilograms using a digital floor scale (calibrated each day according to the manufacturer's instructions). Children wore light clothing (no coats or sweaters) when weights were taken. Body mass index was calculated using the height and weight measurements by the kg/m2 formula. Body mass index scores were then translated into age- and gender-specific percentiles per Centers for Disease Control and Prevention (CDC) growth charts.3
The gross motor sections of the Peabody Developmental Motor Scales 2nd edition (PDMS-2)15 were administered to each participant individually. The PDMS-2 is a standardized norm-referenced test for children from birth to 6 years that measures both fine and gross motor development but also can be used to assess either fine motor or gross motor skills alone, if required. Gross motor scores (quotients) range from 35 to 165 and are divided into categories very poor to very superior based on the gross motor quotient. The PDMS-2 has been shown to have good reliability with an intraclass correlation coefficient of r = 0.84 to r = 0.94.17
Two physical therapists, with more than 40 years' combined pediatric experience, completed the testing. Each had used the PDMS-2 extensively in practice and was actively involved in educating physical therapist students in the use of the tool. In addition, before testing, the therapists reviewed the PDMS-2 together, item by item, and set up the testing area in an attempt to maximize interrater consistency.
Descriptive statistics including age, gender, height, weight, BMI, BMI percentiles, and gross motor quotients (GMQ) categories were calculated for the sample. A Kolmogorov-Smirnov test of normality was performed to determine whether continuous variables exhibited a normal distribution. Body mass index scores were dichotomized into 2 groups according to BMI percentiles as defined by the CDC growth charts3 to be considered at the overweight and obese levels; namely, BMI set one (1): nonoverweight or nonobese, and BMI set two (2): overweight or obese. In addition, GMQ of the PDMS-2 were categorized into predetermined levels (categories) per the PDMS-2 manual17 and coded with numbers ranging from 1 to 7: very poor = 1, poor = 2, below average = 3, average = 4, above average = 5, superior = 6, and very superior = 7. A Pearson chi-square statistic test was used to determine the strength of the relationship between the 2 variables: BMI sets and GMQ categories of the PDMS-2. We also examined the association between BMI and the continuous measure of gross motor quotient, using a Person correlation coefficient. Stepwise linear hierarchical regression analysis was used to further examine the relationship between BMI and gross motor development, specifically to determine predictive power of the independent variables. Body mass index set was added in the first step of the model followed by age and gender to determine how these variables would affect the model. Gross motor quotient was entered as the dependent variable. SPSS version 16 (IBM corporation, Chicago, Illinois) was used for data analysis. An α value of 0.05 or less was considered to be statistically significant.
One hundred twenty recruitment flyers were sent out the last week of February 2009, followed by letters of informed consent. Fifty-seven letters of informed consent were returned for a response rate of 47.5%. Of the 57 children with signed consent forms, 7 were ineligible because they did not meet the inclusion criteria. This left 50 subjects for data analysis, a 41.6% participation rate.
Data collection took place during a 3-week period in March 2009. Of the 50 subjects, 26 were male (52%) and 24 were female (48%). A Kolmogorov-Smirnov test demonstrated that age (P = .26), BMI (P = .41), BMI percentile (P = .40), and GMQ (P = .90) all exhibited a normal distribution. Ages ranged from 36 to 70 months with a mean age of 53 months (SD = 10.5). Body mass index scores ranged from 13.5 to 24.8 with a mean of 16.1 (SD = 1.8), and BMI percentiles ranged from 1% to 99% with a mean of 57.7% (SD = 28.8).
On the basis of the CDC guidelines, 1 subject was considered underweight (2%), 37 were of healthy weight (74%), 8 were considered overweight (16%), and 4 were in the obese category (8%) (see Table 1). When dichotomized into BMI sets, 76% were in the nonoverweight/obese category and 24% were in the overweight/obese category.
On the PDMS-2, GMQ scores ranged from 81 to 111 with a mean of 94.84 (SD = 6.77). GMQ categories ranged from 3 to 5 with a mean of 3.76 (SD = 0.47). Thirteen subjects (26%) scored in the below-average category (GMQ category 3), 36 subjects (72%) scored in the average category (GMQ category 4), and 1 subject (2%) scored in the above-average category (GMQ category 5). No child scored in the very poor (GMQ category 1), poor (GMQ category 2), superior (GMQ category 6), or very superior categories (GMQ category 7).
Seven of the 12 subjects (58%) in the overweight/obese group (BMI set 2) scored in the below-average category on the PDMS-2 compared with 6 of the 38 subjects (15%) in the non overweight/obese group (BMI set 1). One subject, in the nonoverweight category, had a BMI percentile of less than 5% (underweight per CDC growth charts) and scored in the below-average category. The only subject scoring in the above-average category was classified in the overweight/obese category.
The Pearson chi-square statistic identified a significant correlation between BMI sets and GMQ category (P = .002). The Pearson correlation coefficient examining BMI and the continuous measure of gross motor score was not significant (P = .165). Nonsignificant correlations between GMQ and age (−0.041) and GMQ and gender (−0.025) were also identified. Stepwise hierarchical regression analysis demonstrated that neither gender nor age and nor the addition of BMI sets made a significant contribution to the linear regression model.
The purpose of this study was to investigate the relationship between overweight and obesity and gross motor development in children who are developing typically, and to determine whether BMI could predict difficulties in gross motor skill development. In a group of 50 children developing typically aged 3 to 5 years from a daycare center in southern New Hampshire, 24% were determined to be either overweight (16%) or obese (8%). This is similar to national and international statistics (21.2%), demonstrating the alarming trend in childhood obesity. A statistically significant relationship between high BMI at the overweight/obese level and low gross motor skills according to the PDMS-2 was identified with P < .002. This relationship suggests that as BMI levels increase (to the overweight and obese categories), gross motor skills decline. A higher percentage of children in the overweight and obese categories (58%) scored in the below-average range on the PDMS-2 gross motor test compared with children who were not overweight (15%). Children with below-average scores may be described as uncoordinated or clumsy, having difficulty with balance and movement, even if the delay is mild.17 Below-average abilities in gross motor skills may place children at a disadvantage with respect to their peers who score in the average range when participating in group activities such as recess or physical education that may necessitate program accommodation or modification for them. In addition, children with lower gross motor ability may lack confidence or motivation that could further hinder their participation, causing them to refrain from physical activity. This may lead to a more sedentary lifestyle and further weight gain. Having all 3 factors—high BMI, low gross motor skills, and low physical activity levels—could make weight reduction difficult.
Similar to the findings of Jaffe and Kosakov,8 these results demonstrate that children who are overweight or obese are more likely to experience lower gross motor skills than those who are not. The current study, unlike the Jaffe and Kosakov8 study, examined children aged 3 to 5 years. The gross motor skills for this age group are particularly important, as they focus on refining and improving basic motor milestones developed at earlier ages and are foundational for higher-level skills. Unlike many previous studies that examined the relationship between individual components of gross motor skill and BMI, the current study used the PDMS-2 as the gross motor outcome measure. Both of these outcome measures (individual component and whole composite) have value and assist health care providers in understanding difficulties children may have with movement. Despite the use of different outcome measures, data from the previous study8 and the current study yield similar results that children who are overweight or obese experience difficulties with gross motor skill, either as individual components or as a whole. The difficulties with component skills may be associated with or explain the lower total gross motor scores, as the PDMS-2 tool provides a composite score of the combination of gross motor abilities including stationary, locomotor, and ball manipulative skills. For example, McGraw et al10 found that children who were overweight had more difficulty with balance and stability in gait, which would most likely affect their ability to complete other higher-level gross motor tasks such as running, whereas Southall11 and Habib13 found locomotor skills to be negatively affected in children who were obese. Riddeford-Harland et al12 found strength impairment in the lower extremities to be more prevalent in children who were overweight, which affected their ability to rise from sitting to standing and to jump. Although they did not examine the full functional outcome of strength impairments on gross motor developmental level, having decreased lower extremity strength would theoretically make it difficult to complete gross motor skills. D'Hondt et al14 demonstrated that fine motor skills can also be negatively affected by high BMIs, as they found that children who were overweight had more difficulty placing pegs in a pegboard in varied positions. Included in the gross motor testing of the PDMS-2 are ball manipulation tasks that require the use of hand skills while the body is in different postural configurations. Because this combination of component skills examined in these earlier studies8,10–14 is required for many sports and recreational activities, the use of the PDMS-2 may, in some cases, be more meaningful for childhood participation in sports and gross motor activities.
While a relationship was found between overweight/obesity and gross motor skills in this study, it must be emphasized that this is a cross-sectional study of a small sample and the results do not infer causality between BMI and gross motor skills. In addition, the study sample had a limited range of GMQ scores with subjects only falling within 3 of the 7 categories. Whereas this may be related to the small sample size, it may also be due to the curriculum provided by the particular daycare (amount of daily physical activity, instruction in gross motor skills, etc) to which the children in the sample would have been exposed. Perhaps other variables, not examined in this study, could help to further understand the relationship between BMI and motor skill.
Research has shown that physical activity level is 1 variable that has been demonstrated to affect motor skill. In a cross-sectional study of 137 obese children, aged 5 to 9, Morgan et al18 examined the association between levels of physical activity and levels of motor proficiency. Significant positive correlations (r = 0.46 and r = 0.49 with P < .001) were seen between gross motor skills and moderate intensity and vigorous physical activity levels in boys. Girls also demonstrated a significant positive correlation, but with less strength of the association (r = 0.35 with P < .05). In addition, an inverse relationship was seen between age and the level of physical activity. In another study examining the relationships between BMI, motor skills, and activity levels, Graf et al19 also found low, but significant correlations between physical activity and gross motor skills (r = −0.164 with P < .001). Williams et al15 also found low, but significant correlations between motor skills and levels of physical activity in 3- and 4-year-old children. Finally, Wrotniak et al20 found that children with higher levels of physical activity performed better on gross motor skills, as assessed using the Bruininks-Oseretsky Test of Motor Proficiency (BOTMP). In their study, 34 girls and 31 boys aged 8 to 10 years wore accelerators that monitored activity level, a negative correlation (r = −0.308 with P = .012) was seen between sedentary activity and performance on the BOTMP, while a positive correlation (r = 0.329 with P = .008) was seen between moderate-intense activity and BOTMP. This finding is particularly relevant to the current study as it also examined an age group that has not been studied extensively. Our study did not use physical activity level as a variable, which might have shed additional insight into the results.
The evidence reported in the studies described earlier, along with the evidence from this current study, demonstrates that the variables (BMI, motor skills, and physical activity levels) are associated with each other, but it is not clear whether one alone will lead to or predict a change in the other.
Other factors may explain the variance unaccounted for in gross motor skill level in children with high BMI in our study. For instance, socioeconomic demographics were not examined and may have an influence on children's motor development. Future research is needed to investigate other potential variables.
The fact that the only subject who scored in the above-average gross motor category was in the overweight/obese group should also be considered. Outliers, which do not fall within the range of most of the scores (>2 SD from the mean), may cause distortion of the statistical analysis. Thus, we performed a regression analysis with this outlier excluded. Without this outlier, regression results became significant (P = .018), indicating that BMI had predictive power for gross motor skill with the outlier excluded. There may be multiple explanations for why this child scored well in motor skills despite his high BMI. He could have possibly been from a very physically active family or have been involved in sports. This would coincide with the studies correlating physical activity with gross motor skill. In addition, this child's BMI was at the 86th percentile, which just put him into the overweight category by one percentile. A final reason this subject may have scored high may be the fact that he was at the upper end of his age group for the PDMS-2 data tables and this may have given him an advantage as he was compared with younger children who were in the same group.
As health care professionals who are experts in the field of gross motor development, and practitioners who assist in efforts of health promotion and prevention, the findings of this study have important clinical relevance for physical therapists. Despite the unanswered questions that remain concerning the relationship between obesity and gross motor development, physical therapists do have a potential role in preventing or treating childhood obesity. Intervention or consultation is possible in any of the 3 areas identified: motor skills, physical activity, and/or obesity education. For instance, physical therapists could begin by examining children for motor delay, levels of physical activity, and BMI in their local schools or clinics. In 2003, Arkansas legislature passed Act 1220, which charged the state to coordinate efforts to fight childhood obesity, which led to statewide screening of BMI in the public school system.20 As other states pass similar mandates, physical therapists could participate by taking anthropometric measurements and computing BMIs as was performed in our study. In 2000, the Institute of Medicine of the National Academies' report “Preventing Childhood Obesity: Health in the Balance” suggested that health insurance companies should designate obesity screening and prevention programs as priorities.21 Physical therapists could be leaders in assisting in this initiative by educating and informing insurance companies and legislators about obesity research and the associations between BMI, motor skills, and activity levels. In addition to these efforts, physical therapists could develop gross motor and physical activity programs for school, community, and individual use as both rehabilitative and preventative measures.
To understand the relationship between obesity and gross motor development, more research is needed. Future studies should involve larger sample sizes including larger numbers in each of the BMI categories. Examination should include motor skills of children not only in the overweight and obese categories but also in the underweight category. Additional variables such as physical activity levels and socioeconomic status should also be considered. Physical activity programs should consist of 30–60 minutes of moderate to vigorous activity.22
In a small sample of 3- to 5-year-old pre–school-aged children, high BMIs were associated with low gross motor skills, indicating that children who are overweight/obese are more likely to score in the below-average category while lean children score in the average category. Physical therapists can and should participate in efforts to assist in preventing the devastating effects of childhood obesity.
Special thanks to Jennifer Parent-Nichols, MSPT, for her assistance in data collection.
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