It is proposed that in this project reference will be made to the growth charts developed by the US Centre for Disease Control (CDC data). Other similar reference charts have been developed in other countries such as those by the International Obesity Force Task (IOTF). IOTF have formulated BMI centiles charts for children with specific centiles designated to classify obesity, overweight and thinness (Cole et al., 2000). These charts have been widely
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used, but they might not be suitable for use with this study population because even though they were derived from 6 countries, they were based mostly on western populations (Caroli et al., 2007). Also, in order to compare this study’s results with other studies in Saudi Arabia, the CDC charts were chosen. CDC charts have been used more commonly in Saudi Arabia.
The CDC charts have been used, developed and refined for over 40 years and (although they are mainly intended for use in the US) they enable some comparisons between countries and between different research projects. The growth charts comprise of a series of percentile curves that illustrate the distribution of selected body measurements in children. The charts are not intended to be used as a sole diagnostic instrument; rather they are statistical tools that adjust raw weight and height measurements to take account of the body changes that occur with age, and they contribute to forming an overall clinical impression for the child being measured.
Because of the different rates of growth for boys and girls specific charts have been developed for each. They consist of two separate reference lists; one chart showing stature- for-age and weight-for-age, the other referencing BMI-for-age. They show the 3rd, 10th, 25th, 50th, 75th, 90th, and 97th smoothed percentile lines for all charts and the 85th percentile for BMI-for-age and weight-for-stature. Percentiles are commonly used statistical indicators to assess the relative size and children’s growth patterns. An individual’s measurements are plotted and ranked by percentiles, thus indicating where the individual lies in relation to the population of the same age and sex.
Z scores can also be used to evaluate and monitor a child’s growth. A Z-score is principally calculated from the distribution of the reference population. It is “the deviation of the value for an individual from the mean value of the reference population divided by the standard
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deviation for the reference population” (CDC, 2009). Z-scores have a direct relationship with percentiles and so there is a Z-score corresponding to each percentile (Table 3.1). Z scores form a continuous scale including negative values (those below the median) and positive values (those above the median). For example, a Z score of 1 corresponds to the 84th percentile, and -1 to the 16th percentile. An advantage of using Z scores is that they can be used as a continuous variable in analysis and can better quantify growth at the extremes of the distribution (Wang and Chen, 2012).
Table 3.1: List of percentile z-score conversions values.
Percentiles z-scores 0.2nd –3 2.3rd –2 2.5th –1.96 5th –1.64 15th –1.04 16th –1 50th (median) 0 84th +1 85th +1.04 95th +1.64 97.5th +1.96 97.7th +2 99.8th +3
(Wang and Chen, 2012)
A research project which endorsed BMI as a measure of physical change was conducted by Cole et al. (2005). The researchers sought to identify the optimal method of measuring adipose change in growing children across the spectrum of adiposity. Their main focus was on the various methods of applying height/weight ratios and they assessed four versions of calculating BMI: that is, BMI, BMI %, BMI z-score, and BMI centile. Using these different computations the authors tracked the weight and height changes of 135 children for a period of nine months. They reported that even though the BMI z-score was optimal for
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assessing adiposity on a single occasion, it was not necessarily the best scale for measuring change in adiposity for individuals over time because the variability depended on the child's level of adiposity. Their conclusion was that better alternatives are BMI itself or BMI%.
Despite its general acceptance and widespread application BMI is not a useful measure in all situations, critics pointing out that it does not account for extremes of muscle mass, for some rare genetic disorders, the very young, and some individual variations. For instance, an adult with a BMI of less than 25 may still have excess body fat, and others may have a BMI that is above 25 though without much body fat. Moreover, being overweight is not automatically an indicator of ill-health or of health risks. Some of the non-anthropometric methods for determining body fat may be more accurate for individual cases but they come with added complexity.
Another criticism of BMI concerns its inability to reflect the changing health of an individual as a result of change in physical condition or lifestyle. For example Ross and Janiszewski (2008) point out that an individual may adopt a physically active lifestyle, along with a balanced diet so that body fat might be reduced and muscle increased. But as a consequence BMI may appear little changed and this may be interpreted as a failure, resulting in the disappointed individual resuming an inactive lifestyle and unhealthy eating patterns. They argue that weight loss or changes in BMI are not absolutely necessary to observe substantial health benefit from a healthy lifestyle, it being well-established that increased physical activity and associated improvement in cardiorespiratory fitness are associated with marked reductions in coronary heart disease and related mortality independent of weight or BMI.
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In support of their comments on the limitations of BMI, Ross and Janiszewski (2008) also stress that exercise is linked with substantial reduction in cardiometabolic risk factors (such as 20 % improvement in insulin resistance after 1 hour of exercise, 10- 25 % reduction in triglycerides, and increasing about 7-15 % in high-density lipoprotein (HDL)), although it was not accompanied with any change in body weight. Authors also noted that abdominal fat and waist circumferences can be significantly reduced after regular exercise with minimal weight loss. Fat mass reduction often takes place simultaneously with equivalent increase in muscle mass in response to daily exercise for at least 8–16 weeks (Josse et al., 2011). Equal but opposite changes which are not detected by alterations in body weight, and thus BMI remains unchanged.