The Concerns of Obesity in Children and Young People
- Pages: 66
- Word count: 16293
- Category: Obesity
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This paper focuses on childhood and adolescent obesity; physical activity; sedentary behaviours; the relationship between physical activity, sedentary behaviours, and obesity; and other correlates for obesity in childhood. The paper includes issues related to measurement, developmental trends, and predictors particularly of physical activity and sedentary behaviours In addition, this comprehensive study investigates inter-relationships between different health behaviours and obesity, including the relationships between physical activity and sedentary behaviours, as well as interaction effects between physical activity and sedentary behaviours on obesity. Therefore, the current study will add knowledge about complicated relationships between obesity and obesity related health risk behaviours and provide some implications for future interventions for obesity.
Despite steady progress over most of the past century toward ensuring the health of the children, the 21st century begins with a startling setback: an epidemic of childhood obesity. This epidemic is occurring in boys and girls worldwide, in younger children as well as adolescents, across all socioeconomic strata, and among all ethnic groups. At a time when it has been learned that excess weight has significant and troublesome health consequences, we nevertheless see the population, in general; and children, in particular; gaining weight to a dangerous degree and at an alarming rate (IOM 2005). As a consequence, dietary guidance for children has broadened from an earlier focus on issues of underconsumption and nutrient deficiencies to overconsumption and decreased energy expended in physical activity.
Children nowadays live in a society that has changed dramatically in the three decades over which the obesity epidemic has developed. Several of these changes, such as both parents working outside of the home and the availability of convenience foods often affect decisions about what children eat, how much they eat and the amount of energy they expend in school and leisure-time activities. Many of the social and cultural characteristics that the population has accepted as a normal way of life may collectively contribute to the growing levels of childhood obesity (IOM 2005).
Obesity is associated with numerous health complications, and can cause difficulty in many organ systems. Factors contributing to obesity are wide-ranging. Health complications previously diagnosed in adults are steadily increasing in children (Troiano & Flegal 1998). Symptoms associated with overweight and obesity include glucose intolerance, headaches, hip pain or limp, abdominal pain, short stature or growth arrest, depression, and elevated blood pressure (Dietz & Robinson 2005).
Despite the plethora of research, constant media attention regarding the pervasiveness of childhood obesity, and efforts to develop prevention focused strategies; this problem continues to be one for which there is no defined, universal plan of action. Successful interventions are subject to appropriateness, access, commitment, involvement, available resources, and the capacity for implementation (IOM 2005). Because of the multidimensional nature of childhood obesity, no one group can effectively address this issue. Collaborative effort is required to produce measurable outcomes that demonstrate these interventions can make a meaningful difference in childhood obesity prevention. Childhood obesity is a multifactorial problem with a multifactorial solution (IOM 2005).
Furthermore, the cost of obesity and complications attributable to this condition are staggering. Data on the harmful effects of obesity on health quality of life and social interactions in children are emerging as well. The known and potential causes of childhood obesity are many, but can be categorised as genetic, endocrine, prenatal/early life, physical activity, dietary, and socioeconomic. There are a multitude of factors acting on these seemingly simple variables. These factors influence the basic equation contributing to childhood obesity: energy input greater than energy output. Imbalances in this equation can result in obesity equals weight gain. It seems that gaining a foothold in the battle against obesity has never been more important (Strock et al. 2004).
This paper focuses on childhood and adolescent obesity; physical activity; sedentary behaviours; the relationship between physical activity, sedentary behaviours, and obesity; and other correlates for obesity in childhood. The paper includes issues related to measurement, developmental trends, and predictors particularly of physical activity and sedentary behaviours In addition, this comprehensive study investigates inter-relationships between different health behaviours and obesity, including the relationships between physical activity and sedentary behaviours, as well as interaction effects between physical activity and sedentary behaviours on obesity. The paper will finally presents some conclusive remarks.
Basically, excessive energy intake compared to less energy expenditure can cause obesity (Aronne & Segal 2002). However, obesity does not have a simple cause. Inter-relationships between genetic, metabolic, environmental, and lifestyle factors result in obesity (Baur 2002). Health-related behaviours, including physical activity, sedentary behaviours, and eating habits, are important modifiable risk factors. Physical inactivity is a well-known risk factor for the development of obesity. It has been accepted that increased physical activity is related to decreased risk for being obese in youth (Dencker et al. 2006; Janssen, Katzmarzyk, Boyce, King, & Pickett 2004; Kimm et al. 2005). However, the relationship between intensity of physical activity (i.e. moderate and vigorous intensity of activity) and obesity is not clear yet. Some researchers stated that moderate physical activity is negatively related to obesity (Eisenmann, Bartee, & Wang 2002). Others argued that moderate to vigorous is beneficial to reduce obesity (Ekelund et al. 2004; Hernandez et al. 1999). Others suggested that only vigorous activity has beneficial effects (Abbott & Davies 2004; Patric et al. 2004; Ruiz et al. 2006).
Sedentary life style, which involves low energy expenditure, is becoming a pervasive behaviour in children and adolescents (Livingstone, Robson, Wallace, & McKinley 2003). The representative sedentary behaviours, including TV viewing, playing video games, and computer use, have also been reported as risk factors for obesity in youth (Berkey et al. 2000; Hancox & Poulton 2006; Tremblay & Willms 2003). Still, controversial results exist according to the measurement of sedentary behaviours. A summed screen time (hours of TV viewing plus video and computer use) has been presented as a significant risk factor for obesity (Berkey et al. 2000; Utter, Neumark-Sztainer, Jeffery, & Story 2003).
On the other hand, when the relationships between each behaviour and obesity were investigated, inconsistent results have been reported. While some studies have found a positive relationship between frequent video game play and obesity and between computer use and obesity (Crooks 2000; Tremblay & Willms 2003), others have reported that digital game play did not have significant effects on obesity (Giammattei, Blix, Marshak, Wollitzer, & Pettitt 2003; Kautiainen, Koivusilta, Lintonen, Virtanen, & Rimpela 2005; McMurray et al. 2000). In addition, computer use did not show a significant effect on obesity (Giammattei et al. 2003; Hernandez et al. 1999).
Factors related to physical activity and sedentary behaviours have been examined in many studies. Gender, race, age, parental socioeconomic status (SES) and parental activity have been most frequently investigated as predictor (Van Der Horst, Paw, Twisk, & Van Mechelen 2007). The body of research on inter-relationships between physical activity and sedentary behaviours in childhood and adolescence does not provide consistent results. According to previous investigations, while some researchers argue that sedentary behaviours are inversely related to physical activity (Katzmarzyk, Malina, Song, & Bouchard 1998; Marshall, Biddle, Gorely, Cameron, & Murdey 2004; Strauss, Rodzilsky, Burack, & Colin 2001), others claim that active and sedentary behaviours are not associated (Brodersen, Steptoe, Williamson, & Wardle 2005; Grund, Krause, Siewers, Rieckert, & Muller 2001; Parsons, Power, & Manor 2005; Utter et al. 2003). To further complicate the issue, some studies have found a positive relationship between computer use and physical activity (Koezuka et al. 2006; Santos, Gomes, & Mota 2005; Utter et al. 2003). Thus, sedentary behaviours seem to have different contexts. The inter-relationship between physical activity and each sedentary behaviour needs to be investigated.
One of the important underlying factors for understanding obesity is knowledge of eating behaviours. Some studies have found significant relationships between sweetened drinks and fast foods, and obesity (Murray, Frankowski, & Taras 2005; Nicklas, Yang, Baranowski, Zakeri, & Berenson 2003). The Bogalusa Heart Study has also shown that sweetened beverages, snacks, and low quality foods have positive relationships to obesity in young adolescents (Nicklas et al. 2003). On the other hand, many studies have failed to find a significant relationship between obesity and unhealthy eating behaviours, including low intakes of fruits and vegetables, and high intakes of soft drinks, fast foods, and fat (Field, Gillman, Rosner, Rockett, & Colditz 2003; Janssen et al., 2005; Maffeis et al. 2000). However, eating behaviours cannot be excluded in obesity research, because energy imbalance due to excessive energy intake can cause obesity.
Parental influence is one of the strongest risk factors for the development of child obesity. Maffeis et al. (1998) have shown that only parental obesity has significant effects on child obesity, when parental obesity and physical activity of child are included in the same model. Golan and Crow (2004) have suggested that parents play an important role in childhood obesity, in that they provide an environmental context for a child. Parental obesity, activity, and SES are representative parental variables.
Zeller and Daniels (2004) have stated that parental obesity is an important risk factor for childhood obesity, which may be due to sharing genetic and environmental factors. Positive association between parental and child activities have also been reported (Bogaert, Steinbeck, Baur, Brock, & Bermingham 2003; Gilmer et al. 2003; Troiano & Flegal 1998). In addition, parental SES, measured by family income and parental education, has been presented as a significant factor for child obesity (Goodman, Huang, Wade, & Kahn 2003; Langnase, Mast, & Muller 2002). In spite of the strong relationship to child obesity, not many studies have included parental influence in obesity research in youth.
In addition, different determinants of obesity by gender have been found. While sedentary behaviours are more closely related to obesity in girls (Crespo et al. 2001), physical activity is more highly associated with obesity in boys (McMurray et al. 2000). However, not all studies have included these possible moderating variables in their analysis models. Age has been presented as another correlate to physical activity, sedentary behaviours, eating behaviours and obesity. Physical activity decreased with an increase of age (Brodersen, Steptoe, Boniface, & Wardle 2007; Gordon-Larsen, Nelson, & Popkin 2004; Nelson, Neumark-Stzainer, Hannan, Sirard, & Story 2006).
While a group of researchers reported that sedentary behaviours (summed hours of TV, video, and computer use) increased with age in British adolescents (Brodersen et al. 2007), others presented a decreasing trend in time spent in TV and video games with age (Gordon-Larsen, McMurray, & Popkin 1999; Nelson & Gordon-Larsen 2006; Villard, Ryden, Ohrvik, & Stahle 2007). Age also influences the relationship between activity and obesity. Marshall et al. (2004) reported that harmful effects of sedentary behaviours on obesity were greater in young children compared to adolescents. In addition to the relationship to activity, age also relates to eating behaviour. Older children have shown increased fast food intake and increased percentage of energy from fat (Cullen et al. 2004; Schmidt et al. 2005).
To date, few studies have included pubertal status in obesity research. However, distinctive changes in body fat can be found during puberty. Boys show a slight increase in fat accumulation in early puberty and followed by a decrease during adolescence, which seems to be the result of increased muscle development (Johnson, Gerstein, Evans, & Woodward-Lopez 2006). For girls, fat accumulation increases steadily through puberty (Johnson et al. 2006). Hence, pubertal development needs to be included in obesity research in child and adolescent populations.
In terms of obesity measurement, previous studies in children and adolescents have frequently used BMI and skinfold thickness as an obesity index. Not many studies have included waist circumference in children and adolescents. Considering that central adiposity shows a close relationship to cardiovascular disease (Cruz et al. 2005; Haslam & James 2005), assessment of obesity including abdominal obesity index was helpful to broaden our understanding of obesity.
In summary, while increased physical activity and decreased sedentary behaviours have showed beneficial effects on obesity, not many studies have included important contributing factors (i.e., gender, race, age, puberty, eating behaviour, and parental influence) in analyses. Thus, when underlying factors for obesity are considered, the extent of obesity that can be explained by physical activity or sedentary behaviours needs to be investigated. In addition, it is not clear how the intensity level of activities would have beneficial effects on weight status, which has important implications for developing intervention programmes for obesity. Longitudinal relationships between predictors and health behaviours (physical activity and sedentary behaviours) are another topic to be investigated.
More importantly, if physical activity and sedentary behaviours are not related, which mean those behaviours are not in the same continuum of activity, interactions between physical activity and sedentary behaviours on obesity should be examined. That is, studies that examine how physical activity influences obesity in different levels of sedentary behaviours are needed to be examined. Investigation of comprehensive longitudinal relationships between risk factors (physical activity and sedentary behaviours) and obesity will help us understand obesity and produce developmentally suitable intervention programmes for obesity.
Measuring Obesity in Children and Adolescents
The definition of “overweight” and “obesity” is currently based on the standards set by the International Obesity Task Force (IOTF). (Papadimitriou et al. 2006) The non-government organisation based in London collaborated closely with the World Health Organisation as well as other health organisations to develop programmes to help prevent and effectively treat obesity. (Macdiarmid 2002) In a report for the IOTF, in excess of 300 million people are considered obese worldwide. The Body Mass Index (BMI) measures body fat and obesity is on average having a BMI of 30 kg/m2 or more for adults. BMI is calculated by dividing weight (in kilograms) by height (in meters) squared, and expressed as kg/m2. According to the WHO, the normal BMI range for adults is 18.5 to 24.9, while a BMI of less than 18.5 is considered below the normal weight (underweight). Adults in the BMI range of 25 to 29.9 are considered pre-obese while those with BMI values over 30 are considered obese. (International Obesity Task Force 2005)
While the WHO had established international obesity cut off points for adults, such standards have not been established for children and adolescents. Cole et al (2000) developed a table of cut off points to define obesity for male and female children between the ages of 2 to 18 based on growth studies from the Netherlands, Brazil, Great Britain, Singapore, Hong Kong, and the United States (Table 1). Previous to the standards imposed by the IOTF, there were various benchmarks for adults that prevailed in different countries, and no consensus on what the cut-off was for childhood obesity. A 1992 ratios study by Williams et al established that a body fat ratio of 25% classifies a child as overweight for males and 30% for females. A range of 85th to 95th percentile BMI is considered by the Center for Disease Control and Prevention as at-risk for obesity while in Europe researchers considers the 95th percentile as the threshold for childhood obesity (Dehghan et al 2005).
The data was derived from averaging figures from Singapore, Netherlands, Brazil, Hong Kong, Great Britain, and United States (Source: http://www.bmj.com/cgi/reprint/320/7244/1240)
Table 1: The body mass index for overweight and obesity by sex for children between 2 and 18 years old.
The accurate measurement of obesity is critical to assess the status of obesity in each subject and to evaluate the effects of obesity intervention programmes (Field et al. 2003). Frequently used methods for obesity measurement can be divided into relatively direct ways and indirect methods (surrogates of body fat). The former can actually measure the amount of adiposity. These methods include doubly labeled water (DLW), dual-energy X-ray absorptiometry (DXA), computerised tomography (CT), and underwater weighing (hydrodensitometry). However, these expensive methods can not be easily applied in population level studies. Instead, surrogates of body fat, including body weight, BMI, waist circumference, and skinfold thickness, have been used as alternatives for population studies, i.e., 2109 subjects in the study of Harrell et al. (1999) and 1294 subjects in the study of Ekelund et al. (2004).
Surrogates of body fat measures do not assess adiposity directly but estimate it indirectly. Each indicator measures different aspects of obesity. Thus, studies including a variety of surrogate measures are helpful for more comprehensive evaluation and understanding of obesity. Below is a brief description of surrogates of body fat indicator, including BMI, BMI z score, skinfold thickness, and waist circumference.
BMI as a measure of obesity.
While BMI is not a direct measure of adiposity, it has been frequently used as an indicator for obesity because of its convenience and a high correlation to mortality and obesity related disease, such as hypertension, Type 2 Diabetes, and cardiovascular disease risk in adult populations (Aronne & Segal 2002). In addition, researchers have concluded that childhood BMI predicts cardiovascular disease morbidity in later life (Kiess et al. 2001).Therefore, BMI is a useful index for obesity.
However, BMI may not be a good index for fat mass. When BMI was compared to body fat index from DLW in adult Indonesians and Dutch Caucasians, Indonesians showed higher body fat index than Dutch Caucasians with the same BMI (Gurrici, Hartriyanti, Hautvast, & Deurenberg 1998). When analysing body composition indexes from hydrodensitometry, an elevation of BMI during adolescence showed higher correlation to lean mass than fat mass in 387 white adolescents aged 8 to 19 years (Maynard et al. 2001).
Hence, researchers concluded that BMI does not differentiate between lean and fat mass, so muscular subjects can be mistakenly identified as obese (Aronne & Segal 2002). Likewise, reduced BMI may not necessarily mean loss of fat because about a quarter of weight lost is from lean tissue (Prentice & Jebb 2001). Thus, studies including supplemental measures for adiposity as well as BMI are useful as they allow to assess obesity more accurately.
BMI percentile and BMI z score as a measure of obesity. Growth is a distinctive characteristic of children and adolescents. Unlike adults, percentile values from the same age and gender groups provide more appropriate information for youth population. BMI growth charts provide a distribution of BMI change by age and gender in the reference population of those 2 to 20 years of age (http://www.cdc.gov/nchs/about/major/nhanes/growthcharts /background.htm). Age- and gender- specific BMI percentiles indicate where the BMI of a subject is located in percentile rank of the reference populations
(http://www.cdc.gov/nchs/about/major/nhanes/growthcharts/GrowthchartFAQs.htm). For instance, a girl aged 10 years whose BMI is at the 50th percentile indicates that the BMI of the girl is at the median for 10-year-old girls in the reference population.
BMI z score is a standardised value for BMI, which shows how far an individual value is located from a mean or a median value of a reference population. In other words, BMI z score of 1 or 2 means that the BMI of an individual is one or two standard deviations above the mean or the median value of the age and gender specific reference value. According to the CDC, BMI z score and percentile are the same and interchangeable (http://www.cdc.gov/nchs/about/major/nhanes/growthcharts/GrowthchartFAQs.htm). Because BMI z score was developed from BMI, it may have the same weakness as BMI.
Sum of skinfold thickness (SSF) as a measure of obesity.
Skinfold thickness is an index of subcutaneous body fat. It is a measure of thickness of a double layer of skin and can be measured at several body sites (Speiser et al., 2005). It is a more direct measure for body fat than BMI (Sherry & Dietz, 2004). Sum of triceps and subscapular thickness shows an inverse relationship to all-cause mortality in adults (Zhu, Heo, Plankey, Faith, & Allison 2003). Positive relationship between skinfold thickness and cardiovascular risks including blood pressure and lipid levels has been reported in youth (Williams et al. 1992). Therefore, skinfold thickness is a useful measure for fatness.
Empirical evidence has shown that skinfold thickness is better than other surrogate indicators in estimating fat mass. (Piers, Soares, Frandsen, & O’Dea 2000). However, skinfold thickness measures have limitations. First, low inter-rater reliability is a problem (Speiser et al. 2005). Low test-retest reliability is another issue (Speiser et al. 2005). Last, it has been reported that in the case of fat subjects, skinfold thickness is difficult to measure and the value may be less reliable (Speiser et al. 2005). Thus, while skinfold thickness provides an assessment of adiposity, the difficulty in obtaining consistent, reliable measurement makes BMI a more reliable way to assess obesity even though it does not actually measure body fat.
Waist circumference as a measure of Obesity.
Recently, waist circumference (WC) has been a more popular research focus of obesity due to a close association with the Metabolic Syndrome (MS). The MS is a common cluster of risk factors for cardiovascular disease and type 2 diabetes (Burke 2006; Morrison et al. 2005). WC is an indicator of central adiposity (Bray & Bellanger 2006; Speiser et al. 2005). The comparison to a central obesity index measured by a CT showed accurateness of WC in terms of central obesity in adolescents. That is, when BMI and WC were included as predictor variables in the same model, WC significantly predicted central obesity measured with a CT but BMI was not significant (Lee, Bacha, Gungor, & Arslanian 2006). Hence, WC is a meaningful indicator for central obesity.
Trends in Obesity by Age
Increasing trends in obesity with age can be found in many previous studies. According to Project HeartBeat!, which was a longitudinal study of cardiovascular disease risk factors in a total of 678 children and adolescents, BMI and waist circumference for both gender increased from the age of 8 to 19 years (Dai, Labarthe, Grunbaum, Harrist, & Mueller 2002). However, sum of skinfold thickness (SSF) showed different trends; boys decreased and girls increased SSF with age. Similar increasing trend in BMI and waist circumference can be found in a 5-year follow up study in 5,863 adolescents aged 11 to 12 years at baseline (Wardle, Brodersen, Cole, Jarvis, & Boniface 2006).
A group of researchers also reported that BMI increased from the ages of 6-9 to 12-19 years in the sample of 1,302 youth and that a faster increase in BMI was found from the age of 10 to 12 years (Hlaing, Prineas, Zhu, & Leaverton 2001). Similarly, a faster increase in median BMI from the age of 11 to 13 years was also found in a longitudinal study in 4,290 boys and 5,169 girls (Berkey & Colditz 2007). Gender difference in SSF has been found in the literature. SSF increased from the ages of 9-10 to 18-19 years in 2,287 black and white girls from the National Heart, Lung, and Blood Institute’s Growth and Health Study (NGHS) (Kimm et al. 2005). Heude et al. (2006) also presented an increase of SSF with age in French girls. But SSF for boys increased from the age of 5 to 11 years then decreased until the age of 17 years.
No single surrogate of body fat is perfect as a measure of obesity. Clearly, each measure has a significant relationship to health risks related to obesity and it measures different aspects of obesity. For example, skinfold thickness is a sensitive index reflecting subcutaneous fat, waist circumference measures visceral obesity, and BMI is a more reliable indicator of overweight. In addition, while increases in BMI and waist circumference with age and gender difference in SSF have been relatively documented, no research about trend in BMI z score with age was found. Therefore, evaluation of obesity using different surrogates of body fat indexes, including BMI z score, was done to examine obesity with diverse perspectives within the current paper.
Factors Related to Obesity
This section explores eating behaviours, parental influences, gender, and puberty as important factors related to obesity.
Physical activity is an important plausible risk factor for obesity (Crawford & Ball 2002). Investigators have reported that the problem of low activity is deeply rooted from childhood. A 5-year follow-up study from childhood to adolescence showed that physically active girls were more likely to be active during puberty, and inactive boys were more likely to be inactive during adolescence (Janz, Dawson, & Mahoney 2000). A representative longitudinal study also indicated that many adolescents were not physically active and that inactive adolescents became inactive adults (Gordon-Larsen et al. 2004). Hence, empirical data indicate that the problem of low level of physical activity starts from childhood.
A widely accepted definition of physical activity is “any bodily movement produced by skeletal muscles that results in caloric expenditure” (Caspersen Powell, & Christenson, 1985). In other words, physical activity includes every activity that costs energy regardless of the magnitude of energy expenditure. One specific type of physical activity is exercise. Exercise is defined as “physical activity that is planned, structured, repetitive, and results in the improvement or maintenance of one or more facets of physical fitness” (Caspersen et al. 1985). Thus, physical activity is a diverse spectrum of activities from purposeful exercise to any movement that costs energy regardless of a magnitude.
Physical activity can be assessed in four different dimensions: type (aerobic or anaerobic, and occupational, household, or leisure time activities), intensity (low, moderate, or vigorous activity), frequency (how often it is done), and duration (length of time the activity lasted) (Mahar & Rowe 2002). Each physical activity measurement, including direct observation, questionnaires reported from self or proxy, accelerometry and heart rate monitoring, assesses different dimensions of physical activity. For example, while questionnaires can measure all four domains of activity, accelerometer can do only intensity, frequency, and duration (Welk 2002). Frequently used objective measures of physical activity are accelerometry, heart rate monitoring, and the Doubly Labeled Water (DLW) described below. These measures can be used as gold standards for the development and evaluation of a physical activity questionnaire, which is a relatively subjective measure.
Trends in physical activity by age
Decrease in physical activity with age has been well documented. According to Kimm et al. (2002), habitual physical activity measured with questionnaire and accelerometer dropped 21% from the ages of 11-12 to 13-14 years. Vigorous activity also decreased from the ages of 11-12 to 15-16 years, when physical activity was measured as how many days per week subjects participated in vigorous activity (Brodersen et al. 2007). Physical activity, measured with survey and pedometer, decreased from the age of 12 to 17 years in 371 adolescents (Duncan, Duncan, Strycker, & Chaumeton 2007). Hours spent in moderate to vigorous physical activity (MVPA) decreased only for girls in 5-year follow-up study of 806 adolescents aged 11 to 15 years at baseline, (Nelson et al. 2006).
According to the Oxford online dictionary, sedentary is rooted from Latin sedentarius or sedere, and means (1) sitting; seated, (2) tending to sit down a lot; taking little physical exercise, and (3) tending to stay in the same place for much of the time. Therefore, Varo et al. (2003) stated that many researchers have operationalised sedentary behaviours as hours of TV viewing, video game, and computer use. Increasingly children live a sedentary life style spending much time in activities, such as TV viewing, video games, and computer use, that involve low physical activity (Livingstone et al. 2003). A representative sample survey, including 4063 children aged 8 to 16 years, showed that about 26% of children spent more than 4 hours a day in watching TV (Andersen et al., 1998). TV viewing and video games were one of the top 10 most common activities in middle school students (Harrell et al. 2003) as well as elementary school students (Harrell, Gansky, Bradley, & McMurray 1997).
The negative effects of sedentary behaviours on health have been reported. Fung et al. (2000) have found a positive relationship between hours of TV viewing and biological markers of obesity and cardiovascular disease risk, such as leptin, low density lipoprotein and a negative relationship to high density lipoprotein in 468 adult male populations. The survey data from 1999 to 2000 showed that sedentary behaviours were related to the prevalence of the Metabolic Syndrome in 1,626 adults, when measured as time spent in front of TV, video, and computer (Ford, Kohl, Mokdad, & Ajani 2005). A positive association between representative sedentary behaviours, hours of TV viewing, and childhood obesity has also been found (Giammattei et al. 2003; Hancox & Poulton 2006; Wake, Hesketh, & Waters 2003). Questionnaires have frequently been used for the measurement of sedentary behaviours. A frequently used definition of sedentary behaviours is screen time, including TV, video, and computer use.
Trends in sedentary behaviours by age
Not many studies have been done regarding trends in sedentary behaviours with age. An increasing trend were found in summed hours of TV, video, and computer use (2.5 hours per week for boys and 2.8 hours per week for girls) in a 5-year follow up study in 5836 British adolescents aged 11 to 12 years at baseline (Brodersen et al. 2007). On the other hand, decreasing trends with age have been reported. Hours spent in TV and video viewing for girls decreased and no trend was shown in computer use in girls from a 5-year follow up study in middle school students aged 11 to 15 years at baseline (Nelson et al. 2006).
However, boys did not show any trend in TV and video viewing. Instead, hours of computer use for boys increased. There was a decreasing prevalence rate of more than 1 hour per day TV viewing only for girls in the study of Swedish school children from the age of 11 to 13 years (Villard et al. 2007). In addition, they also found a decreasing trend in computer use for both gender. Although it is not longitudinal study, comparisons of screen time (summed hours of TV viewing, video and computer game play) among different age groups also showed a decreasing trend (12-15 years: 23.1, 16-17 years: 20.3 and 18-22 years: 19.8 hours per week) (Gordon-Larsen et al. 1999).
Eating Behaviours and Obesity
Unhealthy eating behaviours have been reported as one of the risk factors for obesity. According to Nielsen et al. (2002), nationally representative data measured during 1977-1978 and 1994-1996 in 63,380 subjects from age 2 and up, shows that total energy intake, snack intake, and frequency of eating-out have increased in all age groups for the last 20 years (Nielsen, Siega-Riz, & Popkin 2002). In particular, consumption of fast foods and soft drinks by youth has rapidly increased (St-Onge, Keller, & Heymsfield 2003). This means that more and more children and adolescents have unhealthy eating behaviours.
Eating behaviours of adolescents are very problematic when compared to the national guidelines. About a half of adolescents did not consume fruits and vegetables compared to the guidelines of at least 5 a day and about 70 percent of them did not eat even 1 serving of a dairy product a day (Story, Neumark-Sztainer, & French 2002). The percentage of energy intake from dietary fat was reported as 40 % in youths (Paulus, Saint-Remy, & Jeanjean 2001).
Conflicting results have been reported regarding relationships between eating behaviours and obesity in youth. Some researchers found no significant relationship between adolescent obesity and unhealthy eating habits, such as low intakes of fruits and vegetables, and high intakes of soft drinks, fast foods, and fat, from descriptive studies (Field et al. 2003; Janssen et al. 2005). According to Maffeis et al. (2000), diet composition was not associated with obesity when adjusting for parental obesity in 530 children aged 7 to 11 years (Maffeis et al. 2000). Data from a comparison study among 137,593 adolescents aged 10-16 in 34 countries data failed to find a significant relationship between obesity and intake of fruit and vegetables, and soft drinks; there was a significant relationship between sweets (candy and chocolate) and BMI (Janssen et al. 2005).
On the other hand, other researchers have reported that poor eating habits are one of the critical risk factors for obesity. A result from an animal experiment was one proof, which showed that high fat diet can induce obesity in rats (Bray, Paeratakul, & Popkin 2004). Descriptive studies found significant associations between unhealthy eating behaviours and obesity in youth: significantly positive relationships between sweetened drinks and fast foods and obesity (Murray et al. 2005; Nicklas et al. 2003). The Bogalusa Heart Study (n=1562 children aged 10 years) showed that sweetened beverages, snacks, and low quality foods (including fats, oils, sweets, and salty snacks) were positively related to obesity in young adolescents (Nicklas et al. 2003). Low BMI was associated with higher intake of vegetable in 210 African American girls aged 8 to 10 years in a cross-sectional study (Cullen et al. 2004).
Effective dietary interventions focusing on healthy eating provide another indirect proof for poor eating habits as a risk factor for obesity. High fruits and vegetables and low fat and sugar intakes produced beneficial effects on reducing body weight (Epstein et al. 2001). Bray and Popkin (1998) concluded in a review paper based on previous 28 clinical trials (mostly adults and small number of sample size) that 10% less fat intake can decrease weight 16 gram/day.
On the contrary, many dietary intervention studies failed to connect change of eating behaviours to health benefits including obesity, while they succeeded in changing unhealthy eating behaviours into healthy ones. According to a review paper based on 21 intervention trials from 1966 to 2001 (intervention for adults), many intervention trials showed positive effects to reduce saturated fat intake and to increase fruit and vegetable intake, but the effects on health outcomes were not clear (Pignone et al. 2003).
In short, it is clear that many children and adolescents have unhealthy eating behaviours. However, it is difficult to clearly identify the relationships between eating behaviours and obesity. This is partly because there are many different types of unhealthy eating behaviours, such as high fat and soft drink intake, low fruit and vegetable intake, frequent eating-out and fast food intake. It is also because objective measurement for eating behaviours is rare. All studies reviewed in this chapter measured eating behaviours with questionnaires or diaries from self-report or proxy (such as parents).
Parental Influence on Obesity
Familial factors have been reported as important risk factors for child obesity. As Baur (2002) explained, obesity is a result of the inter-relation between genetic, metabolic, behavioural and environmental, and lifestyle factors. Family members share genetic, environmental, and lifestyle similarity. Well-known parental factors for child obesity are parental SES, obesity, and activity level.
Parental socioeconomic status and child obesity.
Parental socioeconomic status (SES) has been presented as a risk factor for childhood obesity. SES can be measured with education, occupation, and income. When SES is represented with education level, which is the highest education achieved by mother or father, a significant negative relationship to child obesity has been reported. The highest education level of either mother or father was inversely related to overweight in children in a German sample of children aged 5 to 7 (Langnase et al. 2002). That is, compared with parents with advanced high education, parents had equal to and less than 9 years of education showed much higher odds ratio for being overweight of child. Similar results were found in another German sample in the total of 2631, 5 to 7 years children (Danielzik, Czerwinski-Mast, Langnase, Dilba, & Muller 2004).
Income has also been used for the measurement of SES. In a review paper, Agras and Mascola (2005) stated that family income has a protective effect on childhood overweight. In the sample of 1,871 high school students, subjects from a high income district showed more frequent PE per week and higher frequency of vigorous exercise during PE compared to students from a low income district (Sallis, Zakarian, Hovell, & Hofstetter 1996).
Parental occupation is another indicator for SES. A significantly negative relationship between occupation and obesity has been found. That is, the mean BMI of a professional occupational group was 25.9 and that of an unskilled manual occupational group was 27.2 in an adult female population (Wardle, Waller, & Jarvis, 2002). Similar to income, parental occupation seems to have a protective effect on obesity. According to Tammelin et al. (2003), the prevalence of physical inactivity in children was higher in children with lower status of father’s occupation in 3069 boys at age 14: skilled professional 13.5%, skilled worker 17.1%, unskilled worker 20.4%, and farmer 24.2%.
Parental obesity has been reported as a strong risk factor for obesity in their children. A prospective study in the sample of 150 children, followed up from birth to 9.5 years of age, showed that parental overweight predicted childhood overweight (Agras, Hammer, McNicholas, & Kraemer 2004). In another longitudinal study, which followed a sample of 155 healthy boys and girls from 2 to 20 years of age, standardised BMI score of boys (standard deviation of BMI using age and gender specific BMI percentile curve) was correlated to BMI of the mother and father. For girls, BMI of the father was significantly associated with standardised BMI score of the child, but BMI of the mother was only significantly related to standardised BMI score of the child at age 8 or older (Magarey, Daniels, Boulton, & Cockington 2003).
In addition, a secondary data analysis of a family-based intervention study showed that change of parental BMI z score between pre and post observations significantly predicted change of child BMI z score in 142 families with obese children aged 8 to 12 (Wrotniak, Epstein, Paluch, & Roemmich 2004). In 1350 German children aged 5 to 7 (Langnase et al. 2002), overweight parents showed a higher prevalence of having overweight children (27.9% of child overweight) than parents who were not overweight (normal weight mother: 16.7% of child overweight; underweight mother: 12.8% of child overweight). Hence, close relationships between parental and child obesity have been reported.
Parental activity level
Parental activity may influence child obesity. Although an exact mechanism of how parental activity level is associated with child obesity is not clear, one of the possible pathways is through the relationship between parent and child activities. Parental and child activities, reported by parents, showed a significantly positive correlation among 59 healthy children aged 6 to 9 (Bogaert et al., 2003). According to Gilmer et al. (2003), the activity of 113 children of parents with premature coronary heart disease related to the level of activity of their fathers. Troiano and Flegal (1998) explained the relationship between parent and child activities as shared family environment.
Gender, Health Behaviours and Obesity
Gender is another important demographic variable related to the level of physical activity, which is closely related to obesity. Overall, females show lower physical activity level than males in many investigations. When sedentary was defined as children whose top 3 activities included 2 or more activities with less than 2 or 3 METs, 42.1 to 66.2% of girls were sedentary, while 25.2 to 43.5% of boys were sedentary in the sample of 3rd to 10th graders (Bradley et al. 2000). Tammelin et al. (2004) evaluated physical activity at the ages of 14 and 31 among 5,706 Finnish males and females. At the age of 14, more boys participated in sports daily or every other day than girls.
Caspersen et al. (2000) reported that 16.8% of girls and 10.5% of boys were inactive at age 17 from the analysis of 1992 Youth Health Behaviour Survey. Sallis et al. (1996) also found that time spent in vigorous activity out of school per week for boys was 3.8 hours and for girls was 2.6 hours in 1634 multi-ethnic adolescents populations. In addition, a representative cross-sectional study in the sample of 4063 children aged 8 to 16 years showed that 84.6% of boys did vigorous intensity activity, while 74.5% of girls did (Andersen et al., 1998).
Gender differences can also be found in the relationship between activity and obesity and between sedentary behaviours and obesity. Crespo et al. (2001) presented a significant positive relationship between TV viewing and obesity only for girls, not for boys in 4069, 8 to 16 years of children. Ball et al. (2001) found that physical activity was significantly related to obesity for boys, but not for girls in healthy 106 children. This means that for girls, sedentary behaviours explain obesity better than physical activity. McMurray et al. (2000) also reported that each gender had different predictors for obesity. That is, for males, weight was more closely associated with exercise than with TV or video games, and for girls, video and exercise were not related to obesity, rather SES and ethnicity may be more important. Thus, gender should be included in studies about physical activity, sedentary behaviours, and obesity.
Puberty and Obesity
Puberty is a life transition period, which involves changes in psychological, physiological, and behavioural aspects. In particular, changes of body composition and weight status are associated with pubertal development (Dunger, Ahmed, & Ong 2006). Johnson et al. (2006) describe the patterns of normal growth in young populations. According to them, preschoolers experienced a decrease in BMI and a BMI level rebound during age 4 to 7. During puberty, boys and girls show different growth patterns of adipose cells.
Boys show a slight increase in fat accumulation that is followed by a decrease during adolescence, which seems to be the results of increased muscle development. For girls, fat accumulation increases steadily throughout puberty. In addition, weight status and puberty showed close relationships. Overweight girls begin puberty earlier than others and gain more fat during that period compared to non-obese girls (Biro, Khoury, & Morrison 2006; Johnson et al. 2006). Boys with lower adiposity (more muscle mass) is related to earlier maturation in boys (Biro et al. 2006).
Empirical studies show changes of fat composition in each gender during puberty. McCarthy et al. (2006) reported that adiposity growth curves, derived from the measurement using bio-impedance in the sample of 2085 boys and girls aged 5 to 18 years, were similar in both sex before puberty. However, the curves were different during puberty. That is, boys decreased fatness with maturation and girls gained fatness continuously. Vizmanos and Marti-Henneberg (2000) also found that boys with smaller BMI had earlier onset of puberty and boys with later onset of puberty had greater BMI in the sample of 469 children aged 10 to 15. In contrast, girls did not show significant difference in BMI at the onset of puberty regardless of early or late onset. Thus, while not many studies have included puberty in analyses, pubertal maturation should be included in obesity research in young populations a puberty has distinct effects on body fat.
Relationships between Physical Activity,
Sedentary Behaviours, and Obesity
Risk factors for obesity are inter-related. Knowing how physical activity and sedentary behaviours are associated with each other as well as how each risk factor relates to obesity is important to understand childhood and adolescent obesity.
Physical Activity and Childhood and Adolescent Obesity
Many cross-sectional analyses between objectively measured physical activity and obesity using different measurement methods show an inverse relationship. A significant negative relationship between step counts from pedometer and BMI was found in the sample of 711 US children aged 6 to 12 years (Vincent, Pangrazi, Raustorp, Tomson, & Cuddihy 2003). Vigorous physical activity measured by accelerometry showed a significant association with lower body fat from the sum of 5 skinfold thickness in 780 children aged 9 to 10 years (Ruiz et al. 2006). Vigorous physical activity measured with accelerometry was also negatively related to percentage of body fat in 248 children aged 8 to 11 years (Dencker et al. 2006).
According to Ekelund et al. (2004), time spent in moderate to vigorous physical activity, which was measured by accelerometer, was significantly associated with log-transformed sum of 5 skinfold thickness in 1292 children aged 9 to 10. In addition, a case-control comparison between 133 non-obese and 54 obese children indicated that obese children showed lower total daily counts of moderate and vigorous physical activity when accelerometry was used (Trost, Kerr, Ward, & Pate 2001). Physical activity index, calculated from a difference between heart rate and baseline heart rate divided by a certain interval, was negatively related to body fat in 76 children and adolescents aged 6 to 17 years (Janz et al. 1992). Physical activity measured with DLW showed significant negative relationship to BMI in 47 children aged 5 to 10 years (Abbott & Davies 2004).
In addition, when analysed in each gender, studies found a significant effect of physical activity on obesity only for boys. When physical activity was measured with DLW, it was significantly associated with BMI, fat mass and percentage of body fat for boys but not for girls in the sample of 106 healthy children aged 6 and 9 (E. J. Ball et al. 2001). Similar results (correlation between % body fat and physical activity for boys were shown in the sample of 79, 5 to 14 years children when the same DLW method was used (Rush, Plank, Davies, Watson, & Wall 2003). Hence, cross-sectional analyses including objectively measured physical activity shows an inverse relationship of activity to obesity in youth populations and a possible gender difference in the relationship between physical activity and obesity.
However, some longitudinal studies failed to find a significant relationship between physical activity and obesity, which makes it difficult to finalise the relationship. When physical activity and obesity, measured by questionnaire and BMI, percent body fat (bioimpedance), and sum of 4 skinfold thickness, were treated as continuous variables, a graded association was not found in a 3-year follow-up study from 1999 to 2001 in the sample of normal weight 222 boys and 214 girls aged 8 to 18 (Kettaneh et al. 2005). Instead, group comparisons showed that all obesity indicators were higher in the group of girls with decreased moderate physical activity than other girls during follow-up.
The group of boys with decreased vigorous activity showed higher sum of 4 skinfold thickness at follow-up than the rest of boys (Kettaneh et al. 2005). Another longitudinal study reported that amount of physical activity was not a significant predictor for change of BMI during follow-up from 1992 to 1996 in the sample of 112 prepubertal subjects (Maffeis et al. 1998). Parental obesity was the only significant factor for child obesity, when eating behaviour, physical activity, and parental obesity (BMI) were included in the same model in their research.
Possible reasons for inconsistent results about the relationship between physical activity and obesity are as follows. First, each study controls different factors related to obesity. For instance, parental BMI is one of the most closely related factors to child obesity. However, not all studies controlled for parental obesity. When parental BMI was not included in analyses (Ball et al. 2001; Tremblay & Willms 2003), physical activity was a significant predictor of obesity. On the other hand, when parental BMI was included, physical activity was non-significant (Maffeis et al. 1998) or was significant with very little explained variance of obesity (less than 1 %) (Ekelund et al. 2004).
Gender is another possible confounder, as previous results presents gender difference of the relationship between physical activity and obesity. Thus, it is important to include possible important underlying factors in analyses to examine how much variance of obesity is explained by physical activity. More importantly, how to deal with the physical activity variable is relevant. Some studies dealt with physical activity as a continuous and others as categorical variable. Significant relationship between activity and obesity has been found using a comparison method between the highest and the lowest groups among tertile, quartile, or quintile.
Intervention studies are another source for determining the relationship between physical activity and obesity. Strong et al. (2005) reviewed 850 articles in children and adolescent populations regarding physical activity intervention programmes and concluded that moderate intensity physical activity for 30 to 60 minutes, 3 to 7 days per week can decrease body fat and visceral adiposity in overweight youth but not in normal weight ones. They also suggested that vigorous intensity activity may be required for a beneficial effect on body fat in normal weight children and adolescents. In sum, a negative relationship between physical activity and obesity in youth can be found in many previous studies. However, most studies have used either BMI or skinfold thickness as a measure of obesity.
Relationship between intensity of activity and obesity.
More specifically, some researchers have analysed the relationship between intensity of physical activity and obesity using self-reported activities that were subsequently categorised by intensity of activity (i.e., light, moderate and vigorous physical activity). A survey in a nationally representative sample of 15,143 boys and girls aged 14 to 18 years showed that mean BMI of the highest tertile of frequency of moderate physical activity (MPA) was greater than that of the lowest tertile. The same was true for vigorous physical activity (Eisenmann et al., 2002). Similar results were found in the study of 712 children 9 to 16 years of age in Mexico City when the same method was used (Hernandez et al. 1999).
Studies including an objective measure of activity did not clarify the relationship between intensity of physical activity and obesity. When accelerometry was used for the measurement of physical activity, only time spent in vigorous physical activity (VPA) was reported as a significant factor related to obesity in the sample of 878 girls and boys aged from 11 to 15 years (Patric et al. 2004). Similarly, time spent in vigorous (defined as 2000-3499 counts) and hard activity (defined as 3500 counts), but not in moderate activity, was significantly related to low body fatness in 47 boys and girls aged 5 to 10 years, when an accelerometer was used to measure physical activity (Abbott & Davies 2004). Ruiz et al. (2006) also reported that vigorous physical activity, but not moderate activity, measured with accelerometry was significantly negatively related to the 5 sum of skinfold thickness in 780 children aged 9 to 10 years.
On the contrary, according to the investigation including 1291 children aged 9 to 10 years by Ekelund et al. (2004), moderate to vigorous activities (MVPA), measured with accelerometer, were negatively related to body fat and vigorous activity showed stronger relationship to body fat. A significant negative relationship between MVPA and body fat, measured with accelerometer and DXA, was also found in 12-year-old children (Ness et al. 2007). Hence, while vigorous activities have shown significant beneficial effects on obesity, effects of moderate intensity activities on obesity are not clear yet.
The investigation about whether moderately intense activities are negatively related to weight status is an important topic. Moderate activities are easier to achieve than vigorous ones. Thus, if moderate activities can influence obesity, intervention programmes targeting increased moderate activities would be more achievable and acceptable to subjects. Hence, there is a need to clarify whether moderate activities have beneficial effects on obesity when considering ease of doing moderate activities.
One of the challenges is the variability of definition of physical activity intensity in youth. Studies have used different definitions. Usually, physical activity can be categorised as light, moderate, and vigorous. For example, Utter et al. (2003) used the cut points of 3 MET for mild, 5 MET for moderate, and 9 MET for strenuous intensity. Gordon-Larsen et al. (2004) used 5 to 8 MET for MVPA in the analysis of data from the National Longitudinal Study of Adolescent Health. Hernandez et al. (1999) defined moderate as 3.5-5.9 MET and vigorous intensity as 6 MET or more. In this study, light physical activity will be 2 or 3 MET, moderate will be 5 MET, and vigorous intensity will be 8 METs, as done by McMurray et al. (2000). Therefore, analysis of the intensity of physical activity and its effects on obesity is also important for understanding the relationship between obesity and physical activity.
Sedentary Behaviours and Obesity
A positive association between hours of TV viewing and childhood obesity can be found in many studies (Giammattei et al. 2003; Hancox & Poulton 2006; Wake et al. 2003). Giammattei et al. (2003) reported that amount of TV viewing was positively associated with BMI z score and percentage body fat in 385 adolescents aged 11 to 13. A significantly increased risk for being overweight (BMI 25 or more) was found in subjects who spent 2-3 hours per day and 3-5 hours per day in front of TV compare to subjects who spent less than 2 hours per day in a representative Canadian sample of 7,260, children 7 to 11 years of age (Tremblay & Willms 2003). Among 60 obese and non-obese children aged 5 to 11 years, skinfold thickness as well as BMI were significantly greater in the group watching TV more than 1 hour per day than the others (Grund et al. 2001).
Hence, cross-sectional analyses showed significant harmful effect of hours of TV viewing on obesity. Watching TV more than 2 hours per day was significantly related to obesity in 15,349 adolescents, graded 9 to 12, from the National Youth Behaviour Survey (Lowry, Wechsler, Galuska, Fulton, & Kann 2002). A representative Canadian sample also showed greater time in TV in obese youth than normal weight youth (Janssen, Katzmarzyk, Boyce et al. 2004). Similarly, 2 or more hours per day TV viewing was significantly related to being overweight from the National Nutrition Survey in Colombia (Gomez et al. 2007).
Longitudinal studies have also shown a positive relationship between obesity and TV viewing. In a prospective study, time spent in front of TV was a significant predictor for 3-year change in BMI among 1037 children (Hancox & Poulton 2006). When TV viewing was observed from 5 to 15 years of age and the outcome was health risk at age 26, longer hours of TV viewing during childhood and adolescence showed long-lasting effects on health in later life, including overweight and elevated cholesterol, among about 1000 subjects (Hancox, Milne, & Poulton 2004). Greater BMI increase was found in subjects who watched TV more in a 1-year follow-up study in 6149 girls and 4260 boys aged 9 to 14 years, (Berkey et al. 2000). Greater BMI percentile increase was observed in students with longer hours of TV viewing in a 3-year follow up study among 2223 adolescents (Kaur, Choi, Mayo, & Harris 2003). Therefore, negative effects of long hours of TV viewing on obesity and health have been found in previous longitudinal studies.
Intervention studies, intended to reduce TV viewing hours, have also shown effects on reducing obesity. Gortmarker et al. (1999) investigated a longitudinal effect of reduced TV watching hours on obesity using a randomised and controlled design. The intervention was found to be effective in reducing obesity only for girls, when obesity was defined as age- and gender- specific 85th percentile or greater BMI and triceps skinfold thickness. Robinson (1999) also presented intervention effects of reduced TV hours in 192, 3rd to 4th graders. That is, BMI, sum of skinfold, and waist circumference were significantly reduced in girls and boys. Hence, the association between TV viewing and obesity is supported by many cross-sectional, prospective and intervention studies.
However, not all studies have shown significant effects of TV viewing on obesity. When hours of TV viewing was categorised into 5 groups, no significant relationship to BMI was found (p for trend: 0.47) among 552 girls, 5th to 12th graders (Wolf et al. 1993). The authors explained that no statistical significance of trend was because the lowest quintile TV viewing group had the greatest mean BMI. It reveals a possible significant curve linear relationship between TV viewing and BMI. Robinson et al. (1993) also reported that hours of TV watching were not associated with baseline and longitudinal change of BMI and sum of skinfold thickness in cross-sectional analysis of baseline data in 671, 6th and 7th graders and in longitudinal analysis of 279 subjects.
In addition, a follow-up study from preschool children (mean age: 4) to early adolescence (mean age: 11.1) using mix effects model analysis showed that hours of TV viewing were a predictor of changes in BMI and sum of five skinfold thickness but hours of TV viewing became non-significant after controlling for 51 physical activity measured by accelerometry (Proctor et al. 2003). According to Giammattei et al. (2003), when ethnicity was included in a model, TV viewing was not significant but ethnicity and sweet drink intake were significantly related to obesity. A summed screen time (TV, video tapes, and playing video game) was not related to BMI z score with or without controlling for parental obesity in 173 girls aged 8 to 12 from 4-year follow up study (Must et al. 2007). Therefore, a few aspects need to be considered in future studies to assess the unique influence of TV viewing on obesity: how to deal with the variable of TV viewing, for instance, as a categorical or continuous variable; what to include in analysis models as controlling variables, i.e., race, gender, SES, eating behaviours, and physical activity level.
Video games and use of computers are also popular sedentary behaviours in youth. According to Christakis et al. (2004), children spend more time in video and computer use than in TV viewing. Similar to TV viewing, video games and computer use have been measured as frequency or hours spent in those behaviours. Sometimes video games and computer use have been included in analyses as separate variables so that relationships between each behaviour and obesity can be assessed. More frequently, hours of TV viewing, video, and computer use have been aggregated as one variable, such as total screen time, in analyses. While electronic games more than 1 hour per day was significantly related to obesity, TV or computer use were not significantly related to obesity. Odds ration of being obese were 0.45 for boys and 0.57 for girls in less than 1 hour per day electronic game use (Carvalhal, Padez, Moreira, & Rosado 2007).
Summed hours of TV viewing, video game, and computer use have been reported as a significant factor for obesity. Berkey et al. (2000) found that a summed time spent in front of TV, video and computer games was closely associated with an increased BMI in a year later among 10,769 boys and girls aged 9 to 14. Utter et al. (2003) also reported that mean BMI for boys and girls was significantly different among tertile groups of summed hours of TV and video use (BMI for boys: 23.3±0.2 for the highest, 23.2±0.1 for the middle, and 22.6±0.3 for the lowest group; BMI for girls: 23.8±0.2 for the highest, 23.3±0.2 for the middle, and 22.8±0.2 for the lowest group) in the sample of 4746 middle and high school students. When normal and overweight groups were compared, the overweight group children spent more time in video and computer game play in 54 children aged 8 to 12 years (Crooks 2000). Electronic game play more than 1 hour per day reported by parents was significantly related to BMI in nationally representative sample of Portuguese boys and for girl) aged 7 to 9 years (Carvalhal et al. 2007).
When time spent only in computer or in video game play was used separately in analyses, inconsistent findings have been reported. Some studies have presented a significant relationship to obesity. A comparison between 54 overweight and non-overweight children aged 8 to 12 showed that overweight children engaged in video or computer game play more frequently than non-overweight ones (Crooks 2000). Those who played video games more than once a week showed elevated risk for being overweight (BMI cut point:25) in a representative Canadian sample aged 7 to 11 (Tremblay & Willms 2003).
On the contrary, some researchers failed to find a significant effect of computer use. Computer use and weight status did not show a significant linear or curve linear relationship in the sample of 2831 adolescents aged 9 to 12 (Vandewater, Shim, & Caplovitz 2004). There was no significant association between video games and obesity and between computer use and obesity among 712 adolescents aged 9 to 16 in Mexico City area, while TV viewing showed a significant association with obesity in the same population (Hernandez et al. 1999). Similarly, Kautiainen et al. (2005) presented significant effects of TV viewing and non-significant effects of digital game play on the elevated prevalence of overweight in the sample of 6,515 girls, aged 14, 16, and 18 (Kautiainen et al. 2005).
In short, a common sedentary behaviour, TV viewing, has been reported as a significant factor for obesity in previous literature. However, video games and computer use may have different energy expenditure than TV watching, as the non-significant effect of video and computer use on obesity has been reported frequently. For example, Harrell and associates (2005) reported that energy expenditure while watching TV in children and adolescents was lower than energy expenditure when playing video games.
Thus, using different measures for sedentary behaviours, such as separate measures for TV, video games, or computer use provides more information. In addition, obesity has been frequently defined using BMI and skinfold thickness in previous investigations to assess the relationships between sedentary behaviours and obesity. Waist circumference and standardised BMI are helpful to understand the relationship between sedentary behaviours and obesity in a different perspective.
Physical Activity and Sedentary Behaviours
Significant negative relationships between physical activity and sedentary behaviours have been presented in many studies using diverse measurement methods (Katzmarzyk et al. 1998; Marshall et al. 2004; Strauss et al. 2001). In a cross-sectional study of 104 first graders (median age: 5.4), energy expenditure of physical activity, measured by DLW and accelerometry, and time spent in sedentary behaviours, measured with accelerometry, were negatively correlated (Montgomery et al. 2004). A population study in a representative sample of 15,143 high school students aged 14 to 18 found that elevated levels of moderate to vigorous physical activity, measured by questionnaire, were related to less TV viewing (Eisenmann et al. 2002). Objectively measured physical activity using accelerometry was also inversely associated with video game play in 102 adolescents (mean age 15) (Janz & Mahoney 1997).
On the other hand, other researchers claimed that active and sedentary behaviours were not negatively associated (Allision 2002; Brodersen et al. 2005; Ekelund et al. 2006; Grund et al. 2001; Wolf et al. 1993). Adolescents in the highest and the lowest quintile of TV viewing hours showed no significant difference in energy expenditure and moderate to vigorous physical activity, measured with self-report activity log in the sample of 423 males and 361 females aged 9 to 18 (Katzmarzyk et al. 1998). A British birth cohort study showed that physical activity and TV viewing were significantly related to BMI when both factors were included in the model but they were not related to each other in 11-year-old girls (Parsons et al. 2005).
Utter et al. (2003) failed to find a significant difference of physical activity among the highest, middle, and the lowest groups of TV viewing in 4746 middle and high school students. Hours of TV viewing and video games were not related to physical activity in a cross-sectional analysis among 743 high school students (Feldman, Barnett, Shrier, Rossignol, & Abenhaim 2003). Correlation between physical activity, measured with accelerometry, and TV viewing was not significantly related in 1921 boys and girls aged 9-10 and 15-16 years (Ekelund et al. 2006).Vigorous physical activity measured with survey was also not correlated with TV in 1,041 Canadian boys and girls graded 9 to 11 (Allision, 2002) and in 4360 boys and girls aged 11 to 12 years (Brodersen et al. 2005). Longitudinal relationship between TV and moderate to vigorous physical activity was not significantly related in Canadian sample (Neumark-Sztainer et al. 2003) and in US sample (Taveras et al. 2007).
Non-negative relationship between sedentary behaviours and physical activity have supported by the results that individual can be sedentary and active at the same time. According to de Velde et al. (2007), boys spent more time in TV viewing and computer use than girls but also physically activity than girls in 12,538 adolescents aged 11 years. The National Youth Behaviour Survey also showed that black youth spent more time in watching TV and physically more active than white counterpart in 15,349 high school students (Lowry et al. 2002). Adolescents who spent more time in TV viewing participated more in moderate physical activity in 92 children aged 10 to 16 years (Strauss et al. 2001).
Unlike hours of TV viewing, computer use seems to have a more complicated relationship to physical activity. A positive relationship between computer use and physical activity has been reported. Spearman correlation between computer use and physical activity, measured by questionnaire, was 0.16 for weekdays and 0.10 for the weekend in 500, 7th to 12th graders (mean age: 14.6) (Santos et al. 2005). The authors provided as a possible reason that the nature of computer use is different from TV viewing, in that computer use is associated with working, not playing, in this age population. Utter et al. (2003) also found similar results only for girls.
That is, the highest computer use group showed a greater mean leisure time physical activity than the middle and the lowest. For boys, the middle group showed a greater mean of physical activity than the highest and the lowest group. In addition, more computer use was related to being active only for males in 7982 youth aged 12 to 19 years from the Canadian Community Health Survey (Koezuka et al. 2006). That is, compared to boys with no computer use, less than 6 hours computer use per week appeared to be physically inactive.
Thus, the relationship between physical activity and sedentary behaviours is not clear yet, which makes it difficult to weigh the relative importance of those health behaviours on obesity. In addition, investigation of interrelationships between sedentary behaviours and physical activity needs to be done by each sedentary behaviour. A clearer understanding of inter-relationships between physical activity and sedentary behaviours is needed to guide intervention and clinical recommendations. For instance, if activity and sedentary behaviours are negatively related, intervening on one aspect may be effective. If activity and sedentary behaviours are not related, which means that physical activity and sedentary behaviours may not be in the same continuum of the level of activity but in two different dimensions, we need to assess activity and sedentary patterns of each child so that customised intervention can be more effective.
While many researchers have investigated predictors of physical activity, relatively few researchers have examined how the relationships between predictors and physical activity change as children grow older. In addition, few studies have looked correlates of sedentary behaviours as well as longitudinal relationships between correlates and sedentary behaviours in child and adolescent populations. As for the relationships between physical activity and obesity, the literature, including cross-sectional, longitudinal, and intervention studies shows that children and adolescents with low physical activity are at risk for obesity. In particular, vigorous intensity activity is one of strong predictors for low body fatness, while effects of moderate intensity activity on obesity are still controversial. This paper also shows that sedentary behaviour, especially, hours of TV viewing, is a risk factor for obesity, even though some researchers have suggested that TV viewing has a small effect on obesity (Agras & Mascola 2005; Marshall et al. 2004). Other sedentary behaviours, video games and computer use have not shown a consistent relationship to obesity.
Therefore, it is important to investigate the association between activities of different intensity and childhood obesity for a clearer understanding of the relationships between physical activity and obesity. In the analysis of the longitudinal relationship between obesity and sedentary behaviours, using three popular sedentary behaviours (TV viewing, video games, and computer use) separately provides more information than is currently available. To assess the extent of obesity explained by either physical activity or sedentary behaviours, possible confounders need to be explored.
Including age, gender, race, puberty, parental influence, and eating behaviour, which have shown significant relationships to child obesity, in analysis can help clarify the relationships. At the same time, including those variables in analysis can provide the relative importance of physical activity and sedentary behaviours may differ by gender or by race and etc, which would also have implications for interventions. In particular, not many studies have included pubertal maturation. As mentioned earlier, puberty is a critical period for body composition change. Hence, it is essential to include pubertal development in obesity research in adolescent populations.
In addition, it is also important to assess physical activity, sedentary behaviours, and obesity with more diverse methods. While the literature shows that physical activity decreases with age, frequently used variables are habitual activity, moderate to vigorous physical activity, or vigorous physical activity. BMI, sum of skinfold thickness, and waist circumference, but not BMI z score, have been frequently used in obesity research. Although each sedentary behaviour (TV, video games, computer use) seems to have different characteristics, many researchers have used a combined hours of sedentary behaviours. Therefore, using different indicators for physical activity, sedentary behaviours, and obesity can make it possible to understand the whole picture of complicated relationships between health behaviours and obesity.
Another crucial issue is the inter-relationship and possible interaction between physical activity and sedentary behaviours with obesity. Knowing the inter-relationship is important for developing obesity intervention programmes. If physical activity and sedentary behaviours are negatively related, this would indicate that highly active children spend less time in sedentary behaviours. Thus, a comparison of magnitude of influence on obesity from the two risk factors can help us understand the relative importance on obesity, and, consequently, help guide the choice of behaviour selected as a target behaviour. In contrast, if physical activity and sedentary behaviours are not related, this would indicate that two different dimensions exist in activity, which means individuals can be active and sedentary at the same time. Hence, intervention programmes focusing only one behaviour may not be effective for all subjects because children can have significant amounts of both active and sedentary behaviours.
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