Physical Activity, Fitness and the Energy Cost of Activities

CHAPTER TWO

Physical Activity, Fitness and the Energy Cost of Activities: Implications for Obesity in Children and Adolescents in the Tropics Xiao Chuan Lau*, Kar Hau Chong*, Bee Koon Poh*,1, Mohd Noor Ismail†

*Physical Activity and Energy Metabolism Research Group, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia † Department of Nutrition and Dietetics, Faculty of Health Sciences, MARA University of Technology, Puncak Alam, Selangor, Malaysia 1 Corresponding author: e-mail address: [email protected]

Contents 1. Introduction 2. Overweight and Obesity in the Tropics 2.1 Overweight and obesity 2.2 Health consequences of overweight and obesity 2.3 Factors contributing to overweight and obesity 2.4 Global data on overweight and obesity in the tropics 2.5 Obesity studies conducted in the tropics 3. Physical Activity in the Tropics 3.1 Physical activity 3.2 Global data on PA in the tropics 3.3 PA studies conducted in the tropics 3.4 Discussion 4. Physical Fitness in the Tropics 4.1 Physical fitness 4.2 Relationship between PF and PA 4.3 PF studies conducted in the tropics 4.4 Discussion 5. Energy Cost of Physical Activities in Children and Adolescents in the Tropics 5.1 Energy cost of habitual activities 5.2 Methods of measuring energy cost 5.3 Compilation of energy cost of physical activities in the tropics 6. Implications of PA, PF, and Energy Cost on Obesity in the Tropics 6.1 PA and obesity

Advances in Food and Nutrition Research, Volume 70 ISSN 1043-4526 http://dx.doi.org/10.1016/B978-0-12-416555-7.00002-3

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2013 Elsevier Inc. All rights reserved.

50 53 53 54 55 56 59 59 59 62 65 67 68 68 69 71 72 72 72 73 80 82 82

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Xiao Chuan Lau et al.

6.2 PF and obesity 6.3 Energy cost of PA and obesity 7. Conclusion References

85 86 88 89

Abstract The tropics cover a large section of the world in which both developed and developing countries are situated. Rapid socioeconomic development, modernization, urbanization, and globalization have affected both the food market and physical activity (PA), which in turn have propelled the obesity epidemic in the tropics. There is growing concern that overweight and obesity are emerging as major health problems among children and adolescents in the tropics, despite the fact that undernutrition still exists in many of these countries. Physical inactivity, a low metabolic rate, and lack of physical fitness (PF) have been linked to overweight and obesity. Moreover, PF in several tropical countries is declining, and these changes may be a threat to future health, as low PA and PF levels are important risk factors for noncommunicable chronic diseases. Previous studies have reported that the relationships among PA, PF, overweight, and obesity are inconsistent and inconclusive. There is no indication that variances in the energy cost of physical activities lead to obesity. Despite a lack of definite evidence to prove a causal relationship, there is enough certainty that physical inactivity and low fitness levels are linked to overweight and obesity. Hence, people living in tropical countries need to be encouraged to lead a healthier lifestyle by increasing their PA levels and reducing sedentary behaviors to prevent overweight or obesity.

1. INTRODUCTION In recent decades, obesity has grown into a global epidemic that affects not just the adult population but also children and adolescents. In year 2010, the prevalence of overweight and obesity among preschool children increased by 60% from 1990, affecting some 43 million young children worldwide (de Onis, Blossner, & Borghi, 2010). A similar trend has also been observed in school-age children, with an estimated 200 million classified as either overweight or obese (International Obesity Taskforce (IOTF), 2010). Obesity was once considered a problem only of developed nations; however, obesity rates have dramatically risen even in developing countries (Popkin, 2009), including those in the tropics. Tropical countries are located around the equator and lie between the Tropic of Cancer (23.4
North) and the Tropic of Capricorn (23.4
South) (Encyclopedia of World Geography, 2001). In Asia, the tropics cover Southeast Asia and the southern part of India and Sri Lanka. The tropics also

Physical Activity, Fitness and Energy Cost of Activities

51

include the northern part of Australia and most of the islands in Oceania and South Pacific. In the Americas, all countries in Central America, the Caribbean islands, and the northern parts of South America are in the tropics. Most of the African continent is tropical, except Morocco, Algeria, Tunisia, Libya, and Egypt in the north, and South Africa, Lesotho, and Swaziland in the south. In the Middle East, Yemen and parts of the United Arab Emirates, Oman, and Saudi Arabia are located in the tropics. Reports from several tropical countries have shown that overweight and obesity have become a major concern. In Vietnam (Ho Chi Minh City), the prevalence of adolescent obesity increased significantly from 1.7% to 5.1% over a 5-year period (Trang, Hong, & Dibley, 2012). A similar situation has been described in Malaysia, in which the prevalences of obesity among children and adolescents (<18 years old) increased from 5.4% to 6.1% from year 2006 to 2011 (Institute of Public Health, 2008, 2011). The prevalence rates of obesity of children and adolescents aged from 5 to 19 years old were also found to be high in other tropical countries, such as Mexico (41.8%), Brazil (22.1%) and India (22.0%) (Gupta, Goel, Shah, & Misra, 2012). These facts clearly indicate that this obesity epidemic should be taken as seriously as any infectious disease epidemic and targeted as a health priority in tropical countries. In addition to the increasing prevalence and severity, attention to obesity in childhood and adolescence has increased worldwide for several reasons. First, childhood and adolescent obesity has been proven to track strongly into adult life (Freedman et al., 2009; Wyatt, Winters, & Dubbert, 2006), although adolescent obesity is a stronger predictor of adult obesity compared to childhood obesity status (Goodman & Whitaker, 2002; Hohepa, Schofield, & Kolt, 2004). Second, current evidence demonstrates that obese children and adolescents are not only at a higher risk for numerous physical disorders, such as hypertension, type 2 diabetes mellitus, asthma, and sleep disorders (Ho, 2009; Ko, Chan, & Chan, 2007; Smith, 2007), but also an increased risk of premature mortality and physical morbidity in adulthood (Reilly & Kelly, 2011). Moreover, being obese during childhood and adolescence can negatively impact social and psychological health through maladaptive peer experiences that are particularly important during this stage of development (Daniels et al., 2005; Pearce, Boergers, & Prinstein, 2002). The determinants of obesity are as varied as the people it affects. Obesity is believed to be a chronic disorder that results from the complex interactions of multiple factors (Dehghan, Akhtar-Danesh, & Merchant, 2005). However, at the most basic level, obesity occurs when there is an energy imbalance, specifically a positive energy balance related to either an increase in

52

Xiao Chuan Lau et al.

energy intake or a decrease in energy expenditure (EE) or both (Hills, Andersen, & Byrne, 2011). In this context, physical activity (PA), which accounts for a part of human EE, has long been recognized as one of the important factors that can be modified for obesity prevention in young people (Dulloo, 2010; Stankov, Olds, & Cargo, 2012). PA has been shown to dramatically decline from childhood to adolescence (Bradley, McRitchie, Houts, Nader, & O’Brien, 2011; Troiano et al., 2008; Tudor-Locke et al., 2011), and physical inactivity or low levels of PA has been associated with the rising prevalence of obesity among children and adolescents ( Jimenez-Pavon, Kelly, & Reilly, 2010). In addition, obese children (Planinsˇec & Matejek, 2004) and adolescents (Ekelund et al., 2002; Olds, Ferrar, Schranz, & Maher, 2011) are usually less physically active than their normal weight counterparts, and the reduced PA has accounted for more than two-thirds of the difference in EE between these two groups of individuals (Olds et al., 2011). Conversely, Li, Treuth, and Wang (2010) found that there was no evidence of declining PA among adolescents, despite the continued rise in the obesity prevalence of this population. Wilks, Besson, Lindroos, and Ekelund (2011) further concluded in a prospective study that PA was not strongly related to an excessive gain in adiposity among younger and older populations. These findings suggest that physical inactivity may not be the major contributor to the development of obesity among children and adolescents. Although the relationship between PA and obesity is yet to be fully understood, available evidence supports that increasing PA participation and decreasing sedentary behavior (particularly television viewing) should be the focus when planning obesity prevention programs for children and adolescents (Boone, Gordon-Larsen, Adair, & Popkin, 2007; Hills et al., 2011; Stankov et al., 2012). In addition to PA, much attention has been given to studies investigating the link between physical fitness (PF) and obesity, given that the fitness levels among children and adolescents are declining (Carter & Micheli, 2013; Lobstein, Baur, Uauy, & International Obesity Task Force, 2004). Numerous cross-sectional studies have found that an increased body mass index (BMI) status was associated with declines in PF (Aires et al., 2008; Dumith et al., 2010), and overweight and obese children and adolescents tended to perform worse on fitness tests compared to their normal weight peers (Chen, Fox, Haase, & Wang, 2006; Mak et al., 2010). A large population-based survey in the Republic of Seychelles (African region) also found a strong inverse relationship between fitness and excess body weight in adolescents (Bovet, Auguste, & Burdette, 2007). A review by Ortega,

Physical Activity, Fitness and Energy Cost of Activities

53

Ruiz, Castillo, and Sjo¨stro¨m (2008) further presented strong evidence to support negative associations between PF, or to be specific, cardiorespiratory fitness levels, and total body and abdominal adiposity in childhood and adolescence. While all these of studies showed fairly consistent results, it should be noted that there are various components of fitness measures and most of the published studies used only a single fitness test to represent the fitness component (Kim et al., 2005). Moreover, due to the cross-sectional nature of the study designs, the direction of causation between fitness and obesity could not be clearly established. Although energy intake and EE are known to be equally important in maintaining energy balance, it is difficult to obtain accurate dietary intake information from a young population. Therefore, the FAO/WHO/ UNU (1985) recommended that the estimate of EE in free-living individuals be used for establishing the energy requirements in children and adolescents (Wong, 1994). However, EE can be influenced by many factors and varies among age groups. The energy cost of activities may be greater among children and adolescents compared to adults, and this difference could be due to the smaller muscle mass, shorter legs, and greater proportional amount of internal organs in these young populations (Harrell et al., 2005). These factors may contribute to the higher total daily EE in children and adolescents. Unfortunately, studies on the energy cost of daily activities in children and adolescents worldwide are limited to a few activities. As a result, data from adults are often used as surrogates; hence, the accuracy and validity of the estimates of energy requirements for children and adolescents has often been questioned (Ainsworth et al., 2000; Torun, 1983; Torun, Chew, & Mendoza, 1983). In comparison to the other countries, we found that there is currently no research or literature that systematically examines the PA and fitness levels of children and adolescents in the tropics. Moreover, data regarding the energy costs of daily activities in these populations are scarce. Therefore, this review aimed to report the PA, fitness levels, and energy costs of habitual activities performed by children and adolescents and to extensively review the implications of these components on the increasing prevalence of obesity in the tropics.

2. OVERWEIGHT AND OBESITY IN THE TROPICS 2.1. Overweight and obesity In general, overweight and obesity can be defined as conditions of abnormal or excessive fat accumulation that may impair health (World Health

54

Xiao Chuan Lau et al.

Organization (WHO), 2013). While a variety of terms and cut-off points have been proposed and used to describe overweight and obesity in children and adolescents, there is considerable argument as to whether these classifications are applicable for these young populations. Ideally, the classifications of overweight and obesity should be defined on the basis of whole-body fatness (Cole, Bellizzi, Flegal, & Dietz, 2000), which can be directly measured using several methods, such as densitometry, magnetic resonance imaging, and bioelectrical impedance analysis (Dehghan et al., 2005). However, these methods are usually time consuming and require good cooperation of the subject, which makes it impractical for use in large population-based surveys that involve children and adolescents. Moreover, these measurements are relatively expensive and thus not widely available in most of the developing countries where resources are limited. Most importantly, established criteria or cut-off points for defining excess fatness in children and adolescents who are overweight or obese are still lacking (Flegal & Ogden, 2011; Krebs et al., 2007). Because of these limitations, anthropometric measurements (skinfold thickness, waist circumference, BMI, and waist-to-hip ratio) are often used as surrogates for body fatness (Rao, 2012). Among these measurements, BMI, which is derived from height and weight measures, has been found to be the most useful indicator of increased body fat. Therefore, BMI is recommended for evaluating overweight and obesity in children and adolescents in the clinical setting (Barlow & Committee, 2007; Daniels, 2009). However, the interpretation of BMI in these young populations is complicated by body size and composition changes during growth, and the failure of BMI in distinguishing fat from fat-free mass (FFM) may lead to misclassifications of individuals with larger muscle masses (Daniels, 2009; Dehghan et al., 2005). Moreover, there is still no precise definition of overweight and obesity based on BMI, and the selection of these BMI cut points are generally statistical rather than risk based (Flegal & Ogden, 2011). Therefore, the interpretations and comparisons of the BMI distributions should be performed cautiously, particularly those between countries that have used different reference data sets for BMI. It is also of practical importance to clearly define the terminology of overweight and obesity in the context of children and adolescents, given that the prevalence rates of these conditions are rising at an alarming rate.

2.2. Health consequences of overweight and obesity Overweight and obesity in childhood and adolescence are known to have serious adverse consequences on both physical and psychological health.

Physical Activity, Fitness and Energy Cost of Activities

55

Two reviews by Reilly et al. (2003) and more recently by Raj and Kumar (2010) have summarized that childhood obesity is closely associated with numerous comorbidities in children and adolescents, including metabolic, cardiovascular, psychological, neurological, orthopedic, pulmonary, hepatic, and renal disorders. Cali and Caprio (2008) stated that many obese children and adolescents already manifest some metabolic disturbances (impaired glucose regulation, hypertension, dyslipidemia, and fatty liver disease) without knowing it, placing them at a higher risk for the development of early morbidity. In addition, these children and adolescents are also found to be more susceptible to psychosocial problems than their normal weight counterparts, such as stigmatization and discrimination by peers, lower levels of self-esteem, anxiety, and depression (Daniels et al., 2005; Dockray, Susman, & Dorn, 2009; Vila et al., 2004). Many studies have reported that overweight and obesity are associated with significant decrements in overall quality of life among these young populations (Stern et al., 2007; Williams, Wake, Hesketh, Maher, & Watrs, 2005), and these decrements were somewhat comparable to those of children with cancer undergoing chemotherapy (Schwimmer, Burkwinkle, & Varni, 2003). Moreover, current evidence also confirms the persistence of childhood and adolescence obesity into adult life, and this factor is associated with significantly increased rates of premature death, morbidity, and mortality later in adulthood (Franks et al., 2010; Guo, Wu, Chumlea, & Roche, 2002; Reilly & Kelly, 2011; Whitlock, Williams, Gold, Smith, & Shipman, 2005).

2.3. Factors contributing to overweight and obesity Childhood obesity is a multifactorial disorder that involves complex interactions between genetic, metabolic, neuroendocrine, environmental, sociocultural, and psychological factors (Raj & Kumar, 2010). Ang, Wee, Poh, and Ismail (2013) have critically reviewed the major risk factors involved in the etiology of childhood obesity, and they summarized these determinants into two categories: modifiable factors (socioeconomic status, diet, PA, sleep, and parental determinants) and nonmodifiable factors (genetics, ethnic differences, gestational weight, and intrauterine conditions). In addition, PF has recently been suggested as another potential factor that may be associated with the rising obesity rate among children and adolescents (Ara et al., 2006; Janz, Dawson, & Mahoney, 2002; Nassis, Psarra, & Sidossis, 2005). WHO (2013) has stated that the adverse imbalance between energy intake and EE, which mainly results from the overconsumption of energy-dense foods and an increase in sedentary behavior,

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Xiao Chuan Lau et al.

Energy balance

Total energy expenditure

Energy intake • Diet • Energy-dense food

Childhood and adolescent obesity • • • •

Body mass index Waist circumference Skinfold thickness Body fatness

Other factors • • • • • •

rate • Diet-induced

thermogenesis

Energy cost of physical activity

• Socioeconomic

status Sleep Parental determinants Genetics Ethnic difference Gestational weight Intrauterine condition

• Basal metabolic

Physical fitness • • • • •

Body composition Muscular Flexibility Cardiorespiratory Metabolic

Physical activity • • • •

Frequency Intensity Type Duration

Figure 2.1 Conceptual framework describing the factors associated with childhood and adolescent obesity.

is the major contributor to the increasing childhood obesity rates in both developed and developing countries. Figure 2.1 presents the conceptual framework identifying several major factors that may contribute to childhood and adolescent obesity. On the whole, energy balance varies depending on energy intake and EE, and a positive balance over a period of time is known to be the basic cause of obesity development in childhood and adolescence. It is now known that there are also numerous other factors associated with this global epidemic. However, in this review, we will only focus on the linkages between PA and its energy cost, as well as PF in relation to overweight and obesity status, and their implications for children and adolescents living in tropical countries.

2.4. Global data on overweight and obesity in the tropics The Global School-based Student Health Survey (GSHS) was conducted by WHO to determine the overall health status of adolescents in developing nations since 2003. The survey involved primarily school-age adolescents (13–15 years) in low and middle-income countries (WHO, 2011). Out of 104 countries involved in this survey, 46 are located in the tropical region.

57

Physical Activity, Fitness and Energy Cost of Activities

We have compiled the available published data from the GSHS and reviewed the current overweight and obesity rates among adolescents in these tropical countries. As summarized in Figs. 2.2 (overweight) and 2.3 (obesity), the proportion of overweight adolescents was slightly greater than the proportion of obese adolescents. It was estimated that the percentage of overweight adolescents ranged from 4.5% (Sri Lanka) to 58.5% (Cook Islands). Niue, a 3.4 3

Algeria Barbados Benin British Cook Costa Rica Djibouti∗ Dominica Egypt Fiji Ghana∗ Guatemala Guyana India∗ Indonesia∗

14.6 13.9

1 0

17.9 17.5 19

29

7.6 10.2

6.5

3.2

9.6 8.8

7.1 4.5 6.8 5.9

0.9 0.6

5.4 9.4 3.6 4.6 1.5 2.5 2.4 6.7 5.3 8.5 7.8

Jamaica Kiribati Libya Malawi Malaysia Mauritius Myanmar∗ Nauru Niue∗∗

8 7.9

0.7 0.6 1.1 1

Girls Boys

11.4

4.6

Pakistan Peru Philippines Saint Kitts Seychelles∗

28.7 23.4

0.2 0.4

15.7 17.8

39.9 2.7 3 2.5 3.1

12.1 16.6

10.3

Solomon Sri Lanka Sudan Suriname Thailand∗ Trinidad Uganda∗

0.6 0.4

2.9 1.5

10.9

3.7 3.6 4.5

5.7 8.4

0 0.3 2.3

0

10.7 13.2

1 0.3

Uruguay Vanuatu Yemen∗

6.9 6.2 6.5

5.7

5

10

15

20

25

30

35

40

45

Figure 2.2 Proportion of overweight adolescents (13–15 years) in the tropics. Definition of overweight: >þ1 SD from median for BMI-for-age and sex. *According to World Health Organization (WHO) growth reference for school-aged children and adolescents. **No data for girls. Data source: Global School-based Health Survey (GSHS) (WHO, 2011).

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Xiao Chuan Lau et al.

16.3

Algeria Barbados Benin British Cook Costa Rica Djibouti∗ Dominica Egypt Fiji Ghana∗ Guatemala Guyana India∗ Indonesia∗ Jamaica Kiribati Libya Malawi Malaysia Mauritania Mauritius Myanmar∗ Nauru Niue∗∗ Pakistan Peru Philippines Saint Kitts Seychelles∗ Solomon Sri Lanka Sudan Suriname Thailand∗ Trinidad Uganda∗ Uruguay Vanuatu Yemen∗

10.9

31.8 32.1

13.6

8.4

37.8 35.8 20.5 13.4

25.9 24

34.1 30.7

20.4 17.9

9.9 4.1

27.3 26.8

15.9 14.6

9.7 11.6 14

6.2

25.2

18.1

4

46.4

31.9

8.7 7.6 8.6

3.6

58.9 58.2

27.3 28.2

22.2 25.3 17.619.3 22.7

6

Girls Boys

28

48.9 40

60.3

8.7 5.1

18.7 21.8

9.3 11.3

17.6

4.2 4.8 9.6

13.4 13.2

32.5 32.6

25.9

19.2 19.3 19.8

25 27.5

11.6

2.1

23 22.4

25 29.7

13.6 8.9 11.4 12 0

10

20

30

40

50

60

70

Figure 2.3 Proportion of obese adolescents (13–15 years) in the tropics. Definition of obese: >þ2 SD from median for BMI-for-age and sex. *According to World Health Organization (WHO) growth reference for school-aged children and adolescents. **No data for girls. Data source: Global School-based Health Survey (GSHS) (WHO, 2011).

tropical island in the South Pacific, ranked the highest for the obesity rate (29.7%), indicating that almost 3 in 10 adolescents are obese. This rate is somewhat higher (1.6 times) than the rates that have been reported in the United States, in which 18% of the adolescents aged 12–19 years were found to be obese in 2010 (Ogden, Carroll, Kit, & Flegal, 2012). In European and North American regions (WHO Europe, 2008), the Health Behavior in School-aged Children (HBSC) study that involved 41 countries (primarily European) revealed that the prevalence of overweight or obesity was 14% for 11-year-old children and 13% for both

Physical Activity, Fitness and Energy Cost of Activities

59

13- and 15-year-old students. The country with the highest prevalence of overweight or obesity was the United States for 11- (boys: 25%, girls: 33%), 13- (boys: 27%, girls: 35%), and 15- (boys: 28%, girls: 32%) yearold students. The HBSC survey also reported that in North America and Western Europe, higher levels of overweight and obesity were reported in families with lower income and among boys. In summary, these findings clearly indicate that this obesity epidemic is no longer restricted only to the developed western nations but is also growing rapidly in developing countries in the tropics.

2.5. Obesity studies conducted in the tropics In addition to the GSHS, we have compiled and reviewed the data on overweight and obesity prevalence from nationally representative surveys that are available from several tropical countries. The findings are summarized in Table 2.1. Similar to the global data (GSHS), the findings summarized from local national surveys clearly indicate that there is a rise in the overweight and obesity rate among adolescents in most tropical countries, including lessdeveloped regions, such as Vietnam and Guatemala (particularly among urban populations). Even in Brazil, the prevalence of overweight adolescents was shown to have quadrupled over a period of 28 years (from 1975 to 2003). It must be noted, however, that different cut-off points were used by these countries in classifying the body weight status of adolescents, and therefore the results are not directly comparable. Nevertheless, current evidence clearly showed that overweight and obesity have emerged as serious health problems that must be addressed among children and adolescents living in the tropics, along with addressing the undernutrition problem that still exists in many of these developing countries (Caulfield, de Onis, Blo¨ssner, & Black, 2004).

3. PHYSICAL ACTIVITY IN THE TROPICS 3.1. Physical activity PA has been defined as a behavior involving bodily movement produced by skeletal muscles that results in an increased EE (Caspersen, Powell, & Christenson, 1985). According to the basic concept of human EE, PA accounts for the second largest part of daily EE and is viewed as the most variable component compared to basal metabolic rate (BMR) and thermogenesis (Food & Agriculture Organization (FAO), 2004). Hence,

Table 2.1 Prevalence of overweight and obesity compiled from studies conducted in tropical countries Overweight Obesity Reference Year(s) of Boys Girls Overall Boys Girls Overall system Country Source survey Age

Bolivia

Pe´rez-Cueto, Botti, and Verbeke (2009)

2005–2007 12–18a – 2002–2003 10–19

a

–

13.2

–

–

2.5

IOTF

–

–

16.7

–

–

2.3

IOTF

Brazil

Instituno Brasileiro de Geografia and Estatı´stica (2006)

Colombia

Instituto colombiano de bienestar familiar (ICBF) 2005 (2006)

10–17a –

–

10.3

–

–

–

CDC

Ecuador

Ye´pez (2005)

2006

12–18a –

–

13.7

–

–

8.5

MUST

Fiji

Utter et al. (2008)

2005–2006 12–18

20

–

4

6

–

IOTF

6.7

3.8

–

NA

13 a

21.3 16.4 –

India

Unnithan and Syamakumari (2008)

NA

10–15

India

Bharati, Pal, and Bharati (2008)

NA

10–17a –

–

3.1

–

–

1.2

CDC

Indonesia

Julia, van Weissenbruch, Prawirohartono, Surjono, and Delemarre-van de Waal (2008)

1999

6–8 to 11–13

–

–

5.3%

–

–

2.7%

NA

–

–

8.6%

–

–

3.7%

NA WHO2

Malaysia

Ismail et al. (2009)

2004 2001

6–12

a

2008 <18a

10.9 11.1 11.0

12.1 7.2

9.7

12.4 13.1 12.8

17.7 9.6

13.7

–

7.6

6.1

Institute for Public Health, National Health and Morbidity Survey (2011)

2011

Poh et al. (2013)

2010–2011 0.5–12a 9.0 (Urban)

–

–

10.5 9.7

0.5–12a 10.5 9.2 (Rural)

9.9

4.6

15.1 10.2 12.7 9.9

6.5

8.4

CDC WHO1, WHO2

Mexico

Olaiz et al. (2006)

2006

12–19a 14.8 18.3 16.6

12.9 10.1 11.5

CDC

Peru

Pajuelo (2003)

2003

10–15

18.2

–

5.0

MUST

13.8 –

–

20.5 –

–

CDC

–

26.8

–

–

16.5

IOTF

21.1 12.6 –

–

–

–

ISRS

24

7

15

–

IOTF

Saudi Arabia

Al-Rukban (2003)

2002

12–20

Taiwan

Chen et al. (2006)

2001

6–18a a

–

–

–

Thailand

Mo-suwan (2008)

1997

Tonga

Utter et al. (2008)

2005–2006 12–18

United Arab Emirates

Al-Haddad, Little, and Abdul Ghafoor (2005)

NA

4–18a

17.1 20.1 –

7.7

7.1

–

IOTF

Venezuela

Instituto Nacional de Nutricion Gobierno Bolivariano de Venezuela (2005)

2005

7–14a

–

–

19.3

–

–

–

NCHS/ WHO2

Vietnam

Tang, Dibley, Sibbritt, and Tran (2007)

2004

11–16a –

–

11.7

–

–

–

IOTF

a

5–15

– a

38

–

Prevalence data considered as nationally representative. IOTF, International Obesity Task Force; NCHS, National Center for Health Statistics reference population; CDC, CDC growth charts for the United States 2000, 85th and 95th percentiles; MUST, for the United States 1991, 85th and 95th percentiles; WHO1, child growth standards for 0–5 years (WHO, 2006); WHO2, growth reference data for 5–19 years (WHO, 2007); NA, not available.

62

Xiao Chuan Lau et al.

quantifying the amount of PA, as part of the estimation of energy expended, is necessary and can be performed by assessing the frequency, intensity, duration, and type of PA. Over the decades, regular PA has been found to be beneficial and thus important for the physical, mental, and social health of children and adolescents (Centers for Disease Control, Prevention (CDC), 2011). Moreover, it is an accepted norm that regular PA established during the early years, particularly the period of adolescence, not only protects against physical inactivity in adulthood (Azevedo, Arau´jo, Silva, & Hallal, 2007; Hallal, Victora, Azevedo, & Wells, 2006) but also greatly impacts the mortality and longevity of individuals (Hills, King, & Armstrong, 2007) because people establish many of their lifestyle choices as they proceed through adolescence. In spite of these benefits, evidence clearly indicates that PA levels decline from childhood to adolescence (Allison, Adlaf, Dwyer, Lysy, & Irving, 2007; Dumith, Hallal, Reis, & Kohl, 2011; McMurray, Harrell, Creighton, Wang, & Bangdiwala, 2008; Nader, Bradley, Houts, McRitchie, & O’Brien, 2008), and this decrease is more marked among girls than boys (Allison et al., 2007; Dumith et al., 2011). Nevertheless, there are still relatively few studies or literature on children and adolescents’ PA in tropical countries; furthermore, far less is known about the current PA pattern and its impact on the health of these populations.

3.2. Global data on PA in the tropics In addition to body weight status, the GSHS also assessed the PA levels of adolescents using a standardized questionnaire across countries (WHO, 2011). We have conducted a secondary analysis to estimate the PA patterns and levels of the adolescents in these tropical countries, and the results are summarized in Figs. 2.4 and 2.5. Figure 2.4 presents the percentage of adolescents who were physically active for a total of at least 60 min a day over a 7-day period, while Fig. 2.5 shows the percentage of adolescents who spent 3 or more hours a day in sedentary activities. Figure 2.4 revealed that Vanuatu had the highest percentage of adolescents (46.7% boys and 45.7% girls) who were physically active for at least 60 min a day during the past 7 days, while Venezuela achieved the lowest percentage (8.1%) among the 46 tropical countries included in the analysis. In general, we found that most adolescents in the tropics did not engage in sufficient amounts of PA, as only 25% of the boys (in more than half of the countries) and 16.4% of girls (for all countries except India) met the PA

63

Physical Activity, Fitness and Energy Cost of Activities

11

Algeria Benin Cayman China Colombia Cook Islands Costa Rica Djibouti Dominica Ecuador Egypt Fiji Ghana Grenada Guatemala Guyana India Indonesia Kenya Kiribati Libya Malaysia Maldives Mauritania Mauritius Montserrat Namibia Nauru Niue∗ Peru Philippines Senegal Seychelles Solomon Sri Lanka St Lucia St Vincent Suriname Thailand Trinidad and Uganda Vanuatu Venezuela Yemen Zambia Zimbabwe

31.5 32.9

25.4 11.7 17.2

12.8

19.8 24.9

18.1

34.5

19 9.2

18.8

5.8 10.3

44.3

35.9 22.6

24.8

12.2 23

12.6 13.8 13.1 18.1 19.2 19.1 17.1 15.7 14.2

10.1 10.8

30

28.6 23.8

29.1 31

21.9

32.8

21

15.3

30.2 29.5

24.5 10.9

Girl Boy

21.5

9.7 10.2 12.5

24.5 23.6 22.1

41.4

22.2 14.8 15.4 13.9

11.1 14 11.4

41

17.5 27

13.1 6.2

37.1

24.1 25.1

17.4 17.4 15.6

8.9

30

23.6

30.5

22.3 22.9

36

14.4 16.1 4.8

0

5

46.7 45.7

11.8 12.3 16.8 10.2 9.7 11.6 14.4

10

15

20

25

30

35

40

45

50

Figure 2.4 Proportion of adolescents in the tropics who met physical activity recommendation. Be physically active for a total of at least 60 min a day. *No data for girls. Data source: Global School-based Health Survey (GSHS) (WHO, 2011).

recommendations. In comparison to girls, boys from these countries were found to be more physically active. Globally, this sex difference has now become evident in not only the younger populations (Butcher, Sallis, Mayer, & Woodruff, 2008; Dan, Mohd Nasir, & Zalilah, 2007; Ridgers, Fairclough, & Stratton, 2010) but also among adults (Bauman et al., 2009; Lin, Yeh, Chen, & Huang, 2010). For sedentary activities, which included sitting, watching television, and chatting (Fig. 2.5), more than 50% of the adolescents in the Cayman Islands,

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26.1

Algeria Benin Cayman China Colombia Cook Islands Costa Rica Djibouti Ecuador Fiji Ghana Grenada Guatemala Guyana India Indonesia Kenya Kiribati Libya Malaysia Maldives Mauritania Mauritius Montserrat Namibia Niue∗ Peru Philippines Senegal Seychelles Solomon Sri Lanka St Lucia St Vincent Suriname Thailand Trinidad and Uganda Vanuatu Venezuela Yemen Zambia Zimbabwe

30

15.4 19.6

40.5

33.1 32.1 32.6 30.6 29.4

24.6 12.6 13.8

52 51.3

38.9

36

47.8

42.9 42.6

25.7 25.3 21.2

63.4

51.1

22.6 21.3

36 35

24.5

34.2 33 35.9 40

13.4 15.2

27.4 29.7

48.5 46.4 41.5 43.2 39.6 39.2 42.7 38.3 45.1

24.4 19.9 20.6

Boys

50.5

31.8 29.6 25.7

Girls

29.5 28.4 28.6 56.2 52.7

27.8 27.1 33.5 34.6

54 38.2

42.3 41.5 39.7 40.5 38.5

0

10

20

30

52

42.6

27.7 26.7

16.1 22.6 18.5 22.8 24.7 25.7

56.5

32.7 33.1

42 42.9

40

50

60

70

Figure 2.5 Proportion of adolescents in the tropics who spent 3 or more hours a day in sedentary activities. Sitting, television watching, and playing video games. *No data for girls. Data source: Global School-based Health Survey (GSHS) (WHO, 2011).

Colombia, Saint Lucia, and the Seychelles spent 3 or more hours a day in these activities. The Cayman Islands recorded the highest percentage (57%) of sedentary lifestyle regardless of sex. The proportion of adolescents who were highly sedentary was similar between boys and girls in most of the countries, except in the Cayman Islands, where the percentage of highly sedentary girls was found to be at least 10% higher than the boys. Overall,

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65

the girls were generally more likely to be involved in sedentary activities compared to boys in the tropics, particularly in the Americas. In comparison, the HBSC study, which involved 41 countries in regions of Europe and North America (WHO, 2008), showed that adolescents in these mainly high-income countries had somewhat similar PA patterns to tropical countries. The proportions of adolescents with sufficient PA among boys and girls in these countries were 25% and 15% in 13 year olds and 20% and 12% in 15 year olds, respectively. These findings indicate that physical inactivity is not simply a problem in developing countries but is also a significant problem in developed nations. Nevertheless, the comparability between these two global surveys may be limited due to the application of different guidelines to define PA and inactivity. For example, adolescents involved in the HBSC were not asked to exclude physical education class when reporting their PA and a higher cut-off was used in classifying adolescents with sufficient PA (engaging in activities for at least 60 min a day for 5 or more days a week). In the context of sedentary behavior, we found discrepancies between the findings reported by the HBSC and our analysis of the GSHS data. The HBSC reported that 70% of boys and 69% of girls who were 13 years old and 69% of boys and 67% of girls who were 15 years old were sedentary (WHO, 2008); these values are much higher than the values among adolescents in the tropics (approximately 30%), which we compiled from the GSHS database. This difference is most likely due to the lower cut-off employed in the HBSC (at least 60 min a day for 5 or more days a week) and the assessment of only one specific activity in defining sedentary behavior, namely, television watching. Overall, our compilation of the GSHS data estimated that only 23.9% of boys and 16.4% of girls in the tropics had met the PA recommendation. On the other hand, more than one-fourth of the adolescents spent 3 or more hours a day on sedentary activities (excluding the hours spent sitting at school and on daily homework).

3.3. PA studies conducted in the tropics In Malaysia, Rezali, Chin, and Mohd Yusof (2012) had reported that the majority of adolescents (56.8%) in the state of Selangor (Kajang) reported sedentary lifestyles, followed by light active (35.0%) and moderately active (6.4%) lifestyles. Only a small percentage of the adolescents (1.8%) engaged in vigorous activity. Similarly, in Vietnam (Ho Chi Minh City), the time

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Xiao Chuan Lau et al.

spent by adolescents in moderate to vigorous PA decreased significantly from 87 to 50 min/day over a 5-year period; the time spent in sedentary behaviors increased from 512 to 600 min/day (Trang et al., 2012). With regard to the PA levels among adolescents in Hong Kong, Lee et al. (2005) reported that 65% of the adolescents participated in vigorous exercise for at least 20 min a day for less than 3 days a week. This finding is particularly true among the girls and those students in higher grades in school. Even for moderate exercise, only approximately 13% of the adolescents were found to participate for at least 30 min a day more than 5 days a week. In Macao, only a small percentage of the school-age children (5.7%) and adolescents (4%) were reported to have engaged in moderate exercise for at least 60 min a day (Lee, 2008). On the other hand, more than half of the children and adolescents spent over 2 h daily on sedentary activities, such as watching television, playing video or computer games, and using the computer for nonacademic purposes. Similar patterns of PA and sedentary behavior were reported among adolescents in Taiwan; a national health survey found that only 28.4% of the adolescents met the PA guidelines, whereas nearly 80% of the adolescents reported being sedentary for more than 8 h a day (Chen, Haase, & Fox, 2007). Similar to other countries, girls were found to be more sedentary than boys. In Mexico, Morales-Rua´n, Herna´ndez-Prado, Go´mez-Acosta, ShamahLevy, and Cuevas-Nasu (2009) reported that the adolescents in Mexico are generally sedentary and tend to spend more time on screen-based activities than PA. This result aligns with what had been recorded in a previous longitudinal study, in which the median time of television watching among Mexican adolescents was 3.90 h/day and only a minimal amount of time was dedicated to vigorous- (0.73 h/day) and moderate-intensity (0.58 h/ day) PA (Caballero et al. 2007). Similar findings have also been reported in Saudi Arabia, where only approximately half of the male and less than a quarter of the female adolescents met the current PA recommendation of 1 h daily of moderate-intensity PA (Al-Hazzaa, Abahussain, Al-Sobayel, Qahwaji, & Musaiger, 2011). This result was also supported by previous studies that involved objective methods of PA measurement, in which it was found that 60% of the children and 71% of the adolescents did not engage in any health-enhancing PA for a sufficient duration and frequency (Al-Hazzaa, 2002). Ng et al. (2011) also reported that the proportion of Emirati girls engaged in moderate PA (53%) and vigorous PA (77%) was significantly less than boys (63% for moderate PA, 81% for vigorous PA). The authors also found that the majority of the Emirati girls (75%) tended

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to spend more time in sitting activities (>5 h/weekday) compared to the boys (<50%). In addition, the PA level among females was found to be decreased across the age groups (from childhood to adulthood). In contrast to the above reported findings reported, Wang, Chia, Quek, and Liu (2006) revealed that 60% of Singaporean children 10–14 years old were physically active (>300 min/week of moderate activity or >120 min/ week of vigorous activity) and only 19% of the children reported low or no PA. High levels of PA were also found among the adolescents in several studies conducted in Mozambique (dos Santos et al., 2013; Nhantumbo, Maia, Saranga, & Prista, 2008; Prista et al., 2009), with girls achieving higher levels of PA than boys. This finding can be explained by the social and economic features in Mozambique, where adolescents spend most of their time in subsistence activities (household chores, farming activities) and engaged in outdoor games during their leisure time. A similar scenario was reported in Senegal, where adolescents are generally more physically active (Be´ne´fice et al., 2001; Be´ne´fice & Ndiaye, 2005).

3.4. Discussion Overall, we found that the majority of the children and adolescents in the tropics are engaged in sedentary behavior and that girls are less active compared to boys. Although studies on the rising trends of sedentary lifestyles among children and adolescents in the tropics are few and inconclusive, available data clearly indicate that physical inactivity and sedentary behavior are emerging as important public health issues that need urgent attention. In this context, the sex difference is particularly important considering that the tracking of PA has been shown to be stronger in female adolescents than their male counterparts (Azevedo et al., 2007). To plan and implement effective interventions to promote regular PA among children and adolescents, a greater understanding of the determinants associated with PA participation in these young populations is needed. However, only a small amount of information is available regarding certain parts of the tropical region. For example, in Malaysia, Dan et al. (2007) reported that sex, self-confidence in performing PA, and peer influence are the most significant contributors explaining PA among young adolescents. This finding is somewhat similar to what has been found in Singapore (Wang et al., 2006) and Thailand (Wattanasit, 2009), whereby psychosocial variables, such as peer influence and perceived physical competence, are significant predictors for PA participation among children and adolescents.

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Recent systematic reviews by Park and Kim (2008) and Craggs, Corder, van Sluijs, and Griffin (2011) also revealed that psychosocial variables were positive significant predictive factors in most of the studies. However, the determinants of PA in these studies also included sex, ethnicity, socioeconomic status, and environmental factors (Kahn et al., 2008; Krange & Bjerke, 2011; Park & Kim, 2008); and therefore, findings generated from one population may not be applicable to the others, even if they are living in the same region of the world. Hence, we suggest that more large-scale studies using a multivariate approach should be conducted in tropical countries, particularly in those countries with high levels of physical inactivity, to further establish evidence for the development of effective PA promotion programs among children and adolescents. One of the identified issues related to PA that would require serious consideration is the selection of an appropriate technique or method for assessing PA to avoid under- or overestimation, particularly in the young population. In this context, we found that most of the surveys or studies conducted in tropical countries only used subjective methods of PA assessment, such as a PA questionnaire, to determine the PA levels of the children and adolescents. Caution should be taken when interpreting these data, as this method greatly relies on the subject’s memory and self-perception of PA behavior (Vanhees et al., 2005). Furthermore, an international consensus on PA guidelines or recommendations should be established. Currently, different guidelines have been developed and used by different countries to define the PA levels of children and adolescents, which has caused some difficulties in making comparisons across the countries and has hindered the establishment of global prevalence data. Therefore, we suggest a greater collaboration among researchers toward the standardization of surveillance methods and guidelines are needed to further ensure the quality of the studies conducted to assess PA in children and adolescents in the tropics.

4. PHYSICAL FITNESS IN THE TROPICS 4.1. Physical fitness PF can be defined as “a physiologic state of well-being that allows one to meet the demands of daily living or that provides the basis for sport performance, or both” (Warburton, Nicol, & Bredin, 2006). There are two categories of PF, namely, performance-related fitness and health-related fitness (Howley, 2001). The former refers to attributes that relate to an individual’s

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69

athletic performance (Howley, 2001), while the latter is linked to health and are affected by habitual PA (Suni et al., 1998). Compared to performancerelated fitness, health-related fitness has been found to be significantly associated with not only cardiovascular profile but also other important health components, such as body adiposity, skeletal, and mental health status, in children and adolescents (Ortega et al., 2008). Moreover, it is believed that the functional status of overall systems in humans can be assessed when performing a health-related fitness test. As health-related PF is considered an important indicator of health status in childhood and adolescence, and could play a major role in health monitoring systems, this section focuses only on the components of health-related PF. In general, health-related PF comprises five components, namely: cardiorespiratory fitness, muscular fitness (muscular strength and muscular endurance), flexibility, body composition, and metabolic fitness with specific functions (Percia, Davis, & Dwyer, 2012; Warburton et al., 2006). Cardiorespiratory fitness reflects the functional capabilities of the cardiovascular and respiratory systems to meet the demands of the tissues while performing specific exercise, while muscular fitness is the capacity to carry out work against a resistance. Flexibility, on the other hand, refers to the ability of a joint moving through normal and pain-free range of motions. Body composition relates to the relative proportion of fat and fat-free tissues in the body (Ortega et al., 2008; Percia et al., 2012; Ruiz et al., 2006). Finally, metabolic fitness can be defined as “the ratio between mitochondrial capacity for substrate utilization and maximum oxygen uptake of the muscles” (Saltin & Pilegaard, 2002). Each of these components can be assessed by a variety of methods, either in the laboratory or in the field.

4.2. Relationship between PF and PA It is often assumed that PA is causally related to PF, suggesting that more habitually active individuals are usually fitter. Therefore, these two components are always presumed to be interchangeable especially in large epidemiologic studies, with PF commonly accepted as a more accurate measure of PA compared to the self-reported PA methods (Warburton et al., 2006; Williams, 2001). However, the evidence for this relationship remains inconclusive. Earlier studies suggested that the relationships between regular PA and various indicators of PF had been reported to be generally low to moderate in children (Sallis, McKenzie, & Alcaraz, 1993) and adolescents (Aaron et al., 1993). Katzmarzyk, Malina, Song,

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and Bouchard (1998) also reported a significant but only weak to moderate relationship between PA and health-related PF among adolescents; however, they noted that PA accounted for only a relatively small percentage (11–21%) of the variations in health-related fitness. This finding was further supported by an earlier review by Malina (2001) who concluded that PA was not a strong predictor for the fitness levels across childhood through to adulthood. In a more recent study, Blaes, Baquet, Fabre, Van Praagh, and Berthoin (2011) also reported that PA level was poorly correlated to health-related PF among school-aged children. Notwithstanding the above findings, there are several studies that have reported positive effects of PA on the fitness levels of adolescents. Both longitudinal (Beunen et al., 1992) and cross-sectional (Huang & Malina, 2002) studies have revealed that adolescents who were more physically active tended to be more fit when performing the cardiorespiratory endurance tasks. This result was found despite the fact that the methods used in classifying adolescents as active or inactive differed across studies. Aires et al. (2011) also found that cardiorespiratory fitness was independently and positively associated with PA, which further suggested that increasing overall PA levels may be an effective strategy for improving the fitness levels of adolescents. The magnitude of improvements, however, was found to be strongly determined by the level of PA (Pahkala et al., 2013; Timmons et al., 2010). Nevertheless, a 4-year longitudinal study conducted by Baquet, Twisk, Kemper, Van Praagh, and Berthoin (2006) revealed that increasing or decreasing PA level from childhood to adolescence was not associated with changes in PF, but children who were the most physically active at baseline were the fittest, which further suggests that PF is associated with maintaining a high level of PA since early life. It has often been argued that the current evidence on the relationships between PA and fitness components in children and adolescents is still weak and inconsistent (Hands, Larkin, Parker, Straker, & Perry, 2009), except for cardiorespiratory endurance; evidence strongly suggests that the benefits of habitual PA may be only specific to this fitness component. Moreover, PF is thought to be influenced by several factors other than PA. For example, sedentary behavior, such as television viewing and playing video games, was also found to be negatively related to the indicators of health-related fitness, although the relationship was not strong (Katzmarzyk et al., 1998). To the best of our knowledge, there are currently no data available on the relationships between PA and fitness in adolescents in tropical countries. Hence, it is of the utmost importance to initiate research to better

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understand this relationship because both low fitness levels and physical inactivity have been proven to be independent risk factors for various diseases (cardiovascular disease, cancer, obesity) in childhood and adolescence (Ortega et al., 2008).

4.3. PF studies conducted in the tropics Compared to PA, only limited information is available for fitness performance and its secular changes among children and adolescents in the tropics in recent decades. In Thailand, there was an overall decline in the power test performance, speed test performance, abdominal strength, and endurance but not in upper-body strength among children 8–12–years old over a 13-year period (1990–2003) (Klanarong, 2005). A meta-analysis performed by Macfarlane and Tomkinson (2007) summarized the secular changes in the power, speed, and cardiovascular endurance test performance of over 23.5 million individuals 6–19 years old from seven Asian countries between 1917 and 2003. Only a small change in the power and speed test performance of the Asian children and adolescents in recent decades was found; however, a consistent decline could be observed in the cardiovascular endurance performance of these young people across all the studied Asian nations over the past 10–15 years. A few studies have been conducted to compare the fitness levels of children and adolescents in the tropics with their counterparts in other regions. Ip (1991) compared the fitness performance of children 5–12 years old in Hong Kong with the performance of children from the United States, and the results indicated that children in Hong Kong, on average, performed worse on the sit-up test (32% fewer) and 9-min run (7.2% less distance) but were slightly more flexible than their American counterparts. Huang (1994) reported that adolescents 12–14 years old in America outperformed their Taiwanese counterparts in the 1-mile run. However, another study that used the same fitness protocol showed that Taiwanese boys were generally superior to their American counterparts for these fitness measures; however, no significant differences could be observed between the girls of both countries (Su, 1993). Another recent study involving the comparison of fitness performance (20-m shuttle run test) among children in 37 countries indicated that children in tropical countries, such as Hong Kong, Brazil, and Singapore, were classified as below average compared to children from other regions, such as Northern Europe (Olds, Tomkinson, Le´ger, & Cazorla, 2006).

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4.4. Discussion The current PF level among children and adolescents in tropical countries is largely unknown, given that only a few studies have been conducted in this recent decade. Hence, more comprehensive investigations and periodic measures of the fitness levels in this population are needed not only to fill gaps in the PF data but also to assess the effectiveness of the present sports development and health promotion programs in these tropical countries. As introduced by Bouchard and Shephard (1994), health-related PF can be assessed by five various components. One recent study revealed that all of these health-related PF variables or skills (except flexibility) were strongly associated with each other, and thus a single test of these variables may be sufficient to reflect the overall fitness levels among children and adolescents (Dumith, Van Dusen, & Kohl, 2012). However, it is still unknown which of these components can be used as the single-choice indicator of PF levels for these young populations. This issue is particularly important in those countries where resources are limited (such as a lack of instruments and qualified fitness trainers) for the evaluation of all of these fitness components in large population-based surveys. Moreover, comparisons of the PF levels of children and adolescents may not be appropriate if the assessed fitness components differ among countries. Various fitness batteries have been developed in the recent decades for assessing each fitness component. However, variations exist across the fitness batteries (Mak et al., 2010), and thus controversy may occur over which test truly reflects the actual fitness component assessed. Therefore, we believe that there is a need for the standardization of the indicators of PF levels and their respective validated instruments among children and adolescents.

5. ENERGY COST OF PHYSICAL ACTIVITIES IN CHILDREN AND ADOLESCENTS IN THE TROPICS 5.1. Energy cost of habitual activities The measurement of total daily EE is an important aspect of the assessments of human health and nutrition. The importance of total energy expenditure (TEE) gained further attention when the Joint Consultation of the FAO, WHO, and United Nations University (UNU) published by the FAO (1985, 2004) adopted the factorial method to estimate daily energy requirements based on EE rather than energy intake. Children’s and adolescents’ energy requirements were estimated from the scarce information available.

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Many of the estimates were derived from the energy cost of activities performed by adults and were expressed by unit of body weight; however, Torun (1990) questioned the validity of such estimates of energy costs. Rowland, Auchinachie, Keenan, and Green (1987) found that the weight-related energy cost of exercise is lower in adults than children. The difference is likely due to physiology-related mechanisms (Rowland & Green, 1988). In addition, differences in body height (Bonen, Heyward, Cureton, Boileau, & Massey, 1979), body weight, and body composition (Murray et al., 1993) also contribute. It is thus more appropriate to determine the energy cost of activities in children and adolescents themselves to derive an accurate estimate of daily EE and recommendations for energy intake based on this EE would therefore be more appropriate. TEE includes BMR, resting metabolic rate, the thermic effect of food and activity energy expenditure (AEE). BMR contributes approximately 60–70% of daily EE (Ravussin, Lillioja, Anderson, Christin, & Bogardus, 1986). The BMR is often used for the calculation of TEE by multiplying the time spent in various activities by the respective energy cost calculated based on a BMR multiple (also known as the physical activity ratio, PAR). The PAR is the ratio that expresses the energy cost of an individual activity per minute as multiples of BMR (James & Schofield, 1990). The assumption for using this approach to calculate energy requirements is that it compensates for differences in body weight between individuals. The PAR of activities can be obtained either by direct measurements or from data in the published literature. Published data on the PARs of different activities are available primarily for Caucasian children (Ainsworth et al., 1993, 2000; FAO, 1985, 2004; James & Schofield, 1990). Using data from previously published literature, Torun (1990) compiled the energy cost of physical activities among healthy children in developing countries. The following section summarizes the information currently available in the tropics on the energy cost of habitual activities commonly performed by children.

5.2. Methods of measuring energy cost EE can be estimated by indirect estimates or by direct measurements of oxygen uptake. Indirect methods include PA questionnaires, pedometers, and accelerometers (Kashiwazaki, Inaoka, Suzuki, & Kondo, 1986), heart rate (HR) monitors (Spurr et al., 1988), and doubly labeled water (Stager, Lindeman, & Edwards, 1995). The Douglas bag (DB) technique is the classic

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method of direct measurement of oxygen uptake, while the Kofranyi– Michaelis (KM) respirometer was developed for energy exchange studies in the field. All of these techniques have their own strengths and limitations for estimating EE. Most studies on the energy costs of activities in the tropics (Table 2.2) used either the DB technique, the KM respirometer, or a HR monitor (together with diary). Under trained hands, the DB technique is considered to be the gold standard (Carter & Jeukendrup, 2002); however, potential air leakage is one of the limitations of this method. Hopker, Jobson, Gregson, Coleman, and Passfield (2012) tested the reliability of the DB technique and found that the gas sampling attained a low coefficient of variation (CV), which was less than 0.5% for both oxygen and carbon dioxide. However, the CV for the bag residual volume was approximately 15%, which is considered high. This high variation could lead to errors; however, a large gas sample volume may help to minimize the error. The KM respirometer, developed as an alternative technique for measuring EE, has generally shown good agreement with the DB technique (Louhevaara, Ilmarinen, & Oja, 1985), despite a minor overestimation of VO2. Hence, the KM respirometer is still considered reliable for VO2 measurements in field settings (Louhevaara et al., 1985). Other than the DB technique and the KM respirometer, Louie et al. (1998) and Eston, Ingledew, Fu, and Rowlands (1998) suggested that triaxial accelerometry provides the best assessment of EE. In addition to the techniques discussed earlier, the HR method has often been used to estimate EE due to its ability to record readings over time, its ease of administration, and its reflection of the relative stress on the cardiopulmonary system from PA (Welsman & Armstrong, 1992). However, HR can be impacted by the subject’s emotional status and Nieman (1999) suggested that HR is approximately 10% higher for upper-body dynamic exercises than lower-body dynamic exercises. The level of PF may also affect the relationship between HR and VO2. Physically fit individuals will have a lower HR due to a greater stroke volume compared to those who are less fit (Saris, Binkhorst, Cramwinckel, Van Waesberghe, & Van der VeenHezemans, 1980). Consequently, HR is higher during static exercise (Klausen, Rasmussen, Glensgaard, & Jensen, 1985), as it is associated with the active muscle mass and the percentage of maximum pulmonary ventilatory response rather than VO2. Despite these limitations, studies in children and adults have shown that when HR monitor is used together with motion sensors, less error is

Table 2.2 Compilation of the energy cost of physical activities in the tropics Activity

Age (years)

Ethnicity

BMI status

Method

BMR (kcal/min)

Energy cost kcal/min

PAR

Source

Boys

Lying down and resting

9–12

Malay, Chinese, Indian

OW, OB Douglas Bag

1.07  0.16 0.87  0.23 0.81 Lim (2004)

Lying down and resting

10–12

Malay, Chinese, Indian

NW

Douglas Bag

0.88  0.02 0.78  0.10 0.89 Tan (2003)

Sitting quietly

16–17

Malay

NW

Douglas Bag

NA

Sitting and reading

10–12

Malay, Chinese, Indian

NW

Douglas Bag

0.88  0.02 0.94  0.04 1.07 Tan (2003)

Sitting and reading

16–17

Malay, Chinese

NW

Douglas Bag

1.16  0.09 1.49  0.14 1.28 Poh, Yap, Sia, Ong, and Ismail (2003)

Sitting and reading

16–17

Malay

NW

Douglas Bag

NA

Sitting and reading or writing

9–12

Malay, Chinese, Indian

OW, OB Douglas Bag

1.07  0.16 0.96  0.19 0.90 Lim (2004)

Sitting and writing

10–12

Malay, Chinese, Indian

NW

Douglas Bag

0.88  0.02 1.00  0.03 1.14 Tan (2003)

Sitting and writing

16–17

Malay

NW

Douglas Bag

NA

NA

NA

NA

1.11 Ismail, Ong, and Zawiah (1991)

1.19 Ismail et al. (1991)

1.30 Ismail et al. (1991) Continued

Table 2.2 Compilation of the energy cost of physical activities in the tropics—cont'd Ethnicity

BMI status

Sitting and playing video game

12–14

Malay, Chinese

NW

Standing

9–12

Malay, Chinese, Indian

OW, OB Douglas Bag

1.07  0.16 1.41  0.10 1.32 Lim (2004)

Standing

10–12

Malay, Chinese, Indian

NW

Douglas Bag

0.88  0.02 1.19  0.10 1.35 Tan (2003)

Standing

12–14

Malay, Chinese

NW

Douglas Bag

0.84  0.02 1.32  0.08 1.57 Poh et al. (2003)

Standing

16–17

Malay, Chinese

NW

Douglas Bag

1.16  0.09 1.80  0.29 1.60 Poh et al. (2003)

Walking

9–12

Malay, Chinese, Indian

OW, OB Douglas Bag

1.07  0.16 2.09  0.31 1.95 Lim (2004)

Walking

10–12

Malay, Chinese, Indian

NW

Douglas Bag

0.88  0.02 1.36  0.13 1.55 Tan (2003)

Walking

12–14

Malay, Chinese

NW

Douglas Bag

0.84  0.02 2.04  0.08 2.42 Poh et al. (2003)

Walking

16–17

Malay, Chinese

NW

Douglas Bag

1.16  0.09 2.90  0.29 2.59 Poh et al. (2003)

Sweeping floor

12–14

Malay, Chinese

NW

Douglas Bag

0.84  0.02 2.13  0.19 2.54 Poh et al. (2003)

Activity

Method

Douglas Bag

BMR (kcal/min)

Energy cost

Age (years)

kcal/min

PAR

Source

0.84  0.02 1.21  0.09 1.44 Poh et al. (2003)

Cycling

16–17

Douglas Bag

1.16  0.09 2.99  0.64 2.63 Poh et al. (2003)

Malay, Chinese

NW

Ascending and descending 9–12 stairs

Malay, Chinese, Indian

OW, OB Douglas Bag

1.07  0.16 4.96  1.02 4.64 Lim (2004)

Ascending and descending 10–12 stairs

Malay, Chinese, Indian

NW

Douglas Bag

0.88  0.02 2.75  0.18 3.13 Tan (2003)

Ascending and descending 12–14 stairs

Malay, Chinese

NW

Douglas Bag

0.84  0.02 3.23  0.35 3.84 Poh et al. (2003)

Ascending and descending 16–17 stairs

Malay, Chinese

NW

Douglas Bag

1.16  0.09 4.92  0.80 4.38 Poh et al. (2003)

Girls

Lying down and resting

9–12

Malay, Chinese, Indian

OW, OB Douglas Bag

0.90  0.06 0.87  0.07 0.97 Lim (2004)

Lying down and resting

10–12

Malay, Chinese, Indian

NW

Douglas Bag

0.80  0.03 0.61  0.12 0.76 Tan (2003)

Sitting and reading

10–12

Malay, Chinese, Indian

NW

Douglas Bag

0.80  0.03 0.90  0.10 1.13 Tan (2003)

Sitting and reading

16–17

Malay, Chinese

NW

Douglas Bag

0.88  0.07 1.08  0.17 1.23 Poh et al. (2003)

Sitting and reading or writing

9–12

Malay, Chinese, Indian

OW, OB Douglas Bag

0.90  0.06 0.92  0.07 1.02 Lim (2004) Continued

Table 2.2 Compilation of the energy cost of physical activities in the tropics—cont'd Activity

Sitting and writing

10–12

Malay, Chinese, Indian

NW

Douglas Bag

0.80  0.03 0.94  0.08 1.18 Tan (2003)

Sitting and playing video game

12–14

Malay, Chinese

NW

Douglas Bag

0.80  0.03 0.99  0.05 1.24 Poh et al. (2003)

Standing

9–12

Malay, Chinese, Indian

OW, OB Douglas Bag

0.90  0.06 1.40  0.17 1.56 Lim (2004)

Standing

10–12

Malay, Chinese, Indian

NW

Douglas Bag

0.80  0.03 1.06  0.07 1.33 Tan (2003)

Standing

12–14

Malay, Chinese

NW

Douglas Bag

0.80  0.03 1.06  0.07 1.33 Poh et al. (2003)

Standing

16–17

Malay, Chinese

NW

Douglas Bag

0.88  0.07 1.15  0.18 1.36 Poh et al. (2003)

Walking

9–12

Malay, Chinese, Indian

OW, OB Douglas Bag

0.90  0.06 2.04  0.19 2.27 Lim (2004)

Walking

10–12

Malay, Chinese, Indian

NW

Douglas Bag

0.80  0.03 1.28  0.11 1.60 Tan (2003)

Walking

12–14

Malay, Chinese

NW

Douglas Bag

0.80  0.03 1.76  0.10 2.20 Poh et al. (2003)

Walking

16–17

Malay, Chinese

NW

Douglas Bag

0.88  0.07 2.19  0.30 2.59 Poh et al. (2003)

Ethnicity

BMI status

Method

BMR (kcal/min)

Energy cost

Age (years)

kcal/min

PAR

Source

Sweeping floor

12–14

Malay, Chinese

NW

Douglas Bag

0.80  0.03 1.77  0.11 2.21 Poh et al. (2003)

Cycling

16–17

Malay, Chinese

NW

Douglas Bag

0.88  0.07 1.97  0.35 2.33 Poh et al. (2003)

Ascending and descending 9–12 stairs

Malay, Chinese, Indian

OW, OB Douglas Bag

0.90  0.06 3.55  0.87 3.94 Lim (2004)

Ascending and descending 10–12 stairs

Malay, Chinese, Indian

NW

Douglas Bag

0.80  0.03 2.49  0.37 3.11 Tan (2003)

Ascending and descending 12–14 stairs

Malay, Chinese

NW

Douglas Bag

0.80  0.03 2.97  0.17 3.71 Poh et al. (2003)

Ascending and descending 16–17 stairs

Malay, Chinese

NW

Douglas Bag

0.88  0.07 3.95  0.44 4.60 Poh et al. (2003)

PAR, physical activity ratio (energy cost of activity/BMR); NA, not available; OB, obese; OW, overweight; NW, normal weight.

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produced in the estimation of energy cost (Brage et al., 2004; Corder, Brage, Wareham, & Ekelund, 2005). For a more accurate estimation of EE, the use of multiple methods simultaneously has been suggested. Studies have shown that employing multiple accelerometers provides promising data for accurately estimating EE (Chen et al., 2003; Swartz et al., 2000). Eston et al. (1998) concluded that combined HR monitors and a triaxial accelerometer slightly improve the estimations of EE for specific activities. The SenseWear® Pro2 Armband (SWA; Body-Media, Inc., Pittsburgh, PA) and the Intelligent Device for Energy Expenditure and Activity® (IDEEA; Minisun LLC, Fresno, CA) are activity monitors with multiple sensors that can provide accurate assessments of PA intensity and type, EE, and environmental exposure. The SWA has five different types of sensors, while the IDEEA uses an array of miniaccelerometers attached to different parts of the body. The SWA is suitable for estimating EE during rest, stationary bicycling, motoring and weight-lifting activities among people aged 7–65 years old. However, Arvidsson, Slinde, Larsson, and Hulthe´n (2007) reported that the SWA underestimated the energy cost for most activities, especially for high-intensity activities. In another study, Arvidsson, Slinde, Larsson, and Hulthe´n (2009) concluded that IDEEA had the best ability to assess energy cost and that SWAs are more feasible for use in children in free-living conditions. However, these activity monitors had limitations when assessing the energy cost of playing basketball, stationary bicycling, and jumping on a trampoline (Arvidsson et al., 2009). Recently, Whybrow, Ritz, Horgan, and Stubbs (2013) reported that the IDEEA overestimated EE in both the controlled laboratory and free-living conditions. In summary, more effort has to be made in designing and developing improved models/equipment that are suitable for the estimation of the energy cost of a wide range of activities in children and adolescents. These methods would enable us to derive more accurate estimates of energy requirements and hence the overall energy balance.

5.3. Compilation of energy cost of physical activities in the tropics We conducted electronic and manual searches to obtain published data on the energy costs of physical activities in the tropics. Only studies that reported measurements of energy costs of physical activities and BMR were included in this review. Table 2.2 shows a compilation of the energy cost of activities for boys and girls published after the Torun (1990) publication.

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The energy cost of activities in children and adolescents of tropical countries tended to be lower in comparison to their counterparts living in temperate countries. Where data were available for similar activities in both tropical and temperate countries, the mean energy costs (or PAR) were lower in the tropics. For example, the PAR for sitting activities in French boys (PAR ¼ 1.36) (Vermorel, Vernet, Bitar, Fellmann, & Coudert, 2002) was higher than in Malaysian boys (PAR ¼ 1.11) (Poh et al., 2003). For standing quietly, the energy cost was higher among Malaysian boys (1.80, 16–17 years old) compared to French boys (1.42, 14–16 years old) (Vermorel et al., 2002); however, it must be noted that the Malaysian boys in this case were older than their French counterparts. The energy cost of physical activities for boys were always lower than girls when expressed in absolute values (kcal/min). As shown in Table 2.2, the same can be said when the energy cost is expressed in multiples of BMR, otherwise known as PAR; however, there are certain activities in which the opposite is true. Previous studies have shown similar findings when comparing between the sexes among children and adolescents (Torun, 1990). Table 2.2 also shows that the energy costs for physical activities among overweight and obese children and adolescents were generally higher than those who were of normal weight. These findings were similar to results reported by Maffeis, Schutz, Schena, Zaffanello, and Pinelli (1993) among obese and nonobese prepubertal children in Italy. In terms of age, older adolescents appeared to have higher energy costs for similar activities compared to their younger counterparts (Poh et al., 2003). One could argue that the above studies are not strictly comparable because of differences in methodologies employed, the lack of standardization in the activities performed, and the small number of children who participated in the studies. Therefore, well-designed, standardized studies must be conducted to confirm if there are differences in the energy cost of activities for children and adolescents from differing geographic, ethnic, socioeconomic or nutritional backgrounds in the tropics. Most of the studies were conducted in controlled laboratory environments; therefore, freeliving conditions also should be taken into consideration in future studies. Research on the energy cost of activities in the tropics is scarce and most of the data are old (reported between 1923 and 1986) and had already been compiled by Torun (1990). Our efforts to search for and report on recent studies could not locate any published data from other tropical countries apart from our own data collected in Malaysia. Generally, there are less data reported for girls than boys, and most countries in the tropics still lack the

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resources and infrastructure to conduct EE studies. Sometimes, available data were not released for public use or were not published. There is clearly a need for more studies on the energy cost of activities in a wide range of age groups, ethnicities, and in both tropical and temperate countries to allow for comparison among regions of the world. In addition, the scope of activities to be measured needs to be widened to include classroom activities, sports and games, household chores, and other tasks commonly performed by children and adolescents in the tropics.

6. IMPLICATIONS OF PA, PF, AND ENERGY COST ON OBESITY IN THE TROPICS 6.1. PA and obesity Current evidence clearly indicates that obesity and physical inactivity are two important health issues among children and adolescents. In recent decades, many studies have been conducted to determine the possible relationships between PA and obesity; however, to date, this relationship has not been extensively studied, particularly among these young populations in tropical countries. Evidence from numerous studies conducted in different countries in the tropics consistently suggests that obesity is associated with a reduced level of PA. Studies in Hong Kong (Yu et al., 2002) and the Kingdom of Tonga (Smith, Phongsavan, Havea, Halavatau, & Chey, 2007) reported that a higher level of PA participation was associated with a reduced likelihood of being overweight among adolescents in these countries. Similar findings also have been reported by Ramachandran et al. (2002) and Dancause et al. (2012), in which low PA was associated with overweight among urban adolescents. Bharati et al. (2008) also observed that adolescents who participated in less than 30 min of outdoor games were found to have an increased risk of being overweight or obese. This finding aligns with the findings of two other longitudinal observational studies, which concluded that increased PA could be a protective factor for the relative weight and fatness gain across childhood and adolescence (Must & Tybor, 2005; Reichert, Menezes, Wells, Dumith, & Hallal, 2009). In addition, another recent cross-sectional study by Rezali et al. (2012) revealed that PA accounts for approximately 21% of the variations in body weight status of adolescents in Malaysia (Selangor). In addition to low levels of PA, sedentary behavior also contributes to the increased overweight and obesity rates in children and adolescents. According to Hong (2005), time spent watching television during the

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weekday was strongly associated with overweight/obesity in Vietnamese adolescents. For children, those who spent 4 h or longer each weekday in this sedentary activity were four times more likely to be overweight than those who spent only 1 h or less per weekday. Vicente-Rodrı´guez et al. (2008) also reported that the risk of overweight was increased by 15.8% per each hour of television viewing. Similarly, Sharifah, Nur Hana, Ruzita, Roslee, and Reilly (2011) found that the obese children in their intervention study (MASCOT) spent 89% of their waking day on sedentary activity and only 1%, or approximately 8 min per day, in moderate to vigorous intensity PA. More recently, Al-Nuaim et al. (2012) reported that larger waist circumference was recorded among adolescents who led a more sedentary lifestyle. Nevertheless, there are studies that have reported negative results for the relationships between PA and obesity. In a case–control study, Amini et al. (2009) showed no significant difference in daily PA patterns between the overweight or obese and normal weight groups, suggesting that PA may not be related to overweight or obese among children. Wang et al. (2006) also noticed that there were no significant differences in the time spent watching television and playing electronic games between normal weight and overweight adolescents during both weekdays and weekend days. Based on the available evidence, it is believed that physical inactivity and sedentary behavior are somehow associated with the development of overweight and obesity among children and adolescents. However, the direction of causality cannot be inferred from cross-sectional associations, as the findings could equally suggest that being obese leads to physical inactivity rather than the other way around. Recently, a nonintervention prospective cohort study reported that physical inactivity is the result rather than the cause of obesity (Metcalf et al., 2011). Nevertheless, there is also evidence that indicates that obesity and physical inactivity are interrelated in a cyclic relationship (Hallal et al., 2006). To examine the associations between PA variables and BMI categories (overweight and nonoverweight), we conducted a logistic regression analysis using the data extracted from the GSHS. In this analysis, only 20 tropical countries with complete data for BMI and PA were included and the results are presented in Table 2.3. No significant associations were found among all these countries, except Libya, in which the odds ratio of being overweight among those participants who did not meet WHO PA recommendations (WHO, 2010) was higher than their counterparts (OR: 1.634; 95% confidence interval: 1.036–2.576).

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Table 2.3 Odds ratio for being overweight using prediction models with physical activity variables among adolescents in the tropics (13–15 years old) Physically active for Sitting activities Country the past 7 days p Value (3 or more hours) p Value

Benin

1.160 (0.443–3.039)

0.762

1.127 (0.570–2.229)

0.732

British Virgin Islands

0.678 (0.511–0.898)

0.007**

1.099 (0.886–1.362)

0.391

China

0.773 (0.458–1.304)

0.334

0.781 (0.489–1.247)

0.301

Costa Rica

0.819 (0.649–1.032)

0.090

0.891 (0.748–1.060)

0.193

Djibouti

1.211 (0.603–2.434)

0.591

1.669 (0.889–3.131)

0.111

Egypt

1.487 (0.965–2.289)

0.072

0.645 (0.475–0.876)

0.005**

Ghana

0.936 (0.279–3.134)

0.914

0.771 (0.332–1.789)

0.544

Guatemala

1.049 (0.889–1.237)

0.574

0.819 (0.719–0.933)

0.003**

India

1.246 (0.723–2.146)

0.429

0.515 (0.301–0.883)

0.016**

Indonesia

0.901 (0.375–2.165)

0.816

0.768 (0.401–1.473)

0.427

Jordan

1.280 (0.634–2.586)

0.491

1.465 (0.816–2.629)

0.201

Libya

1.634 (1.036–2.576)

0.035*

1.035 (0.683–1.569)

0.872

Myanmar

0.531 (0.068–4.161)

0.547

0.587 (0.126–2.731)

0.497

Pakistan

0.715 (0.477–1.072)

0.104

0.898 (0.596–1.352)

0.605

Philippines

0.900 (0.564–1.437)

0.659

0.786 (0.609–1.014)

0.064

Seychelles

0.954 (0.512–1.779)

0.882

1.462 (0.904–2.363)

0.122

Sri Lanka

0.117 (0.026–0.527)

0.005**

1.483 (0.331–6.643)

0.607

Suriname

0.858 (0.619–1.188)

0.356

0.894 (0.692–1.154)

0.389

Thailand

0.674 (0.375–1.211)

0.187

0.773 (0.531–1.125)

0.179

Logistic regression analysis: significant at *p < 0.05, **p < 0.01. Data source: Global School-based Health Survey (GSHS) (WHO, 2011).

We also found that for adolescents who spent 3 or more hours a day doing sedentary activities, such as sitting while watching television, playing computer games, talking with friends, or doing other sitting activities, had a lower risk for being overweight compared to those who spent less than 3 h doing these activities on a typical day. This finding was particularly true in Egypt (OR: 0.645; 95% CI: 0.475–0.876), India (OR: 0.515; 95% CI: 0.301–0.883), and Guatemala (OR: 0.819; 95% CI: 0.719–0.933).

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85

However, this study only used a univariate analysis, which does not consider the effects contributed by other obesity-related factors. Nevertheless, this finding is in agreement with a few other studies that reported that PA has only a modest impact on body composition (Wareham, van Sluijs, & Ekelund, 2005), and individuals who are physically active are more susceptible to obesity compared to those who are less active (Harding, Teyhan, Maynard, & Cruickshank, 2008) likely due to an increase in sedentary time among those people who were more active. Although there is innumerable evidence that obesity and PA are linked, our analysis of the GSHS data from the tropics showed that PA may not be associated with the overweight incidence among adolescents in tropical countries. Despite a lack of robust evidence to prove a causal association, it is important that children and adolescents be encouraged to be more physically active and to spend less time in sedentary activities to avoid being overweight or obese. While PA is an important feature of the EE component, the sedentary behavior that typically coexists with eating, particularly snacking, may also cause an extra increase in the energy intake of children and adolescents and thus plays an equally important role in maintaining energy balance (Al-Hazzaa et al., 2011; Blundell, King, & Bryant, 2005; Chou & Pei, 2010). This factor therefore strengthens the need for increasing PA and reducing sedentary behavior in order to decrease the prevalence of overweight and obesity among children and adolescents.

6.2. PF and obesity Because there are only a few studies reporting on the relationships between PF and obesity among the children and adolescents in tropical countries, in this section, we have also reviewed the findings reported by other countries and regions. There are two recent longitudinal studies (Aires et al., 2010; He et al., 2011) that reported an inverse relationship between PF and BMI. The risk of becoming overweight or obese was higher among subjects with low fitness levels compared to those who had higher fitness levels at baseline. This observation is in agreement with a recent study conducted by Monyeki, Neetens, Moss, and Twisk (2012) among children in South Africa. In addition, He et al. (2011) found that boys with low fitness levels at baseline were more likely than the girls to become overweight 3 years later. A few studies in the tropics also reported that overweight and obese children and adolescents tended to perform worse on certain fitness tests than

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their normal weight counterparts (Chen et al., 2006; Shang et al., 2010). Mak et al. (2010) found that both overweight and obese adolescents performed poorly on push-up, sit-up, and endurance running tests but not on the sit-and-reach tests, which mainly assessed flexibility. This result is similar to a later study conducted in Oaxaca, Mexico, where overweight and obese adolescents were found to have less muscular strength and endurance (Malina, Reyes, Tan, & Little, 2011). Compared to other components of PF, cardiorespiratory fitness has often been shown to be more important in evaluating a child’s overall fitness level (Nassis et al., 2005). Therefore, most researchers have focused on and used only the cardiorespiratory fitness component when assessing an individual’s PF. Overall, many studies have reported that a high cardiorespiratory fitness level was significantly associated with lower total or central body fatness among children and adolescents (Ara, Moreno, Leiva, Gutin, & Casaju´s, 2007; Ortega et al., 2007), not only for normal weight individuals but also for overweight and obese children (Nassis et al., 2005). In addition, several studies also showed that cardiorespiratory fitness levels achieved during childhood and adolescence can help predict total and central body fatness in adulthood (Koutedakis & Bouziotas, 2003; Psarra, Nassis, & Sidossis, 2006). Based on the available evidence, it can be concluded that low PF, particularly a low cardiorespiratory fitness level, is significantly associated with higher total or central body fatness among children and adolescents. However, little is known about the relationships between other elements of PF and overweight and obesity development in children and adolescents. Hence, more quality studies that involve different elements of PF should be conducted to further investigate the role of PF in body fatness gain in these young populations.

6.3. Energy cost of PA and obesity Obese children and adolescents have higher body weights and greater FFM and consequently a higher total daily energy expenditure (TDEE), BMR, and AEE than nonobese subjects (Treuth et al., 1998). The higher TDEE could also be explained by the higher body weight in overweight adolescents. Obese children require extra energy to move around due to their higher body mass. However, previous studies have shown that obese children usually spend less time in PA and more time involved in sedentary activities compared to their age-matched counterparts (Chou & Pei, 2010; Ramachandran et al., 2002). This observation raises the question of

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87

the role metabolic rate and energy cost of activities play in the development of obesity in children and adolescents. Ekelund et al. (2002) found that accelerometer-measured PA was lower in obese adolescents even though their EE did not differ significantly from their normal weight counterparts. The authors hypothesized that this result was due to the increased energy cost of moving a larger body mass, even though the obese group was less physically active. Ekelund et al. (2002) suggested that the energy costs of activities were not associated with obesity in children and adolescents, but the duration of PA and the total amount of PA play an important role. Moreover, Goran, Hunter, Nagy, and Johnson (1997) reported that time spent in PA was negatively associated with fat mass, and the authors found no association between body fat mass and the energy cost of PA (using doubly labeled water). On the other hand, Lazzer et al. (2003) reported that the EE associated with physical activities was lower in obese adolescents despite the higher energy cost of the physical activities. These results suggested that energy imbalance impacts the development of overweight and obesity and may result from low PA. The energy cost of walking adjusted for body mass was not significantly different between obese and nonobese individuals (Delextra, Matthew, Cohen, & Brisswalter, 2011), and our finding is in agreement with previous studies (Ekelund et al., 2002; Treuth et al., 1998). However, Browning and Kram (2007) reported that, at the same speed of exercise, the energy cost was significantly greater in obese than in normal weight children, in both boys and girls, especially at high walking speeds (Katch, Becque, Marks, Moorehead, & Rocchini, 1988). Moreover, similar findings were also reported by Maffeis et al. (1993). Schwartz, Koop, Bourke, and Baker (2006) employed a nondimensional normalization scheme in an energy cost comparison for children that compensated for physiological and anthropometric factors. The net walking metabolic rate was approximately 20% greater in obese children compared to their normal weight counterparts. This result may be due to obese children having larger pulmonary ventilatory responses to exercise than nonobese children (Browning & Kram, 2007), to the greater muscle force required by those who are obese having to support excess body fat (Grabowski, Farley, & Kram, 2005), and to the increased rate of fat oxidation and increased cardiac and respiratory work (Bruckner et al., 1991). There are only a few studies that directly compared the energy costs of activities among obese and nonobese adolescents across a range of walking speeds (Beatriz & Oded, 2003; Lazzer et al., 2003). Nevertheless, available findings suggest that walking and running entails more

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Xiao Chuan Lau et al.

energy in obese compared to normal weight children and adolescents. Thus, PA interventions should incorporate these types of activities to assist children and adolescents in body weight reduction. Differences in the energy cost of PA associated with obesity are still largely unknown (Hill & Saris, 1998), although as obesity develops, the energy cost of weight-bearing PA increases (Miller & Blyth, 1955; Passmore, 1956). The degree of adiposity (Murray et al., 1993) and total body mass (Peyrot et al., 2010) have been shown to be determinants of the energy cost of weight-bearing activities, such as walking and running. Peyrot et al. (2010) reported that the energy cost of walking was reduced in weight-reduced adolescents because less leg muscles were required. However, Beatriz and Oded (2003) found that adiposity was not associated with the energy cost of locomotion. On the other hand, laboratory studies show that fatness and the energy costs of specific activities are similar in lean and obese children after adjusting for differences in body composition (Maffeis et al., 1993, 1994). In summary, based on available evidence, walking at a high speed and running could be used in exercise modules to help promote weight loss. However, the duration and intensity of the PA are more important than the energy cost of specific activities for reducing body weight. A major limitation of a majority of the studies examining the role of the energy costs of activities and EE in the etiology of obesity was their cross-sectional design, in which causal relationships could not be determined. Nevertheless, since studies on the energy costs of habitual activities and the implication of these factors on obesity are scarce, more studies are recommended. Knowledge regarding the energy cost of physical activities is essential for planning PA programs to be used in managing and treating children and adolescents with weight problems.

7. CONCLUSION In conclusion, the prevalence of overweight and obesity as well as sedentary behavior is increasing, while PF levels in children and adolescents are declining in the tropics. These findings indicate a potential threat to the future health of this population. The role of PA or PF in predicting overweight or obesity remains inconclusive in the tropics, mostly due to the limitations related to methodological designs. More longitudinal studies and further prospective studies are needed to determine the cause and effect and the types of the relationships among PA, PF, and obesity. In particular, studies that investigated the energy cost of activity and overweight/obesity

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are extremely limited in tropical countries or even in other regions in the world. More research should be conducted to determine the causal relationship, given that the energy cost of PA plays an important role in reducing body fat mass. Despite a lack of concrete evidence to prove a causal association, it is essential that sedentary behaviors in children and adolescents be decreased because of their inherent contribution to a reduction in EE as well as due to their role in the promotion of positive energy balance.

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