Prevalence of Overweight and Obesity among children

Full text

(1)

International Journal in Management and Social Science (Impact Factor- 6.178)

Prevalence of Overweight and Obesity among children

Poonam Singh1, Pramjeet Singh Ghuman2 1

Research Scholar, Poornima University, Jaipur 2

Professor, Poornima University, Jaipur

Abstract

Childhood Obesity is considered as a major health problem in every country. The prevalence of overweight and obesity is increasing nowadays with some health consequences that simultaneously affect the life of large proportion of Indian population both among children and adults. The purpose of the review was to identify the current literature pertaining to the childhood obesity with their possible causes; and to provide health care professionals, nurses, Physical education teachers with the fundamental knowledge that will help them to recognize the obesity problem who are at risk to find the possible interventions. The data was obtained by searching keywords including obesity, childhood, causes, health risks, overweight, interventions, policies, and statistics. The databases that were retrieved for the gathering current literature included PubMed, CINAHL,ProQuest, and Science Direct. The literature was confined to past 10 years with more emphasis laid down on last 5 years research work. In this article on the basis of the keywords 50 research papers were selected for review purpose. These papers reveled that most of the researchers nowadays use BMI as method to assess childhood obesity on large sample due to its time consuming factor. Obesity among children is related to various circumstances, universal themes, social, economical factors, physical activity, public policy, and so on. Every city or country that was suffering from increasing or high rate of childhood obesity problem must acknowledge the core factors that directly or indirectly contribute to this serious health problem in modern scenario. Furthermore, health policies should focus more on this emerging health problem. The policies can be implemented throughout the country through the medium of education, research, and encouragement from the part of physical education teachers, nurses, and doctors by association with the child and his family.

Keywords: Obesity, Childhood Obesity, Overweight, BMI, Factors Introduction

“Obesity refers to a state where excessive amount of fat is diposited in adipose tissue” (Seidell&Visscher, 2004). Body fat of the body can be assessed and measured directly by estimation of total body fat mass and various components of fat free mass. Overweight and obesityare defined as excessive or abnormal accumulation of fat that may leads to various health problems. The WHO proposed classification of categories on the basis of the weight status among adults based upon BMI cut-off points that were independent of sex and age where BMI < 18.5 is considered as underweight, 18.5 ≤ BMI < 25 as normal weight, 25 ≤ BMI < 30 overweight, and BMI ≥ 30.27 as overweight. In terms of children, the BMI is used as age- and gender-specific BMI percentiles to report categories of weight but no standards are there for categorization of weight among children worldwide. Depending on the reference population and the year the percentiles have been established, different BMI-values correspond to the cut-offs of overweight and obesity. For most BMI-centile curves, the respective 90th and 97th percentiles serve as respective cut-offs for overweight and obesity, except for the United States references of the Centers for Disease Control and Prevention (CDC) were the cut-offs used are the 85th and the 95th percentiles. National percentiles are known to be more sensitive than the international ones. For simplicity, the term “overweight” is used for overweight and obesity, unless stated otherwise.

(2)

International Journal in Management and Social Science (Impact Factor- 6.178)

diagnosis of the obesity is based on the body mass index (BMI). Due to increasing prevalence of obesity among children and various adversarial health effects associated with it is being considered as serious concern of public health. The term overweight rather than obesity is sometimes used for children as it is less denouncing.

Obesity among children nowadays has become most worrying trend due to increasing number of overweight and obese individuals is the increasing number of children and adolescent that were becoming overweight and obese. Childhood obesity is a most worrying and serious medical condition with its adverse effects on the growth of young generation. There is BMI-age percentile with age, and gender specific that makes aware about the obesity area.

The high prevalence of childhood overweight and obesity despite of recent stabilization remains of great concern in terms of public health (Ogden, Carroll, Curtin, Lamb, &Flegal, 2010). The increasing overweight has been associated with associatedreduction in physical fitness (Tomkinson& Olds, 2007; Tremblay et al., 2010). Though physical fitness is also determined as genetically, data from large units alsoreveals the importance of environmental factors. These cohort studies among children of 6 to 19 year old from 27 countries revealed a degeneration in aerobic fitness to 0.4% per year between 1970 and 2003 (Tomkinson& Olds, 2007).

Causes of Obesity

There are various causes of obesity that directly or indirectly contributes to it. The most important causes were environment, behavior, and genetics that were considered as the main contributors towards obesity. The Centers for Disease Control has identified these three as the main causes to the complexity of the obesity epidemic.

One of the best strategy for reduction purpose of childhood obesity is to make improvement of exercise and diet habits not only of a children but of entire family members. Preventing and treating childhood obesity will help to protect health of a child today and in future as well.

Risk factors related with the childhood obesity

Risk factors that were associated with the childhood obesity were discussed below:-

 Increase in the sedentary habits with maximum time expenditure while watching television or playing on cell phones and computers

 Lack of sufficientamount of physical activity

 Excessive intake of fast foods and due to more consumption of sugary products and saturated fat rich food

 Lack of time spent by the working parents to cook and prepare different nutritious meals and also less or no time spent for outdoor playtime with children

 Reduced consumption of fresh vegetables and fruits

 High crime rates in urban and suburban areas that makes children to stay at home and avoids outdoor activities.

(3)

International Journal in Management and Social Science (Impact Factor- 6.178)

Table 1. : Solution Approaches for Body Composition

Author (Year)

Input (Sample) Process Outcome Result

Size Gender

(M/F) Age group Sample

Body Composition Parameters

Other Variables Training / Test Statistical

technique Findings

Abiodun,

et al.(2014) 776

M-200 F-576

42.61 ± 14.33

Healthy

individuals BMI, WHR BP, RBS Glucometer t-test, r, ANOVA

RBS, SBP, DBP increased with increasing BMI status. Significant correlation b/w WHR & RBS, SBP & DSP

Nigerian population is at risk of obesity & its related conditions (Hypertension)

Abraham

(2010) 93 M 18-20

22-Sprinters 20-MDR 20-Throwers 31-Jumpers

BMI, %BF Anthropometric Variables

Carter and

Health method ANOVA

Among all groups %BF was lowest in Sprinters and highest in Throwers

T & F athletes exhibit greater endomorphic values.

Acham, et al. (2008)

100 3

M-457

F-546 9-15 Children H, W, BMI - -

r t-test

Significant association

between W, H & BMI H, W, & BMI have significant association with learning

Adeyemo, et al.(2001) 280

M-140 F-140

45-50 42-47

Idikan Urban Community Adults

%BF, BMI, WC Blood Fasting Glucose

BIA, Deuterated water dilution, GD method

Regression Coefficient

Participants showed increased WC, BMI, W, Fasting Insulin

Baseline PA & change in PA were significant predictor of change in FM

Adkins, et al. (2008) 216

M-47%

F-53% 17-22 Students BMI

Behavioral & Psychological factors

MSPANQ, BDQ, & BS scale

r t-test

BS negatively associated with BMI & high discrepancy b/w perceived & ideal body images

BS seems to be prevalent issue among both sexes, but particularly among females

Aires, et al.

(2010) 111 M 11-18 Adolescents BMI PA, CRF

Accelerometer, 20 m SR

t-test r

BMI was significantly

correlated with CRF Low CRF is strongly associated with obesity

Ajith, et al. (2014) 224

M

F 40-70 Patients BMI CABG - ANCOVA

No significant difference from overweight to obese group

Patients with low BMI are at higher risk for reduced functional outcome after cardiac surgery than normal & overweight patients

Alvira, et al. (2013)

463 7

M-2121

F-2516 22-27

Belgium, Greece & Hungary

BMI, WC PA, ,Sedentary

Behavior - r

High association between parental edu. and WC compared to BMI

Daily breakfast consumption increasing sports participation & decreasing use of TV & Computer use

Alwachi, et

al. (2013) 50 F 30-35

PM & PPM women

BMI, WHR,

WHtR, NC - - t-test

Significant difference between central obesity PM & PPM women in NC, WHtR,

(4)

International Journal in Management and Social Science (Impact Factor- 6.178)

WHR & BMI

Anari, et al.

(2014) 406 M 18-65 Patients BMI

Hbconcentra-tion& Iron parameters

- ANOVA, t-test, X2

No significant difference in Hb , serum ion between normal W, overweight & obese patients

Nutritional status of persons & intake of iron food by obese person considered

Anast, et

al. (2008) 501 M 2-12

6 Chicago communities children

BMI - - Multiple logistic

regression

Prevalence of obesity was more in North side of Chicago

Surveillance of childhood obesity epidemic at local level is limited

Andersen, et al.(2014)

13,

572 F 50-69 Women BMI - -

Logistic regression

BMI inversely associated with breast cancer

Among women 50 yrs& older, childhood body fatness inversely associated with breast cancer risk

Angoorani

et al.(2014) 255 M 25-35

Iranian military personnel

BMI, %BF Muscle Strength BIA t-test

No significant correlation between BMI & muscle strength

BMI & %BF affect several health related PF

Arabaci(20 12) 914

M-473 F-441

22±1.9 21±1.6

Turkish college students

BMI, %BF PA Pedometer, BIA ANOVA, r

Number of daily step was not correlated with BMI, %BF & energy consumption

Both F & M college students increase PA & energy intake

Arani, et al.

(2013) 30 M 13-17

Squash

players BMI, %BF -

8 weeks of squash exercise t-test

8 weeks of squash exercise had no effect on W, BMI & %BF in Squash players

8 weeks of squash exercise could not impact on weight loss, BMI & %BF in athletes

Askari, et

al. (2012) 60 M 20-25 Athletes LBM` TBW, BMR, TEE -

t-test, ANOVA, ANCOVA

LBM, TBW, BMR, TEE increased in Quercetin group

Supplementation with Quercetin in athletes may improve performance

Ashwell, et al. (2011)

3,00 ,000

M

F 18-28 Adults

WC, WHtR,

BMI - ROC curve -

WHtR significantly better than WC for CVD, Diabetes & Hypertension

WHtR is superior in detecting cardio-metabolic risk than WC & BMI in both sexes

Ayatollahi( 2005) 596

M-295 F-301

23-42

18-35 Adults WFH, BMI -

BMI & WFH charts t-test

F subjects became heavier & more obese than before, while M subjects W & Obesity stayed constant

BMI & WFH charts are useful tools to assess shape of adults

Baccouche,

et al.(2014) 50 M 24.7 ± 0.9 Rugby players BMI AMPS ImPACT r

BMI negatively correlated with verbal & visual memory & visual motor speed

(5)

International Journal in Management and Social Science (Impact Factor- 6.178)

Bacopoulo u, et al. (2015) 161 0 M-42.2% F-57.8%

12-17 Adolescents

WC, WHR, WHtR, BMI, HC

- BMI for-age percentile t-test

Boys had higher means in all measures than girls, except for BMI

BMI, WC & HC increases with age

Badaruddo za, et al. (2010)

300 F 20-26

150-Sikh religion 150-Hindu religion

WHR, BMI BP, Pulse rate - t-test, r, ANOVA

BMI, WHR & skin fold measurements have significant effect on BP

BMI & WHR are good predictor for chronic disease like Hypertension

Basterfield,

et al.(2014) 609 M 7-12

Sports club participants FM

Sports club participation -

Linear regression analysis

Inverse relation b/w FM & sports club participation

Sports club participation in adolescence may be sassociated with decreased levels of adiposity

Baxter, et al. (2010)

157 1

M-47% F-53% 8-17

School

children BMI DP, EI -

Mixed-effects regression

Average BMI was larger for children with breakfast in classroom than in cafeteria

Positive relation b/w BMI & observed EI at school meals in 4th grade children

Broeder, et

al. (2004) 18 F 18-35

Weight lifter N=30 PG, N=30 NZDAV G

%BF, TBW

VO2max ,Max -imal aerobic capacity

NZDAV supplementati -on (1350 mg)

ANOVA

NZDAV G showed significant improvement in 1 RM values & 1 BW

NZDAV shows positive effect on BC & strength in men undergoing resistance training

Brtkova, et al. (2014) 52

M-29 F-23

22.4 ± 1.9

Undergraduat

e students BMI, %BF -

Skinfold caliper, BIA, Omron BF511

X2

Significant difference of Omron BF511 method from other 3 measurement method

Omron BF511 provided higher values in comparison to 3 other methods of measurement %BF

Bubanj, et al. (2013) 240

M-69 F-171

16.65 ± 1.14

120-Athletes 120-Non Athletes

BMI, %BF RM BIA t-test

Athletes M & F have better BC than non-athletes

Non-athletes has imbalance b/w energy output & dietary caloric intake due to lack of adequate PA

Buehring, et al.(2013) 60

M-30 F-30

18-24

18-23 Athletes H, W, %BF - DEXA

Regression analysis

Males were larger & heavier than females. %BF was higher in F than in M

DEXA BC varies among genders, regions, tissues & mass

Carlsson

(2011) 15 M 60-70 Old people BW, FFM, FM

Probiotic

(Yoghurt) drink MNA scale, BIS X 2

, ANOVA

Independent association found b/w poor nutritional status & had UTI

Ingestion of protein-rich drink after exercise produced no effect on outcome of malnourished people

Carrasco, et al.(2010) 63

M-38

F-25 10-13

Table tennis players

BM, %BF, Skinfolds, Girths, Breadths

- - t-test, r

Higher %BF in females than in males

(6)

International Journal in Management and Social Science (Impact Factor- 6.178)

Carrel, et al. (2005) 50

M-52%

F-48% 12±0.5 Children BMI, %BF

Fasting insulin & glucose level, VO2max

DEXA, Treadmill testing

Wilcoxon rank sum test

Compared to CG, TG demonstrated greater loss of %BF

Modification of PE curriculum demonstrates beneficial effects on BC, fitness &insulin level in children

Carter, et

al. (2011) 244 M 3-7 Children BMI, FM, FFM

PA, Sleep duration, Dietary intake

DEXA, BIA Multiple regression

Each additional hour of sleep at age 3-5 associated with reduction in BMI

Children who do not get enough sleep are at increased risk of becoming overweight

Case (2010) 36 M 25±4 Military

personnel FFM, %BF

Protein intake, HPD

DEXA, SR, 1.5

mile run t-test, r, ANOVA

There was trend for HPD to elicit FFM retention & -vely impact performance

Discrepancies in energy & nutrient intake made dietary comparison difficult

Ceballos, et

al. (2014) 187 F 20-30 Women’s BMI, WHR Saliva sample -

t-test, linear regression

Significant interaction found b/w testosterone & estradiol affecting WHR in fertile phase of menstrual cycle

BMI increased as testosterone increased in female in non-fertile days

Cherian, et al. (2011)

163 4

M-856

F-778 6-15

Urban school

children BMI UIG, LIG

Parental Occupation interview

t-test

UIG had higher BMI in comparison to LIG in both sexes

Prevalence of obesity & overweight found higher in HIG & among girls

Chittibabu,

et al.(2014) 27 M 18-24

Handball

players %BF, LBM, FM VO2max

Yo-Yo intermittent recovery test

r

VO2max showed negative correlation with %BF & FM. LBM showed no relation with VO2max

Players with greater maximal oxygen consumption will have low FM

Christophe r, et al. (2013)

21 F 19.6 ± 1.3

10-Soccer player 5-Dancer 6-Volleyball player

%FM, BC - DEXA, SJ, CMJ,

DJ r

%FM & BC were strong predictors of jump performance

Volleyball had more & stronger correlation with CMJ & DJ

Church, et al. (2004)

219

6 M 23-79

Diabetic

patients BMI - - r

No significant trend across BMI categories for mortality for fitness

Steep inverse gradient b/w fitness & mortality was found

Cole, et al. (2009) 1,92 ,727 M-97876 F-94851

0-25 Children &

Adolescents BMI - - t-test

Garde 2 thinness in adults as BMI was <17

Proposed cutoffs points of BMI help to compare prevalence of thinness in children & adolescents

Crecelius,

et al.(2008) 99 M

43.9 ±

12.1 5 Km Runners %BF, BW RPE

20-point Brog scale

Multiple regression analysis

BW was correlated with RTadj. Both RPE & %BF were

(7)

International Journal in Management and Social Science (Impact Factor- 6.178)

significant by BF & not RPE

Dagan, et al. (2013) 403

M-55%

F-45% 50±8.8

Healthy men

& women BMI, WC VO2max Maximal fitness test r

Correlation b/w both BMI & WC, & VO2max were significant in M & F

Association of BMI, WC with VO2max support clinical use of obesity measures to assess CRF

Das, et al.

(2014) 200 M 15-60

Brick industry

labor BMI PEFR

Wright’s peak

flow meter t-test, r

PEFR was lower in high BMI in all age groups except 15-25 age group

Both age & BMI affects PWFR in brick industry labor but effect of age on PEFR is more than BMI

Das, et al. (2012)

187 2

M-53% F-47% 2-6

Pre-school

children BMI - CDC charts t-test

Bengalese boys & girls had lower BMI than reference population

BMI values were useful for comparing different region & monitoring changes over time

Davis, et al.

(2006) 62 M 25-69 Adults BMI, %BF PA

Accelerometer,

DEXA X

2

Adults with more BMI, %BF engaged in moderate or greater intensity activity than their normal weight counterparts

Daily minutes spent in moderate intensity activity are associated with weight status

Demirkan,

et al.(2014) 114 M 15-17 Wrestlers %BF Body hydration BIS, Usg t-test

Significant difference found b/w 2 method used for assessment of %BF

Body hydration fluctuation or dehydration may affect to assess BC assessment via BIA

Deurenber g, et al. (1991)

122 9

M-521

F-708 7-83 - BMI, %BF -

Densitometry & Anthropometry r

Prediction formulas slightly over estimated %BF

Prediction error obtained by other methods such as BIA or skinfold thickness measurements

Diehl, et al.

(2015) 10 F 10-14

Scout camp

girls BMI - - r

Negative correlation found b/w BMI & self-concept

Adolescence is good time to intervene & influence +ve self-concept via understanding relation of nutrition & PA on health

Eberechuk wu, et al. (2013)

664

9 M 12-21

Nigeria School

students BMI, HWP BP - t-test

Diff. type of obesity with highest prevalence of obesity in urban than rural area

Lifestyle & inactivity are reasons for obesity in urban students

Flint, et al. (2014)

14,9 58

M-51% F-49% -

BMI-7534

%BF-7424 BMI, %BF - -

Linear regression

%BF similar in terms of magnitude significance & direction of effects

(8)

International Journal in Management and Social Science (Impact Factor- 6.178)

Fuemmeler

et al.(2013) 15 M 13-17

ALL & Lymphoma adolescent patient

BMI PA

GT1M Acti-Graph

accelerometer

t-test

Significant difference b/w cases & controls in BMI from baseline to 12 months

Following treatment of ALL & Lymphoma, childhood cancer survivors tend to be less active & at greater risk for obesity than healthy

Gallagher,

et al.(2015) 706 F 20-94

202-Black 504-White women

BMI, %BF - -

t-test, r, multiple regression

Older women showed higher BMI & %BF than younger ones. White were more obese than black women

BMI is age & sex dependent when used as an indicator of body fatness

Gaohua, et al. (2013)

136

4 M 19-22

Univ. of agriculture students

BMI Physical

function - t-test, r

PF indicator among different BC differs from one another

Overweight & obesity have negative effect on students physical function

Garthe, et al. (2011) 24

M-57%

F-43% 22±5

13-SR & 11-FR

elite athletes BW, FM, LBM Strength

Resistance training (4 wk), 1 RM test

t-test

BW & FM decreased in both SR & FR. LBM Increased in SR

Athletes who want to gain LBM should aim for weekly BW loss of 0.7% & combined strength training

Gearhardt, et al.(2009)

37,2

59 M 20-30 Adults BMI AC, SES BAC r

Higher SES & younger age associated with drinking & inverse relation b/w BMI & AC

Attenuation od family history effects on drinking behavior for obese in comparison to non-obese

Ghosh, et

al. (2015) 90 M 9-12

30-Footballer 30-Cricketer 30-Gymnasts

LM, FM, %BF AM - ANOVA

Footballers & Gymnasts have lower %BF & FM & higher LM than cricketer.

Training schedule of footballers should be incorporated for better fitness & overall performance

Giugliano, et al.(2004) 528

M-264

F-264 6-10

School children

BMI, %BF,

WC, HC, WHR - - t-test, r

%BF, WC & HC were significantly correlated with BMI

BMI for age was an adequate indicator of overweight & obesity

Gokhale, et

al. (2012) 118 M 3-15

ALL & NHL

survivor BMI -

CDC growth

charts t-test, X 2

Overweight/obesity observed among survivors who were off therapy for 2 years

Late effect of therapy needed as long term assessment of survivors

Gorely, et

al. (2011) 589 M 7-11 Children

BMI, WC, %BF, SS PA

1 mile school runs/walks, pedometer

t-test

Significant increase in %BF, SS, WC & BMI with increasing age

Facilitating long-term health behavior change in children remains a challenge

Gorner, et al. (2014) 598

M-386

F-212 19-24

218-PT students 380-PE students

BMI, %BF PA, AC GPM caliper,

PWC170 test ANOVA

M & F PE students have lower BMI & %BF, & higher level of AC than PT students

(9)

International Journal in Management and Social Science (Impact Factor- 6.178)

Gothi, et al.

(2010) 90 M 30-40

30-Ahmedabad 30-Gandhinagar 30-Sabarkantha PET

BMI, Fm, F%, TBW, BW

Cardiovascular endurance, Flexibility

12 min. copper

run & walk test ANOVA

No significant difference were observed in all variables except flexibility

BMI, BW, F%, TBW of all PET was in normal range

Gross, et al. (2003) 101

M-53

F-48 15-18

Spanish

adolescents BMI, SS, %BF PF CNT r

Negative correlation b/w %BF & PF, correlation was stronger in M than F. F had lower BMI than M

SS appears to be more suitable than BMI in expressing BC

Guerra, et al. (2014)

28,8

70 M M

15627-EG

13603-CG BMI PA, NE - t-test

PA & NE showed no significant reduction on

children’s &adolescents BMI BMI is related to PA & NE

Guo, et al. (2007) 347

M-166 F-181

3-20 30-39

Childhood &

Adolescence BMI - CDC charts

Logistic regression

Child or adolescent with high BMI has risk of being obese at 35 y of age

Child or adolescent BMI value with a known probability of overweight or obesity in adulthood

Harris, et

al. (2009) - - -

Journals

articles BMI - - -

School based PA did not improve BMI, although they had other beneficial health effects

Increases PA in schools are unlikely to have significant effect on increasing prevalence of childhood obesity

Hassan, et al. (2011)

124

4 F 14-18

Egyptian adolescents

BMI, WC, HC, WHR

6 m balanced caloric deficit diet program

BIA, Skinfold caliper t-test

Reduction in WC, FM, WHR, Skinfold thickness following dietary program & PA

Combined program of diet restriction & exercise is necessary for reduction in BC & BF distribution

Hastuti

(2013) 600 M & F 18-65

Indonesian adults

TBW, FFM, FM, BMI

Body image, eating behavior, PA

Deuterium isotope dilution technique, BIA

r

Males heavier & taller than females. BMI & %BF higher in females than males

BIA is a low cost assessment of BC for Indonesian adults

Heelan, et al. (2006) 100

M-48

F-52 4-7

Young children

BMI, %BF, FM,

FFM PA, Media time DXA,

accelerometer, parental proxy

t-test, r

No significant difference b/w sexes for BC, PA levels or media time

Others factors may influence complex, multi-factorial BC phenotype of young children

Heney, et al. (2015) 181

M-103

F-78 2-18

Africa & Asia

Children BMI - - t-test

BMI increased from 2007 to 2012 from 17.3% to 35.4% obesity

(10)

International Journal in Management and Social Science (Impact Factor- 6.178)

Hooley, et al. (2012)

Revi ew of lit.

M & F 0-18

Children & Adolescents (2004 – 2011)

BMI Dental caries - -

Dental caries was associated with both high & low BMI

Socio-economic status mediate association b/w BMI & dental caries

Hosseini,

et al.(2014) 842 5

M-5044

F-3381` 25-69 Iranian adult BMI BP, (SBP & DBP) -

Linear regression analysis

BP increased with rise in BMI & W, but showed negative correlation with H

BP percentiles are steadily increased by age & BMI

Hubbard

(2005)

19,0

08 M 5-10

I, III & V grade students BMI

Mother’s

employment - t-test

Mother working full time using child care increases risk of obese

Mother’s employment effects BMI status of young children

Hugher, et al. (2004) 129

M-54 F-75

9.4±1.4

60±7.8 Elderly person %BF, LM

PA, metabolic variables

Hydro- densiometry

Linear regression, r

Subcutaneous fat declined, whereas BF increased

Skinfold thickness cannot be used to assess changes in BFM bcoz of age-related F redistribution

Jang, et al.

(2014) 66 M 18-24

20-2day exercises, 29-3day exercises, 17-4day exercises,

BMI, WHR, %BF

Internet addiction degree

BIA Kruskal-wallis test, r

Internet addicting degree associated with duration of SA & daily internet usage

Internet addiction is affected by time of daily use & may be decreased by increasing SA duration

Joy, et al.

(2013) 24 M 21.3 ± 1.9

12-consume 48g rice, 12-Whey protein isolate

LBM, FM, MM

Rating of perceived rec- overy, power

8 W resistance training programme

ANOVA

Due to training LBM, MM, strength & power all increased & FM decreased

Both whey & rice protein isolate followed resistance exercise improved indices of BC& exercise programme

Kao, et al.

(2011) 27 M 18-26 Soccer players FFM -

BIA, DEXA, Underwater weighing

r

Correlations obtained b/w results obtained by DEXA & 3 equations

Predictive equations have best validity among all equations to assess FFM

Kaur, et al.

(2007) 189 M 17-23

N=84.17% - JF N=3.83% - NV Nursing students

BMI, WC, HC,

WHR - - t-test

11 participants were overweight & 4 were obese as per BMI

Markers of central obesity appeared to be most sensitive indicators of obesity followed by WC & BMI

Kavak, et al. (2014)

111 8

M-597

F-521 20-30

Women &

men BMI, WHR - - ANOVA

BMI was primary determinant of female P attractiveness whereas WHR failed as significant predictor

P attractiveness was differ due to gradient of socio-economic development (urban & rural female)

Kesavacha ndran, et al. (2012)

111

1 M 18-69 Men %BF, BMI - BIA, ROC curve

r, f-test, mult-iple linear regression

44% subjects showed higher %BF & BMI & risk factor like Hypertension, type 2

(11)

International Journal in Management and Social Science (Impact Factor- 6.178)

diabetes be lowered (BMI)

Khadilkar,

et al.(2011) 198 34

M-57%

F-43% 6-12 Children BMI - - Skewness

BMI cut-off lower for Indian children in comparison to European children

Age & sex specific BMI cut-offs for Indian children linked to Asian cut-offs for assessment of obesity

Kilduff, et

al. (2007) 55 M 21±1

Healthy

athletes FFM

Cr

Suppleme-ntation HW, ADP t-test, ANOVA

All 5 BC techniques detected change in FFM to a similar degree

All 5 methods provided similar measures of FFM change during acute Cr supplementation

Kilinc, et al. (2013) 19

M-11

F-8 9-17 Adolescents

BMI, HC, WC, BFM, BW

BP (SBP, DBP), Biochemical parameters

- t-test

Percentages of overweight & obese adolescents were 31.6% & 68.4%

16w diet & lifestyle intervention program for overweight & obese adolescents showed significant improvement in obese

Kim, et al.

(2015) 14 F 18-22

Taekwondo athletes

BBW, %BF, TBW, FFM, BFM

PF parameters

50m SR, grip strength, sit & reach, sit ups

t-test

The mean %BF & BFM significant increases

BC, PF, physique is necessary to improve athlete performance of female taekwondo athletes

Kim, et al. (2014)

820

943 F 30-50

HD-4938, BC-3302, ND-7752, BCM-2349

BMI - - Multiple logistic

regression

Excessive gestational W gain contributes most to LGA

Overweight & obesity, excessive gestational W gain, & GDM associated with LGA risk

Komala, et

al. (2015) 60 F 18-25

Arts & Science Students BMI

Agility, flexibility

SR, Sit & reach test t-test

Agility & BMI found more in science students, flexibility more in arts students

Arts women are more active in PA

Koscinski,

et al.(2013) 121

M-52 F-69

18-40 18-31

Women &

men BM, WHR

Attractiveness

traits - ANOVA

Both sexes preferred underweight women with accentuated waist

BMI is proved twice as important for attractiveness as WHR6

Kozera, et al. (2006) 56

M-22

F-34 8-10

Rural Manitoba community school children

BMI, %BF Aerobic performance

20m SR, Pedometer step counts

t-test, r

BC significantly related to aerobic power. Weekday steps related to aerobic performance.

Interval pedometer was capable of identifying differences in activity pattern

Kumar, et

al. (2013) 202 F 25-35

Infertility in female patients

BMI Lifestyle - t-test

Underweight was prevalent in women with infertility

Underweight & overweight contribute to infertility & to be reproductive health

Kumar, et

al. (2009) 513 M 18-54 Mine laborers BMI

Periodontal

status CPI

Multiple logistic regression

Subjects had increased risk of periodontitis for each

(12)

International Journal in Management and Social Science (Impact Factor- 6.178)

increase in BMI assessment

Kurpad, et al. (2003) 285

M-207 F-78

18-76

18-69 Athletes

WC, WHR,

BMI - - r

WC correlated between with BMI than WHR

Prevalence of being overweight & obese among both sexes does not differ from growth chart

Larouche, et al.(2014) 68 stud ies - -

Active & passive travelers BMI, %BF PA, cardiovascular fitness Medline, PubMed, Emb-ase databEmb-ase

-

Majority of studies found active school travelers were more active than passive

AST should be promoted to increase PA levels in children & adolescents to increase cardiovascular fitness

Laviano, et al. (2010)

245 9

M-1247

F-1212 6-11 Children BMI EC, PA Accelerometer X 2

, r

Distribution of BMI by age & gender shows no significant difference in 2 sexes

Obese less active subjects had greater number of episode od asthma than more lean & active one

Laxmi, et

al. (2010) 100 M 18-22

Healthy

subjects BMI VO2max QCT r

Significant negative correlation b/w BMI & VO2max

BF have striking effect on cardiac function & VO2maxby working muscles

Li, et al. (2010)

470

0 M 8-11

Primary school students

FFM, BMI,

FM, %BF - BIA

Multivariate regression

Intervention had effect on W, H, BMI, & BC among obese children than normal weight or overweight children

20 min. of daily moderate to vigorous PA is effective way to prevent excessive gain of BW, BMI & BF in primary school student

Linchey, et al. (2011) 50 Stat es - - School-based health screening

BMI - Telephonic

interviews -

It was revealed that 20 states require BMI or BC screening

Prevalence of obesity is higher in states that require screening than in those that do not

Lo, et al. (2014)

117 618

M-50% F-50% 6-17

Hispanics, Blacks girls & boys

BMI BP -

t-test, Multivariate logistic regression

Highest severe obesity prevalence among 12-17 y old Hispanic boys & black girls

Severe obesity varied by gender & race/ ethnicity

Loenneke,

et al. (2-13) 129

M-63

F-66 18-25

College

students BMI, %BF -

DEXA,

Womersley&Du rninEqn

t-test

No significant %BF difference b/w BMI prediction equation & DEXA

Womersley&Durnin equation for estimating %BF was not found to be a good estimator

Loon, et al.

(2003) 20 M 18-22

Non-vegetarian subjects

FFM CrP, Total Cr content

Cycle

ergometer ANOVA

Cr loading increased muscle free Cr &CrP& total Cr content

Prolonged Cr ingestion induces an increase in FFM

Macran(20 04)

117 83

M-51%

F-49% 25±7

Healthy

survey BMI HRQoL EQ-5D ANOVA

Significant difference in FQ-%D index in female. For male,

(13)

International Journal in Management and Social Science (Impact Factor- 6.178)

BMI associated with poor FQ-5Q index

presence of long-standing illness

Magee, et al. (2013)

107 9 M

4-5:2004 10-11: 2010

Australian

children BMI Sleep duration

Growth mixture modeling approach

Regression analysis

Association found b/w BMI & Sleep duration

Shorter sleep duration associated with BMI in children with onset obesity

Maize, et

al. (2013) 122 M 18-25 Libya students BMI, %BF VO2max - t-test

Significant relationship b/w VO2max & %BF

Level of PF among students teachers in Libya Univ. is average

Mei, et al. (2002)

110

96 M 2-19

Children adolescents

BMI, %BF,

TFM - DEXA r

BMI for age was better than RI-for-age in detecting over-weight in children aged 3-19 y

For children & adolescents aged 2-19 y, BMI for age was better than RI-for age in predicting overweight & underweight

Mihailova,

et al.(2014) 67

M-21 F-46

21.61 ± 0.71

PE & PT students

BMI, BF, MM, B water

Health related PF, PA, AMST, VO2max

IPAQ, dynamometry

X2, t-test, ANOVA

PA duration high in PE student. Most students have normal BC, PT student had high BC values

PA implies a higher level of health related PF, PA positively affects BC

Miller, et al. (2014) 21

M-9

F-12 18-23

9-Footballers, 12-Soccer players

MM, FM, %BF - 6w PCT program , BIA t-test

Males did not significantly alter BC. Females positively altered BC

6w PCT program can positively alter BC particularly for female athletes

Monyeki,

et al.(2012) 256

M-100

F-156 14 Adolescents BMI, BW, %BF PF

EUROFIT fitness standard procedures

Multinomial logistic regression

+ve association b/w PF & BMI for underweight boys & girls. BMI strongly related with %BF

Effects on PF performance noticed among both underweight & overweight boys & girls

Mukherjee

et al.(2012) 126 F 17-20

BDG-87, CG-39

Bengali & Sedentary female

BMI, %BF

Bharatnatyam dance, PF, VO2max

QCT t-test

BMI & %BF of DG individuals lower than CG individuals

Bharatnatyam dancing is a cost effective beneficial way of ex. to maintain healthy BC & fitness status

Muratovic,

et al.(2014) 59 M

24.77 ± 3.00

15-Handball, 14-Basketball, 30-Health sedentary

BMI, %BF - Skinfold caliper ANOVA

Basketballers were taller & heavier than Handballer& subjects of CG. In terms of BMI no difference among grps

These variables help as a selection process method

Navarro, et al. (2004)

168 09

M-7862

F-8947 10-17

School

children BMI - - t-test

Higher overweight rates & lower obesity rates among

(14)

International Journal in Management and Social Science (Impact Factor- 6.178)

both sexes management, policies

Niederer,

et al.(2009) 115 M 4-6

65-Gallen, 50-Lausanne Preschool children

BMI, WC, %BF AF, SD, PA

20m SR, Accelerometer, FFQ, GHQ

X2, t-test

BMI reduced & AF increased in 4 to 6 y old preschool children

Adaptation & implementation of prevention program focusing on preschool children low SES

Nsibambi,

et al.(2013) 192 9

M-901 F-1028 6-9

Central Uganda urban school children

BMI -

CDC-BMI growth chart for sex-age

t-test

Significant gender difference with boys having lower BMI scores than girls

Underweight, overweight & obesity are prevalent among Uganda children

Ogden, et al. (2014)

912

0 M 0-2, 2-19

Unites States

children BMI -

CDC growth

chart r

Significant decrease in obesity among 2-5 y old children & increase in obesity in women aged 60 y & older

Obesity prevalence remain high b/w 2003-04 & 2011-12 & thus it is important to continue surveillance

Olasupo, et

al. (2014) 180 M 7-12

Public &

private school BMI, %BF AE, PRE

Lange skinfold

caliper ANCOVA

AE & PRE enhanced better improvement in %BF & BMI

For good BC, AE training should be considered for primary school children in Nigeria

Oravitan,

et al.(2011) 72 M 21.2± 3.58

Young obese people

BMI, WHR,

%BF BMR

12 month train-ing program t-test

Improvement in BMI & %BF

after 12 month training All BC parameters were reduced & maintained

Orhan(201

5) 103 M 13-16

Rural school

children BMI, %BF PA PAQ, BIA X

2 BMI & %BF low in rural

school boys Boys living in rural participate in school sports more often

Orhan(201 4)

103 0

M-527

F-503 11-13

Turkey rural & urban school children

BMI, BH, BW,

%BF - PAQ r

BMI & %BF were low compared to urban elementary school students

Rural body & girls participated in team sport. Rural students were more active & fit than urban ones

Parkhad, et

al. (2014) 485 M, F 14-18

Maharashtria

n adolescents BMI, WC, FMI BP, VO2max Oscillometry ANOVA, r

VO2max have –ve correlation

with SBP, DBP in M & F Better PF rather than higher PA level can keep BP in check

Passos, et al. (2010)

802

0 M, F 10-15

Brazil public & private school children

BMI - - ANOVA

Both M & F adolescents showed BMI cut-off over international parameters

Use of adequate BMI values for Brazilian adolescent 10-15 y reflect nutritional status

Phyllis, et al. (2010) 161

M-72

F-89 5-18

Native American children

BMI - - X2

BMI at or above 85th percentile had high sensitivity for insulin resistance

BMI at or above 85th percentile was more sensitivity screening tool than acanthuses Nigerian

Polman, et al. (2003) 275

M-112

F-163 16-28 Filipino BMI, WC, HC PA PAQ t-test

Self-motivation with BW & %BF found to be best

(15)

International Journal in Management and Social Science (Impact Factor- 6.178)

predictor of exercise behavior exercise situations

Popovic, et

al. (2013) 40 M

19-27 20-26 N=26-Soccer N=14-Basketball players

BMI, %BF - - ANOVA

No significant difference in BMI among grps, except BW & %BF

Taller players in basketball have advantage bcoz tall height enables to travel shorter distance & rebounds

Potteiger,

et al.(2010) 21 M 20.76± 1.6

Hockey

athletes FFM, FM, %BF

Leg strength, anerobic power

Wingate 30-sec cycle ergometer test, ADP

r

First length skate- average & TLS – average skating times were correlated to%BF& FFM

Targeted & effective strength & conditioning program could be developed to improve on-ice skating speed

Powell, et al. (2009)

290

8 M 0-12 Children BMI - - r

Increased supermarket availability & fewer convenience stores related to low W among low income children

Supermarket assess as policy instrument to address childhood obesity

Pribis, et al. (2010)

510 1

M-51%

F-49% 18-25

College

students BMI, %BF PF, BP

Micro fit test, Oscillometic

Linear regression, r

In last 13 years, %BF increased for M & F. Indirect correlation b/w VO2max & %BF for F

PF among college students is declining & body fatness is increasing

Przbycien,

et al.(2011) 23 M 24.9 ± 5.5

Roman wrestlers

FM, F%, FFM,

BMI Somatotype

Health-carter somatotypes X

2 Heavier wrestlers have higher BMI, FM, F%, FFM than lighter one

Heavier wrestlers were endomorph-mesomorph, whereas lighter were mesomorph Ranasinghe et al.(2013) 111 4 M-29.1% F-50.9%

18-83 Shri Lankan

adults BMI, %BF - BIA r

Positive correlation b/w BMI & %BF in both sexes

BMI strongly correlated with %BF in sub popn of South Asian adult

Randakova

et al.(2005) 131

M-76

F-55 12-15

49-Non-Skiers, 82-Skiers Cross country skier

BBM, BMI, %BF, BCM PP

BIA, Basic motor test t-test

Skier have lower BM, BMI, greater FFM, BCM & better PP than non-skier

Regular training functions is means of prevention of overweight & obesity in adult age

Ray, et al. (2013)

949

8 M 0-18

16 areas of Tripura children

BMI - - X2

Problems of stunting & overweight were more among under 5 y children

Need of under-nutrition above 6 y children

Razak, et

al. (2013) 242 M 18-25

122-Libyan, 120-Malaysian students

BMI, %BF PA, VO2max QCT, PAQ t-test

SUL students had lower BMI

& %BF than UKM students Students were of good fitness & health

Saha, et al.

(2013) 500 M 18-25

250-PE students

H, W, G,8

skinfold - - t-test

PE students had higher BMI, LBM, % skeletal MM, & BSA

(16)

International Journal in Management and Social Science (Impact Factor- 6.178)

250-Non-PE students

thickness, BMI, bone diameter

than non-PE students than non-PE students

Saha(2012) 30 M 18-25

Track & field, & soccer players

BW, %BF, LBM

Anthropometric

variables - t-test

Soccer, & track & field athletes donot differ much on selected anthropometric measurements

Minimum level of fatness are advantageous for soccer, & track & field athletes

Sahu(2015) 40 M 18-22

20-Batsman 20-Hockey players

BMI, Body segements (Leg & Arm length)

-

Stadiometer, weighing machine

t-test

Significant difference b/w cricketer & hockey player in terms of H, BMI, leg & arm length, except W

Hockey players are superior due to effect of strenuous training program that generate more forces on them

Salazar, et al. (2013) 370

M-48.9% F-51.1%

20.98 ± 2.24

Colima Univ.

Students BMI PA IPAQ t-test

M have BMI greater than F. students aged 21 to 25 y have higher BMI than less than 21 y

Sedentary students were mainly obese than active ones

Salmi(2003

) 58 M 36-53

Middle aged subjects

BMI, FM, F%,

FFM -

UWW, SMFBIA method t-test

SMFBIA method may be advantageous in large epidemiological studies as being simple, inexpensive tool

SMFBIA was useful method to evaluate FM, FFM, F% from whole body

Santos, et

al. (2010) 27 M 22.2±2.8 Judo athletes

%FM, FM,

FFM -

DEXA,

4 C model r

Changes in %FM, FM & FFM estimated by DEXA were not significantly difference from those by 4C model

DEXA overestimating at lower ends & under estimating at the upper ends of FM changes

Schmeiser,

et al.(2008) 126

86 F 14-21 Women BMI Family income -

Linear regression, r

Income was significantly raise

BMI & probability of obese Increase in real family income increased obesity prevalence

Schmidt, et

al. (2010) - M 4

Medical students, family medicine, pediatric physician

BMI - - ANOVA, X2

Only 15%respondent identified 4y boy, BMI> 95th percentile, 86% respondent identified 4y boy with normal W

Medical professional & trainees have difficulty visualizing assessing child BMI-for-age

Schoenfeld

et al.(2014) 20 F 22.4±2.8

10-FASTED grp, 10- FED grp. Healthy young female

BW, FM FASTED & FED training

Energy & macronutrient intake

t-test

Both group showed significant loss of W & FM

(17)

International Journal in Management and Social Science (Impact Factor- 6.178)

Shah, et al.

(2013) 850 M, F 12-15 Adolecents BMI, WC -

Non-stretchable

tape t-test, r

High correlation b/w BMI & WC

BMI was strongly correlated with WC

Sharma, et al. (2013) 335

M-188

F-147 12-17

South Indian adolescents

WC, HC, BMI, FFM, %BF, skinfold thickness

CRF, VO2max Rockport walk

fitness test t-test, r

Higher %BF in girls of all age grp than boys. Higher FFM & VO2max in boys than girls

FFM is a stronger determinant of CRF than %BF in adolescents

Shishkova,

et al.(2007) 519 F 30-40

Overweight & obese women

FM, WHR,

WC, HC BMR BIA t-test

Reduction in W, FM, WC, HC & BMR

Use of BIA to estimate changes in FM, is simple to use & requires minimal training

Siahkouhia n, et al. (2011)

200 M 7-8 Children BMI FMS - r

Significant correlation b/w

BMI & FMS There was perceptual-motor deficit in obese children

Sijtsma, et al. (2011)

Stud

ies - 1.5-6

Cross sectional-17; Long. Studies-17;

Children

BMI, %BF PA - -

PA inversely related to %BF. Association b/w PA & BMI remain elusive

More firm conclusions are needed with using further studies measured directlyPA&%BFto define adiposity

Sillanpa, et al. (2011) 215

M-113

F-102 39-77

Middle aged

& older adults BMI, %BF

Blood glucose, insulin

concentration

DEXA, BIA, Ultrasound, TR-IFMA

ANCOVA, t-test

After 21 weeks of training, %BF & BMI decreased in all training grp in M % in endurance trained grp in F

Combined training program was effective in improving BC & both aerobic performance & muscle strength

Silva, et al.

(2003) 23 M 20-36

Brazilian Bodybuilders

BW, %BF, 3 bone breadth, 9 skinfold, MG

- - t-test

Athletes showed higher BW, elbow girth & calf girth

Brazilian elite bodybuilder showed lower %BF & bigger muscular weight when compared to Ross & Wilson model

Silventoine n, et al. (2010)

103 4 M

18-67 21-24 756- Denmark 278-Finland, Twins complete fair

BMI, WC, %BF PA, Protein

intake FFQ, BIA t-test, r

High PA & proportion of protein in diet were associated with higher BMI, WC, %BF

PA is able to modify action of genes responsible for pre-disposition to obesity

Singh, et

al. (2015) 80 M

14-24 15-19

Thang-ta-practitioners Footballers

BMI, %F, FM,

FFM, TBW - BIA, BC analyzer t-test

Footballers were having more FFM, TBW, BMI than thang-ta-practitioners

Optimal BC is important for fitness & to enhance performance

Spartali, et

al. (2014) 868 M 19.9±1.6

Hellenic army academy cadets

BMI, %BF Physical tests

Push up, 50m swimming, obstacle course

X2

Cadets group with lower BMI & %BF has good PA levels

(18)

International Journal in Management and Social Science (Impact Factor- 6.178)

Spessato,

et al.(2013) 264

M-132

F-132 5-10

5y-31, 6y-31, 7y-40, 8y-64, 9y-64, 10y-34 PE children

BMI PA, Motor

competence TGMD-2 ANOVA, r

+ve correlation b/w MC & PA. BMI was not significantly correlated with MC & PA

MC was a better predictor of PA than BMI. Children with higher MC are more active

Stanforth,

et al.(2014) 211 F 19.2±1.2

38-BB, 47-SOC, 52-SW, 26-VB, 49-Track

LM, %BF - DEXA Linear

regression

LM increased in VB & SW; %BF increased in BB. No change in SOC or Track

Female collegiate athletes expected to maintain their %BF

Stevens, et

al. (2007) 984 F 9-14

6th& 8th grade

students BMI, %BF MVPA Accelerometer t-test

Association b/w MVPA in 6th grade & incidence of overweight in 8th grade were not detected

Clear & consistent association b/w PA & BC (both BMI, %BF)

Subhedar,

et al.(2014) 100

M-50

F-50 40-50

Grp A-50 M, Grp B-50 F; subjects of PT dept., Bomlar

%BF, TBW% - Tanita machine (BIA) t-test

%BF was more & TBW% was less than desired range.

BIA was proved as advanced diagnostic tech. for evaluating body fitness in today’s fast lifestyle

Swami, et

al. (2005) 682 F 20-30

Urban & rural

Female BMI, WHR - - ANOVA

Bmi was primary determinant of F attractiveness whereas WHR failed as significant predictor

P attractiveness was differ due to gradient of socio-economic development (urban & rural F)

Tatsukawa,

et al.(2013) 268 6

M-834

F-1852 0-40

ATB bomb

survivor BMI - DEXA, AHS t-test

BMI decrease with increased radiation dose in both M & F

Among F younger than age 15 ATB, abdominal obesity index tended to increase with radiation dose

Taylor, et al. (2015) 580

M-300

F-280 3-19

Children & Adolescents

WC, WHR, high trunk FM -

DEXA, conicity index

Linear regression

WC was significantly better performer index of trunk FM than WHR or conicity index

WC provides effective measure of truncal adiposity in children & adolescents

Teisala, et

al. (2014) 81 M 26-40 Healthy BMI, %BF CRF, PA, HRV - X 2

PA, CRF & BC significantly associated with level of stress & recovery on workdays

HRV method associated with self-reported burnout symptoms

Teramoto,

et al.(2015) 28 M 20±2.7

Physical disabilities adults

BMI, %BF PA DEXA t-test

Individuals with PD who are physically active have normal BMI range

PA can reduce risk of obesity & diseases & maintain BMI & BC

Tesfaye, et al. (2007) 250

M-125

F-125 25-64

Vietram, Indonesia rural & urban

BMI BP - Regression, r

High prevalence of overweight/ obese & risk of hypertension found among Indonesian M & F in

(19)

International Journal in Management and Social Science (Impact Factor- 6.178)

comparison to Vietnam

Tiwana(20

13) 70 F 18-25

25-High jumper, 20-Long jumper, 25-High jumper

BMI, %BF, WHR, Pondreal index

- Harpanden

skinfold caliper ANOVA

High jumper were higher in H & lower in W as compared to long & triple jumper

High jumper have thin & lean body types which help them to jump to a higher level due to less BW

Tripathi, et al. (2014) 225 9 M-56.1% F-43.9%

2-17 Children BMI - - Logistic

regression

Using W for age, 326 were obese while using BMI for age 363 were obese

Limited validity in using W-for-age cut-offs as definition for obesity in research in children with acute injuries

Urniaz, et al. (2009) 377

M-206

F-171 25-35

235-PE students 142-PT students

BM, BMI, %BF Flexibility BIA, Modified

sit & reach test ANOVA

Lower BM, F content, BMI & higher level of flexibility among PE students than PT

Positive impact of increased PA on the correct BM as well as very good level of flexibility

Vishaw, et

al. (2010) 63 M 18-25

36-VB players 27-BB players

CC, %BF, TBF,

FFM - - t-test

BB players have greater %BF, TBF, FFM as compared to VB players

BB & VB players have higher %BF with lower BH & BW than their international counterparts

Visnes, et al. (2011) 169

M-69,F-72; M-22,F-6 16-18 141-Healthy Volleyballers 28-Jumper’s Knee

BC - - t-test, ANOVA

No significant difference b/w group in BC

M Gender, high volume of VB training & match exposure were risk factors for developing jumper’s knee

Wesolowsk i, et al. (2013)

20 M 27.7±7.1

Winter swimmers & normal people

BMI, %BF, FM, FFM,

FM/FFM%, TBW, MM

- BIA t-test

Winter swimmer showed higher BMI, %BF, FM/FFM% than people who so not swimming except MM

Choice of bathing in cold water as a form of recreation depend on content of adipose tissue

Wickramas inghe, et al. (2009)

282 M-158

F-124 5-15

Children & adolescents

HC, WC, BMI, TBW, FM -

Deuterium

dilution t-test

Percentage FM & BMI had very low but significant association among girls & boys

FM had significant association with WC & HC

Wijtzes, et al. (2014)

591

3 M 6 Children

BMI, %BF, W status

Sedentary behavior & PA behavior

DEXA, Parent-

reported Q ANOVA, X 2

, r

Sports participant was inversely associated with FM

Sports participants is inversely associated with %BF among ethnically diverse 6-y-old children

Yildiz, et al. (2012) 99

M-45

F-54 14-18

9th, 10th& 11th grade students

BMI, %BF PA Pedometer, BIA t-test, ANOVA

%BF of F students were higher than M students & no significant differences found

(20)

International Journal in Management and Social Science (Impact Factor- 6.178)

in BMI

Zamani, et

al. (2012) 106 F 10-14

51-Active 55-Sedentary girls

WC, HC, BMI, %BF

Blood lipid

profile Beak PAQ t-test, r

Significant relation b/w BMI & %BF. No significant difference found b/w low & high density lipo-protein & total cholesterol in both grps

Based on the level of PA, no difference found b/w active & sedentary participants

Zheng, et al. (2015)

566 2

M-2800 F-2862 6-18

Children &

adolescents BMI, WC, HC - - t-test

BMI scores strongly correlated with HC for children younger than 14 y, while no correlation found in females

Association of BMI with WHtR& WC indicate obesity presents as abdominal obesity in children

Zwierzcho wska, et al. (2014)

14 M 32.6±5.1 Wheelchair

Rugby BM, WC -

BIA, trunk fat analyzer r

High correlation b/w Vfat& WC, WHR &Tfat. Weak correlation b/w Vfat& BMI

BMI has low sensitivity for predicting obesity risk in wheelchair rugby players

(21)

International Journal in Management and Social Science (Impact Factor- 6.178)

Statistical Analysis of Body Composition

The issue wise statistical analysis was adopted in the area of Body Composition. One hundred sixty eight (168) research papers were reviewed and on the basis of the statistical analysis, commonly used parameters, statistical techniques adopted, gender, sport and subject selection were analyzed and represented in the form of graph as shown:

Fig. 1. Critical analysis on the basis of Body Composition Parameters

Figure 1. Indicates the statistical analysis of body composition parameters in which the analysis was on the basis of the 8 parameters that are BMI, %BF, WC, HC, WHR, FM, LBM, and FFM. Further it indicates that the Body Mass Index (BMI) parameter was demanded more as body composition parameter to assess overweight and obesity in the obesity area as its value is highest that is 77.98%. On the other hand, it was further indicated that the Lean Body Mass (LBM) was demanded less as body composition parameter to assess overweight and obesity in the obesity area as its value is lowest that is 5.95%.

Fig. 2. The Gender selection (Male/Female) in Body Composition research area

Figure 2. Shows the Gender selection (Male/Female) assumed in which the analysis was on the basis of the 2 gender state that are male and female. Further it directs that the Male gender was given preference in the Body Composition area as its value is highest that is 44.05%. On the other hand, it was further indicated that the studies with Female Gender subjects were conducted less to assess obesity in the obesity area as its value is lowest that is 12.5% which shows discrimination in gender selection.

BMI %BF

WHR WC

HC

FM

LBM

FFM 77.98

39.88

15.48 14.88

6.55 14.29

5.95 10.12

Body Composition Parameters

BMI %BF WHR WC HC FM LBM FFM

Male

Female

Both 44.05

12.5 36.9

Gender

(22)

International Journal in Management and Social Science (Impact Factor- 6.178)

Fig.3. The Sport selection in Body Composition research area

Figure 3. indicates the Sport selection in Body Composition research area in which the analysis was done on the basis of 2 sports that are team sport (Volleyball, Soccer, Basketball, Rugby) and individual sport (Swimming, Track & Field, Combative). Further it shows that the study was conducted more on individual sport as statistical analysis in the Body Composition area indicated highest value that is 24.40%.

Fig.4.The Subject Selection in Body Composition research rea

Figure 4. Shows the Subject selection assumed in which the analysis was on the basis of the 6 subject were selected that are Children, Physiotherapy students, Physical Education students, Adolescent, Adult and Patient. Further it directs that the most of the research work was conducted with children subject selection in the Body Composition area as its value is highest that is 42.85%. On the other hand, it was further indicated that the studies with subjects Physiotherapy students were conducted less to assess obesity in the obesity area as its value is lowest that is 3.57%.

Strengths and Weaknesses

After reviewing 168 research papers in the sub area “Body Composition” in Physical Education, some strengths and weaknesses which were found while reviewing papers were discussed in this chapter. Strengths are the strong points of the papers on the other hand weaknesses are the lacking areas in which improvement is to be needed. This chapter also deals with the gaps in the area of Body

Team Sport

Individual Sport

17.26 24.4

Sport

Team Sport Individual Sport

Children PT

PE

Adolescent

Adult

Patient

42.85

3.57 6.55 35.12 26.79

4.76

Subject Selection

(23)

International Journal in Management and Social Science (Impact Factor- 6.178)

Composition in which studies could be done further. This chapter would enlist the strengths and weaknesses of the various methods and algorithms used.

Strengths of the Body Composition area are as mentioned below:

1. Strengths of the Work in the Area of Overweight and Obesity

 The studies confirm that refugee children are at risk of becoming overweight and obese within the first year following resettlement and that their risk increases by the third year post-resettlement.

 With 29% of the subjects were considered overweight or obeseusing BMI cut off points, these values closely reflect the status of children asmeasured in the ‘National longitudinal survey of children and youth: childhood obesity’.

 Children in the study were divided into rural sub categories of rural-town and rural-out of town. Rural town children were found to be significantly more active and lower BMI than out of urban town children.

 The waist circumference cutoffs, which correctly identified most of the children with high trunk fat mass while minimizing misclassification, were obtained and presented for girls and boys of each year of age from 3 to 19 y.

 WC showed an increasing trend with age among both girls and boys. This was an expected finding during puberty, as it represented a critical period for body fat development and distribution.

 Physical activity level had a significant effect on BMI, %BF, VO2max among both genders i.e. males and females. It was further indicated that females were less physically active than males having a prevalence of moderate physical activity level comparing with 90.5% of high physical activity level in males.

 BMI and WHtR charts were useful tools to study shape of adults. While former may be used when age was unknown, the latter was more useful to study age related thin, overweight and obese young adults. The BMI chart provided local reference curves for clinical work and public health purposes in urban areas.

 The findings indicated that due to the regular practice and training program designed, the improvement in the %BF, muscle mass, FM, BMI (body composition parameters) obtained.

 Female adult students have greater mean for height, weight, BMI, WHR, three skinfolds (biceps, triceps and subcsapular) SBP and DBP when compared to males.

 Waist circumference correlates better with body mass index than waist-to-hip ratio. The prevalence of abdominal obesity using waist circumference was higher than that with waist-to-hip ratio.

 The positive relationship found between body composition parameters and the physical activity level among adolescents and adults.

 Men and women who commuted to work by active and public modes of transport had significantly lower BMI and percentage body fat than their counterparts who used private transport.

 Higher cut-offs of BMI and %BF indicated that these higher values were strongly associated with the type 2 diabetes, hypertension and other coronary heart diseases.

2. Weaknessesof the Work in the Area of Overweight and Obesity

 The selection of the body composition parameter was the weakness of the researchers, utmost of the studies were conducted on the parameters as BMI, %BF, FFM, WHR, FM, LBM, WC and HC and among them BMI was the parameter which was assessed maximally as its value is about 78%.

Figure

Table 2. Indicated the solution approach adopted in the Body Composition issue was discussed under the BC outcome

Table 2.

Indicated the solution approach adopted in the Body Composition issue was discussed under the BC outcome p.20
Fig. 1.   Critical analysis on the basis of Body Composition Parameters
Fig. 1. Critical analysis on the basis of Body Composition Parameters p.21
Figure 1. Indicates the statistical analysis of body composition parameters in which the analysis was on the basis of the 8 parameters that are BMI, %BF, WC, HC, WHR, FM, LBM, and FFM

Figure 1.

Indicates the statistical analysis of body composition parameters in which the analysis was on the basis of the 8 parameters that are BMI, %BF, WC, HC, WHR, FM, LBM, and FFM p.21
Fig.4.The Subject Selection in Body Composition research rea
Fig.4.The Subject Selection in Body Composition research rea p.22
Fig.3. The Sport selection in Body Composition research area
Fig.3. The Sport selection in Body Composition research area p.22