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.
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.
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,
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
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
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
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
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
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
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
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
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,
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
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
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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
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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
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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
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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
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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
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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
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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
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
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%.