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Examination of related some morphometric parameters of forearm and hand with hand-
grip strength
Serpil Çilingiroğlu Anlı1, Rahmi Ertan Anlı2
1Kırıkkale University School of Medicine, Department of Anatomy, Kırıkkale, TURKEY 2Ankara University Faculty of Sport Sciences, Ankara, TURKEY
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INTRODUCTION• Handgrip strength may be an indicator of
muscular strength of an individual (1,2). Because
of its close association with the neural and
musculoskeletal systems, grip strength is
routinely utilized in wide range of clinical settings
including evaluation of individuals with
pathologies affecting the upper limb function (3).
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INTRODUCTION
• Grip strength refers to the ability of the fingers and
hand to generate muscle power and force. In
athletes, grip strength is an important component of
participating in many sports, including rock
climbing, judo, weight lifting, baseball, martial arts,
racquets sports etc. (4).
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INTRODUCTION
• If the forearm muscles have been overused, the
resulting decrease in grip strength or fatigue of
the forearm muscles may result in the muscles
not being able to maintain force output. This
would result in decreasing the overall
effectiveness of an athlete’s sports ability (5).
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INTRODUCTION
• Kickboxing and Muay Thai are two very popular
striking combat sports that have similar
characteristics as the operating principles, as
punching and kicking techniques are used[6,7].
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PURPOSE• Specific anthropometric characteristics play an important
role for the perfect performance of the players in sportive
activites.
• Especially, hand is a very complex structure capable of
motor function and handgrip strength is also very
important for the maximal performance for the players.
• The purpose of this study was performed to evaluate
forearm and hand anthropometric characteristics and
handgrip strength in kickbox athletes.
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3
METHOD
The present study were based on 60
healthy male licensed kickboks atlethes,
between the age of 17 to 25, attached to
Turkish Kickboxing Federation.
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METHOD
Anthropometric characteristics were
measured on each subject following
standard techniques.*
*Lohmann TG, Roche AF, Martorell R. Anthropometric Standardization Reference Manual. Champaign, IL: Human Kinetics Books.1988
4
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METHOD
A digital caliper (Asimeto, Resolution:
0.0005 inch/0.01mm) and a medical tape
measure was used for morphometric
measurement of some morphometric
parameters of upper extremity.
5
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METHOD
A digital hand dynamometer (Takei) was
used for hand-grip strength measurement.
Athletes were tested for grip strength,
which is an indication of upper extremity
strength.
6
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METHOD • The hand dynamometer was set according
to the athlete hand measurement. • The athletes held the dynamometer on the
side of their bodies, without bending their elbows and making an angle of 10-15 degrees from their shoulders.
• It was demanded from the athletes to tighten the dynamometer by applying as much force as possible.
.
7
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Figure 1: The hand dynamometer
8
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METHOD
• After 2 attempts with both hands, the best
performance was determined in kilograms.
• The best performance to evaluate was
taken.
9
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METHOD These obtained data were analyzed by
response surface methodology (RSM). (It was performed using Stat-Ease Design Expert 7.0.0 software
Stat-Ease Inc., Minneapolis, USA).
9
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METHOD
Response Surface Methodology (RSM)
was used to determine the optimal
conditions for the upper extremity
morphology and handgrip strength in the
present study.
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METHOD• Response surface methodology (RSM) is a
collection of mathematical and statistical techniques for empirical model building.
• By careful design of experiments, the objective is to optimize a response (output variable) which is influenced by several independent variables (input variables).
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METHOD
An experiment is a series of tests, called
runs, in which changes are made in the
input variables in order to identify the
reasons for changes in the output
response.
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METHOD
Re la t i ve mode l equa t ions were
established with independent variables of
morphometric parameters,
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Right wrist circumference (RWC), Right hand lenght (RHL), Right hand width (RHW), Right forearm lenght (RFL),
METHOD
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Left wrist circumference (LWC), Left hand lenght (LHL), Left hand width (LHW), Left forearm lenght (LFL),
METHOD
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Other upper extremity morphometric parameters:
Shoulder width (SW), Right arm lenght (RAL), Left arm lenght (LAL) Right arm lenght (elbow flexion-900)(RALEF).
METHOD
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The model equation was found by establishing the relationship matrix with age, height, weight and body fat ratio independent variables and the right handgrip strength dependent variable. The dependent variable was revealed by 54.40% by independent variables (R2 = 0.5440).
1-Right handgrip strength & Age, height, weight and body fat ratio
RESULTS
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Right handgrip strength & Age, height, weight and body fat ratio
• Results of ANOVA showed that the model was significant with low P-value of 0.0003 for right handgrip strength.
• Age, height, weight and body fat ratio parameters was found insignificant for right handgrip strength.
• The model was demonstrated as significant (p<0.05).
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The model equation was found by establishing the relationship matrix with age, height, weight and body fat ratio independent variables and the left handgrip strength dependent variable. The dependent variable was revealed by 55,62% by independent variables (R2= 0,5562).
2-Left handgrip strength & Age, height, weight and body fat ratio
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• Results of ANOVA showed that the model was significant with low P-value of 0.0002 for right handgrip strength.
• Age, height, weight and body fat ratio parameters was found insignificant for left handgrip strength.
• The model was demonstrated as significant (p<0.05).
Left handgrip strength & Age, height, weight and body fat ratio
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The model equation was found by establishing the relationship matrix with RWC, RHL, RHW and RFL independent variables and the right handgrip strength dependent variable. The dependent variable was revealed by 57,60% by independent variables (R2= 0,5760).
3-Right handgrip strength & RWC, RHL, RHW and RFL
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Right handgrip strength & RWC, RHL, RHW and RFL
• Results of ANOVA showed that the model was highly significant with low P-value of 0.0001 for right handgrip strength.
• The model was found significant. For the left handgrip strength; LWC parameter was shown significant, but LHL, LHW and LFL parameters was found insignificant.
• The model was demonstrated as significant (p<0.05).
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The model equation was found by establishing the relationship matrix with RAL, RFL and SW independent variables and the right handgrip strength dependent variable. The dependent variable was revealed by 53,48% by independent variables (R2= 0,5348).
4-Right handgrip strength & RAL, RFL and SW
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Right handgrip strength & RAL, RFL and SW • Results of ANOVA showed that the model was
highly significant with low P-value of 0.0245 for right handgrip strength.
• The model was found significant. RAL, SW and the dual interaction of RAL-RFL was determined as significant model parameter. While RFL parameter is not significant for the right handgrip strength.
• The model was demonstrated as significant (p<0.05).
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Optimum point: RAL: 35.60, RFL: 28.40, SW: 46.60, Right Handgrip Strength: 45.3008
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The model equation was found by establishing the relationship matrix with RAL, RAL-EF and SW independent variables and the right handgrip strength dependent variable. The dependent variable was revealed by 53,69% by independent variables (R2= 0,5369).
5-Right handgrip strength & RAL, RALEF and SW
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• Results of ANOVA showed that the model was highly significant with P-value of 0.0238 for right handgrip strength.
• The model was found significant. The dual interaction
of RAL-SW and RALEF-SW was determined as significant model parameter.
• The model was demonstrated as significant (p<0.05).
Right handgrip strength & RAL, RALEF and SW
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Optimum point: RAL: 35.00, RALEF: 33.80,
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Optimum point: RAL: 35.00, RALEF: 33.80,
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The model equation was found by establishing the relationship matrix with LWC, LHL, LHW and LFL independent variables and the left handgrip strength dependent variable. The dependent variable was revealed by 51,04% by independent variables (R2= 0,5104).
6-Left handgrip strength & LWC, LHL, LHW and LFL
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Left handgrip strength & LWC, LHL, LHW and LFL
• Results of ANOVA showed that the model was significant with low P-value of 0.0019 for left handgrip strength.
• The model was found significant. For the left
handgrip strength; LWC parameter was shown significant, but LHL, LHW and LFL parameters was found insignificant.
• The model was demonstrated as significant (p<0.05).
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The model equation was found by establishing the relationship matrix with LAL, LALEF, LFL and SW independent variables and the left handgrip strength dependent variable. The dependent variable was revealed by 51,14% by independent variables (R2= 0,5414).
7-Left handgrip strength & LAL, LALEF, LFL and SW
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CORPORATE SPORT ORGANISATION15/29, rue Guilleminot, 75014 PARIS - FRANCEPhone: +33 (0)1 77 70 65 15 - Fax: +33 (0)1 77 70 65 14Email: [email protected] - Website: www.medigames.com
Left handgrip strength & LAL, LALEF, LFL and SW
• Results of ANOVA showed that the model was significant with P-value of 0.0091 for left handgrip strength.
• The model was found significant. For the left handgrip strength; The intrinsic interaction of LFL parameter was shown significant.
• The model was demonstrated as significant (p<0.05).
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The model equation was found by establishing the relationship matrix with LAL, LFL and SW independent variables and the left handgrip strength dependent variable. The dependent variable was revealed by 51,11% by independent variables (R2= 0,5111).
8-Left handgrip strength & LAL, LFL and SW
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Left handgrip strength & LAL, LALEF, LFL and SW
• Results of ANOVA showed that the model was significant with P-value of 0.0426 for left handgrip strength.
• The model was found significant and SW parameters
was found significant.
• The model was demonstrated as significant (p<0.05).
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SUMMARY Hand-grip strength selected as dependent variable
• RFL, p-value:0.0051 • LWC, p-value:0.0230 • SW, p-value:0.0362 • RAL-RFL, p-value:0.0129 • LFL, p-value:0.0159
was determined as the significant model parameter. Moreover, the dual interaction of RALEF-SW was
determinedas significant model parameter (p-value:0.0478).
10
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CONCLUSION
Consequently, the different anthropometric
variables of forearm and hand are
posit ively correlated with handgrip
strength.
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REFERENCES• (1)LingCH,TaekemaD,deCraenAJ,GusseklooJ,WestendorpRG,Maier
AB. Handgrip strength and mortality in the oldest old populaDon: theLeiden85-plusstudy.CMAJ.2010;182(5):429–435.
• (2) Foo LH, ZhangQ, ZhuK,MaG,GreenfieldH, FraserDR. Influenceofbody composiDon, muscle strength, diet and physical acDvity on totalbody and forearm bone mass in Chinese adolescent girls. Br J Nutr.2007;98(6):1281–1287.
• (3) Abaraogu, UO, Ezema,CI , Ofodile, UN Igwe , SE. AssociaDon of gripstrength with anthropometric measures: Height, forearm diameter, andmiddlefinger length inyoungadults.PolishAnal.OfMedicine.ArDcle inPress.2017;hep//dx.doi.org/10.1006/pomed2016.11.008.
• (4) Mathiowetz, V., Kashman, N., Volland, G., Weber, K., Dowe, M., &Rogers, S. (1985). Grip and pinch strength: normaDve data for adults.ArchivesofPhysicalMedicineandRehabilitaDon,66,69e74
38World Medical & Health Games& INTERNATIONAL SPORT MEDECINE SYMPOSIUM
CORPORATE SPORT ORGANISATION15/29, rue Guilleminot, 75014 PARIS - FRANCEPhone: +33 (0)1 77 70 65 15 - Fax: +33 (0)1 77 70 65 14Email: [email protected] - Website: www.medigames.com
REFERENCES
• (5)Chang,H-Y,Kun-Yu,K-Y,Lin,J-J,Lin,J-F,Wang,C-H. Immediateeffectofforearm Kinesio taping on maximal grip strength and force sense inhealthycollegiateathletes.PhysicalTherapyinSport11(2010)122e127.
• (6) Silva JJR, Del Vecchio FB, Picanc¸o LM, TakitoMY, FranchiniE. Time-moDon analysis in Muay Thai and kickboxing amateurmatches. J HumSportExerc2011;6(3):490-496.
• [7]SlimaniM,ChaabeneH,MiarkaB,ChamariK.TheacDvityprofileofelitelow-kick kickboxing compeDDon. Int J SportsPhysiol Perform 2016 [inpress].
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HAND IN HAND TO KEEP THE BLUE ALIVE…
THANKS J
