Allometric Scaling of Grip Strength by Body Mass in College-Age Men and Women

Vanderburgh, Paul M.; Mahar, Matthew T.; Chou, Chia Hao

doi:10.1080/02701367.1995.10607658

Cited by 110 publications

(92 citation statements)

References 14 publications

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“…However, in order to estimate valid and unbiased body mass exponents when investigating the power-law association between a physiological output variable (e.g., Kleiber (1950) Metabolic rate Allometry Stroke volume Kory et al (1961) Pulmonary function, FEV 1 Linear regression Jolicoeur and Heusner (1971) Oxygen consumption Allometry Cole (1975) FEV Albrecht et al (1993) Facial height Ratios, regression, and allometry Nevill (1994) Physiological variables Allometry Nevill and Holder (1994) VO 2max Log-linear model Rogers et al (1995) VO 2 sm Allometry Jungers et al (1995) Crania of male monkeys Ratios and allometry Nevill and Holder (1995a) Percentage body fat, Body Mass Index, Lean Body Mass Index Allometry and linear regression Vanderburgh et al (1995) Grip strength Allometry VO 2max Allometry Performance time Allometry Welsman et al (1996) Peak VO 2 Allometry Batterham and Tolfrey (1996) VO 2 Allometry Heil (1997) VO 2peak Allometry Simple ratio Nevill et al (1997b) Systolic and diastolic blood pressure Allometry and log-linear regression Batterham et al (1997b) VO 2peak \Full" allometry Intercept Batterham and George (1997) Maximal muscular function Allometry Batterham et al (1997a) Left ventricular mass Allometry West et al (1997) Metabolism Allometry George et al (1998) Left . In humans, there is strong evidence to suggest that the muscle mass of both the arms and legs increases at a greater proportion to body mass than that expected by geometric similarity (Nevill et al, 2003a(Nevill et al, , 2004c.…”

Section: Controlling For Differences In Body Size Historical Backgroundmentioning

confidence: 99%

Modeling Physiological and Anthropometric Variables Known to Vary with Body Size and Other Confounding Variables

Nevill

Bate

Holder

2005

Am. J. Phys. Anthropol.

100

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This review explores the most appropriate methods of identifying population differences in physiological and anthropometric variables known to differ with body size and other confounding variables. We shall provide an overview of such problems from a historical point of view. We shall then give some guidelines as to the choice of body-size covariates as well as other confounding variables, and show how these might be incorporated into the model, depending on the physiological dependent variable and the nature of the population being studied. We shall also recommend appropriate goodness-of-fit statistics that will enable researchers to confirm the most appropriate choice of model, including, for example, how to compare proportional allometric models with the equivalent linear or additive polynomial models. We shall also discuss alternative body-size scaling variables (height, fat-free mass, body surface area, and projected area of skeletal bone), and whether empirical vs. theoretical scaling methodologies should be reported. We shall offer some cautionary advice (limitations) when interpreting the parameters obtained when fitting proportional power function or allometric models, due to the fact that human physiques are not geometrically similar to each other. In conclusion, a variety of different models will be identified to describe physiological and anthropometric variables known to vary with body size and other confounding variables. These include simple ratio standards (e.g., per body mass ratios), linear and additive polynomial models, and proportional allometric or power function models. Proportional allometric models are shown to be superior to either simple ratio standards or linear and additive polynomial models for a variety of different reasons. These include: 1) providing biologically interpretable models that yield sensible estimates within and beyond the range of data; and 2) providing a superior fit based on the Akaike information criterion (AIC), Bayes information criterion (BIC), or maximum log-likelihood criteria (resulting in a smaller error variance). As such, these models will also: 3) naturally lead to a more powerful analysis-of-covariance test of significance, which will 4) subsequently lead to more correct conclusions when investigating population (epidemiological) or experimental differences in physiological and anthropometric variables known to vary with body size. Yrbk Phys Anthropol 48: 141-153, 2005.

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Section: Controlling For Differences In Body Size Historical Backgroundmentioning

confidence: 99%

Modeling Physiological and Anthropometric Variables Known to Vary with Body Size and Other Confounding Variables

Nevill

Bate

Holder

2005

Am. J. Phys. Anthropol.

100

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“…Essa indicação é baseada principalmente em estudos que identificaram um aumento progressivo da FM absoluta (sem normalização) de acordo com a idade cronológica e maturação biológica. A partir disso, diversos estudos utilizaram a alometria para normalizar adequadamente os dados de FM e TM, na tentativa de elucidar o real comportamento da FM em crianças e adolescentes 3,10,24,35 . Um dos primeiros estudos a utilizar a alometria para normalizar os dados de FM em crianças foi o de Vanderburgh et al 24 .…”

Section: Crianças E Adolescentesunclassified

“…Adicionalmente, muitos estudos têm indicado a alometria como método mais apropriado para ajustar os efeitos da MC sobre a FM [15][16][17][18] ou TM [19][20][21][22][23][24] . Essa metodologia é baseada na aplicação da equação alométrica (Y=aX b ), que estabelece um índice que permite comparar indivíduos heterogêneos em tamanho e composição corporal.…”

Section: Introductionunclassified

Normalização da força e torque muscular em crianças e adolescentes.<br>DOI:10.5007/1980-0037.2011v13n6p468

Cunha¹,

Vaz²,

Oliveira³

2011

Rev. Bras. Cineantropom. Desempenho Hum.

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“…Research into the association between muscle strength and body mass has had a major focus on allometric scaling [1]- [3] based around the equation…”

Section: A Allometric Scaling Of Maximum Isometric Grip Strengthmentioning

confidence: 99%

Implementing an Online Student Laboratory Data Repository in a First Year Undergraduate Health Science Course

Engstrom¹,

Bulmer²,

Newcombe³

et al. 2017

IJIET

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Abstract-In many large first year undergraduate courses in the health sciences, students participate in practical sessions (pracs) in which they are taught about and provided with opportunities to practice basic skills and techniques for measuring biophysical and psychological variables. We have successfully developed and implemented an Online Student Laboratory Data Repository in an introductory health science course which provides rich opportunities for using a large, student centric dataset in lecture components of the course to explore and evaluate underlying principles, models and theories within the discipline or profession. In future work we will examine its effectiveness in improving collaboration between undergraduate students from different health science programs.

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Allometric Scaling of Grip Strength by Body Mass in College-Age Men and Women

Cited by 110 publications

References 14 publications

Modeling Physiological and Anthropometric Variables Known to Vary with Body Size and Other Confounding Variables

Modeling Physiological and Anthropometric Variables Known to Vary with Body Size and Other Confounding Variables

Normalização da força e torque muscular em crianças e adolescentes.<br>DOI:10.5007/1980-0037.2011v13n6p468

Implementing an Online Student Laboratory Data Repository in a First Year Undergraduate Health Science Course

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