Body surface area equations for physically active men and women

Looney, David P.; Potter, Adam W.; Arcidiacono, Danielle M; Santee, William R.; Friedl, Karl E.

doi:10.1002/ajhb.23823

Cited by 8 publications

(6 citation statements)

References 42 publications

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“…The choice of the underlying models to calculate the body surface area and metabolic rate may influence the outcome. For instance, the equation from Du Bois and Du Bois for body surface area can be replaced by a more contemporary version for physically active men and women such as that from Looney et al [ 27 ]. Likewise, the Pandolf equation for heat production can be replaced by a different equation that is specific to the metabolic costs of standing and walking for military age adults [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

We are all exposed, but some are more exposed than others

Kingma

Sullivan-Kwantes

Castellani

et al. 2023

International Journal of Circumpolar Health

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This paper defines functional cold exposure zones that illustrate whether a person is at risk of developing physical performance loss or cold weather injuries. Individual variation in body characteristics, activity level, clothing and protective equipment all contribute to variation in the effective exposure. Nevertheless, with the right education, training, and cold-adapted behaviours the exposure differences might not necessarily lead to increased risk for cold injury. To support the preparation process for cold weather operations, this paper presents a biophysical analysis explaining how much cold exposure risk can vary between individuals in the same environment. The results suggest that smaller persons are prone to be underdressed for moderate activity levels and larger persons are prone to be overdressed. The consequences of these discrepancies place people at different risks for performance loss or cold weather injuries. Nonetheless, even if all are well-dressed at the whole-body level, variation in hand morphology is also expected to influence hand skin temperatures that can be maintained; with smaller hands being more prone to reach skin temperatures associated with dexterity loss or cold weather injuries. In conclusion, this work focusses on bringing cold science to the Arctic warrior, establishing that combating cold stress is not a one size fits all approach.

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Section: Discussionmentioning

confidence: 99%

We are all exposed, but some are more exposed than others

Kingma

Sullivan-Kwantes

Castellani

et al. 2023

International Journal of Circumpolar Health

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“…In Tables 1 and 2, minimum, maximum, average (X), standard deviation (SD), and optimal values of indicators are presented for male and female students. Optimal data values for men and women of this age group were chosen for evaluation based on research data and recommendations of other authors [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53].…”

Section: The Indicatorsmentioning

confidence: 99%

“…Values of indicator variables for women (optimal indicators of parameters are determined according to research data from sources[36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53]). …”

mentioning

confidence: 99%

Algorithm for Determination of Indicators Predicting Health Status for Health Monitoring Process Optimization

Krylovas,

Kosareva,

Dadelo

2024

Mathematics

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This article proposes an algorithm that allows the selection of prognostic variables from a set of 21 variables describing the health statuses of male and female students. The set of variables could be divided into two groups—body condition indicators and body activity indicators. For this purpose, we propose applying the multiple criteria decision methods WEBIRA, entropy-ARAS, and SAW in modelling the general health index, a latent variable describing health status, which is used to rank the alternatives. In the next stage, applying multiple regression analysis, the most informative indicators influencing health status are selected by reducing the indicator’s number to 9–11, and predictor indicators by reducing their number to 5. A methodology for grouping students into three groups is proposed, using selected influencing indicators and predictor indicators in regression equations with the dependent variable of group number. Our study revealed that two body condition indicators and three body activity indicators have the greatest influence on men’s general health index. It was established that two body condition indicators have the greatest influence on women’s general health index. The determination of the most informative indicators is important for predicting health status and optimizing the health monitoring process.

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“…In fact, BSA has been classically defined as a measure of the extent of an individual’s skin surface área [ 21 ] and represents human dimensionality and predicts metabolic activity in clinical applications and metabolic heat production in physiology [ 22 ]. It is calculated from the height and weight of an individual, using classical mathematical formulas, such as the Dubois formula [ 23 ] or others.…”

Section: Introductionmentioning

confidence: 99%

Prediction of fat-free mass from body surface area in young basketball players

de Moraes,

Vidal-Espinoza,

Bergamo

et al. 2024

BMC Sports Sci Med Rehabil

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Background Fat Free Mass (FFM) is an important and essential indicator in various sports populations, since greater muscle and bone mass generates greater strength, endurance and speed in athletes. Objective The purpose of the study was to validate Body Surface Area (BSA) as an anthropometric indicator to estimate FFM in young basketball players. Methods A descriptive cross-sectional study was carried out in 105 male basketball players of the Brazilian Basketball Confederation of Sao Paulo (Campinas), Brazil. The age range was 11 to 15 years. Weight and height were evaluated. BSA, body mass index (BMI) and maturity status (MS) were calculated. Total body scanning was performed by dual X-ray absorptiometry (DXA). The components were extracted: Fat mass (FM), Fat free mass (FFM), percentage of fat mass (%FM) and bone mass (BM). The data were analyzed using the correlation coefficient of concordance (CCC) in terms of precision and accuracy. Results Three regression equations were generated: equation 1 had age and body weight as predictors [FFM= -30.059+(2.926*age)+(0.625*Weight)] (R2 = 92%, precision = 0.96 and accuracy = 0.99), equation 2 used age and BSA [FFM=-45.719+(1.934*age)+(39.388*BSA)] (R2 = 94%, precision = 0.97 and accuracy = 0.99) and equation 3 was based on APHV and BSA [FFM=-15.284+(1.765*APHV)+(37.610*(BSA)] (R2 = 94%, precision = 0.96 and accuracy = 0.99). Conclusions The results suggest the use of anthropometric equation using decimal age and BSA to estimate FFM in young basketball players. This new method developed can be used to design, evaluate and control training programs and monitor the weight status of athletes.

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Body surface area equations for physically active men and women

Cited by 8 publications

References 42 publications

We are all exposed, but some are more exposed than others

We are all exposed, but some are more exposed than others

Algorithm for Determination of Indicators Predicting Health Status for Health Monitoring Process Optimization

Prediction of fat-free mass from body surface area in young basketball players

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