Prediction of adult height in girls: The Beunen-Malina-Freitas method

Abstract

The purpose of this study was to validate and cross-validate the Beunen-Malina-Freitas method for non-invasive prediction of adult height in girls. A sample of 420 girls aged 10–15 years from the Madeira Growth Study were measured at yearly intervals and then 8 years later. Anthropometric dimensions (lengths, breadths, circumferences, and skinfolds) were measured; skeletal age was assessed using the Tanner-Whitehouse 3 method and menarcheal status (present or absent) was recorded. Adult height was measured and predicted using stepwise, forward, and maximum R ² regression techniques. Multiple correlations, mean differences, standard errors of prediction, and error boundaries were calculated. A sample of the Leuven Longitudinal Twin Study was used to cross-validate the regressions. Age-specific coefficients of determination (R ²) between predicted and measured adult height varied between 0.57 and 0.96, while standard errors of prediction varied between 1.1 and 3.9 cm. The cross-validation confirmed the validity of the Beunen-Malina-Freitas method in girls aged 12–15 years, but at lower ages the cross-validation was less consistent. We conclude that the Beunen-Malina-Freitas method is valid for the prediction of adult height in girls aged 12–15 years. It is applicable to European populations or populations of European ancestry.     

Prediction equation for head volume of Japanese young adults

Accurate measurement of head volume is indispensable for precise assessments of body composition determined by hydrostatic weighing without head submersion. The purpose of this study was to establish a prediction equation for head volume measured by the immersion method from multiple regression analysis using head parameters (head circumference, head length, head breadth, neck girth and head thickness) as independent variables. The participants were 106 Japanese young adults (55 males and 51 females) aged 17-27 years. Intra-class correlation coefficients (ICCs) for each head parameter and head volume in males and females were very high (ICC = 0.993-0.999, 0.992-0.998). Head circumference was closely related to head volume measured by the immersion method (r = 0.719, 0.861, P < 0.05), and was the most important parameter for the prediction equation in both sexes. Head breadth was related poorly (r = 0.475, 0.500, P < 0.05) and showed a small individual difference. It was, therefore, excluded from the independent variables. The prediction equation for males was predicted head volume = 122.10X1 + 106.19X3 + 37.16X4 - 89.46X5 - 4754.93, R = 0.909, SEE = 121.75 ml, and that for females was predicted head volume = 213.83X1 + 45.24X3 + 36.85X4 - 74.34X5 - 8912.43, R = 0.913, SEE = 136.26 ml (where X1 = head circumference, X3 = head length, X4 = neck girth, X5 = head thickness, and SEE = standard error of the estimate). The limits of agreement for predicted and measured head volume were -234.5 to 234.1 ml for males, and -261.0 to 261.0 ml for females. In cross-validation groups of both sexes, there were no significant differences between measured head volume and predicted head volume. The correlation coefficients between measured head volume and predicted head volume in males and females were 0.894 and 0.908, respectively. The predicted head volume from prediction equations was considered to have high reliability and validity.     

Prediction of segmental lean mass using anthropometric variables in young adults

The aim of the present study was to develop and cross-validate anthropometrical prediction equations for segmental lean tissue mass (SLM). One hundred and seventeen young healthy Caucasians (67 men and 50 women; mean age: 31.9 ± 10.0 years; Body Mass Index: 24.3 ± 3.2 kg · m(-2)) were included. Body mass (BM), stretch stature (SS), 14 circumferences (CC), 13 skinfolds (SF) and 4 bone breadths (BB) were used as anthropometric measurements. Segmental lean mass of both arms, trunk and both legs were measured by dual energy X-ray absorptiometry as the criterion method. Three prediction equations for SLM were developed as follows: arms = 40.394(BM) + 169.836(CCarm-tensed) + 399.162(CCwrist) - 85.414(SFtriceps) - 39.790(SFbiceps) - 7289.190, where Adj.R (2) = 0.97, P < 0.001, and standard error of estimate (SEE) = 355 g;trunk = 181.530(BM) + 155.037(SS) + 534.818(CCneck) + 175.638(CCchest) - 88.359(SFchest) - 147.232(SFsupraspinale) - 46522.165, where Adj.R(2) = 0.97, P < 0.001, and SEE = 1077g; and legs = 55.838(BM) + 88.356(SS) + 235.579(CCmid-thigh) + 278.595(CCcalf) + 288.984(CCankle) - 84.954(SFfront-thigh) - 53.009(SFmedial calf) - 28522.241, where Adj.R (2) = 0.96, P < 0.001, and SEE = 724 g. Cross-validation statistics showed no significant differences (P < 0.05) between observed and predicted SLM. Root mean squared errors were smallest for arms (362 g), followed by legs (820 g) and trunk (1477 g). These new prediction equations allow an accurate estimation of segmental lean mass in groups of young adults, but estimation errors of 8 to 14% can occur in certain individuals.     

Validation of maturity offset in a longitudinal sample of Polish girls

Abstract

This study attempted to validate an anthropometric equation for predicting age at peak height velocity (PHV) in 198 Polish girls followed longitudinally from 8 to 18 years. Maturity offset (years before or after PHV) was predicted from chronological age, mass, stature, sitting height and estimated leg length at each observation; predicted age at PHV was the difference between age and maturity offset. Actual age at PHV for each girl was derived with Preece–Baines Model 1. Predicted ages at PHV increased from 8 to16 years and varied relative to time before and after actual age at PHV. Predicted and actual ages at PHV did not differ at 9 years, but predicted overestimated actual age at PHV from 10 to 16 years. Girls of contrasting maturity status differed in predicted age at PHV from 8 to 14 years. In conclusion, predicted age at PHV is dependent upon age at prediction and individual differences in actual age at PHV, which limits its utility as an indicator of maturity timing in general and in sport talent programmes. It may have limited applicability as a categorical variable (pre-, post-PHV) among average maturing girls during the interval of the growth spurt, ~11.0–13.0 years.     

The validity and reliability of relative body fat estimates and the construction of new prediction equations for young Japanese adult males.

The aims of this study were to assess the reliability and validity of three methods of bioelectrical impedance analysis (based on induction between the hand and foot, between one foot and the other foot and between one hand and the other hand) and the skinfold method, and to construct prediction equations for total body density by examining cross-validity in young Japanese adult males. The participants were 50 Japanese males aged 18-27 years (height 1.72 +/- 0.06 m, body mass 64.9 +/- 9.0 kg; mean +/- s), each of whom was measured twice using each of the four methods. Relative body fat based on underwater weighing was used as the criterion for validity. To construct prediction equations for body density, we used multiple regression analysis, whereby all possible combinations were examined. The reliability of all three bioelectrical impedance methods was high (R = 0.999). Three new prediction equations were constructed for the hand-foot method, foot-foot method and skinfold method. The cross-validity of the equations was guaranteed. The relative body fat calculated using the new equations did not differ from that based on the underwater weighing method.     

Estimation of body density based on hydrostatic weighing without head submersion in young Japanese adults

This study examined a method of predicting body density based on hydrostatic weighing without head submersion (HWwithoutHS). Donnelly and Sintek (1984) developed a method to predict body density based on hydrostatic weight without head submersion. This method predicts the difference (D) between HWwithoutHS and hydrostatic weight with head submersion (HWwithHS) from anthropometric variables (head length and head width), and then calculates body density using D as a correction factor. We developed several prediction equations to estimate D based on head anthropometry and differences between the sexes, and compared their prediction accuracy with Donnelly and Sintek's equation. Thirty-two males and 32 females aged 17-26 years participated in the study. Multiple linear regression analysis was performed to obtain the prediction equations, and the systematic errors of their predictions were assessed by Bland-Altman plots. The best prediction equations obtained were: Males: D(g) = -164.12X1 - 125.81X2 - 111.03X3 + 100.66X4 + 6488.63, where X1 = head length (cm), X2 = head circumference (cm), X3 = head breadth (cm), X4 = head thickness (cm) (R = 0.858, R2 = 0.737, adjusted R2 = 0.687, standard error of the estimate = 224.1); Females: D(g) = -156.03X1 - 14.03X2 - 38.45X3 - 8.87X4 + 7852.45, where X1 = head circumference (cm), X2 = body mass (g), X3 = head length (cm), X4 = height (cm) (R = 0.913, R2 = 0.833, adjusted R2 = 0.808, standard error of the estimate = 137.7). The effective predictors in these prediction equations differed from those of Donnelly and Sintek's equation, and head circumference and head length were included in both equations. The prediction accuracy was improved by statistically selecting effective predictors. Since we did not assess cross-validity, the equations cannot be used to generalize to other populations, and further investigation is required.     

Non-Invasive Measurements of Physical Maturity in Children

Abstract

Serial data for 268 normal children (144 boys, 124 girls) have been used to calculate predicted adult statures without using skeletal age. Present statures at an age have been used with these predicted values to obtain ratios that are significantly correlated (p <.05 or < .01) with accepted measures of physical maturity from 5 to 15 years in boys, and from 3 to 13 years in girls. Reference values for this ratio are provided. It is recommended that this non-invasive method of measuring maturity be used when irradiation cannot be justified or when the invasion of personal privacy is inappropriate.     

Evaluating the prediction of maximal heart rate in children and adolescents

In this study, we compared measured maximal heart rate (HRmax) to two different HRmax prediction equations [22 - age and 208 - 0.7(age)] in 52 children ages 7-17 years. We determined the relationship of chronological age, maturational age, and resting HR to measured HRmax and assessed seated resting HR and HRmax during a graded exercise test. Maturational age was calculated as the maturity offset in years from the estimated age at peak height velocity. Measured HRmax was 201 +/- 10 bpm, whereas predicted HRmax ranged from 199 to 208 bpm. Measured HRmax and the predicted value from the 208 - 0.7(age) prediction were similar but lower (p < .05) than the 220 - age prediction. Absolute differences between measured and predicted HRmax were 8 +/- 5 and 10 +/- 8 bpm for the 208 - 0.7 (age) and 220 - age equations, respectively, and were greater than zero (p < .05). Regression equations using resting HR and maturity offset or chronological age significantly predicted HRmax, although the R2 < .30 and the standard error of estimation (8.2-8.5) limits the accuracy. The 208 - 0.7(age) equation can closely predict mean HRmax in children, but individual variation is still apparent.     

Evaluating the Prediction of Maximal Heart Rate in Children and Adolescents

In this study, we compared measured maximal heart rate (HRmax) to two different HRmax prediction equations [220 — age and 208 — 0.7(age)] in 52 children ages 7-17 years. We determined the relationship of chronological age, maturational age, and resting HR to measured HRmax and assessed seated resting HR and HRmax during a graded exercise test. Maturational age was calculated as the maturity offset in years from the estimated age at peak height velocity. Measured HRmax was 201 ± 10 bpm, whereas predicted HRmax ranged from 199 to 208 bpm. Measured HRmax and the predicted value from the 208 — 0.7(age) prediction were similar but lower (p < .05) than the 220 — age prediction. Absolute differences between measured and predicted HRmax were 8 ± 5 and 10 ± 8 bpm for the 208 — 0.7 (age) and 220 — age equations, respectively, and were greater than zero (p < .05). Regression equations using resting HR and maturity offset or chronological age significantly predicted HRmax, although the R2 < .30 and the standard error of estimation (8.2-8.5) limits the accuracy. The 208 — 0.7(age) equation can closely predict mean HRmax in children, but individual variation is still apparent.     

国家高水平体育后备人才基地选材测试指标体系对青少年运动员运动项目定位的应用性探索

目的：通过构建运动项目定位模型，探索“国家高水平体育后备人才基地”选材测试指标体系对青少年运动员的运动项目定位的效果，进一步获得有助于人才识别的选材特征指标。方法：以近五年（2015—2019年）上海市两所市级体育运动学校的11~18岁青少年运动员（男性663名、女性662名）为研究对象，每两岁为一组，采用标准式判别分析探索基地选材测试体系对运动员所从事运动项目的正确定位能力，步进式判别分析更进一步筛选出在运动项目定位中相对重要的特征指标，皆由留一法进行交叉验证。结果：通过标准判别分析，基地选材测试体系对男性青少年运动员初始案例进行分类的正确率为11~12岁的77.0%，13~14岁的51.2%，15~16岁的60.6%，以及17~18岁的77.4%，经交叉验证后的正确分类率分别降为48.0%、36.5%、40.4%和63.2%；对女性青少年运动员初始案例进行分类的正确率为11~12岁的61.7%、13~14岁的54.0%、15~16岁的61.1%和17~18岁的90.1%，交叉验证后的正确分类率分别降为31.6%、32.5%、37.3%和71.8%。通过步进式判别分析，有助于项目正确定位的特征指标有男运动员11~12岁的肩宽和小腿长A，13~14岁的下肢长B、大腿围、肺活量和背力，15~16岁的下肢长B、小腿围、大腿围和背力，以及17~18岁的身高、胸围和背力；女运动员的项目定位特征指标为11~12岁的小腿长A和肺活量，13~14岁的体重、指距、下肢长B和背力，15~16岁的下肢长B、背力和皮褶厚度和，以及17~18岁的身高、体重、小腿围、皮褶厚度和与背力。结论：基地选材测试指标体系对青少年运动员的运动项目定位表现出中到高的有效性，会受到年龄和项目的影响。下肢长B和背力指标在青春中后期表现出较强的运动项目区分能力，小腿长A指标则在青春前期表现出区分优势，可作为运动项目定位时的特征指标。     

Vertical jumping and leg power normative data for English school children aged 10–15 years

Abstract

Although vertical jumping is often incorporated into physical activity tests for both adults and children, normative data for children and adolescents are lacking in the literature. The objectives of this study were to provide normative data of jump height and predicted peak leg power for males and females aged 10.0–15.9 years. Altogether, 1845 children from 12 state primary and secondary schools in the East of England participated in the study. Each child performed two countermovement jumps, and jump height was calculated using a NewTest jump mat. The highest jump was used for analysis and in the calculation of predicted peak power. Jump height and predicted peak leg power were significantly higher for males than females from the age of 11 years. Jump height and peak power increased significantly year on year for males. For females, jump height and predicted peak leg power reached a plateau after age 12 and 13 years respectively. This study provides normative data that can be used as a tool to classify jumping performance in children aged 10–15 years.     

Relationships among values, achievement orientations, and attitudes in youth sport

This research examines the value-expressive function of attitudes and achievement goal theory in predicting moral attitudes. In Study 1, the Youth Sport Values Questionnaire (YSVQ; Lee, Whitehead, & Balchin, 2000) was modified to measure moral, competence, and status values. In Study 2, structural equation modeling on data from 549 competitors (317 males, 232 females) aged 12-15 years showed that moral and competence values predicted prosocial attitudes, whereas moral (negatively) and status values (positively) predicted antisocial attitudes. Competence and status values predicted task and ego orientation, respectively, and task and ego orientation partially mediated the effect of competence values on prosocial attitudes and of status values on antisocial attitudes, respectively. The role of sport values is discussed, and new research directions are proposed.     

Book reviews

Thigh muscle volume is a useful determinant of functional fitness. However, anthropometric prediction of muscle content is influenced by the variability of adipose tissue accumulation. The aims of this study were to predict thigh muscle and adipose tissue volumes from anthropometry and to assess the validity of the method by examining the various components of the measurements and the assumptions involved. The 19 participants (9 men, 10 women; age 23-49 years) varied in adiposity. They all underwent magnetic resonance imaging (MRI) of the upper leg and the eight men and two women with the lowest adiposity underwent detailed anthropometry involving girths and skinfolds. Using MRI as the reference method, muscle volume was predicted from anthropometry using a circular concentric model, and the assumptions inherent in the method were tested further using the MRI data alone. Muscle volume was best predicted by anthropometry in the 10 leanest participants using a five-slice truncated cone model that overestimated the mean MRI value by 30% ( R 2 = 0.95; standard error of estimate = 288 cm 3 ; P ? 0.001). A single skinfold plus girth measurement at the mid-thigh almost matched its predictive ability, but with an increased bias. Measurements of leg circumference by means of the two techniques agreed well. The assumption of a circular cross-section was valid. In contrast, the agreement between skinfold thickness measured by caliper and superficial adipose tissue thickness by MRI was poor, contributing to the scatter of fat and lean area comparisons. An anterior skinfold thickness measurement underestimated the area of superficial adipose tissue at that level, particularly at the most proximal and distal sites. Although these limitations increase the uncertainties of muscle volume determination by anthropometry, they do not prevent its valid prediction in leaner individuals. The prediction of superficial adipose tissue was poorer.     

Determination of thigh volume in youth with anthropometry and DXA: Agreement between estimates

Abstract

This study examined the agreement between estimates of thigh volume (TV) with anthropometry and dual-energy x-ray absorptiometry (DXA) in healthy school children. Participants (n=168, 83 boys and 85 girls) were school children 10.0–13.9 years of age. In addition to body mass, height and sitting height, anthropometric dimensions included those needed to estimate TV using the equation of Jones & Pearson. Total TV was also estimated with DXA. Agreement between protocols was examined using linear least products regression (Deming regressions). Stepwise regression of log-transformed variables identified variables that best predicted TV estimated by DXA. The regression models were then internally validated using the predicted residual sum of squares method. Correlation between estimates of TV was 0.846 (95%CI: 0.796–0.884, S_y·x=0.152L). It was possible to obtain an anthropometry-based model to improve the prediction of TVs in youth. The total volume by DXA was best predicted by adding body mass and sum of skinfolds to volume estimated with the equation of Jones & Pearson (R=0.972; 95%CI: 0.962–0.979; R ²=0.945).     

Prediction equation for head volume of Japanese young adults

Accurate measurement of head volume is indispensable for precise assessments of body composition determined by hydrostatic weighing without head submersion. The purpose of this study was to establish a prediction equation for head volume measured by the immersion method from multiple regression analysis using head parameters (head circumference, head length, head breadth, neck girth and head thickness) as independent variables. The participants were 106 Japanese young adults (55 males and 51 females) aged 17?–?27 years. Intra-class correlation coefficients (ICCs) for each head parameter and head volume in males and females were very high (ICC = 0.993?–?0.999, 0.992?–?0.998). Head circumference was closely related to head volume measured by the immersion method (r = 0.719, 0.861, P <?0.05), and was the most important parameter for the prediction equation in both sexes. Head breadth was related poorly (r = 0.475, 0.500, P <?0.05) and showed a small individual difference. It was, therefore, excluded from the independent variables. The prediction equation for males was predicted head volume = 122.10X ₁ + 106.19X ₃ + 37.16X ₄ - 89.46X ₅ - 4754.93, R = 0.909, SEE = 121.75?ml, and that for females was predicted head volume = 213.83X ₁ + 45.24X ₃ + 36.85X ₄ - 74.34X ₅ - 8912.43, R = 0.913, SEE = 136.26?ml (where X ₁ = head circumference, X ₃ = head length, X ₄ = neck girth, X ₅ = head thickness, and SEE = standard error of the estimate). The limits of agreement for predicted and measured head volume were –?234.5 to 234.1?ml for males, and ??261.0 to 261.0?ml for females. In cross-validation groups of both sexes, there were no significant differences between measured head volume and predicted head volume. The correlation coefficients between measured head volume and predicted head volume in males and females were 0.894 and 0.908, respectively. The predicted head volume from prediction equations was considered to have high reliability and validity.     

Do physical maturity and birth date predict talent in male youth ice hockey players?

The aim of this study was to examine the relationships among biological maturity, physical size, relative age (i.e. birth date), and selection into a male Canadian provincial age-banded ice hockey team. In 2003, 619 male ice hockey players aged 14-15 years attended Saskatchewan provincial team selection camps, 281 of whom participated in the present study. Data from 93 age-matched controls were obtained from the Saskatchewan Pediatric Bone Mineral Accrual Study (1991-1997). During the initial selection camps, birth dates, heights, sitting heights, and body masses were recorded. Age at peak height velocity, an indicator of biological maturity, was determined in the controls and predicted in the ice hockey players. Data were analysed using one-way analysis of variance, logistic regression, and a Kolmogorov-Smirnov test. The ice hockey players selected for the final team were taller, heavier, and more mature (P < 0.05) than both the unselected players and the age-matched controls. Furthermore, age at peak height velocity predicted (P < 0.05) being selected at the first and second selection camps. The birth dates of those players selected for the team were positively skewed, with the majority of those selected being born in the months January to June. In conclusion, team selectors appear to preferentially select early maturing male ice hockey players who have birth dates early in the selection year.     

Development of an aerobic capacity prediction model from one-mile run/walk performance in adolescents aged 13–16 years

A popular algorithm to predict VO_2Peak from the one-mile run/walk test (1MRW) includes body mass index (BMI), which manifests practical issues in school settings. The purpose of this study was to develop an aerobic capacity model from 1MRW in adolescents independent of BMI. Cardiorespiratory endurance data were collected on 90 adolescents aged 13–16 years. The 1MRW was administered on an outside track and a laboratory VO_2Peak test was conducted using a maximal treadmill protocol. Multiple linear regression was employed to develop the prediction model. Results yielded the following algorithm: VO_2Peak = 7.34 × (1MRW speed in m s^?1) + 0.23 × (age × sex) + 17.75. The New Model displayed a multiple correlation and prediction error of R = 0.81, standard error of the estimate = 4.78 ml kg^?1·min^?1, with measured VO_2Peak and good criterion-referenced (CR) agreement into FITNESSGRAM’s Healthy Fitness Zone (Kappa = 0.62; percentage agreement = 84.4%; Φ = 0.62). The New Model was validated using k-fold cross-validation and showed homoscedastic residuals across the range of predicted scores. The omission of BMI did not compromise accuracy of the model. In conclusion, the New Model displayed good predictive accuracy and good CR agreement with measured VO_2Peak in adolescents aged 13–16 years.     

Estimation of thigh muscle and adipose tissue volume using magnetic resonance imaging and anthropometry

Thigh muscle volume is a useful determinant of functional fitness. However, anthropometric prediction of muscle content is influenced by the variability of adipose tissue accumulation. The aims of this study were to predict thigh muscle and adipose tissue volumes from anthropometry and to assess the validity of the method by examining the various components of the measurements and the assumptions involved. The 19 participants (9 men, 10 women; age 23-49 years) varied in adiposity. They all underwent magnetic resonance imaging (MRI) of the upper leg and the eight men and two women with the lowest adiposity underwent detailed anthropometry involving girths and skinfolds. Using MRI as the reference method, muscle volume was predictedfrom anthropometry using a circular concentric model, and the assumptions inherent in the method were tested further using the MRI data alone. Muscle volume was best predicted by anthropometry in the 10 leanest participants using a five-slice truncated cone model that overestimated the mean MRI value by 30% (R2 = 0.95; standard error of estimate = 288 cm3; P < 0.001). A single skinfold plus girth measurement at the mid-thigh almost matched its predictive ability, but with an increased bias. Measurements of leg circumference by means of the two techniques agreed well. The assumption of a circular cross-section was valid. In contrast, the agreement between skinfold thickness measured by caliper and superficial adipose tissue thickness by MRI was poor, contributing to the scatter of fat and lean area comparisons. An anterior skinfold thickness measurement underestimated the area of superficial adipose tissue at that level, particularly at the most proximal and distal sites. Although these limitations increase the uncertainties of muscle volume determination by anthropometry, they do not prevent its valid prediction in leaner individuals. The prediction of superficial adipose tissue was poorer.     

Estimated maturity status and perceptions of adult autonomy support in youth soccer players

In this study, we examined the relations between biological maturity status, body mass index, age, and perceptions of adult autonomy support in the context of youth soccer. A total of 70 female and 43 male soccer players, aged 9 - 15 years, completed three adult-specific versions (i.e. mother, father, coach) of the perceived autonomy support subscale from the Interpersonal Style Scale. The participants' percent predicted adult stature was used as an estimate of biological maturity status. Multiple linear regression analyses revealed that advanced maturity status in male players predicted lower perceptions of autonomy support from the coach. Maturity status was unrelated to perceptions of autonomy support from the coach in female soccer players, and paternal and maternal autonomy support in male and female players. Age and body mass index were unrelated to perceptions of adult (i.e. coach, mother, father) autonomy support in male and female players.     

Developmental changes and predictability of static strength in individuals of different maturity: A 30-year longitudinal study

Abstract

This longitudinal study analyses the development and predictability of static strength and their interactions with maturation in youth. Of 515 children followed annually from age 6 to 18 years, 59 males and 60 females were measured again at age 35. Early, average, and late maturity groups were established. Body height and mass were assessed. Static strength was measured using handgrip dynamometry. Pearson correlations were used as tracking coefficients. From 6 to 12 years of age, no static strength differences were found to exist between the maturity groups of both sexes. Static strength is significantly higher in early than in average and late maturing boys (age 13–16). In girls, a dose–response effect exists (age 11–14). Adult static strength predictability is low in early maturing boys and late maturing girls. It is moderate to high (50–76%) in the other maturity groups up to age 14. Predictors for adult static strength are childhood and adolescent handgrip dynamometry (in females only), medicine ball throw, sit-up, hockey ball throw, and 25-m sprint. Handgrip is a fair predictor of adult static strength at most ages in early and average maturing females; in average maturing males, it is a predictor at age 11. Other indicators of strength (e.g. hockey ball throw) are predictors in males.