Home Print this page Email this page Small font sizeDefault font sizeIncrease font size
Users Online: 328

 

Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Subscribe Advertise Contacts Login 
     

  Table of Contents  
ORIGINAL ARTICLE
Year : 2014  |  Volume : 55  |  Issue : 4  |  Page : 314-320  

Age-predicted vs. measured maximal heart rate in young team sport athletes


Department of Physical and Cultural Education, Hellenic Army Academy, Athens; Exercise Physiology Laboratory, Nikaia, Greece

Date of Web Publication21-Jul-2014

Correspondence Address:
Pantelis Theo Nikolaidis
Department of Physical and Cultural Education, Hellenic Army Academy, Athens, Greece

Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0300-1652.137192

Rights and Permissions
   Abstract 

Background: Although maximal heart rate (HR) max is used widely to assess exercise intensity in sport training and particularly in various team sports, there are limited data with regards to the use of age-based prediction equations of HR max in sport populations. The aim of this study was to compare the measured-HR max with three prediction equations (Fox-HR max = 220-age and Tanaka-HR max = 208-0.7×age and Nikolaidis-HR max = 223-1.44×age) in young team sport athletes. Materials and Methods: Athletes of soccer, futsal, basketball and water polo, classified into three age groups (u-12, 9−12 years, n = 50; u-15, 12−15 years, n = 40; u-18, 15−18 years, n = 57), all members of competitive clubs, voluntarily performed a graded exercise field test (20 m shuttle run endurance test) to assess HR max . Results: Fox-HR max and Nikolaidis-HR max overestimated measured-HR max , while Tanaka-HR max underestimated it (P < 0.001). However, this trend was not consistent when examining each group separately; measured-HR max was similar with Tanaka-HR max in u-12 and u-15, while it was similar with Nikolaidis-HR max in u-18. Conclusion: The results of this study failed to validate two widely used and one recently developed prediction equations in a large sample of young athletes, indicating the need for specific equation in different age groups. Therefore, coaches and fitness trainers should prefer Tanaka-HR max when desiring to avoid overtraining, while Fox-HR max and Nikolaidis-HR max should be their choice in order to ensure adequate exercise intensity.

Keywords: Age groups, athletes, cardiac rate, graded exercise test, prediction equations


How to cite this article:
Nikolaidis PT. Age-predicted vs. measured maximal heart rate in young team sport athletes. Niger Med J 2014;55:314-20

How to cite this URL:
Nikolaidis PT. Age-predicted vs. measured maximal heart rate in young team sport athletes. Niger Med J [serial online] 2014 [cited 2020 Feb 28];55:314-20. Available from: http://www.nigeriamedj.com/text.asp?2014/55/4/314/137192


   Introduction Top


Sport training is based on the optimal prescription of exercise mode, duration and intensity. A daily task of coaches and fitness trainers is to plan an exercise program of adequate intensity. On the contrary, special care is given in order the exercise intensity not to increase the likelihood of overtraining. When working with athletes, coaches and fitness trainers often establish training heart rate (HR) intensities for aerobic exercise based on maximal HR (HR max ), for example Karvonen method. [1] HR max is measured as the maximal value recorded in the end of graded exercise test (GXT) either in a laboratory or in field. However, occasionally it is desirable not to perform a GXT, for example to avoid the fatigue induced by maximal testing during the competitive period.

When it is not possible to measure HR max , its prediction from an age-based equation is an alternative, which is widely used by coaches and fitness trainers. Two popular equations used in the daily sport practice are those of Fox, Naughton and Haskell (Fox-HR max = 220-age) [2] and Tanaka, Monahan and Seals (Tanaka-HR max = 208-0.7×age). [3] The validity of these equations has been examined extensively in large samples of adults (e.g. [3],[4],[5],[6],[7],[8],[9],[10],[11] ) and in specific categories of population, for example healthy, [3],[12] sedentary, [5],[10] overweight, [7] sport [8],[13] and individuals with mental retardation. [6] The aforementioned studies have used a GXT in a laboratory setting to elicit HR max . In contrast, only a few studies have been conducted in children and adolescents [9],[14],[15] and using a field protocol. [16] Few studies had a longitudinal design. [17],[18]

While available studies provide important data regarding the estimation of HR max , the research is by no means complete nor has it has been consistent. One particular area of concern is that athletes and adolescents are under-represented in this body of research. In a recent study, it was shown that athletes of speed/power sports had similar measured-HR max with endurance athletes and both had lower values than those who were untrained. [19] This difference between trained and untrained individuals highlights the need to examine the popular prediction equations in sport samples. In addition, the various protocols of GXT in laboratory and in field may elicit different values of HR max . For instance, a study on soccer players revealed higher HR max in a field test (multistage shuttle run) than in a GXT on treadmill. [20] In a recent study of the validity of prediction equations in soccer players, Fox-HRmax overestimated and Tanaka-HRmax underestimated measured-HRmax, a new formula was suggested for adolescent soccer players (223-1.44×age) and the need to examine the validity of these equations in more sport populations was highlighted. [21]

Therefore, the aim of this study was to examine the validity of Fox-HR max , Tanaka-HR max and Nikolaidis-HR max in a large sample of young team sport athletes. In addition, we investigated whether these relationships vary according to age group (U-12, 9-12 years vs. U-15, 12-15 years vs. U-18, 15-18 years).


   Materials and methods Top


A total of 147 athletes from soccer, futsal, basketball and water polo clubs in the region of Athens were recruited to participate in this study, which was conducted in 2 days. On the 1 st day, the participants visited the laboratory, where they were examined for anthropometry. On the 2 nd day, within a week from the first session, they performed a GXT (20 m shuttle run test, SRT) in an indoor court.

Anthropometry. Height, weight and skinfolds were measured with subjects barefoot and in minimal clothing. An electronic weight scale (HD-351 Tanita, Illinois, USA) was employed for weight measurement (in the nearest 0.1 kg), a portable stadiometer (SECA, Leicester, UK) for height (0.001 m) and a caliper (Harpenden, West Sussex, UK) for skinfolds (0.5 mm). Body mass index (BMI) was calculated as the quotient of body mass (kg) to height squared (m 2 ), and body fat percentage (BF) was estimated from the sum of 10 skinfolds (cheek, wattle, chest I, triceps, subscapular, abdominal, chest II, suprailiac, thigh and calf; BF = -41.32 + 12.59 × log e x, where x the sum of 10 skinfolds). [22]

GXT. Aerobic capacity was tested with the widely used 20 m SRT. [23],[24] Briefly, after a 20 min warm-up including jogging and stretching exercises, participants performed an incremental running test in an indoor court between two lines 20 m apart. Initial speed was set at 8.5 km.h -1 and increased every minute by 0.5 km.h -1 till exhaustion. During the late stages of the test, participants were cheered vigorously to make maximal effort. In addition, they had been instructed to adhere strictly to the speed that was dictated by audio signals. Measured-HR max was defined as the highest value attained during the test. HR was recorded continuously during the test by Team2 Pro (Polar Electro Oy, Kempele, Finland).

Statistical analyses

Statistical analyses were performed using IBM SPSS v.20.0 (SPSS, Chicago, USA). Data were expressed as mean and standard deviations of the mean (SD). One-way analysis of variance (ANOVA) was used to examine differences between the age groups (U-12, U-15 and U-18). One-way repeated measures ANOVA was used to examine differences between measured and predicted HR max. To interpret effect sizes for statistical differences in the ANOVA we used eta square classified as small (0.010 < η2 ≤ 0.059), medium (0.059 < η2 ≤ 0.138) and large (η2 > 0.138). [25] Bland-Altman [26] analysis was used to examine the accuracy and variability of prediction equations. Associations between measured HR max and age were examined using Pearson's product moment correlation coefficient (r). Magnitude of correlation coefficients were considered as trivial (r ≤ 0.1), small (0.1 ≤ r < 0.3) moderate (0.3 ≤ r < 0.5), large (0.5 ≤ r < 0.7), very large (0.7 ≤ r <0.9) and nearly perfect (r ≥ 0.9) and perfect (r = 1). The level of significance was set at α = 0.05.


   Results Top


The basic characteristics of participants can be seen in [Table 1]. Briefly, our sample was comprised of U-12 (34%), U-15 (27%) and U-18 athletes (39%). There were significant differences between age groups for age, weight, height and BMI. The older the age group, the heavier, taller with higher BMI and aerobic capacity were the athletes. Moreover, U-18 had lower BF than U-12 (−2.3%) and U-15 (−2.9%), while there was no difference with regard to the measured-HR max (F 2,144 = 1.2, P0 = 0.308, η2 = 0.02).
Table 1: Descriptive characteristics, shown as mean (standard deviation) values of participants by age group

Click here to view


The measured-HR max and predicted-HR max can be found in [Table 2]. When using an ANOVA with repeated measures with a Greenhouse-Geisser correction, the mean score for HR max differed statistically significantly between measured and predicted values in the total sample (F 1.072,156.441 = 103.0, P < 0.001, η2 = 0.41), in U-12 (F 1.007,49.348 = 51.2, P < 0.001, η2 = 0.51), in U-15 (F 1.006,39.224 = 26.4, P < 0.001, η2 = 0.40) and in U-18 (F 1.010,56.569 = 43.0, P < 0.001, η2 = 0.43). Post hoc tests using the Bonferroni correction revealed that in the total sample, Fox-HR max and Nikolaidis-HR max overestimated measured-HR max [5.5 bpm (3.7; 7.2), mean difference (95% confidence intervals) and 2.5 bpm (0.7; 4.3), respectively], while Tanaka-HR max underestimated measured-HR max [−2.4 bpm (-4.2; −0.7)].
Table 2: Measured-heart rate (HR)max and age-predicted HRmax, shown as mean (standard deviation) values of participants by age group

Click here to view


In addition, we examined this relationship separately for each age group. In U-12, Fox-HR max and Nikolaidis-HR max overestimated measured-HR max [7.1 bpm (3.8; 10.3) and 5.4 bpm (2.1; 8.6), respectively], whereas Tanaka-HR max provided similar values as measured-HR max [−1.7 bpm (-5.0; 1.5) -. In U-15, Fox-HR max overestimated measured-HR max [6.2 bpm (2.4; 10.3)], while Nikolaidis-HR max and Tanaka-HR max provided similar values as measured-HR max [3.3 bpm (−0.6;7.1) and −1.8 bpm (-5.6;2.0), respectively]. In U-18, Fox-HR max overestimated [3.6 bpm (1.2; 6.0)], Tanaka-HR max underestimated [−3.5 bpm (-5.9; −1.1)], while Nikolaidis-HR max provided similar values as measured-HR max [−0.6 bpm (−3.0; 1.8)].

The relationship between measured-HR max and age is depicted in [Figure 1]. HR max was not correlated with age in the total sample (r = −0.11, P = 0.201). The respective correlations separately for U-12, U-15 and U-18 were also trivial to small and non-significant: r = 0.03 (P = 0.829), r = 0.22 (P = 0.181) and r = −0.16 (P = 0.242), respectively.
Figure 1: Relationship between age and maximal heart rate (HRmax) in participants

Click here to view


[Figure 2], [Figure 3] and [Figure 4] show Bland-Altman plots of the difference between predicted-HR max and measured-HR max in total and in each age group for Fox-HR max Tanaka-HR max and Nikolaidis-HR max , respectively. In general, we observed that there was overestimation of HR max at low values of HR max and underestimation of HR max at high values of HR max . This trend was noticed consistently for all age groups and prediction equations.
Figure 2: Bland-Altman plots of the difference between Fox-HRmax and measured-HRmax in the total sample (a), u-12 (b), u-15 (c) and u-18 participants (d)

Click here to view
Figure 3: Bland-Altman plots of the difference between Tanaka-HRmax and measured-HRmax in the total sample (a), u-12 (b), u-15 (c) and u-18 participants (d)

Click here to view
Figure 4: Bland-Altman plots of the difference between Nikolaidis-HRmax and measured-HRmax in the total sample (a), u-12 (b), u-15 (c) and u-18 participants (d)

Click here to view



   Discussion Top


The main finding of this study was that neither Fox, Tanaka nor Nikolaidis equation provide accurate values of HR max in the total sample of young athletes [Table 3]. Fox-HR max overestimated measured-HR max in total as well as in each age group. Tanaka-HR max underestimated measured-HR max in total and in U-18. Nikolaidis-HR max overestimated measured-HR max in total and in U-12. Thus, Tanaka-HR max was valid in U-12 and U-15, while Nikolaidis-HR max was valid in U-15 and U-18.
Table 3: Summary of the main findings

Click here to view


Our study did not confirm the findings of previous research supporting that Fox-HR max underestimates HR max with increasing age, [3],[17] which should be attributed to the younger age of the participants in the present study. In contrast, our findings confirmed that Fox-HR max overestimate HR max in adolescents. [15] This finding practically implies that adopting this widely used prediction equation in young athletes leads athletes to work at higher intensities than what it is desired.

The basic characteristics of participants were similar with those reported recently [27] and the differences in weight, height, BMI and endurance between adolescent and adult players were in line with previous research. [27],[28] The comparison between age groups with regard to their mean HR max revealed no statistical difference, despite a trend for lower values in U15 (−1.9 bpm) and U18 (−2.2 bpm) than in the younger group, finding which was in accordance with the trivial, but not statistically significant, negative correlation between HR max and age.

However, these findings on the relationship between HR max and age were not in agreement with the existing literature. The variation of the span of chronological age may explain the discrepancy between this study and previous research with regard to the above mentioned correlation. In a previous study covering a relatively short span of ages (10−16 years) the correlation between HR max and age was −0.10, [15] while in studies with large span we observed large to very large correlations (e.g. 15-75 years, r = −0.56, [8] 14-77 years, r = −0.60, [11] 16-71 years, r = −0.67, [29] 19-89 years, r = −0.60, [12] 16-65 years, r = −0.60, [10] 18-81 years, r = −0.79 [3] ). Therefore, it should not be a surprise the lack of significant and large correlation when the sample of participants, independently from its size, covers only a few years. Compared with boys of similar age (10-16 years) [15] who performed a GXT on treadmill, the athletes in the present study achieved similar HR max . In addition, we found also similar values with another study on individuals aged 7-17 years. [9]

The main limitation of this study was that it presents the common drawbacks of any field GXT; in opposition to the criteria of maximal effort [e.g. plateau of oxygen uptake, HR >90%-95% of HR max , respiratory quotient >1.15 and lactate >9-10 mmol.L -130 ] used typically in a laboratory setting, the only criterion to evaluate participants' effort was their oral confirmation that they have run till exhaustion. In addition, we examined only the relationship between HR max and age and not the effect of other confounders on this relationship. It has been suggested that the overestimation of HR max might be associated with increased weight and smoking, while its underestimation with rest HR. [11]

However, an issue with important practical implications is to recognize the risks that coaches and fitness trainers undertake depending on which their choice of prediction equation is. Adopting Tanaka equation, which consistently tends to provide low values of HR max , might result in prescribing exercise of lower intensity than what it is desired. In contrast, using Fox or Nikolaidis equation, which tend to overestimate HR max , might result in prescribing high exercise intensity. To deal with this issue, it is recommended to apply higher intensity in the first case and lower intensity in the other two cases.[30]


   Conclusion Top


The results of this study failed to validate two widely used and one recently developed prediction equations in a large sample of young athletes, indicating the need for specific equation in different age groups. Based on the findings of the present study, coaches and fitness trainers are advised to prefer Tanaka-HR max when desiring to avoid overtraining, while Fox-HR max and Nikolaidis-HR max should be their choice in order to ensure adequate exercise intensity.


   Acknowledgement Top


The participation of all athletes and the collaboration with coaches and parents are gratefully acknowledged

 
   References Top

1.Karvonen MJ, Kentala E, Mustala O. The effects of training on heart rate; a longitudinal study. Ann Med Exp Biol Fenn 1957;35:307-15.  Back to cited text no. 1
[PUBMED]    
2.Fox SM 3rd, Naughton JP, Haskell WL. Physical activity and the prevention of coronary heart disease. Ann Clin Res 1971;3:404-32.  Back to cited text no. 2
[PUBMED]    
3.Tanaka H, Monahan KD, Seals DR. Age-predicted maximal heart rate revisited. J Am Coll Cardiol 2001;37:153-6.  Back to cited text no. 3
    
4.Balassiano DH, Araújo CG. Maximal heart rate: Influence of sport practice during childhood and adolescence. Arq Bras Cardiol 2013;100:333-8.  Back to cited text no. 4
    
5.Camarda SR, Tebexreni AS, Páfaro CN, Sasai FB, Tambeiro VL, Juliano Y, et al. Comparison of maximal heart rate using the prediction equations proposed by Karvonen and Tanaka. Arq Bras Cardiol 2008;91:311-4.  Back to cited text no. 5
    
6.Fernhall B, McCubbin JA, Pitetti KH, Rintala P, Rimmer JH, Millar AL, et al. Prediction of maximal heart rate in individuals with mental retardation. Med Sci Sports Exerc 2001;33:1655-60.  Back to cited text no. 6
    
7.Franckowiak SC, Dobrosielski DA, Reilley SM, Walston JD, Andersen RE. Maximal heart rate prediction in adults that are overweight or obese. J Strength Cond Res 2011;25:1407-12.  Back to cited text no. 7
    
8.Lester M, Sheffield LT, Trammell P, Reeves TJ. The effect of age and athletic training on the maximal heart rate during muscular exercise. Am Heart J 1968;76:370-6.  Back to cited text no. 8
[PUBMED]    
9.Mahon AD, Marjerrison AD, Lee JD, Woodruff ME, Hanna LE. Evaluating the prediction of maximal heart rate in children and adolescents. Res Q Exerc Sport 2010;81:466-71.  Back to cited text no. 9
    
10.Sarzynski MA, Rankinen T, Earnest CP, Leon AS, Rao DC, Skinner JS, et al. Measured maximal heart rates compared to commonly used age-based prediction equations in the heritage family study. Am J Hum Biol 2013;25:695-701.  Back to cited text no. 10
    
11.Whaley MH, Kaminsky LA, Dwyer GB, Getchell LH, Norton JA. Predictors of over- and underachievement of age-predicted maximal heart rate. Med Sci Sports Exerc 1992;24:1173-9.  Back to cited text no. 11
    
12.Nes BM, Janszky I, Wisløff U, Støylen A, Karlsen T. Age-predicted maximal heart rate in healthy subjects: The HUNT Fitness Study. Scand J Med Sci Sports 2013;23:697-704.  Back to cited text no. 12
    
13.Faff J, Sitkowski D, Ladyga M, Klusiewicz A, Borkowski L, Starczewska-Czapowska J. Maximal heart rate in athletes. Biol Sport 2007;24:129-42.  Back to cited text no. 13
    
14.Colantonio E, Kiss MA. Is the HRmax=220-age equation valid to prescribe exercise training in children? J Exerc Physiol Online 2013;16:19-7.  Back to cited text no. 14
    
15.Machado FA, Denadai BS. Validity of maximum heart rate prediction equations for children and adolescents. Arq Bras Cardiol 2011;97:136-40.  Back to cited text no. 15
    
16.Cleary MA, Hetzler RK, Wages JJ, Lentz MA, Stickley CD, Kimura IF. Comparisons of age-predicted maximum heart rate equations in college-aged subjects. J Strength Cond Res2011;25:2591-7.  Back to cited text no. 16
    
17.Gellish RL, Goslin BR, Olson RE, McDonald A, Russi GD, Moudgil VK. Longitudinal modeling of the relationship between age and maximal heart rate. Med Sci Sports Exerc 2007;39:822-9.  Back to cited text no. 17
    
18.Zhu N, Suarez-Lopez JR, Sidney S, Sternfeld B, Schreiner PJ, Carnethon MR, et al. Longitudinal examination of age-predicted symptom-limited exercise maximum HR. Med Sci Sports Exerc 2010;42:1519-27.  Back to cited text no. 18
    
19.Kusy K, Zielinski J. Aerobic capacity in speed-power athletes aged 20-90 years vs endurance runners and untrained participants. Scand J Med Sci Sports 2014;24:68-79.  Back to cited text no. 19
    
20.Aziz AR, Tan FH, Teh KC. A pilot study comparing two field tests with the treadmill run test in soccer players. J Sports Sci Med 2005;4:105-12.  Back to cited text no. 20
[PUBMED]    
21.Nikolaidis PT. Maximal heart rate in soccer players: Measured vs. age-predicted. Biomed J 2014.  Back to cited text no. 21
    
22.Parizkova J. Lean body mass and depot fat during autogenesis in humans. In: Parizkova J, Rogozkin V, editors. Nutrition, Physical Fitness and Health: International Series on Sport Sciences. Baltimore: University Park Press; 1978.  Back to cited text no. 22
    
23.Adam C, Klissouras V, Ravazzolo M, Renson R, Tuxworth W. The Eurofit Test of European Physical Fitness Tests. Strasbourg: Council of Europe; 1988.  Back to cited text no. 23
    
24.Olds T, Tomkinson G, Léger L, Cazorla G. Worldwide variation in the performance of children and adolescents: An analysis of 109 studies of the 20-m shuttle run test in 37 countries. J Sports Sci 2006;24:1025-38.  Back to cited text no. 24
    
25.Cohen J. Statistical power analysis for the behavioral sciences. 2 nd ed. Hillsdale: Lawrence Erlbaum Associates; 1988.  Back to cited text no. 25
    
26.Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307-10.  Back to cited text no. 26
[PUBMED]    
27.Nikolaidis PT, Vassilios Karydis N. Physique and body composition in soccer players across adolescence. Asian J Sports Med 2011;2:75-82.  Back to cited text no. 27
    
28.Nikolaidis PT. Cardiorespiratory power across adolescence in male soccer players. Fiziologiia Cheloveka 2011;37:137-42.  Back to cited text no. 28
    
29.Inbar O, Oren A, Scheinowitz M, Rotstein A, Dlin R, Casaburi R. Normal cardiopulmonary responses during incremental exercise in 20- to 70-yr-old men. Med Sci Sports Exerc 1994;26:538-46.  Back to cited text no. 29
    
30.Howley ET, Bassett DR Jr, Welch HG. Criteria for maximal oxygen uptake: Review and commentary. Med Sci Sports Exerc 1995;27:1292-301.  Back to cited text no. 30
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]


This article has been cited by
1 Field-Based Tests for the Assessment of Physical Fitness in Children and Adolescents Practicing Sport: A Systematic Review within the ESA Program
Garden Tabacchi,Guillermo F. Lopez Sanchez,Fatma Nese Sahin,Meltem Kizilyalli,Rosario Genchi,Michele Basile,Musa Kirkar,Carlos Silva,Nuno Loureiro,Eduardo Teixeira,Yolanda Demetriou,David Joseph Sturm,Simona Pajaujene,Ilona J. Zuoziene,Manuel Gómez-López,Ante Rada,Jelena Pausic,Nemanja Lakicevic,Luca Petrigna,Kaltrina Feka,Ana Ribeiro,Marianna Alesi,Antonino Bianco
Sustainability. 2019; 11(24): 7187
[Pubmed] | [DOI]
2 Validity of Prediction Equations of Maximal Heart Rate in Physically Active Female Adolescents and the Role of Maturation
Michael R. Papadopoulou,Michael R. Papadopoulou,Michael R. Alipasali,Michael R. Hatzimanouil,Michael R. Rosemann,Michael R. Knechtle,Michael R. Nikolaidis
Medicina. 2019; 55(11): 735
[Pubmed] | [DOI]
3 Maximum heart rate predicted by formulas versus values obtained in graded exercise tests in Brazilian jiu-jitsu athletes
Braulio Henrique Magnani Branco,Fabiano de Oliveira Mendes,Gabriel Fassina Ladeia,Sônia Maria Marques Gomes Bertolini,Pablo Valdés Badilla,Leonardo Vidal Andreato
Sport Sciences for Health. 2019;
[Pubmed] | [DOI]
4 Age-Based Prediction of Maximal Heart Rate in Children and Adolescents: A Systematic Review and Meta-Analysis
Zackary S. Cicone,Clifton J. Holmes,Michael V. Fedewa,Hayley V. MacDonald,Michael R. Esco
Research Quarterly for Exercise and Sport. 2019; : 1
[Pubmed] | [DOI]
5 Age-Predicted Maximal Heart Rate Equations Are Inaccurate for Use in Youth Male Soccer Players
Zackary S. Cicone,Oleg A. Sinelnikov,Michael R. Esco
Pediatric Exercise Science. 2018; : 1
[Pubmed] | [DOI]
6 Predictive maximal heart rate equations in child and adolescent athletes: a systematic review
Anderson Sartor Pedroni,Aniuska Schiavo,Eléia de Macedo,Natália E de Campos,Aline Dill Winck,João Paulo Heinzmann-Filho
Fisioterapia em Movimento. 2018; 31(0)
[Pubmed] | [DOI]
7 Influence of Different Methods to Determine Maximum Heart Rate on Training Load Outcomes in Basketball Players
Daniel M. Berkelmans,Vincent J. Dalbo,Jordan L. Fox,Robert Stanton,Crystal O. Kean,Kate E. Giamarelos,Masaru Teramoto,Aaron T. Scanlan
Journal of Strength and Conditioning Research. 2018; 32(11): 3177
[Pubmed] | [DOI]
8 HEART RATE MONITORING IN BASKETBALL
Daniel M. Berkelmans,Vincent J. Dalbo,Crystal O. Kean,Zoran Milanovic,Emilija Stojanovic,Nenad Stojiljkovic,Aaron T. Scanlan
Journal of Strength and Conditioning Research. 2017; : 1
[Pubmed] | [DOI]
9 The acute effect of exercise intensity on free throws in young basketball players
Eleni Mokou,Pantelis T. Nikolaidis,Johnny Padulo,Nikos Apostolidis
Sport Sciences for Health. 2016;
[Pubmed] | [DOI]



 

Top
  
 
  Search
 
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

 
  In this article
    Abstract
   Introduction
    Materials and me...
   Results
   Discussion
   Conclusion
   Acknowledgement
    References
    Article Figures
    Article Tables

 Article Access Statistics
    Viewed1963    
    Printed44    
    Emailed2    
    PDF Downloaded201    
    Comments [Add]    
    Cited by others 9    

Recommend this journal