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Phys Act Nutr > Volume 23(4); 2019 > Article
Park: Using physical activity levels to estimate energy requirements of female athletes

Abstract

[Purpose]

The goal of this study was to review data on physical activity level (PAL), a crucial index for determining estimated energy requirement (EER), calculated as total energy expenditure (TEE, assessed with doubly labeled water [DLW]) divided by resting metabolic rate (RMR, PAL = TEE/RMR) in female athletes and to understand the methods of assessing athletes’ EERs in the field.

[Methods]

For the PAL data review among female athletes, we conducted a PubMed search of the available literature related to the DLW method. DLW studies measuring TEE and RMR were included for the present review.

[Results]

Briefly, the mean PAL was 1.71 for collegiate swimmers with moderate training, which was relatively low, but the mean PAL was 3.0 for elite swimmers during summer training camp. This shows that PAL can largely vary even within the same sport depending on the amount of training, and the differences in PAL were remarkable depending on the sport. Aside from the DLW method, there is currently no research tool related to athletes’ EERs that can be used in the field.

[Conclusion]

Briefly, the mean PAL was 1.71 for collegiate swimmers with moderate training, which was relatively low, but the mean PAL was 3.0 for elite swimmers during summer training camp. This shows that PAL can largely vary even within the same sport depending on the amount of training, and the differences in PAL were remarkable depending on the sport. Aside from the DLW method, there is currently no research tool related to athletes’ EERs that can be used in the field.

INTRODUCTION

In female endurance runners or gymnasts, chronic energy deficiency, when energy intake cannot meet the energy expenditure from high-intensity training, leads to amenorrhea and osteoporosis, creating the female athlete triad (FAT)1,2. These interrelated problems lead to not only decline in performance due to stress and chronic fatigue, but also sports injuries such as fatigue fractures3-5. To reduce the problems of the FAT, estimating daily calorie intake is essential for establishing detailed nutritional intake strategies1,2.
Currently, the doubly labeled water method (DLW) is the gold standard for measuring total energy expenditure (TEE) and calculating the estimated energy requirement (EER) in athletes6. A crucial index, EER is measured by the physical activity level (PAL) and the TEE divided by the resting metabolic rate (RMR). As individual RMR has a very narrow range of variance, the differences in the PAL determine the differences in the EER6. However, there are no reviews on PAL data obtained by the DLW method in female athletes or methods of calculating EER in athletes. Therefore, our study aims to review PAL data from DLW studies focusing on female athletes and to understand the methods of assessing athletes’ EERs in the field.

Data review method

For the PAL data review among female athletes, we conducted a PubMed search of the available English literature related to the DLW method. DLW studies measuring TEE and resting metabolic rate (RMR) for calculating PAL were included for the present review.

DLW method

The DLW method using stable isotopes of hydrogen and oxygen (2H and 18O) has been used worldwide in humans for about 60 years7. As the gold standard for TEE measurement, the DLW method is known as the most objective and accurate one7-9. When there is no weight change in the athlete during a week or two of the test period for obtaining DLW data, it is assumed that the TEE is identical to the daily EER6. The following briefly describes the DLW method used by our research team6,10-14. The DLW is produced by combining 10% 18O and 99.9% 2H, and the DLW composed of 1.2-1.8 g of 18O and 0.06 - 0.07 g of 2H is administered according to the recipient’s body weight. Urine samples are collected before DLW administration (baseline) and multiple times during the week. After the pretreatment process of vaporizing the isotopes through the equilibration method using catalysts such as platinum or zinc for hydrogen in the urine and carbon dioxide gas for 18O, the elimination rates of 2H and 18O are analyzed using an isotope-ratio mass spectrometer. Using the stable isotope ratio obtained from the urine samples collected at multiple time points after the administration of DLW, the elimination rates of 2H and 18O (kh and ko) are calculated using a natural logarithm. The total body water (TBW) is calculated as the mean value of the dilution volume of 2H divided by 1.041, and the dilution volume of 18O is divided by 1.007. Carbon dioxide production is calculated by the equation rCO2 (mol/day) = 0.4554TBW (1.007ko - 1.041kh), and TEE is calculated by the Weir equation TEE = 3.9(VCO2/food quotient (FQ))+1.1(VCO2)15 using carbon dioxide production and FQ estimated from the dietary intake investigation.

PAL and EER predictions for non-athletes

To assess EER in the field, PAL is used as the most critical index. According to the Japanese standard of dietary intake, the EER is calculated as the RMR multiplied by the PAL (EER = RMR × PAL)16. In Korea, an EER equation following the US standard of dietary intake is used6. PAL acts as a major factor in EER data as the coefficient of physical activity (PA) in the EER equation, which varies depending on the differences in PAL6. For instance, if the PAL is around 1.6, a low active (1.11 and 1.12 for men and women, respectively) PA coefficient is applied, and if the PAL is 1.9-2.22, indicating high-intensity activities, a very active (1.48 and 1.45 for men and women, respectively) PA coefficient is applied.
EER for men and women aged 19 years and older (kcal/day):
Men: 662 - 9.53 × age (years) + PA [15.91 × weight (kg) + 539.6 × height (m)],
where PA = 1.0 (sedentary), 1.11 (low active), 1.25 (active), or 1.48 (very active).
Women: 354 - 6.91 × age (years) + PA [9.36 × weight (kg) + 726 × height (m)],
where PA = 1.0 (sedentary), 1.12 (low active), 1.27 (active), or 1.45 (very active).
In Japan, it was reported that Japanese adults have a low PAL distribution of around 1.6, and the normal range is around 1.75, although the data were not from a national representative sample17. In Korea, the majority of general adults have a low PAL distribution of around 1.46 in women and 1.55 in men, although the data were not from a national representative sample18.

PAL data for female athletes

PAL data derived from the DLW method in Asian female athletes show that the mean PAL of collegiate tennis players was 1.9719, and the mean PAL of Japanese synchronized swimming athletes was 2.1820. PAL data of collegiate dinghy sailors during a training camp season show that the mean was 2.4121, and the mean of collegiate endurance runners was 2.68, both of which were relatively high22. PAL data in Western female athletes show that the mean PAL of basketball players was relatively high at 2.6023. The mean PAL of the US female endurance runners from a national team during its training period was 1.99, which was relatively low24, and the mean PAL of US female cross-country runners was 2.2425. The mean PAL of collegiate swimmers with moderate levels of training was relatively low at 1.7126, but the mean PAL of elite swimmers during summer training camp was 3.027. Notably, the mean PAL of cross-country skiers was the highest among elite female athletes at 3.4028. This indicates that PAL among female athletes can vary largely, ranging from 1.71 to 3.40. Due to the number of cases with PAL far exceeding 2.20 in the sports event and training environment, there are limitations to applying the EER prediction method developed for the general public to athletes.

EER prediction methods for athletes

Currently, there is no established method of calculating EER that considers the amount of training, except for estimating PAL using the DLW method. Although the DLW method is currently the most accurate for calculating EER, stable isotopes and the costs of the analysis are expensive, and it requires a high level of expertise to conduct the experiments, which makes it difficult to be applied in the field. Currently, to calculate EER in athletes, an equation developed by Japan Institute of Sports Sciences (JISS), which is to multiply 2.0 by RMR for athletes in ball games and to multiply 2.5 by RMR for endurance runners, is being used indiscriminately29-31. However, these calculations are restricted to one country, and these EER equations have the huge limitation of not considering the amount of training32. Currently, no countries have research methods related to EER that can be conveniently used for actively training athletes playing sports. For instance, if there is no weight change during one week of the test period, it can be assumed that TEE achieves balance with energy intake (EI), and therefore, EER calculation through EI measurement has been attempted33. However, large errors can occur during this EI measurement, and it is impossible to estimate the amounts of energy requirements during training, which fluctuate frequently.
Recently, Sagayama et al.34 measured the metabolic equivalents (MET) used in table tennis players during training and reported a total of 7 METs expended. Furthermore, the PAL of the table tennis players was a mean of 2.53 measured by the DLW method. Among the 7 METs, converting 6 METs (excluding 1 MET from resting) multiplied by 3 hours of training into PAL data yields approximately 0.75. Adding the PAL of 0.75 from training to a PAL of 1.75 from the average level of daily PAL yields a PAL of approximately 2.5, showing a similar value to the PAL of 2.53 calculated by the DLW method described above. Thus, Sagayama et al.34 suggested that it should be possible to develop an EER equation considering the amount of the intensity of training (METs) and estimated daily PAL. Recently, Yoshida et al.22 attempted to calculate EER by estimating the amount of training with the session-Rating of Perceived Exertion (RPE) method. To briefly describe the session-RPE method, it was developed by Foster et al.35 to quantify the amount of training, and it determines the RPE for each session and multiplies it by the training time. The session-RPE method is also used for endurance runners and swimmers36,37, and its validity and reproducibility have been proved38. Yoshida et al.22 attempted to estimate energy expenditure in endurance runners during training using the session-RPE based activity record, and they reported that the energy expenditure during training can be estimated with high accuracy. In addition, the energy expenditure from daily activities excluding training was estimated using a triaxial accelerometer. The accelerometer used by Yoshida et al.22 has the advantage of assessing the metabolism from daily physical activities with high accuracy using a unique algorithm that can precisely distinguish between locomotive and non-locomotive activity39-42. They reported that the TEE and PAL of endurance runners can be estimated with high accuracy (r > 0.8) using a combination of methods, using the session-RPE based activity record during training and the triaxial accelerometer to estimate daily energy expenditure22. Although the energy expenditure of endurance runners during training can be estimated using the heart rate method, this method requires expertise and the inconvenience of using a regression equation9. It is highly likely that the session-RPE based activity record can be very useful for not only assessing EER but also monitoring amounts of training and guiding endurance athletes. However, further studies on a larger number of subjects and sports are needed in order to apply the session-RPE based activity record in the field.
Figure 1.

Physical activity levels (PALs) in female athletes. The data on PALs were derived from DLW studies identified in PubMed. Numbers in parentheses are PALs. Numbers in superscript are reference numbers.

JENB_2019_v23n4_1_f001.jpg

CONCLUSION

The present review describes research showing that PAL among female athletes was within a wide range of 1.71-3.40. Since the range of PAL in female athletes is very wide and it can exceed far over 2.20, depending on the event and training environment, there are limitations in applying the EER prediction developed for the general public to athletes. Further studies on PAL data from more diverse sports and environments during seasons or training camps obtained by the DLW method should be conducted, and the development of simplified measurement methods of predicting EER that can be easily used in the field is an urgent task.

Acknowledgments

This research was supported by the College of Education, Korea University Grant in 2019. We would like to thank Editage (www.editage.co.kr) for English language editing.

References

1.
Brown KA, Dewoolkar AV, Baker N, Dodich C. The female athlete triad: special considerations for adolescent female athletes. Transl Pediatr. 2017;6:144-9.
crossref
Brown KA, Dewoolkar AV, Baker N, Dodich C. The female athlete triad: special considerations for adolescent female athletes. Transl Pediatr 2017;6:144-9. PMID: 10.21037/tp.2017.04.04. PMID: 28795004.
2.
De Souza MJ, Koltun KJ, Etter CV, Southmayd EA. Current Status of the Female Athlete Triad: Update and Future Directions. Curr Osteoporos Rep. 2017;15:577-87.
crossref pdf
De Souza MJ, Koltun KJ, Etter CV, Southmayd EA. Current Status of the Female Athlete Triad: Update and Future Directions. Curr Osteoporos Rep 2017;15:577-87. PMID: 10.1007/s11914-017-0412-x. PMID: 29027102.
3.
Zanker CL, Swaine IL. Relation between bone turnover, oestradiol, and energy balance in women distance runners. Br J Sports Med. 1998;32:167-71.
crossref
Zanker CL, Swaine IL. Relation between bone turnover, oestradiol, and energy balance in women distance runners. Br J Sports Med 1998;32:167-71. PMID: 10.1136/bjsm.32.2.167. PMID: 9631227.
4.
Meeusen R, Duclos M, Foster C, Fry A, Gleeson M, Nieman D, Raglin J, Rietjens G, Steinacker J, Urhausen A; European College of Sport Science; American College of Sports Medicine. Prevention, diagnosis, and treatment of the overtraining syndrome: joint consensus statement of the European College of Sport Science and the American College of Sports Medicine. Med Sci Sports Exerc. 2013;45:186-205.
crossref
Meeusen R, Duclos M, Foster C, Fry A, Gleeson M, Nieman D, Raglin J, Rietjens G, Steinacker J, Urhausen AEuropean College of Sport Science; American College of Sports Medicine; Prevention, diagnosis, and treatment of the overtraining syndrome: joint consensus statement of the European College of Sport Science and the American College of Sports Medicine. Med Sci Sports Exerc. 2013. 45:p. 186-205. PMID: 10.1249/MSS.0b013e318279a10a. PMID: 23247672.
5.
Nicoll JX, Hatfield DL, Melanson KJ, Nasin CS. Thyroid hormones and commonly cited symptoms of overtraining in collegiate female endurance runners. Eur J Appl Physiol. 2018;118:65-73.
crossref pdf
Nicoll JX, Hatfield DL, Melanson KJ, Nasin CS. Thyroid hormones and commonly cited symptoms of overtraining in collegiate female endurance runners. Eur J Appl Physiol 2018;118:65-73. PMID: 10.1007/s00421-017-3723-9. PMID: 29159669.
6.
Park J, Kazuko IT, Kim E, Kim J, Yoon J. Estimating free-living human energy expenditure: Practical aspects of the doubly labeled water method and its applications. Nutr Res Pract. 2014;8:241-8.
crossref
Park J, Kazuko IT, Kim E, Kim J, Yoon J. Estimating free-living human energy expenditure: Practical aspects of the doubly labeled water method and its applications. Nutr Res Pract 2014;8:241-8. PMID: 10.4162/nrp.2014.8.3.241. PMID: 24944767.
7.
Schoeller DA. Recent advances from application of doubly labeled water to measurement of human energy expenditure. J Nutr. 1999;129:1765-8.
crossref pdf
Schoeller DA. Recent advances from application of doubly labeled water to measurement of human energy expenditure. J Nutr 1999;129:1765-8. PMID: 10.1093/jn/129.10.1765. PMID: 10498745.
8.
Schoeller DA, van Santen E. Measurement of energy expenditure in humans by doubly labeled water method. J Appl Physiol Respir Environ Exerc Physiol. 1982;53:955-9.
crossref
Schoeller DA, van Santen E. Measurement of energy expenditure in humans by doubly labeled water method. J Appl Physiol Respir Environ Exerc Physiol 1982;53:955-9. PMID: 10.1152/jappl.1982.53.4.955. PMID: 6759491.
9.
Westerterp KR. Assessment of physical activity: a critical appraisal. Eur J Appl Physiol. 2009;105:823-8.
crossref pdf
Westerterp KR. Assessment of physical activity: a critical appraisal. Eur J Appl Physiol 2009;105:823-8. PMID: 10.1007/s00421-009-1000-2. PMID: 19205725.
10.
Park J, Ishikawa-Takata K, Tanaka S, Hikihara Y, Ohkawara K, Watanabe S, Miyachi M, Morita A, Aiba N, Tabata I. Relation of body composition to daily physical activity in free-living Japanese adult women. Br J Nutr. 2011;106:1117-27.
crossref
Park J, Ishikawa-Takata K, Tanaka S, Hikihara Y, Ohkawara K, Watanabe S, Miyachi M, Morita A, Aiba N, Tabata I. Relation of body composition to daily physical activity in free-living Japanese adult women. Br J Nutr 2011;106:1117-27. PMID: 10.1017/S0007114511001358. PMID: 21736836.
11.
Park J, Ishikawa-Takata K, Tanaka S, Hikihara Y, Ohkawara K, Watanabe S, Miyachi M, Morita A, Aiba N, Tabata I. The relationship of body composition to daily physical activity in free-living Japanese adult men. Br J Nutr. 2014;111:1882-8.
crossref
Park J, Ishikawa-Takata K, Tanaka S, Hikihara Y, Ohkawara K, Watanabe S, Miyachi M, Morita A, Aiba N, Tabata I. The relationship of body composition to daily physical activity in free-living Japanese adult men. Br J Nutr 2014;111:1882-8. PMID: 10.1017/S0007114513001918.
12.
Park JH, Ishikawa-Takata K, Lee SJ, Kim EK, Lim KW, Kim HR, Lee IS, Tanaka S. Association between daily step counts and physical activity level among Korean elementary schoolchildren. J Exerc Nutrition Biochem. 2016;20:51-5.
crossref
Park JH, Ishikawa-Takata K, Lee SJ, Kim EK, Lim KW, Kim HR, Lee IS, Tanaka S. Association between daily step counts and physical activity level among Korean elementary schoolchildren. J Exerc Nutrition Biochem 2016;20:51-5. PMID: 10.20463/jenb.2016.09.20.3.8.
13.
Park JH, Ishikawa-Takata K, Lee S, Kim E, Lim K, Kim H, Lee IS, Tanaka S. Comparison of daily physical activity parameters using objective methods between overweight and normal-weight children. J Sport Health Sci. 2018;7:210-7.
crossref
Park JH, Ishikawa-Takata K, Lee S, Kim E, Lim K, Kim H, Lee IS, Tanaka S. Comparison of daily physical activity parameters using objective methods between overweight and normal-weight children. J Sport Health Sci 2018;7:210-7. PMID: 10.1016/j.jshs.2017.01.008. PMID: 30356488.
14.
Kim EK, Ndahimana D, Ishikawa-Takata K, Lee S, Kim H, Lim K, Lee IS, Tanaka S, Kim YJ, Choi YJ, Ju MJ, Park J. Validation of Dietary Reference Intakes for predicting energy requirements in elementary school-age children. Nutr Res Pract. 2018;12:336-41.
crossref
Kim EK, Ndahimana D, Ishikawa-Takata K, Lee S, Kim H, Lim K, Lee IS, Tanaka S, Kim YJ, Choi YJ, Ju MJ, Park J. Validation of Dietary Reference Intakes for predicting energy requirements in elementary school-age children. Nutr Res Pract 2018;12:336-41. PMID: 10.4162/nrp.2018.12.4.336. PMID: 30090171.
15.
Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol. 1949;109:1-9.
crossref
Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol 1949;109:1-9. PMID: 10.1113/jphysiol.1949.sp004363. PMID: 15394301.
16.
Ministry of Health, Labour and Welfare. Dietary Reference Intake for Japanese. 2015.

Ministry of Health, Labour and Welfare. Dietary Reference Intake for Japanese 2015.
17.
Ishikawa-Takata K, Tabata I, Sasaki S, Rafamantanantsoa HH, Okazaki H, Okubo H, Tanaka S, Yamamoto S, Shirota T, Uchida K, Murata M. Physical activity level in healthy free-living Japanese estimated by doubly labelled water method and International Physical Activity Questionnaire. Eur J Clin Nutr. 2008;62:885-91.
crossref pdf
Ishikawa-Takata K, Tabata I, Sasaki S, Rafamantanantsoa HH, Okazaki H, Okubo H, Tanaka S, Yamamoto S, Shirota T, Uchida K, Murata M. Physical activity level in healthy free-living Japanese estimated by doubly labelled water method and International Physical Activity Questionnaire. Eur J Clin Nutr 2008;62:885-91. PMID: 10.1038/sj.ejcn.1602805. PMID: 17522602.
18.
Kim EK, Kim JH, Kim MH, Ndahimana D, Yean SE, Yoon JS, Kim JH, Park J, Ishikawa-Takata K. Validation of dietary reference intake equations for estimating energy requirements in Korean adults by using the doubly labeled water method. Nutr Res Pract. 2017;11:300-6.
crossref
Kim EK, Kim JH, Kim MH, Ndahimana D, Yean SE, Yoon JS, Kim JH, Park J, Ishikawa-Takata K. Validation of dietary reference intake equations for estimating energy requirements in Korean adults by using the doubly labeled water method. Nutr Res Pract 2017;11:300-6. PMID: 10.4162/nrp.2017.11.4.300. PMID: 28765776.
19.
Ndahimana D, Lee SH, Kim YJ, Son HR, Ishikawa-Takata K, Park J, Kim EK. Accuracy of dietary reference intake predictive equation for estimated energy requirements in female tennis athletes and non-athlete college students: comparison with the doubly labeled water method. Nutr Res Pract. 2017;11:51-6.
crossref
Ndahimana D, Lee SH, Kim YJ, Son HR, Ishikawa-Takata K, Park J, Kim EK. Accuracy of dietary reference intake predictive equation for estimated energy requirements in female tennis athletes and non-athlete college students: comparison with the doubly labeled water method. Nutr Res Pract 2017;11:51-6. PMID: 10.4162/nrp.2017.11.1.51. PMID: 28194265.
20.
Ebine N, Feng JY, Homma M, Saitoh S, Jones PJ. Total energy expenditure of elite synchronized swimmers measured by the doubly labeled water method. Eur J Appl Physiol. 2000;83:1-6.
crossref pdf
Ebine N, Feng JY, Homma M, Saitoh S, Jones PJ. Total energy expenditure of elite synchronized swimmers measured by the doubly labeled water method. Eur J Appl Physiol 2000;83:1-6. PMID: 10.1007/s004210000253. PMID: 11072766.
21.
Sagayama H, Toguchi M, Yasukata J, Yonaha K, Higaki Y, Tanaka H. Total Energy Expenditure, Physical Activity Level, and Water Turnover of Collegiate Dinghy Sailors in a Training Camp. Int J Sport Nutr Exerc Metab. 2019;29:350-3.
crossref
Sagayama H, Toguchi M, Yasukata J, Yonaha K, Higaki Y, Tanaka H. Total Energy Expenditure, Physical Activity Level, and Water Turnover of Collegiate Dinghy Sailors in a Training Camp. Int J Sport Nutr Exerc Metab 2019;29:350-3. PMID: 10.1123/ijsnem.2018-0204. PMID: 30299186.
22.
Yoshida A, Ishikawa-Takata K, Tanaka S, Suzuki N, Nakae S, Murata H, Taguchi M, Higuchi M. Validity of combination use of activity record and accelerometry to measure free-living total energy expenditure in female endurance runners. J Strength Cond Res. 2019;33:2962-70.
crossref
Yoshida A, Ishikawa-Takata K, Tanaka S, Suzuki N, Nakae S, Murata H, Taguchi M, Higuchi M. Validity of combination use of activity record and accelerometry to measure free-living total energy expenditure in female endurance runners. J Strength Cond Res 2019;33:2962-70. PMID: 10.1519/JSC.0000000000002205. PMID: 29389693.
23.
Silva AM, Santos DA, Matias CN, Minderico CS, Schoeller DA, Sardinha LB. Total energy expenditure assessment in elite junior basketball players: a validation study using doubly labeled water. J Strength Cond Res. 2013;27:1920-7.
crossref
Silva AM, Santos DA, Matias CN, Minderico CS, Schoeller DA, Sardinha LB. Total energy expenditure assessment in elite junior basketball players: a validation study using doubly labeled water. J Strength Cond Res 2013;27:1920-7. PMID: 10.1519/JSC.0b013e31827361eb. PMID: 22990574.
24.
Schulz LO, Alger S, Harper I, Wilmore JH, Ravussin E. Energy expenditure of elite female runners measured by respiratory chamber and doubly labeled water. J Appl Physiol(1985). 1992;72:23-8.
crossref
Schulz LO, Alger S, Harper I, Wilmore JH, Ravussin E. Energy expenditure of elite female runners measured by respiratory chamber and doubly labeled water. J Appl Physiol(1985) 1992;72:23-8. PMID: 10.1152/jappl.1992.72.1.23. PMID: 1537719.
25.
Edwards JE, Lindeman AK, Mikesky AE, Stager JM. Energy balance in highly trained female endurance runners. Med Sci Sports Exerc. 1993;25:1398-404.
crossref
Edwards JE, Lindeman AK, Mikesky AE, Stager JM. Energy balance in highly trained female endurance runners. Med Sci Sports Exerc 1993;25:1398-404. PMID: 10.1249/00005768-199312000-00014. PMID: 8107549.
26.
Jones PJ, Leitch CA. Validation of doubly labeled water for measurement of caloric expenditure in collegiate swimmers. J Appl Physiol(1985). 1993;74:2909-14.
crossref
Jones PJ, Leitch CA. Validation of doubly labeled water for measurement of caloric expenditure in collegiate swimmers. J Appl Physiol(1985) 1993;74:2909-14. PMID: 10.1152/jappl.1993.74.6.2909. PMID: 8396112.
27.
Trappe TA, Gastaldelli A, Jozsi AC, Troup JP, Wolfe RR. Energy expenditure of swimmers during high volume training. Med Sci Sports Exerc. 1997;29:950-4.
crossref
Trappe TA, Gastaldelli A, Jozsi AC, Troup JP, Wolfe RR. Energy expenditure of swimmers during high volume training. Med Sci Sports Exerc 1997;29:950-4. PMID: 10.1097/00005768-199707000-00015. PMID: 9243495.
28.
Sjödin AM, Andersson AB, Högberg JM, Westerterp KR. Energy balance in cross-country skiers: a study using doubly labeled water. Med Sci Sports Exerc. 1994;26:720-4.
crossref
Sjödin AM, Andersson AB, Högberg JM, Westerterp KR. Energy balance in cross-country skiers: a study using doubly labeled water. Med Sci Sports Exerc 1994;26:720-4. PMID: 10.1249/00005768-199406000-00011. PMID: 8052113.
29.
Japan Institute of Sports Sciences. Health management for female athletes Ver.2. 2016.

Japan Institute of Sports Sciences. Health management for female athletes Ver.2 2016.
30.
Koshimizu T, Yanagisawa K, Higuchi M. Estimated energy requirement in Japanese elite athletes. J Train Sci Exerc Sport. 2005;7:245-50.

Koshimizu T, Yanagisawa K, Higuchi M. Estimated energy requirement in Japanese elite athletes. J Train Sci Exerc Sport 2005;7:245-50.
31.
Koshimizu T, Matsushima Y, Yokota Y, Yanagisawa K, Nagai S, Okamura K, Komatsu Y, Kawahara T. Basal metabolic rate and body composition of elite Japanese male athletes. J Med Invest. 2012;59:253-60.
crossref
Koshimizu T, Matsushima Y, Yokota Y, Yanagisawa K, Nagai S, Okamura K, Komatsu Y, Kawahara T. Basal metabolic rate and body composition of elite Japanese male athletes. J Med Invest 2012;59:253-60. PMID: 10.2152/jmi.59.253. PMID: 23037196.
32.
Institute of Medicine of the National Academies (US). Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients). Washington (D.C.): The National Academies Press. 2002.

Institute of Medicine of the National Academies (US). Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients). Washington (D.C.): The National Academies Press; 2002.
33.
Ebine N, Rafamantanantsoa HH, Nayuki Y, Yamanaka K, Tashima K, Ono T, Saitoh S, Jones PJ. Measurement of total energy expenditure by the doubly labelled water method in professional soccer players. J Sports Sci. 2002;20:391-7.
crossref
Ebine N, Rafamantanantsoa HH, Nayuki Y, Yamanaka K, Tashima K, Ono T, Saitoh S, Jones PJ. Measurement of total energy expenditure by the doubly labelled water method in professional soccer players. J Sports Sci 2002;20:391-7. PMID: 10.1080/026404102317366645. PMID: 12043828.
34.
Sagayama H, Hamaguchi G, Toguchi M, Ichikawa M, Yamada Y, Ebine N, Higaki Y, Tanaka H. Energy Requirement Assessment in Japanese Table Tennis Players Using the Doubly Labeled Water Method. Int J Sport Nutr Exerc Metab. 2017;27:421-8.
crossref
Sagayama H, Hamaguchi G, Toguchi M, Ichikawa M, Yamada Y, Ebine N, Higaki Y, Tanaka H. Energy Requirement Assessment in Japanese Table Tennis Players Using the Doubly Labeled Water Method. Int J Sport Nutr Exerc Metab 2017;27:421-8. PMID: 10.1123/ijsnem.2017-0022. PMID: 28530485.
35.
Foster C, Florhaug JA, Franklin J, Gottschall L, Hrovatin LA, Parker S, Doleshal P, Dodge C. A new approach to monitoring exercise training. J Strength Cond Res. 2001;15:109-15.

Foster C, Florhaug JA, Franklin J, Gottschall L, Hrovatin LA, Parker S, Doleshal P, Dodge C. A new approach to monitoring exercise training. J Strength Cond Res 2001;15:109-15. PMID: 10.1519/00124278-200102000-00019. PMID: 11708692.
36.
Minganti C, Ferragina A, Demarie S, Verticchio N, Meeusen R, Piacentini MF. The use of session RPE for interval training in master endurance athletes: should rest be included? J Sports Med Phys Fitness. 2011;51:547-54.

Minganti C, Ferragina A, Demarie S, Verticchio N, Meeusen R, Piacentini MF. The use of session RPE for interval training in master endurance athletes: should rest be included? J Sports Med Phys Fitness 2011;51:547-54. PMID: 22212255.
37.
Wallace LK, Slattery KM, Coutts AJ. The ecologicalvalidity and application of the session-RPE methodfor quantifying training loads in swimming. J Strength Cond Res. 2009;23:33-8.
crossref
Wallace LK, Slattery KM, Coutts AJ. The ecologicalvalidity and application of the session-RPE methodfor quantifying training loads in swimming. J Strength Cond Res 2009;23:33-8. PMID: 10.1519/JSC.0b013e3181874512. PMID: 19002069.
38.
Rodoriguez-Marroyo JA, Villa JG, Fernandez G, Foster C. Effect of cycling competition type on effort based on heat rate and session rating of perceived exertion. J Sports Med Phys Fitness. 2012;53:154-61.

Rodoriguez-Marroyo JA, Villa JG, Fernandez G, Foster C. Effect of cycling competition type on effort based on heat rate and session rating of perceived exertion. J Sports Med Phys Fitness 2012;53:154-61.
39.
Oshima Y, Kawaguchi K, Tanaka S, Ohkawara K, Hikihara Y, Ishikawa-Takata K, Tabata I. Classifying household and locomotive activities using a triaxial accelerometer. Gait Posture. 2010;31:370-4.
crossref
Oshima Y, Kawaguchi K, Tanaka S, Ohkawara K, Hikihara Y, Ishikawa-Takata K, Tabata I. Classifying household and locomotive activities using a triaxial accelerometer. Gait Posture 2010;31:370-4. PMID: 10.1016/j.gaitpost.2010.01.005. PMID: 20138524.
40.
Ohkawara K, Oshima Y, Hikihara Y, Ishikawa-Takata K, Tabata I, Tanaka S. Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm. Br J Nutr. 2011;105:1681-91.
crossref
Ohkawara K, Oshima Y, Hikihara Y, Ishikawa-Takata K, Tabata I, Tanaka S. Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm. Br J Nutr 2011;105:1681-91. PMID: 10.1017/S0007114510005441. PMID: 21262061.
41.
Park J, Ishikawa-Takata K, Tanaka S, Mekata Y, Tabata I. Effects of walking speed and step frequency on estimation of physical activity using accelerometers. J Physiol Anthropol. 2011;30:119-27.
crossref
Park J, Ishikawa-Takata K, Tanaka S, Mekata Y, Tabata I. Effects of walking speed and step frequency on estimation of physical activity using accelerometers. J Physiol Anthropol 2011;30:119-27. PMID: 10.2114/jpa2.30.119. PMID: 21636955.
42.
Park J, Ishikawa-Takata K, Tanaka S, Bessyo K, Tanaka S, Kimura T. Accuracy of estimating step counts and intensity using accelerometers in older people with or without assistive devices. J Aging Phys Act. 2017;25:41-50.
crossref
Park J, Ishikawa-Takata K, Tanaka S, Bessyo K, Tanaka S, Kimura T. Accuracy of estimating step counts and intensity using accelerometers in older people with or without assistive devices. J Aging Phys Act 2017;25:41-50. PMID: 10.1123/japa.2015-0201. PMID: 27180730.


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