Associations of age, body size, and maturation with physical activity intensity in different laboratory tasks in children

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY-NC 4.0 https://creativecommons.org/licenses/by-nc/4.0/ Associations of age, body size, and maturation with physical activity intensity in different laboratory tasks in children © 2021 Informa UK Limited, trading as Taylor & Francis Group Accepted version (Final draft) Haapala, Eero A.; Gao, Ying; Rantalainen, Timo; Finni, Taija Haapala, E. A., Gao, Y., Rantalainen, T., & Finni, T. (2021). Associations of age, body size, and maturation with physical activity intensity in different laboratory tasks in children. Journal of Sports Sciences, 39(12), 1428-1435. https://doi.org/10.1080/02640414.2021.1876328 2021 1 1 Associations of age, body size, and maturation with physical activity intensity in 2 different laboratory tasks in children 3 4 Eero A. Haapala1,2, Ying Gao1,3, Timo Rantalainen1, Taija Finni1 5 6 1Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland; 2Institute 7 of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland; 8 3Department of Sports Science, College of Education, Zhejiang University, Hangzhou, China. 9 10 ORCID /twitter: 11 EAH: 0000-0001-5096-851X / @EeroHaapala 12 YG: 0000-0003-1440-0681 / NA 13 TR: 0000-0001-6977-4782 / @tjrantal 14 TF: 0000-0002-7697-2813 / @TaijaFinni 15 16 Corresponding author: Eero Haapala, PhD, Sports & Exercise Medicine, Faculty of Sport and 17 Health Sciences, University of Jyväskylä, PO Box 35, FI-40014 University of Jyväskylä, 18 Jyväskylä, Finland. Tel: +358 40 805 4210, Fax: +35817 162 131, Email: 19 eero.a.h[email protected], OrcID: 0000-0001-5096-851X 20 21 22 23 24 2 ABSTRACT 25 We investigated the associations of age, sex, body size, body composition, and maturity with 26 measures of physical activity (PA) intensity in children. PA intensity was assessed using V O2 27 as % of V O2reserve or V O2 at ventilatory threshold (VT), muscle activity measured by textile 28 electromyography, mean amplitude deviation (MAD) measured by accelerometry, and 29 metabolic equivalent of task (MET) during laboratory activities. Age, stature, and skeletal 30 muscle mass were inversely associated with V O2 as % of V O2reserve and V O2 as % of V O2 at 31 VT, during walking or running on a treadmill for 4, 6, and 8 km/h (Spearman r=-0.645 to - 32 0.358, p<0.05). Age was inversely associated with MAD during walking on treadmill for 4 33 km/h (r=-0.541, p<0.05) and positively associated with MAD during running on a treadmill 34 for 8 km/h, playing hopscotch, and during self-paced running (r=0.368 to 0.478, p<0.05). Fat 35 mass was positively associated with V O2 as % of V O2reserve and V O2 as % of V O2 at VT and 36 waist circumference was positively associated with V O2 as a % of V O2reserve and muscle 37 activity during stair climbing (r=0.416 to 0.519, p<0.05). Fixed accelerometry cut-offs used 38 to define PA intensities should be adjusted for age, sex, body size, and body composition. 39 40 Key words: child; physical activity; accelerometry; electromyography; body composition 41 Disclosure statement: No potential conflict of interest was reported by the authors. 42 Data availability statement: The datasets generated during and/or analysed during the 43 current study are available from the corresponding author on reasonable request. 44 45 46 47 48 49 3 INTRODUCTION 50 Accelerometry has become the preferred device-based method to assess volume and intensity 51 of habitual physical activity (PA) 1. Higher levels of moderate-to-vigorous intensity physical 52 activity (MVPA) and vigorous PA (VPA) assessed by accelerometry have been consistently 53 associated with lower levels of adiposity, cardiometabolic risk, arterial stiffness, and higher 54 cardiorespiratory fitness in children and adolescents 1,2. 55 56 Cut-offs defining light PA, moderate PA, and vigorous PA in children and adolescents have 57 been created using specific calibration activities 3–5. These calibration activities have been 58 used to investigate the point of acceleration magnitude separating e.g. slow walking as light 59 PA from brisk walking and climbing up and down the stairs as moderate PA 3–5. Although 60 some of these calibration studies have measured oxygen uptake (V O2) during the calibration 61 activities, PA intensity cut-offs have been mainly created using subjective criteria based on 62 calibration activities rather than physiological responses 3–5. Furthermore, converting 63 accelerometry metrics to metabolic equivalents of tasks (METs) and utilising commonly used 64 MET-based cut-offs for PA intensities have been suggested to improve comparability 65 between studies 6. However, physiological rationale for the usage of METs in the assessment 66 of PA intensity is lacking 7 and fixed MET-based cut-offs have been found to underestimate 67 PA intensity and volume in unfit and obese adults8 and to misclassify PA intensity in children 68 9. 69 70 Growth and maturation are dominant biological processes during childhood and adolescence 71 characterised by increased body size, sexually dimorphic changes in body composition, and 72 morphological and functional changes in cardiorespiratory, neuromuscular, and metabolic 73 systems 10,11. Exercise capacity also increases with increasing age and advancing maturity 10. 74 4 Nevertheless, none of the previous calibration studies have taken body size, maturation, or 75 body composition into account in the proposed cut-offs 3–5,12. Furthermore, previous large 76 scale PA studies in paediatric populations have used the same fixed accelerometry cut-offs 77 for the assessment of PA intensity for children and adolescents with age of the participants 78 varying from 3 to 18 years 13–17. However, because of growth and maturation and 79 accompanying changes in locomotor economy 18,19, using the same cut-offs in children and 80 adolescent with different ages, body sizes, and body compositions may lead to large errors in 81 the estimation of PA prevalence and the associations of PA with health and wellbeing. 82 Nevertheless, some evidence also suggests that the same accelerometer cut-offs could be 83 applied for adolescents and adults 20. Furthermore, some studies suggest that muscle activity 84 measured by electromyography (EMG) could provide a direct and useful measure of PA 85 intensity in children 21, but the knowledge on the associations of age, sex, body size, body 86 composition, and maturity with muscle activity in different PA intensity calibration activities 87 is limited. Therefore, research on the role of age, growth, and maturation on the PA intensity 88 during the calibration activities is warranted. 89 90 Available and commonly used acceleration magnitude cut-offs utilised to define PA intensity 91 in children and youth3–5,12 are based on several different methods and calibration tasks. 92 However, these fixed PA intensity cut-offs provide absolute values which are used to define 93 light PA, moderate PA, and vigorous PA in children and adolescents without consideration 94 whether proposed PA intensity cut-offs describe the same intensity among children with 95 different body sizes and body compositions or among different ageand maturation groups. 96 Therefore, we investigated the associations of age, sex, body size, body composition, 97 estimated years from the peak height velocity (YPHV), and pubertal status with acceleration 98 magnitude and MET-based PA intensity in children. We further investigated whether age, 99 5 sex, body size, body composition, estimated years from the peak height velocity (YPHV), 100 and pubertal status were associated with individualised measures of PA intensity defined 101 using V O2reserve, ventilatory threshold (VT), and muscle activity. 102 103 METHODS 104 Participants 105 This study was based on the laboratory phase of the Children’s Physical Activity Spectrum 106 (CHIPASE) study 22. A total of 35 children (21 girls, 14 boys) aged 7–11 years were recruited 107 from local schools by leaflets and word of mouth advertisement to participate in the study. 108 Volunteering children were accepted into to the study sample in the order of enrolment. The 109 applicability of the children was checked by the research staff and children were included if 110 they were apparently healthy and were able to perform the physical activities at moderate and 111 vigorous intensities. Children with chronic conditions or disabilities were excluded from the 112 study. The number of participants in the current data analyses varied from 27 to 35 113 participants with acceptable data quality. Most missing data was from the activity where the 114 participants were asked to run around an indoor track at self-paced speed (27 participants 115 with acceptable METs data). The study protocol was approved by the Ethics Committee of 116 the University of Jyväskylä. All children gave their assent and their parents/caregivers gave 117 their written informed consents. The study was conducted in agreement with the Declaration 118 of Helsinki. 119 120 Based on the main research question of the CHIPASE Study, a sample size of 30 was 121 estimated to provide sufficient statistical power for differentiating METs between sitting 122 (1.33 ± 0.24) and standing (1.59 ± 0.37) based on the data of Mansoubi et al.23 with 80% 123 power and 5% α-error level. 124 6 125 126 Study protocol 127 The participants visited the laboratory on three occasions. At the first visit, research staff 128 explained the research protocol to children and their parents. They were also familiarised to 129 the laboratory environment and measurement equipment. At the second visit, children arrived 130 at the laboratory in the morning after 10-12 hour overnight fast for assessment of 131 anthropometrics, body composition, and resting V O2. At the third visit, children were asked 132 to perform following activities for 4.5 minutes in a random order interspersed with 1-minute 133 rest: sitting quietly, sitting while playing a mobile game, standing quietly, standing while 134 playing a mobile game, playing hopscotch, walking up and down the stairs, and walking or 135 running on a treadmill at 4, 6, and 8km/h. They were also asked to walk and run around an 136 indoor track at self-chosen speed for 4.5 minutes. At the end of the third visit, children 137 performed maximal cardiopulmonary exercise test on a bicycle ergometer. 138 139 Assessments 140 Body size and body composition 141 Stature was measured to the nearest 0.1 cm using a wall-mounted stadiometer. Body mass 142 (BM), skeletal muscle mass (SMM), fat mass (FM), fat free mass, and body fat percentage 143 were measured by InBody 770 bioelectrical impedance device (Biospace Ltd., Seoul, Korea). 144 Body mass index (BMI) was calculated by dividing body weight with body height squared 145 and body mass index standard deviation score (BMI-SDS) was computed using the Finnish 146 references 24. Waist circumference (WC) was measured to the nearest 0.1 cm using a 147 unstrechable measuring tape at mid-distance between the top of the iliac crest and the bottom 148 7 of the rib cage. Hip circumference (HC) was measured at the widest circumference over the 149 great trochanter. 150 151 Years from peak height velocity and pubertal status 152 Years from peak height velocity and pubertal status (YPHV) was estimated using a sex153 specific formula described by Mirwald et al. 25. Pubertal status was assessed according to 154 self-reported genital development in boys and breast development in girls on the basis of the 155 five-stage criteria described by Tanner 26. We defined those at Stage I as pre-pubertal and 156 those at Stage II as those who had entered puberty. 157 158 Oxygen uptake 159 Mobile metabolic cart (Oxycon mobile, CareFusion Corp, USA) was calibrated and dead 160 space was adjusted to 78 mL for the petite size of the face mask following the manufacturer’s 161 recommendations. V O2, carbon dioxide production (V CO2) and respiratory exchange ratio 162 (RER) were collected breath by breath and computed in non-overlapping 1 second epoch 163 lengths. Resting V O2 was determined as the mean value between the 15th and 25th minute of 164 30 minutes of supine rest when the steady state was reached. When steady stated was not 165 observed between 15th and 25th minute, the steady state was visually selected for further 166 analysis. In physical activities, V O2 was averaged over 2 minutes from the 3rd and 4th 167 minutes of each task when plateau in V O2 and V CO2 was observed 22. V O2 reserve as a 168 percentage of V O2peak during different physical activities was calculated as (V O2 during PA 169 task / (V O2peak - V O2 during rest)) x 100. V O2 at VT was determined individually by two 170 exercise physiologists using modified V-slope method and any disagreements were solved by 171 these two exercise physiologists. V O2 at VT was verified utilising the equivalents for V E / 172 V CO2 and V E / V O2. 173 8 174 Accelerometry 175 Movement was measured by triaxial accelerometer (X6-1a, Gulf Coast Data Concepts Inc., 176 Waveland, USA). We used raw acceleration data in actual g-units, with range up to 6g, 16-bit 177 A/D conversion, and sampling at 40 Hz. The resultant acceleration of the triaxial 178 accelerometer signal was calculated from√𝑥2+ 𝑦2+ 𝑧2, where x, y and z are the 179 measurement sample of the raw acceleration signal in x-, y-, and z-directions. The X6-1a 180 accelerometer has been shown to produce congruent results with the ActiGraph GT3X 181 accelerometer in children 27. Mean amplitude deviation (MAD) was calculated from the 182 resultant acceleration in non-overlapping 1 s epoch. MAD was calculated as the mean 183 distance of data points about the mean ( 1 𝑛∑|𝑟𝑖 −𝑟| 𝑛 𝑖=1 where n is the number of samples in 184 the epoch, 𝑟𝑖 is the 𝑖𝑡ℎresultant sample within the epoch and r  is the mean resultant value of the 185 epoch)20,28. The mean of the 1 s MAD values (g) calculated in 2 minute epochs for each 186 activity and in 10 minute epoch for lying down are reported as the outcomes. 187 188 Textile electromyography 189 Textile EMG electrodes embedded into elastic garments were used to assess muscle activity 190 from the quadriceps and the hamstring muscles and has been described in detail previously 22. 191 Four different sizes of EMG shorts (120, 130, 140, and 150 cm) with zippers located at the 192 inner sides of short legs and adhesive elastic band in the hem ensured proper fit in every 193 child. The conductive area of the electrodes over the muscle bellies of the left and the right 194 quadriceps was 9 × 2 cm2 (length × width) in all short sizes, while the corresponding sizes for 195 the hamstring muscles were 6 × 2 cm2 in sizes of 120, 130, and 140 cm and 6.5 × 2 cm2 in 196 size of 150 cm. The conductive area of the reference electrodes was 11 × 2 cm2, and they 197 were located longitudinally over the iliotibial band. Water or electrode gel (Parker 198 15 influence the results. V O2peak and VT were assessed during a maximal cycle ergometer test 349 and V O2peak was adjusted using the data from treadmill running or self-paced running if 350 higher V O2 was observed during those tasks. Therefore, it is possible that we have 351 underestimated true V O2max in some participants and this may have had a minor effect on 352 V O2reserve estimation. Furthermore, we estimated APHV and assessed pubertal status using 353 self-reports instead of measuring circulating sex steroids or using clinical examination of 354 secondary sex-characteristics. In addition, the increasing error in the estimation of APHV 355 with increasing time to PHV could have an effect on the estimated maturity status in our 356 sample of relatively young children. Further studies on the effect of maturation on PA 357 intensity utilising more accurate methods, such as skeletal age, in the assessment of maturity 358 are warranted. Because of relatively small sample size, we were not able to study whether age 359 or maturity groups modified the observed associations of body size and composition with PA 360 intensity in different tasks. Finally, the relatively large number of analyses increases the 361 possibility that some statistically significant associations were observed by chance. 362 363 CONCLUSION 364 In conclusion, we found inverse association of age, stature, and SMM with PA intensity 365 defined using V O2reserve and VT. However, MADs and METs did not reliably capture these 366 associations and our results suggest that PA intensity estimated by MAD may overestimate 367 PA intensity in older and taller children. Therefore, our results suggest that studies validating 368 accelerometry or muscle activity cut-offs used to define PA intensities should be adjusted for 369 age, sex, body size, and body composition. Further studies on the role of these adjustments of 370 the prevalence of children meeting the PA recommendations are warranted. 371 372 373 16 REFERENCES 374 1 Ekelund U, Tomkinson G, Armstrong N. What proportion of youth are physically active? 375 Measurement issues, levels and recent time trends. British Journal of Sports Medicine 376 2011; 45(11):859–865. Doi: 10.1136/bjsports-2011-090190. 377 2 Poitras VJ, Gray CE, Borghese MM, et al. Systematic review of the relationships between 378 objectively measured physical activity and health indicators in school-aged children and 379 youth. Appl Physiol Nutr Metab 2016; 41(6 (Suppl. 3)):S197–S239. Doi: 10.1139/apnm380 2015-0663. 381 3 Evenson KR, Catellier DJ, Gill K, et al. Calibration of two objective measures of physical 382 activity for children. Journal of Sports Sciences 2008; 26(14):1557–1565. Doi: 383 10.1080/02640410802334196. 384 4 Treuth MS, Schmitz K, Catellier DJ, et al. Defining Accelerometer Thresholds for Activity 385 Intensities in Adolescent Girls. Medicine & Science in Sports & Exercise 2004; 386 36(7):1259–1266. Doi: 10.1249/01.MSS.0000074670.03001.98. 387 5 Puyau MR, Adolph AL, Vohra FA, et al. Validation and Calibration of Physical Activity 388 Monitors in Children. Obesity Research 2002; 10(3):150–157. Doi: 10.1038/oby.2002.24. 389 6 Sievänen H, Kujala UM. Accelerometry—Simple, but challenging. Scandinavian Journal 390 of Medicine & Science in Sports 2017; 27(6):574–578. Doi: 10.1111/sms.12887. 391 7 Tompuri TT. Metabolic equivalents of task are confounded by adiposity, which disturbs 392 objective measurement of physical activity. Front Physiol 2015; 6. Doi: 393 10.3389/fphys.2015.00226. 394 8 Kujala UM, Pietilä J, Myllymäki T, et al. Physical Activity: Absolute Intensity versus 395 Relative-to-Fitness-Level Volumes. Medicine & Science in Sports & Exercise 2017; 396 49(3):474. Doi: 10.1249/MSS.0000000000001134. 397 9 Haapala EA, Gao Y, Vanhala A, et al. Validity of traditional physical activity intensity 398 calibration methods and the feasibility of self-paced walking and running on individualised 399 calibration of physical activity intensity in children. Scientific Reports 2020; 10(1):11031. 400 Doi: 10.1038/s41598-020-67983-7. 401 10 Armstrong N, Barker AR, McManus AM. Muscle metabolism changes with age and 402 maturation: How do they relate to youth sport performance? Br J Sports Med 2015; 403 49(13):860–864. Doi: 10.1136/bjsports-2014-094491. 404 11 Hochberg Z. Evo–devo of child growth II: human life history and transition between its 405 phases. European Journal of Endocrinology 2009; 160(2):135–141. Doi: 10.1530/EJE-08406 0445. 407 12 Sirard JR, Trost SG, Pfeiffer KA, et al. Calibration and Evaluation of an Objective 408 Measure of Physical Activity in Preschool Children. Journal of Physical Activity and 409 Health 2005; 2(3):345–357. Doi: 10.1123/jpah.2.3.345. 410 13 Ekelund U, Luan J, Sherar LB, et al. Moderate to Vigorous Physical Activity and 411 Sedentary Time and Cardiometabolic Risk Factors in Children and Adolescents. JAMA 412 2012; 307(7):704–712. Doi: 10.1001/jama.2012.156. 413 14 Wijndaele K, White T, Andersen LB, et al. Substituting prolonged sedentary time and 414 cardiovascular risk in children and youth: a meta-analysis within the International 415 Children’s Accelerometry database (ICAD). International Journal of Behavioral Nutrition 416 and Physical Activity 2019; 16(1):96. Doi: 10.1186/s12966-019-0858-6. 417 15 Kwon S, Andersen LB, Grøntved A, et al. A closer look at the relationship among 418 accelerometer-based physical activity metrics: ICAD pooled data. International Journal of 419 Behavioral Nutrition and Physical Activity 2019; 16(1):40. Doi: 10.1186/s12966-019420 0801-x. 421 17 16 Werneck AO, Silva DR, Oyeyemi AL, et al. Physical activity attenuates metabolic risk of 422 adolescents with overweight or obesity: the ICAD multi-country study. Int J Obes 2020:1– 423 7. Doi: 10.1038/s41366-020-0521-y. 424 17 Thivel D, Tremblay MS, Katzmarzyk PT, et al. Associations between meeting 425 combinations of 24-hour movement recommendations and dietary patterns of children: A 426 12-country study. Preventive Medicine 2019; 118:159–165. Doi: 427 10.1016/j.ypmed.2018.10.025. 428 18 Frost G, Bar-Or O, Dowling J, et al. Explaining differences in the metabolic cost and 429 efficiency of treadmill locomotion in children. Journal of Sports Sciences 2002; 430 20(6):451–461. Doi: 10.1080/02640410252925125. 431 19 Unnithan, VB E RG. Stride Frequency and Submaximal Treadmill Running Economy in 432 Adults and Children. Pediatric Exercise Science n.d.; 2:149–155. Doi: 433 http://dx.doi.org/10.1123/pes.2.2.149. 434 20 Aittasalo M, Vähä-Ypyä H, Vasankari T, et al. Mean amplitude deviation calculated from 435 raw acceleration data: A novel method for classifying the intensity of adolescents’ physical 436 activity irrespective of accelerometer brand. BMC Sports Science, Medicine and 437 Rehabilitation 2015; 7:18. Doi: 10.1186/s13102-015-0010-0. 438 21 Gao Y, Melin M, Mäkäräinen K, et al. Children’s physical activity and sedentary time 439 compared using assessments of accelerometry counts and muscle activity level. PeerJ 440 2018; 6:e5437. Doi: 10.7717/peerj.5437. 441 22 Gao Y, Haapala EA, Vanhala A, et al. Sedentary Thresholds for Accelerometry-Based 442 Mean Amplitude Deviation and Electromyography Amplitude in 7–11 Years Old 443 Children. Front Physiol 2019; 10. Doi: 10.3389/fphys.2019.00997. 444 23 Mansoubi M, Pearson N, Clemes SA, et al. Energy expenditure during common sitting and 445 standing tasks: examining the 1.5 MET definition of sedentary behaviour. BMC Public 446 Health 2015; 15(1):516. Doi: 10.1186/s12889-015-1851-x. 447 24 Saari A, Sankilampi U, Hannila M-L, et al. New Finnish growth references for children 448 and adolescents aged 0 to 20 years: Length/height-for-age, weight-for-length/height, and 449 body mass index-for-age. Annals of Medicine 2011; 43(3):235–248. Doi: 450 10.3109/07853890.2010.515603. 451 25 Mirwald RL, G. Baxter-Jones AD, Bailey DA, et al. An assessment of maturity from 452 anthropometric measurements. Medicine & Science in Sports & Exercise 2002; 34(4):689– 453 694. 454 26 Taylor, Whincup, Hindmarsh, et al. Performance of a new pubertal self-assessment 455 questionnaire: a preliminary study. Paediatric and Perinatal Epidemiology 2001; 456 15(1):88–94. Doi: 10.1046/j.1365-3016.2001.00317.x. 457 27 Laukkanen A, Pesola A, Havu M, et al. Relationship between habitual physical activity 458 and gross motor skills is multifaceted in 5to 8-year-old children. Scandinavian Journal of 459 Medicine & Science in Sports 2014; 24(2):e102–e110. Doi: 10.1111/sms.12116. 460 28 Vähä-Ypyä H, Vasankari T, Husu P, et al. A universal, accurate intensity-based 461 classification of different physical activities using raw data of accelerometer. Clinical 462 Physiology and Functional Imaging 2014; 35. Doi: 10.1111/cpf.12127. 463 29 Radnor JM, Oliver JL, Waugh CM, et al. The Influence of Growth and Maturation on 464 Stretch-Shortening Cycle Function in Youth. Sports Med 2018; 48(1):57–71. Doi: 465 10.1007/s40279-017-0785-0. 466 30 Moritani T, Oddsson L, Thorstensson A, et al. Neural and biomechanical differences 467 between men and young boys during a variety of motor tasks. Acta Physiologica 468 Scandinavica 1989; 137(3):347–355. Doi: 10.1111/j.1748-1716.1989.tb08763.x. 469 18 31 Armstrong N, Welsman J. Development of peak oxygen uptake from 11–16 years 470 determined using both treadmill and cycle ergometry. Eur J Appl Physiol 2019; 471 119(3):801–812. Doi: 10.1007/s00421-019-04071-3. 472 32 Cureton KJ, Sparling PB. Distance running performance and metabolic responses to 473 running in men and women with excess weight experimentally equated. Medicine & 474 Science in Sports & Exercise 1980; 12(4):288–294. 475 33 Schepens B, Willems PA, Cavagna GA. The mechanics of running in children. J Physiol 476 1998; 509(Pt 3):927–940. Doi: 10.1111/j.1469-7793.1998.927bm.x. 477 34 Gil-Rey E, Maldonado-Martin S, Gorostiaga E. Individualized Accelerometer Activity 478 Cut-Points for the Measurement of Relative Physical Activity Intensity Levels. Research 479 Quarterly for Exercise and Sport 2019; 90:1–9. Doi: 10.1080/02701367.2019.1599801. 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 19 Figure legends 501 Figure 1. Differences in the measures of physical activity intensity between girls and boys. 502 503 504 505 20 Figure 2. Differences in the measures of physical activity intensity between prepubertal and 506 pubertal children. 507 508