Individual calibration of accelerometers in children and their health-related 2 implications

Lynne Boddy, Conor Cuningham, Stuart Fairclough, MH Murphy, Gavin Breslin, Laurence Foweather, Dagger Rebecca, Lee Graves

Research output: Contribution to journalArticle

1 Citation (Scopus)
4 Downloads (Pure)

Abstract

This study compared children’s physical activity (PA) levels, the prevalence of children 73 meeting current guidelines of ≥60 minutes of daily moderate to vigorous PA (MVPA), and 74 PA-health associations using individually calibrated (IC) and empirical accelerometer 75 cutpoints. Data from 75 (n = 32 boys) 10-12 year old children were included in this study. 76 Clustered cardiometabolic (CM) risk, directly measured cardiorespiratory fitness (CRF), 77 anthropometric and 7 day accelerometer data were included within analysis. PA data were 78 classified using Froude anchored IC, Evenson et al., 2008 (Ev) and Mackintosh et al., 2012 79 (Mack) cutpoints. The proportion of the cohort meeting ≥60mins MVPA/day ranged from 80 37%-56% depending on the cutpoints used. Reported PA differed significantly across the 81 cutpoint sets. IC LPA and MPA were predictors of CRF (LPA: standardised β = 0.32, p = 82 0.002, MPA: standardised β = 0.27 p = 0.013). IC MPA also predicted BMI Z-score 83 (standardised β = -0.35, p = 0.004). Ev VPA was a predictor of BMI Z-score (standardised β 84 = -0.33, p = 0.012). Cutpoint choice has a substantial impact on reported PA levels though no 85 significant associations with CM risk were observed. Froude IC cut points represent a 86 promising approach towards classifying children’s PA data.
Original languageEnglish
JournalJournal of Sport Sciences
Volume36
DOIs
Publication statusAccepted/In press - 5 Sep 2017

Fingerprint

Calibration
Exercise
Child Health
Guidelines
Health

Keywords

  • physical activity
  • accelerometry
  • threshold
  • children

Cite this

Boddy, Lynne ; Cuningham, Conor ; Fairclough, Stuart ; Murphy, MH ; Breslin, Gavin ; Foweather, Laurence ; Rebecca, Dagger ; Graves, Lee. / Individual calibration of accelerometers in children and their health-related 2 implications. In: Journal of Sport Sciences. 2017 ; Vol. 36.
@article{20446a2b6c374b90a5c1ad87d7eba809,
title = "Individual calibration of accelerometers in children and their health-related 2 implications",
abstract = "This study compared children’s physical activity (PA) levels, the prevalence of children 73 meeting current guidelines of ≥60 minutes of daily moderate to vigorous PA (MVPA), and 74 PA-health associations using individually calibrated (IC) and empirical accelerometer 75 cutpoints. Data from 75 (n = 32 boys) 10-12 year old children were included in this study. 76 Clustered cardiometabolic (CM) risk, directly measured cardiorespiratory fitness (CRF), 77 anthropometric and 7 day accelerometer data were included within analysis. PA data were 78 classified using Froude anchored IC, Evenson et al., 2008 (Ev) and Mackintosh et al., 2012 79 (Mack) cutpoints. The proportion of the cohort meeting ≥60mins MVPA/day ranged from 80 37{\%}-56{\%} depending on the cutpoints used. Reported PA differed significantly across the 81 cutpoint sets. IC LPA and MPA were predictors of CRF (LPA: standardised β = 0.32, p = 82 0.002, MPA: standardised β = 0.27 p = 0.013). IC MPA also predicted BMI Z-score 83 (standardised β = -0.35, p = 0.004). Ev VPA was a predictor of BMI Z-score (standardised β 84 = -0.33, p = 0.012). Cutpoint choice has a substantial impact on reported PA levels though no 85 significant associations with CM risk were observed. Froude IC cut points represent a 86 promising approach towards classifying children’s PA data.",
keywords = "physical activity, accelerometry, threshold, children",
author = "Lynne Boddy and Conor Cuningham and Stuart Fairclough and MH Murphy and Gavin Breslin and Laurence Foweather and Dagger Rebecca and Lee Graves",
note = "Reference text: Alexander, R. M. (1989). Optimization and gaits in the locomotion of vertebrates. Physiol 389 Rev, 69(4), 1199-1227 390 Andersen, L. B., Hasselstrom, H., Gronfeldt, V., Hansen, S. E., & Karsten, F. (2004). The 391 relationship between physical fitness and clustered risk, and tracking of clustered 392 risk from adolescence to young adulthood: eight years follow-up in the Danish 393 Youth and Sport Study. Int J Behav Nutr Phys Act, 1(1), 6 394 Andersen, L. B., Riddoch, C., Kriemler, S., & Hills, A. P. (2011). Physical activity and 395 cardiovascular risk factors in children. Br J Sports Med, 45(11), 871-876. doi: 396 10.1136/bjsports-2011-090333 397 Anderssen, S. A., Cooper, A. R., Riddoch, C., Sardinha, L. B., Harro, M., Brage, S., & 398 Andersen, L. B. (2007). Low cardiorespiratory fitness is a strong predictor for 399 clustering of cardiovascular disease risk factors in children independent of country, 400 age and sex. Eur J Cardiovasc Prev Rehabil, 14(4), 526-531. doi: 401 10.1097/HJR.0b013e328011efc1 402 Bailey, D. P., Boddy, L. M., Savory, L. A., Denton, S. J., & Kerr, C. J. (2013). Choice of activity-403 intensity classification thresholds impacts upon accelerometer-assessed physical 404 activity-health relationships in children. PLoS One, 8(2), e57101. doi: 405 10.1371/journal.pone.0057101 406 Baquet, G., Stratton, G., Van Praagh, E., & Berthoin, S. (2007). Improving physical activity 407 assessment in prepubertal children with high-frequency accelerometry monitoring: 408 a methodological issue. Prev Med, 44(2), 143-147. doi: 409 10.1016/j.ypmed.2006.10.004 410 Biddle, S. J., & Asare, M. (2011). Physical activity and mental health in children and 411 adolescents: a review of reviews. Br J Sports Med, 45(11), 886-895. doi: 412 10.1136/bjsports-2011-090185 413 Boddy, L. M., Murphy, M. H., Cunningham, C., Breslin, G., Foweather, L., Gobbi, R., . . . 414 Stratton, G. (2014). Physical activity, cardiorespiratory fitness, and clustered 415 cardiometabolic risk in 10- to 12-year-old school children: The REACH Y6 study. Am 416 J Hum Biol, 26(4), 446-451. doi: 10.1002/ajhb.22537 417 Boreham, C. A., & McKay, H. A. (2011). Physical activity in childhood and bone health. Br J 418 Sports Med, 45(11), 877-879. doi: 10.1136/bjsports-2011-090188 419 Buchan, D. S., Young, J. D., Boddy, L. M., & Baker, J. S. (2014). Independent associations 420 between cardiorespiratory fitness, waist circumference, BMI, and clustered 421 cardiometabolic risk in adolescents. Am J Hum Biol, 26(1), 29-35. doi: 422 10.1002/ajhb.22466 423 Cain, K., Sallis, J. F., Conway, T. L., Van Dyck, D., & Calhoon, L. (2013). Using accelerometers 424 in youth physical activity studies: a review of methods. Journal of Physical Activity 425 & Health, 10, 437-450 426 Catellier, D. J., Hannan, P. J., Murray, D. M., Addy, C. L., Conway, T. L., Yang, S., & Rice, J. C. 427 (2005). Inputation of missing data when measuring activity by accelerometry. Med 428 Sci Sports Exerc, 37(Suppl 11), S555-S562 429 Chu, E. Y., McManus, A. M., & Yu, C. C. (2007). Calibration of the RT3 accelerometer for 430 ambulation and nonambulation in children. Med Sci Sports Exerc, 39(11), 2085-431 2091. doi: 10.1249/mss.0b013e318148436c 432 Cole, T. J., Freeman, J. V., & Preece, M. A. (1995). Body mass index reference curves for the 433 UK, 1990. Arch Dis Child, 73(1), 25-29 434 Ekelund, U., Tomkinson, G., & Armstrong, N. (2011). What proportion of youth are 435 physically active? Measurement issues, levels and recent time trends. Br J Sports 436 Med, 45(11), 859-865. doi: 10.1136/bjsports-2011-090190 437 16 Evenson, K. R., Catellier, D. J., Gill, K., Ondrak, K. S., & McMurray, R. G. (2008). Calibration of 438 two objective measures of physical activity for children. J Sports Sci, 26(14), 1557-439 1565. doi: 10.1080/02640410802334196 440 Fischer, C., Yildirim, M., Salmon, J., & Chinapaw, M. J. M. (2012). Comparing different 441 accelerometer cut-points for sedentary time in children. Pediatric Exercise Science, 442 24(2), 220-228 443 Freedson, P., Pober, D., & Janz, K. F. (2005). Calibration of accelerometer output for 444 children. Med Sci Sports Exerc, 37(11 Suppl), S523-530 445 Harrell, J. S., McMurray, R. G., Baggett, C. D., Pennell, M. L., Pearce, P. F., & Bangdiwala, S. I. 446 (2005). Energy costs of physical activities in children and adolescents. Med Sci 447 Sports Exerc, 37(2), 329-336 448 Hislop, J., Bulley, C., Mercer, T., & Reilly, J. (2012). Comparison of accelerometry cut points 449 for physical activity and sedentary behavior in preschool children: a validation 450 study. Pediatric Exercise Science, 24(4), 563-576 451 Kram, R., Domingo, A., & Ferris, D. P. (1997). Effect of reduced gravity on the preferred 452 walk-run transition speed. J Exp Biol, 200(Pt 4), 821-826 453 Kristensen, P. L., Moeller, N. C., Korsholm, L., Kolle, E., Wedderkopp, N., Froberg, K., & 454 Andersen, L. B. (2010). The association between aerobic fitness and physical 455 activity in children and adolescents: the European youth heart study. Eur J Appl 456 Physiol, 110(2), 267-275. doi: 10.1007/s00421-010-1491-x 457 Lohman, T., Roche, A. F., & Martorell, R. (1988). Anthropometric standardization reference 458 manual. Champaign, Illinois: Human Kinetics. 459 Mackintosh, K. A., Fairclough, S. J., Stratton, G., & Ridgers, N. D. (2012). A calibration 460 protocol for population-specific accelerometer cut-points in children. PLoS One, 461 7(5), e36919. doi: 10.1371/journal.pone.0036919 462 Mattocks, C., Ness, A., Leary, S., Tilling, K., Blair, S., Shield, J., . . . Riddoch, C. (2008). Use of 463 accelerometers in a large field-based study of children: Protocols, design issues, 464 and effects on precision. Journal of Physical Activity & Health, 5(S1), S98-S111 465 McMurray, R. G., & Ondrak, K. S. (2013). Cardiometabolic risk factors in children: The 466 importance of physical activity. American Journal of Lifestyle Medicine, 7(5), 292-467 303 468 Minetti, A. E. (2001). Walking on other planets. Nature, 409(6819), 467-469 469 Mirwald, R., Baxter-Jones, A., Bailey, D., & Beunen, G. (2002). An assessment of maturity 470 from anthropometric measurements. Medicine & Science in Sports & Exercise, 471 34(4), 689-694 472 Reilly, J. J., Penpraze, V., Hislop, J., Davies, G., Grant, S., & Paton, J. (2008). Objective 473 measurement of physical activity and sedentary behaviour: review with new data. 474 Arch Dis Child, 93(7), 614-619 475 Ridley, K., & Olds, T. S. (2008). Assigning energy costs to activities in children: a review and 476 synthesis. Med Sci Sports Exerc, 40(8), 1439-1446. doi: 477 10.1249/MSS.0b013e31817279ef 478 Rowlands, A. V. (2007). Accelerometer assessment of physical activity in children: an 479 update. Pediatr Exerc Sci, 19(3), 252-266 480 Rowlands, A. V., Thomas, P. W., Eston, R. G., & Topping, R. (2004). Validation of the RT3 481 triaxial accelerometer for the assessment of physical activity. Med Sci Sports Exerc, 482 36(3), 518-524 483 Trost, S. G., Loprinzi, P. D., Moore, R., & Pfeiffer, K. A. (2011). Comparison of accelerometer 484 cut points for predicting activity intensity in youth. Med Sci Sports Exerc, 43(7), 485 1360-1368. doi: 10.1249/MSS.0b013e318206476e 486 Vanhelst, J., Beghin, L., Rasoamanana, P., Theunynck, D., Meskini, T., Iliescu, C., . . . 487 Gottrand, F. (2010). Calibration of the RT3 accelerometer for various patterns of 488 17 physical activity in children and adolescents. J Sports Sci, 28(4), 381-387. doi: 489 10.1080/02640410903508821 490 WHO. (2010). Global Recommendations on Physical Activity for Health. Geneva, 491 Switzerland.",
year = "2017",
month = "9",
day = "5",
doi = "10.1080/02640414.2017.1377842",
language = "English",
volume = "36",
journal = "Journal of Sports Sciences",
issn = "0264-0414",

}

Individual calibration of accelerometers in children and their health-related 2 implications. / Boddy, Lynne; Cuningham, Conor; Fairclough, Stuart; Murphy, MH; Breslin, Gavin; Foweather, Laurence; Rebecca, Dagger; Graves, Lee.

In: Journal of Sport Sciences, Vol. 36, 05.09.2017.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Individual calibration of accelerometers in children and their health-related 2 implications

AU - Boddy, Lynne

AU - Cuningham, Conor

AU - Fairclough, Stuart

AU - Murphy, MH

AU - Breslin, Gavin

AU - Foweather, Laurence

AU - Rebecca, Dagger

AU - Graves, Lee

N1 - Reference text: Alexander, R. M. (1989). Optimization and gaits in the locomotion of vertebrates. Physiol 389 Rev, 69(4), 1199-1227 390 Andersen, L. B., Hasselstrom, H., Gronfeldt, V., Hansen, S. E., & Karsten, F. (2004). The 391 relationship between physical fitness and clustered risk, and tracking of clustered 392 risk from adolescence to young adulthood: eight years follow-up in the Danish 393 Youth and Sport Study. Int J Behav Nutr Phys Act, 1(1), 6 394 Andersen, L. B., Riddoch, C., Kriemler, S., & Hills, A. P. (2011). Physical activity and 395 cardiovascular risk factors in children. Br J Sports Med, 45(11), 871-876. doi: 396 10.1136/bjsports-2011-090333 397 Anderssen, S. A., Cooper, A. R., Riddoch, C., Sardinha, L. B., Harro, M., Brage, S., & 398 Andersen, L. B. (2007). Low cardiorespiratory fitness is a strong predictor for 399 clustering of cardiovascular disease risk factors in children independent of country, 400 age and sex. Eur J Cardiovasc Prev Rehabil, 14(4), 526-531. doi: 401 10.1097/HJR.0b013e328011efc1 402 Bailey, D. P., Boddy, L. M., Savory, L. A., Denton, S. J., & Kerr, C. J. (2013). Choice of activity-403 intensity classification thresholds impacts upon accelerometer-assessed physical 404 activity-health relationships in children. PLoS One, 8(2), e57101. doi: 405 10.1371/journal.pone.0057101 406 Baquet, G., Stratton, G., Van Praagh, E., & Berthoin, S. (2007). Improving physical activity 407 assessment in prepubertal children with high-frequency accelerometry monitoring: 408 a methodological issue. Prev Med, 44(2), 143-147. doi: 409 10.1016/j.ypmed.2006.10.004 410 Biddle, S. J., & Asare, M. (2011). Physical activity and mental health in children and 411 adolescents: a review of reviews. Br J Sports Med, 45(11), 886-895. doi: 412 10.1136/bjsports-2011-090185 413 Boddy, L. M., Murphy, M. H., Cunningham, C., Breslin, G., Foweather, L., Gobbi, R., . . . 414 Stratton, G. (2014). Physical activity, cardiorespiratory fitness, and clustered 415 cardiometabolic risk in 10- to 12-year-old school children: The REACH Y6 study. Am 416 J Hum Biol, 26(4), 446-451. doi: 10.1002/ajhb.22537 417 Boreham, C. A., & McKay, H. A. (2011). Physical activity in childhood and bone health. Br J 418 Sports Med, 45(11), 877-879. doi: 10.1136/bjsports-2011-090188 419 Buchan, D. S., Young, J. D., Boddy, L. M., & Baker, J. S. (2014). Independent associations 420 between cardiorespiratory fitness, waist circumference, BMI, and clustered 421 cardiometabolic risk in adolescents. Am J Hum Biol, 26(1), 29-35. doi: 422 10.1002/ajhb.22466 423 Cain, K., Sallis, J. F., Conway, T. L., Van Dyck, D., & Calhoon, L. (2013). Using accelerometers 424 in youth physical activity studies: a review of methods. Journal of Physical Activity 425 & Health, 10, 437-450 426 Catellier, D. J., Hannan, P. J., Murray, D. M., Addy, C. L., Conway, T. L., Yang, S., & Rice, J. C. 427 (2005). Inputation of missing data when measuring activity by accelerometry. Med 428 Sci Sports Exerc, 37(Suppl 11), S555-S562 429 Chu, E. Y., McManus, A. M., & Yu, C. C. (2007). Calibration of the RT3 accelerometer for 430 ambulation and nonambulation in children. Med Sci Sports Exerc, 39(11), 2085-431 2091. doi: 10.1249/mss.0b013e318148436c 432 Cole, T. J., Freeman, J. V., & Preece, M. A. (1995). Body mass index reference curves for the 433 UK, 1990. Arch Dis Child, 73(1), 25-29 434 Ekelund, U., Tomkinson, G., & Armstrong, N. (2011). What proportion of youth are 435 physically active? Measurement issues, levels and recent time trends. Br J Sports 436 Med, 45(11), 859-865. doi: 10.1136/bjsports-2011-090190 437 16 Evenson, K. R., Catellier, D. J., Gill, K., Ondrak, K. S., & McMurray, R. G. (2008). Calibration of 438 two objective measures of physical activity for children. J Sports Sci, 26(14), 1557-439 1565. doi: 10.1080/02640410802334196 440 Fischer, C., Yildirim, M., Salmon, J., & Chinapaw, M. J. M. (2012). Comparing different 441 accelerometer cut-points for sedentary time in children. Pediatric Exercise Science, 442 24(2), 220-228 443 Freedson, P., Pober, D., & Janz, K. F. (2005). Calibration of accelerometer output for 444 children. Med Sci Sports Exerc, 37(11 Suppl), S523-530 445 Harrell, J. S., McMurray, R. G., Baggett, C. D., Pennell, M. L., Pearce, P. F., & Bangdiwala, S. I. 446 (2005). Energy costs of physical activities in children and adolescents. Med Sci 447 Sports Exerc, 37(2), 329-336 448 Hislop, J., Bulley, C., Mercer, T., & Reilly, J. (2012). Comparison of accelerometry cut points 449 for physical activity and sedentary behavior in preschool children: a validation 450 study. Pediatric Exercise Science, 24(4), 563-576 451 Kram, R., Domingo, A., & Ferris, D. P. (1997). Effect of reduced gravity on the preferred 452 walk-run transition speed. J Exp Biol, 200(Pt 4), 821-826 453 Kristensen, P. L., Moeller, N. C., Korsholm, L., Kolle, E., Wedderkopp, N., Froberg, K., & 454 Andersen, L. B. (2010). The association between aerobic fitness and physical 455 activity in children and adolescents: the European youth heart study. Eur J Appl 456 Physiol, 110(2), 267-275. doi: 10.1007/s00421-010-1491-x 457 Lohman, T., Roche, A. F., & Martorell, R. (1988). Anthropometric standardization reference 458 manual. Champaign, Illinois: Human Kinetics. 459 Mackintosh, K. A., Fairclough, S. J., Stratton, G., & Ridgers, N. D. (2012). A calibration 460 protocol for population-specific accelerometer cut-points in children. PLoS One, 461 7(5), e36919. doi: 10.1371/journal.pone.0036919 462 Mattocks, C., Ness, A., Leary, S., Tilling, K., Blair, S., Shield, J., . . . Riddoch, C. (2008). Use of 463 accelerometers in a large field-based study of children: Protocols, design issues, 464 and effects on precision. Journal of Physical Activity & Health, 5(S1), S98-S111 465 McMurray, R. G., & Ondrak, K. S. (2013). Cardiometabolic risk factors in children: The 466 importance of physical activity. American Journal of Lifestyle Medicine, 7(5), 292-467 303 468 Minetti, A. E. (2001). Walking on other planets. Nature, 409(6819), 467-469 469 Mirwald, R., Baxter-Jones, A., Bailey, D., & Beunen, G. (2002). An assessment of maturity 470 from anthropometric measurements. Medicine & Science in Sports & Exercise, 471 34(4), 689-694 472 Reilly, J. J., Penpraze, V., Hislop, J., Davies, G., Grant, S., & Paton, J. (2008). Objective 473 measurement of physical activity and sedentary behaviour: review with new data. 474 Arch Dis Child, 93(7), 614-619 475 Ridley, K., & Olds, T. S. (2008). Assigning energy costs to activities in children: a review and 476 synthesis. Med Sci Sports Exerc, 40(8), 1439-1446. doi: 477 10.1249/MSS.0b013e31817279ef 478 Rowlands, A. V. (2007). Accelerometer assessment of physical activity in children: an 479 update. Pediatr Exerc Sci, 19(3), 252-266 480 Rowlands, A. V., Thomas, P. W., Eston, R. G., & Topping, R. (2004). Validation of the RT3 481 triaxial accelerometer for the assessment of physical activity. Med Sci Sports Exerc, 482 36(3), 518-524 483 Trost, S. G., Loprinzi, P. D., Moore, R., & Pfeiffer, K. A. (2011). Comparison of accelerometer 484 cut points for predicting activity intensity in youth. Med Sci Sports Exerc, 43(7), 485 1360-1368. doi: 10.1249/MSS.0b013e318206476e 486 Vanhelst, J., Beghin, L., Rasoamanana, P., Theunynck, D., Meskini, T., Iliescu, C., . . . 487 Gottrand, F. (2010). Calibration of the RT3 accelerometer for various patterns of 488 17 physical activity in children and adolescents. J Sports Sci, 28(4), 381-387. doi: 489 10.1080/02640410903508821 490 WHO. (2010). Global Recommendations on Physical Activity for Health. Geneva, 491 Switzerland.

PY - 2017/9/5

Y1 - 2017/9/5

N2 - This study compared children’s physical activity (PA) levels, the prevalence of children 73 meeting current guidelines of ≥60 minutes of daily moderate to vigorous PA (MVPA), and 74 PA-health associations using individually calibrated (IC) and empirical accelerometer 75 cutpoints. Data from 75 (n = 32 boys) 10-12 year old children were included in this study. 76 Clustered cardiometabolic (CM) risk, directly measured cardiorespiratory fitness (CRF), 77 anthropometric and 7 day accelerometer data were included within analysis. PA data were 78 classified using Froude anchored IC, Evenson et al., 2008 (Ev) and Mackintosh et al., 2012 79 (Mack) cutpoints. The proportion of the cohort meeting ≥60mins MVPA/day ranged from 80 37%-56% depending on the cutpoints used. Reported PA differed significantly across the 81 cutpoint sets. IC LPA and MPA were predictors of CRF (LPA: standardised β = 0.32, p = 82 0.002, MPA: standardised β = 0.27 p = 0.013). IC MPA also predicted BMI Z-score 83 (standardised β = -0.35, p = 0.004). Ev VPA was a predictor of BMI Z-score (standardised β 84 = -0.33, p = 0.012). Cutpoint choice has a substantial impact on reported PA levels though no 85 significant associations with CM risk were observed. Froude IC cut points represent a 86 promising approach towards classifying children’s PA data.

AB - This study compared children’s physical activity (PA) levels, the prevalence of children 73 meeting current guidelines of ≥60 minutes of daily moderate to vigorous PA (MVPA), and 74 PA-health associations using individually calibrated (IC) and empirical accelerometer 75 cutpoints. Data from 75 (n = 32 boys) 10-12 year old children were included in this study. 76 Clustered cardiometabolic (CM) risk, directly measured cardiorespiratory fitness (CRF), 77 anthropometric and 7 day accelerometer data were included within analysis. PA data were 78 classified using Froude anchored IC, Evenson et al., 2008 (Ev) and Mackintosh et al., 2012 79 (Mack) cutpoints. The proportion of the cohort meeting ≥60mins MVPA/day ranged from 80 37%-56% depending on the cutpoints used. Reported PA differed significantly across the 81 cutpoint sets. IC LPA and MPA were predictors of CRF (LPA: standardised β = 0.32, p = 82 0.002, MPA: standardised β = 0.27 p = 0.013). IC MPA also predicted BMI Z-score 83 (standardised β = -0.35, p = 0.004). Ev VPA was a predictor of BMI Z-score (standardised β 84 = -0.33, p = 0.012). Cutpoint choice has a substantial impact on reported PA levels though no 85 significant associations with CM risk were observed. Froude IC cut points represent a 86 promising approach towards classifying children’s PA data.

KW - physical activity

KW - accelerometry

KW - threshold

KW - children

U2 - 10.1080/02640414.2017.1377842

DO - 10.1080/02640414.2017.1377842

M3 - Article

C2 - 28922063

VL - 36

JO - Journal of Sports Sciences

JF - Journal of Sports Sciences

SN - 0264-0414

ER -