Prospective Association Patterns for the Physical Activity Intensity Spectrum With Body Mass Index and Lower Body Muscle Strength in Norwegian Children Aged 3–9 Years

in Journal of Physical Activity and Health

Click name to view affiliation

Eivind AadlandDepartment of Sport, Food and Natural Sciences, Faculty of Education, Arts and Sports, Western Norway University of Applied Sciences, Sogndal, Norway

Search for other papers by Eivind Aadland in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-9654-2653*
,
Einar YlvisåkerDepartment of Sport, Food and Natural Sciences, Faculty of Education, Arts and Sports, Western Norway University of Applied Sciences, Sogndal, Norway

Search for other papers by Einar Ylvisåker in
Current site
Google Scholar
PubMed
Close
,
Kjersti JohannessenDepartment of Sport, Food and Natural Sciences, Faculty of Education, Arts and Sports, Western Norway University of Applied Sciences, Sogndal, Norway

Search for other papers by Kjersti Johannessen in
Current site
Google Scholar
PubMed
Close
, and
Ada Kristine Ofrim NilsenDepartment of Sport, Food and Natural Sciences, Faculty of Education, Arts and Sports, Western Norway University of Applied Sciences, Sogndal, Norway

Search for other papers by Ada Kristine Ofrim Nilsen in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-0865-7739
Restricted access

Background: Limited evidence exists regarding prospective associations for physical activity (PA) and sedentary time (SED) with body mass index (BMI) and muscle strength in young children. We aimed to determine prospective associations for PA and SED with change in BMI and standing long jump over 2 and 4 years in children aged 3–5 years at baseline. Methods: A sample of 262 Norwegian children (50% girls) was followed from 2015 to 2017 and/or 2019. PA and SED (hip-worn ActiGraph GT3X+) were measured at baseline and BMI and standing long jump at baseline and at follow-ups. Multivariate pattern analysis was used to determine prospective associations between the triaxial PA intensity spectrum (0–99 to ≥15,000 counts per minute) and the change in outcomes. Results: We found significant prospective associations between the PA intensity spectrum and standing long jump at 2- (explained variance = 5.8%–7.7%) and 4-year (explained variance = 4.8%–5.6%) follow-ups. Associations were negative for SED and positive for all PA intensities. We found no associations between PA/SED and BMI. Conclusions: Our findings suggest that PA and SED can predict future lower body muscle strength but not BMI in early childhood.

Supplementary Materials

    • Supplementary Table S1 (PDF 499 KB)
    • Supplementary Figure S1 (PDF 100 KB)
  • Collapse
  • Expand
  • 1.

    The Global Burden of Disease 2015 Obesity Collaborators. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377(1):1327. doi:10.1056/NEJMoa1614362

    • Search Google Scholar
    • Export Citation
  • 2.

    Monteiro POA, Victora CG. Rapid growth in infancy and childhood and obesity in later life — A systematic review. Obes Rev. 2005;6(2):143154. PubMed ID: 15836465 doi:10.1111/j.1467-789X.2005.00183.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3.

    Poitras VJ, Gray CE, Borghese MM, et al. Systematic review of the relationships between objectively measured physical activity and health indicators in school-aged children and youth. Appl Physiol Nutr Metabol. 2016;41(6 suppl 3):S197S239. doi:10.1139/apnm-2015-0663

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    Aadland E, Kvalheim OM, Anderssen SA, Resaland GK, Andersen LB. The multivariate physical activity signature associated with metabolic health in children. Int J Behav Nutr Phys Act. 2018;15:77. doi:10.1186/s12966-018-0707-z

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5.

    Jimenez-Pavon D, Kelly J, Reilly JJ. Associations between objectively measured habitual physical activity and adiposity in children and adolescents: systematic review. Int J Pediatr Obes. 2010;5:318. PubMed ID: 19562608 doi:10.3109/17477160903067601

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6.

    Cooper AR, Goodman A, Page AS, et al. Objectively measured physical activity and sedentary time in youth: the international children’s accelerometry database (ICAD). Int J Behav Nutr Phys Act. 2015;12:113. doi:10.1186/s12966-015-0274-5

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Wiersma R, Haverkamp BF, van Beek JH, et al. Unravelling the association between accelerometer-derived physical activity and adiposity among preschool children: a systematic review and meta-analyses. Obes Rev. 2020;21(2):15. doi:10.1111/obr.12936

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8.

    Veldman SL, Paw MJCA, Altenburg TM. Physical activity and prospective associations with indicators of health and development in children aged < 5 years: a systematic review. Int J Behav Nutr Phys Act. 2021;18(1):111. doi:10.1186/s12966-020-01072-w

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Aadland E, Holmøy OK, Nilsen AKO. The multivariate physical activity signature associated with body mass index in young children. Prev Med. 2021;145:106437. PubMed ID: 33493523 doi:10.1016/j.ypmed.2021.106437

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10.

    Burgi F, Meyer U, Granacher U, et al. Relationship of physical activity with motor skills, aerobic fitness and body fat in preschool children: a cross-sectional and longitudinal study (ballabeina). Int J Obes. 2011;35:937944. doi:10.1038/ijo.2011.54

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11.

    Butte NF, Puyau MR, Wilson TA, et al. Role of physical activity and sleep duration in growth and body composition of preschool-aged children. Obesity. 2016;24(6):13281335. PubMed ID: 27087679 doi:10.1002/oby.21489

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12.

    Janz KF, Kwon S, Letuchy EM, et al. Sustained effect of early physical activity on body fat mass in older children. Am J Prev Med. 2009;37(1):3540. PubMed ID: 19423269 doi:10.1016/j.amepre.2009.03.012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    Jáuregui A, Villalpando S, Rangel-Baltazar E, Lara-Zamudio YA, Castillo-García MM. Physical activity and fat mass gain in Mexican school-age children: a cohort study. BMC pediatr. 2012;12(1):17. doi:10.1186/1471-2431-12-109

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14.

    Metcalf BS, Voss LD, Hosking J, Jeffery AN, Wilkin TJ. Physical activity at the government-recommended level and obesity-related health outcomes: a longitudinal study (early bird 37). Arch Dis Child. 2008;93(9):772777. PubMed ID: 18591181 doi:10.1136/adc.2007.135012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15.

    Moore LL, Gao D, Bradlee ML, et al. Does early physical activity predict body fat change throughout childhood? Prev Med. 2003;37(1):1017. PubMed ID: 12799124 doi:10.1016/S0091-7435(03)00048-3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16.

    Remmers T, Sleddens E, Gubbels JS, et al. Relationship between physical activity and the development of body mass index in children. Med Sci Sports Exerc. 2014;46(1):177184. PubMed ID: 23846163 doi:10.1249/MSS.0b013e3182a36709

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17.

    Wiersma R, Hartman E, Boezen HM, Corpeleijn E. Adiposity and high blood pressure during childhood: a prospective analysis of the role of physical activity intensity and sedentary time in the gecko drenthe cohort. Int J Environ Res Publ Health. 2020;17(24):9526. doi:10.3390/ijerph17249526

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18.

    Carter PJ, Taylor BJ, Williams SM, Taylor RW. Longitudinal analysis of sleep in relation to BMI and body fat in children: the flame study. Br Med J. 2011;342:d2712. doi:10.1136/bmj.d2712

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19.

    Ip EH, Saldana S, Trejo G, et al. Physical activity states of preschool-aged latino children in farmworker families: predictive factors and relationship with BMI percentile. J Phys Act Health. 2016;13(7):726732. PubMed ID: 26800568 doi:10.1123/jpah.2015-0534

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20.

    Leppänen M, Henriksson P, Nyström CD, et al. Longitudinal physical activity, body composition, and physical fitness in preschoolers. Med Sci Sports Exerc. 2017;49(10):20782085. doi:10.1249/MSS.0000000000001313

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    Schoenfeld BJ, Ogborn D, Krieger JW. Dose-response relationship between weekly resistance training volume and increases in muscle mass: a systematic review and meta-analysis. J Sports Sci. 2017;35(11):10731082. PubMed ID: 27433992 doi:10.1080/02640414.2016.1210197

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22.

    Nilsen AKO, Anderssen SA, Johannessen K, et al. Bi-directional prospective associations between objectively measured physical activity and fundamental motor skills in children: a two-year follow-up. Int J Behav Nutr Phys Act. 2020;17(1):1. PubMed ID: 31898547 doi:10.1186/s12966-019-0902-6

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 23.

    Garcia-Hermoso A, Cavero-Redondo I, Ramirez-Velez R, et al. Muscular strength as a predictor of all-cause mortality in an apparently healthy population: a systematic review and meta-analysis of data from approximately 2 million men and women. Arch Phys Med Rehabil. 2018;99:21002113.e5. PubMed ID: 29425700 doi:10.1016/j.apmr.2018.01.008

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 24.

    Ortega FB, Silventoinen K, Tynelius P, Rasmussen F. Muscular strength in male adolescents and premature death: cohort study of one million participants. Br Med J. 2012;345:e7279. doi:10.1136/bmj.e7279

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 25.

    Wang DX, Yao J, Zirek Y, Reijnierse EM, Maier AB. Muscle mass, strength, and physical performance predicting activities of daily living: a meta‐analysis. J Cachexia Sarcopenia Muscle. 2020;11(1):325. PubMed ID: 31788969 doi:10.1002/jcsm.12502

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 26.

    Aadland E, Andersen LB, Anderssen SA, Resaland GK, Kvalheim OM. Accelerometer epoch setting is decisive for associations between physical activity and metabolic health in children. J Sports Sci. 2020;38(3):256263. PubMed ID: 31735120 doi:10.1080/02640414.2019.1693320

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 27.

    Madsen R, Lundstedt T, Trygg J. Chemometrics in metabolomics — A review in human disease diagnosis. Anal Chim Acta. 2010;659(1–2):2333. PubMed ID: 20103103 doi:10.1016/j.aca.2009.11.042

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 28.

    Rajalahti T, Kroksveen AC, Arneberg R, et al. A multivariate approach to reveal biomarker signatures for disease classification: application to mass spectral profiles of cerebrospinal fluid from patients with multiple sclerosis. J Proteome Res. 2010;9(7):36083620. PubMed ID: 20499859 doi:10.1021/pr100142m

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 29.

    van der Ploeg HP, Hillsdon M. Is sedentary behaviour just physical inactivity by another name? Int J Behav Nutr Phys Act. 2017;14(1):8. doi:10.1186/s12966-017-0601-0

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30.

    Wold S, Ruhe A, Wold H, Dunn WJ. The collinearity problem in linear-regression — The partial least-squares (PLS) approach to generalized inverses. Siam J Sci Stat Comput. 1984;5(3):735743. doi:10.1137/0905052

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 31.

    Kvalheim OM, Karstang TV. Interpretation of latent-variable regression-models. Chemometr Intell Lab Syst. 1989;7(1–2):3951. doi:10.1016/0169-7439(89)80110-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32.

    Aadland E, Kvalheim OM, Anderssen SA, Resaland GK, Andersen LB. The triaxial physical activity signature associated with metabolic health in children. Med Sci Sports Exerc. 2019;51(10):21732179. PubMed ID: 31525174 doi:10.1249/MSS.0000000000002021

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 33.

    Nilsen AKO, Anderssen SA, Ylvisåker E, Johannessen K, Aadland E. Physical activity among Norwegian preschoolers varies by sex, age, and season. Scand J Med Sci Sports. 2019;29(6):862873. PubMed ID: 30740779 doi:10.1111/sms.13405

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 34.

    Aadland E, Nilsen AKO, Ylvisåker E, Johannessen K, Anderssen SA. Reproducibility of objectively measured physical activity: reconsideration needed. J Sports Sci. 2020;38(10):11321139. PubMed ID: 32202469 doi:10.1080/02640414.2020.1743054

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 35.

    Mattocks C, Leary S, Ness A, et al. Intraindividual variation of objectively measured physical activity in children. Med Sci Sports Exerc. 2007;39(4):622629. PubMed ID: 17414799 doi:10.1249/mss.0b013e318030631b

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 36.

    Wickel EE, Welk GJ. Applying generalizability theory to estimate habitual activity levels. Med Sci Sports Exerc. 2010;42(8):15281534. PubMed ID: 20139788 doi:10.1249/MSS.0b013e3181d107c4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 37.

    John D, Freedson P. Actigraph and actical physical activity monitors: a peek under the hood. Med Sci Sports Exerc. 2012;44(suppl 1):S86S89. PubMed ID: 22157779 doi:10.1249/MSS.0b013e3182399f5e

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 38.

    Aadland E, Nilsen AKO. Accelerometer epoch length influence associations for physical activity intensities with body mass index and locomotor skills in young children. J Sports Sci. 2022;40(14):15681577. doi:10.1080/02640414.2022.2092979

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 39.

    Esliger DW, Copeland JL, Barnes JD, Tremblay MS. Standardizing and optimizing the use of accelerometer data for free-living physical activity monitoring. J Phys Act Health. 2005;2(3):366. doi:10.1123/jpah.2.3.366

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 40.

    Aadland E, Andersen LB, Skrede T, Ekelund U, Anderssen SA, Resaland GK. Reproducibility of objectively measured physical activity and sedentary time over two seasons in children; comparing a day-by-day and a week-by-week approach. PLoS One. 2017;12(12):e0189304. doi:10.1371/journal.pone.0189304

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 41.

    Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008;26(14):15571565. PubMed ID: 18949660 doi:10.1080/02640410802334196

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 42.

    Aadland E, Nilsen AKO, Haugland ES, Vabø KB, Aadland KN. The multivariate physical acitivity signatures associated with body mass index and waist-to-height ratio in 3–5-year-old Norwegian children. Prev Med Reports. 2022;29:101930. doi:10.1016/j.pmedr.2022.101930

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 43.

    World Health Organization. Who Guidelines on Physical Activity, Sedentary Bahavior and Sleep for Children under 5 years of Age. World Health Organization; 2019.

    • Search Google Scholar
    • Export Citation
  • 44.

    Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. Br Med J. 2000;320(7244):1240. doi:10.1136/bmj.320.7244.1240

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 45.

    Ortega FB, Cadenas-Sanchez C, Sanchez-Delgado G, et al. Systematic review and proposal of a field-based physical fitness-test battery in preschool children: the prefit battery. Sports Med. 2015;45(4):533555. PubMed ID: 25370201 doi:10.1007/s40279-014-0281-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 46.

    Cadenas-Sanchez C, Martinez-Tellez B, Sanchez-Delgado G, et al. Assessing physical fitness in preschool children: feasibility, reliability and practical recommendations for the prefit battery. J Sci Med Sport. 2016;19(11):910915. PubMed ID: 26947061 doi:10.1016/j.jsams.2016.02.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 47.

    Artero EG, Espana-Romero V, Castro-Pinero J, et al. Reliability of field-based fitness tests in youth. Int J Sports Med. 2011;32(03):159169. PubMed ID: 21165805 doi:10.1055/s-0030-1268488

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 48.

    Ortega FB, Artero EG, Ruiz JR, et al. Reliability of health-related physical fitness tests in European adolescents. The Helena study. Int J Obesity. 2008;32(suppl 1):S49S57. doi:10.1038/ijo.2008.183

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 49.

    Nilsen AKO, Anderssen SA, Loftesnes JM, Johannessen K, Ylvisaaker E, Aadland E. The multivariate physical activity signature associated with fundamental motor skills in preschoolers. J Sports Sci. 2019;38(3):264272. PubMed ID: 31774369 doi:10.1080/02640414.2019.1694128

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 50.

    Kvalheim OM, Arneberg R, Grung B, Rajalahti T. Determination of optimum number of components in partial least squares regression from distributions of the root-mean-squared error obtained by monte carlo resampling. J Chemometrics. 2018;32:e2993. doi:10.1002/cem.2993

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 51.

    Rajalahti T, Kvalheim OM. Multivariate data analysis in pharmaceutics: a tutorial review. Int J Pharm. 2011;417(1–2):280290. PubMed ID: 21335075 doi:10.1016/j.ijpharm.2011.02.019

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 52.

    Rajalahti T, Arneberg R, Berven FS, Myhr KM, Ulvik RJ, Kvalheim OM. Biomarker discovery in mass spectral profiles by means of selectivity ratio plot. Chemometr Intell Lab Syst. 2009;95(1):3548. doi:10.1016/j.chemolab.2008.08.004

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 53.

    Rajalahti T, Arneberg R, Kroksveen AC, Berle M, Myhr KM, Kvalheim OM. Discriminating variable test and selectivity ratio plot: quantitative tools for interpretation and variable (biomarker) selection in complex spectral or chromatographic profiles. Anal Chem. 2009;81(7):25812590. PubMed ID: 19228047 doi:10.1021/ac802514y

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 54.

    Aadland E, Andersen LB, Resaland GK, Kvalheim OM. Interpretation of multivariate association patterns between multicollinear physical activity accelerometry data and cardiometabolic health in children—A tutorial. Metabolites. 2019;9(7):129. doi:10.3390/metabo9070129

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 55.

    Aadland E, Kvalheim OM, Anderssen SA, Resaland GK, Andersen LB. Multicollinear physical activity accelerometry data and associations to cardiometabolic health: challenges, pitfalls, and potential solutions. Int J Behav Nutr Phys Act. 2019;16:74. doi:10.1186/s12966-019-0836-z

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 56.

    Kvalheim OM. Scaling of analytical data. Analytica Chimica Acta. 1985;177:7179.

  • 57.

    Cattuzzo MT, Henrique RD, Re AHN, et al. Motor competence and health related physical fitness in youth: a systematic review. J Sci Med Sport. 2016;19(2):123129. PubMed ID: 25554655 doi:10.1016/j.jsams.2014.12.004

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 58.

    Utesch T, Bardid F, Büsch D, Strauss B. The relationship between motor competence and physical fitness from early childhood to early adulthood: a meta-analysis. Sports Med. 2019;49(4):541551. PubMed ID: 30747376 doi:10.1007/s40279-019-01068-y

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 59.

    Hutcheon JA, Chiolero A, Hanley JA. Random measurement error and regression dilution bias. Br Med J. 2010;340:c2289. doi:10.1136/bmj.c2289

  • 60.

    Aadland E, Okely AD, Nilsen AKO. Trajectories of physical activity and sedentary time in Norwegian children aged 3–5 years: a 5-year longitudinal study. Int J Behav Nutr Phys Act. 2022;19(1):67. PubMed ID: 35690755 doi:10.1186/s12966-022-01286-0

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 61.

    Bornstein DB, Beets MW, Byun W, McIver K. Accelerometer-derived physical activity levels of preschoolers: a meta-analysis. J Sci Med Sport. 2011;14(6):504511. PubMed ID: 21684809 doi:10.1016/j.jsams.2011.05.007

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 62.

    Metcalf BS, Hosking J, Jeffery AN, Voss LD, Henley W, Wilkin TJ. Fatness leads to inactivity, but inactivity does not lead to fatness: a longitudinal study in children (earlybird 45). Arch Dis Child. 2011;96(10):942947. PubMed ID: 20573741 doi:10.1136/adc.2009.175927

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 63.

    Hjorth MF, Chaput JP, Ritz C, et al. Fatness predicts decreased physical activity and increased sedentary time, but not vice versa: support from a longitudinal study in 8-to 11-year-old children. Int J Obes. 2014;38(7):959965. doi:10.1038/ijo.2013.229

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 384 384 158
Full Text Views 15 15 9
PDF Downloads 26 26 15