The Physical Behaviour Intensity Spectrum and Body Mass Index in School-Aged Youth: A Compositional Analysis of Pooled Individual Participant Data
Jazyk angličtina Země Švýcarsko Médium electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
35886629
PubMed Central
PMC9320124
DOI
10.3390/ijerph19148778
PII: ijerph19148778
Knihovny.cz E-zdroje
- Klíčová slova
- CoDa, accelerometer, adiposity, adolescents, children, intensity spectrum, physical activity,
- MeSH
- cvičení * MeSH
- dítě MeSH
- index tělesné hmotnosti MeSH
- lidé MeSH
- mladiství MeSH
- sedavý životní styl * MeSH
- školy MeSH
- zrychlení MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
- mladiství MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
We examined the compositional associations between the intensity spectrum derived from incremental acceleration intensity bands and the body mass index (BMI) z-score in youth, and investigated the estimated differences in BMI z-score following time reallocations between intensity bands. School-aged youth from 63 schools wore wrist accelerometers, and data of 1453 participants (57.5% girls) were analysed. Nine acceleration intensity bands (range: 0−50 mg to ≥700 mg) were used to generate time-use compositions. Multivariate regression assessed the associations between intensity band compositions and BMI z-scores. Compositional isotemporal substitution estimated the differences in BMI z-score following time reallocations between intensity bands. The ≥700 mg intensity bandwas strongly and inversely associated with BMI z-score (p < 0.001). The estimated differences in BMI z-score when 5 min were reallocated to and from the ≥700 mg band and reallocated equally among the remaining bands were −0.28 and 0.44, respectively (boys), and −0.39 and 1.06, respectively (girls). The time in the ≥700 mg intensity band was significantly associated with BMI z-score, irrespective of sex. When even modest durations of time in this band were reallocated, the asymmetrical estimated differences in BMI z-score were clinically meaningful. The findings highlight the utility of the full physical activity intensity spectrum over a priori-determined absolute intensity cut-point approaches.
Centre for Adolescent Health Murdoch Children's Research Institute Melbourne VIC 3052 Australia
Department of Psychology University of Liverpool Liverpool L69 7ZA UK
Faculty of Physical Culture Palacký University Olomouc CZ 779 00 Olomouc Czech Republic
Zobrazit více v PubMed
Office for National Statistics National Child Measurement Programme, England 2020–21 School Year. [(accessed on 13 May 2022)]. Available online: https://digital.nhs.uk/data-and-information/publications/statistical/national-child-measurement-programme/2020-21-school-year#.
Jago R., Drews K.L., McMurray R.G., Baranowski T., Galassetti P., Foster G.D., Moe E., Buse J.B. BMI change, fitness change and cardiometabolic risk factors among 8th grade youth. Pediatr. Exerc. Sci. 2013;2:52–68. doi: 10.1123/pes.25.1.52. PubMed DOI PMC
Kolsgaard M.L.P., Joner G., Brunborg C., Anderssen S.A., Tonstad S., Andersen L.F. Reduction in BMI z-score and improvement in cardiometabolic risk factors in obese children and adolescents. The Oslo Adiposity Intervention Study—A hospital/public health nurse combined treatment. BMC Pediatr. 2011;11:47. doi: 10.1186/1471-2431-11-47. PubMed DOI PMC
Halfon N., Larson K., Slusser W. Associations between obesity and comorbid mental health, developmental, and physical health conditions in a nationally representative sample of US children aged 10 to 17. Acad. Pediatr. 2013;13:6–13. doi: 10.1016/j.acap.2012.10.007. PubMed DOI
Brown T., Moore T.H., Hooper L., Gao Y., Zayegh A., Ijaz S., Elwenspoek M., Foxen S.C., Magee L., O’Malley C., et al. Interventions for preventing obesity in children. Cochrane Database Syst. Rev. 2019;23:CD001871. doi: 10.1002/14651858.CD001871.pub4. PubMed DOI PMC
Poitras V.J., Gray C.E., Borghese M.M., Carson V., Chaput J.-P., Janssen I., Katzmarzyk P.T., Pate R.R., Connor Gorber S., Kho M.E., et al. Systematic review of the relationships between objectively measured physical activity and health indicators in school-aged children and youth. Appl. Physiol. Nutr. Metab. 2016;41((Suppl. 3)):S197–S239. doi: 10.1139/apnm-2015-0663. PubMed DOI
Hamer M., Stamatakis E. Relative proportion of vigorous physical activity, total volume of moderate to vigorous activity, and body mass index in youth: The Millennium Cohort Study. Int. J. Obes. 2018;42:1239–1242. doi: 10.1038/s41366-018-0128-8. PubMed DOI
Carson V., Hunter S., Kuzik N., Gray C.E., Poitras V.J., Chaput J.-P., Saunders T.J., Katzmarzyk P.T., Okely A.D., Connor Gorber S., et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth: An update. Appl. Physiol. Nut.r Metab. 2016;41((Suppl. 3)):S240–S265. doi: 10.1139/apnm-2015-0630. PubMed DOI
Migueles J.H., Aadland E., Andersen L.B., Brønd J.C., Chastin S.F., Hansen B.H., Konstabel K., Kvalheim O.M., McGregor D.E., Rowlands A.V., et al. GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studies. Br. J. Sports Med. 2022;56:376–384. doi: 10.1136/bjsports-2020-103604. PubMed DOI PMC
Rowlands A.V. Moving forward with accelerometer-assessed physical activity: Two strategies to ensure meaningful, interpretable, and comparable measures. Pediatr. Exerc. Sci. 2018;30:450–456. doi: 10.1123/pes.2018-0201. PubMed DOI
Aadland E., Kvalheim O.M., Anderssen S.A., Resaland G.K., Andersen L.B. 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. PubMed DOI PMC
Aadland E., Kvalheim O.M., Hansen B.H., Kriemler S., Ried-Larsen M., Wedderkopp N., Sardinha L.B., Møller N.C., Hallal P.C., Anderssen S.A. The multivariate physical activity signature associated with metabolic health in children and youth: An International Children’s Accelerometry Database (ICAD) analysis. Prev. Med. 2020;141:106266. doi: 10.1016/j.ypmed.2020.106266. PubMed DOI
Aadland E., Nilsen A.K.O., Andersen L.B., Rowlands A.V., Kvalheim O.M. A comparison of analytical approaches to investigate associations for accelerometry-derived physical activity spectra with health and developmental outcomes in children. J. Sports Sci. 2021;39:430–438. doi: 10.1080/02640414.2020.1824341. PubMed DOI
Aadland E., Andersen L.B., Migueles J.H., Ortega F.B., Kvalheim O.M. Interpretation of associations between the accelerometry physical activity spectrum and cardiometabolic health and locomotor skills in two cohorts of children using raw, normalized, log-transformed, or compositional data. J. Sports Sci. 2020;38:2708–2719. doi: 10.1080/02640414.2020.1796462. PubMed DOI
Verloigne M., Van Lippevelde W., Maes L., Yildirim M., Chinapaw M., Manios Y. Levels of physical activity and sedentary time among 10- to 12-year-old boys and girls across 5 European countries using accelerometers: An observational study within the ENERGY-project. Int J Behav Nutr. Phys. Act. 2012;9:34. doi: 10.1186/1479-5868-9-34. PubMed DOI PMC
Beltran-Valls M.R., Janssen X., Farooq A., Adamson A.J., Pearce M.S., Reilly J.K., Basterfield L., Reilly J.J. Longitudinal changes in vigorous intensity physical activity from childhood to adolescence: Gateshead Millennium Study. J. Sci. Med. Sport. 2019;22:450–455. doi: 10.1016/j.jsams.2018.10.010. PubMed DOI
DHSC . UK Chief Medical Officers’ Physical Activity Guidelines. DHSC; London, UK: 2019.
Costigan S.A., Eather N., Plotnikoff R.C., Taaffe D.R., Lubans D.R. High-intensity interval training for improving health-related fitness in adolescents: A systematic review and meta-analysis. Br. J. Sports Med. 2015;49:1253–1261. doi: 10.1136/bjsports-2014-094490. PubMed DOI
Janssen I., Ross R. Vigorous intensity physical activity is related to the metabolic syndrome independent of the physical activity dose. Int. J. Epidemiol. 2012;41:1132–1140. doi: 10.1093/ije/dys038. PubMed DOI PMC
Fairclough S.J., Dumuid D., Mackintosh K.A., Stone G., Dagger R., Stratton G., Davies I., Boddy L.M. Adiposity, fitness, health-related quality of life and the reallocation of time between children’s school day activity behaviours: A compositional data analysis. Prev. Med. Rep. 2018;11:254–261. doi: 10.1016/j.pmedr.2018.07.011. PubMed DOI PMC
Fairclough S.J., Dumuid D., Taylor S., Curry W., McGrane B., Stratton G., Maher C., Olds T. Fitness, fatness and the reallocation of time between children’s daily movement behaviours: An analysis of compositional data. Int. J. Behav. Nutr. Phys. Act. 2017;14:64. doi: 10.1186/s12966-017-0521-z. PubMed DOI PMC
Fairclough S.J., Tyler R., Dainty J.R., Dumuid D., Richardson C., Shepstone L., Atkin A.J. Cross-sectional associations between 24-hour activity behaviours and mental health indicators in children and adolescents: A compositional data analysis. J. Sports Sci. 2021;39:1602–1614. doi: 10.1080/02640414.2021.1890351. PubMed DOI
Burns R.D., Kim Y., Byun W., Brusseau T.A. Associations of school day sedentary behavior and physical activity with gross motor skills: Use of compositional data analysis. J. Phys. Act. Health. 2019;16:811–817. doi: 10.1123/jpah.2018-0549. PubMed DOI
Talarico R., Janssen I. Compositional associations of time spent in sleep, sedentary behavior and physical activity with obesity measures in children. Int. J. Obes. 2018;42:1508–1514. doi: 10.1038/s41366-018-0053-x. PubMed DOI
Dumuid D., Stanford T.E., Pedišić Ž., Maher C., Lewis L.K., Martín-Fernández J.-A., Katzmarzyk P.T., Chaput J.-P., Fogelholm M., Standage M., et al. Adiposity and the isotemporal substitution of physical activity, sedentary time and sleep among school-aged children: A compositional data analysis approach. BMC Public Health. 2018;18:311. doi: 10.1186/s12889-018-5207-1. PubMed DOI PMC
Dumuid D., Maher C., Lewis L.K., Stanford T.E., Martin Fernandez J.A., Ratcliffe J., Katzmarzyk P.T., Barreira T.V., Chaput J.P., Fogelholm M., et al. Human development index, children’s health-related quality of life and movement behaviors: A compositional data analysis. Qual. Life Res. 2018;27:473–482. doi: 10.1007/s11136-018-1791-x. PubMed DOI PMC
Carson V., Tremblay M.S., Chaput J.-P., Chastin S.F.M. Associations between sleep duration, sedentary time, physical activity, and health indicators among Canadian children and youth using compositional analyses. Appl. Physiol. Nutr. Metab. 2016;41((Suppl. 3)):S294–S302. doi: 10.1139/apnm-2016-0026. PubMed DOI
Taylor S.L., Noonan R.J., Knowles Z.R., Owen M.B., McGrane B., Curry W.B., Fairclough S.J. Predictors of segmented school day physical activity and sedentary time in children from a northwest England low-income community. Int. J. Environ. Res. Public Health. 2017;14:534. doi: 10.3390/ijerph14050534. PubMed DOI PMC
Taylor S.L., Noonan R.J., Knowles Z.R., Owen M.B., McGrane B., Curry W.B., Fairclough S.J. Evaluation of a pilot school-based physical activity clustered randomised controlled trial-Active Schools: Skelmersdale. Int. J. Environ. Res. Public Health. 2018;15:1011. doi: 10.3390/ijerph15051011. PubMed DOI PMC
Hurter L., Rowlands A.V., Fairclough S.J., Gibbon K.C., Knowles Z.R., Porcellato L.A., Cooper-Ryan A.M., Boddy L.M. Validating the Sedentary Sphere method in children: Does wrist or accelerometer brand matter? J. Sports Sci. 2019;37:1910–1918. doi: 10.1080/02640414.2019.1605647. PubMed DOI
Foweather L., Crotti M., Foulkes J.D., O’Dwyer M.V., Utesch T., Knowles Z.R., Fairclough S.J., Ridgers N.D., Stratton G. Foundational movement skills and play behaviors during recess among preschool children: A compositional analysis. Children. 2021;8:543. doi: 10.3390/children8070543. PubMed DOI PMC
Noonan R.J., Boddy L.M., Kim Y., Knowles Z.R., Fairclough S.J. Comparison of children’s free-living physical activity derived from wrist and hip raw accelerations during the segmented week. J. Sports Sci. 2017;35:2067–2072. doi: 10.1080/02640414.2016.1255347. PubMed DOI
Owen M., Kerner C., Taylor S., Noonan R., Newson L., Kosteli M.-C., Curry W., Fairclough S.J. The feasibility of a novel school peer-led mentoring model to improve the physical activity levels and sedentary time of adolescent girls: The Girls Peer Activity (G-PACT) project. Children. 2018;5:67. doi: 10.3390/children5060067. PubMed DOI PMC
Lohman T.G., Roche A.F.M., Martorell R. Anthropometric Standardization Reference Manual. Human Kinetics Books; Champaign, IL, USA: 1991.
Cole T.J., Freeman J.V., Preece M.A. Body mass index reference curves for the UK, 1990. Arch. Dis. Child. 1995;73:25–29. doi: 10.1136/adc.73.1.25. PubMed DOI PMC
Cole T.J., Bellizzi M., Flegal K., Dietz W. Establishing a standard definition for child overweight and obesity worldwide: International survey. Br. Med. J. 2000;320:1240–1243. doi: 10.1136/bmj.320.7244.1240. PubMed DOI PMC
Department for Communities and Local Government The English Indices of Deprivation. [(accessed on 12 December 2017)];2015 Available online: https://www.gov.uk/government/statistics/english-indices-of-deprivation-2015.
Ministry of Housing, Communities, and Local Government English Indices of Deprivation 2019. [(accessed on 24 April 2022)]; Available online: https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019.
Migueles J.H., Rowlands A.V., Huber F., Sabia S., Van Hees V.T. GGIR: A Research community–driven open source R package for generating physical activity and sleep outcomes from multi-day raw accelerometer data. J. Meas. Phys. Behav. 2019;2:188–196. doi: 10.1123/jmpb.2018-0063. DOI
van Hees V.T., Fang Z., Langford J., Assah F., Mohammad A., da Silva I.C., Trenell M.I., White T., Wareham N.J., Brage S. Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: An evaluation on four continents. J. Appl. Physiol. 2014;11:738–744. doi: 10.1152/japplphysiol.00421.2014. PubMed DOI PMC
van Hees V.T., Renstrom F., Wright A., Gradmark A., Catt M., Chen K.Y., Lof M., Bluck L., Pomeroy J., Wareham N.J. Estimation of daily energy expenditure in pregnant and non-pregnant women using a wrist-worn tri-axial accelerometer. PLoS ONE. 2011;6:e22922. doi: 10.1371/journal.pone.0022922. PubMed DOI PMC
van Hees V.T., Gorzelniak L., Dean León E.C., Eder M., Pias M., Taherian S., Ekelund U., Renström F., Franks P.W., Horsch A. Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PLoS ONE. 2013;8:e61691. doi: 10.1371/journal.pone.0061691. PubMed DOI PMC
Rowlands A.V., Plekhanova T., Yates T., Mirkes E.M., Davies M., Khunti K., Edwardson C.L. Providing a basis for harmonization of accelerometer-assessed physical activity outcomes across epidemiological datasets. J. Meas. Phys. Behav. 2019;2:131–142. doi: 10.1123/jmpb.2018-0073. DOI
Phillips L.R., Parfitt G., Rowlands A.V. Calibration of the GENEA accelerometer for assessment of physical activity intensity in children. J. Sci. Med. Sport. 2012;16:124–128. doi: 10.1016/j.jsams.2012.05.013. PubMed DOI
Hildebrand M., Van Hees V.T., Hansen B.H., Ekelund U. Age-group comparibility of raw accelerometer output from wrist- and hip-worn monitors. Med. Sci. Sports Exerc. 2014;46:1816–1824. doi: 10.1249/MSS.0000000000000289. PubMed DOI
Sterne J.A.C., White I.R., Carlin J.B., Spratt M., Royston P., Kenward M.G., Wood A.M., Carpenter J.R. Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. Br. Med. J. 2009;338:b2393. doi: 10.1136/bmj.b2393. PubMed DOI PMC
van den Boogaart K.G., Tolosana-Delgado R. ‘Compositions’: A unified R package to analyze compositional data. Comput. Geosci. 2008;34:320–338. doi: 10.1016/j.cageo.2006.11.017. DOI
Dumuid D., Stanford T.E., Martin-Fernandez J.A., Pedisic Z., Maher C.A., Lewis L.K., Hron K., Katzmarzyk P.T., Chaput J.P., Fogelholm M. Compositional data analysis for physical activity, sedentary time and sleep research. Stat. Methods Med. Res. 2018;27:3736–3738. doi: 10.1177/0962280217710835. PubMed DOI
Dumuid D., Pedišić Ž., Palarea-Albaladejo J., Martín-Fernández J.A., Hron K., Olds T. Compositional data analysis in time-use epidemiology: What, why, how. Int. J. Environ. Res. Public Health. 2020;17:2220. doi: 10.3390/ijerph17072220. PubMed DOI PMC
Zeileis A., Hothorn T. Diagnostic checking in regression relationships. R News. 2002;2:7–10.
Fox J., Weisberg S. An R Companion to Applied Regression. Sage; Thousand Oaks, CA, USA: 2019.
Ludecke D., Ben-Shachar M.S., Patil I., Waggoner P., Makowski D. performance: An R package for assessment, comparison and testing of statistical models. J. Open Sci. Softw. 2021;60:3139. doi: 10.21105/joss.03139. DOI
Dumuid D., Wake M., Clifford S., Burgner D., Carlin J.B., Mensah F.K., Fraysse F., Lycett K., Baur L., Olds T. The association of the body composition of children with 24-hour activity composition. J. Pediatr. 2019;208:43–49. doi: 10.1016/j.jpeds.2018.12.030. PubMed DOI
Stanford T.E. Tystan/Deltacomp: Deltacomp Version 0.2.2. 2022. [(accessed on 5 May 2022)]. Available online: https://zenodo.org/record/6340017.
Aadland E., Andersen L.B., Anderssen S.A., Resaland G.K., Kvalheim O.M. Associations of volumes and patterns of physical activity with metabolic health in children: A multivariate pattern analysis approach. Prev. Med. 2018;115:12–18. doi: 10.1016/j.ypmed.2018.08.001. PubMed DOI
Aadland E., Kvalheim O.M., Anderssen S.A., Resaland G.K., Andersen L.B. 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. PubMed DOI PMC
Štefelová N., Dygrýn J., Hron K., Gába A., Rubín L., Palarea-Albaladejo J. Robust compositional analysis of physical activity and sedentary behaviour data. Int. J. Environ. Res. Public Health. 2018;15:2248. doi: 10.3390/ijerph15102248. PubMed DOI PMC
Rubín L., Gába A., Pelclová J., Štefelová N., Jakubec L., Dygrýn J., Hron K. Changes in sedentary behavior patterns during the transition from childhood to adolescence and their association with adiposity: A prospective study based on compositional data analysis. Arch. Public Health. 2022;80:1–9. doi: 10.1186/s13690-021-00755-5. PubMed DOI PMC
Carson V., Tremblay M.S., Chaput J.-P., McGregor D., Chastin S. Compositional analyses of the associations between sedentary time, different intensities of physical activity, and cardiometabolic biomarkers among children and youth from the United States. PLoS ONE. 2019;14:e0220009. doi: 10.1371/journal.pone.0220009. PubMed DOI PMC
Pedišić Ž., Bauman A. Accelerometer-based measures in physical activity surveillance: Current practices and issues. Br. J. Sports Med. 2015;49:219–223. doi: 10.1136/bjsports-2013-093407. PubMed DOI
Trost S.G. Population-level physical activity surveillance in young people: Are accelerometer-based measures ready for prime time? Int. J. Behav. Nutr. Phys. Act. 2020;17:28. doi: 10.1186/s12966-020-00929-4. PubMed DOI PMC
Hurter L., Fairclough S.J., Knowles Z., Porcellato L., Cooper-Ryan A., Boddy L. Establishing raw acceleration thresholds to classify sedentary and stationary behaviour in children. Children. 2018;5:172. doi: 10.3390/children5120172. PubMed DOI PMC
Chaput J.-P., Gray C.E., Poitras V.J., Carson V., Gruber R., Olds T., Weiss S.K., Connor Gorber S., Kho M.E. Systematic review of the relationships between sleep duration and health indicators in school-aged children and youth. Appl. Physiol. Nutr. Metab. 2016;41((Suppl. 3)):S266–S282. doi: 10.1139/apnm-2015-0627. PubMed DOI
Kwon S., Janz K.F., Burns T.L., Levy S.M. Association between light-intensity physical activity and adiposity in childhood. Pediatr Exerc Sci. 2011;23:218–229. doi: 10.1123/pes.23.2.218. PubMed DOI PMC
Rowlands A.V., Fairclough S.J., Yates T.O.M., Edwardson C., Davies M., Munir F., Khunti K., Stiles V. Activity intensity, volume, and norms: Utility and interpretation of accelerometer metrics. Med. Sci. Sports Exerc. 2019;51:2410–2422. doi: 10.1249/MSS.0000000000002047. PubMed DOI
Gába A., Dygrýn J., Štefelová N., Rubín L., Hron K., Jakubec L. Replacing school and out-of-school sedentary behaviors with physical activity and its associations with adiposity in children and adolescents: A compositional isotemporal substitution analysis. Environ. Health Prev. Med. 2021;26:16. doi: 10.1186/s12199-021-00932-6. PubMed DOI PMC
Sattelmair J., Pertman J., Ding E.L., Kohl H.W., Haskell W., Lee I.M. Dose response between physical activity and risk of coronary heart disease: A meta-analysis. Circulation. 2011;124:789–795. doi: 10.1161/CIRCULATIONAHA.110.010710. PubMed DOI PMC
Fairclough S.J., Hackett A., Davies I., Gobbi R., Mackintosh K., Warburton G., Stratton G., van Sluijs E., Boddy L. Promoting healthy weight in primary school children through physical activity and nutrition education: A pragmatic evaluation of the CHANGE! randomised intervention study. BMC Public Health. 2013;13:626. doi: 10.1186/1471-2458-13-626. PubMed DOI PMC
Chastin S.F.M., Palarea-Albaladejo J., Dontje M.L., Skelton D.A. Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: A novel compositional data analysis approach. PLoS ONE. 2015;10:e0139984. doi: 10.1371/journal.pone.0139984. PubMed DOI PMC