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Environmental Health and Preventive Medicine

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Open Access 01-12-2021 | Obesity | Research article

Associations of novel 24-h accelerometer-derived metrics with adiposity in children and adolescents

Authors: Jan Dygrýn, María Medrano, Pablo Molina-Garcia, Lukáš Rubín, Lukáš Jakubec, David Janda, Aleš Gába

Published in: Environmental Health and Preventive Medicine | Issue 1/2021

Abstract

Background

Further research is required to explore the associations between 24-h movement behaviours and health outcomes in the paediatric population. Therefore, this study aimed to examine the associations between novel data-driven 24-h activity metrics and adiposity among children and adolescents.

Methods

The sample included 382 children (8–13 years) and 338 adolescents (14–18 years). The average acceleration (AvAcc) of activity, intensity gradient (IG), and metrics representing the initial acceleration for the most active time periods of the 24-h cycle were calculated from raw acceleration data. Adiposity measures included body mass index z-score, fat mass percentage (FM%), and visceral adipose tissue (VAT). Data analysis was performed using multiple linear regression adjusted for wear time, sex, maternal education level, and maternal overweight and obesity.

Results

Children demonstrated higher values in all 24-h activity metrics than did adolescents (p < 0.001 for all). For children, the initial acceleration for the most active 2, 5, 15, and 30 min of the 24-h cycle were negatively associated with FM% (p ≤ 0.043 for all) and VAT (p <0.001 for all), respectively. For adolescents, the IG was negatively associated with FM% (p = 0.002) and VAT (p = 0.007). Moreover, initial acceleration for the most active 2, 5, 15, 30, 60, and 120 min were associated with FM% (p ≤ 0.007 for all) and with VAT (p ≤ 0.023 for all).

Conclusions

The intensity distribution of activity and initial acceleration for the most active 2, 5, 15, 30, 60, and 120 min within the 24-h cycle are beneficial for the prevention of excess adiposity in the paediatric population.

Han JC, Lawlor DA, Kimm SYS. Childhood obesity. Lancet. 2010;375(9727):1737–48. https://doi.org/10.1016/S0140-6736(10)60171-7.CrossRefPubMedPubMedCentral

Weihrauch-Blüher S, Wiegand S. Risk factors and implications of childhood obesity. Curr Obes Rep. 2018;7(4):254–9. https://doi.org/10.1007/s13679-018-0320-0.CrossRefPubMed

Lindberg L, Hagman E, Danielsson P, Marcus C, Persson M. Anxiety and depression in children and adolescents with obesity: a nationwide study in Sweden. BMC Med. 2020;18(1):30. https://doi.org/10.1186/s12916-020-1498-z.CrossRefPubMedPubMedCentral

Lindberg L, Danielsson P, Persson M, Marcus C, Hagman E. Association of childhood obesity with risk of early all-cause and cause-specific mortality: a Swedish prospective cohort study. PLoS Med. 2020;17(3):e1003078. https://doi.org/10.1371/journal.pmed.1003078.CrossRefPubMedPubMedCentral

Abarca-Gómez L, Abdeen ZA, Hamid ZA, Abu-Rmeileh NM, Acosta-Cazares B, Acuin C, et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet. 2017;390(10113):2627–42. https://doi.org/10.1016/S0140-6736(17)32129-3.CrossRef

Lobstein T, Jackson-Leach R. Planning for the worst: estimates of obesity and comorbidities in school-age children in 2025. Pediatr Obes. 2016;11(5):321–5. https://doi.org/10.1111/ijpo.12185.CrossRefPubMed

Lin Y, Tremblay MS, Katzmarzyk PT, Fogelholm M, Hu G, Lambert EV, et al. Temporal and bi-directional associations between sleep duration and physical activity/sedentary time in children: an international comparison. Prev Med. 2018;111:436–41. https://doi.org/10.1016/j.ypmed.2017.12.006.CrossRefPubMed

Chaput JP, Saunders TJ, Carson V. Interactions between sleep, movement and other non-movement behaviours in the pathogenesis of childhood obesity. Obes Rev. 2017;18(Suppl 1):7–14. https://doi.org/10.1111/obr.12508.CrossRefPubMed

Saunders TJ, Gray CE, Poitras VJ, Chaput JP, Janssen I, Katzmarzyk PT, et al. Combinations of physical activity, sedentary behaviour and sleep: relationships with health indicators in school-aged children and youth. Appl Physiol Nutr Metab. 2016;41(6 Suppl 3):283–93. https://doi.org/10.1139/apnm-2015-0626.CrossRef

10.

Jakubec L, Gába A, Dygrýn J, Rubín L, Šimůnek A, Sigmund E. Is adherence to the 24-hour movement guidelines associated with a reduced risk of adiposity among children and adolescents? BMC Public Health. 2020;20(1):1119. https://doi.org/10.1186/s12889-020-09213-3.CrossRefPubMedPubMedCentral

11.

Troiano RP, Stamatakis E, Bull FC. How can global physical activity surveillance adapt to evolving physical activity guidelines? Needs, challenges and future directions. Br J Sports Med. 2020;54(24):1468–73. https://doi.org/10.1136/bjsports-2020-102621.CrossRefPubMedPubMedCentral

12.

Rowlands AV, Edwardson CL, Davies MJ, Khunti K, Harrington DM, Yates T. Beyond cut points: accelerometer metrics that capture the physical activity profile. Med Sci Sports Exerc. 2018;50(6):1323–32. https://doi.org/10.1249/MSS.0000000000001561.CrossRefPubMed

13.

Rowlands AV, Dawkins NP, Maylor B, Edwardson CL, Fairclough SJ, Davies MJ, et al. Enhancing the value of accelerometer-assessed physical activity: meaningful visual comparisons of data-driven translational accelerometer metrics. Sports Med Open. 2019;5(1):47. https://doi.org/10.1186/s40798-019-0225-9.CrossRefPubMedPubMedCentral

14.

Migueles JH, Aadland E, Andersen LB, Brønd JC, Chastin SF, Hansen BH, 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. 2021. https://doi.org/10.1136/bjsports-2020-103604.

15.

Doherty A, Jackson D, Hammerla N, Plotz T, Olivier P, Granat MH, et al. Large scale population assessment of physical activity using wrist worn accelerometers: the UK Biobank study. PLoS One. 2017;12(2):e0169649. https://doi.org/10.1371/journal.pone.0169649.CrossRefPubMedPubMedCentral

16.

Fairclough SJ, Taylor S, Rowlands AV, Boddy LM, Noonan RJ. Average acceleration and intensity gradient of primary school children and associations with indicators of health and well-being. J Sports Sci. 2019;37(18):2159–67. https://doi.org/10.1080/02640414.2019.1624313.CrossRefPubMed

17.

Donnelly S, Buchan DS, McLellan G, Arthur R. Relationship between parent and child physical activity using novel acceleration metrics. Res Q Exerc Sport. 2020:1–9. https://doi.org/10.1080/02701367.2020.1817295.

18.

Marshall ZA, Mackintosh KA, Lewis MJ, Ellins EA, McNarry MA. Association of physical activity metrics with indicators of cardiovascular function and control in children with and without type 1 diabetes. Pediatr Diabetes. 2020;22(2):320–8. https://doi.org/10.1111/pedi.13159.CrossRefPubMed

19.

Aadland E, Nilsen AKO, Andersen LB, Rowlands AV, Kvalheim OM. 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(4):430–8. https://doi.org/10.1080/02640414.2020.1824341.CrossRefPubMed

20.

Javed A, Jumean M, Murad MH, Okorodudu D, Kumar S, Somers VK, et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta-analysis. Pediatr Obes. 2015;10(3):234–44. https://doi.org/10.1111/ijpo.242.CrossRefPubMed

21.

Karchynskaya V, Kopcakova J, Klein D, Gaba A, Madarasova-Geckova A, van Dijk JP, et al. Is BMI a valid indicator of overweight and obesity for adolescents? Int J Environ Res Public Health. 2020;17(13). https://doi.org/10.3390/ijerph17134815.

22.

Gába A, Dygrýn J, Štefelová N, Rubín L, Hron K, Jakubec L, et al. How do short sleepers use extra waking hours? A compositional analysis of 24-h time-use patterns among children and adolescents. Int J Behav Nutr Phys Act. 2020;17(1):104. https://doi.org/10.1186/s12966-020-01004-8.CrossRefPubMedPubMedCentral

23.

Migueles JH, Rowlands AV, Huber F, Sabia S, van Hees VT. 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(3):188–96. https://doi.org/10.1123/jmpb.2018-0063.CrossRef

24.

Rowlands AV. Moving forward with accelerometer-assessed physical activity: two strategies to ensure meaningful, interpretable, and comparable measures. Pediatr Exerc Sci. 2018;30(4):450–6. https://doi.org/10.1123/pes.2018-0201.CrossRefPubMed

25.

Migueles JH, Cadenas-Sanchez C, Ekelund U, Delisle Nystrom C, Mora-Gonzalez J, Lof M, et al. Accelerometer data collection and processing criteria to assess physical activity and other outcomes: a systematic review and practical considerations. Sports Med. 2017;47(9):1821–45. https://doi.org/10.1007/s40279-017-0716-0.CrossRefPubMedPubMedCentral

26.

Group WHOMGRS. WHO child growth standards based on length/height, weight and age. Acta Paediatr Suppl. 2006;450:76–85. https://doi.org/10.1111/j.1651-2227.2006.tb02378.x.CrossRef

27.

Lim JS, Hwang JS, Lee JA, Kim DH, Park KD, Jeong JS, et al. Cross-calibration of multi-frequency bioelectrical impedance analysis with eight-point tactile electrodes and dual-energy X-ray absorptiometry for assessment of body composition in healthy children aged 6-18 years. Pediatr Int. 2009;51(2):263–8. https://doi.org/10.1111/j.1442-200X.2008.02698.x.CrossRefPubMed

28.

Rubín L, Gába A, Dygrýn J, Jakubec L, Materová E, Vencálek O. Prevalence and correlates of adherence to the combined movement guidelines among Czech children and adolescents. BMC Public Health. 2020;20(1):1692. https://doi.org/10.1186/s12889-020-09802-2.CrossRefPubMedPubMedCentral

29.

Cohen J. A power primer. Psychological Bulletin. 1992;112(1):155–9. https://doi.org/10.1037/0033-2909.112.1.155.CrossRefPubMed

30.

Farooq A, Martin A, Janssen X, Wilson MG, Gibson AM, Hughes A, et al. Longitudinal changes in moderate-to-vigorous-intensity physical activity in children and adolescents: a systematic review and meta-analysis. Obes Rev. 2020;21(1):e12953. https://doi.org/10.1111/obr.12953.CrossRefPubMed

31.

Reilly JJ. When does it all go wrong? Longitudinal studies of changes in moderate-to-vigorous-intensity physical activity across childhood and adolescence. J Exerc Sci Fit. 2016;14(1):1–6. https://doi.org/10.1016/j.jesf.2016.05.002.CrossRefPubMedPubMedCentral

32.

Hildebrand M, VAN Hees VT, Hansen BH, Ekelund U. Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. Med Sci Sports Exerc. 2014;46(9):1816–24. https://doi.org/10.1249/MSS.0000000000000289.CrossRefPubMed

33.

Stamatakis E, Huang BH, Maher C, Thogersen-Ntoumani C, Stathi A, Dempsey PC, et al. Untapping the health enhancing potential of vigorous intermittent lifestyle physical activity (VILPA): rationale, scoping review, and a 4-Pillar research framework. Sports Med. 2021;51(1):1–10. https://doi.org/10.1007/s40279-020-01368-8.CrossRefPubMed

34.

Rowlands AV, Fairclough SJ, Yates T, Edwardson CL, Davies M, Munir F, et al. Activity intensity, volume, and norms: utility and interpretation of accelerometer metrics. Med Sci Sports Exerc. 2019;51(11):2410–22. https://doi.org/10.1249/MSS.0000000000002047.CrossRefPubMed

35.

Ensenyat A, Serra-Paya N, Sagarra-Romero L. Objectively measured sedentary behaviour in overweight and obese prepubertal children: challenging the school. Int J Environ Health Res. 2020;30(5):533–44. https://doi.org/10.1080/09603123.2019.1609656.CrossRefPubMed

36.

Jones MA, Skidmore PM, Stoner L, Harrex H, Saeedi P, Black K, et al. Associations of accelerometer-measured sedentary time, sedentary bouts, and physical activity with adiposity and fitness in children. J Sports Sci. 2020;38(1):114–20. https://doi.org/10.1080/02640414.2019.1685842.CrossRefPubMed

37.

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(1):16. https://doi.org/10.1186/s12199-021-00932-6.CrossRefPubMedPubMedCentral

38.

Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR Jr, Tudor-Locke C, et al. 2011 Compendium of physical activities: a second update of codes and MET values. Med Sci Sports Exerc. 2011;43(8):1575–81. https://doi.org/10.1249/MSS.0b013e31821ece12.CrossRefPubMed

39.

Buchan DS, McLellan G, Donnelly S, Arthur R. The use of the intensity gradient and average acceleration metrics to explore associations with BMI z-score in children. J Sports Sci. 2019;37(23):2751–8. https://doi.org/10.1080/02640414.2019.1664536.CrossRefPubMed

40.

Fairclough SJ, Rowlands AV, Taylor S, Boddy LM. Cut-point-free accelerometer metrics to assess children’s physical activity: an example using the school day. Scand J Med Sci Sports. 2020;30(1):117–25. https://doi.org/10.1111/sms.13565.CrossRefPubMed

Title: Associations of novel 24-h accelerometer-derived metrics with adiposity in children and adolescents
Authors: Jan Dygrýn
María Medrano
Pablo Molina-Garcia
Lukáš Rubín
Lukáš Jakubec
David Janda
Aleš Gába
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Obesity
Obesity
Published in: Environmental Health and Preventive Medicine / Issue 1/2021
Print ISSN: 1342-078X
Electronic ISSN: 1347-4715
DOI: https://doi.org/10.1186/s12199-021-00987-5

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Springer Medicine

Associations of novel 24-h accelerometer-derived metrics with adiposity in children and adolescents

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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