Skip to main content

Advertisement

SpringerLink
Log in

Association between sociodemographic, dietary, and substance use factors and accelerometer-measured 24-hour movement behaviours in Brazilian adolescents

  • Original Article
  • Published:
European Journal of Pediatrics Aims and scope Submit manuscript

Abstract

Sociodemographic factors and lifestyle behaviours were evidenced as correlates of self-reported 24-hour movement behaviours in high-income settings. However, it is unclear how these relations occur in a middle-income country setting, with unique cultural and social characteristics. This study aimed to examine the association between sociodemographic, dietary, and substance use factors with accelerometer-measured 24-hour movement behaviours in Brazilian adolescents. Information on sex, age, socioeconomic status (SES), family structure, dietary behaviours, and history of substance use were collected by a questionnaire. Sleep duration, sedentary behaviour, and light- and moderate-to-vigorous physical activity (LPA and MVPA) were measured using wrist-worn accelerometers. On average, females slept more (β = 21.09, 95%CI 13.18; 28.98), engaged in more LPA (β = 17.60, 95%CI 8.50; 27.13), and engaged in less sedentary behaviour (β = −16.82, 95%CI −30.01; −4.30) and MVPA (β = −4.76, 95%CI −7.48; −1.96) than males. Age and sedentary behaviour were positively associated (β = 8.60, 95%CI 2.53; 14.64). Unprocessed foods were positively related to LPA (β = 2.21, 95%CI 0.55; 3.92), whereas processed foods were positively related to sedentary behaviour (β = 3.73, 95%CI 0.03; 7.38) and inversely related to MVPA (β = −0.89, 95%CI −1.68; −0.10). Family structure, SES, and substance use factors were not significantly associated with any 24-hour movement behaviour.

Conclusions: Sex, age, and dietary behaviours, unlike SES or substance use, were associated with 24-hour movement behaviours in this sample of Brazilian adolescents and are important factors to consider in interventions, policies, and practice.

What is Known:

• The 24-hour movement behaviours are composed of sleep, sedentary behaviour, and physical activity and are important determinants of health.

• Most adolescents do not engage in adequate levels of physical activity, sedentary behaviour, and sleep, and there is a need to better understand factors related to these behaviours.

What is New:

• Sex, age, and dietary behaviours were associated with the 24-hour movement behaviours.

• No associations were found between socioeconomic status and substance use with the 24-hour movement behaviours.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Data availability

The participants’ individual data are not publicly available as the original approval by the Ethics Committee of Research with Human Beings of the Universidade Federal de Santa Catarina (protocol number: 3.168.745) and the informed consent from the participants and their respective legal guardians stated the individual information will not be shared.

Code availability

Not applicable

Abbreviations

ELEVA:

Estudo Longitudinal do Estilo de Vida de Adolescentes (Longitudinal Study of the Lifestyle of Adolescents)

LPA:

Light physical activity

MVPA:

Moderate-to-vigorous physical activity

PeNSE:

Pesquisa Nacional de Saúde do Escola (National School Health Survey)

SES:

Socioeconomic status

References

  1. Chaput J-P, Gray CE, Poitras VJ et al (2016) Systematic review of the relationships between sleep duration and health indicators in school-aged children and youth. Appl Physiol Nutr Metab Physiol Appl Nutr Metab 41:S266–S282. https://doi.org/10.1139/apnm-2015-0627

    Article  Google Scholar 

  2. Poitras VJ, Gray CE, Borghese MM et al (2016) Systematic review of the relationships between objectively measured physical activity and health indicators in school-aged children and youth. Appl Physiol Nutr Metab 41:S197–S239. https://doi.org/10.1139/apnm-2015-0663

    Article  PubMed  Google Scholar 

  3. Carson V, Hunter S, Kuzik N et al (2016) Systematic review of sedentary behaviour and health indicators in school-aged children and youth: an update. Appl Physiol Nutr Metab 41:S240–S265. https://doi.org/10.1139/apnm-2015-0630

    Article  PubMed  Google Scholar 

  4. da Costa BGG, Barreto PS, da Silveira PM et al (2020) The association between practicing sport and non-sport physical activities and health-related quality of life of Brazilian adolescents: a cross-sectional study. Sci Sports

  5. Guthold R, Stevens GA, Riley LM, Bull FC (2020) Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1·6 million participants. Lancet Child Adolesc Health 4:23–35. https://doi.org/10.1016/S2352-4642(19)30323-2

    Article  PubMed  PubMed Central  Google Scholar 

  6. LeBlanc AG, Katzmarzyk PT, Barreira TV et al (2015) Correlates of total sedentary time and screen time in 9-11 year-old children around the world: the international study of childhood obesity, lifestyle and the environment. PLoS One 10:e0129622. https://doi.org/10.1371/journal.pone.0129622

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Chattu VK, Manzar MD, Kumary S et al (2018) The global problem of insufficient sleep and its serious public health implications. Healthcare 7. https://doi.org/10.3390/healthcare7010001

  8. Janz KF, Burns TL, Levy SM (2005) Tracking of activity and sedentary behaviors in childhood: the Iowa Bone Development Study. Am J Prev Med 29. https://doi.org/10.1016/j.amepre.2005.06.001

  9. Boreham C, Robson PJ, Gallagher AM et al (2004) Tracking of physical activity, fitness, body composition and diet from adolescence to young adulthood: The Young Hearts Project, Northern Ireland. Int J Behav Nutr Phys Act 1:14. https://doi.org/10.1186/1479-5868-1-14

    Article  PubMed  PubMed Central  Google Scholar 

  10. Chaput J-P, Barnes JD, Tremblay MS et al (2018) Inequality in physical activity, sedentary behaviour, sleep duration and risk of obesity in children: a 12-country study. Obes Sci Pract 4:229–237. https://doi.org/10.1002/osp4.271

    Article  PubMed  Google Scholar 

  11. Bauman AE, Reis RS, Sallis JF et al (2012) Correlates of physical activity: why are some people physically active and others not? Lancet 380:258–271. https://doi.org/10.1016/s0140-6736(12)60735-1

    Article  PubMed  Google Scholar 

  12. Frank S, Gonzalez K, Lee-Ang L et al (2017) Diet and sleep physiology: public health and clinical implications. Front Neurol 8. https://doi.org/10.3389/fneur.2017.00393

  13. St-Onge M-P, Mikic A, Pietrolungo CE (2016) Effects of diet on sleep Quality12. Adv Nutr 7:938–949. https://doi.org/10.3945/an.116.012336

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hobbs M, Pearson N, Foster PJ, Biddle SJH (2015) Sedentary behaviour and diet across the lifespan: an updated systematic review. Br J Sports Med 49:1179–1188. https://doi.org/10.1136/bjsports-2014-093754

    Article  PubMed  Google Scholar 

  15. Richter A, Heidemann C, Schulze MB et al (2012) Dietary patterns of adolescents in Germany — associations with nutrient intake and other health related lifestyle characteristics. BMC Pediatr 12:35. https://doi.org/10.1186/1471-2431-12-35

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Thivel D, Tremblay MS, Katzmarzyk PT et al (2018) Associations between meeting combinations of 24-hour movement recommendations and dietary patterns of children: A 12-country study. Prev Med. https://doi.org/10.1016/j.ypmed.2018.10.025

  17. Sampasa-Kanyinga H, Chaput J-P (2016) Consumption of sugar-sweetened beverages and energy drinks and adherence to physical activity and screen time recommendations among adolescents. Int J Adolesc Med Health:29. https://doi.org/10.1515/ijamh-2015-0098

  18. Pearson N, Biddle SJH (2011) Sedentary behavior and dietary intake in children, adolescents, and adults. A systematic review. Am J Prev Med 41:178–188. https://doi.org/10.1016/j.amepre.2011.05.002

    Article  PubMed  Google Scholar 

  19. Fletcher EA, McNaughton SA, Crawford D et al (2018) Associations between sedentary behaviours and dietary intakes among adolescents. Public Health Nutr 21:1115–1122. https://doi.org/10.1017/S136898001700372X

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lowry R, Michael S, Demissie Z, et al (2015) Associations of physical activity and sedentary behaviors with dietary behaviors among US high school students. In: J. Obes. https://www.hindawi.com/journals/jobe/2015/876524/. Accessed 8 Jul 2020

  21. Sanchez ZM, Prado MCO, Sanudo A et al (2015) Trends in alcohol and tobacco use among Brazilian students: 1989 to 2010. Rev Saúde Pública 49. https://doi.org/10.1590/S0034-8910.2015049005860

  22. Pengpid S, Peltzer K (2019) Leisure-time sedentary behavior is associated with psychological distress and substance use among school-going adolescents in five Southeast Asian countries: a cross-sectional study. Int J Environ Res Public Health:16. https://doi.org/10.3390/ijerph16122091

  23. Terry-McElrath YM, Maslowsky J, O’Malley PM et al (2016) Sleep and substance use among US adolescents, 1991–2014. Am J Health Behav 40:77–91. https://doi.org/10.5993/AJHB.40.1.9

    Article  PubMed  PubMed Central  Google Scholar 

  24. Moore MJ, Werch C (2008) Relationship between vigorous exercise frequency and substance use among first-year drinking college students. J Am Coll Health J ACH 56:686–690. https://doi.org/10.3200/JACH.56.6.686-690

    Article  Google Scholar 

  25. Colrain IM, Nicholas CL, Baker FC (2014) Alcohol and the sleeping brain. Handb Clin Neurol 125:415–431. https://doi.org/10.1016/B978-0-444-62619-6.00024-0

    Article  PubMed  PubMed Central  Google Scholar 

  26. Brazil M of H of (2014) Dietary guidelines for the Brazilian population. Ministry of Health of Brazil Brasilia

    Google Scholar 

  27. Landmann-Szwarcwald C, Macinko J (2016) A panorama of health inequalities in Brazil. Int J Equity Health 15. https://doi.org/10.1186/s12939-016-0462-1

  28. Portes LH, Machado CV, Turci SRB et al (2018) Tobacco control policies in Brazil: a 30-year assessment. Ciênc Amp Saúde Coletiva 23:1837–1848. https://doi.org/10.1590/1413-81232018236.05202018

    Article  Google Scholar 

  29. Sasaki JE, da Silva KS, da Costa BGG, John D (2016) Measurement of physical activity using accelerometers. In: Computer-assisted and web-based innovations in psychology, special education, and health. Elsevier, pp 33–60

  30. Migueles JH, Cadenas-Sanchez C, Ekelund U et al (2017) Accelerometer data collection and processing criteria to assess physical activity and other outcomes: a systematic review and practical considerations. Sports Med Auckl NZ 47:1821–1845. https://doi.org/10.1007/s40279-017-0716-0

    Article  Google Scholar 

  31. van Hees VT, Renström F, Wright A et al (2011) Estimation of daily energy expenditure in pregnant and non-pregnant women using a wrist-worn tri-axial accelerometer. PLoS One 6:e22922. https://doi.org/10.1371/journal.pone.0022922

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Migueles JH, Rowlands AV, Huber F et al (2019) 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 2:188–196. https://doi.org/10.1123/jmpb.2018-0063

    Article  Google Scholar 

  33. Hildebrand M, VAN Hees VT, Hansen BH, Ekelund U (2014) Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. Med Sci Sports Exerc 46:1816–1824. https://doi.org/10.1249/MSS.0000000000000289

    Article  PubMed  Google Scholar 

  34. Hildebrand M, Hansen BH, van Hees VT, Ekelund U (2017) Evaluation of raw acceleration sedentary thresholds in children and adults. Scand J Med Sci Sports 27:1814–1823. https://doi.org/10.1111/sms.12795

    Article  PubMed  Google Scholar 

  35. van Hees VT, Sabia S, Jones SE et al (2018) Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep 8:1–11. https://doi.org/10.1038/s41598-018-31266-z

    Article  CAS  Google Scholar 

  36. Tavares LF, de Castro IRR, Levy RB et al (2014) Relative validity of dietary indicators from the Brazilian National School-Based Health Survey among adolescents in Rio de Janeiro, Brazil. Cad Saúde Pública 30:1029–1041. https://doi.org/10.1590/0102-311X00000413

    Article  PubMed  Google Scholar 

  37. Instituto Brasileiro de Geografia e Estatística (2016) Pesquisa nacional de saúde do escolar, 2015. IBGE, Rio de Janeiro

  38. Associação Brasileira de Empresas de Pesquisa (2019) Critério de classificação econômica Brasil 2019

  39. UNICEF (2016) Harnessing the power of data for girls: taking stock and looking ahead to 2030. N Y UNICEF Www Unicef OrggenderfilesHarnessingthe-Power--Data--Girls-Broch-2016-1-1 Pdf

  40. Mong JA, Cusmano DM (2016) Sex differences in sleep: impact of biological sex and sex steroids. Philos Trans R Soc B Biol Sci 371:20150110. https://doi.org/10.1098/rstb.2015.0110

    Article  CAS  Google Scholar 

  41. Dias PJP, Domingos IP, Ferreira MG et al (2014) Prevalence and factors associated with sedentary behavior in adolescents. Rev Saúde Pública 48:266–274. https://doi.org/10.1590/S0034-8910.2014048004635

    Article  PubMed  PubMed Central  Google Scholar 

  42. Felden ÉPG, Leite CR, Rebelatto CF et al (2015) Sleep in adolescents of different socioeconomic status: a systematic review. Rev Paul Pediatr 33:467–473. https://doi.org/10.1016/j.rpped.2015.01.011

    Article  PubMed  PubMed Central  Google Scholar 

  43. Sullivan SM, Broyles ST, Barreira TV et al (2017) Associations of neighborhood social environment attributes and physical activity among 9-11 year old children from 12 countries. Health Place 46:183–191. https://doi.org/10.1016/j.healthplace.2017.05.013

    Article  PubMed  Google Scholar 

  44. Mielke GI, Brown WJ, Nunes BP et al (2017) Socioeconomic correlates of sedentary behavior in adolescents: systematic review and meta-analysis. Sports Med 47:61–75. https://doi.org/10.1007/s40279-016-0555-4

    Article  PubMed  Google Scholar 

  45. Pearson N, Atkin AJ, Biddle SJ et al (2009) Patterns of adolescent physical activity and dietary behaviours. Int J Behav Nutr Phys Act 6:45. https://doi.org/10.1186/1479-5868-6-45

    Article  PubMed  PubMed Central  Google Scholar 

  46. Manz K, Mensink GBM, Finger JD et al (2019) Associations between physical activity and food intake among children and adolescents: results of KiGGS wave 2. Nutrients 11. https://doi.org/10.3390/nu11051060

  47. Dmitruk A, Kunicka I, Popławska H, Hołub W (2016) Relationship between diet and physical activity level in adolescents from post-grammar schools. Rocz Panstw Zakl Hig 67:37–44

    PubMed  Google Scholar 

  48. Leech RM, McNaughton SA, Timperio A (2014) The clustering of diet, physical activity and sedentary behavior in children and adolescents: a review. Int J Behav Nutr Phys Act 11:4. https://doi.org/10.1186/1479-5868-11-4

    Article  PubMed  PubMed Central  Google Scholar 

  49. da Costa BGG, Chaput J-P, Lopes MVV et al (2021) Associations between sociodemographic, dietary, and substance use factors with self-reported 24-hour movement behaviors in a sample of Brazilian adolescents. Int J Environ Res Public Health 18:2527. https://doi.org/10.3390/ijerph18052527

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the students for their voluntary participation in the study. In addition, we thank the teachers and staff of the participating schools, as well as the team of the Núcleo de Pesquisa em Atividade Física e Saúde of the Universidade Federal de Santa Catarina that supported the conduction of the ELEVA study.

Funding

The Brazilian National Council for Scientific and Technological Development provided funding for the ELEVA study (grant number 406258/2018-0). The Brazilian Coordination for the Improvement of Higher Education Personnel provided scholarships (BGGC, MVVL).

Author information

Authors and Affiliations

  1. Núcleo de Pesquisa em Atividade Física e Saúde, Universidade Federal de Santa Catarina, Florianópolis, SC, Brasil

    Bruno Gonçalves Galdino da Costa, Marcus Vinicius Veber Lopes & Kelly Samara Silva

  2. Healthy Active Living and Obesity Research Group, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada

    Jean-Philippe Chaput

  3. Projeto Esporte Brasil, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brasil

    Anelise Reis Gaya

  4. Núcleo de Pesquisa em Cineantropometria e Desempenho Humano, Universidade Federal de Santa Catarina, Florianópolis, SC, Brasil

    Diego Augusto Santos Silva

Authors
  1. Bruno Gonçalves Galdino da Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jean-Philippe Chaput
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcus Vinicius Veber Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anelise Reis Gaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Diego Augusto Santos Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kelly Samara Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: BGGC, JPC, KSS; Data curation: BGGC, MVVL; Formal Analysis: BGGC; Funding acquisition: BGGC, KSS; Investigation: BGGS, MVVL; Methodology: BGGC, KSS; Project administration: BGGC, KSS; Supervision: JPC, KSS; Writing — original draft: BGGC; Writing — Review & editing: JPC, MVVL, DASS, ARG, KSS.

Corresponding author

Correspondence to Bruno Gonçalves Galdino da Costa.

Ethics declarations

Ethics approval

The present research project was approved by the Ethics Committee in Research with Human Beings of the Universidade Federal de Santa Catarina (protocol number: 3.168.745). The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Consent to participate

All participants provided written informed consent signed by their legal guardians.

Consent for publication

Not applicable

Conflict of interest

The authors declare no competing interests.

Additional information

Communicated by Gregorio Paolo Milani

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(DOCX 13 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

da Costa, B.G.G., Chaput, JP., Lopes, M.V.V. et al. Association between sociodemographic, dietary, and substance use factors and accelerometer-measured 24-hour movement behaviours in Brazilian adolescents. Eur J Pediatr 180, 3297–3305 (2021). https://doi.org/10.1007/s00431-021-04112-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00431-021-04112-0

Keywords

Search

Navigation