Skip to main content

Advertisement

SpringerLink
Log in

Clustering of Physical Activity, Sleep, Diet, and Screen-Based Device Use Associated with Self-Rated Health in Adolescents

  • Full length manuscript
  • Published:
International Journal of Behavioral Medicine Aims and scope Submit manuscript

Abstract

Background

Little is known about how the interplay among health-related behaviors impacts self-rated health (SRH). We examined the clustering of physical activity (PA), sleep, diet, and specific screen-based device use, and the associations between the emergent clusters and SRH among Brazilian adolescents.

Method

The data used in this cross-sectional study were from the baseline of the Movimente Program. Self-reported data were analyzed. SRH was recorded as a 5-point scale (from poor to excellent). Daily duration of exposure to the computer, the television, the cell phone, and games; PA; sleep; and weekly consumption of fruits and vegetables and ultra-processed foods were included in a Two-Step cluster analysis. Multilevel ordered logistic regressions assessed the associations between the clusters and SRH.

Results

The data of 750 students (girls: 52.8%, 13.1 ± 1.0 years) were analyzed. Good SRH was more prevalent (52.8%). Three clusters were identified: the Phubbers (50.53%; characterized by the longest cell phone use duration, shortest gaming and computer use, lowest PA levels, and low consumption of fruits and vegetables), the Gamers (22.80%; longest gaming and computer use duration, PA < sample average, highest intake of ultra-processed foods), and a Healthier cluster (26.67%; physically active, use of all screen-based devices < sample average, and healthier dietary patterns). For both Gamers (−0.85; 95% CI −1.24, −0.46) and Phubbers (−0.71; 95% CI −1.04, −0.38), it was found a decrease in the log-odds of being in a higher SRH category compared with the Healthier cluster.

Conclusion

Specific clusters represent increased health-related risk. Assuming the interdependence of health-related behaviors is indispensable for accurately managing health promotion actions for distinguishable groups.

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

References

  1. Garbarski D. Research synthesis research in and prospects for the measurement of health using self-rated health. Public Opin Q. 2016;80:977–97.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Sirois FM. Who looks forward to better health? Personality factors and future self-rated health in the context of chronic illness. Int J Behav Med. 2015;22:569–79.

    Article  PubMed  Google Scholar 

  3. Hetlevik Ø, Vie TL, Meland E, Breidablik HJ, Jahanlu D. Adolescent self-rated health predicts general practice attendance in adulthood: results from the Young-HUNT1 survey. Scand J Public Health. 2019;47:37–44.

    Article  PubMed  Google Scholar 

  4. Vie TL, Hufthammer KO, Meland E, Breidablik HJ. Self-rated health (SRH) in young people and causes of death and mortality in young adulthood. A prospective registry-based Norwegian HUNT-study. SSM - Popul Heal. 2019;7:100364.

  5. Vie TL, Hufthammer KO, Holmen TL, Meland E, Breidablik HJ. Is self-rated health a stable and predictive factor for allostatic load in early adulthood? Findings from the Nord Trøndelag Health Study (HUNT). Soc Sci Med. 2014;117:1–9.

    Article  PubMed  Google Scholar 

  6. Breidablik H-J, Meland E, Lydersen S. Self-rated health in adolescence: a multifactorial composite. Scand J Public Health. 2008;36:12–20.

    Article  PubMed  Google Scholar 

  7. Meireles AL, Xavier CC, de Souza Andrade AC, Proietti FA, Caiaffa WT. Self-rated health among urban adolescents: the roles of age, gender, and their associated factors. Clark JL, editor. PLoS One. 2015;10:e0132254.

  8. Herman KM, Hopman WM, Sabiston CM. Physical activity, screen time and self-rated health and mental health in Canadian adolescents. Prev Med (Baltim). 2015;73:112–6.

    Article  Google Scholar 

  9. Werneck AO, Silva DR, Agostinete RR, Fernandes RA, Ronque ERV, Oyeyemi AL, et al. Relationship of parental and adolescents’ screen time to self-rated health: a structural equation modeling. Heal Educ Behav. 2018;45:764–71.

    Article  Google Scholar 

  10. Craig BA, Morton DP, Morey PJ, Kent LM, Gane AB, Butler TL, et al. The association between self-rated health and social environments, health behaviors and health outcomes: a structural equation analysis. BMC Public Health. 2018;18:440.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Nuutinen T, Lehto E, Ray C, Roos E, Villberg J, Tynjälä J. Clustering of energy balance-related behaviours, sleep, and overweight among Finnish adolescents. Int J Public Health. 2017;62:929–38.

    Article  PubMed  Google Scholar 

  12. Matias TS, Silva KS, Da SJA, De MGT, Salmon J. Clustering of diet, physical activity and sedentary behavior among Brazilian adolescents in the national school-based health survey (PeNSE 2015). BMC Public Health BMC Public Health. 2018;18:1–9.

    Google Scholar 

  13. El Ansari W, Ssewanyana D, Stock C. Behavioral health risk profiles of undergraduate university students in England, Wales, and Northern Ireland: a cluster analysis. Front Public Heal. 2018;6:1–8.

    Google Scholar 

  14. Spengler S, Mess F, Mewes N, Mensink GB, Woll A. A cluster-analytic approach towards multidimensional health-related behaviors in adolescents: the MoMo-Study. BMC Public Health. 2012;12:1128.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Mandic S, Bengoechea EG, Coppell K, Spence J. Clustering of (un)healthy behaviors in adolescents from Dunedin. New Zealand Am J Health Behav. 2017;41:266–75.

    Article  PubMed  Google Scholar 

  16. Parker KE, Salmon J, Costigan SA, Villanueva K, Brown HL, Timperio A. Activity-related behavior typologies in youth: a systematic review. Int J Behav Nutr Phys Act. 2019;16:44.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Matias TS, Lopes MVV, de Mello GT, Silva KS. Clustering of obesogenic behaviors and association with body image among Brazilian adolescents in the national school-based health survey (PeNSE 2015). Prev Med Reports. 2019;16:101000.

  18. Dumuid D, Olds T, Lewis LK, Martin-Fernández JA, Katzmarzyk PT, Barreira T, et al. Health-related quality of life and lifestyle behavior clusters in school-aged children from 12 countries. J Pediatr Elsevier Inc. 2017;183:178–183.e2.

  19. Zhang J, Tang B, Liu M, Yuan S, Yu H, Zhang R, et al. Association of adverse childhood experiences with health risk behaviors among college students in Zambia. Int J Behav Med. 2020;27:400–5.

    Article  PubMed  Google Scholar 

  20. Costa BGG, Salmon J, Santos PC, Minatto G, Silva KS. Clustering of screen time behaviours in adolescents and its association with waist circumference and cardiorespiratory fitness. Sports Medicine Australia. J Sci Med Sport. 2020;23:487–92.

  21. Silva KS, Silva da JA, Barbosa Filho VC, Santos dos PC, Silveira da PM, Lopes MVV, et al. Protocol paper for the Movimente school-based program. Medicine (Baltimore). 2020;99:e21233.

  22. Jürges H, Avendano M, Mackenbach JP. Are different measures of self-rated health comparable? An assessment in five European countries. Eur J Epidemiol. 2008;23:773–81.

    Article  PubMed  Google Scholar 

  23. Tavares LF, Castro IR, Levy RB, Cardoso LO, Passos MD, Brito FD. Relative validity of dietary indicators from the Brazilian National School-Based Health Survey among adolescents in Rio de Janeiro, Brazil. Cadernos de Saúde Pública. 2014;30:1029-41.

  24. da Costa BG, Chaput JP, Lopes MV, da Costa RM, Malheiros LE, Silva KS. Association between lifestyle behaviors and health-related quality of life in a sample of Brazilian adolescents. Int J Environ Res Public Health. 2020;17(19):7133.

  25. Farias Júnior JC, Lopes AD, Mota J, Santos MP, Ribeiro JC, Hallal PC. Validity and reproducibility of a physical activity questionnaire for adolescents: adapting the Self-Administered Physical Activity Checklist. Rev Bras Epidemiol. 2012;15(1):198-210.

  26. Collese TS, De Moraes ACF, Fernández-Alvira JM, Michels N, De Henauw S, Manios Y, et al. How do energy balance-related behaviors cluster in adolescents? Int J Public Health. 2019;64:195–208.

    Article  PubMed  Google Scholar 

  27. Leech RM, McNaughton SA, Timperio A. Clustering of diet, physical activity and sedentary behaviour among Australian children: cross-sectional and longitudinal associations with overweight and obesity. Int J Obes (Lond). 2015;39:1079–85.

    Article  CAS  Google Scholar 

  28. Ssewanyana D, Abubakar A, Newton CR, Otiende M, Mochamah G, Nyundo C, Walumbe D, Nyutu G, Amadi D, Doyle AM, Ross DA. Clustering of health risk behaviors among adolescents in Kilifi, Kenya, a rural Sub-Saharan African setting. PloS one. 2020;15(11):e0242186.

  29. Leech RM, McNaughton SA, Timperio A. The clustering of diet, physical activity and sedentary behavior in children and adolescents: a review. Int J Behav Nutr Phys Act. 2014;11:4.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Weaver E, Gradisar M, Dohnt H, Lovato N, Douglas P. The effect of presleep video-game playing on adolescent sleep. J Clin Sleep Med. 2010;6:184–9.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Chaput JP, Visby T, Nyby S, Klingenberg L, Gregersen NT, Tremblay A, et al. Video game playing increases food intake in adolescents: a randomized crossover study. Am J Clin Nutr. 2011.

  32. Delfino LD, Dos Santos Silva DA, Tebar WR, Zanuto EF, Codogno JS, Fernandes RA, et al. Screen time by different devices in adolescents: association with physical inactivity domains and eating habits. J Sports Med Phys Fitness. 2018;58:318–25.

    Article  PubMed  Google Scholar 

  33. Huh J, Riggs NR, Spruijt-Metz D, Chou C-P, Huang Z, Pentz M. Identifying patterns of eating and physical activity in children: a latent class analysis of obesity risk. Nature Publishing Group. Obesity (Silver Spring). 2011;19:652–8.

  34. Uncapher MR, Wagner AD. Minds and brains of media multitaskers: current findings and future directions. Proc Natl Acad Sci. 2018;115:9889–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Mesch GS. Parental mediation, online activities, and cyberbullying. CyberPsychology Behav. 2009;12:387–93.

    Article  Google Scholar 

  36. Cain MS, Leonard JA, Gabrieli JDE, Finn AS. Media multitasking in adolescence. Psychon Bull Rev. 2016;23:1932–41.

    Article  PubMed  Google Scholar 

  37. Gorely T, Marshall SJ, Biddle SJH, Cameron N. Patterns of sedentary behaviour and physical activity among adolescents in the United Kingdom: project STIL. J Behav Med. 2007;30:521–31.

    Article  PubMed  Google Scholar 

  38. Decelis A, Jago R, Fox KR. Objectively assessed physical activity and weight status in Maltese 11–12 year-olds. Eur J Sport Sci. 2014;14:S257–66.

    Article  PubMed  Google Scholar 

  39. Seghers J, Rutten C. Clustering of multiple lifestyle behaviours and its relationship with weight status and cardiorespiratory fitness in a sample of Flemish 11- to 12-year-olds. Public Health Nutr. 2010;13:1838–46.

    Article  PubMed  Google Scholar 

  40. Pereira S, Borges A, Gomes TN, Santos D, Souza M, dos Santos FK, et al. Correlates of children’s compliance with moderate-to-vigorous physical activity recommendations: a multilevel analysis. Scand J Med Sci Sports. 2017;27:842–51.

    Article  CAS  PubMed  Google Scholar 

  41. Abi-Jaoude E, Naylor KT, Pignatiello A. Smartphones, social media use and youth mental health. Can Med Assoc J. 2020;192:E136–41.

    Article  Google Scholar 

  42. Busch V, Van Stel HF, Schrijvers AJ, De Leeuw JR. Clustering of health-related behaviors, health outcomes and demographics in Dutch adolescents: a cross-sectional study. BMC Public Health. 2013;13.

  43. Council on Communications and Media. Children, adolescents, and the media. Pediatrics. 2013;132:958–61.

    Article  Google Scholar 

  44. Zagalaz-Sánchez ML, Cachón-Zagalaz J, Sánchez-Zafra M, Lara-Sánchez A. Mini Review of the use of the mobile phone and its repercussion in the deficit of physical activity. Front Psychol. 2019;10.

  45. Best P, Manktelow R, Taylor B. Online communication, social media and adolescent wellbeing: a systematic narrative review. Child Youth Serv Rev. 2014;41:27–36.

    Article  Google Scholar 

  46. Iannotti RJ, Janssen I, Haug E, Kololo H, Annaheim B, Borraccino A. Interrelationships of adolescent physical activity, screen-based sedentary behaviour, and social and psychological health. Int J Public Health. 2009;54:191–8.

    Article  PubMed  Google Scholar 

  47. Yeo SC, Jos AM, Erwin C, Lee SM, Lee XK, Lo JC, et al. Associations of sleep duration on school nights with self-rated health, overweight, and depression symptoms in adolescents: problems and possible solutions. Sleep Med. 2019;60:96–108.

    Article  PubMed  Google Scholar 

  48. Keats E, Rappaport A, Shah S, Oh C, Jain R, Bhutta Z. The dietary intake and practices of adolescent girls in low- and middle-income countries: a systematic review. Nutrients. 2018;10:1978.

    Article  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank the Federal University of Santa Catarina for the technical support and the Movimente Program working group. The authors also thank Editage (www.editage.com) for English language editing.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil (The National Council for Scientific and Technological Development, Brazil), grant: CNPq/Edital Universal 14/2014, process number: 446227/2014–5 and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil (The Coordination for the Improvement of Higher Education Personnel, Brazil) provided scholarship grants. The funders had no role in the design, conduction, data collection, analysis, and interpretation of the results, nor in the preparation, writing, review, or approval of the manuscript.

Author information

Authors and Affiliations

  1. School of Sports, Research Centre in Physical Activity and Health, Federal University of Santa Catarina, Florianópolis, SC, 88040-900, Brazil

    Margarethe Thaisi Garro Knebel, Thiago Sousa Matias, Marcus Vinicius Veber Lopes, Priscila Cristina dos Santos, Alexsandra da Silva Bandeira & Kelly Samara da Silva

Authors
  1. Margarethe Thaisi Garro Knebel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thiago Sousa Matias
    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. Priscila Cristina dos Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexsandra da Silva Bandeira
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kelly Samara da Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Margarethe Thaisi Garro Knebel, Thiago Sousa Matias, and Marcus Vinicius Veber Lopes participated in the conception and design of the study, contributed to data collection, analysis, and interpretation of results; Priscila Cristina dos Santos and Alexsandra da Silva Bandeira participated in the conception and design of the study, contributed to data collection, and interpretation of results; and Kelly Samara da Silva participated in the conception and design of the study. All authors contributed to the manuscript writing and reviewing. All authors have read and approved the final version of the manuscript and agree on the order in which their names are listed in the manuscript.

Corresponding author

Correspondence to Margarethe Thaisi Garro Knebel.

Ethics declarations

Ethical Approval

All procedures involving human participants were approved by the Federal University of Santa Catarina Ethics Committee (Certificate: CAAE 49462015.0.0000.0121. Protocol number: 1.807.825 on November 7, 2016). All participants were protected by the ethical principles of the Resolution number 466/2012 from the Brazilian Health Council, which is in accordance with the ethical standards of the Declaration of Helsinki. This article does not contain any studies with animals.

Informed Consent

All adolescents and their parents/legal guardians approved the study protocols and provided written consent forms.

Protocol Registration

Movimente Program (ClinicalTrials.gov Identifier: NCT02944318).

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 49 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Knebel, M.T.G., Matias, T.S., Lopes, M.V.V. et al. Clustering of Physical Activity, Sleep, Diet, and Screen-Based Device Use Associated with Self-Rated Health in Adolescents. Int.J. Behav. Med. 29, 587–596 (2022). https://doi.org/10.1007/s12529-021-10043-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12529-021-10043-9

Keywords

Search

Navigation