IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i17p3151-d262000.html
   My bibliography  Save this article

Healthy or Unhealthy? The Cocktail of Health-Related Behavior Profiles in Spanish Adolescents

Author

Listed:
  • Javier Sevil-Serrano

    (Faculty of Health and Sport Sciences, Department of Didactics of the Musical, Plastic and Corporal Expression, University of Zaragoza, 22001 Huesca, Spain)

  • Alberto Aibar-Solana

    (Faculty of Social Sciences and Humanities, Department of Didactics of the Musical, Plastic and Corporal Expression, University of Zaragoza, 22003 Huesca, Spain)

  • Ángel Abós

    (Faculty of Health and Sport Sciences, Department of Didactics of the Musical, Plastic and Corporal Expression, University of Zaragoza, 22001 Huesca, Spain)

  • José Antonio Julián

    (Faculty of Social Sciences and Humanities, Department of Didactics of the Musical, Plastic and Corporal Expression, University of Zaragoza, 22003 Huesca, Spain)

  • Luis García-González

    (Faculty of Health and Sport Sciences, Department of Didactics of the Musical, Plastic and Corporal Expression, University of Zaragoza, 22001 Huesca, Spain)

Abstract

The aim of this study was to identify the prevalence and clustering of health-related behaviors in Spanish adolescents and to examine their association with sex, body mass index (BMI), different types of sedentary screen time, and adherence to 24-hour movement guidelines. A final sample of 173 students (M = 12.99 ± 0.51) participated in this study. Cluster analysis was conducted based on five health-related behaviors: PA and sedentary time derived from accelerometers, as well as healthy diet, sedentary screen time, and sleep duration derived from self-reported scales. Recommendations for 24-hour movement guidelines (i.e., physical activity (PA), screen time, and sleep duration) were analyzed both independently and combined. A total of 8.9% of the sample did not meet any of the guidelines, whereas 72.3%, 17.3%, and 1.7% of the sample met 1, 2, or all 3 guidelines, respectively. Six distinct profiles were identified, most of them showing the co-occurrence of healthy- and unhealthy-related behaviors. Given that most of the adolescents failed to meet the combination of PA, screen time, and sleep duration guidelines, these findings suggest the necessity to implement school-based interventions that target multiple health behaviors, especially because (un)healthy behaviors do not always cluster in the same direction.

Suggested Citation

  • Javier Sevil-Serrano & Alberto Aibar-Solana & Ángel Abós & José Antonio Julián & Luis García-González, 2019. "Healthy or Unhealthy? The Cocktail of Health-Related Behavior Profiles in Spanish Adolescents," IJERPH, MDPI, vol. 16(17), pages 1-14, August.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:17:p:3151-:d:262000
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/17/3151/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/17/3151/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Valerie Carson & Guy Faulkner & Catherine Sabiston & Mark Tremblay & Scott Leatherdale, 2015. "Patterns of movement behaviors and their association with overweight and obesity in youth," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 60(5), pages 551-559, July.
    2. O'Neil, A. & Quirk, S.E. & Housden, S. & Brennan, S.L. & Williams, L.J. & Pasco, J.A. & Berk, M. & Jacka, F.N., 2014. "Relationship between diet and mental health in children and adolescents: A systematic review," American Journal of Public Health, American Public Health Association, vol. 104(10), pages 31-42.
    3. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    4. Javier Sevil & Luis García-González & Ángel Abós & Eduardo Generelo Lanaspa & Alberto Aibar Solana, 2018. "Which School Community Agents Influence Adolescents’ Motivational Outcomes and Physical Activity? Are More Autonomy-Supportive Relationships Necessarily Better?," IJERPH, MDPI, vol. 15(9), pages 1-21, August.
    5. Teija Nuutinen & Elviira Lehto & Carola Ray & Eva Roos & Jari Villberg & Jorma Tynjälä, 2017. "Clustering of energy balance-related behaviours, sleep, and overweight among Finnish adolescents," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 62(8), pages 929-938, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lukáš Jakubec & Karel Frömel & František Chmelík & Dorota Groffik, 2020. "Physical Activity in 15–17-Year-Old Adolescents as Compensation for Sedentary Behavior in School," IJERPH, MDPI, vol. 17(9), pages 1-14, May.
    2. David Manzano-Sánchez & María Victoria Palop-Montoro & Milagros Arteaga-Checa & Alfonso Valero-Valenzuela, 2022. "Analysis of Adolescent Physical Activity Levels and Their Relationship with Body Image and Nutritional Habits," IJERPH, MDPI, vol. 19(5), pages 1-14, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Enza Mozzillo & Eugenio Zito & Valeria Calcaterra & Nicola Corciulo & Mario Di Pietro & Anna Di Sessa & Roberto Franceschi & Maria Rosaria Licenziati & Giulio Maltoni & Giuseppe Morino & Barbara Predi, 2021. "Poor Health Related Quality of Life and Unhealthy Lifestyle Habits in Weight-Loss Treatment-Seeking Youth," IJERPH, MDPI, vol. 18(17), pages 1-14, September.
    2. Liu, Pei-chen Barry & Hansen, Mark & Mukherjee, Avijit, 2008. "Scenario-based air traffic flow management: From theory to practice," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 685-702, August.
    3. Hélène Syed Zwick & S. Ali Shah Syed, 2017. "The polarization impact of the crisis on the Eurozone labour markets: a hierarchical cluster analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 24(7), pages 472-476, April.
    4. Goethner, Maximilian & Hornuf, Lars & Regner, Tobias, 2021. "Protecting investors in equity crowdfunding: An empirical analysis of the small investor protection act," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    5. Pennings, J.S.J. & van Kranenburg, H.L. & Hagedoorn, J., 2005. "Past, present and future of the telecommunications industry," Research Memorandum 016, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    6. Li-Xuan Qin & Steven G. Self, 2006. "The Clustering of Regression Models Method with Applications in Gene Expression Data," Biometrics, The International Biometric Society, vol. 62(2), pages 526-533, June.
    7. Caroline Méjean & Pauline Macouillard & Sandrine Péneau & Camille Lassale & Serge Hercberg & Katia Castetbon, 2014. "Association of Perception of Front-of-Pack Labels with Dietary, Lifestyle and Health Characteristics," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    8. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    9. Marin, Giovanni & Vona, Francesco, 2019. "Climate policies and skill-biased employment dynamics: Evidence from EU countries," Journal of Environmental Economics and Management, Elsevier, vol. 98(C).
    10. Luis García-González & Ángel Abós & Sergio Diloy-Peña & Alexander Gil-Arias & Javier Sevil-Serrano, 2020. "Can a Hybrid Sport Education/Teaching Games for Understanding Volleyball Unit Be More Effective in Less Motivated Students? An Examination into a Set of Motivation-Related Variables," Sustainability, MDPI, vol. 12(15), pages 1-16, July.
    11. Hyeri Choi & Min Jae Park, 2019. "Evaluating the Efficiency of Governmental Excellence for Social Progress: Focusing on Low- and Lower-Middle-Income Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 111-130, January.
    12. Alessandra Cepparulo & Antonello Zanfei, 2019. "The diffusion of public eServices in European cities," Working Papers 1904, University of Urbino Carlo Bo, Department of Economics, Society & Politics - Scientific Committee - L. Stefanini & G. Travaglini, revised 2019.
    13. Ana Helena Tavares & Jakob Raymaekers & Peter J. Rousseeuw & Paula Brito & Vera Afreixo, 2020. "Clustering genomic words in human DNA using peaks and trends of distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 57-76, March.
    14. Noelia Caceres & Luis M. Romero & Francisco J. Morales & Antonio Reyes & Francisco G. Benitez, 2018. "Estimating traffic volumes on intercity road locations using roadway attributes, socioeconomic features and other work-related activity characteristics," Transportation, Springer, vol. 45(5), pages 1449-1473, September.
    15. Michele Cincera, 2005. "Firms' productivity growth and R&D spillovers: An analysis of alternative technological proximity measures," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 14(8), pages 657-682.
    16. André Lucas & Julia Schaumburg & Bernd Schwaab, 2019. "Bank Business Models at Zero Interest Rates," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 542-555, July.
    17. Wang, Ketong & Porter, Michael D., 2018. "Optimal Bayesian clustering using non-negative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 395-411.
    18. Douglas L. Steinley & M. J. Brusco, 2019. "Using an Iterative Reallocation Partitioning Algorithm to Verify Test Multidimensionality," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 397-413, October.
    19. Lin Chang-Ching & Ng Serena, 2012. "Estimation of Panel Data Models with Parameter Heterogeneity when Group Membership is Unknown," Journal of Econometric Methods, De Gruyter, vol. 1(1), pages 1-14, August.
    20. Rong, Rong & Houser, Daniel, 2015. "Growing stars: A laboratory analysis of network formation," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 380-394.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:16:y:2019:i:17:p:3151-:d:262000. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.