Skip to main content

Advertisement

SpringerLink
Log in

A person-centred analysis of the time-use, daily activities and health-related quality of life of Irish school-going late adolescents

  • Published:
Quality of Life Research Aims and scope Submit manuscript

Abstract

Purpose

The health, well-being and quality of life of the world’s 1.2 billion adolescents are global priorities. A focus on their patterns or profiles of time-use and how these relate to health-related quality of life (HRQoL) may help to enhance their well-being and address the increasing burden of non-communicable diseases in adulthood. This study sought to establish whether distinct profiles of adolescent 24-h time-use exist and to examine the relationship of any identified profiles to self-reported HRQoL.

Method

This cross-sectional study gathered data from a random sample of 731 adolescents (response rate 52 %) from 28 schools (response rate 76 %) across Cork city and county. A person-centred approach, latent profile analysis, was used to examine adolescent 24-h time-use and relate the identified profiles to HRQoL.

Results

Three male profiles emerged, namely productive, high leisure and all-rounder. Two female profiles, higher study/lower leisure and moderate study/higher leisure, were identified. The quantitative and qualitative differences in male and female profiles support the gendered nature of adolescent time-use. No unifying trends emerged in the analysis of probable responses in the HRQoL domains across profiles. Females in the moderate study/higher leisure group were twice as likely to have above-average global HRQoL.

Conclusion

Distinct time-use profiles can be identified amongst adolescents, but their relationship with HRQoL is complex. Rich mixed-method research is required to illuminate our understanding of how quantities and qualities of time-use shape lifestyle patterns and how these can enhance the HRQoL of adolescents in the twenty-first century.

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
Fig. 2

Similar content being viewed by others

Notes

  1. The Irish second-level school system comprises a 3-year junior cycle and a 3-year senior cycle. The first year of senior cycle is typically referred to as Transition Year, while the second year of senior cycle is referred to as Fifth Year. Sixth (final)-year students were not included as informal consultation with school principals had indicated that accessing this cohort in their final State examination year would be problematic.

References

  1. World Health Organisation (2014). Health for the world’s adolescents. World Health Organisation. http://www.who.int/maternal_child_adolescent/topics/adolescence/second-decade/en/. Accessed 28 June 2014.

  2. Sawyer, S., Afifi, R., Bearinger, L., Blakemore, S.-J., Dick, B., Ezeh, A., et al. (2012). Adolescence: A foundation for future health. The Lancet, 379, 1630–1640.

    Article  Google Scholar 

  3. United Nations Children’s Fund/UNICEF (2014). The state of the world’s children 2014—every child counts. UNICEF. http://www.unicef.org/sowc2014/numbers/. Accessed 28 June 2014.

  4. Gore, F. M., Bloem, G. C., Patton, G. C., Ferguson, J., Joseph, V., Coffey, C., et al. (2011). Global burden of disease in young people aged 10–24 years: A systematic analysis. Lancet, 377(9783), 2093–2102.

    Article  PubMed  Google Scholar 

  5. Zuzanek, J. (2005). Adolescent time-use and well-being from a comparative perspective. Society and Leisure, 28(2), 379–423.

    Google Scholar 

  6. Olds, T., Ferrar, K. E., Gomersall, S. R., Maher, C., & Walters, J. L. (2012). The elasticity of time: Associations between physical activity and use of time in adolescents. Health Education and Behaviour, 39(6), 732–736.

    Article  Google Scholar 

  7. Harvey, A. S. (1993). Quality of life measurement and the use of time theory and measurement. Journal of Occupational Science: Australia, 1(2), 27–30.

    Article  Google Scholar 

  8. Hocking, C. (2013). Occupation for public health. New Zealand Journal of Occupational Therapy, 60(1), 33–37.

    Google Scholar 

  9. Erlandsson, L.-K. (2013). Fresh perspectives on occupation: Creating health in everyday patterns of doing. New Zealand Journal of Occupational Therapy, 60(1), 16–23.

    Google Scholar 

  10. Gershuny, J., & Fisher, K. (2014). Multinational time use study. In A. C. Michalos (Ed.), Encyclopedia of quality of life and well-being research (pp. 4197–4202). New York, USA: Springer Science + Business Media.

    Chapter  Google Scholar 

  11. United Nations Economic Commission for Europe (2013). Guidelines for harmonising time-use surveys. Luxembourg: United Nations Economic Commission for Europe. http://www.unece.org/publications/time_use_surveys.html. Accessed 28 June 2014.

  12. Gershuny, J. (2011). Time-use surveys and the measurement of national well-being. http://www.ons.gov.uk/ons/rel/environmental/time-use-surveys-and-the-measurement-of-national-well-being/article-by-jonathan-gershuny/index.html. Accessed 28 June 2014.

  13. National Research Council (2012). The subjective well-being module of the American time use survey: Assessment for its continuation. Panel on measuring subjective well-being in a policy-relevant framework. Committee on National Statistics, Division of Behavioural and Social Sciences and Education. Washington, DC: The National Academies Press.

  14. Ben-Arieh, A., & Ofir, A. (2002). Time for (more) time-use studies: Studying the daily activities of children. Childhood, 9, 225–248.

    Article  Google Scholar 

  15. Hagell, A., Peck, S. C., Zarrett, N., Gimenez-Nadal, J. I., & Symonds, J. (2012). Trends in adolescent time-use in the United Kingdom. In A. Hagell (Ed.), Changing adolescence: Social trends and mental health (pp. 47–73). Bristol: The Policy Press.

    Chapter  Google Scholar 

  16. Leech, R. M., McNaughton, S. A., & Timperio, A. (2014). The clustering of diet, physical activity and sedentary behaviour in children and adolescents: A review. International Journal of Behavioural Nutrition and Physical Activity,. doi:10.1186/1479-5868-11-4.

    Google Scholar 

  17. Michelson, W. (2005). Time use—expanding the explanatory power of the social sciences. London: Paradigm Publishers.

    Google Scholar 

  18. Mekary, R. A., Willett, W. C., Hu, F. B., & Ding, E. L. (2009). Isotemporal substitution paradigm for physical activity epidemiology and weight. American Journal of Epidemiology, 170(4), 519–527.

    Article  PubMed Central  PubMed  Google Scholar 

  19. Larson, R. W., & Verma, S. (1999). How children and adolescents spend time across the world: Work, play and developmental opportunities. Psychological Bulletin, 125, 701–736.

    Article  CAS  PubMed  Google Scholar 

  20. American Academy of Pediatrics. (2011). Policy statement—children, adolescents, obesity, and the media. Pediatrics, 128, 201–208.

    Article  Google Scholar 

  21. Vandewater, E. A., Shim, M. S., & Caplovitz, G. A. (2004). Linking obesity and activity level with children’s television and video game use. Journal of Adolescence, 27, 71–85.

    Article  PubMed  Google Scholar 

  22. Ferrar, K., Chang, C., Li, M., & Olds, T. (2013). Adolescent time-use clusters: A systematic review. Journal of Adolescent Health, 52(3), 259–270.

    Article  PubMed  Google Scholar 

  23. Clark, F. (1997). Reflections on the human as an occupational being: Biological need, tempo and temporality. Journal of Occupational Science, 4(3), 86–92.

    Article  Google Scholar 

  24. Christiansen, C. H., & Townsend, E. A. (2010). An introduction to occupation. In C. H. Christiansen & E. A. Townsend (Eds.), Introduction to occupation—the art and science of living (pp. 1–34). Upper Saddle River, NJ: Pearson.

    Google Scholar 

  25. Busch, V., Van Steel, H. F., Schrijvers, A. J. P., & de Leeuw, J. R. J. (2013). Clustering of health-related behaviours, health outcomes and demographics in Dutch adolescents: A cross-sectional study. BMC Public Health, 13, 1118. doi:10.1186/1471-2458-13-1118.

    Article  PubMed Central  PubMed  Google Scholar 

  26. Ottovaere, C., Huybrechts, I., Benser, J., De Bourdeaudhuij, I., Cuenca-Garcia, M., & Dallongeville, J. (2011). Clustering patterns of physical activity, sedentary and dietary behaviour among European adolescents: The HELENA study. BMC Public Health, 11, 328. doi:10.1186/1471-2458-11-328.

    Article  Google Scholar 

  27. Sattoe, J. N., Hilberink, S. R., van Staa, A., & Bal, R. (2014). Lagging behind or not? Four distinctive social participation patterns among young adults with chronic conditions. Journal of Adolescent Health, 54, 397–403.

    Article  PubMed  Google Scholar 

  28. Zeiders, K. H., Roosa, M. W., Knight, G. P., & Gonzales, N. A. (2013). Mexican American adolescents’ profiles of risk and mental health: A person-centered longitudinal approach. Journal of Adolescence, 36, 603–612.

    Article  PubMed Central  PubMed  Google Scholar 

  29. Nelson, M. C., Gordon-Larson, P., Adair, L. S., & Popkin, B. M. (2005). Adolescent physical activity and sedentary behaviour: Patterning and long-term maintenance. American Journal of Preventive Medicine, 28(3), 259–265.

    Article  PubMed  Google Scholar 

  30. von Eye, A., Bogat, G. A., & Rhodes, J. E. (2006). Variable-oriented and person-oriented perspectives of analysis: The example of alcohol consumption in adolescence. Journal of Adolescence, 29, 981–1004.

    Article  Google Scholar 

  31. Zarrett, N., Fay, K., Li, Y., Carrano, J., Phelps, E., & Lerner, R. M. (2009). More than child’s play: Variable- and pattern-centred approaches for examining effects of sports participation on youth development. Developmental Psychology, 45(2), 368–382.

    Article  PubMed  Google Scholar 

  32. Zarrett, N., & Bell, B. A. (2014). The effects of out-of-school time on changes in youth risk of obesity across the adolescent years. Journal of Adolescence, 37(1), 85–96.

    Article  PubMed  Google Scholar 

  33. Regan, A., & Heary, C. (2013). Patterns of sedentary behaviours in Irish female adolescents. Journal of Adolescence, 36, 269–278.

    Article  PubMed  Google Scholar 

  34. Bergman, L. R. (2001). A person approach in research on adolescence: Some methodological challenges. Journal of Adolescent Research, 16, 28. doi:10.1177/0743558401161004.

    Article  Google Scholar 

  35. Linver, M. R., Roth, J. L., & Brooks-Gunn, J. (2009). Patterns of adolescents’ participation in organised activities: Are sports best when combined with other activities? Developmental Psychology, 45, 354–367.

    Article  PubMed  Google Scholar 

  36. Bartko, W. T., & Eccles, J. S. (2003). Adolescent participation in structured and unstructured activities: A person-oriented analysis. Journal of Youth and Adolescence, 32, 233–241.

    Article  Google Scholar 

  37. Orban, K., Edberg, A.-K., & Erlandsson, L.-K. (2012). Using a time-geographical diary method in order to facilitate reflections on changes in patterns of daily occupations. Scandinavian Journal of Occupational Therapy, 19, 249–259.

    Article  PubMed  Google Scholar 

  38. Ferrar, K., Olds, T., & Maher, C. (2012). More than just physical activity: Time-use clusters and profiles of Australian youth. Journal of Science and Medicine in Sport, 16(5), 427–432.

    Article  PubMed  Google Scholar 

  39. Ferrar, K., Olds, T., Maher, C., & Maddison, R. (2013). Time-use clusters of New Zealand adolescents are associated with weight status, diet and ethnicity. Australian and New Zealand Journal of Public Health, 37(1), 39–46.

    Article  PubMed  Google Scholar 

  40. Shanahan, M. J., & Flaherty, B. P. (2001). Dynamic patterns of time-use in adolescence. Child Development, 72(2), 385.

    Article  CAS  PubMed  Google Scholar 

  41. Larson, R. W., & Tran, S. P. (2014). Positive youth development and human complexity. Journal of Youth and Adolescence, 43, 1012–1017.

    Article  PubMed  Google Scholar 

  42. Matza, L. S., Swensen, A. R., Flood, E. M., Secnik, K., & Leidy, N. K. (2004). Assessment of health-related quality of life in children: A review of conceptual, methodological, and regulatory issues. Value in Health, 7(1), 79–92.

    Article  PubMed  Google Scholar 

  43. Ford, C. A. (2014). Celebrating progress in adolescent health and optimism for the future. Journal of Adolescent Health, 55, 458–462.

    Article  PubMed  Google Scholar 

  44. Ben-Arieh, A. (2010). Developing indicators for child well-being in a changing context. In C. McAuley & W. Rose (Eds.), Child well-being: Understanding children’s lives (pp. 129–148). London: Jessica Kingsley.

    Google Scholar 

  45. Huang, I.-C., Revicki, D. A., & Schwartz, C. E. (2014). Measuring pediatric patient-reported outcomes: Good progress but a long way to go. Quality of Life Research, 23, 747–750.

    Article  PubMed Central  PubMed  Google Scholar 

  46. Gopinath, B., Hardy, L. L., Baur, L. A., Burlutsky, G., & Mitchell, P. (2012). Physical activity and sedentary behaviors and health-related quality of life in adolescents. Pediatrics, 130(1), e167–e174.

    Article  PubMed  Google Scholar 

  47. Lacy, K. E., Allender, S. E., Kremer, P. J., de Silva-Sanigorski, A. M., Millar, L. M., Moodie, M., et al. (2012). Screen time and physical activity behaviours are associated with health-related quality of life in Australian adolescents. Quality of Life Research, 21, 1085–1099.

    Article  PubMed  Google Scholar 

  48. Finne, E., Bucksch, J., Lampert, T., & Kolip, P. (2014). Physical activity and screen-based media use: Cross-sectional associations with health-related quality of life and the role of body satisfaction in a representative sample of German adolescents. Health Psychology and Behavioural Medicine, 1(1), 15–30.

    Article  Google Scholar 

  49. Iannotti, R. J., Kogan, M. D., Janssen, I., & Boyce, W. F. (2009). Patterns of adolescent physical activity, screen-based media use, and positive and negative health indicators in the U.S. and Canada. Journal of Adolescent Health, 44, 493–499.

    Article  PubMed Central  PubMed  Google Scholar 

  50. Mathers, M., Canterford, L., Olds, T., Hesketh, K., Ridley, K., & Wake, M. (2009). Electronic media use and adolescent health and well-being: Cross-sectional community study. Academic Pediatrics, 9, 307–314.

    Article  PubMed  Google Scholar 

  51. Lin, C.-Y., Su, C.-T., & Ma, H.-I. (2012). Physical activity patterns and quality of life of overweight boys: A preliminary study. Hong Kong Journal of Occupational Therapy, 22, 31–37.

    Article  CAS  Google Scholar 

  52. Xu, F., Chen, G., Stevens, K., Zhou, H., Qi, S., Wang, Z., et al. (2014). Measuring and valuing health-related quality of life among children and adolescents in mainland China—a pilot study. PLoS ONE,. doi:10.1371/journal.pone.0089222.

    Google Scholar 

  53. Zuzanek, J., & Mannell, R. (2005). Adolescent time-use and well-being: Challenges of cross-national research. Society and Leisure, 28(2), 377–378.

    Google Scholar 

  54. Sonnenberg, B., Riediger, M., Wrzus, C., & Wagner, G. G. (2012). Measuring time-use in surveys—concordance of survey and experience sampling measures. Social Science Research, 41, 1037–1052.

    Article  PubMed  Google Scholar 

  55. Phipps, P. A., & Vernon, M. K. (2009). 24 hours: An overview of the recall diary method and data quality in the American time-use survey. In R. F. Belli, F. P. Stafford, & D. F. Alwin (Eds.), Calendar and time diary methods in life course research (pp. 109–128). Los Angeles, CA: Sage.

    Google Scholar 

  56. Robinson, J. P., & Godbey, G. (1999). Time for life—the surprising way Americans use their time (2nd ed.). University Park, PA: Pennsylvania State University Press.

    Google Scholar 

  57. McGinnity, F., Russell, H., Williams, J., & Blackwell, S. (2005). Time-use in Ireland 2005: Survey report. Dublin: Economic and Social Research Institute.

    Google Scholar 

  58. Gillen-O’Neel, Huynh, V. W., & Fuligni, A. J. (2013). To study or sleep? The academic costs of extra studying at the expense of sleep. Child Development, 84(1), 133–142.

    Article  PubMed  Google Scholar 

  59. Witkow, M. R. (2009). Academic achievement and adolescent’s daily time-use in the social and academic domains. Journal of Research on Adolescence, 19(1), 151–172.

    Article  Google Scholar 

  60. Fuligni, A. J., & Stevenson, H. W. (1995). Time-use and mathematics achievement among America, Chinese, and Japanese high school students. Child Development, 66, 830–842.

    Article  Google Scholar 

  61. Hofferth, S. L., & Sandberg, J. F. (2001). How American children spend their time. Journal of Marriage and Family, 63, 295–308.

    Article  Google Scholar 

  62. Ravens-Sieberer, U., Gosch, A., Rajmil, L., Erhart, M., Bruil, J., Duer, W., et al. (2005). KIDSCREEN-52 quality-of-life measure for children and adolescents. Expert Review of Pharmacoeconomics and Outcomes Research, 5(3), 353–364.

    Article  PubMed  Google Scholar 

  63. Ravens-Sieberer, U., Herdman, M., Devine, J., Otto, C., Bullinger, M., Rose, M., & Klasen, F. (2014). The European KIDSCREEN approach to measure quality of life and well-being in children: Development, current application, and future advances. Quality of Life Research, 23, 791–803.

    Article  PubMed Central  PubMed  Google Scholar 

  64. Erhart, M., Ottova, V., Gaspar, T., Jericek, H., Schnohr, C., Mujgan, A., et al. (2009). Measuring mental health and Well-being of school-children in 15 European countries using the KIDSCREEN-10 Index. International Journal of Public Health, 54, S160–S166.

    Article  Google Scholar 

  65. Ravens-Sieberer, U., Erhart, M., Rajmil, L., Herdman, M., Auquier, P., Bruil, J., et al. (2010). Reliability, construct and criterion validity of the KIDSCREEN-10 score: A short measure for children and adolescents’ well-being and health-related quality of life. Quality of Life Research, 19(10), 1487–1500.

    Article  PubMed Central  PubMed  Google Scholar 

  66. Keenaghan, C., & Kilroe, J. (2008). A study on the quality of life tool KIDSCREEN for children and adolescents in Ireland—results of the KIDSCREEN national survey 2005. Dublin: Office of the Minister for Children.

    Google Scholar 

  67. KIDSCREEN Group Europe. (2006). The KIDSCREEN questionnaires handbook. Lengerish: Pabst Science Publishers.

    Google Scholar 

  68. Ravens-Sieberer, U., Gosch, A., Rajmil, L., Erhart, M., Bruil, J., Power, M., et al. (2008). The KIDSCREEN-52 quality of life measure for children and adolescents: Psychometric results from a cross-cultural survey in 13 European countries. Value in Health, 11(4), 645–658.

    Article  PubMed  Google Scholar 

  69. Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis with application in the social, behavioural, and health sciences. Hoboken, NJ: Wiley.

    Google Scholar 

  70. Hunt, E., McKay, E., Fitzgerald, A., & Perry, I. (2014). Time-use and daily activities of late adolescents in contemporary Ireland. Journal of Occupational Science, 21(1), 42–64.

    Article  Google Scholar 

  71. Ferrar, K., Olds, T. S., & Walters, J. L. (2012). All the stereotypes confirmed: Differences in how Australian boys and girls use their time. Health Education and Behaviour, 39(5), 589–595.

    Article  Google Scholar 

  72. Masyn, K. E. (2013). Latent class analysis and finite mixture modeling. In T. Little (Ed.), The Oxford handbook of quantitative methods (Vol. II, pp. 551–611)., statistical analysis Oxford: Oxford University Press.

    Google Scholar 

  73. Herman, K. C., Ostrander, R., Walkup, J. T., Silva, S. G., & March, J. S. (2007). Empirically derived subtypes of adolescent depression: Latent profile analysis of co-occurring symptoms in the treatment for adolescents with depression study (TADS). Journal of Consulting and Clinical Psychology, 75(5), 716–728.

    Article  PubMed  Google Scholar 

  74. Arbeit, M. R., Johnson, S. K., Champine, R. B., Greenman, K. N., Lerner, J. V., & Lerner, R. M. (2014). Profiles of problematic behaviours across adolescence: Covariations with indicators of positive youth development. Journal of Youth and Adolescence, 43, 971–990.

    Article  PubMed  Google Scholar 

  75. Nylund, K. L., Asparouhov, T., & Muthén, B. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–569.

    Article  Google Scholar 

  76. Samuelson, K., & Raczynski, K. (2013). Latent class/profile analysis. In Y. M. Petscher, C. Schatschneider, & D. L. Compton (Eds.), Applied quantitative analysis in education and the social sciences (pp. 304–328). New York: Routledge.

    Google Scholar 

  77. McIntosh, C. N., et al. (2013). Pitfalls in subgroup analysis based on growth mixture models: A commentary of van Leeuwen et al. 2012. Quality of Life Research, 22, 2625–2629.

    Article  PubMed  Google Scholar 

  78. Asparouhov, T., & Muthén, B. (2013). Auxiliary variables in mixture modeling: 3-step approaches using Mplus. http://www.statmodel.com/download/3stepOct28.pdf. Accessed 28 June 2014.

  79. Lanza, S. T., Tan, X., & Bray, B. C. (2013). Latent class analysis with distal outcomes: A flexible model-based approach. Structural Equation Modeling, 20, 1–26.

    Article  PubMed Central  PubMed  Google Scholar 

  80. Machin, D., Campbell, M. J., & Walters, S. J. (2007). Medical statistics—a textbook for the health sciences (4th ed.). West Sussex: John Wiley.

    Google Scholar 

  81. Muthén, L.K., & Muthén, B.O. (1998–2012). Mplus user’s guide. 7th Edition. Los Angeles, CA: Muthén and Muthén.

  82. Currie, C., Zanotti, C., Morgan, A., Currie, D., de Looze, M., Roberts, C., et al. (2012). Social determinants of health and well-being among young people. Health behaviour in school-aged children (HBSC) study: International report from the 2009/2010 survey. Copenhagen: WHO Regional Office for Europe. http://www.euro.who.int/__data/assets/pdf_file/0003/163857/Social-determinants-of-health-and-well-being-among-young-people.pdf. Accessed 28 June 2014.

  83. World Health Organisation (2011). Evidence for gender responsive actions to prevent and manage overweight and obesity. Geneva: World Health Organization. http://www.euro.who.int/__data/assets/pdf_file/0016/158101/316637_WHO_brochure_226x226_2-overweight_obesity.pdf. Accessed 28 June 2014.

  84. Agans, J. P., Champine, R. B., DeSouza, L. M., Mueller, M. K., Johnson, S. K., & Lerner, R. M. (2014). Activity involvement as an ecological asset: Profiles of participation and youth outcomes. Journal of Youth and Adolescence, 43, 919–932.

    Article  PubMed  Google Scholar 

  85. Matuska, K., & Christiansen, C. (2008). A proposed model of lifestyle balance. Journal of Occupational Science, 15, 9–19.

    Article  Google Scholar 

  86. Håkansson, C., Dahlin-Ivanoff, S., & Sonn, U. (2006). Achieving balance in everyday life. Journal of Occupational Science, 13(1), 74–82.

    Article  Google Scholar 

  87. Zuzanek, J. (2009). Time-use imbalance—developmental and emotional costs. In K. Matuska & C. Christiansen (Eds.), Life balance-multidisciplinary theories and research (pp. 207–222). Thorofare, NJ: Slack Inc./AOTA.

    Google Scholar 

  88. Dollman, J., Okely, A. D., Hardy, L., Timperio, A., Salmon, J., & Hills, A. P. (2009). A hitchhiker’s guide to assessing young people’s physical activity: Deciding what method to use. Journal of Science and Medicine in Sport, 12, 518–525.

    Article  PubMed  Google Scholar 

  89. Patnode, C. D., Lytle, L. A., Erickson, D. J., Sirard, J. R., Barr-Anderson, D. J., & Storey, M. (2011). Physical activity and sedentary activity patterns among children and adolescents: A latent class analysis approach. Journal of Physical Activity and Health, 8, 457–467.

    PubMed  Google Scholar 

  90. De Leeuw, J., van der Heijden, P. G. M., & Verboon, P. (1990). A latent time-budget model. Statistica Neerlandica, 41(1), 1–22.

    Article  Google Scholar 

  91. Aitchison, J. (2003). The statistical analysis of compositional data. London: The Blackburn Press.

    Google Scholar 

  92. Kucera, M., & Malmgren, B. A. (1998). Logratio transformation of compositional data—a resolution of the constant sum constraint. Marine Micropaleontology, 34, 117–120.

    Article  Google Scholar 

  93. Brooks, A.-M., & Hanafin, S. (2005). Measuring child well-being: an inventory of key indicators, domains and indicator selection criteria to support the development of a national set of child well-being indicators. National Children’s Office. http://www.dcya.gov.ie/documents/publications/Well_Being_Indicators.pdfndicators. Accessed 28 June 2014.

  94. Lippman, L. H., Moore, K. A., & McIntosh, H. (2009). Positive indicators of child well-being: A conceptual framework, measures and methodological issues. Innocenti Working Paper No. 2009-21. UNICEF Innocenti Research Centre. http://www.unicef-irc.org/publications/pdf/iwp_2009_21.pdf. Accessed 28 June 2014.

  95. NicGabhainn, S., & Sixsmith, J. (2005). Children’s understandings of well-being. The National Children’s Office. http://www.nuigalway.ie/hbsc/documents/childrenunderstandingofWell-being.pdf. Accessed 28 June 2014.

  96. Hagell, A., Coleman, J., & Brooks, F. (2013). Key data on adolescence 2013. London: Association for Young People’s Health. http://www.ayph.org.uk/publications/457_AYPH_KeyData2013_WebVersion.pdf. Accessed 28 June 2014.

  97. Magnusson, D. (2003). The person approach: Concepts, measurement models, and research strategy. New Directions for Child and Adolescent Development, 101, 3–23.

    Article  PubMed  Google Scholar 

  98. Larson, R. W. (2011). Positive development in a disorderly world. Journal of Research on Adolescence, 21(2), 317–334.

    Article  Google Scholar 

  99. Larson, R. (2001). How U.S. children and adolescents spend their time: What it does (and doesn’t) tell us about their development. Current Directions in Psychological Science, 10(5), 160–162.

    Article  Google Scholar 

  100. Hamilton, S. F. (2014). On the 4-H study of positive youth development. Journal of Youth and Adolescence, 43, 1008–1011.

    Article  PubMed  Google Scholar 

  101. Masten, A. S. (2014). Resilience and positive youth development frameworks in developmental science. Journal of Youth and Adolescence, 43, 1018–1024.

    Article  PubMed  Google Scholar 

  102. Trilk, J. L., Pate, R. R., Pfeiffer, K. A., Dowda, M., Addy, C. L., Ribisl, K. M., et al. (2012). A cluster analysis of physical activity and sedentary behaviour patterns in middle school girls. Journal of Adolescent Health, 51, 292–298.

    Article  PubMed Central  PubMed  Google Scholar 

  103. Cuenca-García, M., Huybrechts, I., Ruiz, J. R., Ortega, F. B., Ottovaere, C., González-Gross, M., et al. (2013). Clustering of multiple lifestyle behaviours and health-related fitness in European adolescents. Journal of Nutrition Education and Behaviour, 45(6), 549–557.

    Article  Google Scholar 

  104. Marques, E. S., Pizarro, A. N., Figueiredo, P., Mota, J., & Santos, M. P. (2013). Modifiable lifestyle behaviour patterns, sedentary time and physical activity contexts: A cluster analysis among middle school boys and girls in the SALTA study. Preventive Medicine, 56, 413–415.

    Article  PubMed  Google Scholar 

  105. Spengler, S., Mess, F., Mewes, N., Mensink, G. B. M., & Woll, A. (2012). A cluster-analytic approach towards multidimensional health-related behaviors in adolescents: The MoMo-Study. BMC Public Health, 12, 1128.

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Ethical standards

Ethical approval was granted by the University College Cork Research Ethics Committee of the Cork Teaching Hospitals. Therefore, this study has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. All participants provided written consent/assent.

Author information

Authors and Affiliations

  1. Department of Occupational Science and Occupational Therapy, University College Cork, Room G24, Brookfield Health Sciences Complex, College Road, Cork, Ireland

    Eithne Hunt

  2. Department of Occupational Therapy, Brunel University, London, UK

    Elizabeth A. McKay

  3. Department of Epidemiology and Public Health, University College Cork, Cork, Ireland

    Darren L. Dahly, Anthony P. Fitzgerald & Ivan J. Perry

  4. School of Mathematical Sciences, University College Cork, Cork, Ireland

    Anthony P. Fitzgerald

Authors
  1. Eithne Hunt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elizabeth A. McKay
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Darren L. Dahly
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anthony P. Fitzgerald
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ivan J. Perry
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eithne Hunt.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hunt, E., McKay, E.A., Dahly, D.L. et al. A person-centred analysis of the time-use, daily activities and health-related quality of life of Irish school-going late adolescents. Qual Life Res 24, 1303–1315 (2015). https://doi.org/10.1007/s11136-014-0863-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-014-0863-9

Keywords

Search

Navigation