Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Quality of Life Research
Erschienen in: Quality of Life Research 6/2015

01.06.2015

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

verfasst von: Eithne Hunt, Elizabeth A. McKay, Darren L. Dahly, Anthony P. Fitzgerald, Ivan J. Perry

Erschienen in: Quality of Life Research | Ausgabe 6/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.
Vorheriger Artikel Strategies to improve success of pediatric cancer cooperative group quality of life studies: a report from the Children’s Oncology Group
Nächster Artikel Integrating health-related quality of life findings from randomized clinical trials into practice: an international study of oncologists’ perspectives

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
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.
 
Literatur
1.
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.CrossRef
3.
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.PubMedCrossRef
5.
Zuzanek, J. (2005). Adolescent time-use and well-being from a comparative perspective. Society and Leisure, 28(2), 379–423.
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.CrossRef
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.CrossRef
8.
Hocking, C. (2013). Occupation for public health. New Zealand Journal of Occupational Therapy, 60(1), 33–37.
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.
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.CrossRef
11.
12.
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.CrossRef
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.CrossRef
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.
17.
Michelson, W. (2005). Time use—expanding the explanatory power of the social sciences. London: Paradigm Publishers.
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.PubMedCentralPubMedCrossRef
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.PubMedCrossRef
20.
American Academy of Pediatrics. (2011). Policy statement—children, adolescents, obesity, and the media. Pediatrics, 128, 201–208.CrossRef
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.PubMedCrossRef
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.PubMedCrossRef
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.CrossRef
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.
25.
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.CrossRef
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.PubMedCrossRef
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.PubMedCentralPubMedCrossRef
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.PubMedCrossRef
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.CrossRef
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.PubMedCrossRef
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.PubMedCrossRef
33.
Regan, A., & Heary, C. (2013). Patterns of sedentary behaviours in Irish female adolescents. Journal of Adolescence, 36, 269–278.PubMedCrossRef
34.
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.PubMedCrossRef
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.CrossRef
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.PubMedCrossRef
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.PubMedCrossRef
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.PubMedCrossRef
40.
Shanahan, M. J., & Flaherty, B. P. (2001). Dynamic patterns of time-use in adolescence. Child Development, 72(2), 385.PubMedCrossRef
41.
Larson, R. W., & Tran, S. P. (2014). Positive youth development and human complexity. Journal of Youth and Adolescence, 43, 1012–1017.PubMedCrossRef
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.PubMedCrossRef
43.
Ford, C. A. (2014). Celebrating progress in adolescent health and optimism for the future. Journal of Adolescent Health, 55, 458–462.PubMedCrossRef
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.
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.PubMedCentralPubMedCrossRef
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.PubMedCrossRef
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.PubMedCrossRef
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.CrossRef
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.PubMedCentralPubMedCrossRef
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.PubMedCrossRef
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.CrossRef
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.
53.
Zuzanek, J., & Mannell, R. (2005). Adolescent time-use and well-being: Challenges of cross-national research. Society and Leisure, 28(2), 377–378.
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.PubMedCrossRef
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.
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.
57.
McGinnity, F., Russell, H., Williams, J., & Blackwell, S. (2005). Time-use in Ireland 2005: Survey report. Dublin: Economic and Social Research Institute.
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.PubMedCrossRef
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.CrossRef
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.CrossRef
61.
Hofferth, S. L., & Sandberg, J. F. (2001). How American children spend their time. Journal of Marriage and Family, 63, 295–308.CrossRef
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.PubMedCrossRef
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.PubMedCentralPubMedCrossRef
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.CrossRef
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.PubMedCentralPubMedCrossRef
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.
67.
KIDSCREEN Group Europe. (2006). The KIDSCREEN questionnaires handbook. Lengerish: Pabst Science Publishers.
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.PubMedCrossRef
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.
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.CrossRef
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.CrossRef
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.
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.PubMedCrossRef
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.PubMedCrossRef
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.CrossRef
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.
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.PubMedCrossRef
78.
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.PubMedCentralPubMedCrossRef
80.
Machin, D., Campbell, M. J., & Walters, S. J. (2007). Medical statistics—a textbook for the health sciences (4th ed.). West Sussex: John Wiley.
81.
Muthén, L.K., & Muthén, B.O. (1998–2012). Mplus user’s guide. 7th Edition. Los Angeles, CA: Muthén and Muthén.
82.
83.
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.PubMedCrossRef
85.
Matuska, K., & Christiansen, C. (2008). A proposed model of lifestyle balance. Journal of Occupational Science, 15, 9–19.CrossRef
86.
Håkansson, C., Dahlin-Ivanoff, S., & Sonn, U. (2006). Achieving balance in everyday life. Journal of Occupational Science, 13(1), 74–82.CrossRef
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.
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.PubMedCrossRef
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
90.
De Leeuw, J., van der Heijden, P. G. M., & Verboon, P. (1990). A latent time-budget model. Statistica Neerlandica, 41(1), 1–22.CrossRef
91.
Aitchison, J. (2003). The statistical analysis of compositional data. London: The Blackburn Press.
92.
Kucera, M., & Malmgren, B. A. (1998). Logratio transformation of compositional data—a resolution of the constant sum constraint. Marine Micropaleontology, 34, 117–120.CrossRef
93.
94.
95.
96.
97.
Magnusson, D. (2003). The person approach: Concepts, measurement models, and research strategy. New Directions for Child and Adolescent Development, 101, 3–23.PubMedCrossRef
98.
Larson, R. W. (2011). Positive development in a disorderly world. Journal of Research on Adolescence, 21(2), 317–334.CrossRef
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.CrossRef
100.
Hamilton, S. F. (2014). On the 4-H study of positive youth development. Journal of Youth and Adolescence, 43, 1008–1011.PubMedCrossRef
101.
Masten, A. S. (2014). Resilience and positive youth development frameworks in developmental science. Journal of Youth and Adolescence, 43, 1018–1024.PubMedCrossRef
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.PubMedCentralPubMedCrossRef
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.CrossRef
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.PubMedCrossRef
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.PubMedCentralPubMedCrossRef
Metadaten
Titel
A person-centred analysis of the time-use, daily activities and health-related quality of life of Irish school-going late adolescents
verfasst von
Eithne Hunt
Elizabeth A. McKay
Darren L. Dahly
Anthony P. Fitzgerald
Ivan J. Perry
Publikationsdatum
01.06.2015
Verlag
Springer International Publishing
Erschienen in
Quality of Life Research / Ausgabe 6/2015
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-014-0863-9

Weitere Artikel der Ausgabe 6/2015

Quality of Life Research 6/2015 Zur Ausgabe

OriginalPaper

Identifying key domains of health-related quality of life for patients with chronic obstructive pulmonary disease: interviews with healthcare professionals

OriginalPaper

Evaluating quality of life and response shift from a couple-based perspective: a study among patients with colorectal cancer and their partners

OriginalPaper

Measurement properties of the 13-item sense of coherence scale using Rasch analysis

OriginalPaper

Strategies to improve success of pediatric cancer cooperative group quality of life studies: a report from the Children’s Oncology Group

OriginalPaper

Girls’ quality of life prior to detention in relation to psychiatric disorders, trauma exposure and socioeconomic status

OriginalPaper

Predictors of health-related quality of life perceived by end-stage renal disease patients under online hemodiafiltration

Premium Partner

    Bildnachweise