Adolescent Time Use Clusters: A Systematic Review

  • Katia Ferrar
    Address correspondence to: Katia E. Ferrar, University of South Australia, GPO Box 2471, Adelaide, SA, 5001, Australia
    Health and Use of Time Group, School of Health Sciences, University of South Australia, Adelaide, South Australia, Australia

    Sansom Institute for Health Research, University of South Australia, Adelaide, South Australia, Australia
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  • Cindy Chang
    Health and Use of Time Group, School of Health Sciences, University of South Australia, Adelaide, South Australia, Australia

    Sansom Institute for Health Research, University of South Australia, Adelaide, South Australia, Australia
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  • Ming Li
    Sansom Institute for Health Research, University of South Australia, Adelaide, South Australia, Australia

    School of Health Sciences, University of South Australia, Adelaide, South Australia, Australia
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  • Tim S. Olds
    Health and Use of Time Group, School of Health Sciences, University of South Australia, Adelaide, South Australia, Australia

    Sansom Institute for Health Research, University of South Australia, Adelaide, South Australia, Australia
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      Recent research suggests that patterns or clusters of time use may affect health in ways that cannot be explained by the effect of individual behaviors alone. The aim of this research was to systematically review the literature examining adolescent time use clusters and associated correlates.


      Systematic searches of six online databases for relevant observational studies were conducted. At least two authors reviewed abstract and full text selection meeting eligibility criteria. Included studies were quality scored, had data extracted, and cluster types and cluster associations interpreted.


      Nineteen studies were identified for inclusion, and 18 of them investigated cluster–correlate associations. Twenty-nine cluster types were identified, characterized by both individual (e.g., church) and co-occurring behaviors (e.g., physical activity and screen [technoactive]). Nineteen correlate categories were identified (e.g., socioeconomic and weight status). Consistent patterns of cluster–correlate association were found. For example, the technoactive cluster type is more likely to be male and to have low school orientation.


      Despite the between-study differences, consistent cluster and cluster–correlate patterns were still evident. Cluster analysis of adolescent time use behaviors appears to be an emerging and useful classification technique, one which may have implications for targeted health-related interventions.


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        • Biddle S.J.
        • Gorely T.
        • Stensel D.J.
        Health-enhancing physical activity and sedentary behaviour in children and adolescents.
        J Sports Sci. 2004; 22: 679-701
        • Iannotti R.J.
        • Kogan M.D.
        • Janssen I.
        • Boyce W.F.
        Patterns of adolescent physical activity, screen-based media use, and positive and negative health indicators in the U.S. and Canada.
        J Adolesc Health. 2009; 44: 493-499
        • Hulshof K.F.
        • Wedel M.
        • Löwik M.R.
        • et al.
        Clustering of dietary variables and other lifestyle factors (Dutch nutritional surveillance system).
        J Epidemiol Community Health. 1992; 46: 417-424
        • Kreuter M.W.
        • Wray R.J.
        Tailored and targeted health communication: Strategies for enhancing information relevance.
        Am J Health Behav. 2003; 27: S227-S232
        • Everitt B.
        Cluster analysis [electronic resource].
        Wiley, Chichester, UK2011
        • Panagiotakos D.B.
        • Pitsavos C.
        • Polychronopoulos E.
        • et al.
        Can a Mediterranean diet moderate the development and clinical progression of coronary heart disease? A systematic review.
        Med Sci Monit. 2004; 10: RA193-RA198
        • Hodges K.
        • Wotring J.
        Client typology based on functioning across domains using the CAFAS: Implications for service planning.
        J Behav Health Serv Res. 2000; 27: 257-270
        • Moore W.C.
        • Meyers D.A.
        • Wenzel S.E.
        • et al.
        Identification of asthma phenotypes using cluster analysis in the Severe Asthma Research Program.
        Am J Respir Crit Care Med. 2010; 181: 315-323
        • Sullivan C.
        • Oakden J.
        • Young J.
        • et al.
        Obstacles to action: A study of New Zealanders' physical activity and nutrition.
        AC Nielson, Wellington, New Zealand2003
        • von Elm E.
        • Altman D.G.
        • Egger M.
        • et al.
        The strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies.
        J Clin Epidemiol. 2008; 61: 344-349
        • Cardoso L.O.
        • Engstrom E.M.
        • Leite I.C.
        • et al.
        Socioeconomic, demographic, environmental and behavioral factors associated with overweight in adolescents: A systematic literature review.
        Bras Epidemiol. 2009; 12: 1-24
        • Li M.
        • Dibley M.J.
        • Sibbritt D.
        • Yan H.
        Factors associated with adolescents' physical inactivity in Xi'an city, China.
        Med Sci Sports Exerc. 2006; 38: 2075-2085
      1. Kazdin A.E. Ecological systems theory. Oxford University Press, Washington, DC2000
        • Shanahan M.J.
        • Flaherty B.P.
        Dynamic patterns of time use in adolescence.
        Child Dev. 2001; 72: 385-401
        • Wang C.K.J.
        • Chia Y.H.M.
        • Quek J.J.
        • et al.
        Patterns of physical activity, sedentary behaviors, and psychological determinants of physical activity among Singaporean school children.
        Int J Sport Exerc Psychol. 2006; 4: 227-249
        • Olds T.
        • Dollman J.
        • Ridley K.
        • et al.
        Children and sport.
        Australian Sports Commission, Belconnen, South Australia2004
        • Marshall S.J.
        • Biddle S.J.H.
        • Sallis J.F.
        • et al.
        Clustering of sedentary behaviors and physical activity among youth: A cross-national study.
        Pediatr Exerc Sci. 2002; 14: 401-417
        • Telama R.
        • Nupponen H.
        • Piéron M.
        Physical activity among young people in the context of lifestyle.
        Eur Phys Educ Rev. 2005; 11: 115-137
        • te Velde S.J.
        • De Bourdeaudhuij I.
        • Thorsdottir I.
        • et al.
        Patterns in sedentary and exercise behaviors and associations with overweight in 9–14-year-old boys and girls—a cross-sectional study.
        BMC Public Health. 2007; 7: 16
        • Nuviala A.
        • Izquierdo D.
        • Martinez A.
        • et al.
        Typologies of occupation of leisure-time Spanish adolescents. The case of the participants in physical activities organized.
        J Hum Sports Exerc. 2009; 4: 29-39
        • Jago R.
        • Fox K.R.
        • Page A.S.
        • et al.
        Physical activity and sedentary behaviour typologies of 10–11 year olds.
        Int J Behav Nutr Phys Act. 2010; 7: 59
        • Gorely T.
        • Marshall S.J.
        • Biddle S.J.H.
        • Cameron N.
        Patterns of sedentary behaviour and physical activity among adolescents in the United Kingdom: Project STIL.
        J Behav Med. 2007; 30: 521-531
        • Liu J.
        • Kim J.
        • Colabianchi N.
        • et al.
        Co-varying patterns of physical activity and sedentary behaviors and their long-term maintenance among adolescents.
        J Phys Act Health. 2010; 7: 465-474
        • Nelson M.C.
        • Gordon-Larsen P.
        • Adair L.S.
        • Popkin B.M.
        Adolescent physical activity and sedentary behavior: Patterning and long-term maintenance.
        Am J Prev Med. 2005; 28: 259-266
        • Linver M.R.
        • Roth J.L.
        • Brooks-Gunn J.
        Patterns of adolescents' participation in organized activities: Are sports best when combined with other activities?.
        Dev Psychol. 2009; 45: 354-367
        • Zarrett N.
        • Fay K.
        • Li Y.
        • et al.
        More than child's play: Variable- and pattern-centered approaches for examining effects of sports participation on youth development.
        Dev Psychol. 2009; 45: 368-382
        • Metzger A.
        • Crean H.F.
        • Forbes-Jones E.L.
        Patterns of organized activity participation in urban, early adolescents: Associations with academic achievement, problem behaviors, and perceived adult support.
        J Early Adolesc. 2009; 29: 426-442
        • Peck S.C.
        • Roeser R.W.
        • Zarrett N.
        • Eccles J.S.
        Exploring the roles of extracurricular activity quantity and quality in the educational resilience of vulnerable adolescents: Variable- and pattern-centered approaches.
        J Soc Issues. 2008; 64: 135-156
        • Nelson I.A.
        • Gastic B.
        Street ball, swim team and the sour cream machine: A cluster analysis of out of school time participation portfolios.
        J Youth Adolesc. 2009; 38: 1172-1186
        • Newby P.K.
        • Tucker K.L.
        Empirically derived eating patterns using factor or cluster analysis: A review.
        Nutr Rev. 2004; 62: 177-203
        • Beets M.W.
        • Foley J.T.
        Comparison of 3 different analytic approaches for determining risk-related active and sedentary behavioral patterns in adolescents.
        J Phys Act Health. 2010; 7: 381
        • Zabinski M.F.
        • Norman G.J.
        • Sallis J.F.
        • et al.
        Patterns of sedentary behavior among adolescents.
        Health Psychol. 2007; 26: 113-120
        • Bartko W.T.
        • Eccles J.S.
        Adolescent participation in structured and unstructured activities: A person-oriented analysis.
        J Youth Adolesc. 2003; 32: 233-241
        • Vandenbroucke J.P.
        • Elm E.
        • Altman D.G.
        • et al.
        Strengthening the reporting of observational studies in epidemiology (STROBE): Explanation and elaboration.
        Ann Intern Med. 2007; 147: W-163
        • Klesges L.M.
        • Baranowski T.
        • Beech B.
        Social desirability bias in self-reported dietary, physical activity and weight concerns measures in 8- to 10-year-old African-American girls: Results from the girls health enrichment multisite studies (GEMS).
        Prev Med. 2004; 38: S78-S87
      2. Ferrar KE, Olds TS, Walters JL. All the stereotypes confirmed: Differences in how Australian boys and girls use their time. Health Educ Behav (in press).

        • Olds T.
        • Wake M.
        • Patton G.
        • et al.
        How do school-day activity patterns differ with age and gender across adolescence?.
        J Adolesc Health. 2009; 44: 64-72
        • Larson R.W.
        • Verma S.
        How children and adolescents spend time across the world: Work, play, and developmental opportunities.
        Psychol Bull. 1999; 125: 701-736
        • Fuligni A.J.
        The academic achievement of adolescents from immigrant families: The roles of family background, attitudes, and behavior.
        Child Dev. 1997; 68: 351-363
        • Hair J.F.
        Multivariate data analysis.
        Prentice Hall, Upper Saddle River, NJ1998
        • Olds T.S.
        • Ferrar K.E.
        • Schranz N.K.
        • Maher C.A.
        Obese adolescents are less active than their normal-weight peers, but wherein lies the difference?.
        J Adolesc Health. 2011; 48: 189-195
        • Must A.
        • Tybor D.J.
        Physical activity and sedentary behavior: A review of longitudinal studies of weight and adiposity in youth.
        Int J Obes Relat Metab Disord. 2005; 29: S84-S96
        • Han J.
        • Kamber M.
        Data mining [electronic resource]: Concepts and techniques.
        2nd edition. Elsevier, Amsterdam2006
        • Skinner C.S.
        • Campbell M.K.
        • Rimer B.K.
        • et al.
        How effective is tailored print communication?.
        Ann Behav Med. 1999; 21: 290-298
        • Christensen A.J.
        Patient-by-treatment context interaction in chronic disease: A conceptual framework for the study of patient adherence.
        Psychosom Med. 2000; 62: 435-443