Review article
Adolescent Time Use Clusters: A Systematic Review

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Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusions

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.

Section snippets

Rationale

There is overwhelming evidence that behaviors such as sleep, physical activity (PA), cognitive activities, and sedentary pastimes can have profound effects on physical and mental health [1], [2]. Until recently, time use has largely been investigated as a series of linear relationships between individual activities (such as screen time or PA) and health outcomes. Yet, recent health research suggests that combinations or patterns of behaviors may affect health in ways that cannot be explained by

Methods

Studies were identified by searching electronic databases and pearling reference lists. No limits were applied for language, but foreign language articles were subsequently excluded. This search was applied to Scopus (1996––present), ProQuest (1997–present), Ebscohost ([databases included Academic Search Premier, Cumulative Index to Nursing and Allied Health Literature, Clinical Reference Systems, E-Journals, Educational Resources Information Centre (ERIC) database, Health Source:

Study selection

Nineteen studies were identified for inclusion in the review. The process for determining inclusion and exclusion of articles is detailed in Figure 1.

Table 1 provides a summary of included studies investigating use of time clusters detailing study sample information, instruments, and cluster analysis methodological information and cluster names. In Table 1, each included study has been assigned a manuscript number, which is different from the individual study reference numbers in text. These

Discussion

In the past 10 years, there have been at least 19 studies published that used cluster analysis to investigate adolescent time use patterns, and 18 of those explored the relationship between time use clusters and sociodemographic correlates.

Despite all the between-study differences—country, participant age range, time use measurement tools, numbers and types of cluster input variables, correlate variables, data treatment, and cluster-specific methodological decisions—consistent cluster and

References (45)

  • K. Hodges et al.

    Client typology based on functioning across domains using the CAFAS: Implications for service planning

    J Behav Health Serv Res

    (2000)
  • W.C. Moore et al.

    Identification of asthma phenotypes using cluster analysis in the Severe Asthma Research Program

    Am J Respir Crit Care Med

    (2010)
  • C. Sullivan et al.

    Obstacles to action: A study of New Zealanders' physical activity and nutrition

    (2003)
  • L.O. Cardoso et al.

    Socioeconomic, demographic, environmental and behavioral factors associated with overweight in adolescents: A systematic literature review

    Bras Epidemiol

    (2009)
  • M. Li et al.

    Factors associated with adolescents' physical inactivity in Xi'an city, China

    Med Sci Sports Exerc

    (2006)
  • M.J. Shanahan et al.

    Dynamic patterns of time use in adolescence

    Child Dev

    (2001)
  • C.K.J. Wang et al.

    Patterns of physical activity, sedentary behaviors, and psychological determinants of physical activity among Singaporean school children

    Int J Sport Exerc Psychol

    (2006)
  • T. Olds et al.

    Children and sport

    (2004)
  • S.J. Marshall et al.

    Clustering of sedentary behaviors and physical activity among youth: A cross-national study

    Pediatr Exerc Sci

    (2002)
  • R. Telama et al.

    Physical activity among young people in the context of lifestyle

    Eur Phys Educ Rev

    (2005)
  • S.J. te Velde 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)

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