In recent times, the heath of adolescents and young people has been considered a priority area by governments around the world. The New Zealand (NZ) Ministry of Health has identified the current health issues in young people (12–24 years) as mental health problems, alcohol abuse, increasing rates of obesity and decreasing rates of physical activity.1 Significant health inequalities exist among NZ adolescents, with young Māori and Pacific Island people in particular experiencing poorer health outcomes than their peers.2 Māori and Pacific adolescents (10–14 years) are more likely to suffer chronic health conditions, asthma, skin conditions, poorer dental health and overweight and obesity.3 Recent data suggest that 10% of 10–18 year olds in NZ are obese and an additional 24% are overweight.4
Recent interventions to improve the health and well-being of adolescents have been implemented at government, community and individual levels.5–7 Yet it appears NZ adolescents are receiving only small benefits, with health trend data suggesting little change1 and health-related interventions yielding generally small effect sizes.8–10 Relationships between individual aspects of time use, such as screen time and health outcomes, have been investigated.11,12 However, there is a suggestion that multi-dimensional patterns of behaviour may affect health in ways not explained when such one-dimensional relationships are investigated.13 Exploring young people's multi-dimensional time use behaviours may further our understanding of the complex health and well-being relationships, and offer insights into the design of targeted health interventions.
Cluster analysis is classified as an unsupervised data mining algorithm which attempts to group the data into classes or clusters, such that ‘cases’ within the clusters are similar to each other and relatively dissimilar to the ‘cases’ in the other clusters. Cluster analysis allows empirical definition of data patterns, and does not rely on current theory or knowledge in the related field of study. In recent years, the research fields of dietary patterning14 and disease symptomology15 have successfully utilised cluster analysis to identify underlying patterns in data.16 Cluster analysis to identify adolescent time use patterns has not been a common approach to date. Only 19 adolescent time use clustering studies could be sourced as part of a recent systematic review of the literature.17 The studies reported on adolescents from different countries and relative socioeconomic backgrounds. Regardless of the differences, some similar multi-dimensional time use cluster patterns were identifiable, as were patterns of relationship with socio-demographic variables. No time use clustering studies have reported the time use clusters or patterns of NZ adolescents.
The aims of this study are to investigate the time use clusters among NZ adolescents (10–16 years) and determine which time use activity, socio-demographic, anthropometric, physical activity and diet variables best characterise each cluster. To achieve this aim, the study will cluster-analyse 24-hour recall data from a nationally representative sample of NZ adolescents.
Authors: Katia Ferrar, Tim Olds, Carol Maher, Ralph Maddison
Australian and New Zealand Journal of Public Health, Volume 37, Issue 1, pages 39–46, February 2013