Aggregation of Health Behaviors among Fourth Graders in Northern Taiwan

Yen, Lee-Lan; Chiu, Ching Ju; Wu, Wen Chi; Pan, Ling Yen

doi:10.1016/j.jadohealth.2006.01.010

Cited by 17 publications

(28 citation statements)

References 24 publications

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“…Excessive screen time was significantly related to being overweight (Cluster 4, Table 3), while compulsive screen time was significantly more prevalent among students that also indicated psychosocial problems, problems with GSE and behaviors such as bullying/being bullied (behavioral factor 2) and risk-prone behaviors (behavioral factor 1) (Table 3). The findings related to excessive screen time behavior were in accordance with previous studies [18,31,48,51]. However, similar clustering studies that integrated the compulsive aspect of these behaviors in adolescents were not retrieved, although previous research has shown that, separately, compulsive and excessive screen time behaviors differ in their relation to outcomes such as psychosocial problems [52], educational outcomes [53] or physical health indicators [23].…”

Section: Discussionsupporting

confidence: 86%

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Clustering of health-related behaviors, health outcomes and demographics in Dutch adolescents: a cross-sectional study

et al. 2013

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BackgroundRecent studies show several health-related behaviors to cluster in adolescents. This has important implications for public health. Interrelated behaviors have been shown to be most effectively targeted by multimodal interventions addressing wider-ranging improvements in lifestyle instead of via separate interventions targeting individual behaviors. However, few previous studies have taken into account a broad, multi-disciplinary range of health-related behaviors and connected these behavioral patterns to health-related outcomes. This paper presents an analysis of the clustering of a broad range of health-related behaviors with relevant demographic factors and several health-related outcomes in adolescents.MethodsSelf-report questionnaire data were collected from a sample of 2,690 Dutch high school adolescents. Behavioral patterns were deducted via Principal Components Analysis. Subsequently a Two-Step Cluster Analysis was used to identify groups of adolescents with similar behavioral patterns and health-related outcomes.ResultsFour distinct behavioral patterns describe the analyzed individual behaviors: 1- risk-prone behavior, 2- bully behavior, 3- problematic screen time use, and 4- sedentary behavior. Subsequent cluster analysis identified four clusters of adolescents. Multi-problem behavior was associated with problematic physical and psychosocial health outcomes, as opposed to those exerting relatively few unhealthy behaviors. These associations were relatively independent of demographics such as ethnicity, gender and socio-economic status.ConclusionsThe results show that health-related behaviors tend to cluster, indicating that specific behavioral patterns underlie individual health behaviors. In addition, specific patterns of health-related behaviors were associated with specific health outcomes and demographic factors. In general, unhealthy behavior on account of multiple health-related behaviors was associated with both poor psychosocial and physical health. These findings have significant meaning for future public health programs, which should be more tailored with use of such knowledge on behavioral clustering via e.g. Transfer Learning.

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Section: Discussionsupporting

confidence: 86%

“…self-efficacy or resilience) are underexposed, while they are often targets of health promoting interventions [29,30]. Also, only few studies have focused on adolescents as the population of interest, while they form such a unique population in which many health-related behavior habits find their origin [1,2,31]. …”

Section: Introductionmentioning

confidence: 99%

Clustering of health-related behaviors, health outcomes and demographics in Dutch adolescents: a cross-sectional study

et al. 2013

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“…Various studies have reported a similar pattern (9,10,(13)(14)(15)(16)18,22,25) , sometimes with the additional inclusion of high levels of PA (23)(24)(25)(26)28) and/or low levels of sedentary behaviour (24,28) . It is not clear why …”

Section: Discussionmentioning

confidence: 85%

“…The sedentary-snack pattern included television watching and intake of relatively unhealthy sweet and savoury snacks. This is a very common pattern in children, encountered in many studies (22)(23)(24)(25)(26)(27)(28) . In addition, various of the studies that only included dietary intake items reported similar clustering of high intakes of snacks and other unhealthy energy-dense foods (10,(13)(14)(15)(16)18,19) .…”

Section: Discussionmentioning

confidence: 91%

Energy balance-related behavioural patterns in 5-year-old children and the longitudinal association with weight status development in early childhood

Gubbels

Kremers

Goldbohm

et al. 2011

Public Health Nutr.

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Objective: The current study examined clustering of dietary intake and activity behaviours (i.e. physical activity (PA) and sedentary behaviour) in 5-year-old children, as well as the longitudinal association with BMI and overweight development. Design: Principal component analysis (PCA) was used to identify behavioural patterns. Backward regression analyses were used to examine the association of behavioural patterns with parent and child background characteristics, as well as the association of the patterns with BMI Z-score and overweight development up to ages 7 and 8 years. Setting: The Netherlands. Subjects: Data originated from the KOALA Birth Cohort Study (n 2074 at age 5 years). Results: Four patterns emerged: a 'sedentary-snacking' pattern (including television viewing and snack consumption), a 'healthy intake' pattern (including healthy items such as fruit, vegetables and fish), a 'sandwich' pattern (including bread and sandwich fillings) and a 'sporty-traditional meal' pattern (including sports, meat and potatoes). The patterns were related to child gender, general appetite and various parental characteristics. The sedentary-snacking pattern was positively associated with longitudinal BMI development. Conclusions: Cross-behavioural energy balance-related behavioural patterns exist at age 5 years. Different activity types (e.g. sports, television viewing) clustered differently with the behavioural patterns, indicating the need to incorporate these different types instead of one general PA measure in energy balance-related pattern analyses. The clustering and potential synergy between activity behaviours and dietary intake, as well as the associations with weight status development, stress the importance of an integrated approach to PA and healthy nutrition promotion in preventing childhood overweight.

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“…Failure to establish health-promoting behaviors during childhood contributes to health problems later in life (Yen, Chiu, Wu, & Pan, 2006). Child health behaviors include activities that promote health and prevent injury and were measured with the 25-item Lifestyle Questionnaire with response options of 1for never to 4 for always.…”

Section: Protective Resourcesmentioning

confidence: 99%