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青少年时间使用聚类:系统评价。

Adolescent time use clusters: a systematic review.

机构信息

Health and Use of Time Group, School of Health Sciences, University of South Australia, Adelaide, South Australia, Australia.

出版信息

J Adolesc Health. 2013 Mar;52(3):259-70. doi: 10.1016/j.jadohealth.2012.06.015. Epub 2012 Aug 14.

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.

摘要

目的

最近的研究表明,时间利用模式或聚类可能以不能仅用个体行为的影响来解释的方式影响健康。本研究的目的是系统地回顾考察青少年时间利用聚类及其相关关联的文献。

方法

对六个在线数据库进行了系统的文献检索,以寻找相关的观察性研究。至少有两位作者对符合入选标准的摘要和全文进行了审查。对纳入的研究进行了质量评分、数据提取,并对聚类类型和聚类关联进行了解释。

结果

确定了 19 项研究进行纳入,其中 18 项研究调查了聚类-相关关联。确定了 29 种聚类类型,其特征既有个体行为(例如,教堂),也有同时发生的行为(例如,体育活动和屏幕[技术活动])。确定了 19 个相关类别(例如,社会经济地位和体重状况)。发现了聚类-相关关联的一致模式。例如,技术活动聚类类型更可能是男性,并且对学校的取向较低。

结论

尽管存在研究间差异,但仍然存在一致的聚类和聚类相关模式。青少年时间利用行为的聚类分析似乎是一种新兴且有用的分类技术,可能对有针对性的健康相关干预具有重要意义。

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