J Phys Act Health. 2019 May 1;16(5):325-332. doi: 10.1123/jpah.2018-0205. Epub 2019 Apr 11.
: Although levels of physical activity (PA) have been researched, no information on how university students organize their PA across different life domains is available. The purpose of this study is to explore if and how students organize their PA across transport and recreational domains, and to identify the psychosocial factors related to these patterns. : Students from 31 Irish universities completed a supervised online survey measuring participant characteristics, psychosocial factors, and PA. Two-step cluster analysis was used to identify specific PA patterns in students. Binary logistic regressions identified factors associated with cluster membership while controlling for age, sex, household income, and perceived travel time to a university. : Analysis was performed on 6951 students (50.7% male; 21.51 [5.55] y). One Low Active cluster emerged. Four clusters containing a form of PA emerged including Active Commuters, Active in University, Active Outside University, and High Active. Increases in motivation and planning improved the likelihood of students being categorized in a cluster containing PA. : One size does not fit all when it comes to students PA engagement, with 5 patterns identified. Health professionals are advised to incorporate strategies for increasing students' motivation, action planning, and coping planning into future PA promotion efforts.
尽管已经对身体活动(PA)水平进行了研究,但关于大学生如何在不同的生活领域组织他们的 PA 却没有信息。本研究的目的是探讨学生是否以及如何在交通和娱乐领域组织他们的 PA,并确定与这些模式相关的心理社会因素。
来自 31 所爱尔兰大学的学生完成了一项在线监督调查,调查内容包括参与者的特征、心理社会因素和 PA。两步聚类分析用于识别学生的特定 PA 模式。二元逻辑回归在控制年龄、性别、家庭收入和到大学的感知旅行时间的情况下,确定了与聚类成员身份相关的因素。
对 6951 名学生(50.7%为男性;21.51[5.55]岁)进行了分析。一个低活跃的聚类出现了。四个包含某种 PA 的聚类包括活跃的通勤者、活跃在大学、活跃在大学外和高度活跃。动机和计划的增加提高了学生被归类于包含 PA 的聚类的可能性。
当涉及到学生的 PA 参与时,一种方法并不适合所有人,共确定了 5 种模式。健康专业人员被建议将提高学生动机、行动计划和应对计划的策略纳入未来的 PA 促进工作中。