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Identifying and sharing data for secondary data analysis of physical activity, sedentary behaviour and their determinants across the life course in Europe: general principles and an example from DEDIPAC

机译:识别和共享数据,以对欧洲整个生命过程中的体育活动,久坐行为及其决定因素进行二次数据分析:一般原理和来自DEDIPAC的示例

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摘要

Background The utilisation of available cross-European data for secondary data analyses on physical activity, sedentary behaviours and their underlying determinants may benefit from the wide variation that exists across Europe in terms of these behaviours and their determinants. Such reuse of existing data for further research requires Findable; Accessible; Interoperable; Reusable (FAIR) data management and stewardship. We here describe the inventory and development of a comprehensive European dataset compendium and the process towards cross-European secondary data analyses of pooled data on physical activity, sedentary behaviour and their correlates across the life course.ududMethods A five-step methodology was followed by the European Determinants of Diet and Physical Activity (DEDIPAC) Knowledge Hub, covering the (1) identification of relevant datasets across Europe, (2) development of a compendium including details on the design, study population, measures and level of accessibility of data from each study, (3) definition of key topics and approaches for secondary analyses, (4) process of gaining access to datasets and (5) pooling and harmonisation of the data and the development of a data harmonisation platform.ududResults A total of 114 unique datasets were found for inclusion within the DEDIPAC compendium. Of these datasets, 14 were eventually obtained and reused to address 10 exemplar research questions. The DEDIPAC data harmonisation platform proved to be useful for pooling, but in general, harmonisation was often restricted to just a few core (crude) outcome variables and some individual-level sociodemographic correlates of these behaviours.ududConclusions Obtaining, pooling and harmonising data for secondary data analyses proved to be difficult and sometimes even impossible. Compliance to FAIR data management and stewardship principles currently appears to be limited for research in the field of physical activity and sedentary behaviour. We discuss some of the reasons why this might be the case and present recommendations based on our experience.
机译:背景技术利用现有的跨欧洲数据进行有关身体活动,久坐行为及其根本决定因素的二次数据分析,可能会受益于整个欧洲在这些行为及其决定因素方面的广泛差异。重复使用现有数据以进行进一步研究需要Findable;无障碍;可互操作;可重用(FAIR)数据管理和管理。我们在这里描述了全面的欧洲数据集纲要的清单和开发,以及对跨生命过程中身体活动,久坐行为及其相关性的汇总数据进行跨欧洲二级数据分析的过程。 ud udMethods五步方法是其次是欧洲饮食和体育活动决定因素(DEDIPAC)知识中心,内容包括:(1)识别整个欧洲的相关数据集;(2)编制简编,其中包括有关设计,研究人群,措施和可及性水平的详细信息来自每项研究的数据,(3)二次分析的关键主题和方法的定义,(4)获取数据集的过程以及(5)数据的合并和统一以及数据统一平台的开发。 ud udResults共发现114个唯一数据集包含在DEDIPAC纲要中。在这些数据集中,最终获得了14个数据集,然后再用于解决10个示例性研究问题。事实证明,DEDIPAC数据协调平台可用于合并,但总的来说,协调通常仅限于几个核心(粗略)结果变量以及这些行为的某些个人级别的社会人口统计学相关性。 ud ud结论,获取,合并和协调用于二次数据分析的数据被证明是困难的,有时甚至是不可能的。目前,对于身体活动和久坐行为领域的研究而言,遵守FAIR数据管理和管理原则似乎受到限制。我们讨论了可能出现这种情况的一些原因,并根据我们的经验提出建议。

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