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

Abstract

BackgroundThe 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.MethodsA 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.ResultsA 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.ConclusionsObtaining, 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.

Determinants of diet and physical activity (DEDIPAC): a summary of findings

“…A data pooling taskforce that spanned the WPs in TA1 and TA2 was established to develop a strategy for secondary data analysis. This group devised a five-step methodology covering 1) the identification of relevant datasets across Europe; 2) the development of a dataset compendium that included details on the design, study population, measures, and level of accessibility of data from each study; 3) the definition of key topics and approaches for secondary analyses; 4) the acquisition of access to datasets; and 5) the development of a data harmonisation platform, and pooling and harmonisation of the data [ 43 ]. Based on this, a variety of approaches to secondary data analysis were identified, including re-analysis of a single, existing dataset, ‘federated’ meta-analyses of two or more datasets based on a common data-analytical syntax applied to locally stored data, and the pooling, harmonisation and re-analyses of multiple datasets.…”

“…The DEDIPAC data warehouse proved to be useful for pooling datasets, but in general, the available data -or rather the lack thereof- often restricted harmonisation to just a few core (crude) outcome variables and some individual-level, socio-demographic correlates of these behaviours. The stepwise approach to secondary data analysis used was described in a ‘methods’ paper [ 43 ], as well as in a position paper that draws on the possibilities and impossibilities of secondary data analyses of pooled and harmonised data on determinants of sedentary behaviour. The main gaps identified were lack of datasets that specifically emphasise determinants of behaviour - especially at the more macro level and with a systems approach; too few longitudinal studies examining determinants; and inadequate coverage of European nations and age groups.…”

Associations of self-reported physical activity and anxiety symptoms and status among 7,874 Irish adults across harmonised datasets: a DEDIPAC-study

“…Briefly, the platform for the current research was the DEterminants of DIet and Physical ACtivity Knowledge Hub (DEDIPAC-KH) [45,46]. Within DEDIPAC, accessible cross-sectional datasets with relevant data on physical activity and anxiety were identified in a large compendium for harmonisation [47]. Two datasets, The Irish Longitudinal Study on Ageing (TILDA) and The Mitchelstown Cohort Study were harmonised for the present study.…”

Harmonising data on the correlates of physical activity and sedentary behaviour in young people: Methods and lessons learnt from the international Children’s Accelerometry database (ICAD)

“…Data pooling has been widely employed in some fields of research [ 19 ] but has been used less frequently in the physical activity domain, particularly with young people. [ 20 , 21 ]…”

“…Perhaps the most frequently discussed consideration relevant to data pooling, however, is the derivation of analytical variables that are comparable, or at least more comparable, across contributing studies, a process known as data harmonisation. [ 19 , 21 , 27 , 30 ] Central to the harmonisation process is a judgement on whether data from contributing studies are ‘inferentially equivalent’, meaning that the constructs assessed are sufficiently comparable in their format, function or meaning. This requires consideration not just of whether data can be combined, but whether it should be combined.…”