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
posted on 2017-12-21, 09:35authored byJeroen Lakerveld, Anne Loyen, Fiona Chun Man Ling, Marieke De Craemer, Hidde P. van der Ploeg, Donal J. O'Gorman, Angela Carlin, Laura Capranica, Joeri Kalter, Jean-Michel Oppert, Sebastien Chastin, Greet Cardon, Johannes Brug, Ciaran MacDonnchaCiaran MacDonncha
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.
Methods 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.
Results 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.
Conclusions 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
Funding
Development of a structure identification methodology for nonlinear dynamic systems
Belgium: Research Foundation – Flanders, France: Institut National de la Recherche Agronomique (INRA), Germany: Federal Ministry of Education and Research, Italy: Ministry of Education, University and Research, Ministry of Agriculture Food and Forestry Policies; Ireland, HRB, The Netherlands Organisation for Health Research and Development (ZonMw), UK: The Medical Research Council (MRC)