BACKGROUND: In recent years, behaviourally driven policies such as nudges have been increasingly implemented to steer desired outcomes in public health. This study examines the different nudges and the socio-demographic characteristics and lifestyle behaviours that are associated with public acceptance of lifestyle nudges. METHODS: The study used data from the nationwide Knowledge, Attitudes and Practices study (KAP) on diabetes in Singapore. Three types of nudges arranged in increasing order of intrusiveness were examined: (1) information government campaigns, (2) government mandated information and (3) default rules and choice architecture. Acceptance was assessed based upon how much respondents 'agreed' with related statements describing heathy lifestyle nudges. Multivariable linear regressions were performed with socio-demographics and lifestyle behaviours using scores calculated for each nudge. RESULTS: The percentage of respondents who agreed to all statements related to each nudge were: 75.9% (information government campaigns), 73.0% (government mandated information), and 33.4% (default rules and choice architecture). Respondents of Malay/Others ethnicity (vs. Chinese) were more likely to accept information government campaigns. Respondents who were 18 – 34 years old (vs 65 years and above), female, of Malay/Indian ethnicity (vs Chinese), were sufficiently physically active, and with a healthier diet based on the DASH (Dietary Approach to Stop Hypertension) score were more likely to accept nudges related to government mandated information. Respondents of Malay/Indian ethnicity (vs Chinese), and who had a healthier diet were more likely to accept default rules and choice architecture. CONCLUSION: Individuals prefer less intrusive approaches for promoting healthy lifestyle. Ethnicity and lifestyle behaviours are associated with acceptance of nudges and should be taken into consideration during the formulation and implementation of behaviourally informed health policies. SUPPLEMENTARY INFORMATION: The online ...
Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence–absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals. ; This paper emerged from the first two workshops of the Horizon 2020 project GLOBIS‐B (GLOBal Infrastructures for Supporting Biodiversity research; http://www.globis‐b.eu/). Financial support came from the European Commission (grant 654003). C. A. additionally received funding from the LifeWatchGreece infrastructure (MIS 384676), funded by the Greek Government under the General Secretariat of Research and Technology (GSRT), ESFRI Projects and National Strategic Reference Framework (NSRF). M. O. was supported by the Swedish LifeWatch project funded by the Swedish Research Council (Grant no. 829‐2009‐6278), and J.E. by the Australian Research Council (grant FT0991640).
