Neighbourhood typology based on virtual audit of environmental obesogenic characteristics.

Virtual audit (using tools such as Google Street View) can help assess multiple characteristics of the physical environment. This exposure assessment can then be associated with health outcomes such as obesity. Strengths of virtual audit include collection of large amount of data, from various geographical contexts, following standard protocols. Using data from a virtual audit of obesity-related features carried out in five urban European regions, the current study aimed to (i) describe this international virtual audit dataset and (ii) identify neighbourhood patterns that can synthesize the complexity of such data and compare patterns across regions. Data were obtained from 4,486 street segments across urban regions in Belgium, France, Hungary, the Netherlands and the UK. We used multiple factor analysis and hierarchical clustering on principal components to build a typology of neighbourhoods and to identify similar/dissimilar neighbourhoods, regardless of region. Four neighbourhood clusters emerged, which differed in terms of food environment, recreational facilities and active mobility features, i.e. the three indicators derived from factor analysis. Clusters were unequally distributed across urban regions. Neighbourhoods mostly characterized by a high level of outdoor recreational facilities were predominantly located in Greater London, whereas neighbourhoods characterized by high urban density and large amounts of food outlets were mostly located in Paris. Neighbourhoods in the Randstad conurbation, Ghent and Budapest appeared to be very similar, characterized by relatively lower residential densities, greener areas and a very low percentage of streets offering food and recreational facility items. These results provide multidimensional constructs of obesogenic characteristics that may help target at-risk neighbourhoods more effectively than isolated features

Mismatch between perceived and objectively measured environmental obesogenic features in European neighbourhoods.

Findings from research on the association between the built environment and obesity remain equivocal but may be partly explained by differences in approaches used to characterize the built environment. Findings obtained using subjective measures may differ substantially from those measured objectively. We investigated the agreement between perceived and objectively measured obesogenic environmental features to assess (1) the extent of agreement between individual perceptions and observable characteristics of the environment and (2) the agreement between aggregated perceptions and observable characteristics, and whether this varied by type of characteristic, region or neighbourhood. Cross-sectional data from the SPOTLIGHT project (n = 6037 participants from 60 neighbourhoods in five European urban regions) were used. Residents' perceptions were self-reported, and objectively measured environmental features were obtained by a virtual audit using Google Street View. Percent agreement and Kappa statistics were calculated. The mismatch was quantified at neighbourhood level by a distance metric derived from a factor map. The extent to which the mismatch metric varied by region and neighbourhood was examined using linear regression models. Overall, agreement was moderate (agreement < 82%, kappa < 0.3) and varied by obesogenic environmental feature, region and neighbourhood. Highest agreement was found for food outlets and outdoor recreational facilities, and lowest agreement was obtained for aesthetics. In general, a better match was observed in high-residential density neighbourhoods characterized by a high density of food outlets and recreational facilities. Future studies should combine perceived and objectively measured built environment qualities to better understand the potential impact of the built environment on health, particularly in low residential density neighbourhoods

Comparative analyses on medium optimization using one-factor-at-a-time, response surface methodology, and artificial neural network for lysine–methionine biosynthesis by Pediococcus pentosaceus RF-1

Optimization strategy that encompassed one-factor-at-a-time (OFAT), response surface methodology (RSM), and artificial neural network method was implemented during medium formulation with specific aim for lysine-methionine biosynthesis employing a newly isolated strain of Pediococcus pentosaceus RF-1. OFAT technique was used in the preliminary screening of factors (molasses, nitrogen sources, fish meal, glutamic acid and initial medium pH) before proceeded to optimization study. Implementation of central composite design of experiment subsequently generated 30 experimental runs based on four factors (molasses, fish meal, glutamic acid, and initial medium pH). From RSM analysis, a quadratic polynomial model can be devoted to describing the relationship between various medium components and responses. It also suggested that using molasses (9.86 g/L), fish meal (10.06 g/L), glutamic acid (0.91 g/L), and initial medium pH (5.30) would enhance the biosynthesis of lysine (15.77 g/L) and methionine (4.21 g/L). Alternatively, a three-layer neural network topography at 4-5-2 predicted a further improvement in the biosynthesis of lysine (16.52 g/L) and methionine (4.53 g/L) by using formulation composed of molasses (10.02 g/L), fish meal (18.00 g/L), and glutamic acid (1.17 g/L) with initial medium pH (4.26), respectively

Using remote sensing to define environmental characteristics related to physical activity and dietary behaviours: a systematic review (the SPOTLIGHT project).

We performed a systematic literature review on the use of free geospatial services as potential tools to assess built environmental characteristics related to dietary behaviour and physical activity. We included 13 studies, all published since 2010 and conducted in urban contexts, with Google Earth and Google Street View as the two main free geospatial services used. The agreement between virtual and field audit was higher for items related to objectively verifiable measures (e.g. presence of infrastructure and equipment) and lower for subjectively assessed items (e.g. aesthetics, street atmosphere, etc.). Free geospatial services appear as promising alternatives to field audit for assessment of objective dimensions of the built environment

The SPOTLIGHT virtual audit tool: a valid and reliable tool to assess obesogenic characteristics of the built environment

BACKGROUND: A lack of physical activity and overconsumption of energy dense food is associated with overweight and obesity. The neighbourhood environment may stimulate or hinder the development and/or maintenance of a healthy lifestyle. To improve research on the obesogenicity of neighbourhood environments, reliable, valid and convenient assessment methods of potential obesogenic characteristics of neighbourhood environments are needed. This study examines the reliability and validity of the SPOTLIGHT-Virtual Audit Tool (S-VAT), which uses remote sensing techniques (Street View feature in Google Earth) for desk-based assessment of environmental obesogenicity. METHODS: A total of 128 street segments in four Dutch urban neighbourhoods - heterogeneous in socio-economic status and residential density - were assessed using the S-VAT. Environmental characteristics were categorised as walking related items, cycling related items, public transport, aesthetics, land use-mix, grocery stores, food outlets and physical activity facilities. To assess concordance of inter- and intra-observer reliability of the Street View feature in Google Earth, and validity scores with real life audits, percentage agreement and Cohen\u27s Kappa (k) were calculated. RESULTS: Intra-observer reliability was high and ranged from 91.7% agreement (k = 0.654) to 100% agreement (k = 1.000) with an overall agreement of 96.4% (k = 0.848). Inter-observer reliability results ranged from substantial agreement 78.6% (k = 0.440) to high agreement, 99.2% (k = 0.579), with an overall agreement of 91.5% (k = 0.595). Criterion validity was substantial to high for most of the categories ranging from 87.3% agreement (k = 0.539) to 99.9% agreement (k = 0.887) with an overall score of 95.6% agreement (k = 0.747). CONCLUSION: These study results suggest that the S-VAT is a highly reliable and valid remote sensing tool to assess potential obesogenic environmental characteristics