OBJECTIVE: Online supermarkets are increasingly used both by consumers and as a source of data on the food environment. We compared product availability, nutritional information, front-of-pack (FOP) labelling, price and price promotions for food and drink products between physical and online supermarkets. DESIGN: For physical stores, we collected data on price, price promotions, FOP nutrition labels and nutrition information from a random sample of food and drinks from six UK supermarkets. For online stores, we used foodDB, a research-ready dataset of over 14 million observations of food and drink products available in online supermarkets. SETTING: Six large supermarket stores located near Oxford, UK. PARTICIPANTS: General sample with 295 food and drink products, plus boost samples for both fruit and vegetables, and alcohol. RESULTS: In the general sample, 85 % (95 % CI 80, 90 %) of products found in physical stores could be matched with an online product. Nutritional information found in the two settings was almost identical, for example, concordance correlation coefficient for energy = 0·995 (95 % CI 0·993, 0·996). The presence of FOP labelling and price promotions differed between the two settings (Cohen's kappa = 0·56 (95 % CI 0·45, 0·66) and 0·40 (95 % CI 0·26, 0·55), respectively). Prices were similar between online and physical supermarkets (concordance correlation coefficient > 0·9 for all samples). CONCLUSIONS: Product availability, nutritional information and prices sourced online for these six retailers are good proxies of those found in physical stores. Price promotions and FOP labelling vary between the two settings. Further research should investigate whether this could impact on health inequalities.
OBJECTIVE: Online supermarkets are increasingly used both by consumers and as a source of data on the food environment. We compared product availability, nutritional information, front-of-pack (FOP) labelling, price and price promotions for food and drink products between physical and online supermarkets. DESIGN: For physical stores, we collected data on price, price promotions, FOP nutrition labels and nutrition information from a random sample of food and drinks from six UK supermarkets. For online stores, we used foodDB, a research-ready dataset of over 14 million observations of food and drink products available in online supermarkets. SETTING: Six large supermarket stores located near Oxford, UK. PARTICIPANTS: General sample with 295 food and drink products, plus boost samples for both fruit and vegetables, and alcohol. RESULTS: In the general sample, 85 % (95 % CI 80, 90 %) of products found in physical stores could be matched with an online product. Nutritional information found in the two settings was almost identical, for example, concordance correlation coefficient for energy = 0·995 (95 % CI 0·993, 0·996). The presence of FOP labelling and price promotions differed between the two settings (Cohen's kappa = 0·56 (95 % CI 0·45, 0·66) and 0·40 (95 % CI 0·26, 0·55), respectively). Prices were similar between online and physical supermarkets (concordance correlation coefficient > 0·9 for all samples). CONCLUSIONS: Product availability, nutritional information and prices sourced online for these six retailers are good proxies of those found in physical stores. Price promotions and FOP labelling vary between the two settings. Further research should investigate whether this could impact on health inequalities.
Authors: Devorah Riesenberg; Kathryn Backholer; Christina Zorbas; Gary Sacks; Anna Paix; Josephine Marshall; Miranda R Blake; Rebecca Bennett; Anna Peeters; Adrian J Cameron Journal: Am J Public Health Date: 2019-08-15 Impact factor: 9.308
Authors: Stephanie B Jilcott Pitts; Shu Wen Ng; Jonathan L Blitstein; Alison Gustafson; Mihai Niculescu Journal: Public Health Nutr Date: 2018-10-19 Impact factor: 4.022
Authors: Amber L Pearson; Pieta R Winter; Ben McBreen; Georgia Stewart; Rianda Roets; Daniel Nutsford; Christopher Bowie; Niamh Donnellan; Nick Wilson Journal: PLoS One Date: 2014-03-20 Impact factor: 3.240
Authors: Ashkan Afshin; José L Peñalvo; Liana Del Gobbo; Jose Silva; Melody Michaelson; Martin O'Flaherty; Simon Capewell; Donna Spiegelman; Goodarz Danaei; Dariush Mozaffarian Journal: PLoS One Date: 2017-03-01 Impact factor: 3.240
Authors: Dagfinn Aune; Edward Giovannucci; Paolo Boffetta; Lars T Fadnes; NaNa Keum; Teresa Norat; Darren C Greenwood; Elio Riboli; Lars J Vatten; Serena Tonstad Journal: Int J Epidemiol Date: 2017-06-01 Impact factor: 7.196
Authors: Peter Scarborough; Vyas Adhikari; Richard A Harrington; Ahmed Elhussein; Adam Briggs; Mike Rayner; Jean Adams; Steven Cummins; Tarra Penney; Martin White Journal: PLoS Med Date: 2020-02-11 Impact factor: 11.069
Authors: Damian Maganja; Mia Miller; Kathy Trieu; Tailane Scapin; Adrian Cameron; Jason H Y Wu Journal: Curr Atheroscler Rep Date: 2022-02-09 Impact factor: 5.967
Authors: Louise Mc Grath-Lone; Matthew A Jay; Ruth Blackburn; Emma Gordon; Ania Zylbersztejn; Linda Wiljaars; Ruth Gilbert Journal: Int J Popul Data Sci Date: 2022-04-27