Literature DB >> 33925712

Dietary Patterns Derived from UK Supermarket Transaction Data with Nutrient and Socioeconomic Profiles.

Stephen D Clark1, Becky Shute2, Victoria Jenneson1, Tim Rains2, Mark Birkin1, Michelle A Morris3.   

Abstract

Poor diet is a leading cause of death in the United Kingdom (UK) and around the world. Methods to collect quality dietary information at scale for population research are time consuming, expensive and biased. Novel data sources offer potential to overcome these challenges and better understand population dietary patterns. In this research we will use 12 months of supermarket sales transaction data, from 2016, for primary shoppers residing in the Yorkshire and Humber region of the UK (n = 299,260), to identify dietary patterns and profile these according to their nutrient composition and the sociodemographic characteristics of the consumer purchasing with these patterns. Results identified seven dietary purchase patterns that we named: Fruity; Meat alternatives; Carnivores; Hydrators; Afternoon tea; Beer and wine lovers; and Sweet tooth. On average the daily energy intake of loyalty card holders -who may buy as an individual or for a household- is less than the adult reference intake, but this varies according to dietary purchase pattern. In general loyalty card holders meet the recommended salt intake, do not purchase enough carbohydrates, and purchase too much fat and protein, but not enough fibre. The dietary purchase pattern containing the highest amount of fibre (as an indicator of healthiness) is bought by the least deprived customers and the pattern with lowest fibre by the most deprived. In conclusion, supermarket sales data offer significant potential for understanding population dietary patterns.

Entities:  

Keywords:  big data; dietary assessment; dietary patterns; nutrients; nutrition analytics; socioeconomic; transaction data

Year:  2021        PMID: 33925712     DOI: 10.3390/nu13051481

Source DB:  PubMed          Journal:  Nutrients        ISSN: 2072-6643            Impact factor:   5.717


  15 in total

1.  Seven unique food consumption patterns identified among women in the UK Women's Cohort Study.

Authors:  D C Greenwood; J E Cade; A Draper; J H Barrett; C Calvert; A Greenhalgh
Journal:  Eur J Clin Nutr       Date:  2000-04       Impact factor: 4.016

2.  The Automated Self-Administered 24-hour dietary recall (ASA24): a resource for researchers, clinicians, and educators from the National Cancer Institute.

Authors:  Amy F Subar; Sharon I Kirkpatrick; Beth Mittl; Thea Palmer Zimmerman; Frances E Thompson; Christopher Bingley; Gordon Willis; Noemi G Islam; Tom Baranowski; Suzanne McNutt; Nancy Potischman
Journal:  J Acad Nutr Diet       Date:  2012-06-15       Impact factor: 4.910

3.  Update of the Healthy Eating Index: HEI-2015.

Authors:  Susan M Krebs-Smith; TusaRebecca E Pannucci; Amy F Subar; Sharon I Kirkpatrick; Jennifer L Lerman; Janet A Tooze; Magdalena M Wilson; Jill Reedy
Journal:  J Acad Nutr Diet       Date:  2018-09       Impact factor: 4.910

4.  Development of a UK Online 24-h Dietary Assessment Tool: myfood24.

Authors:  Michelle C Carter; Salwa A Albar; Michelle A Morris; Umme Z Mulla; Neil Hancock; Charlotte E Evans; Nisreen A Alwan; Darren C Greenwood; Laura J Hardie; Gary S Frost; Petra A Wark; Janet E Cade
Journal:  Nutrients       Date:  2015-05-27       Impact factor: 5.717

5.  Cohort Profile: The UK Women's Cohort Study (UKWCS).

Authors:  Janet E Cade; Victoria J Burley; Nisreen A Alwan; Jayne Hutchinson; Neil Hancock; Michelle A Morris; Diane E Threapleton; Darren C Greenwood
Journal:  Int J Epidemiol       Date:  2017-04-01       Impact factor: 7.196

6.  Large-scale loyalty card data in health research.

Authors:  Jaakko Nevalainen; Maijaliisa Erkkola; Hannu Saarijärvi; Turkka Näppilä; Mikael Fogelholm
Journal:  Digit Health       Date:  2018-11-29

7.  The use of commercial food purchase data for public health nutrition research: A systematic review.

Authors:  Lauren Bandy; Vyas Adhikari; Susan Jebb; Mike Rayner
Journal:  PLoS One       Date:  2019-01-07       Impact factor: 3.240

8.  Tesco Grocery 1.0, a large-scale dataset of grocery purchases in London.

Authors:  Luca Maria Aiello; Daniele Quercia; Rossano Schifanella; Lucia Del Prete
Journal:  Sci Data       Date:  2020-02-18       Impact factor: 6.444

9.  Assessing diet in a university student population: a longitudinal food card transaction data approach.

Authors:  E L Wilkins; M Galazoula; M A Morris; S D Clark; M Birkin
Journal:  Br J Nutr       Date:  2020-03-05       Impact factor: 3.718

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  3 in total

Review 1.  Dietary Fibre Intervention for Gut Microbiota, Sleep, and Mental Health in Adults with Irritable Bowel Syndrome: A Scoping Review.

Authors:  Ran Yan; Lesley Andrew; Evania Marlow; Kanita Kunaratnam; Amanda Devine; Ian C Dunican; Claus T Christophersen
Journal:  Nutrients       Date:  2021-06-23       Impact factor: 5.717

2.  Exploring the Geographic Variation in Fruit and Vegetable Purchasing Behaviour Using Supermarket Transaction Data.

Authors:  Victoria Jenneson; Graham P Clarke; Darren C Greenwood; Becky Shute; Bethan Tempest; Tim Rains; Michelle A Morris
Journal:  Nutrients       Date:  2021-12-30       Impact factor: 5.717

3.  A systematic review of supermarket automated electronic sales data for population dietary surveillance.

Authors:  Victoria L Jenneson; Francesca Pontin; Darren C Greenwood; Graham P Clarke; Michelle A Morris
Journal:  Nutr Rev       Date:  2022-05-09       Impact factor: 6.846

  3 in total

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