Association Between Household Online Grocery Delivery Service Use and Food and Drink Purchase Behavior in England: Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND: Online grocery delivery services (OGDSs) are a popular way of acquiring food. However, it is unclear whether OGDS use is associated with the healthiness of purchases and whether there are sociodemographic differences in OGDS use. If so, the increased prevalence of OGDS use may have implications for population diet, and differential OGDS use could contribute to diet inequalities. OBJECTIVE: This study aimed to examine whether OGDS use varies by sociodemographic characteristics and is associated with the amount and types of groceries purchased. METHODS: Item-level take-home food and drink purchase data (n=3,233,920 items) from households in London and the North of England were available from the 2019 UK Kantar fast-moving consumer goods panel (N=1911). Purchases were categorized as being bought online or in-store. We used logistic regression to estimate the likelihood of an above-median frequency of OGDS use by sociodemographic characteristics. We used Poisson regression to estimate the differences in energy and nutrients purchased by households that had above- and below-median OGDS use and the proportion of energy purchased from products high in fat, salt, and sugar (HFSS) online versus in-store among households that used both shopping methods (n=665). RESULTS: In total, 668 (35%) households used OGDSs at least once in 2019. Of the households that used OGDSs, the median use was 5 occasions in 2019. Households were more likely to have above-median use in London versus in the North of England (odds ratio 1.29, 95% CI 1.01-1.65) and if they had a higher annual household income (odds ratio 1.56, 95% CI 1.02-2.38 for ≥£50,000 [US $64,000] vs <£20,000 [$25,600]). Households with above-median OGDS use had a higher weekly mean purchase of energy by 1461 (95% CI 1448-1474) kcal per person compared with households with below-median OGDS use. For households that used a combination of in-store and online shopping, HFSS products made up a lower proportion (-10.1%, 95% CI -12% to -8.1%) of energy purchased online compared to in-store. CONCLUSIONS: Differences in grocery purchases between households with above- and below-median OGDS use could have positive or negative consequences. The extra energy purchased among households with above-median OGDS use could lead to overconsumption or food waste, which has negative consequences for population and environmental health. Alternatively, this extra energy may be replacing out-of-home purchasing, which tends to be less healthy, and may be beneficial for the population diet. Households made fewer HFSS purchases when shopping online compared to in-store, which may be due to differences in the shopping environment or experience, such as fewer promotions and advertisements when shopping online or not having to transport and carry purchases home. As higher-income households used OGDS more frequently, the implications of this sociodemographic pattern on dietary inequalities must be explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".