Eating at Food Outlets and “On the Go” Is Associated with Less Healthy Food Choices in Adults: Cross-Sectional Data from the UK National Diet and Nutrition Survey Rolling Programme (2008–2014)
Bibliographic record
Abstract
Eating location has been linked with variations in diet quality including the consumption of low-nutrient energy-dense food, which is a recognised risk factor for obesity. Cross-sectional data from 4736 adults aged 19 years and over from Years 1–6 of the UK National Diet and Nutrition Survey (NDNS) Rolling Programme (RP) (2008–2014) were used to explore food consumption patterns by eating location. Eating location was categorized as home, work, leisure places, food outlets and “on the go”. Foods were classified into two groups: core (included in the principal food groups and considered important/acceptable within a healthy diet) and non-core (all other foods). Out of 97,748 eating occasions reported, the most common was home (67–90% of eating occasions). Leisure places, food outlets and “on the go” combined contributed more energy from non-core (30%) than from core food (18%). Analyses of modulating factors revealed that sex, income, frequency of eating out and frequency of drinking were significant factors affecting consumption patterns (p < 0.01). Our study provides evidence that eating patterns, behaviours and resulting diet quality vary by location. Public health interventions should focus on availability and access to healthy foods, promotion of healthy food choices and behaviours across multiple locations, environments and contexts for food consumption.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".