The impact of eating alone on food intake and everyday eating routines: A cross-sectional study of community-living 70- to 75-year-olds in Sweden
Bibliographic record
Abstract
Abstract Background Eating is fundamental not only to survival and health, but also to how humans organise their social lives. Eating together with others is often seen as the healthy ideal, while eating alone is highlighted as a risk factor for negative health outcomes, especially among older adults. This paper, therefore, investigates the relationship between the frequency and subjective experience of eating alone and food-related outcomes among 70- to 75-year-olds in Sweden. Methods A survey was distributed to a nationally representative random sample of 1500 community-living in Sweden, aged 70–75 years. Two different constructs of eating alone (objective and subjective) were measured, along with the following food-related outcomes: a food index, intake of food groups, consumption of ready-made meals, number of main meals per day, and body mass index (BMI). Results In total, 695 respondents were included in the final sample. A quarter of the respondents were categorised as eating alone, of which a small proportion reported that doing so bothered them. There were no associations between eating alone and food index scores, BMI, or intake frequencies of fruits and berries, or fish and shellfish. However, people eating alone were less likely to report eating three meals per day (OR: 0.53, CI: 0.37–0.76, p = 0.006), less likely to report higher intake frequencies of vegetables and snacks, sugary foods, and sweet drinks (adjusted OR: 0.68, CI: 0.48–0.95, p = 0.023 resp. OR: 0.59, CI: 0.43–0.81, p = 0.001), and more likely to report higher intake frequencies of ready-made meals (adjusted OR: 3.71, CI: 2.02–6.84, p < 0.001) compared to those eating together with others. The subjective experience of eating alone did not have an impact on food-related outcomes. Conclusion Eating alone or with others played a role in participants’ food intake, and seemed to influence aspects of the organisation of everyday eating routines rather than overall dietary healthiness or weight status. Our findings add to the previous body of research on commensality, eating alone, and health among the older population, providing insights into the development of future health policies and research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".