Assessing the impact of dietary choices on fiber deficiency: insights from the 2017–2020 Polish national adult nutrition survey
Bibliographic record
Abstract
Introduction Dietary fiber is a key component of a healthy diet, associated with a reduced risk of cardiovascular disease, obesity, type 2 diabetes, certain cancers, chronic inflammation, or depression. The aim of the study was to perform an in-depth analysis of dietary fiber intake in the Polish population, taking account of the consumption of groups of products that are fiber sources and identify any age-related differences in the dietary fiber intake of the subjects. Methods We analyzed data obtained from two representative cross-sectional studies on the diet and nutritional status of adult Polish residents including the total of 4,000 individuals aged 19 years and more. Two 24-h recalls were used per individual to assess the diet using the computer-assisted personal interview (CAPI) technique. Total fiber content and fiber contained in cereal products, vegetables, fruits, legumes, nuts and seeds were calculated. Fiber intake was compared to the recommendations: 25 g/d for adults up to 65 years of age and 20 g/d for those aged 66 years and older. All statistical analyses, including the Pearson’s chi-squared test, the Student’s t -test, and the Analysis of Variance (ANOVA), were conducted using STATISTICA ™ version 13.3, with the results being adjusted for demographic distribution biases to enhance the representativeness. Results The average daily fiber intake was 17.83 ± 0.14 g/day (78% of the recommended intake), with 20.5% of respondents meeting the requirement. More men than women (27.05% vs. 14.3%;) met the requirement and men were characterized by a higher average intake (19.34 ± 0.20 g/day) than women (16.43 ± 0.19 g/day). The main fiber sources were cereals (44.1%), vegetables (23.6%), and fruits (16.0%). As regards men, the sources included refined bread (25.8%), vegetables (23.1%), and fruits (10.2%) and for women, they were vegetables (24.0%), fruits (17.2%), and refined bread (16.3%). Although refined bread is not recommended as a primary fiber source due to its lower fiber content compared to whole grain bread, its high consumption significantly contributed to the total fiber intake. Conclusion The prevalence of widespread dietary fiber deficiency calls for the intensification of educational efforts that address the health advantages and sources of dietary fiber, as well as methods for its inclusion in daily meals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".