Prevalence and Factors Associated with the Use of Artificial Sweeteners in Nonpregnant, Nonlactating Females of Reproductive Age – A Systematic Review
Bibliographic record
Abstract
Background Artificial or non-nutritive sweeteners (AS/NNS) are widely consumed as sugar substitutes globally. While research often focuses on specific groups like those with diabetes or pregnant women, non-pregnant, non-lactating women of reproductive age remain understudied despite the significant implications of dietary patterns on women‘s health. Objectives This systematic review aims: (1) to summarise the global prevalence of AS/NNS consumption among non-pregnant, non-lactating women aged 15-49, (2) to identify the factors associated with AS/NNS use within this population. Methods By following PRISMA reporting guidelines and registering the protocol with PROSPERO (CRD42023450145), we conducted a comprehensive search across multiple databases, including The Cochrane Database of Systematic Reviews, CENTRAL, Campbell Library, MEDLINE, EMBASE, CINAHL, SCOPUS, Web of Science, and eLANA (World Health Organisation), covering studies up to May 2024. We included observational and experimental studies involving non-pregnant and non-lactating women aged 15-49, focusing on AS/NNS consumption and its associated factors. Data extraction was performed in duplicates, and quality assessment was conducted using the NHLBI Quality Assessment Tool. Results This review identified 15 eligible studies. The pooled prevalence of AS/NNS use among women of reproductive age was 37.1% (95% CI: 25.1% to 49.9%; heterogeneity: I 2 =99.9%). Most of the studies were conducted in high-income countries, with only one from an upper-middle-income country. All studies were cross-sectional (n= 15), with 33% high and 67% moderate quality ratings. Factors associated with AS/NNS use included people living with obesity, ethnicity (higher prevalence among white participants), and physical activity levels. However, due to a lack of sex-disaggregated data, these factors could not be analysed specifically for women, thus, a narrative synthesis was presented. Conclusion This review highlights significant AS/NNS consumption among non-pregnant, non-lactating women of reproductive age. The findings underscore the need for further research on the long-term health implications of AS/NNS use in this population.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".