Effect of flavour manipulation on ENDS (JUUL) users’ experiences, puffing behaviour and nicotine exposure among US college students
Bibliographic record
Abstract
SIGNIFICANCE: Electronic nicotine delivery system (ENDS) use has continued to increase exponentially among young people in the USA, with unique flavours being one of the most cited reasons for use. Yet, controlled studies examining the effects of restricting flavour are lacking. This study evaluates the impact of ENDS flavour manipulation on user's puffing behaviour, subjective experience, harm perception and nicotine exposure among college-aged ENDS users. METHODS: ENDS use sessions (JUUL preferred flavour vs JUUL classic tobacco flavour) in a cross-over design. Puff topography and plasma nicotine concentration were measured, and participants completed subjective experience questionnaires. RESULTS: Increases were observed on measures of satisfaction, taste, enjoyment, urges to vape/smoke, pleasure, product appeal and increased concentration following using the preferred flavour pod (p values <0.05). Compared with preferred flavour, participants in the tobacco flavour were less motivated to use it in the future (70.9 vs 19.1 scores, p<0.001), even if it was the only product on the market (75.8 vs 30.7 scores, p<0.001). While nicotine levels significantly increased in both conditions from pre to post session (p values <0.001), no significant differences were observed in nicotine boost levels or on puff topography parameters when comparing both flavour conditions. CONCLUSIONS: This pilot study provides evidence that ENDS flavours have a substantial effect in enhancing young current ENDS users' experiences, product appeal and motivation to use the product in the future. It highlights that limiting flavours could play a potential role when designing strategic policies to reduce the appeal of ENDS use among young people.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".