Exposure to Potentially Harmful E-Cigarette Emissions via Vape Tricks: Protocol for a Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: The number of adolescents and adults using e-cigarettes, referred to as vaping, has dramatically increased. E-cigarettes can be used to perform vape tricks by inhaling and exhaling the e-cigarette aerosol in patterns to create visual effects or large clouds. To create these effects, the puffing patterns associated with vape tricks may be different than standard ad-lib e-cigarette usage. The prevalence of vape tricks and the harm associated with exposure to e-cigarette emissions when performing vape tricks is currently unknown. OBJECTIVE: Our objectives are to characterize duration, heart rate, respiratory rate, tidal volume, minute volume, and physical activity metrics associated with the performance of vape tricks and to characterize the emission of e-cigarettes when performing vape tricks in a manner suitable to inform novel exposure modeling. METHODS: The study will recruit e-cigarette users with a history of performing vape tricks. Data collection will occur in two different sessions. In the first session, participants will be asked to puff on their e-cigarette as they normally would for 20 minutes. The second session will be a vape tricks session, where users will be asked to perform a series of up to five different vape tricks with their e-cigarette. Data will be collected through screener surveys, in-person interviews, video recordings, a personal exposure monitor, and a biometric garment. RESULTS: Data analysis is pending and scheduled to take place in the fall of 2019. CONCLUSIONS: This study will be used to assess the feasibility of using a biometric garment to complement environmental and observational data. The approach may provide greater insight into the health risks of performing vape tricks compared to typical e-cigarette use. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12304.
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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 | 0.036 | 0.024 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.070 | 0.025 |
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".