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Record W4312066884 · doi:10.2196/42544

A Novel Puff Recording Electronic Nicotine Delivery System for Assessing Naturalistic Puff Topography and Nicotine Consumption During Ad Libitum Use: Ancillary Study

2022· article· en· W4312066884 on OpenAlexvenueno aff
Xiang Gao, Melody Fewx, John Sprock, Huanhuan Jiang, Yong Gao, Yatao Liu

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsNicotineAbuse liabilityElectronic cigaretteMedicineSmoking cessationConsumption (sociology)PharmacologyPsychiatryPathology

Abstract

fetched live from OpenAlex

Background Assessing the naturalistic puff topography and associated nicotine consumption during e-cigarette use is important as such information will not only unveil how these products are being consumed in real-world conditions, but also enable investigators and regulatory bodies to conduct quantitative, accurate, and realistic harmful exposure and nicotine abuse liability risk assessments based on actual e-cigarette use. Conventional approaches cannot accurately, conveniently, and noninvasively determine e-cigarette puff topography in a natural use environment. Thus, novel technology-enabled systems that do not primarily rely on self-report mechanisms or intrusive measurements to monitor e-cigarette product use behaviors are highly desired. Objective This study aimed to explore and demonstrate the feasibility of a novel puff recording electronic nicotine delivery system (PR-ENDS) device for measuring naturalistic puff topography and estimating nicotine consumption during the ad libitum use of products among smokers and vapers. Methods An ancillary data analysis based on a completed parent study was conducted. The parent study was a 1-way randomized controlled open-label puff topography and nicotine pharmacokinetic assessment carried out in 24 healthy adults (12 smokers and 12 vapers). Participants were assigned a randomized product use sequence of a PR-ENDS device within 5 site visits for both controlled and ad libitum product use sessions. Blood samples were obtained for plasma nicotine analysis, and questionnaires were administered at various time points. During the ad libitum use session, puff topography was measured using a Clinical Research Support System (CReSS) device as a benchmark, as well as the PR-ENDS device with a built-in puff recording feature. Results There were no significant differences in representative puff topography parameters (number of puffs, total puff duration, and average puff duration) between the PR-ENDS and CReSS devices at the populational level across different device powers, e-liquid nicotine strengths, and flavors. The nicotine consumption estimated by the PR-ENDS device suggested that this device can be employed as a convenient monitoring tool for estimating nicotine use without measuring e-liquid weight loss between puffs. The linear relationship between nicotine consumption estimated by the PR-ENDS device and the pharmacokinetic parameter AUCad lib (plasma concentration-time curve for 1-hour ad libitum use) substantiated the potential of using this device as a pragmatic, noninvasive, and convenient means for estimating nicotine intake in the human body without blood collection. Conclusions The novel PR-ENDS device was feasible for assessing naturalistic puff topography and estimating nicotine consumption and intake in the human body during ad libitum use. Several key factors can influence users’ puff topography, including device power levels, e-liquid nicotine strengths, and flavors. The study results pave the way for further research in the real-time measurement of naturalistic puff topography and puffing behaviors in the real world.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.083
GPT teacher head0.398
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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