Comparison of Ecological Momentary Assessment Versus Direct Measurement of E-Cigarette Use With a Bluetooth-Enabled E-Cigarette: A Pilot Study
Notice bibliographique
Résumé
Background: Assessing the frequency and intensity of e-cigarette use presents special challenges beyond those posed by cigarette use. Accurate measurement of e-cigarette consumption, puff duration, and the stability of these measures over time will be informative for estimating the behavioral and health effects of e-cigarette use. Objective: The purpose of this pilot study was to compare the accuracy of self-reported e-cigarette puff counts collected via ecological momentary assessment (EMA) to objective puff count data collected by a Bluetooth-enabled e-cigarette device and to examine the feasibility and acceptability of using a second-generation e-cigarette among adult smokers. Methods: A total of 5 adult smokers were enrolled in a longitudinal parent study assessing how e-cigarette use affects cigarette use among e-cigarette–naïve smokers. Using a text message–based EMA system, participants reported e-cigarette puffs for 2 weeks. Participants were also given a Bluetooth-enabled e-cigarette (Smokio) that passively collected puff counts and puff duration. Comparisons between mean reports of Smokio (device-report) and EMA (self-report) use were evaluated using paired t tests. Correlation and agreement between device- and self-reports were evaluated using Pearson correlation and the concordance correlation coefficient (CCC), respectively. A linear mixed effect model was used to determine the fixed effect of timing and Smokio-reported daily puffs on report accuracy. We examined the relationship between time of day and reporting accuracy using Tukey's test for multiple pairwise comparisons. Results: A total of 5 African American participants, 4 men and 1 woman, who ranged in age from 24 to 59 years completed the study, resulting in 5180 observations (device-report) of e-cigarette use. At baseline, participants reported smoking for 5 to 25 years and consumed a mean of 7 to 13 cigarettes per day (CPD); 4 smoked within 30 minutes of waking. At the 30-day follow-up, CPD range decreased to 1 to 3 cigarettes; 4 participants reported past 7-day e-cigarette use, and 1 participant reported no cigarette smoking in the past 7 days. Over 2 weeks of e-cigarette use, participants took an average of 1074 e-cigarette (SD 779.0) puffs per person as captured by the device reports. Each participant took a mean of 75.0 (SD 58.8) puffs per day, with each puff lasting an average of 3.6 (SD 2.4) seconds. Device reports captured an average of 33.3 (SD 47.8) more puffs per person per day than the self-reported e-cigarette puffs. In 87% of days, participants underestimated the number of puffs they had taken on the Smokio. There was significant moderate correlation (r=.47, P<.001) but poor agreement (pc=0.31, 95% CI 0.15-0.46) between the device- and self-reported data. Reporting accuracy was affected by amount and timing of e-cigarette use. Conclusions: Compared to self-reported e-cigarette use, the Bluetooth-enabled device captured significantly more e-cigarette use and allowed for examination of puff duration in addition to puff counts. A Bluetooth-enabled e-cigarette is a powerful and feasible tool for objective collection of e-cigarette use behavior in the real world.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».