Post-puff respiration measures on smokers of different tar yield cigarettes

Secondary indoor air pollution and passive smoking associated with cannabis smoking using electric cigarette device-demonstrative in silico study

With electronic (e)-liquids containing cannabis components easily available, many anecdotal examples of cannabis vaping using electronic cigarette devices have been reported. For electronic cigarette cannabis vaping, there are potential risks of secondary indoor air pollution from vapers. However, quantitative and accurate prediction of the inhalation and dermal exposure of a passive smoker in the same room is difficult to achieve due to the ethical constraints on subject experiments. The numerical method, i.e., in silico method, is a powerful tool to complement these experiments with real humans. In this study, we adopted a computer-simulated person that has been validated from multiple perspectives for prediction accuracy. We then conducted an in silico study to elucidate secondary indoor air pollution and passive smoking associated with cannabis vaping using an electronic cigarette device in an indoor environment. The aerosols exhaled by a cannabis vaper were confirmed to be a secondary emission source in an indoor environment; non-smokers were exposed to these aerosols via respiratory and dermal pathways. Tetrahydrocannabinol was used as a model chemical compound for the exposure study. Its uptake by the non-smoker through inhalation and dermal exposure under a worst-case scenario was estimated to be 5.9% and 2.6% of the exhaled quantity from an e-cigarette cannabis user, respectively.

How can we best mitigate unintended passive smoking in an indoor environment? As marijuana tends to be legalized in more countries, there is an increasing number of cases of vaping cannabis using e-cigarette devices. E-cigarette vaping is presumed to cause relatively low levels of indoor air pollution due to the absence of a direct combustion process. In this study, we developed a numerical simulation model to quantitatively predict the impact of first- and second-hand cannabis vaping in an indoor environment. The study was conducted in response to vulnerable residents who are concerned regarding the deterioration of indoor air quality and informs policymakers of the potential risk of second-hand cannabis vaping exposure.

A numerical investigation of the potential effects of e-cigarette smoking on local tissue dosimetry and the deterioration of indoor air quality

Electronic (e)-cigarette smoking is considered to be less harmful than traditional tobacco smoking because of the lack of a combustion process. However, e-cigarettes have the potential to release harmful chemicals depending on the constituents of the vapor. To date, there has been significant evidence on the adverse health effects of e-cigarette usage. However, what is less known are the impacts of the chemicals contained in exhaled air from an e-cigarette smoker on indoor air quality, the second-hand passive smoking of residents, and the toxicity of the exhaled air. In this study, we develop a comprehensive numerical model and computer-simulated person to investigate the potential effects of e-cigarette smoking on local tissue dosimetry and the deterioration of indoor air quality. We also conducted demonstrative numerical analyses for first-hand and second-hand e-cigarette smoking in an indoor environment. To investigate local tissue dosimetry, we used newly developed physiologically based pharmacokinetic/toxicokinetic models that reproduce inhalation exposure by way of the respiratory tract and dermal exposure through the human skin surface. These models were integrated into the computer-simulated person. Our numerical simulation results quantitatively demonstrated the potential impacts of e-cigarette smoking in enclosed spaces on indoor air quality.

Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review

Globally, cigarette smoking is widespread among all ages, and smokers struggle to quit. The design of effective cessation interventions requires an accurate and objective assessment of smoking frequency and smoke exposure metrics. Recently, wearable devices have emerged as a means of assessing cigarette use. However, wearable technologies have inherent limitations, and their sensor responses are often influenced by wearers' behavior, motion and environmental factors. This paper presents a systematic review of current and forthcoming wearable technologies, with a focus on sensing elements, body placement, detection accuracy, underlying algorithms and applications. Full-texts of 86 scientific articles were reviewed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines to address three research questions oriented to cigarette smoking, in order to: (1) Investigate the behavioral and physiological manifestations of cigarette smoking targeted by wearable sensors for smoking detection; (2) explore sensor modalities employed for detecting these manifestations; (3) evaluate underlying signal processing and pattern recognition methodologies and key performance metrics. The review identified five specific smoking manifestations targeted by sensors. The results suggested that no system reached 100% accuracy in the detection or evaluation of smoking-related features. Also, the testing of these sensors was mostly limited to laboratory settings. For a realistic evaluation of accuracy metrics, wearable devices require thorough testing under free-living conditions.

Development of a Multisensory Wearable System for Monitoring Cigarette Smoking Behavior in Free-Living Conditions

This paper presents the development and validation of a novel multi-sensory wearable system (Personal Automatic Cigarette Tracker v2 or PACT2.0) for monitoring of cigarette smoking in free-living conditions. The contributions of the PACT2.0 system are: (1) the implementation of a complete sensor suite for monitoring of all major behavioral manifestations of cigarette smoking (lighting events, hand-to-mouth gestures, and smoke inhalations); (2) a miniaturization of the sensor hardware to enable its applicability in naturalistic settings; and (3) an introduction of new sensor modalities that may provide additional insight into smoking behavior e.g., Global Positioning System (GPS), pedometer and Electrocardiogram(ECG) or provide an easy-to-use alternative (e.g., bio-impedance respiration sensor) to traditional sensors. PACT2.0 consists of three custom-built devices: an instrumented lighter, a hand module, and a chest module. The instrumented lighter is capable of recording the time and duration of all lighting events. The hand module integrates Inertial Measurement Unit (IMU) and a Radio Frequency (RF) transmitter to track the hand-to-mouth gestures. The module also operates as a pedometer. The chest module monitors the breathing (smoke inhalation) patterns (inductive and bio-impedance respiratory sensors), cardiac activity (ECG sensor), chest movement (three-axis accelerometer), hand-to-mouth proximity (RF receiver), and captures the geo-position of the subject (GPS receiver). The accuracy of PACT2.0 sensors was evaluated in bench tests and laboratory experiments. Use of PACT2.0 for data collection in the community was validated in a 24 h study on 40 smokers. Of 943 h of recorded data, 98.6% of the data was found usable for computer analysis. The recorded information included 549 lighting events, 522/504 consumed cigarettes (from lighter data/self-registered data, respectively), 20,158/22,207 hand-to-mouth gestures (from hand IMU/proximity sensor, respectively) and 114,217/112,175 breaths (from the respiratory inductive plethysmograph (RIP)/bio-impedance sensor, respectively). The proposed system scored 8.3 ± 0.31 out of 10 on a post-study acceptability survey. The results suggest that PACT2.0 presents a reliable platform for studying of smoking behavior at the community level.

Cigarette Filter Ventilation and its Relationship to Increasing Rates of Lung Adenocarcinoma

The 2014 Surgeon General's Report on smoking and health concluded that changing cigarette designs have caused an increase in lung adenocarcinomas, implicating cigarette filter ventilation that lowers smoking machine tar yields. The Food and Drug Administration (FDA) now has the authority to regulate cigarette design if doing so would improve public health. To support a potential regulatory action, two weight-of-evidence reviews were applied for causally relating filter ventilation to lung adenocarcinoma. Published scientific literature (3284 citations) and internal tobacco company documents contributed to causation analysis evidence blocks and the identification of research gaps. Filter ventilation was adopted in the mid-1960s and was initially equated with making a cigarette safer. Since then, lung adenocarcinoma rates paradoxically increased relative to other lung cancer subtypes. Filter ventilation 1) alters tobacco combustion, increasing smoke toxicants; 2) allows for elasticity of use so that smokers inhale more smoke to maintain their nicotine intake; and 3) causes a false perception of lower health risk from "lighter" smoke. Seemingly not supportive of a causal relationship is that human exposure biomarker studies indicate no reduction in exposure, but these do not measure exposure in the lung or utilize known biomarkers of harm. Altered puffing and inhalation may make smoke available to lung cells prone to adenocarcinomas. The analysis strongly suggests that filter ventilation has contributed to the rise in lung adenocarcinomas among smokers. Thus, the FDA should consider regulating its use, up to and including a ban. Herein, we propose a research agenda to support such an effort.

Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research

Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Laboratory studies have elucidated the importance of several key physiological and behavioral biomarkers in the study of psychiatric disorders, but much less is known about the role of these biomarkers in naturalistic settings. These gaps are largely driven by methodological barriers to assessing biomarker data rapidly, reliably, and frequently outside the clinic or laboratory. Mobile health (mHealth) tools offer new opportunities to study relevant biomarkers in concert with other types of data (e.g., self-reports, global positioning system data). This review provides an overview on the state of this emerging field and describes examples from the literature where mHealth tools have been used to measure a wide array of biomarkers in the context of psychiatric functioning (e.g., psychological stress, anxiety, autism, substance use). We also outline advantages and special considerations for incorporating mHealth tools for remote biomarker measurement into studies of psychiatric illness and treatment and identify several specific opportunities for expanding this promising methodology. Integrating mHealth tools into this area may dramatically improve psychiatric science and facilitate highly personalized clinical care of psychiatric disorders.

Comparative Risks of Aldehyde Constituents in Cigarette Smoke Using Transient Computational Fluid Dynamics/Physiologically Based Pharmacokinetic Models of the Rat and Human Respiratory Tracts

Computational fluid dynamics (CFD) modeling is well suited for addressing species-specific anatomy and physiology in calculating respiratory tissue exposures to inhaled materials. In this study, we overcame prior CFD model limitations to demonstrate the importance of realistic, transient breathing patterns for predicting site-specific tissue dose. Specifically, extended airway CFD models of the rat and human were coupled with airway region-specific physiologically based pharmacokinetic (PBPK) tissue models to describe the kinetics of 3 reactive constituents of cigarette smoke: acrolein, acetaldehyde and formaldehyde. Simulations of aldehyde no-observed-adverse-effect levels for nasal toxicity in the rat were conducted until breath-by-breath tissue concentration profiles reached steady state. Human oral breathing simulations were conducted using representative aldehyde yields from cigarette smoke, measured puff ventilation profiles and numbers of cigarettes smoked per day. As with prior steady-state CFD/PBPK simulations, the anterior respiratory nasal epithelial tissues received the greatest initial uptake rates for each aldehyde in the rat. However, integrated time- and tissue depth-dependent area under the curve (AUC) concentrations were typically greater in the anterior dorsal olfactory epithelium using the more realistic transient breathing profiles. For human simulations, oral and laryngeal tissues received the highest local tissue dose with greater penetration to pulmonary tissues than predicted in the rat. Based upon lifetime average daily dose comparisons of tissue hot-spot AUCs (top 2.5% of surface area-normalized AUCs in each region) and numbers of cigarettes smoked/day, the order of concern for human exposures was acrolein > formaldehyde > acetaldehyde even though acetaldehyde yields were 10-fold greater than formaldehyde and acrolein.

Effect of cigarette design on biomarkers of exposure, puffing topography and respiratory parameters

Despite the lack of evidence, many reports exist which have implied that smokers inhale low-yield cigarette smoke more deeply than that of high-yield cigarettes. The objective of this study was to investigate the effect of short-term switching between smoker’s own brand and test cigarettes with different smoke yields on puffing topography, respiratory parameters and biomarkers of exposure. Participants were randomly assigned to smoke either a Test Cigarette-High Tar (TCH), for two days, and then switched to a Test Cigarette-Low Tar (TCL), for two days or the reverse order (n = 10 each sequence). Puffing topography (CReSS microdevice), respiratory parameters (inductive plethysmography) and biomarkers of exposure (BOE, urinary nicotine equivalents – NE and blood carboxyhemoglobin – COHb) were measured at baseline and on days 2 and 4. The average puffs per cigarette, puff volume and puff durations were statistically significantly lower, and inter-puff interval was significantly longer for the TCH compared to the TCL groups. Respiratory parameters were not statistically significantly different between the TCH and TCL groups. Post-baseline NE and COHb were statistically significantly lower in the TCL compared to the TCH groups. Under the conditions of this study, we found no indication of changes in respiratory parameters, particularly inhalation time and volume, between study participants smoking lower versus higher yield cigarettes. Likewise, the BOE provides no indication of deeper inhalation when smoking low- versus high-yield cigarettes. These findings are consistent with the published literature indicating smoking low-yield cigarettes does not increase the depth of inhalation.

Component-specific, cigarette particle deposition modeling in the human respiratory tract

Inhalation of cigarette smoke particles (CSP) leads to adverse health effects in smokers. Determination of the localized dose to the lung of the inhaled smoke aids in determining vulnerable sites, and identifying components of the smoke that may be responsible for the adverse effects; thus providing a roadmap for harm reduction of cigarette smoking. A particle deposition model specific to CSP was developed for the oral cavity and the lung by accounting for cigarette particle size growth by hygroscopicity, phase change and coagulation. In addition, since the cigarette puff enters the respiratory tract as a dense cloud, the cloud effect on particle drag and deposition was accounted for in the deposition model. Models of particle losses in the oral cavities were developed during puff drawing and subsequent mouth-hold. Cigarette particles were found to grow by hygroscopicity and coagulation, but to shrink as a result of nicotine evaporation. The particle size reached a plateau beyond which any disturbances in the environmental conditions caused the various mechanisms to balance each other out and the particle size remain stable. Predicted particle deposition considering the cloud effects was greater than when treated as a collection of non-interacting particles (i.e. no cloud effects). Accounting for cloud movement provided the necessary physical mechanism to explain the greater than expected, experimentally observed and particle deposition. The deposition model for CSP can provide the necessary input to determine the fate of inhaled CSP in the lung. The knowledge of deposition will be helpful for health assessment and identification and reduction of harmful components of CSP.

Lung deposition analyses of inhaled toxic aerosols in conventional and less harmful cigarette smoke: a review

Inhaled toxic aerosols of conventional cigarette smoke may impact not only the health of smokers, but also those exposed to second-stream smoke, especially children. Thus, less harmful cigarettes (LHCs), also called potential reduced exposure products (PREPs), or modified risk tobacco products (MRTP) have been designed by tobacco manufacturers to focus on the reduction of the concentration of carcinogenic components and toxicants in tobacco. However, some studies have pointed out that the new cigarette products may be actually more harmful than the conventional ones due to variations in puffing or post-puffing behavior, different physical and chemical characteristics of inhaled toxic aerosols, and longer exposure conditions. In order to understand the toxicological impact of tobacco smoke, it is essential for scientists, engineers and manufacturers to develop experiments, clinical investigations, and predictive numerical models for tracking the intake and deposition of toxicants of both LHCs and conventional cigarettes. Furthermore, to link inhaled toxicants to lung and other diseases, it is necessary to determine the physical mechanisms and parameters that have significant impacts on droplet/vapor transport and deposition. Complex mechanisms include droplet coagulation, hygroscopic growth, condensation and evaporation, vapor formation and changes in composition. Of interest are also different puffing behavior, smoke inlet conditions, subject geometries, and mass transfer of deposited material into systemic regions. This review article is intended to serve as an overview of contributions mainly published between 2009 and 2013, focusing on the potential health risks of toxicants in cigarette smoke, progress made in different approaches of impact analyses for inhaled toxic aerosols, as well as challenges and future directions.

Methodologies for the quantitative estimation of toxicant dose to cigarette smokers using physical, chemical and bioanalytical data

Methodologies have been developed, described and demonstrated that convert mouth exposure estimates of cigarette smoke constituents to dose by accounting for smoke spilled from the mouth prior to inhalation (mouth-spill (MS)) and the respiratory retention (RR) during the inhalation cycle. The methodologies are applicable to just about any chemical compound in cigarette smoke that can be measured analytically and can be used with ambulatory population studies. Conversion of exposure to dose improves the relevancy for risk assessment paradigms. Except for urinary nicotine plus metabolites, biomarkers generally do not provide quantitative exposure or dose estimates. In addition, many smoke constituents have no reliable biomarkers. We describe methods to estimate the RR of chemical compounds in smoke based on their vapor pressure (VP) and to estimate the MS for a given subject. Data from two clinical studies were used to demonstrate dose estimation for 13 compounds, of which only 3 have urinary biomarkers. Compounds with VP > 10(-5) Pa generally have RRs of 88% or greater, which do not vary appreciably with inhalation volume (IV). Compounds with VP < 10(-7) Pa generally have RRs dependent on IV and lung exposure time. For MS, mean subject values from both studies were slightly greater than 30%. For constituents with urinary biomarkers, correlations with the calculated dose were significantly improved over correlations with mouth exposure. Of toxicological importance is that the dose correlations provide an estimate of the metabolic conversion of a constituent to its respective biomarker.

The Remote Monitoring of Smoking in Adolescents

BACKGROUND/OBJECTIVES

Cigarette smoking remains the leading cause of preventable death in the United States with the vast majority of adult smokers starting prior to the age of 18. Despite the public health relevance and implications of studying smoking in adolescents, little is known about the initiation of quit attempts, the process of relapse, and the most efficacious treatment interventions in this high-risk and underserved population. Issues such as retention in research studies and accuracy of self-reports have prompted investigators to explore innovative technology-based systems to integrate into treatment studies and services delivery.

METHODS

This paper will review the remote monitoring of smoking through means of ecological momentary assessment, biochemical verification of smoking verified through video capture, physiological monitoring, and mobile-delivered interventions using self-reported smoking outcomes in adolescents, when applicable.

RESULTS

Use of remote monitoring methods in adolescent smokers has been limited thus far, though monitoring technology in adults has shown promise for understanding relapse and delivering treatment interventions.

CONCLUSIONS

Comprehensive technology-based systems that do not rely primarily on self-report to monitor smoking would be a highly fruitful and innovative avenue to explore with adolescent smokers. Technology integration holds great promise to improve health-related research, treatment delivery, cost-effectiveness, and just-in-time interventions, but its novelty comes with unique problems and concerns to be carefully considered.

The health effects of menthol cigarettes as compared to non-menthol cigarettes

Since the 1920s, menthol has been added to cigarettes and used as a characterizing flavor. The health effects of cigarette smoking are well documented, however the health effects of menthol cigarettes as compared to non-menthol cigarettes is less well studied. This review discusses menthol’s effects on 1) biomarkers of tobacco smoke exposure, 2) toxicity and cellular effects, 3) lung function and respiration, 4) pulmonary and/or vascular function, 5) allergic reactions and inflammation, and 6) tobacco-related diseases. It is concluded that menthol is a biologically active compound that has effects by itself and in conjunction with nicotine, however much of the data on the other areas of interest are inconclusive and firm conclusions cannot be drawn.

Reconciling human smoking behavior and machine smoking patterns: implications for understanding smoking behavior and the impact on laboratory studies

BACKGROUND

Recent Food and Drug Administration legislation enables the mandating of product performance standards for cigarette smoke and the evaluation of manufacturers' health claims for modified tobacco products. Laboratory studies used for these evaluations and also for understanding tobacco smoke toxicology use machines to generate smoke. The goal of this review is to critically evaluate methods to assess human smoking behavior and replicate this in the laboratory.

METHODS

Smoking behavior and smoking machine studies were identified using PubMed and publicly available databases for internal tobacco company documents.

RESULTS

The smoking machine was developed to generate smoke to allow for comparing cigarette tar and nicotine yields. The intent was to infer relative human disease risk, but this concept was flawed because humans tailor their smoking to the product, and chemical yields and toxicologic effects change with different smoking profiles. Although smoking machines also allow for mechanistic assessments of smoking-related diseases, the interpretations also are limited. However, available methods to assess how humans puff could be used to provide better laboratory assessments, but these need to be validated. Separately, the contribution of smoke mouth-holding and inhalation to dose need to be assessed, because these parts of smoking are not captured by the smoking machine. Better comparisons of cigarettes might be done by tailoring human puff profiles to the product based on human studies and comparing results across regimens.

CONCLUSIONS

There are major research gaps that limit the use of smoking machine studies for informing tobacco control regulation and mechanistic studies.