Associated Changes in E-cigarette Puff Duration and Cigarettes Smoked per Day

Abstract

Introduction

To examine whether changes in select measures of e-cigarette puffing topography are associated with changes in smoking behavior.

Methods

Sixteen current cigarette smokers were instructed to completely switch from smoking combustible cigarettes to using e-cigarettes over a 2-week period. The study was completed in the Southern Midwestern region of the United States. Measures included demographics, smoking history, and cigarette dependence, as well as baseline and 2-week follow-up self-reported cigarettes per day, cigarette craving and urges, exhaled carbon monoxide readings, and e-cigarette usage data (puff number, puffing time, and average puff duration) collected via the e-cigarette built-in puff counter.

Results

Over the 2-week switching period, participants significantly reduced their cigarettes per day (~80% reduction, p < .0001). Although the number of e-cigarette puffs/day remained relatively stable (p > .05), the average total e-cigarette daily puffing time increased significantly (p = .001). Users’ average puff duration increased by 91 ms/puff/d (p < .001). The percentage decrease in cigarettes smoked per day was significantly and directly related to the slope of subjects’ average puff duration over time (r(13) = .62, p = .01), such that as cigarettes per day decreased, puff duration increased. Self-reported smoking urges remained relatively stable from baseline to the end of the 2-week period (p > .05).

Conclusions

Among smokers switching to an e-cigarette, greater increases in e-cigarette puff duration was associated with greater reductions in cigarette smoking.

Implications

The current study is one of the first to examine changes in smokers’ e-cigarette puffing behavior and associated changes in cigarette consumption as they attempt to completely switch to e-cigarettes. During a 2-week switching period, participants reduced their cigarettes per day. Moreover, although e-cigarette puffs per day remained relatively stable, users’ average puff duration increased significantly. Greater increases in e-cigarette puff duration were associated with greater reductions in cigarette smoking. Understanding how to effectively use an e-cigarette to best reduce and eventually quit smoking will be necessary as smokers increasingly turn to these products to facilitate possible cessation.

Introduction

The use of electronic cigarettes (e-cigarettes) among smokers remains common, with recent estimates indicating that 10.8 million US adults (4.5%) have used an e-cigarette in the past 30 days.¹ Most adult smokers report using e-cigarettes to help them quit or reduce smoking²; however, research examining both the efficacy of e-cigarettes for smoking cessation and the mechanisms that influence whether a smoker will be successful in switching to an e-cigarette remain unclear.³

The available literature examining smokers’ experimenting with or switching to e-cigarettes suggests that there is a learning curve. Cross-sectional examinations of experienced e-cigarette users indicate that they are able to extract more nicotine from e-cigarettes than naive users.^4–6 It has been suggested that the increase in nicotine delivery is due to changes in puffing topography, including longer puff duration.⁷ Indeed, recent research suggests that smokers switching to e-cigarettes quickly adapt their e-cigarette puffing behavior, by taking longer and slower puffs, within about 1 week of use.⁸ However, many of these studies are limited, resorting to examining use behavior within a short time frame, in a laboratory setting, or using cig-a-like e-cigarette devices.^8–12 While finding similar results, only one study completed in a small sample of veterans, examined daily changes in smokers’ topography following switching to an e-cigarette.¹³ As such, additional research is needed examining what individuals’ e-cigarette use-patterns look like in their everyday lives over time and how they influence smoking behaviors, especially while using more advanced devices with improved nicotine delivery.

To continue to address these questions, the present pilot study examined real-time changes in e-cigarette topography and subsequent changes in smoking behavior among a sample of smokers switching to a more advanced tank-style e-cigarette that incorporated objective usage tracking, including puff frequency and duration.

Methods

Participants

Participants were recruited from a Southern Midwestern city via newspaper, flyer, radio, and social media advertisements from August 2013 to December 2013. Interested individuals contacted the study team and were screened for eligibility. Thirty-three participants were recruited as part of a larger study investigating neurophysiological and immunological effects of the transition from combustible cigarettes to e-cigarettes and randomized to a 2-week continued smoking control (n = 17) or to 2 weeks of e-cigarette use (n = 16). The present study examines the 16 participants who were instructed to switch to the study e-cigarette over the 2-week period. All participants were 18–50 years old and reported current smoking of three or more cigarettes per day for ≥1 year, biochemically confirmed with a exhaled carbon monoxide (eCO) reading of ≥4 ppm. Exclusion criteria included (1) ever use of an e-cigarette (cig-a-like or tank style); (2) current Axis I psychiatric disorder; (3) receiving anticonvulsants, stimulants, or antipsychotic medications for 3 weeks prior to baseline procedures; (4) use of sleep medications within 72 hours of the baseline assessment; (5) medical conditions or taking concomitant medications that may influence cerebral blood flow or neurological function; (6) a history of drug (other than nicotine) or alcohol abuse within 1 year; (7) current pregnancy or breast feeding; (8) a primary language other than English; or (9) meet general MRI exclusion criteria such as magnetic implants or claustrophobia.

Procedures

Eligible participants were invited to attend three in-laboratory study visits. Participants provided written informed consent at the baseline study visit, prior to beginning any study procedures. During the baseline visit, participants completed assessment measures. Participants were instructed to refrain from cigarette smoking and completely switch from combustible cigarettes to the provided e-cigarette but to record in a calendar provided the number of cigarettes they smoked per day, if they were unable to abstain.

At the baseline visit, participants completed self-report measures, eCO measurement, and were provided an e-cigarette and three 10-mL bottles of either tobacco- or menthol-flavored e-liquid depending on their cigarette flavor preference. Participants were also provided a calendar to track their cigarette use over the next week. Participants returned to the laboratory 1 week after the baseline visit to record usage data from the e-cigarette device, return their 7-day smoking calendar, and provided a new 7-day calendar. At the final visit, 2 weeks after baseline, participants returned their calendar and e-cigarette device to record usage data and completed eCO measurement.

E-cigarette Device and Software

Participants were provided a second-generation e-cigarette (eCom-BT, Joyetech Co., variable wattage 5–10 W, 1.5-mL refillable tank, 650 mah battery) equipped with Bluetooth capabilities and trained on its use. Participants were provided e-liquid with a concentration of 18 mg/mL (50:50 PG/VG ratio), the median nicotine concentration used by former cigarette smokers upon initiation of e-cigarette use according to a large, representative study.¹⁴ E-cigarette usage data were viewed using the Joyetech myVapors application software, which allows a smartphone to connect to the eCom-BT e-cigarette via Bluetooth. The e-cigarette stores puffing data, and once the user connects to the myVapors app, their puffing data upload and are available for staff to record.

Measures

Demographics

At baseline, participants completed self-report measures of age, sex, ethnicity, employment status, and household income.

Nicotine Dependence and Cigarette Use History

At baseline, participants completed the Fagerstrom Test for Nicotine Dependence (FTND),¹⁵ a six-item measure of nicotine dependence. Items were summed to create total scores. FTND scores of ≥8 are indicative of high nicotine dependence. Self-reported number of years smoking, number of previous 24-hour quit attempts, and number of cigarettes per day were also assessed at baseline.

Outcome Measures

Cigarette Smoking

Participants were provided a 7-day calendar at the baseline and week 1 visit to track the number of cigarettes smoked per day during the study period. Participants returned these calendars at the Week 1 and 2 visits.

E-cigarette Puffing Behavior

Participant e-cigarette puffing patterns were measured via the eCom-BT e-cigarette device. Measures included total number of puffs and total puff duration per day, which was used to calculate total puffing time per day. Measures of e-cigarette puffing behavior were collected in real-time.

Exhaled Carbon Monoxide

eCO, a measure of smoke exposure, was collected at baseline and at the final 2-week study visit. eCO was measured using the Micro Smokerlyzer carbon monoxide monitor (Bedfont Scientific Ltd).

Smoking Urges

Cravings and urges for nicotine were measured at baseline and 2-week follow-up via the Questionnaire on Smoking Urges-Brief (QSU-Brief).¹⁶ The QSU-Brief is a 10-item, validated measure of nicotine cravings and urges. Items were summed to create a total score. Higher scores are indicative of greater nicotine craving and urge to smoke.

Data Analytic Plan

Descriptive data (M/SD) are presented for demographic and tobacco use variables. A series of paired samples t-tests were conducted to assess changes in cigarettes per day, eCO, and smoking urges from baseline to 2-week follow-up. Linear regression models were conducted to examine changes in average daily puff duration and total number of puffs per day from baseline to Week 2 follow-up. Finally, a correlation was conducted to examine the association between changes in puff duration and cigarettes per day. One participant reported loss of their e-cigarette device and is therefore missing topography data; this participant was retained in all other analyses.

Results

Participants reported smoking an average of 12.9 (SD = 5.8) cigarettes per day at baseline. Complete participant characteristics are presented in Table 1. Participants reported significantly reducing their cigarettes per day over the study period from an average of 12.9 (SD = 5.8) cigarettes per day at baseline to 2.6 (SD = 3.2) at the end of Week 2 (t(15) = 7.42, p < .0001), with corresponding reductions in eCO approaching significance (baseline: M = 15.7 ppm, SD = 7.2; Visit 2: M = 9.7 ppm, SD = 9.3; t(15) = 2.05, p = .058) and smoking urges remained relatively stable from baseline (M = 22.4, SD = 15.0) to Visit 2 (M = 20.3, SD = 10.5; t(15) = 0.72, p = .48).

Table 1.

Participant Demographics and Smoking History

Variable	N (%)/M(SD)
Age	34.2 (8.6)
Sex
Male	8 (50)
Female	8 (50)
Race
White/Caucasian	12 (75)
Native American	2 (12.5)
Not reported	2 (12.5)
Ethnicity
Hispanic/Latino	2 (12.5)
Employment statusa
Full time	6 (37.5)
Unemployed	3 (18.75)
Other	2 (12.5)
Total household incomeb
<$25 000	3 (18.75)
25 000–49 999	2 (12.5)
50 000–74 999	3 (18.75)
>$75 000	4 (25.0)
FTND	3.6 (2.0)
Years smoked	16.87 (9.85)
Number of previous 24-h quit attempts	1.43 (1.28)
Baseline CPD	12.9 (5.8)

FTND = Fagerstrom Test of Nicotine Dependence; CPD = cigarettes per day.

aFive participants chose not to provide employment status data.

^bFour participants chose not to provide household income data.

Over the 2-week period, the number of puffs per day remained relatively stable (p = .89; see Figure 1), whereas the average total daily puffing time increased by 18 s/d (r(12) = .77, r² = .59, p = .001). Users’ average duration of each puff increased by 91 ms/puff/d (r(12) = .93, r² = .86, p < .001). The percentage decrease in cigarettes smoked per day was significantly and directly related to the slope of subjects’ average puff duration over time (r(13) = .62, p = .01), such that as cigarettes per day decreased, puff duration increased.

Figure 1.

Average duration of e-cigarette use per day and number of puffs per day, measured by day over a 14-d period.

Discussion

The present study is among the first to examine how changes in smokers’ e-cigarette puffing behavior is associated with changes in their cigarette use as they attempt to completely switch to e-cigarettes. We found that while users’ number of puffs per day remained relatively stable, their puff duration increased over the 2-week study period, and those who exhibited the greatest increase in e-cigarette puff duration reported the greatest reduction in cigarette smoking. On average, users reduced their daily cigarette intake by 80%, with corresponding reductions in eCO of almost 40% and no significant change in their self-reported smoking urges.

Although users’ nicotine level was not examined here, previous research suggests that increased puff duration yields increased nicotine delivery (assuming constant e-cigarette device and e-liquid characteristics).⁷ Therefore, the pattern of results may suggest that users who are more effective in learning how to increase their nicotine yield with the e-cigarette by increasing their puff duration may be more successful in completely switching from smoking to vaping. These results may also help clarify why some smokers who try e-cigarettes remain dual users. It is possible that a large number are failed switchers who have not learned the proper technique to extract satisfying levels of nicotine from their e-cigarette to replace cigarette smoking. Given the high rate of dual e-cigarette/cigarette use, future research should examine whether educating dual users regarding how to effectively extract nicotine from their e-cigarette will increase switching rates.

The study also replicates previous findings from laboratory-based studies examining changes in smokers’ e-cigarette puffing behavior while attempting to switch. The current study demonstrated these changes with a newer generation, tank-style device and using a more ecologically valid Bluetooth-enabled e-cigarette that was able to passively collect puffing behavior in real-time. Finding valid and reliable measures of frequency, quantity, and patterns of e-cigarette use has proved problematic. Unlike conventional cigarettes that are easily quantified into cigarettes per day, it is unclear how best to measure e-cigarette use. Thus far, studies have been limited to examining usage behavior via questionably valid self-report items such as “puffs per day” or “mL of e-liquid used.” The results of the study suggest that Bluetooth-enable e-cigarettes can passively collect detailed and important e-cigarette usage behavior.

Although the present study has several important implications, it also has limitations. First, the sample size was small and therefore the results may not generalize to a larger population. Second, we did not validate the e-cigarette’s passive collection of puff duration and number of puffs, with a validated e-cigarette topography device in the laboratory, nor did we remove puffs of very short duration (<1 s), which may have been due to false presses. However, this concern is somewhat mitigated given the consistency between the present study’s results and previous studies examining changes in e-cigarette topography over time and that false presses were rarely seen in a previous study using a similar device.¹⁷ Third, although we examined changes in nicotine craving over the 2-week period, we did not examine changes in user’s nicotine levels over the same period to assess the interaction between changes in e-cigarette topography and nicotine uptake. Lastly, we did not independently verify the nicotine concentrations of the e-liquid and previous studies have found that the labeled nicotine levels in e-cigarette liquids can differ from the actual content of the product.^18,19

Overall, this novel study has important implications and generates new questions for future research. Continuing to examine mechanisms associated with switching to e-cigarettes is necessary. Understanding how to effectively use an e-cigarette to best reduce and eventually quit smoking will be necessary as smokers increasingly turn to these products to facilitate possible cessation. Large and prospective investigations with the ability to measure changes in e-cigarette usage, including topography, will probably yield important information to help achieve this goal.

Funding

This work was supported by a seed grant from the Oklahoma Tobacco Research Center (OTRC). The OTRC is funded in part by the Oklahoma Tobacco Settlement Endowment Trust. TLW is supported by funding from the US National Institutes of Health and US Food and Drug Administration (R01CA204891, U01DA045537, R21DA046333).

Author Contribution

TLW, JAA, and WKS contributed to the conception, design, interpretation and write-up. JAA and WKS the acquisition and analysis of the data; ELSL also contributed to the analysis of the data and in drafting the manuscript. All authors read and revised the final manuscript.

Declaration of Interests

None declared.