An electronic, smart lighter to measure cigarette smoking: A pilot study to assess feasibility and initial validity

Measurement of cigarette smoking: Comparisons of global self-report, returned cigarette filters, and ecological momentary assessment

Prior work suggests that prospective measurement of cigarette use may be more reliable and valid than retrospective self-reports. Despite several studies comparing retrospective and prospective methods, there are a myriad of prospective methods that have not been directly compared, including spent cigarette filters that are returned to the laboratory by participants and diary logs of cigarette use on an electronic device via ecological momentary assessment. The current secondary data analysis compared the reliability of retrospective global self-report, returned cigarette filters, and electronic diary logs among a sample of cigarette smokers that also use smokeless tobacco (SLT; N = 51) over two consecutive weeks. CPD values also were compared to salivary cotinine levels to determine whether any method was associated more strongly with nicotine/tobacco exposure. Results indicated that CPD values via global self-report were significantly larger than returned filter and diary log daily averages across both weeks (t(50) = 8.28 to 9.35; p < .001). Both prospective measures showed less digit bias and more variation in smoking behavior across days than global self-reports. Only returned CPD values were correlated significantly with salivary cotinine levels (r(593) = 0.09, p = .024). Importantly, most reliability outcomes for returned filters and logged CPD did not differ significantly, suggesting that they may be comparable prospective methods for measuring cigarette use. Because returned filters and diary logs did not differ from one another, researchers' selection of a prospective measurement method should rely on considerations of participant compliance, protocol burden, and specific research questions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Characterizing and Modeling Smoking Behavior Using Automatic Smoking Event Detection and Mobile Surveys in Naturalistic Environments: Observational Study

Background

There are 1.1 billion smokers worldwide, and each year, more than 8 million die prematurely because of cigarette smoking. More than half of current smokers make a serious quit every year. Nonetheless, 90% of unaided quitters relapse within the first 4 weeks of quitting due to the lack of limited access to cost-effective and efficient smoking cessation tools in their daily lives.

Objective

This study aims to enable quantified monitoring of ambulatory smoking behavior 24/7 in real life by using continuous and automatic measurement techniques and identifying and characterizing smoking patterns using longitudinal contextual signals. This work also intends to provide guidance and insights into the design and deployment of technology-enabled smoking cessation applications in naturalistic environments.

Methods

A 4-week observational study consisting of 46 smokers was conducted in both working and personal life environments. An electric lighter and a smartphone with an experimental app were used to track smoking events and acquire concurrent contextual signals. In addition, the app was used to prompt smoking-contingent ecological momentary assessment (EMA) surveys. The smoking rate was assessed based on the timestamps of smoking and linked statistically to demographics, time, and EMA surveys. A Poisson mixed-effects model to predict smoking rate in 1-hour windows was developed to assess the contribution of each predictor.

Results

In total, 8639 cigarettes and 1839 EMA surveys were tracked over 902 participant days. Most smokers were found to have an inaccurate and often biased estimate of their daily smoking rate compared with the measured smoking rate. Specifically, 74% (34/46) of the smokers made more than one (mean 4.7, SD 4.2 cigarettes per day) wrong estimate, and 70% (32/46) of the smokers overestimated it. On the basis of the timestamp of the tracked smoking events, smoking rates were visualized at different hours and were found to gradually increase and peak at 6 PM in the day. In addition, a 1- to 2-hour shift in smoking patterns was observed between weekdays and weekends. When moderate and heavy smokers were compared with light smokers, their ages (P<.05), Fagerström Test of Nicotine Dependence (P=.01), craving level (P<.001), enjoyment of cigarettes (P<.001), difficulty resisting smoking (P<.001), emotional valence (P<.001), and arousal (P<.001) were all found to be significantly different. In the Poisson mixed-effects model, the number of cigarettes smoked in a 1-hour time window was highly dependent on the smoking status of an individual (P<.001) and was explained by hour (P=.02) and age (P=.005).

Conclusions

This study reported the high potential and challenges of using an electronic lighter for smoking annotation and smoking-triggered EMAs in an ambulant environment. These results also validate the techniques for smoking behavior monitoring and pave the way for the design and deployment of technology-enabled smoking cessation applications.

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2018-028284

Comparing video observation to electronic topography device as a method for measuring cigarette puffing behavior

BACKGROUND

Smoking topography, or puffing behavior, is an important measure of how consumers may use tobacco products. However, numerous issues may prevent collection of this data via in-person, electronic topography device (e.g., CReSS). This study compared cigarette topography measures collected by video observation and electronic device.

METHODS

Laboratory smoking sessions were video recorded and scored for 96 cigarettes collected from 34 daily, adult non-treatment-seeking smokers (73.5 % male, 82.4 % White). Participants smoked three of their preferred brand cigarettes using an electronic topography device, providing carbon monoxide (CO) samples before and after each cigarette. Analyses compared measures from both assessment methods and examined associations with device-obtained total puff volume and CO boost.

RESULTS

Agreement analyses indicated robust similarity between methods for measures of puff count and total interpuff interval (Intraclass Correlation Coefficient [ICC]'s > 0.96,p's < 0.001; Bland-Altman [B-A] plotted differences within a priori limit of clinical significance) but diverged on total duration (ICC's > .93, p's < .001, yet B-A plots outside a priori limits). Regardless of assessment method, total duration and puff count (but not total interpuff interval) predicted total puff volume (p's < .001). None predicted CO boost (p's = .07-.90)."

CONCLUSIONS

Although some topography outcomes (e.g., total puff volume) cannot be assessed via video observation, video-observed measures of puff count, total duration, and total interpuff interval are generally interchangeable with their topography device-obtained counterparts. Thus, video observation is likely a sufficient substitute method for assessing cigarette topography when using an electronic device is not possible.

Mobile, Remote, and Individual Focused: Comparing Breath Carbon Monoxide Readings and Abstinence Between Smartphone-Enabled and Stand-Alone Monitors

INTRODUCTION

Newly available, smartphone-enabled carbon monoxide (CO) monitors are lower in cost than traditional stand-alone monitors and represent a marked advancement for smoking research. New products are promising, but data are needed to compare breath CO readings between smartphone-enabled and stand-alone monitors. The purpose of this study was to (1) determine the agreement between the mobile iCO (Bedfont Scientific Ltd) with two other monitors from the same manufacturer (Micro+ pro and Micro+ basic) and (2) determine optimal, monitor-specific, cotinine-confirmed abstinence cutoff values.

METHODS

Adult (≥18) smokers (n = 26) and nonsmokers (n = 21) provided three breath CO samples (using three different monitors) in each of 10 sessions, and urine cotinine was measured for gold standard determination of abstinence. CO comparisons (N = 437) were analyzed using regression-based Bland-Altman Analysis of Agreement; receiver operating characteristics curves were used to determine optimal abstinence cutoffs.

RESULTS

Bland-Altman analyses indicated that the iCO monitor provided higher CO results than both Micro+ monitors. Sensitivity and specificity analyses showed that the optimal CO cutoff for determining abstinence was <3 ppm for the Micro+ pro (88% sensitivity, 93% specificity) and Micro+ basic (83% sensitivity, 98% specificity), but was higher for the iCO (<6 ppm; 73% sensitivity, 100% specificity).

CONCLUSIONS

Relative to both Micro+ monitors, the smartphone-enabled iCO provided systematically higher CO values and required a higher cutoff to reliably determine smoking abstinence. This does not indicate that CO values obtained using the iCO are not valid; instead, these results suggest that monitor-specific abstinence cutoffs are needed to ensure accurate bioverification of smoking status.

IMPLICATIONS

Results from this study indicate that CO values from the smartphone-enabled iCO should not be used interchangeably with the stand-alone Micro+ pro and Micro+ basic, particularly when lower CO values (<10 ppm) are critical (ie, determination of abstinence vs confirming smoking status for study inclusion). Optimal CO cutoffs recommended for determining abstinence on Micro+ and iCO monitors are at <3 and <6 ppm, respectively.