Variations in Daily Cigarette Consumption on Work Days Compared With Nonwork Days and Associations With Quitting: Findings From the International Tobacco Control Four-Country Survey

Cooper, Jae; Borland, Ron; Yong, Hua‐Hie; Hyland, Andrew; Cummings, K. Michael

doi:10.1093/ntr/nts110

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…We also assessed daily CPD over just 3 consecutive weekdays of 1 week, and longer duration, inclusion of the weekend, or a longer interval between assessments of daily CPD may reveal greater (or lesser) variability in the frequency of daily cigarette intake (e.g., Cooper, Borland, Yong, Hyland, & Cummings, 2012). Yet, to facilitate comparison between the methods of assessing CPD, we wanted to conduct the retrospective screening and prospective daily self-monitoring assessments in close temporal proximity (mean of about 2 weeks between screening and end of self-monitoring assessments).…”

Section: Discussionmentioning

confidence: 99%

Consistency of daily cigarette smoking amount in dependent adults.

Perkins

Jao

Karelitz

2013

Psychology of Addictive Behaviors

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Self-reported cigarettes per day (CPD) is a very common screening, as well as dependent or independent, measure in clinical and non-clinical research on smoking, but consistency of CPD across days in dependent smokers is uncertain. Adult dependent smokers (N=357; 170 men, 187 women) retrospectively reported “usual” CPD at screening and then prospectively self-monitored CPD on 3 consecutive days of one week during an ad libitum baseline period. Participants were those recruited for later tests of brief medication effects in those with high (n=170) versus low (n=187) interest in quitting smoking soon (within 3 months). Consistency was determined by intraclass correlation (ICC). Prospective daily CPD was generally consistent (ICC = 0.78, 95% CI of 0.74–0.81), but CPD changed (increase or decrease) by 5 cigarettes/day or more in 40% of participants and by at least 10/day in 10%. Consistency in CPD was greater in higher dependent smokers and in women with low (vs. high) quit interest, but consistency tended to be greater in men with high (vs. low) quit interest. Although retrospectively reported CPD at screening was consistent with the overall mean for prospectively monitored daily CPD, 15% of participants differed by at least 5/day between methods, and digit bias was twice as likely with retrospective versus prospective CPD, which was at chance levels. Understanding variability in CPD may improve knowledge of dependence and factors that foster or discourage daily smoking amount, but precise assessment of daily CPD likely requires prospective monitoring.

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Section: Discussionmentioning

confidence: 99%

Consistency of daily cigarette smoking amount in dependent adults.

Perkins

Jao

Karelitz

2013

Psychology of Addictive Behaviors

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“…However, participants who were inconsistent with the number of butts returned (e.g., ϮϾ2 cigarettes), relative the number of cigarettes provided, were disqualified from the study, and thus not included in these analyses. As mentioned previously, the observed pattern of smoking behavior may also have been influenced by various environmental factors not measured here (Chapman, Haddad, & Sindhusake, 1997;Cooper, Borland, Yong, Hyland, & Cummings, 2013;Hatsukami, Morgan, Pickens, & Champagne, 1990;Henningfield & Griffiths, 1981). For instance, butt counts were measured only during weekdays (Tuesday to Friday), and smoking behavior has shown to differ during the weekends (Bailey et al, 2012).…”

Section: Limitationsmentioning

confidence: 91%

Measurement of smoking behavior: Comparison of self-reports, returned cigarette butts, and toxicant levels.

Blank¹,

Breland²,

Enlow³

et al. 2016

Experimental and Clinical Psychopharmacology

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A basic tenet of empirical research on cigarette smoking behavior is the systematic assessment of patterns of use. However, the large majority of extant research relies on smokers’ retrospective reports of their average number of cigarettes per day (CPD), a measure that may be variable in terms of reliability and validity. Using data from three previously published studies of non-treatment seeking daily smokers (combined N=89), this analysis examined the reliability of self-reported CPD, the consistency of returned cigarette butts each day over four consecutive 24-hour periods, the validity of self-reported CPD as compared to returned cigarette butts, and the relationship of CPD and returned cigarette butts to toxicant exposure. Results showed that self-reported CPD was reliable across telephone and in-person screening interviews (r = 0.87; p <.01). While average self-reported CPD and returned cigarette butt counts did not differ significantly [t(87) = −1.5 to 0.3; all n.s.], butt counts revealed a wider range of variability in daily smoking behavior. In addition, self-reported cigarette use exhibited substantial digit bias (Whipple’s Index = 413.8), meaning that participants tended to round their estimates to values ending in zero or five. Cigarette butt counts, but not self-reported CPD, were associated significantly with exposure to smoke toxicants. However, this former relationship was revealed to be linear, but not curvilinear, in nature. These findings have implications for both research and treatment efforts, as researchers often rely on accurate assessment of CPD to predict a variety of smoking-related outcomes.

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“…Nation-wide surveys have shown differences in cigarette consumption between nonwork days and workdays [ 27 ]. As the smokers in this study were office workers, we assessed the differences between the days at work and at home, so the smoking records from all smokers during the study period of 4 weeks were aggregated and rescaled by the maximums for the weekdays and weekends.…”

Section: Resultsmentioning

confidence: 99%

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

Zhai¹,

Stiphout²,

Schiavone³

et al. 2022

JMIR Mhealth Uhealth

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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

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Variations in Daily Cigarette Consumption on Work Days Compared With Nonwork Days and Associations With Quitting: Findings From the International Tobacco Control Four-Country Survey

Cited by 5 publications

References 14 publications

Consistency of daily cigarette smoking amount in dependent adults.

Consistency of daily cigarette smoking amount in dependent adults.

Measurement of smoking behavior: Comparison of self-reports, returned cigarette butts, and toxicant levels.

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

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