Smoking Antecedents: Separating Between- and Within-Person Effects of Tobacco Dependence in a Multiwave Ecological Momentary Assessment Investigation of Adolescent Smoking

Piasecki, Thomas M.; Trela, Constantine J.; Hedeker, Donald; Mermelstein, Robin J.

doi:10.1093/ntr/ntt132

Cited by 26 publications

(25 citation statements)

References 20 publications

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“…A number of EMA studies (Cronk & Piasecki, 2010; Piasecki, Trela, Hedeker, & Mermelstein, 2014; Shiffman et al, in press; Shiffman, Dunbar, et al, 2015; Shiffman et al, 2013; Shiffman et al, 2014b; Shiffman & Paty, 2006; Thrul, Bühler, & Ferguson, 2014) have now provided evidence that smoking behavior is influenced by stimulus control. The classical model of addiction holds that smoking is maintained primarily through withdrawal avoidance, with smokers smoking regularly across the day in order to maintain nicotine levels.…”

Section: Discussionmentioning

confidence: 99%

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Higher stimulus control is associated with less cigarette intake in daily smokers.

Ferguson¹,

Shiffman²,

Dunbar³

et al. 2016

Psychology of Addictive Behaviors

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It is well established that environmental stimuli influence smoking in light, and to a lesser degree, heavy smokers. A two-factor model of dependence suggests that the influence of stimulus control is masked among heavier smokers who primarily smoke for nicotine maintenance. The current study aimed to assess the influence of stimulus control across a range of moderate to heavy daily smokers. Furthermore, as local tobacco control policies may change the role of stimulus control, the study aimed to replicate previous US findings on stimulus control in an Australian setting marked by strong tobacco control policies. In two Ecological Momentary Assessment studies, 420 participants monitored antecedents of smoking and non-smoking situations. In a set of idiographic logistic regression analyses, situational antecedents were used to predict smoking occasions within each individual’s data. Linear regression analysis was used to test for the association between stimulus control and smoking rate, and to test for differences between the two samples. Daily smokers’ smoking was under considerable stimulus control, which was weaker at higher smoking rates. Overall, there was greater stimulus control in the Australian sample. Daily smokers also experience a degree of stimulus control, which is less influential in heavier smokers.

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Section: Discussionmentioning

confidence: 99%

“…This finding is also consistent with the predictions of the two-factor model of dependence. Further work is required to trace how stimulus control loses its influence as a smoker progresses to higher levels of dependence, perhaps using multiple waves of monitoring over time (Piasecki et al, 2014). …”

Section: Discussionmentioning

confidence: 99%

Higher stimulus control is associated with less cigarette intake in daily smokers.

Ferguson¹,

Shiffman²,

Dunbar³

et al. 2016

Psychology of Addictive Behaviors

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“…The binary dependent variable (i.e., whether a smoking or non-smoking prompt is reported) was captured repeatedly and was thus nested within individuals. Therefore, the outcome was modeled as a function of each individual contextual predictor with random intercepts to account for individual variation in baseline smoking probability (Piasecki et al, 2014). Data analysis was conducted using PROC GLIMMIX on SAS v. 9.4.…”

Section: Methodsmentioning

confidence: 99%

“…In combination, this information can provide a richer picture of smokers’ inner states and environments and highlight opportunities for promoting cessation and relapse prevention. A number of studies have used EMA methodologies to explore between-and within-person contexts of smoking, including heterogeneity in smoking patterns among non-daily smokers (Shiffman, Kirchner, Ferguson, & Scharf, 2009), the influence of varying levels of nicotine dependence on contextual correlates of smoking among adolescents (Piasecki, Trela, Hedeker, & Mermelstein, 2014), immediate psychological antecedents among heavy smokers (Shiffman et al, 2002), and the role of negative affect on subsequent smoking lapse (Shiffman et al, 2007). Micro-level information of this nature is not readily captured using standard cross-sectional methods, which rely heavily on participants’ ability to recall and synthesize information (e.g., “Do you smoke when you feel stressed?”).…”

mentioning

confidence: 99%

Using Ecological Momentary Assessment to Identify Common Smoking Situations Among Korean American Emerging Adults

et al. 2016

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The present study provides detailed contextual information about smoking habits among young Korean American smokers with the goal of characterizing situations where they are most at risk for smoking. Relevant situational factors included location, social context, concurrent activities, time of day, affective states, and food and beverage consumption. Using ecological momentary assessment (EMA) over 7 days, participants (N=78) were instructed to respond to smoking prompts (n=2,614) and non-smoking prompts (n=2,136) randomly scheduled throughout the day. At each prompt, participants completed a short survey about immediate contextual factors. We used multilevel models to evaluate the association between contextual factors and smoking and further explored the distribution of smoking locations and concurrent activities across each social context and reason for smoking. Compared to non-smoking events, smoking events were associated with being outside, the presence of Korean friends, socializing, consuming alcohol, and experiencing more stress relative to one’s average stress level (all p’s < 0.01). Further analyses involving only smoking events showed that when participants smoked alone, they were most commonly at home (50%) and most often studying/working (28%). When smoking with Korean friends, participants were most often outside (38%) and socializing (54%). When smoking to reduce craving, participants were most often at home (39%) and studying/working (25%). To our knowledge, this is the first study to provide detailed descriptions of real-time smoking contexts among young Korean American smokers. Information with this level of granularity is needed to develop effective just-in-time adaptive interventions (JITAIs) for smoking cessation.

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“…These overall rates of compliance are slightly lower than what is reported in EMA studies, which tends to be around 75-85% (47-49). EMA compliance rates specifically among youth smokers typically fall anywhere between 70-90% (22, 23, 50-52), though rates as low as 50% have been reported (53). It should be noted that compliance in this study included sessions that should have been prompted, but were not prompted due to the underreporting of cigarettes.…”

Section: Discussionmentioning

confidence: 99%

Acceptability and compliance with a remote monitoring system to track smoking and abstinence among young smokers

McClure

Tomko

Carpenter

et al. 2018

The American Journal of Drug and Alcohol Abuse

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Background Similar to adult smokers, quit attempts among younger smokers almost inevitably result in relapse. Unlike adults, less is known about the process of relapse in this younger age group. A technology-based remote monitoring system may allow for detailed and accurate characterization of smoking and abstinence and would help to improve cessation strategies. Objectives This study describes a mobile system that captures smoking using breath carbon monoxide (CO) and real-time self-reports of smoking behavior. Compliance, feasibility, acceptability, and accuracy of the system were measured during a quit attempt and subsequent monitoring period. Methods The mobile application (My Mobile Monitor; M3) combined breath CO with ecological momentary assessment, delivered via smartphone. Participants (N=16; 75% female) were daily smokers between the ages of 19-25, who used the app for 11 days during which they agreed to make a quit attempt. Acceptability, compliance, and abstinence were measured. Results Participants averaged 22.3 ± 2.0 years old and smoked an average of 13.0 ± 6.1 cigarettes per day. Overall session compliance was 69% and during the quit attempt, 56% of participants abstained from smoking for at least 24 hours. Agreement between self-reported smoking compared to breath CO was generally high, when available for comparison, though underreporting of cigarettes was likely. Conclusions This study demonstrates feasibility of a remote monitoring app with younger smokers, though improvements to promote compliance are needed. Remote monitoring to detect smoking and abstinence represents a step forward in the improvement of cessation strategies, but user experience and personalization are vital.

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Smoking Antecedents: Separating Between- and Within-Person Effects of Tobacco Dependence in a Multiwave Ecological Momentary Assessment Investigation of Adolescent Smoking

Cited by 26 publications

References 20 publications

Higher stimulus control is associated with less cigarette intake in daily smokers.

Higher stimulus control is associated with less cigarette intake in daily smokers.

Using Ecological Momentary Assessment to Identify Common Smoking Situations Among Korean American Emerging Adults

Acceptability and compliance with a remote monitoring system to track smoking and abstinence among young smokers

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