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. Author manuscript; available in PMC: 2023 Nov 1.
Published in final edited form as: Addict Behav. 2022 Jun 16;134:107413. doi: 10.1016/j.addbeh.2022.107413

Understanding daily life experiences of women who smoke: The role of smoking-related weight control expectancies

Tyler B Mason 1, Cheldy Martinez 1, Genevieve F Dunton 1,2, Britni R Belcher 1, Raina D Pang 1,2
PMCID: PMC9755458  NIHMSID: NIHMS1846915  PMID: 35728423

Abstract

Smoking-related weight control expectancies are a motivational factor for maintaining cigarette use, particularly among women. Yet, less research has investigated the physiological and behavioral daily life weight-related experiences of women with smoking-related weight control expectancies. Increased research could contribute to understanding of maintenance factors for this group of smokers as well as unique intervention targets. Female smokers completed a baseline survey of smoking-related weight control expectancies and 35-days of ecological momentary assessment of physiological (i.e., smoking-related reduction in hunger, end-of-day perceived weight gain and bloating) and behavioral (i.e., daily exercise and sitting) weight-related experiences. Higher smoking-related weight control expectancies were associated with perceived smoking-related reductions in hunger, and end-of-day perceived weight gain. Smoking-related weight control expectancies did not significantly associate with end-of-day bloating, daily exercise, or sitting. Given these findings, smoking-related weight control expectancies may maintain smoking to reduce hunger and to cope with perceived fluctuations in weight in daily life. It is critical for smoking cessation programs to assess smoking-related weight control expectancies and implement targeted treatments for these women.

Keywords: Smoking for weight control, hunger, perceived weight gain, ecological momentary assessment, physical activity, female smokers

1. Introduction

Smoking is a global leading cause of premature death (Lariscy et al., 2018). Studies have shown that compared to men, women have a harder time successfully quitting smoking (Smith et al, 2015; 2017). Thus, understanding factors maintaining cigarette smoking in women could inform efforts for tailored smoking cessation interventions for women. Given that nicotine is an appetite suppressant (Farris et al., 2018; Piñeiro et al., 2016), one factor that may be particularly relevant to female smokers is smoking-related weight control expectancies, or the expectations of the utility of smoking to assist with weight control. Studies suggest that women generally report higher smoking-related weight control expectancies compared to men (Brandon & Baker, 1991; Thomas et al., 2008; Urb & Demetrovics, 2010).

Smoking-related weight control expectancies may be particularly important for women because of societal pressure for thinness that disproportionally affects women (Buote et al., 2011; Fredrickson & Roberts, 1997). Consistently, Fiissel and Lafreniere (2006) showed that women who were current smokers exhibited higher body surveillance, internalization of cultural body standards, and stronger beliefs that one has control over physical appearance compared to never smokers. In addition, in a cross-sectional study of undergraduate women, Copeland and Carney (2003) found that greater smoking-related weight control expectancies mediated the relationship between dietary restraint and disinhibited eating and smoking status, suggesting a pathway by which elevated dysregulated eating may lead to smoking. Research has shown that smoking days were associated with less food responsiveness compared to non-smoking days among individuals who are highly restrained (Perkins et al., 1995).

Smoking-related weight control expectancies are associated with lower self-efficacy to quit smoking (Thomas et al., 2008). Additionally, smoking-related weight control expectancies mediated the association between sex and maladaptive smoking characteristics (i.e., smoking experiential avoidance and barriers to cessation) with women exhibiting greater smoking-related weight control expectancies and in turn higher maladaptive smoking characteristics (Garey et al., 2019); although higher smoking-related weight control expectancies w associated with greater 1-week abstinence. This research highlights the importance of smoking-related weight control expectancies in relation to women’s smoking outcomes, yet less research has studied associations between smoking-related weight control expectancies and women’s daily life experiences. Specifically, understanding the association of smoking-related weight control expectancies with weight- and appetite-related perceptions and behaviors in daily life could lead to novel within-day treatment targets.

The current secondary analysis aimed understand how smoking-related weight control expectancies predicts women’s daily life experiences. Therefore, the current study used ecological momentary assessment (EMA) to examine associations between smoking-related weight control expectancies and weight- and appetite-related perceptions and behaviors in women who currently smoke. EMA captures real-time data on experiences and behaviors across the day for a short period of time. Obesity status and nicotine dependence were included in models to account for possible confounding. The study had three aims:

Aim 1 assessed whether smoking-related weight control expectancies associated with reduced hunger cigarette effects. While research has supported the role of nicotine as a physiological appetite suppressant (Miyata et al., 1999) and reduced food responsiveness on smoking days (Perkins et al., 1995), hunger has both objective and subjective elements (e.g., hedonic hunger; Howard et al., 2020). As such, more research is needed to understand the extent to which women who smoke for weight control actually experience reduced hunger following smoking a cigarette in daily life. We hypothesized that smoking-related weight control expectancies would be positively associated with greater perceived reduction in hunger after smoking.

Aim 2 assessed whether smoking-related weight control expectancies associated with more perceived negative weight-related symptoms (i.e., perceived weight gain and bloating)? Women who smoke who experience more perceived daily weight gain or bloating may be more likely to report using smoking for weight control in order to manage these fluctuations, either actual or perceived. Consistently, research has shown that women with smoking-related weight control expectancies report more body image concerns (Fiissel & Lafreniere, 2006). Understanding how smoking-related weight control expectancies predicts women’s daily life body experiences can provide insight into what factors may maintain these expectancies and points of opportunity for modifying these expectancies (e.g., such as body interoception interventions; Fischer et al., 2017). We hypothesized that smoking-related weight control expectancies would be positively associated with daily perceived weight gain and feeling bloated.

Aim 3 assessed whether are smoking-related weight control expectancies associated with less exercise and more sitting? It is possible that smoking may be used for weight control opposed to more healthful strategies (such as exercise). In addition, women who spend a high degree of time sitting may use alternative weight management, such as smoking, strategies to counteract lower activity levels. We hypothesized that smoking-related weight control expectancies would be associated with lower exercise and greater sitting.

2. Method

2.1. Participants and Procedure

This was a secondary data analysis of a larger study focused on hormones and smoking across the menstrual cycle (Pang et al., 2020). Participants included non-treatment seeking premenopausal female smokers recruited from the Los Angeles metropolitan area. This study was approved by an institutional review board, and all participants provided informed consent. Inclusion criteria required participants to (a) be 18-40 years of age, (b) be a female with self-reported regular menstrual cycles lasting 24-35 days in the past 3 months, (c) be a regular smoker (≥8 cigarettes/day) for at least the past year, (d) have normal or corrected eyesight, (e) be fluent in English, and (f) own a smartphone. Exclusion criteria included: (a) a baseline breath carbon monoxide (CO) level of < 9 ppm, (b) current use of nicotine replacement therapy, psychiatric medication implicated in smoking cessation, or regular use of any other non-cigarette tobacco products, (c) past 3-month use of hormonal medication including birth control or intent to start hormonal medication in the next 35 days, (d) history of hysterectomy or intent to obtain hysterectomy in the next 35 days, (e) a desire to quit or to substantially reduce smoking in the next 35 days, (f) pregnancy or breastfeeding in the past 6 months or intent to get pregnant in the next 35 days.

Eligibility was assessed during in-person screening as part of a baseline session. A total of 121 women were screened; 101 female smokers were eligible at baseline and enrolled in the study and 20 were ineligible. Eligible participants completed baseline measures administered using REDCap electronic data capture tools (Harris et al., 2009), downloaded the commercially available LifeData app (www.lifedatacorp.com) onto their own smartphone, and were trained on EMA and saliva collective protocols. Participants were then enrolled for 35-days of EMA including: 1) event-contingent prompts indicating they are about to smoke the first cigarette of the day or another cigarette; 2) event-contingent prompts upon the completion of smoking the first cigarette of the day; 3) four signal-contingent prompts randomly delivered to their phones; 4) one fixed interval prompt completed upon waking up for the day; and 5) one fixed interval prompt prior to going to bed. Participants were contacted approximately twice per week throughout participation to review compliance and were paid at the end of every week they were enrolled in the study.

2.2. Measures

2.2.1. Baseline session measures.

2.2.1.1. Demographics and smoking characteristics.

An author-constructed questionnaire assessed demographics (e.g., age, race/ethnicity), cigarette use history (e.g., cigarettes per day, years of regular smoking) and physical characteristics (e.g., weight, height). Body mass index (BMI) was calculated using self-reported height and weight, and women were categorized as having obesity (BMI≥30) or not (BMI<30). The 6-item Fagerström Test for Cigarette Dependence (FTCD; Fagerstrom, 2012) assessed cigarette dependence severity. Scores range from 0-10 with higher scores indicating greater cigarette dependence. FTCD was included as a planned covariate given the association of cigarette dependence with weight control motives (Pinto et al, 1999).

2.2.1.2. Smoking-related weight control expectancies.

The 5-item Weight Control subscale (e.g., “Smoking controls my appetite”) of the Smoking Consequence Questionnaire (SCQ; Bandon & Baker, 1991; Wetter et al.,1994) was used to assess smoking-related weight control expectancies. Participants rated each item on a visual analogue scale from “not true of me (0) to “very true of me” (100). The rating scale was changed from the original measure. As such, the psychometric properties of this modified SCQ-Weight Control subscale were examined. A factor analysis showed that the items represented one unidimensional 5-item factor, which accounted for 81.39% of the variance. The items showed good reliability (Cronbach’s alpha=.94). Scores were also positively correlated with scores on the Smoking Abstinence Questionnaire – Weight subscale (r=.53, p<.001).

2.2.2. EMA measures.

2.2.2.1. Reduction in hunger from smoking.

Following indication they just smoked the first cigarette of the day and at random signal contingent prompts, participants rated the extent to which they experienced cigarette effects. At random prompts, women received this item regardless of when they indicated last smoking a cigarette. The current study used one item: “Did smoking reduce your hunger for food?”. Responses were recorded on a scale ranging from “not at all” (1) to “extremely” (6).

2.2.2.2. Perceived weight gain and bloating.

At the fixed time bedtime survey, participants completed the 10-item Premenstrual Assessment Form (PAF; Allen et al, 1991) that measures negative affect and physical symptoms that tend to increase during the luteal phase of menstrual cycles. However, the items were asked using a daily timeframe and did not refer to them as menstrual symptoms. Two items were used from this scale (i.e., “Today, did you experience any weight gain?; “Today, did you experience feeling bloated?”); items were examined separately. Participants were instructed to rate the severity of symptoms experienced “right now” on a slider scale from “not at all” (1) to “extremely” (6).

2.2.2.3. Exercise and sitting.

At the fixed time bed survey, participants reported on daily exercise and sitting. Daily exercise was measured by the following question, “Today, how much time did you spend doing exercise or sports? (only include activities that make your heart beat faster and your breathing harder).” Responses included: “None” (1), “Less than 15 minutes” (2), “15 to almost 30 minutes” (3), “30 minutes to almost an hour” (4), “1 hour to almost 2 hours” (5) or, “2 hours or more” (6). Sedentary behavior was assessed with the following question, “Today, how much time did you spend sitting? (please add together all the time spent sitting including while-driving, at work, in class, at home, watching TV, etc.).” Participants responded with “Less than 2 hours” (1), “2 to almost 4 hours” (2), “4 to almost 6 hours” (3), “6 to almost 8 hours” (4), “8 to almost 10 hours” (5), “10 to almost 12 hours” (6), or “12 or more hours” (7).

2.3. Statistical Analyses

Analyses were completed using SPSS version 25.0 (IBM; Armonk, NY). Descriptive statistics and EMA compliance were computed. Also, analyses comparing women who did versus did not receive the EMA perceived hunger item on demographics, study variables, and compliance were conducted. Separate general estimating equations (GEEs) using an AR1 serial autocorrelation with gamma functions, due to non-normality of outcomes, were used to assess associations between baseline smoking-related weight control expectancies and EMA and daily outcomes (i.e., EMA perceived hunger reduction, end-of-day weight gain, end-of-day bloating, end-of-day exercise, and end-of-day sitting). Models also included obesity versus no obesity and smoking dependence score. As a sensitivity analysis, the model for EMA perceived hunger reduction was ran again only including times when a cigarette was just smoked.

3. Results

Two participants were removed from analyses: one who experienced an episode of psychosis and one who became pregnant during the EMA data collection period. Of the remaining 99 participants, 76 were completers, having completed all 35-days of EMA, and 23 were non-completers. Reasons for non-completion were low EMA compliance (<65% for a week and a half; n=12), technical issues with their phone (e.g., excessive survey issues, data not being received; n=7), and loss of interest (n=4). Day 1 EMA data were used as practice days for learning the EMA protocol, and this data was excluded from all analyses, leaving up to 34 days of data for each participant. Participants completed an average of 28.80 study days (SD=9.97). Compliance to random signals was 71.78% (SD=30.86%). Analyses included all available data from completers and non-completers, but one person had no data for the EMA items used in the current study, leaving 98 participants for analysis. There were no differences between completers and non-completers on age, education, race/ethnicity, baseline cigarettes per day, years of regular smoking, or cigarette dependence severity (ps>.05).

The hunger reduction item was added later in the study and thus was only completed by 55 participants during EMA. Women who received the EMA perceived hunger item differed on race/ethnicity but no other measures (see Supplementary Table 1). Race/ethnicity was added as a covariate in EMA perceived hunger analyses to account for unequal representation. Across all participants, there were a total of 2433 bedtime surveys completed and 3771 surveys (with the hunger reduction item) completed after smoking. Mean compliance for bedtime surveys were 72.28 (SD=31.64), and the mean. Participants completed post-smoking surveys (with the hunger reduction item) on 24.53 days (SD=10.85).

Regarding race, the sample was 30% White, 31% Black, 34% Other, and 4% were missing race information; and regarding ethnicity, the sample was 15.2% Hispanic. Twenty-eight percent of women met criteria for obesity (BMI≥30). Most of the sample had some college (46%) or a college degree (32%) with 19% having a high school degree and 3% less than high school. Most women were single, never married (66%) with 13% currently living with a partner, 7% married, 13% divorced, and 1% separated. Descriptive statistics for other demographic and study variables are displayed in Table 1.

Table 1.

Descriptive Statistics of Demographics and Study Variables

M SD Min Max
Age, years 31.66 5.63 20.00 40.00
BMI, kg/m2 35.99 31.05 16.11 81.14
Years smoking 12.48 6.32 2.00 26.00
FTND score 4.69 1.99 0.00 9.00
Smoking weight control motives 35.98 31.05 0.00 100.00
EMA reduce hunger 2.76 1.65 1.00 5.00
EMA relief 3.25 1.54 1.00 6.00
EMA weight gain 1.85 1.24 1.00 6.00
EMA bloating 2.06 1.41 1.00 6.00
EMA physical activity 2.74 1.60 1.00 6.00
EMA sitting 3.46 1.55 1.00 7.00
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Note. BMI=body mass index; FTND=Fagerstrom Test of Nicotine Dependence; EMA=ecological momentary assessment

Table 2 shows estimates for the GEE model for perceived reduction in hunger. Race and nicotine dependence were unrelated to EMA perceived reduction in hunger. Higher smoking-related weight control expectancies were associated with greater EMA perceived reduction in hunger. Obesity status predicted lower EMA perceived reduction in hunger such that women with obesity reported less reduction in hunger from smoking. A sensitivity analysis including only EMA prompts in which women indicated just having smoked a cigarette were conducted for EMA perceived reduction in hunger; the results remained the same in this model (see Supplementary Table 2).

Table 2.

General Estimating Equations of Smoking-Related Weight Control Expectancies and Obesity Status Predicting EMA Perceived Hunger Reduction

EMA Perceived hunger reduction
Estimate SE df p
Intercept 0.17 0.24 1 .47
Race/ethnicity1
 Asian −0.30 0.19 1 .12
 Black 0.06 0.16 1 .72
 Hispanic 0.19 0.17 1 .28
 Multiracial 0.10 0.16 1 .54
 Other Race 0.36 0.20 1 .07
FTND 0.03 0.03 1 .29
Obesity −0.39 0.12 1 .001
SCQWC 0.01 0.001 1 <.001
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Note. Level 1 N=3771; Level 2 N=55; Quasi Likelihood under Independence Model Criterion (QIC)=1608.09; EMA=ecological momentary assessment; FTND=Fagerstrom Test of Nicotine Dependence; SCQWC=Smoking Consequence Questionnaire – Weight Control;

1

Non-Hispanic White was the reference group

Table 3 shows estimates for the GEE models for end-of-day perceived weight gain and bloating as well as daily exercise and sitting. Higher weight control motives were associated with greater end-of-day perceived weight gain, but there were no significant predictors of end-of-day bloating or swelling. There were no predictors of daily exercise or sitting.

Table 3.

General Estimating Equations of Smoking-Related Weight Control Expectancies and Obesity Status Predicting EMA Experiences and Behaviors

EMA Bloating EMA Weight gain
. Level 1 N=2406; Level 2 N=98; QIC=1042.98 . Level 1 N=2404; Level 2 N=98; QIC=957.97
Estimate SE df p Estimate SE Df P
Intercept 0.60 0.13 1 <.001 0.47 0.14 1 <.001
FTND 0.02 0.02 1 .30 0.02 0.03 1 .56
Obesity −0.02 0.10 1 .87 0.03 0.11 1 .76
SCQWC 0.000 0.001 1 .94 0.003 0.001 1 .048
EMA Physical activity EMA Sitting
. Level 1 N=2388; Level 2 N=98; QIC=1013.96 . Level 1 N=2274; Level 2 N=93; QIC=663.40
Estimate SE df p Estimate SE df p
Intercept 1.12 0.13 1 <.001 1.30 0.14 1 <.001
FTND −0.02 0.02 1 .26 −0.01 0.02 1 .65
Obesity 0.04 0.09 1 .69 0.07 0.10 1 .47
SCQWC 0.001 0.001 1 .66 0.001 0.001 1 .39
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Note. EMA=ecological momentary assessment; QIC= Quasi Likelihood under Independence Model Criterion; FTND=Fagerstrom Test of Nicotine Dependence; SCQWC=Smoking Consequence Questionnaire – Weight Control

4. Discussion

This study examined three research questions related to the association of smoking-related weight control expectancies on weight- and appetite-related perceptions and behaviors in daily life. Support was found for our first hypothesis such that women with greater smoking-related weight control expectancies reported less pereived hunger after smoking. This provides evidence that women who smoke for weight control do experience lower perceived feelings of hunger after smoking in daily life, which could include both objective and subjective hunger. Given that smoking for weight control is associated with disordered eating and associated eating cognitions (George & Waller, 2005; Mason et al., 2022; Pomerleau et al., 1993), women may experience less food cravings and urges to eat after smoking; thus, contributing to maintenance of smoking behavior.

There was partial support for our second hypothesis, such that women who reported higher smoking weight control motives reported more perceived daily weight gain but not bloating. Given the self-report nature of the weight gain measure, women may have experienced actual or perceived weight gain. Associations between smoking weight control motives and perceived weight gain would be consistent with previous research on smoking weight control motives and body dissatisfaction (Fiissel & Lafreniere, 2006). That is, women who are dissatisfied with their body are more likely to use unhealthy weight control strategies, such as smoking for weight control. Further, women who experience more day-to-day fluctuations in weight might be more likely to report smoking for weight control to try to manage these fluctuations.

The hypothesis for physical activity and sitting was not supported as there were no associations between smoking weight control motives and daily exercise or sitting. Some research suggests those who smoke have lower physical activity levels, yet one review determined the relationship is confounded by other factors and may differ by age, gender, and exercise intensity (Bloom et al, 2012; Kaczynski et al., 2008).

There were several study limitations. This study included only self-report measures in EMA and did not examine diet, caloric consumption, or eating behaviors. Future studies should examine associations between smoking-related weight control expectancies and dietary intake, eating behaviors, actual daily weight gain, and objectively measured physical activity and sedentary time. In addition, we only studied individual differences in smoking-related weight control expectancies measured at the start of the study; smoking-related weight control expectancies may fluctuate within-day and in certain contexts (Brandon et al., 1999; McKee et al., 2003), which is an important area for future research. Further, examining smoking-related weight control expectancies only at baseline did not allow for examination of more complex bi-directional predictive associations between smoking-related weight control expectancies and daily experiences. The hunger reduction item was not given to all participants since it was added later in the study, yet women who received versus did not receive this item did not differ on key study variables. Future research is necessary to ensure the generalizability of the findings from this study. Also, the rating scale for the measure of smoking-related weight control expectancies used in the current study was different from the original measure; although, good psychometric properties were found for the measure. Finally, this study did not assess eating disorders; it will be important for future studies to examine differences in associations by eating disorder pathology (Mason et al., 2022).

Current findings showed that women with elevated smoking-related weight control expectancies may maintain smoking to reduce hunger and due to perceived fluctuations in weight in daily life. Given this evidence, it is imperative for smoking cessation programs to assess for smoking-related weight control expectancies to provide appropriate treatment and to achieve optimal outcomes. Smoking cessation interventions should provide healthy weight control practices to women who report smoking-related weight control expectancies. For example, Sallit and colleagues (2009) found evidence of the efficacy of a cognitive-behavioral weight control program for women who smoked and had elevated weight concerns in improving eating and smoking outcomes.

Supplementary Material

Supplementary Tables

Role of Funding Sources:

This project was supported in part by grants K01DA040043 from the National Institute on Drug Abuse (NIDA) and K01DK124435 from the National Institute of Diabetes and Digestive and Kidney Diseases Award Number (NIDDK). The content is solely the responsibility of the authors and does not necessarily represent the official views of NIDA or NIDDK. The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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