Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Nov 10.
Published in final edited form as: J Ethn Subst Abuse. 2024 May 10;25(2):389–406. doi: 10.1080/15332640.2024.2349308

Racial/Ethnic Minority Females Smoke more Cigarettes after Social Interaction with Others who Smoke

Crystal X Wang a, Mariel S Bello b, Matthew G Kirkpatrick c,d, Raina D Pang c,d
PMCID: PMC11550262  NIHMSID: NIHMS1992898  PMID: 38727204

Abstract

The present study investigated the effects of social interaction with others who smoke on daily cigarette use among diverse females via ecological momentary assessment methods. Ninety-eight premenopausal females (29.6% White, 70.4% racial/ethnic minority) who smoke daily reported their social interactions and cigarette use over 35-days. Greater than usual levels of social interaction with others who smoke was associated with increased cigarette use that day among racial/ethnic minority females. Future smoking cessation interventions targeting racial/ethnic minority females should consider the impact of social environments on smoking behaviors, such as the frequency of peer interactions with others who smoke.

Keywords: females who smoke, smoking, racial/ethnic minorities, social interaction

Introduction

Rates of current cigarette smoking have substantially declined overall since 1965 (American Lung Association, 2023), yet inequities in cigarette use remain persistent among racial/ethnically diverse groups, as racial/ethnic minorities continue to suffer disproportionately from tobacco-related disease, disability, and death compared to non-Latinx White populations (henceforth referred to as Whites; Cornelius et al., 2022; Centers for Disease Control and Prevention, 2016; US Department of Health and Human Services, 2014; Mowery et al., 2015; Rostron et al., 2022). For instance, American Indian/Alaskan Native populations have been shown to have the highest prevalence of current cigarette smoking relative to other racial/ethnic minorities in the U.S. (Cornelius et al., 2022), and are more likely to be disproportionately burdened by tobacco-related disease and mortality (Mowery et al., 2015). Black and Hispanic/Latinx populations are also at greater risk of lung cancer morbidity and mortality (US Department of Health and Human Services, 2014; Haiman et al., 2006; Ellis et al., 2018; Fagan et al., 2007; Howe et al., 2009), despite smoking fewer cigarettes per day (Trinidad et al., 2009; Pulvers et al., 2015; Trinidad et al., 2011) and being more likely to smoke non-daily (Pulvers et al., 2015).

Although studies have shown a lower prevalence of current cigarette smoking and fewer tobacco-related consequences among Asian Americans compared to other racial/ethnic minorities (Cornelius et al., 2022; US Department of Health and Human Services, 2014), these studies may potentially yield inaccurate estimates as the continued use of English language measures as primary assessments of tobacco use may systematically exclude or underrepresent non-English speaking Asian populations who smoke in the U.S. (Baluja et al., 2003; Lew et al., 2003). In addition, recent data demonstrates significant differences in smoking prevalence across Asian subgroups, as the prevalence of current cigarette smoking ranged from 7.6% among Chinese and Asian Indians to 20% among Korean Americans (Centers for Disease Control and Prevention, 2016). Moreover, Vietnamese and Korean Americans smoke significantly more than Whites, and nearly one-third of South Asians use smokeless tobacco, contributing to dangerously high rates of lung cancer and tobacco-related cerebrovascular diseases in these populations (Han et al., 2019; Martell et al., 2016; Truth Initiative, 2020). Finally, smoking inequalities also exist with treatment and cessation—for example, healthcare providers are significantly less likely to ask their Asian, Black, and Hispanic/Latinx patients about their smoking behaviors, advise them to quit, or provide them with appropriate smoking cessation resources than their White patients (Cokkinides et al., 2008; Franks et al., 2005; Mukherjea at al., 2014; Trinidad et al., 2011). Thus, it is necessary to determine potential mechanisms that maintain smoking behavior and contribute to tobacco-related health inequities among racial/ethnic minorities, given that in recent years, tobacco companies have continued to target their advertising to diverse, disadvantaged neighborhoods (Irvin Vidrine, 2009), placing racial/ethnic minorities at an even greater risk for tobacco-related consequences.

One critical factor that may contribute to smoking in ethnic minorities is social interaction. In general, cross-sectional research finds that having friends or family members who smoke is associated with a greater likelihood of smoking for individuals across diverse cultural groups (e.g., Martin et al., 2019; Saari et al., 2014; Thomeer et al., 2019). In a naturalistic, ecological momentary assessment (EMA) study by Huh and colleagues (2014), social interaction with peers also predicted subsequent smoking behaviors with Korean American young adults. Specifically, the authors found that being with friends at any given moment was associated with an increased likelihood of cigarette smoking (Huh et al. 2014). In a diary study, Otsuki (2009) found that Asian American college students smoked more in the presence of peers than when alone. Furthermore, Otsuki (2009) cited the cultural value of collectivism—a way of evaluating the world such that the group’s needs surpass the individual’s (Markus & Kitayama, 1991)—as a reason for their greater susceptibility to social influences on smoking. Additionally, there is literature to suggest that all racial/ethnic minorities may be higher in collectivism than Whites, and more sensitive to the perceived social norms of their peers, thus explaining engagement in problem behavior to “fit in” (Bond & Smith, 1996; Wang, 2022; Ybarra & Trafimow, 1998). Indeed, a meta-analysis found that adolescents were twice as likely to smoke if their peers smoked, and that this relationship was especially pronounced with racial/ethnic minority adolescents (Liu et al., 2017).

The impact of social interaction with others who smoke on smoking may be especially prominent for racial/ethnic minority females. In general, there are sex differences in smoking motivation with social influences more strongly predicting smoking among females (e.g., Buckner & Vinci, 2013), and some preliminary evidence for intersectionality of sex and racial/ethnic minority and social-related factors associations with smoking. For example, Black females predominantly reported that social interaction with family members who smoke contributed to their own subsequent smoking behaviors (Hanson, 1997; Woo, 2022). Other studies found that Hispanic and Asian American females were significantly more likely to smoke upon exposure to American social norms regarding smoking, and interaction with peers who smoke, whereas there is no or even an inverse relationship between familiarity with social norms and smoking behaviors among their male counterparts (Bethel & Schenker, 2005; Choi et al., 2005; Weiss & Garbanati, 2005).

Previous studies examining tobacco-related health disparities primarily utilized a cross-sectional design, focusing on differences between racial/ethnic groups. However, there have been more recent movements to deconstruct the categories of race/ethnicity and instead focus on mechanisms underlying disparities (Manly, 2006; Ross et al., 2020). Given that within-group variation is greater than between-group variation, relying on only comparisons between racial/ethnic groups may not be as informative as considering mechanisms that explain these differences, such as education, socioeconomic status, and neighborhood context (Deater-Deckard et al., 2018; Manly, 2006). In terms of social interaction, individuals experience vastly different fluctuations in social interactions on a day-to-day basis. As a result, it is especially important to consider both between-subjects (e.g., difference in amount of social interaction experienced between individuals) and within-subjects (e.g., difference in amount of social interaction an individual experiences in comparison to their own typical level of social interaction) variations in social interaction in understanding its impact on health behaviors. Although prior research found that Asian Americans smoked more in the presence of others than alone (Huh et al., 2014; Otsuki, 2009), the authors did not assess whether their peers also smoked. Furthermore, it is unclear whether these effects extend to other racial/ethnic minority groups. Finally, racial/ethnic minority females may be especially susceptible to the effects of social interaction, but prior studies have primarily focused on comparing the effects of social interaction on smoking between White and racial/ethnic minority males, and it is unclear whether this will generalize to racial/ethnic minority females. Thus, the present study will assess gaps in the literature by examining the extent of social interaction with other smokers on cigarette use with diverse females who smoke daily in a naturalistic, EMA study design. To our knowledge, this is the first study investigating the effects of within- and between-subjects variation in social interaction on smoking behaviors with females.

We hypothesized that increased within- and between-subjects variation in social interaction with other smokers will predict increased smoking behavior across participants. Based on previous research demonstrating the importance of social influence on cigarette use within racial/ethnic minority young adults (e.g., Huh et al., 2014; Liu et al., 2018; Otsuki, 2009), and particularly with minority females (e.g., Woo, 2022; Bethel & Schenker, 2005; Weiss & Garbanati, 2005), we also hypothesized an interaction between ethnicity and social interaction with people who smoke, such that the effects of social interaction with people who smoke on smoking will be greater for racial/ethnic minority than White females. Results from this study have implications for informing future culturally-sensitive smoking cessation interventions for racial/ethnic minority females who smoke.

Method

Participants

The current study is a secondary analysis of a 35-day EMA study investigating the smoking behaviors of premenopausal females who smoke (Pang et al. 2020). One hundred and one females who smoke were recruited via advertisements from communities in the greater Los Angeles area to participate in a 35-day EMA study. Inclusion criteria required participants to be: (1) 18–40 years old, (2) female with regular menstrual cycles lasting 24–35 days, (3) regular smokers of > 7 cigarettes a day for at least the past year, (4) fluent in English, (5) have normal eyesight, and (6) in possession of a smart device that was compatible with the LifeData software. Participants were excluded from the study if they had the following: (1) baseline breath carbon monoxide level above 9 ppm, (2) current use of nicotine replacement therapy or smoking cessation medication, (3) regular use of any non-cigarette tobacco products, (4) use of hormonal medication including birth control within the past 3 months, or intent to begin use in the next 35 days, (5) history of hysterectomy or intent to receive hysterectomy within the next 35 days, (6) pregnancy or breastfeeding within the past 6 months, or intention to become pregnant in the next 35 days, and (7) any medical condition that might affect the menstrual cycle.

Procedure

Participants first arrived at the laboratory to provide informed consent, complete baseline questionnaires, and have their smart devices programmed with LifeData (www.lifedatacorp.com). Next, they completed 35 days of EMA, with the first day used for practice, and the remaining 34 days included for data analyses. During the 35 days of EMA, participants could self-initiate surveys for the first and last cigarettes of the day, and also received 4 signal-contingent (random) prompts a day asking them to fill out surveys assessing smoking, social interaction, and other relevant variables. A bedtime prompt asked them to report the number of cigarettes they had smoked that day. Participants were paid weekly based on EMA compliance and returned for a second lab visit upon the completion of the 35-days of EMA. The University of Southern California Institutional Review Board approved all procedures. See Pang et al. (2020) for additional study information.

Measures

Baseline Measures were collected via REDCap. All participants completed baseline questionnaires that assessed demographic variables (e.g., race/ethnicity, age, education) at the first lab visit. Race/ethnicity was dichotomized as either White or racial/ethnic minority, given the small sample sizes, and prior research demonstrating all ethnic minorities may be higher in collectivism than Whites (e.g., Oyserman et al., 2002), and thus more susceptible to social influence (Wang, 2022). Education was categorized as: (1) less than high school, (2) high school diploma or GED, (3) some college completed or currently enrolled in college, and (4) college degree or higher. The Fagerström Test of Cigarette Dependence (FTCD) comprised of 6 self-report items assessing level of cigarette dependence severity (Fagerström, 2003; Fagerström, 2012).

EMA Measures.

Social Interaction.

For the present study, social interaction was operationalized as any direct or indirect contact with others who smoke throughout the 35 days of EMA, and was assessed during random prompts by, “Who have you been with in the last 30 minutes?” Participants could select “No one, I was alone”, “smokers”, “Non-smokers”, “Mixed; smokers and non-smokers”. Responses were coded dichotomously as with other people who smoke (1; i.e., “smokers”, “mixed; smokers and non-smokers”) or not with other people who smoke (0, i.e., “non-smokers,” “alone”). Responses from the 4 different random prompts were summed to create a day level social contact with people who smoke variable, with scores ranging from 0–4, and higher scores indicating a greater level of daily social interactions with people who smoke.

Affect.

Affect was measured through self-report questionnaires assessing the extent of participants’ experience of positive affect (i.e., happy, content, relaxed, cheerful) and negative affect (i.e., tense or anxious, sad or blue, irritable or easily angered, unable to cope or overwhelmed by ordinary demands) during the past 30 minutes on a 6-point scale. Responses from the 4 prompts each day were averaged for a composite of daily positive affect and negative affect.

Age, education, cigarette dependence, positive affect, and negative affect were included in analyses as planned covariates.

Daily Smoking Behaviors.

At the end of each day, participants completed a survey that asked, “How many cigarettes did you smoke today?” Participants selected the number of cigarettes they smoked. In total, participants completed 1,663 surveys assessing daily smoking behaviors.

Data Analysis

Multilevel linear regression models (MLM) with simple slopes were conducted for the EMA data, with repeated measures (i.e., days) nested within participants. The social contact variable was disaggregated to test for level 2 between-subjects (BS) and level 1 within-subjects (WS) variation in contact with other people who smoke. The BS variation in daily social interaction with people who smoke (BS-SI) was the individual mean deviation from the grand mean for all participants and the WS variation in social interaction with people who smoke (WS-SI) was the participant’s deviation from her own mean at any given prompt. Model 1A tested the effects of BS-SI, WS-SI, and race/ethnicity on daily smoking behavior. Model 2A included the interactions between BS-SI or WS-SI and race/ethnicity on daily smoking behaviors. Relevant covariates (i.e., age, education, cigarette dependence, positive affect, negative affect) were added to the models for Models 1B and 2B.

Results

Sample characteristics

Of the 101 participants at recruitment, 3 were excluded from analyses for disclosing a potentially psychotic episode (n =1), becoming pregnant during the study (n = 1), and for only having completed 1 day of EMA (n = 1), leaving a final sample of 98 participants for EMA analyses. The sample was diverse, with approximately 29.6% of participants identifying as White and 70.4% as racial/ethnic minorities. Of the racial/ethnic minority participants, 43.5% identified as Black, 23.2% as Hispanic, 15.9% as multiracial, 11.6% as Asian, and 5.8% as Other. There were no significant differences between Whites and racial/ethnic minorities in age, education, level of cigarette dependence, or negative affect. Throughout the 35 days of EMA, the dependent variable of number of cigarettes smoked that day (i.e., daily smoking behaviors) was normally distributed, with a mean of 8.47 cigarettes per day. Racial/ethnic minority participants reported greater positive affect than Whites (i.e., 3.55 vs. 3.24, p < .01). White participants smoked significantly more than racial/ethnic minorities (i.e., 9.82 vs. 7.87, p < .01). The intraclass correlation (ICC), which assesses the proportion of variance that can be attributed to between-group differences, was calculated to be .609, 95% CI [.547, .668], indicating that the data was highly clustered within individuals. Throughout the course of the 35 days of EMA, on average participants reported .75 daily social interactions with other people who smoke, with ethnic minority participants reporting significantly greater social interaction than Whites (i.e., .85 vs. .50, p < .01).

Main Analyses

First, analyses were run without covariates. The main analyses showed a significant association of race/ethnicity with daily smoking behavior, such that White participants smoked significantly more than racial/ethnic minorities throughout the course of the EMA study (β = −2.53, SE = .73, p < .01; Model 1A). There was not a significant association of WS-SI (β = .04, SE = .07, p >.05) or BS-SI (β = .47, SE = .55, p >.05) with daily smoking behavior. The interaction between WS-SI and race/ethnicity on daily smoking behavior (β = .42, SE = .17, p = .01; Model 2A; Figure 1) was significant. Specifically, the WS differences in social interactions with people who smoke was more predictive of cigarettes smoked that day for racial/ethnic minority participants than Whites. There was no significant interaction of race/ethnicity and BS-SI with daily smoking behavior (β = −.86, SE = 1.87, p > .05).

Figure 1.

Figure 1.

Open in a new tab

Interaction between race/ethnicity and within-subjects variation in social interaction (WS-SI) on daily smoking behaviors

Next, covariates (i.e., age, education, and cigarette dependence) were added to the model. Results showed that even when controlling for relevant covariates, White participants smoked more than ethnic minorities (β = −2.91, SE = .76, p < .01; Model 1B). There was also a significant interaction between WI-SI and race/ethnicity on daily smoking behavior (β = .41, SE = .16, p = .02; Model 2B), such that greater social interaction than the participant’s average amount was associated with more cigarettes smoked that day. There were no main effects of WS-SI (β = .03, SE = .07, p > .05) or BS-SI (β = 1.01, SEs = .61, s > .05); nor was there a significant interaction between BS-SI and race/ethnicity on daily smoking behavior (β = −1.62, SE = 1.91, p > .05). This suggests that differences in social interaction between participants had no association with cigarette use.

Results with the racial/ethnic minority group disaggregated into specific racial/ethnic groups showed similar results (e.g., stronger WS-SI association with daily cigarette use among racial/ethnic minority groups than Whites), and can be found in Appendix A.

Discussion

Our study investigated racial/ethnic differences in the effects of social interaction with people who smoke on smoking behaviors among premenopausal females in a naturalistic environment. Consistent with our hypotheses, we found a significant interaction between race/ethnicity and social interaction with people who smoke, such that the effects of within-subject variations in social interaction on cigarette use were greater for racial/ethnic minority than White females. Specifically, the racial/ethnic minority females smoked significantly more cigarettes on days they had more social interaction with people who smoke than their usual levels. This finding expands on extant literature on social influences on smoking by demonstrating social interaction with people who smoke (vs. being alone or with people who did not smoke) was associated with increased cigarette use, and that racial/ethnic minority females were more affected by social interactions with people who smoke than their White peers. Unlike prior EMA studies focusing on only Asian American populations (Huh et al., 2014; Otsuki, 2009), we showed that effects generalize to all racial/ethnic minorities, with no differences by specific racial/ethnic minority group (Appendix A).

The greater impact of social interaction with people who smoke on racial/ethnic minority females’ smoking behaviors may be due to cultural values related to intergroup relations and conformity. As noted previously, racial/ethnic minorities tend to endorse higher levels of collectivism than Whites (e.g., Oyserman et al., 2002), which may contribute to their desire to adhere to societal norms. For example, some research finds that greater conformity motives with racial/ethnic minority college students were associated with increased engagement in problem behavior, while White students’ problem behaviors are more influenced by self-enhancement motives (LaBrie et al., 2011l; Perkins et al., 1999). As for cigarette use, peer influence was associated with increased odds of smoking among individuals of all racial/ethnic backgrounds, but the effects of peer influence were especially robust among racial/ethnic minorities, who tend to be higher in collectivism (Liu et al., 2017). In a large-scale study of smoking trends across 25 countries, researchers found that rates of smoking cessation were actually slower in collectivistic societies that were primarily non-White, and explained that social pressures to start or continue smoking inhibit change (Lang et al., 2015). The authors concluded that smoking cessation strategies must be tailored towards each cultural groups, and that the usual strategies (e.g., increasing taxes on cigarettes, warnings about health risks related to tobacco use) are individualistic in nature, and thus less effective for racial/ethnic minorities (Lang et al., 2015). Our study strengthened this argument by providing information about the social environments of racial/ethnic minorities who smoke, and demonstrating the effects of social pressure on their smoking behavior on a day-to-day basis. Therefore, traditional smoking cessation interventions, which typically consist of limited sessions, may not be as effective for racial/ethnic minority females, who experience varying degrees of social pressure throughout their day.

In recent years, researchers have begun to focus on personalizing behavioral interventions for each individual, based on their specific habits and preferences, and strive to intervene in a timely manner. For example, personalized interventions have been increasingly popular for substance use disorders (e.g., Garey et al., 2021; Tomko et al., 2016; Webb et al., 2005), and demonstrate better effects than traditional interventions (e.g., Wang & Miller, 2020). However, more information is needed about momentary states and environments that predict engagement in problem behaviors before tailoring the interventions to the individual. Unlike traditional studies that only provide information about general differences between groups, EMA studies track how within-subjects variations in an individual’s daily routine affect behavior, which is especially important for assessing where and when to intervene for the best efficacy. By demonstrating the increased sensitivity racial/ethnic minority females have to social context, our study may help inform future personalized interventions with racial/ethnic minority females who smoke. For example, intervention when the racial/ethnic minority female participant is in the presence of other people who smoke may be critical for their success in quitting. Moreover, it is possible for these tailored interventions to be implemented on a widespread basis, rather than just individual substance use treatment or therapy. For example, social norms interventions have been successful in reducing engagement in problematic behaviors, such as alcohol use and racial discrimination, even when distributed through quick video presentations or poster advertisements (Murrar et al., 2020; Neighbors et al., 2011). Thus, it is possible that social norms interventions may be especially effective in reducing cigarette use in racial/ethnic minority females who smoke, as our study found them to more sensitive to social influence than the White participants, but more research is needed.

It is important to note that we did not find effects of within-subjects variation in social interaction with people who smoke on the full sample, which highlights the necessity of considering intersectionality in research. Due to the complex interactions between multiple social identities, assessing trends in each group separately omits important information about subgroups (e.g., Cole, 2009). Furthermore, the effects of the multiple marginalized social identities an individual adheres to are not additive; for example, combining research about racial/ethnic minority males and White females does not provide us with information about the experiences of racial/ethnic females. Instead, intersectionality researchers would argue that the experience of being a racial/ethnic minority woman is unique and cannot be captured through assessing each minoritized identity separately. For our study, if we did not consider gender by race factors, we might have falsely concluded that within-subjects social interaction with people who smoke did not predict smoking for our participants. We also did not find any effects of between-subjects social interaction with people who smoke on smoking behavior; nor did race/ethnicity moderate this relationship. This suggests that racial/ethnic minority females only smoked more if they had more social interactions with people who smoke that day than their usual amount, regardless of whether this differed from the grand mean. Indeed, racial/ethnic minority females reported significantly more social interactions with people who smoke than Whites (i.e., .85 vs. .50) throughout the course of EMA, but this difference did not predict higher smoking frequency among racial/ethnic minority than White females. Our results provide further evidence demonstrating the importance of assessing variation within (vs. between) groups (e.g., Manly, 2006,), and suggests that greater social influence on smoking behavior with racial/ethnic minority females is possibly due to their increased sensitivity to social norms regarding smoking—and not that they simply had more interactions with people who smoke than White females. Thus, special attention must be given to the social environment of racial/ethnic minority females when considering problematic smoking behaviors.

Although the results of our study are promising, there are several limitations that must be addressed. First, our sample only consisted of premenopausal females who smoke daily, and did not consider males. It is unclear whether our results demonstrating the effects of social interaction with people who smoke on smoking with racial/ethnic minority females would generalize to their male counterparts. While some studies show the effects of social interaction on all racial/ethnic minorities’ smoking behaviors regardless of gender (e.g., Huh et al., 2014; Otsuki, 2009), others show that females may be more sensitive to social norms regarding smoking than males (Bethel & Schenker, 2005; Choi et al., 2005; Weiss & Garbanati, 2005); thus, future studies should compare social smoking motives with a heterogenous sample of both males and females who smoke to clarify these seemingly conflicting findings in the literature. Second, we had to combine all racial/ethnic minorities into a single category for analysis, due to our small sample size. Although there is evidence that all racial/ethnic minorities may be higher in collectivism than Whites (e.g., Oyserman, 2002), differences may arise when disaggregating the racial/ethnic minority groups. For example, some research finds that Black/African Americans may be more individualistic than Latinx or Asian Americans (Coon & Kemmelmeier, 2001), potentially making them less susceptible to social norms than other racial/ethnic minorities. Third, we did not measure the strength of the participant’s relationship with smoker they had contact with, or the context in which the interaction took place. Most females who smoke, and especially racial/ethnic minority females, report having family members or friends who also smoke. Thus, contact with a family member or friend who smokes might be more impactful than contact with a less familiar individual. In addition, our racial/ethnic minority female participants may have experienced vastly different social situations based on their racial/ethnic backgrounds. Given the disparate levels of intimacy, interactions with these different peers may also have had varying levels of influence on the participant. Fourth, we did not explicitly measure collectivism or other cultural values, nor did we measure conformity motives. Given that within-group variation in cultural values is greater than between-group variation (Deater-Deckard et al., 2018), we might have found greater effects of social interaction with people who smoke on smoking with collectivistic females, regardless of racial/ethnic background. Finally, the primary aim of the parent study was to investigate hormones in a community sample of premenopausal who smoke daily, so our findings may not be generalizable to all females, or to lighter/social smokers.

In summary, the present study tested the effects of within- and between-subjects variations in social interaction with people who smoke on cigarette use with a diverse sample of premenopausal females who smoke daily. We found a significant interaction between race/ethnicity and within-subjects variation in social interaction with people who smoke, such that the effects were greater for racial/ethnic minority than White females. Our findings clarify past literature on effects of social interaction on smoking with Asian Americans (Huh et al., 2014; Otsuki, 2009) by demonstrating that 1) this association was found across different racial/ethnic minority groups, 2) the strength of the association was greater for racial/ethnic minorities than Whites and that 3) the social interactions had to be with other individuals who smoked. By utilizing a naturalistic, repeated-measures design, we were also able to expand on epidemiological studies on the effects of social pressure on smoking with racial/ethnic minorities by demonstrating that differences in even daily social interactions with people who smoke influenced cigarette use. Because smoking behavior can vary significantly from day to day, dependent on the situation, mood, and other internal or external factors, ecological momentary assessment provides special utility for capturing these nuances. Our study has implications for future personalized interventions for racial/ethnic minority females who smoke, and provides evidence that intervention when the female is in the presence of others who smoke may be especially effective for reducing smoking. Furthermore, our study provides preliminary evidence to suggest that social norms interventions may be particularly effective for reducing smoking in racial/ethnic minority females. Future studies should measure collectivism, the specific social context, and include larger sample sizes with males to test for differences in effects between genders and specific racial/ethnic minority groups.

Table 1.

Participant characteristics by racial/ethnic group

Full sample Racial/ethnic minority females White females p
Age (y) 31.90 (5.18) 32.33(5.44) 30.89(4.47) .40
Education (y) 3.13(.77) 3.12(.74) 3.17(.86) .52
FTCD total score 4.58(1.93) 4.61 (1.81) 4.50 (2.16) .20
Cigarettes/day 8.47(3.94) 7.87(3.65) 9.82(4.21) <.01
Average positive affect/day 3.46(1.14) 3.55(1.13) 3.24(1.14) <.01
Average negative affect/day 1.99(.91) 1.98(90) 2.01(.91) .50
Social interactions/day .75(.97) .85(1.03) .50(.77) <.01
Open in a new tab

Note. Values represent M(SD) unless otherwise noted. Full sample N range = 60–98 due to missing data; racial/ethnic minority N = 42–69 due to missing data; White N = 18–29 due to missing data; Education was categorized as 1 = less than high school, 2 = high school diploma or GED, 3 = some college completed or currently enrolled in college, and 4 = college degree or higher; FTCD = Fagerström Test of Cigarette Dependence.

Table 2.

Models of race/ethnicity and social interaction with people who smoke on daily smoking behavior

Model 1A Estimate (SE) Model 1B Estimate (SE) Model 2A Estimate (SE) Model 2B Estimate (SE)
Intercept 9.98 (.61)*** 2.60 (2.89) 10.18 (.75)*** 3.07 (2.94)
Race/Ethnicity −2.53(.73)*** −2.91 (.76)*** −2.72 (.85)** −3.27 (.87)***
WS-SI .04 (.07) .03 (.07) −.28 (.14)* −.28 (.14)*
BS-SI .47 (.55) 1.01 (.61) 1.24 (1.78) 2.48 (1.83)
Race/Ethnicity x WS-SI - - .42 (.17)** .41 (.16)*
Race/Ethnicity x BS-SI - - −.86 (1.87) −1.62 (1.91)
Open in a new tab

Note. Significance codes:

*

p ≤ .05

**

p ≤ .01

***

p ≤ .001. WS-SI = within-subjects variation in social interaction. BS-SI = between-subjects variation in social interaction. Reference group for race/ethnicity is racial/ethnic minorities. Models 1A & 1B are unadjusted for covariates. Models 1B& 2B are adjusted for age, education, cigarette dependence, positive affect, and negative affect.

Acknowledgments

This research was supported by funds from NIDA grant K01-DA040043. Data from the parent study has been used in two prior publications (Pang et al. 2020 & Pang et al. 2022).

Appendix

Additional analyses separating the racial/ethnic groups into the categories of White (N = 29), Black (N = 30), Hispanic/Latinx (N = 16) yielded no differences in results than when combining all racial/ethnic minority females into a single group. Specifically, WS-SI was associated with greater cigarette use among Black and Hispanic/Latinx females than White females, with post-hoc tests finding the differences between Black and White females to be significant (β = −2.77, p < .01). The sample size for the Hispanic/Latinx group was too small to conduct meaningful post-hoc analyses, but the directionality of results was in the predicted direction (i.e., WS-SI more predictive of smoking with Hispanic/Latinx than White females; β = −1.80, p > .05). There were no differences between the Black and Hispanic/Latinx females (β = −.98, p > .05), and WS-SI was positively associated with smoking for both groups. We were unable to perform any additional nuanced analyses with the multiracial (N = 11), Asian (N = 8), or other race/ethnicity (N = 4) females.

Footnotes

We have no known conflicts of interest to disclose.

Data availability statement:

The data that support the findings of this study are available from the corresponding author, [CXW], upon reasonable request.

References

  1. Aguirre CG, Bello MS, Andrabi N, Pang RD, Hendricks PS, Bluthenthal RN, & Leventhal AM (2016). Gender, Ethnicity, and Their Intersectionality in the Prediction of Smoking Outcome Expectancies in Regular Cigarette Smokers. Behavior Modification, 40(1–2), 281–302. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. American Lung Association (2023). Overall Tobacco Trends. American Lung Association. https://www.lung.org/research/trends-in-lung-disease/tobacco-trends-brief/overall-tobacco-trends [Google Scholar]
  3. Arevalo I, So D, & McNaughton-Cassill M (2015). The role of collectivism among Latino American college students. Journal of Latinos and Education, 15(1): 1–9. [Google Scholar]
  4. Baluja KF, Park J, & Myers D (2003). Inclusion of immigrant status in smoking prevalence statistics. American Journal of Public Health, 93(4): 642–646. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bates D, Mächler M, Bolker B, & Walker S (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1): 1–48. [Google Scholar]
  6. Bethel JW, & Schenker MB (2005). Acculturation and smoking patterns among Hispanics: A review. American Journal of Preventive Medicine, 29(2): 143–148. [DOI] [PubMed] [Google Scholar]
  7. Bond R, & Smith PB (1996). Culture and conformity: A meta-analysis of studies using Asch’s (1952b, 1956) line judgment task. Psychological Bulletin, 119(1): 111–137. [Google Scholar]
  8. Buckner JD, Heimberg RG, Ecker AH, & Vinci C (2013). A biopsychosocial model of social anxiety and substance use. Depression & Anxiety, 30(3): 276–284. [DOI] [PubMed] [Google Scholar]
  9. Carson LR (2009). “I am because we are.” Collectivism as a foundational characteristic of African American college student identity and academic achievement. Social Psychology of Education, 12(3): 327–344. [Google Scholar]
  10. Centers for Disease Control and Prevention. (2016). Some racial, ethnic groups continue smoking cigarettes at higher rates. Centers for Disease Control and Prevention. https://www.cdc.gov/media/releases/2016/p0804-ethnic-groups-smoking.html [Google Scholar]
  11. Choi S, Rankin SH, Stewart A, & Oka R (2008). Effects of acculturation on smoking behavior in Asian Americans: A meta-analysis. Journal of Cardiovascular Nursing, 23(1): 67–73. [DOI] [PubMed] [Google Scholar]
  12. Cokkinides VE, Halpern MT, Barbeau EM, Ward E, & Thun MT (2008). Racial and Ethnic Disparities in Smoking-Cessation Interventions: Analysis of the 2005 National Health Interview Survey. American Journal of Preventive Medicine, 34(5): 404–12. [DOI] [PubMed] [Google Scholar]
  13. Cole ER (2009). Intersectionality and research in psychology. American Psychologist, 64(3): 170–180. [DOI] [PubMed] [Google Scholar]
  14. Coon HM, & Kemmelmeier M (2001). Cultural orientations in the United States: (Re)examining differences among ethnic groups. Journal of Cross-Cultural Psychology, 32(3): 348–364. [Google Scholar]
  15. Cornelius ME, Loretan CG, Wang TW, Jamal A, & Homa DM (2022). Tobacco Product Use Among Adults — United States, 2020 (Morbidity and Mortality Weekly Report 71, Issue 11). Centers for Disease Control and Prevention. https://www.cdc.gov/mmwr/volumes/71/wr/mm7111a1.htm?s_cid=mm7111a1_w [Google Scholar]
  16. Deater-Deckard K, Godwin J, Lansford JE, Bacchini D, Silvia Bombi A, Bornstein MH, Chang L, Di Giunta L, Dodge KA, Malone PS, Oburu P, Pastorelli C, Skinner AT, Sorbring E, Steinberg L, Tapanya S, Pena Alampay L, Uribe Tirado LM, Zelli A, & Al-Hassan SM (2018). Within- and between-person and group variance in behavior and beliefs in cross-cultural longitudinal data. Journal of Adolescence, 62, 207–217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Ellis L, Canchola AJ, Spiegel D, Ladabaum U, Haile R, & Gomez SL (2018). Racial and ethnic disparities in cancer survival: the contribution of tumor, sociodemographic, institutional, and neighborhood characteristics. Journal of Clinical Oncology, 36(1): 25–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fagan P, Moolchan ET, Lawrence D, Fernander A, Ponder PK (2007). Identifying health disparities across the tobacco continuum. Addiction, Suppl 2: 5–29. [DOI] [PubMed] [Google Scholar]
  19. Fagerström K (2003). Time to first cigarette; the best single indicator of tobacco dependence? [Research Support, Non-U.S. Gov’t]. Monaldi Archives for Chest Disease, 59(1), 91–94. http://www.ncbi.nlm.nih.gov/pubmed/14533289 [PubMed] [Google Scholar]
  20. Fagerström K (2012). Determinants of tobacco use and renaming the FTND to the Fagerström Test for Cigarette Dependence. Nicotine & Tobacco Research, 14(1), 75–78. 10.1093/ntr/ntr137 [DOI] [PubMed] [Google Scholar]
  21. Franks P, Fiscella K, & Meldrum S (2005). Racial disparities in the content of primary care office visits. Journal of General Internal Medicine, 20(7): 599–603. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Garey L, Smit T, Neighbors C, Gallagher MW, & Zvolensky MJ (2021). Personalized Feedback for Smoking and Anxiety Sensitivity: A Randomized Controlled Trial. Substance Use & Misuse, 56(7), 929–940. 10.1080/10826084.2021.1900255 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Haiman CA, Stram DO, Wilkens LR, Pike MC, Kolonel LN, Hendersen BE, & Le Marchand L (2006). Ethnic and racial differences in the smoking-related risk of lung cancer. New England Journal of Medicine, 354(4): 333–342. [DOI] [PubMed] [Google Scholar]
  24. Han BH, Wyatt LC, Sherman SE, Islam NS, & Kwon SC (2020). Prevalence and correlates of cultural smokeless tobacco products among South Asian Americans in New York City. Journal of Community Health, 44(3): 479–486. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hanson MJS (1997). The theory of planned behavior applied to cigarette smoking in African-American, Puerto Rican, and Non-Hispanic White teenage females. Nursing Research, 46(3): 155–162. [DOI] [PubMed] [Google Scholar]
  26. Howe HL, Lake A, Schymura MJ, & Edwards BK (2009). Indirect method to estimate specific Hispanic group cancer rates. Cancer Causes Control, 20(7): 1215–1226. [DOI] [PubMed] [Google Scholar]
  27. Huh J, Shin H, Leventhal AM, Spruijt-Metz D, Abramova Z, Cerrada C, Hedeker D, & Dunton G (2014). Momentary negative mood and being with friends precede cigarette use among Korean American emerging adults. Nicotine & Tobacco Research, 16(9): 1248–1254. [DOI] [PubMed] [Google Scholar]
  28. Irvin Vidrine J, Reitzel LR, & Wetter DW (2009). Smoking and health disparities. Current Cardiovascular Risk Reports, 3, 403–408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kazak AE (2018). Editorial: Journal article reporting standards. American Psychologist, 73(1): 1–2. [DOI] [PubMed] [Google Scholar]
  30. Kuznetsova A, Brockhoff PB, & Christensen RHB (2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13): 1–26. [Google Scholar]
  31. LaBrie JW, Lac A, Kenney SR, & Mirza T (2011). Protective behavioral strategies mediate the effect of drinking motives on alcohol use among heavy drinking college students: Gender and race differences, Addictive Behaviors, 36, 354–361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Lang JC, Abrams DM, & De Sterck H (2015). The influence of societal individualism on a century of tobacco use: Modelling the prevalence of smoking. BMC Public Health, 15(1): 1280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Lew R, & Tanjasiri SP (2003). Slowing the epidemic of tobacco use among Asian Americans and Pacific Islanders. American Journal of Public Health, 93(5): 764–768. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Liu J, Zhao S, Chen X, Falk E, & Albarracin D (2017). The influence of peer behavior as a function of social and cultural closeness: A meta-analysis of normative influence on adolescent smoking initiation and continuation. Psychological Bulletin, 143(10): 1082–1115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Manly JJ (2006). Deconstructing race and ethnicity: Implications for measurement of health outcomes. Medical Care, 44(11): S10–16. [DOI] [PubMed] [Google Scholar]
  36. Markus HR, & Kitayama S (1991). Culture and the self: Implications for cognition, emotion, and motivation. Psychological Review, 98(2): 224–253. [Google Scholar]
  37. Murrar M, Campbell MR, & Brauer M (2020). Exposure to peers’ pro-diversity attitudes increases inclusion and reduces the achievement gap. Nature Human Behavior, 4, 889–897. [DOI] [PubMed] [Google Scholar]
  38. Martell BN, Garrett BE, & Caraballo RS (2016). Disparities in Adult Cigarette Smoking—United States, 2002–2005 and 2010–2013. (Morbidity and Mortality Weekly Report 65, Issue 30). Centers for Disease Control and Prevention. https://www.cdc.gov/mmwr/volumes/65/wr/mm6530a1.htm [DOI] [PubMed] [Google Scholar]
  39. Mowery PD, Dube SR, Thorne SL, Garrett BE, Homa DM, & Nez Henderson P (2015). Disparities in Smoking-Related Mortality Among American Indians/Alaska Natives. American Journal of Preventative Medicine, 49(5): 738–744. [DOI] [PubMed] [Google Scholar]
  40. Mukherjea A, Wackowski OA, Lee YO, & Delnevo CD (2014). Asian American, Native Hawaiian and Pacific Islander tobacco use patterns. American Journal of Health Behavior, 38(3): 362–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Neighbors C, Jensen M, Tidwell J, Walter T, Fossos N, & Lewis MA (2011). Social-norms interventions for light and nondrinking students. Group Processes & Intergroup Relations, 14(5), 651–669. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Otsuki M (2009). Social connectedness and smoking behaviors among Asian American college students: An electronic diary study. Nicotine & Tobacco Research, 11(4): 418–426. [DOI] [PubMed] [Google Scholar]
  43. Oyserman D, Coon HM, & Kemmelmeier M (2002). Rethinking individualism and collectivism: Evaluation of theoretical assumptions and meta-analyses. Psychological Bulletin, 128(1): 3–72. [PubMed] [Google Scholar]
  44. Pang RD, Guillot CR, Liautaud MM, Bello MS, Kirkpatrick MG, Huh J, & Leventhal AM (2020). Subjective effects from the first cigarette of the day vary with pre-cigarette affect in premenopausal female daily smokers. Experimental and Clinical Psychopharmacology, 28(3): 299–305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Pang RD, Tucker CJ, D’Orazio LM, Weinberger AH, & Guillot CR (2022). Affect and subjective cognitive functioning by depression symptom levels during naturalistic cigarette smoking in premenopausal females who smoke daily. Psychology of Addictive Behaviors, 36(1): 90–99. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Perkins HW, Meilman PW, Leichliter JS, Cashin JR, & Presley CA (1999). Misperceptions of the norms for the frequency of alcohol and other drug use on college campuses. Journal of American College Health, 47(6): 253–258. [DOI] [PubMed] [Google Scholar]
  47. Pulvers K, Romero DR, Blanco L, Sakuma KLK, Ahluwalia JS, & Trinidad DR (2015). Light and intermittent smoking among California Black, Hispanic/Latino, and non-Hispanic White men and women. Nicotine & Tobacco Research, 17(6): 755–759. [DOI] [PubMed] [Google Scholar]
  48. Ross PT, Hart-Johnson T, Santen SA, & Bibler Zaidi NL (2020). Considerations for using race and education as quantitative variables in medical education research. Perspectives on Medical Education, 9: 318–323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Rostron BL, Lynn BCD, Chang CM, Ren C, Salazar E, & Ambrose BK (2022). The contribution of smoking-attributable mortality to differences in mortality and life expectancy among US African-American and white adults, 2000–2019. Demographic research, 46(31), 905–918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Saari AJ, Kentala J, & Mattila KJ (2014). The smoking habit of a close friend or family member—how deep is the impact? A cross-sectional study. BMJ Open, 4(2): e003218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Thomeer MB, Hernandez E, Umberson D, & Thomas PA (2019). Influence of social connections on smoking behavior across the life course. Advances in Life Course Research, 42: 100294. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Tomko RL, Bountress KE, & Gray KM (2016). Personalizing substance use treatment based on pre-treatment impulsivity and sensation seeking: A review. Drug and Alcohol Dependence, 167, 1–7. 10.1016/j.drugalcdep.2016.07.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Trinidad DR, Pérez-Stable EJ, White MM, Emery SL, & Messer K (2011). A nationwide analysis of US racial/ethnic disparities in smoking behaviors, smoking cessation, and cessation-related factors. American Journal of Public Health,101(4): 699–706. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Trinidad DR, Pérez-Stable EJ, Emery SL, White MM, Grana RA, & Messer KS (2009). Intermittent and light daily smoking across racial/ethnic groups in the United States. Nicotine & Tobacco Research, 11(2): 203–210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Initiative Truth (2020). Tobacco use in the Asian American community. https://truthinitiative.org/research-resources/targeted-communities/tobacco-use-asian-american-community
  56. US Department of Health and Human Services (2014). The health consequences of smoking—50 years of progress: a report of the Surgeon General https://www.ncbi.nlm.nih.gov/books/NBK179276/pdf/Bookshelf_NBK179276.pdf
  57. Wang CX (2022). Social Norms Intervention to Promote Help-Seeking with Depressed Asian and European Americans: A Pilot Study. [Doctoral dissertation, University of Southern California]. [Google Scholar]
  58. Wang L & Miller LC (2020). Just-in-the-Moment Adaptive Interventions (JITAI): A meta-analytical review. Health Communication, 35(12); 1531–1544. [DOI] [PubMed] [Google Scholar]
  59. Webb MS, Simmons VN, & Brandon TH (2005). Tailored Interventions for Motivating Smoking Cessation: Using Placebo Tailoring to Examine the Influence of Expectancies and Personalization. Health Psychology, 24(2), 179–188. 10.1037/0278-6133.24.2.179 [DOI] [PubMed] [Google Scholar]
  60. Weiss JW, & Garbanati JA (2004). Relationship of acculturation and family functioning to smoking attitudes and behaviors among Asian-American adolescents. Journal of Child and Family Studies, 13(2): 193–204. [Google Scholar]
  61. Woo J (2022) An intersectional life-course perspective on Black women’s smoking trajectories. SSM- Qualitative Research in Health, 2: 100196. [Google Scholar]
  62. Ybarra O, & Trafimow D (1998). How priming the private self or collective self affects the relative weights of attitudes and subjective norms. Personality and Social Psychology Bulletin, 24(4): 362–370. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, [CXW], upon reasonable request.

RESOURCES