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. Author manuscript; available in PMC: 2023 Sep 1.
Published in final edited form as: Health Psychol. 2022 Jul 28;41(9):621–629. doi: 10.1037/hea0001184

Dispositional optimism and optimistic bias: Associations with cessation motivation, confidence, and attitudes

Nicole Senft Everson 1,2, William M P Klein 2, Scott S Lee 1, Rebecca Selove 3, Maureen Sanderson 4, William J Blot 1, Rachel F Tyndale 5, Stephen King 1, Karen Gilliam 1, Suman Kundu 1, Mark Steinwandel 1, Sarah J Sternlieb 1, Shaneda Warren Andersen 1,6, Debra L Friedman 1, Erin Connors 1, Mary Kay Fadden 4, Matthew S Freiberg 1,7, Quinn S Wells 1, Juan Canedo 1, Robert P Young 8, Raewyn J Scott 8, Ebele M Umeukeje 1, Derek M Griffith 9, Hilary A Tindle 1,7
PMCID: PMC9830640  NIHMSID: NIHMS1856424  PMID: 35901400

Abstract

Objective:

To test whether two conceptually overlapping constructs, dispositional optimism (generalized positive expectations) and optimistic bias (inaccurately low risk perceptions), may have different implications for smoking treatment engagement.

Methods:

Predominantly Black, low-income Southern Community Cohort Study smokers (n=880) self-reported dispositional optimism and pessimism (Life Orientation Test-Revised subscales: 0=neutral, 12=high optimism/pessimism), comparative lung cancer risk (Low/Average/High), and information to calculate objective lung cancer risk (Low/Med/High). Perceived risk was categorized as accurate (perceived=objective), optimistically-biased (perceived<objective), or pessimistically-biased (perceived>objective). One-way ANOVAs tested associations between dispositional optimism/pessimism and perceived risk accuracy. Multivariable logistic regressions tested independent associations of optimism/pessimism and perceived risk accuracy with cessation motivation (Low/High), confidence (Low/High), and precision treatment attitudes (Favorable/Unfavorable), controlling for sociodemographics and nicotine dependence.

Results:

Mean dispositional optimism/pessimism scores were 8.41 (SD=2.59) and 5.65 (SD=3.02), respectively. Perceived lung cancer risk was 38% accurate, 27% optimistically-biased, and 35% pessimistically-biased. Accuracy was unrelated to dispositional optimism (F(2, 641)=1.23, p=0.29), though optimistically-biased (vs. pessimistically-biased) smokers had higher dispositional pessimism (F(2, 628)=3.17, p=.043). Dispositional optimism was associated with higher confidence (Adjusted odds ratio [AOR]=1.71 [95% CI 1.42–2.06], p<.001) and favorable precision treatment attitudes (AOR=1.66 [95% CI 1.37–2.01], p<.001). Optimistically-biased (vs. accurate) risk perception was associated with lower motivation (AOR=0.64 [95% CI 0.42–0.98], p=.041) and less favorable precision treatment attitudes (AOR=0.59 [95% CI 0.38–0.94], p=.029).

Conclusions:

Dispositional optimism and lung cancer risk perception accuracy were unrelated. Dispositional optimism was associated with favorable engagement-related outcomes and optimistically-biased risk perception with unfavorable outcomes, reinforcing the distinctiveness of these constructs and their implications for smoking treatment.

Keywords: Tobacco, optimism, risk perception, health engagement, precision medicine

Dispositional optimism, or the generalized tendency to hold positive expectations about the future, is broadly associated with favorable health outcomes (Carver et al., 2010; Rasmussen et al., 2009; Scheier & Carver, 2018; Taylor & Broffman, 2011; Tindle et al., 2009). Dispositional optimism is associated with nonsmoking (Carvajal et al., 1998; Giltay et al., 2007; Steptoe et al., 2006), and, prospectively, with smoking cessation following a cardiac emergency (Ronaldson et al., 2015) or advanced cancer diagnosis (Krane et al., 2018). However, dispositional optimism may hinder smoking cessation if tied to unrealistically low perceived risk for lung cancer and other smoking-related diseases. Accurate health risk assessment is a key goal of health education and efforts to help patients make informed health decisions, including in the context of promoting smoking cessation (Epstein & Street, 2007). Smokers tend to overestimate their ability to quit smoking (Weinstein et al., 2004) and underestimate their likelihood of developing lung cancer and other smoking-related diseases (McKenna et al., 1993; Weinstein et al., 2005). Smokers with these optimistically-biased (i.e., unrealistically low) risk perceptions are in turn more likely than those with accurate or pessimistically-biased (i.e., overestimated) risk perceptions to endorse inaccurate beliefs about smoking, are less motivated to quit smoking, and are less likely to actually quit smoking (Borrelli et al., 2010; Dillard et al., 2006). In summary, leveraging or augmenting (Malouff & Schutte, 2017) dispositional optimism may enhance smoking cessation efforts, whereas optimistically-biased risk perceptions could undermine them.

Dispositional optimism and optimistically-biased risk perceptions conceptually overlap in that both indicate positive expectations about the future, yet they are also conceptually and empirically distinct. Dispositional optimism is a generalized tendency with no metric for accuracy, whereas optimistic bias (or unrealistic optimism) is ascertained about specific events (e.g., lung cancer risk) and, by definition, is inaccurate (Shepperd & Howell, 2020). Past work has demonstrated no or weak associations between dispositional optimism and optimistically-biased risk perceptions in the contexts of alcohol use (Dillard et al., 2009; Klein et al., 2007) and oncology clinical trials (Jansen et al., 2016). Yet, concerns persist that dispositional optimism may engender optimistic bias (Shepperd et al., 2015), and a recent report demonstrated that dispositional optimists tended to underestimate their own perceived Covid-19 risk relative to that of others, though that study did not include an objective measure of risk, which is needed to determine bias in risk perceptions (Monzani et al., 2021). The associations of dispositional optimism and optimistic bias remain understudied in the context of lung cancer risk and smoking behavior. Understanding the associations of these constructs with smoking-related outcomes is critical to maximizing their potential benefits and mitigating their potential risks. The question of how dispositions and risk perceptions relate to smoking-related outcomes may be particularly relevant to low-income and Black smokers residing in southeastern states. Members of these often overlapping groups suffer from disproportionately high rates of smoking-related disease and mortality (Singh et al., 2011; U.S. Department of Health and Human Services, 2014) and low rates of smoking cessation treatment engagement (Cokkinides et al., 2008; Pacek et al., 2018; Trinidad et al., 2011).

Among smokers in the Southern Community Cohort Study (SCCS), this study examines the associations of dispositional optimism and risk perception accuracy with three key predictors of smoking behavior: motivation to quit, confidence in quitting, and attitudes towards smoking cessation treatments (Christiansen et al., 2012; DiClemente et al., 1991; Gwaltney et al., 2009; Heckman et al., 2010; Ryan et al., 2011). Dispositional optimism and risk perceptions are regarded as largely stable constructs and we were particularly interested in their ability to explain independent variance in these outcomes, thereby justifying their use as exogenous variables. With regard to attitudes towards cessation treatment, this study focuses on attitudes towards two gene-based precision smoking treatments: nicotine metabolism-informed pharmacotherapy, which can increase the efficacy of smoking cessation medication (Lerman et al., 2015), and Respiragene™, a polygenic risk score for lung cancer, which can motivate healthy behavior change by helping smokers to understand their risk (Viron et al., 2012; Young et al., 2012). Our analysis included both dispositional optimism and dispositional pessimism (i.e., negative future expectation), based on evidence that these may be related, but independent constructs (Scheier et al., 2020). We hypothesized that dispositional optimism and pessimism would be unrelated to lung cancer risk perception accuracy. Further, we hypothesized that higher dispositional optimism (or perhaps lower dispositional pessimism) would be associated with higher motivation, higher cessation confidence, and more favorable attitudes towards precision smoking treatment, whereas having an optimistically-biased (vs. accurate) lung cancer risk perception would be negatively associated with these outcomes. Pessimistically-biased (vs. accurate) lung cancer risk perceptions are understudied, and were included for exploratory purposes.

Methods

Study Population and Participants

The SCCS, a prospective study sponsored by the National Cancer Institute initiated in 2001, includes approximately 85,000 adults throughout the southeastern United States. Most individuals in the cohort are Black, low-income adults, members of demographic groups that are underrepresented in health research (Signorello et al., 2010). Active SCCS participants in Tennessee or Mississippi who identified as current smokers in the ongoing SCCS Follow-up 3 survey (collected 2015–2018, N=1,407) were eligible for the Precision Smoking Cessation Survey, a cross-sectional survey linked to existing SCCS data (collected 2017). A total of 988 responses were collected (70% response rate). Of these, 108 were excluded (72 did not identify as current smokers, 31 lacked data on smoking status, five were older than 80 years and hence outside the validated age range for the Tammemagi risk predictor (Tammemägi et al., 2013), see Measures), resulting in an analytic sample of 880 respondents. Respondents provided informed consent to participate in the SCCS; additional documentation of consent was not required for the current study. Procedures were approved by the Vanderbilt University Medical Center IRB.

Measures

Dispositional optimism and pessimism.

The Life Orientation Test-Revised (LOT-R; (Scheier et al., 1994) is a standardized 10-item measure of dispositional optimism and pessimism, which can be scored to consider these as either a single bipolar trait or two unipolar traits. To disaggregate optimism and pessimism, we score them as two unipolar traits. Two 3-item subscales of the LOT-R measure dispositional optimism (e.g., “In uncertain times, I usually expect the best;” α=.71) and pessimism (e.g., “If something can go wrong for me, it will;” α=.76). The LOT-R also includes four filler items, which were excluded from our survey to minimize participant burden. Items are rated on a 5-point scale (0=Strongly Disagree to 4=Strongly Agree). Scores range from 0–12, with higher scores indicating stronger optimism or pessimism, respectively, and lower scores indicating neutrality. The subscales were not significantly correlated in this sample (r(763)=.05, p=.21), consistent with research in other populations (see Scheier et al., 2020).

Lung cancer risk perception biases.

Risk perception bias was measured using the crosstabulation of perceived and objective lung cancer risks. A single item assessed perceived comparative lung cancer risk, “Do you think your risk of [lung cancer] is lower, about the same, or higher than other smokers your age?” (Ayanian & Cleary, 1999). Objective lung cancer risk was calculated using the Tammemagi risk predictor, which predicts risk of developing lung cancer in the next six years based on age, education, race/ethnicity, diagnosis of chronic obstructive pulmonary disease (COPD) (measured at SCCS Baseline), body mass index (BMI), family history of cancer, personal history of cancer (SCCS Follow-up 3), current smoking status, current cigarettes per day, and years smoked (Precision Smoking Cessation Survey) (Tammemägi et al., 2013). This model was shown to perform well among non-Hispanic Black and White smokers (Katki et al., 2018). To align objective risk scores with the comparative lung cancer risk measure, we performed quantile regression of risk on age, age2, and age3 to estimate the 33rd and 67th percentiles risk scores (Supplementary Figure). Respondents were categorized as higher, average, or lower risk according to these age-specific terciles. Respondents were then categorized as having an accurate risk perception (perceived risk=objective risk), optimistic bias (perceived risk<objective risk), or pessimistic bias (perceived risk>objective risk; Table 1).

Table 1.

Crosstabulation of perceived and objective lung cancer risk to define biased vs. accurate risk perceptions.1

Objective Lung Cancer Risk2
Lower Average Higher Missing Total
Perceived Lung Cancer Risk 3 Lower 40 36 20 8 104 Optimistic Bias
(n=185, 27%)
About the Same 133 138 129 38 438
Higher 47 57 80 19 203 Accurate
(n=258, 38%)
Missing 46 40 37 12 135
Total 266 271 266 78 880 Pessimistic Bias
(n=237, 35%)
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1

Accurate risk perception: objective risk=perceived risk; Pessimistic bias: objective risk<perceived risk; Optimistic bias: objective risk>perceived risk.

2

Based on age-specific terciles of objective lung cancer risk as calculated via Tammemagi lung cancer risk calculator.

3

Self-reported, “Do you think your risk of [lung cancer] is lower, about the same, or higher than other smokers your age?”

Motivation to quit.

Motivation to quit was assessed using two items often used to test the Transtheoretical Model (DiClemente et al., 1991): “Are you thinking of quitting cigarettes in the next six months?” (Yes/No), and “Are you planning to quit smoking in the next 30 days?” (Yes/No). Motivation levels were categorized into high (planning to quit in the next 30 days) and low (not yet thinking of quitting or thinking about quitting in the next six months but not planning to quit in the next 30 days), consistent with clinical guidelines that assign distinct treatment approaches to smokers that are vs. are not currently willing to quit (Fiore et al., 2008).

Confidence in quitting.

A single item assessed confidence in quitting, “I am confident that I can quit smoking,” (Abrams et al., 2003; Bandura, 1977) rated on a 5-point scale (1=strongly disagree to 5=strongly agree). Confidence was categorized into high (strongly agree, agree) and low (neutral, disagree, strongly disagree), reflecting clinical use of this construct.

Attitudes towards precision smoking treatments.

Seven items assessed attitudes towards precision smoking treatments (e.g., “If a blood test could help my doctor choose the best medicine for me to quit smoking, I would take that medicine,” α=0.89). Items were rated on a 5-point scale (1=strongly disagree to 5=strongly agree). Scores were dichotomized to reflect generally favorable (mean≥3.5) and not favorable (mean<3.5) attitudes (Senft et al., 2019). Sensitivity analyses showed that results reported below were not altered by operationalizing this measure as a continuous score (Supplementary Table 1).

Sociodemographics and Nicotine Dependence.

Sociodemographic items included age, sex, race and ethnicity, highest education completed (assessed at SCCS baseline, 2002–2009), annual household income, and insurance status (assessed at SCCS follow up, 2015–2018). Nicotine dependence was calculated via the Heaviness of Smoking Index (HSI, (Heatherton et al., 1991)), which combines reported time to first cigarette (0=after 60 minutes, 1=31–60 minutes, 2=6–30 minutes, 3=within 5 minutes) and number of cigarettes smoked per day (0=10 or fewer, 1=11–20, 2=21–30, 3=31 or more; Low=0–1, Medium=2–4, High=5–6).

Statistical Analyses

We first described baseline characteristics of the sample. T-tests, one way Analysis of Variance (ANOVA), and Pearson correlations tested whether continuous measures of optimism and pessimism were associated with baseline characteristics. Chi-square and t-test analyses tested whether baseline characteristics of smokers with an optimistic or pessimistic bias significantly differed from smokers with accurate risk perceptions. One way ANOVAs tested whether dispositional optimism or pessimism were related to inaccurate risk perceptions.

Three multivariable logistic regressions tested independent associations of dispositional optimism, dispositional pessimism, optimistically biased (vs. accurate) risk perceptions, and pessimistically biased (vs. accurate) risk perceptions with (1) motivation, (2) confidence in quitting, and (3) attitudes towards precision smoking treatment. Dispositional optimism and pessimism were standardized for regression analyses, so that odds ratios reflect associations with 1 standard deviation higher scores. Dispositional optimism, pessimism, and perceived risk accuracy were each included in models1, which were further adjusted for sociodemographic characteristics (age, race, sex, education, insurance) and nicotine dependence, based on evidence of associations of these factors with outcomes. Models testing associations with confidence and treatment attitudes also adjusted for motivation, and models testing associations with motivation and treatment attitudes adjusted for confidence, because past research with these data showed that motivation and confidence were related to each other and that both were associated with attitudes towards precision smoking treatment (Senft et al., 2019).

There was substantial missingness in some key variables, including comparative perceived risk (8.86%), objective risk (15.3%), dispositional optimism (7.27%), and dispositional pessimism (9.66%). To handle missing values, multiple imputations using chained equations (MICE) with five imputed datasets were generated based on predictive mean matching using the mice library of R programming language. Regression models were fitted in each imputed dataset and finally combined to obtain pooled odds ratios and standard errors. Sensitivity analyses restricted to respondents with complete data yielded comparable results and are presented in Supplementary Table 2, suggesting that missing data did not bias the associations among variables of interest. Analyses were performed using Stata 15 SE and R software version 3.5.2.

Results

Sample Characteristics

Consistent with demographic characteristics of the SCCS cohort, smokers in this study were predominantly Black and low-income (Table 2). Mean dispositional optimism scores were 8.41 (1–12 scale, SD=2.59) and dispositional pessimism scores were 5.65 (1–12 scale, SD=3.02). About a quarter of smokers had an optimistic lung cancer risk bias (n=185, 27%), compared to over a third each with a pessimistic bias (n=237, 35%) and accurate lung cancer risk perceptions (n=258, 38%; Table 1). Prevalence of high motivation, confidence, and favorable attitudes towards precision smoking treatment were high at 38%, 49%, and 70%, respectively (Table 2). Smokers with lower nicotine dependence tended to have higher levels of dispositional optimism (r(805)=−.08, p=.023) and pessimistically-biased lung cancer risk perceptions (t(469.60)=6.90, p<.001). Smokers with higher nicotine dependence tended to have higher levels of dispositional pessimism (r(785)=.08, p=.029) and optimistically-biased lung cancer risk perceptions (t(412.48)=−7.28, p<.001). Additionally, Black smokers tended to report higher levels of dispositional optimism than white smokers (F(2, 810)=9.59, p<.001), whereas white smokers were more likely to hold optimistically-biased lung cancer risk perceptions than Black smokers (X2(1, 439)=11.53, p<.001). Smokers with higher levels of dispositional pessimism tended to be less highly educated (F(2, 772)=15.22, p<.001). Other associations with sociodemographic characteristics were statistically nonsignificant.

Table 2.

Characteristics of SCCS smokers, among smokers high in dispositional optimism, dispositional pessimism, and with optimistically-biased, accurate, and pessimistically-biased lung cancer risk perceptions.

Dispositional Optimism Lung Cancer Risk Perception1
Full Sample (n=880) High Optimism2 (n=161; 20%) High Pessimism3 (n=183; 23%) Optimistic Bias (n=185; 27%) Accurate (n=258; 38%) Pessimistic Bias (n=237; 35%)
Age (mean, SD) 60.34 (5.84) 60.35 (5.83) 59.82 (5.09) 60.21 (5.42) 60.10 (5.74) 60.37 (5.80)
Female Sex 510 (58.0) 91 (56.5) 117 (63.9) 100 (54.1) 143 (55.4) 156 (65.8)
Race
 Black 739 (84.0) 147 (91.3) 152 (83.1) 134 (72.4) 220 (85.3) 212 (89.5)
 White 125 (14.2) 11 (6.8) 30 (16.4) 50 (27.0) 35 (13.6) 22 (9.3)
 Other 13 (1.5) 1 (0.6) 0 (0.0) 1 (0.5) 3 (1.2) 3 (1.3)
 Missing 3 (0.3) 2 (1.2) 1 (0.5) 0 0 0
Education
 <High School 257 (29.2) 44 (27.3) 63 (34.4) 57 (30.8) 68 (26.4) 59 (24.9)
 High School 329 (37.4) 67 (41.6) 84 (45.9) 65 (35.1) 105 (40.7) 90 (38.0)
 >High School 272 (30.9) 46 (28.6) 32 (17.5) 63 (34.1) 85 (32.9) 88 (37.1)
 Missing 22 (2.5) 4 (2.5) 4 (2.2) 0 0 0
Income
 <$15,000 553 (62.8) 94 (58.4) 115 (62.8) 119 (64.3) 156 (60.5) 153 (64.6)
 $15-$25,000 167 (19.0) 34 (21.1) 34 (18.6) 34 (18.4) 50 (19.4) 46 (19.4)
 $25-$50,000 66 (7.5) 17 (10.6) 11 (6.0) 13 (7.0) 22 (8.5) 20 (8.4)
 >$50,000 24 (2.7) 2 (1.2) 3 (1.6) 8 (4.3) 5 (1.9) 9 (3.8)
 Missing 70 (8.0) 14 (8.7) 20 (10.9) 11 (5.9) 25 (9.7) 9 (3.8)
Insured 654 (74.3) 127 (78.9) 131 (71.6) 145 (78.4) 186 (72.1) 184 (77.6)
Nicotine Dependence4 (Mean, SD) 2.11 (1.51) 1.99 (1.57) 2.30 (1.63) 3.08 (1.35) 2.09 (1.47) 1.28 (1.13)
Motivation5
 Low 517 (58.8) 84 (52.2) 104 (56.8) 126 (68.1) 148 (57.4) 137 (57.8)
 High 333 (37.8) 74 (46.0) 72 (39.3) 55 (29.7) 103 (39.9) 98 (41.4)
 Missing 30 (3.4) 3 (1.9) 7 (3.8) 4 (2.2) 7 (2.7) 2 (0.8)
Confidence6
 Low 411 (46.7) 61 (37.9) 75 (41.0) 114 (61.6) 126 (48.8) 84 (35.4)
 High 427 (48.5) 98 (60.9) 101 (55.2) 68 (36.8) 124 (48.1) 141 (59.5)
 Missing 42 (4.8) 2 (1.2) 7 (3.8) 3 (1.6) 8 (3.1) 12 (5.1)
Attitudes Towards Precision Treatment7
 Not Favorable 260 (29.5) 27 (16.8) 43 (23.5) 58 (31.4) 57 (22.1) 64 (27.0)
 Favorable 613 (69.7) 133 (82.6) 139 (76.0) 126 (68.1) 201 (77.9) 173 (73.0)
 Missing 7 (0.8) 1 (0.6) 1 (0.5) 1 (0.5) 0 (0.0) 0 (0.0)
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1

Based on the relationship between each respondent’s perceived and objective lung cancer risk. Accurate risk perception: objective risk=perceived risk; Pessimistic bias: objective risk<perceived risk; Optimistic bias: objective risk>perceived risk (Table 2). Reported percentages are based on a valid n of 680.

2

Respondents scoring in the upper quartile of the optimism subscale of the Life Orientation Test-Revised (Score≥11). Percentage based on a valid n of 816.

3

Respondents scoring in the upper quartile of the pessimism subscale of the Life Orientation Test-Revised (Score≥9). Percentage based on a valid n of 795.

4

Nicotine dependence is measured via the heaviness of smoking index, a composite of time to first cigarette and cigarettes per day.

5

Respondents who planning on quitting smoking in the next 30 days were classified as having high motivation, while those not yet planning on quitting smoking were classified as having low motivation.

6

Respondents who agreed or strongly agreed that they were confident in their ability to quit smoking were classified as having high confidence, while those who strongly disagreed, disagreed, or were neutral were classified as having low confidence.

7

Respondents whose average scores were ≥3.5 out of 5 were classified as having favorable generalized attitudes towards precision smoking treatment, while those whose scores were <3.5 were classified as having not favorable generalized attitudes.

Hypothesis Testing

Risk perception biases were not associated with dispositional optimism (F(2, 641)=1.23, p=0.29), but were associated with dispositional pessimism (F(2, 628)=3.17, p=.043), such that smokers with optimistically-biased risk perceptions tended to have higher levels of dispositional pessimism than those with pessimistically-biased risk perceptions (Figure 1). In adjusted models, higher dispositional optimism was associated with greater odds of having high confidence (Adjusted odds ratio [AOR]=1.71 [95% CI 1.42–2.06], p<.001) and favorable attitudes towards precision smoking treatment (AOR=1.66 [95% CI 1.37–2.01], p<.001), but not high motivation (AOR=1.04 [95% CI 0.88–1.24], p=.62) (Table 3). Dispositional pessimism was not associated with outcomes. Smokers with optimistically-biased (vs. accurate) lung cancer risk perceptions had lower odds of having high motivation (AOR=0.64 [95% CI 0.42–0.98], p=.041) and favorable attitudes towards precision smoking treatment (AOR=0.59 [95% CI 0.38–0.94], p=.029), but not high confidence (AOR=1.00 [95% CI 0.63–1.58], p>.99). Having pessimistically-biased (vs. accurate) risk perceptions was not associated with outcomes.

Figure 1.

Figure 1

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Boxplots depicting associations between lung cancer risk perceptions and dispositional optimism (upper panel) and pessimism (lower panel).

Note. Boxes represent the 25th-75th percentile scores, with median scores marked by horizontal lines within boxes. Whiskers represent the upper and lower bounds of scores, with dots representing outliers.

Table 3.

Results of multivariable logistic regression models testing associations with confidence, motivation, and attitudes towards precision smoking treatment a, b

Exposure Confidence (n=880)
 Motivation (n=880)
 Attitudes (n=880)
AOR 95% CI p AOR 95% CI p AOR 95% CI p
Dispositional Optimism 1.71 1.42 – 2.06 <.001 1.04 0.88 – 1.24 0.617 1.66 1.37 – 2.01 <.001
Dispositional Pessimism 0.95 0.79 – 1.15 0.628 0.98 0.83 – 1.16 0.792 1.14 0.95 – 1.36 0.152
Lung Cancer Risk Perception
Optimistic (vs. Accurate) 1 0.63 – 1.58 0.996 0.64 0.42 – 0.98 0.041 0.59 0.38 – 0.94 0.029
Pessimistic (vs. Accurate) 1.59 0.9 – 2.82 0.122 0.91 0.6 – 1.39 0.675 0.78 0.51 – 1.19 0.253
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a

Models included dispositional optimism and pessimism and lung cancer risk perceptions and adjusted for sociodemographic factors (age, race, sex, education, insurance), nicotine dependence, motivation (in models with outcomes of confidence and attitudes towards precision smoking treatment), and confidence (in models with outcomes of motivation and attitudes towards precision smoking treatment).

b

Missing data were imputed using multiple imputations with five complete datasets.

Discussion

In this sample of predominantly low-income, Black smokers in the Southeastern US, smokers with high levels of dispositional optimism were not necessarily optimistically biased about their lung cancer risk, re-demonstrating the uniqueness of these constructs. Furthermore, higher dispositional optimism was associated with 1.5–2.0-fold higher odds of having high confidence and favorable attitudes towards precision smoking treatment, whereas optimistically-biased perceived lung cancer risk was associated with lower motivation and less favorable attitudes toward precision smoking treatment. These results suggest that tempering dispositional optimism out of concern that it produces unrealistically low risk perceptions is unnecessary and could theoretically undermine cessation efforts. Future research should explore whether leveraging smokers’ dispositional optimism and promoting accurate risk perceptions can increase engagement in smoking cessation treatment and enhance long term treatment outcomes.

Results of this study do not support concerns that dispositional optimism may engender optimistically-biased lung cancer risk perceptions, underscoring conceptual and empirical arguments that these constructs are distinct (Dillard et al., 2009; Jansen et al., 2016; Klein et al., 2007; Radcliffe & Klein, 2002; Shepperd & Howell, 2020). Dispositional pessimism, not optimism, was associated with optimistically-biased lung cancer risk perceptions. Importantly, as hypothesized, dispositional optimism was associated with high confidence in quitting and favorable attitudes toward precision smoking treatment, whereas optimistically-biased risk perception was associated with lower motivation and less favorable attitudes towards smoking treatment. Counter to hypotheses, dispositional optimism was not associated with motivation to quit, suggesting that addressing optimism in interventions may be helpful both for smokers who are not motivated to quit and those highly motivated to quit. On the other hand, future research may focus on whether targeting smokers’ risk perceptions is a useful motivational tool.

Together, these findings are consistent with past research associating optimistic bias with poorer smoking-related outcomes (Borrelli et al., 2010; Dillard et al., 2006) and contribute to a robust body of work detailing health benefits associated with dispositional optimism (Carver et al., 2010; Rasmussen et al., 2009; Scheier et al., 2020; Scheier & Carver, 2018; Taylor & Broffman, 2011; Tindle et al., 2009). The alignment of this study’s findings with past research in other populations and settings supports their generalizability across different groups of smokers and their applicability to smokers from groups who tend to be under-engaged in smoking treatment and disproportionately burdened by tobacco-related disease. The association of dispositional optimism, but not pessimism, with confidence and attitudes towards treatment also adds to an ongoing discourse on the independence of these constructs and their relative impacts on health behaviors and outcomes (Scheier et al., 2020). Scheier et al. (2020) found that the absence of pessimism was more strongly associated with physical health outcomes than was the presence of optimism. It is possible that the pathways linking dispositional optimism and pessimism to health outcomes are more complex and outcome-specific, with dispositional optimism potentially having greater direct impact on health cognitions and health behaviors.

One important limitation is that this study lacks longitudinal measures of smoking behavior. Motivation and confidence facilitate successful cessation, and precision approaches appear acceptable and efficacious (Bierut & Tyndale, 2018; Lerman et al., 2015; Senft et al., 2019; Wells et al., 2017), supporting a potential benefit of dispositional optimism and harm of optimistically-biased lung cancer risk perceptions for smoking cessation. Though dispositional optimism is largely stable and risk perceptions are difficult to change, we cannot eliminate the potential for reverse causality or third-variable bias in these cross-sectional associations. More research is needed to formally test the longitudinal associations of dispositional optimism and optimistic bias with smoking behavior. Additionally, the associations between optimistically-biased risk perceptions and smoking outcomes should be replicated in future research, as the confidence intervals around these odds approached one.

A further limitation may lie in some measures. Confidence was measured using a single item to reduce survey burden, though a multi-item scale may have been more valid. Risk perception accuracy was operationalized by comparing participants’ perceived risk relative to other smokers their age with their age-specific objective risk (Radcliffe & Klein, 2002). This approach is more individualized than the more common approach of measuring optimistic bias at the group level. However, age-specific objective risk terciles were based on data from this sample so may not be readily transferable to other populations. Another approach would be to provide smokers with absolute thresholds for lung cancer screening and use these to measure perceived risk. However, this measurement is subject to health numeracy concerns, with many smokers unlikely to know their absolute risk. Replicating these findings using diverse measures of lung cancer risk perception would increase confidence in reported results. Several measures, including perceived lung cancer risk, had a nontrivial amount of missing data. Though this survey was created in collaboration with a community advisory board to ensure comprehension, participants may still have been unsure how to respond to this item (Kiviniemi et al., 2020). While our results were robust to these missing data, future work may continue to improve measurement of these complex and sensitive constructs. Finally, though this study found racial differences in levels of dispositional optimism and optimistic bias, the study was not powered to test whether race moderated the associations of these constructs with smoking-related outcomes. As the science of personalization evolves, future work may test whether the effects of constructs such as dispositional optimism and risk perceptions vary across different groups of smokers.

Despite these limitations, our results have implications for future implementation and intervention research. Borelli et al. (2018) identified different psychological profiles of people who are not motivated to quit, suggesting that tailoring interventions and communication based on psychological factors might be needed to motivate smoking behavior change. For example, past research found that optimists were more interested and engaged when treatment importance was highlighted (Geers et al., 2010), suggesting a strategy for tailoring communication to optimistic smokers by emphasizing the importance of treatment for helping them quit. Leveraging smokers’ dispositional optimism may be especially impactful for low-income Black individuals who tend to report high levels of self-reported optimism (Graham & Pinto, 2019). Additionally, though both dispositional optimism and risk perceptions are largely considered to be stable constructs, interventions that do succeed in changing risk perceptions can promote healthy behavior (Sheeran et al., 2014), and there is preliminary evidence that dispositional optimism may be malleable through intervention (Malouff & Schutte, 2017; Mohammadi et al., 2018). Small differences in optimism scores are associated with health outcomes (Tindle et al., 2009), but it is not clear whether increasing optimism translates to improved health outcomes. This project lays groundwork for future research to examine the prospective associations of dispositional optimism, attitudes, and smoking behavior and test whether encouraging accurate risk perceptions and optimistic attitudes improves cessation outcomes.

In summary, this study demonstrates the distinctiveness of dispositional optimism and optimistically biased risk perceptions and the differential associations of these constructs with factors related to smoking treatment engagement. The associations of dispositional optimism with higher confidence in quitting and more favorable attitudes towards precision smoking treatment suggest future research should explore strategies for integrating dispositional optimism in smoking cessation interventions and clinical care. For example, encouraging higher optimism through interventions and leveraging naturally occurring optimism through tailored health communication may promote smokers’ treatment engagement and successful smoking cessation. Novel smoking cessation engagement strategies are especially important for groups with disproportionately high risks of smoking-related diseases and mortality, like those represented in the Southern Community Cohort Study. Though more research is needed to test the effectiveness of such interventions, these findings suggest that researchers and clinicians can have confidence that efforts to increase or leverage dispositional optimism are unlikely to interfere with complementary efforts to educate smokers about the health risks of smoking.

Supplementary Material

Supplemental Material

Acknowledgments

This work was supported by the National Cancer Institute (U54CA163072-09S1, to principal investigator (PI) H.L. Moses, sub-project 6540, PI H.A. Tindle; U54CA163069, to PI S.E. Adunyah, sub-project 6962, PI M. Sanderson; and U54CA163066, to PI B.A. Husaini, sub-project 6610, PI R. Selove), the Vanderbilt Center for Tobacco, Addiction, and Lifestyle (directed by H.A. Tindle), and Clinical and Translational Science from the National Center for Advancing Translational Sciences (UL1TR002243). The Southern Community Cohort Study is funded by NCI (R01CA92447 to PIs W.J. Blot and W. Zheng), including special allocations from the American Recovery and Reinvestment Act (R01CA092447-08S1). N. Senft Everson received postdoctoral support from the Agency for Healthcare Research and Quality (T32 HS026122). S. Warren Andersen is supported by R00CA207848, P30CA014520, University of Wisconsin-Madison, Office of Vice Chancellor for Research and Graduate Education, Wisconsin Alumni Research Foundation. RF Tyndale has consulted for Quinn Emanuel and Ethismos Research Inc on unrelated topics. Other authors declare no potential conflicts of interest. Dr. Tindle acknowledges support of the William Anderson Spickard, Jr., Chair in Medicine and the National Cancer Institute Moonshot Initiative (P30CA068485-22S3). Dr. Freiberg acknowledges support of the Dorothy and Laurence Grossman Chair in Cardiology. Dr. Tyndale acknowledges the support of the Canada Research Chair in Pharmacogenomics.

Acronyms:

AOR

adjusted odds ratio

SCCS

Southern Community Cohort Study

Footnotes

Disclaimer: The opinions expressed by the authors are their own and this material should not be interpreted as representing the official viewpoint of the U.S. Department of Health and Human Services, the National Institutes of Health or the National Cancer Institute.

1

In sensitivity analyses using only one exposure per model, associations did not differ from reported results.

References

  1. Abrams DB, Niaura R, Brown RA, Emmons KM, Goldstein MG, & Monti PM (2003). The tobacco dependence treatment handbook: A guide to best practices Guilford Press. [Google Scholar]
  2. Ayanian JZ, & Cleary PD (1999). Perceived Risks of Heart Disease and Cancer Among Cigarette Smokers. JAMA, 281(11), 1019–1021. 10.1001/jama.281.11.1019 [DOI] [PubMed] [Google Scholar]
  3. Bandura A (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. 10.1037/0033-295X.84.2.191 [DOI] [PubMed] [Google Scholar]
  4. Bierut LJ, & Tyndale RF (2018). Preparing the Way: Exploiting Genomic Medicine to Stop Smoking. Trends in Molecular Medicine, 24(2), 187–196. 10.1016/j.molmed.2017.12.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Borrelli B, Gaynor S, Tooley E, Armitage CJ, Wearden A, & Bartlett YK (2018). Identification of three different types of smokers who are not motivated to quit: Results from a latent class analysis. Health Psychology, 37(2), 179–187. 10.1037/hea0000561 [DOI] [PubMed] [Google Scholar]
  6. Borrelli B, Hayes RB, Dunsiger S, & Fava JL (2010). Risk Perception and Smoking Behavior In Medically Ill Smokers: A Prospective Study. Addiction (Abingdon, England), 105(6), 1100–1108. 10.1111/j.1360-0443.2010.02900.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Carvajal SC, Clair SD, Nash SG, & Evans RI (1998). Relating Optimism, Hope, and Self-Esteem to Social Influences in Deterring Substance Use in Adolescents. Journal of Social and Clinical Psychology, 17(4), 443–465. 10.1521/jscp.1998.17.4.443 [DOI] [Google Scholar]
  8. Carver CS, Scheier MF, & Segerstrom SC (2010). Optimism. Clin Psychol Rev, 30(7), 879–889. 10.1016/j.cpr.2010.01.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cokkinides VE, Halpern MT, Barbeau EM, Ward E, & Thun MJ (2008). Racial and Ethnic Disparities in Smoking-Cessation Interventions: Analysis of the 2005 National Health Interview Survey. American Journal of Preventive Medicine, 34(5), 404–412. [DOI] [PubMed] [Google Scholar]
  10. DiClemente CC, Prochaska JO, Fairhurst SK, Velicer WF, Velasquez MM, & Rossi JS (1991). The process of smoking cessation: An analysis of precontemplation, contemplation, and preparation stages of change. Journal of Consulting and Clinical Psychology, 59(2), 295–304. [DOI] [PubMed] [Google Scholar]
  11. Dillard AJ, McCaul KD, & Klein WMP (2006). Unrealistic Optimism in Smokers: Implications for Smoking Myth Endorsement and Self-Protective Motivation. Journal of Health Communication, 11(sup001), 93–102. 10.1080/10810730600637343 [DOI] [PubMed] [Google Scholar]
  12. Dillard AJ, Midboe AM, & Klein WMP (2009). The Dark Side of Optimism: Unrealistic Optimism About Problems With Alcohol Predicts Subsequent Negative Event Experiences. Personality and Social Psychology Bulletin, 35(11), 1540–1550. 10.1177/0146167209343124 [DOI] [PubMed] [Google Scholar]
  13. Epstein R, & Street R (2007). Patient-centered communication in cancer care: Promoting healing and reducing suffering National Cancer Institute, US Department of Health and Human Services, National Institutes of Health.
  14. Fiore MC, Jaén CR, Baker TB, et al. Clinical Interventions for Tobacco Use and Dependence. In Treating Tobacco Use and Dependence: 2008 Update. Clinical Practice Guideline Rockville, MD: U.S. Department of Health and Human Services. Public Health Service. May 2008. [Google Scholar]
  15. Geers AL, Wellman JA, Seligman LD, Wuyek LA, & Neff LA (2010). Dispositional optimism, goals, and engagement in health treatment programs. Journal of Behavioral Medicine, 33(2), 123–134. 10.1007/s10865-009-9238-z [DOI] [PubMed] [Google Scholar]
  16. Giltay EJ, Geleijnse JM, Zitman FG, Buijsse B, & Kromhout D (2007). Lifestyle and dietary correlates of dispositional optimism in men: The Zutphen Elderly Study. Journal of Psychosomatic Research, 63(5), 483–490. 10.1016/j.jpsychores.2007.07.014 [DOI] [PubMed] [Google Scholar]
  17. Graham C, & Pinto S (2019). Unequal hopes and lives in the USA: Optimism, race, place, and premature mortality. Journal of Population Economics, 32(2), 665–733. 10.1007/s00148-018-0687-y [DOI] [Google Scholar]
  18. Heatherton TF, Kozlowski LT, Frecker RC, & Fagerstrom KO (1991). The Fagerström Test for Nicotine Dependence: A revision of the Fagerstrom Tolerance Questionnaire. British Journal of Addiction, 86(9), 1119–1127. 10.1111/j.1360-0443.1991.tb01879.x [DOI] [PubMed] [Google Scholar]
  19. Jansen LA, Mahadevan D, Appelbaum PS, Klein WM, Weinstein ND, Mori M, Daffé R, & Sulmasy DP (2016). Dispositional optimism and therapeutic expectations in early-phase oncology trials. Cancer, 122(8), 1238–1246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Katki HA, Kovalchik SA, Petito LC, Cheung LC, Jacobs E, Jemal A, Berg CD, & Chaturvedi AK (2018). Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening. Annals of Internal Medicine, 169(1), 10–19. 10.7326/M17-2701 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kiviniemi MT, Ellis EM, Orom H, Waters EA, & Hay JL (2020). ‘Don’t know’ responding and estimates of perceived risk: Failing to provide a ‘don’t know’ response systematically biases laypeople’s perceived risk estimates. Health, Risk & Society, 22(1), 69–85. 10.1080/13698575.2020.1714557 [DOI] [Google Scholar]
  22. Klein W, Geaghan T, & MacDonald T (2007). Unplanned Sexual Activity as a Consequence of Alcohol Use: A Prospective Study of Risk Perceptions and Alcohol Use Among College Freshmen. Journal of American College Health, 56(3), 317–323. 10.3200/JACH.56.3.317-324 [DOI] [PubMed] [Google Scholar]
  23. Krane A, Terhorst L, Bovbjerg DH, Scheier MF, Kucinski B, Geller DA, Marsh W, Tsung A, & Steel JL (2018). Putting the life in lifestyle: Lifestyle choices after a diagnosis of cancer predicts overall survival. Cancer, 124(16), 3417–3426. 10.1002/cncr.31572 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Lerman C, Schnoll RA, Hawk LW, Cinciripini P, George TP, Wileyto EP, Swan GE, Benowitz L, Heitjan DF, Tyndale RF, & PGRN-PNAT Research Group. (2015). Use of the nicotine metabolite ratio as a genetically informed biomarker of response to nicotine patch or varenicline for smoking cessation: A randomised, double-blind placebo-controlled trial. The Lancet. Respiratory Medicine, 3(2), 131–138. 10.1016/S2213-2600(14)70294-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Malouff JM, & Schutte NS (2017). Can psychological interventions increase optimism? A meta-analysis. The Journal of Positive Psychology, 12(6), 594–604. 10.1080/17439760.2016.1221122 [DOI] [Google Scholar]
  26. McKenna FP, Warburton DM, & Winwood M (1993). Exploring the limits of optimism: The case of smokers’ decision making. British Journal of Psychology, 84(3), 389–394. 10.1111/j.2044-8295.1993.tb02490.x [DOI] [PubMed] [Google Scholar]
  27. Mohammadi N, Aghayousefi A, Nikrahan GR, Adams CN, Alipour A, Sadeghi M, Roohafza H, Celano CM, & Huffman JC (2018). A randomized trial of an optimism training intervention in patients with heart disease. General Hospital Psychiatry, 51, 46–53. 10.1016/j.genhosppsych.2017.12.004 [DOI] [PubMed] [Google Scholar]
  28. Monzani D, Gorini A, Mazzoni D, & Pravettoni G (2021). Brief report - “Every little thing gonna be all right” (at least for me): Dispositional optimists display higher optimistic bias for infection during the Italian COVID-19 outbreak. Personality and Individual Differences, 168, 110388. 10.1016/j.paid.2020.110388 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Pacek LR, McClernon FJ, & Bosworth HB (2018). Adherence to Pharmacological Smoking Cessation Interventions: A Literature Review and Synthesis of Correlates and Barriers. Nicotine & Tobacco Research, 20(10), 1163–1172. 10.1093/ntr/ntx210 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Radcliffe NM, & Klein WMP (2002). Dispositional, Unrealistic, and Comparative Optimism: Differential Relations with the Knowledge and Processing of Risk Information and Beliefs about Personal Risk. Personality and Social Psychology Bulletin, 28(6), 836–846. 10.1177/0146167202289012 [DOI] [Google Scholar]
  31. Rasmussen HN, Scheier MF, & Greenhouse JB (2009). Optimism and Physical Health: A Meta-analytic Review. Annals of Behavioral Medicine, 37(3), 239–256. 10.1007/s12160-009-9111-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Ronaldson A, Molloy GJ, Wikman A, Poole L, Kaski J, & Steptoe A (2015). Optimism and Recovery After Acute Coronary Syndrome: A Clinical Cohort Study. Psychosomatic Medicine, 77(3), 311–318. 10.1097/PSY.0000000000000155 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Scheier MF, & Carver CS (2018). Dispositional optimism and physical health: A long look back, a quick look forward. American Psychologist, 73(9), 1082–1094. 10.1037/amp0000384 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Scheier MF, Carver CS, & Bridges MW (1994). Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): A reevaluation of the Life Orientation Test. Journal of Personality and Social Psychology, 67(6), 1063–1078. [DOI] [PubMed] [Google Scholar]
  35. Scheier MF, Swanson JD, Barlow MA, Greenhouse JB, Wrosch C, & Tindle HA (2020). Optimism versus pessimism as predictors of physical health: A comprehensive reanalysis of dispositional optimism research. American Psychologist, No Pagination Specified-No Pagination Specified. 10.1037/amp0000666 [DOI] [PubMed]
  36. Senft N, Sanderson M, Selove R, Blot WJ, King S, Gilliam K, Kundu S, Steinwandel M, Sternlieb SJ, Warren Andersen S, Friedman DL, Connors E, Fadden MK, Freiberg M, Wells QS, Canedo J, Tyndale RF, Young RP, Hopkins RJ, & Tindle HA (2019). Attitudes towards precision treatment of smoking in the Southern Community Cohort Study. Cancer Epidemiology Biomarkers & Prevention, cebp.0179.2019 10.1158/1055-9965.EPI-19-0179 [DOI] [PMC free article] [PubMed]
  37. Sheeran P, Harris PR, & Epton T (2014). Does heightening risk appraisals change people’s intentions and behavior? A meta-analysis of experimental studies. Psychological Bulletin, 140(2), 511–543. 10.1037/a0033065 [DOI] [PubMed] [Google Scholar]
  38. Shepperd JA, & Howell JL (2020). Unrealistic Optimism and Health. In The Wiley Encyclopedia of Health Psychology (pp. 773–780). John Wiley & Sons, Ltd. 10.1002/9781119057840.ch131 [DOI] [Google Scholar]
  39. Shepperd JA, Waters E, Weinstein ND, & Klein WMP (2015). A Primer on Unrealistic Optimism. Current Directions in Psychological Science, 24, 232–237. 10.1177/0963721414568341 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Signorello LB, Hargreaves MK, & Blot WJ (2010). The Southern Community Cohort Study: Investigating health disparities. Journal of Health Care for the Poor and Underserved, 21(1 Suppl), 26–37. 10.1353/hpu.0.0245 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Singh GK, Williams SD, Siahpush M, & Mulhollen A (2011). Socioeconomic, Rural-Urban, and Racial Inequalities in US Cancer Mortality: Part I—All Cancers and Lung Cancer and Part II—Colorectal, Prostate, Breast, and Cervical Cancers [Research article]. Journal of Cancer Epidemiology 10.1155/2011/107497 [DOI] [PMC free article] [PubMed]
  42. Steptoe A, Wright C, Kunz-Ebrecht SR, & Iliffe S (2006). Dispositional optimism and health behaviour in community-dwelling older people: Associations with healthy ageing. British Journal of Health Psychology, 11(Pt 1), 71–84. 10.1348/135910705X42850 [DOI] [PubMed] [Google Scholar]
  43. Tammemägi MC, Katki HA, Hocking WG, Church TR, Caporaso N, Kvale PA, Chaturvedi AK, Silvestri GA, Riley TL, Commins J, & Berg CD (2013). Selection Criteria for Lung-Cancer Screening. New England Journal of Medicine, 368, 728–36. 10.1056/NEJMoa1211776 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Taylor SE, & Broffman JI (2011). Chapter one - Psychosocial Resources: Functions, Origins, and Links to Mental and Physical Health. In Olson JM & Zanna MP (Eds.), Advances in Experimental Social Psychology (Vol. 44, p. 1–57). Academic Press. 10.1016/B978-0-12-385522-0.00001-9 [DOI] [Google Scholar]
  45. Tindle HA, Chang Y-F, Kuller LH, Manson JE, Robinson JG, Rosal MC, Siegle GJ, & Matthews KA (2009). Optimism, Cynical Hostility, and Incident Coronary Heart Disease and Mortality in the Women’s Health Initiative. Circulation, 120(8), 656–662. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. 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, 699–706. 10.2105/AJPH.2010.191668 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. U.S. Department of Health and Human Services. (2014). The Health Consequences of Smoking: 50 Years of Progress. A Report of the Surgeon General. Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health
  48. Viron S. de, der Heyden JV Ambrosino E, Arbyn M, Brand A, & Oyen HV (2012). Impact of Genetic Notification on Smoking Cessation: Systematic Review and Pooled-Analysis. PLOS ONE, 7(7), e40230. 10.1371/journal.pone.0040230 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Weinstein ND, Marcus SE, & Moser RP (2005). Smokers’ unrealistic optimism about their risk. Tobacco Control, 14(1), 55–59. 10.1136/tc.2004.008375 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Weinstein ND, Slovic P, & Gibson G (2004). Accuracy and optimism in smokers’ beliefs about quitting. Nicotine & Tobacco Research : Official Journal of the Society for Research on Nicotine and Tobacco, 6(Suppl), NaN-NaN. 10.1080/14622200412331320789 [DOI] [PubMed] [Google Scholar]
  51. Wells QS, Freiberg MS, Greevy JR, Tyndale RF, Kundu S, Duncan MS, King S, Scoville E, Beaulieu D, Gatskie V, & Tindle HA (2017). Nicotine Metabolism-informed Care for Smoking Cessation: A Pilot Precision RCT. Nicotine & Tobacco Research : Official Journal of the Society for Research on Nicotine and Tobacco 10.1093/ntr/ntx235 [DOI] [PMC free article] [PubMed]
  52. Young RP, Hopkins RJ, & Gamble GD (2012). Clinical applications of gene-based risk prediction for lung cancer and the central role of chronic obstructive pulmonary disease. Frontiers in Genetics, 3, 210. 10.3389/fgene.2012.00210 [DOI] [PMC free article] [PubMed] [Google Scholar]

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