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. Author manuscript; available in PMC: 2020 Sep 1.
Published in final edited form as: Drug Alcohol Depend. 2019 Jul 8;202:93–103. doi: 10.1016/j.drugalcdep.2019.07.001

SMOKING RELAPSE RISK IS INCREASED AMONG INDIVIDUALS IN RECOVERY

Amanda J Quisenberry 1, Jami Pittman 2, Renee D Goodwin 3,4, Warren K Bickel 5, Giordano D’Urso 6, Christine E Sheffer 1
PMCID: PMC6685745  NIHMSID: NIHMS1535012  PMID: 31325822

Abstract

Background:

The prevalence of cigarette smoking among individuals with a history of substance use disorders (SUDs) remains up to four times higher than those without a history of SUDs. More than half of individuals who attain sustained remission from SUDs will die of tobacco-related diseases. The aim of this secondary data analysis was to compare the risk for smoking relapse among smokers with no history of SUDs and smokers in recovery from SUDs after multi-component, cognitive-behavioral treatment for tobacco dependence.

Methods:

Participants were randomized to receive 6 sessions of multicomponent cognitive-behavioral therapy (adapted for lower socioeconomic groups or standard), 8 weeks of nicotine patches, and were followed for 6 months in the parent randomized clinical trial. Participants passed a urine drug test prior to enrollment. Recovery was assessed at baseline by self-report to the question, “Do consider yourself in recovery from drugs or alcohol?” Relapse was defined as any smoking for 7 consecutive days.

Results:

Participants were primarily lower SES and identified as racial and/or ethnic minorities. Cox proportional hazards models revealed that the risk of smoking relapse following tobacco dependence treatment was greater among smokers in long-term (HR: 1.44; 95 % CI: 1.01, 2.05) and short-term (HR: 1.98; 95% CI: 1.30, 3.03) recovery than for smokers with no history of SUDs.

Conclusions:

Our findings indicate that smokers in recovery from SUDs have 1.5 – 2 times the risk of relapse than smokers with no history of SUDs. More effective relapse prevention interventions are needed for this vulnerable, high-risk group of smokers.

Keywords: Recovery, smoking cessation, tobacco dependence treatment, substance use disorders, relapse risk

More than half of individuals who attain sustained remission from non-tobacco Substance Use Disorders (SUDs) will die of tobacco-related diseases (Hurt et al., 1996). Recovery from alcohol and drug problems is considered a process of change through which individuals achieve abstinence from substances and improve health, wellness and quality of life (Administration, 2012). Yet, the prevalence of cigarette smoking among individuals in recovery is up to four times greater than the prevalence in the general population (Campbell et al., 2019; Guydish et al., 2016; Guydish et al., 2011; Kalman et al., 2005; Weinberger et al., 2015), and cigarette smoking may increase the risk of relapse to substance use (Weinberger et al., 2017). Although public health tobacco control efforts have greatly reduced the prevalence of cigarette smoking overall, these efforts have been remarkably ineffective among individuals in remission or recovery from other SUDs (Department of Health and Human Services, 2014; Forouzanfar et al., 2015; Guydish et al., 2011; Weinberger et al., in press).

Cessation of cigarette use at any age provides significant health benefits, but achieved before age 40 reduces the risk of death from smoking-related diseases by 90% (Gellert et al., 2012; Jha et al., 2013). Evidence-based treatments for tobacco dependence significantly increase the odds of attaining long-term abstinence, but the degree to which individuals in recovery benefit from these treatments remains unclear (Department of Health and Human Services, 2014; Fiore et al., 2008; Hughes and Kalman, 2006; Prochaska et al., 2004; Thurgood et al., 2016). Relevant research shows mixed findings with positive results indicating some smoking cessation success among individuals in substance abuse treatment, though the likelihood of long-term cessation is limited (Prochaska et al., 2004; Thurgood et al., 2016).

Tobacco dependence is highly correlated with other SUDs, and the relationship among these disorders is complex. Smokers are five times more likely to use alcohol or illicit drugs than non-smokers (Administration, 2013; Breslau, 1995; Goodwin et al., 2018; Moeller et al., 2018; Weinberger et al., 2016; Weinberger et al., in press). A history of alcohol or drug dependence is strongly associated with the maintenance of tobacco dependence (Goodwin et al., 2014). Individuals with SUDs start smoking earlier, smoke for longer, and become more tobacco dependent than those without SUDs (De Dios et al., 2009; Goldstein et al., 2012; Goodwin et al., 2011; Hughes, 2002; Myers and Kelly, 2006; Richter et al., 2002). Cigarette smoking and drug and alcohol use share similar environmental, social, and behavioral factors, in addition to similar biochemical endogenous reinforcement suggesting common underlying mechanisms (Biederman et al., 2006). For instance, nicotine and other substances produce similar reinforcing dopaminergic effects in the ventral tegmental area of the mid-brain and the nucleus accumbens (Nestler, 2005) and chronic exposure to nicotine and other substances can impair dopaminergic responses to reward and cause long-term sensitization of the dopaminergic system after discontinuation of use (Koob, 2009; Koob and Le Moal, 2008a, b). Thus, the neuroplasticity that supports the development and maintenance of SUDs might influence the ability to discontinue tobacco use and vice versa. Specifically, several recent studies suggest that cigarette use is associated with increased risk of SUD relapse (Weinberger et al., 2017), and alcohol and cannabis use are associated with increased risk of persistence of cigarette use and relapse among former smokers (Weinberger et al., 2018).

Research on the treatment of tobacco dependence among individuals in recovery is primarily focused on individuals in treatment for or recently discharged from treatment for non-tobacco SUDs (Prochaska et al., 2004; Thurgood et al., 2016). The prevalence of smoking generally decreases as the length of recovery increases (Monti et al., 1995), but many factors are likely to contribute to this trend such as increases in socioeconomic status or increased coping strategies. Some evidence suggests improved tobacco dependence treatment outcomes among smokers who have been abstinent for a longer period of time, but given the enormous variability in the studies, conclusions about the role of duration of recovery in tobacco dependence treatment are tentative at best (Prochaska et al., 2004). Understanding the relationship between recovery from and length of recovery from SUDs and tobacco dependence treatment outcomes has implications for tailoring and optimizing treatment delivery for individuals with remitted substance use problems.

Conceptual and empirical models of health disparities and smoking cessation indicate that differences in tobacco treatment outcomes emerge through complex reciprocal relations among demographic, psychosocial, and tobacco treatment-related factors (Adler and Stewart, 2010; Businelle et al., 2011; Ferguson et al., 2005; Fernander et al., 2007; Honjo et al., 2006; Manfredi et al., 2007; Sheffer et al., 2012a; Sheffer et al., 2012b; Stronks et al., 1997; Varghese et al., 2014; Witkiewitz and Marlatt, 2004). Participants in this study were enrolled in a parent randomized controlled trial (RCT) for smoking cessation. The aims of this analysis were to: 1) characterize the sample to identify demographic and psychosocial features that vary by recovery status and may contribute to differences in tobacco treatment outcomes; 2) examine the relative risk of smoking relapse, defined as 7 consecutive days of any smoking following quit day, between smokers in recovery and smokers with no additional SUDs (NR); and 3) to delineate the risk of smoking relapse between smokers in short-term (STR) and long-term (LTR) recovery compared with NR. We expected STR and LTR smokers to demonstrate common characteristics associated with poor tobacco dependence treatment outcomes such as greater nicotine dependence levels (Hughes, 1993; Marks et al., 1997; Reich et al., 2008), less motivation and self-efficacy (Gwaltney et al., 2009), and greater negative affect (Baker et al., 2004; Degenhardt and Hall, 2001; Krentzman et al., 2015). We expected few differences in risk of smoking relapse between smokers in STR and LTR, but significant differences between STR/LTR smokers and NR in this treatment-seeking sample (Okoli and Khara, 2014).

METHOD

Participants

Participants were recruited by word of mouth, fliers placed in the community, and newspaper advertisements seeking smokers who were ready to quit smoking. Inclusion criteria included age 18 or older, smoking cigarettes daily, motivated to quit smoking, no regular use of other tobacco products, able to engage in treatment, attend at least one session of treatment, no contra-indication for use of the nicotine patch, no current use of medications for smoking cessation, drinking ≤ 20 alcoholic drinks per week, and providing a negative urine screen for drugs of abuse (i.e., 12 panel drug screen).

Procedure

Detailed methods of the RCT are published elsewhere (Sheffer et al., 2017). In brief, participants completed a baseline assessment that included a recovery status measure and were randomized to six weekly group sessions of standard or adapted cognitive-behavior treatment (CBT) for tobacco dependence (Evans et al., 2015), The quit day was on the day of the third session. Outcome assessments were completed three and six months after the quit day. Figure 1 depicts the study design. Adapted CBT consisted of a systematic revision of the standard treatment manual that included: 1) a focus on eight modifiable factors identified in the literature specific to socioeconomic smoking disparities, 2) the addition of behavioral rehearsal components that were introduced early and encouraged frequently, and 3) cultural specificities such as referring to strategies as “tools,” and tailoring with relevant examples and scenarios, such as financial stress. Daily smoking was assessed weekly after the quit day during treatment, monthly by telephone during follow-up, and reviewed in person during the outcome assessment visits using the timeline follow back (Sobell and Sobell, 1992). Participants were provided $5 for travel and $25 compensation for each visit.

Figure 1.

Figure 1.

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Parent study timeline and procedures.

Measures

Standard demographic information was collected during the baseline assessment. Table 1 shows the descriptive statistics for each of the measures described below for the overall sample and by recovery status.

Table 1.

Demographic, environmental and clinical, psychosocial and treatment utilization differences among participants in recovery from substance use disorders and with no history of substance use disorders

Variable Category, or Range Percent (n) or Mean (SD) No SUDs (NR) In Recovery from SUDs
Short-term Long-term
(≤12 mos.) (>12 mos.)
n=180 n=80 n = 38 n = 62
Demographic Age, y 21–73 48.4 (9.1) 48.0 (9.1) 47.1 (11.7) 49.7 (7.0)
Sex Male 71.1 (128) 71.2 (57) 68.4 (26) 72.6 (45)
Partnered Status Partnered 21.7 (39) 27.5 (22) 21.1 (8) 14.5 (9)
Race and Ethnicity White or Caucasian 18.3 (33) 17.5 (14) 7.9 (3) 25.8 (16)
African American or Black 66.1 (119) 66.2 (53) 73.7 (28) 61.3 (38)
Asian/Pacific Islander, American Indian, or multiethnic 5.6 (10) 5.0 (4) 10.5 (4) 3.2 (2)
Other 10.0 (18) 11.2 (9) 7.9 (3) 9.7 (6)
Non-Hispanic 80.0 (144) 85.0 (68) 84.2 (32) 71.0 (44)
Work status** Full time 10.0 (18) 16.2 (13) 2.6 (1) 6.5 (4)
Part time 13.3 (24) 21.2 (17) 7.9 (3) 6.5 (4)
Retired 3.9 (7) 3.8 (3) 2.6 (1) 4.8 (3)
Disabled 13.9 (25) 10.0 (8) 10.5 (4) 21.0 (13)
Unemployed 57.8 (104) 48.8 (39) 76.3 (29) 58.1 (36)
Homemaker 1.1 (2) 0.0 (0) 0.0 (0) 3.2 (2)
Household income ≤ $10,000 57.2 (103) 45.0 (36) 73.7 (28) 62.9 (39)
$10,000 - $14,999 16.7 (30) 20.0 (16) 7.9 (3) 17.7 (11)
$15,000 - $24,999 8.3 (15) 10.0 (8) 5.3 (2) 8.1 (5)
$25,000 - $34,999 6.1 (11) 8.8 (7) 7.9 (3) 1.6 (1)
$35,000 - $49,999 6.7 (12) 8.8 (7) 2.6 (1) 6.5 (4)
≥ $50,000 5.0 (9) 7.5 (6) 2.6 (1) 3.2 (2)
Education, y 7–20 12.3 (2.2) 12.7 (2.3) 11.8 (1.6) 12.1 (2.3)
Education, categories < 12 years 30.6 (55) 23.8 (19) 26.3 (10) 41.9 (26)
12 years 36.1 (65) 36.2 (29) 50.0 (19) 27.4 (17)
13–14 years 16.7 (30) 17.5 (14) 18.4 (7) 14.5 (9)
> 15 years 16.7 (30) 22.5 (18) 5.3 (2) 16.1 (10)
Health insurance status** Medicaid and/or Medicare 90.6 (163) 82.5 (66) 100.0 (38) 95.2 (59)
None 3.9 (7) 7.5 (6) 0.0 (0) 1.6 (1)
Private 5.6 (10) 10.0 (8) 0.0 (0) 3.2 (2)
Subjective Social Status 0–10 4.8 (1.9) 4.7 (1.8) 4.7 (2.2) 5.0 (2.0)
Deprivation of basic needs 0–10 7.0 (2.9) 7.2 (2.8) 6.1 (3.3) 7.4 (2.6)
Psychosocial Number of cigarettes per day 2–40 13.7 (7.3) 13.7 (7.2) 13.2 (7.4) 14.0 (7.3)
Cigarettes per day, categories ≤10 41.7 (75) 40.0 (32) 47.4 (18) 40.3 (25)
11–20 44.4 (80) 48.8 (39) 36.8 (14) 43.5 (27)
21–30 10.6 (19) 8.8 (7) 13.2 (5) 11.3 (7)
≥31 3.3 (6) 2.5 (2) 2.6 (1) 4.8 (3)
Partner Smoking1 Partner smokes 47.7 (53) 39.6 (21) 52.0 (13) 57.6 (19)
Timing of last quit attempt Never 11.1 (20) 10.0 (8) 13.2 (5) 11.3 (7)
<6 months ago 31.7 (57) 30.0 (24) 39.5 (15) 29.0 (18)
6–12 months ago 10.6 (19) 11.2 (9) 10.5 (4) 9.7 (6)
>12 months ago 46.7 (84) 48.8 (39) 36.8 (14) 50.0 (31)
Duration of last quit in weeks 0–1032 61.4 (125) 63.6 (147) 69.5 (124) 53.6 (94.1)
Smokes menthol Yes 88.9 (160) 87.5 (70) 94.7 (36) 87.1 (54)
Age started smoking 7–53 16.7 (5.6) 17.2 (4.2) 16.8 (7.7) 15.9 (5.7)
Years smoking 2–50 26.6 (11.0) 26.7 (10.7) 25.7 (12.8) 27.1 (10.3)
Motivation 0–10 8.8 (1.9) 9.0 (1.5) 8.8 (2.0) 8.4 (2.3)
Self efficacy 1–10 7.6 (2.3) 7.7 (2.2) 7.9 (2.5) 7.4 (2.4)
Concern about weight gain 0–10 5.2 (3.8) 5.1 (3.9) 5.1 (3.8) 5.5 (3.8)
FTND 0–9 4.5 (2.2) 4.3 (2.5) 4.6 (2.1) 4.6 (2.1)
SCQ Negative affect reduction (0–9) 5.9 (2.1) 5.8 (2.0) 5.9 (2.2) 6.0 (2.1)
Stimulation/state enhancement (08.7) 3.5 (2.2) 3.7 (2.1) 3.5 (2.2) 3.4 (2.2)
Health risk (0–9) 7.8 (1.7) 7.8 (1.8) 7.7 (1.8) 7.8 (1.6)
Taste/sensorimotor manipulation (08.9) 4.6 (2.1) 4.8 (2.0) 4.3 (2.1) 4.6 (2.1)
Social facilitation (0–9) 3.9 (2.3) 4.1 (2.3) 3.6 (1.8) 3.9 (2.4)
Weight control (09) 4.1 (2.4) 4.2 (2.5) 3.7 (2.3) 4.4 (2.5)
Addiction (0–9) 6.2 (1.8) 6.0 (1.9) 6.2 (1.9) 6.6 (1.7)
Negative physical feelings (0–9) 3.9 (2.4) 3.6 (2.4) 4.2 (2.5) 4.3 (2.4)
Boredom reduction (0–9) 5.5 (2.2) 5.3 (2.2) 5.3 (2.5) 5.8 (2.0)
Negative social impression (0–9) 4.5 (2.4) 4.1 (2.5) 4.6 (2.4) 4.8 (2.4)
Smoking policy at work2 Smoke-free indoors 82.8 (77) 80.9 (38) 84.2 (16) 85.2 (23)
Smoking policy at home** Smoke-free indoors 56.7 (102) 48.8 (39) 81.6 (31) 51.6 (32)
Epworth’s Sleepiness Scale 0–23 6.7 (3.9) 6.2 (3.6) 6.5 (3.6) 7.6 (4.2)
Physical Yes 73.3 (132) 81.2 (65) 65.8 (25) 67.7 (42)
activity in past month3
Number of events (0–31) 10.9 (11.5) 12.3 (11.3) 9.2 (11.5) 10.3 (11.6)
Number of minutes per event3 (0–120) 38.8 (35.4) 38.8 (32.9) 43.0 (39.5) 36.1 (36.3)
MSPSS Total Significant other (4–28) 21.2 (6.1) 20.9 (6.2) 22.6 (5.2) 20.7 (6.4)
Friends (4–28) 19.9 (6.1) 19.8 (5.5) 19.6 (7.1) 20.3 (6.3)
Family (4–28) 20.5 (6.9) 19.8 (6.7) 21.0 (7.8) 21.0 (6.7)
Total (18–84) 61.6 (16.5) 60.5 (16.2) 63.2 (17.2) 62.1 (16.5)
PANAS Positive* (15–50) 35.8 (7.8) 34.0 (8.0)a 38.3 (6.6)a 36.5 (7.8)
Negative (10–43) 16.7 (6.5) 16.4 (6.2) 16.0 (5.4) 17.5 (7.3)
CES-D 0–43 13.2 (8.8) 13.1 (9.9) 14.1 (8.6) 12.8 (7.3)
PSS-4 0–12 5.6 (2.8) 5.9 (2.7) 5.2 (3.2) 5.4 (2.6)
LOCS 1–17 9.2 (3.2) 9.2 (3.6) 8.9 (2.9) 9.5 (2.9)
Life Event Checklist 0–15 3.7 (3.6) 3.5 (3.7) 4.0 (3.5) 3.8 (3.6)
Life Event Stress Scale 12–880 182.6 (145) 166.9 (154) 196.4 (102) 194.5 (155)
William’s Everyday Discriminati on Scale 0–22 6.6 (5.0) 6.3 (4.9) 6.9 (5.9) 6.7 (4.5)
Perceived Discrimination* 3–15 6.9 (2.7) 6.5 (2.7)a 8.0 (2.7) a 6.8 (2.6)
Coping SelfEfficacy Scale Problem-focused coping (10–60) 43.4 (11.8) 42.1 (11.9) 44.6 (11.5) 44.3 (11.9)
Stop unpleasant emotions/thoughts (0–40) 27.5 (9.3) 26.5 (9.9) 27.5 (8.6) 28.8 (8.8)
Get support from friends/family* (0–30) 20.9 (7.3) 19.5 (7.8)a 21.4 (7.3) 22.4 (6.2)a
Total (67–260) 186.2 (49.3) 178.3 (52.7) 189.5 (43.3) 194.3 (47.3)
BRCI Positive* (0–21) 14.5 (6.0) 13.6 (6.0)a 16.6 (5.4)a 14.4 (6.2)
Negative (0–20) 2.6 (3.7) 2.3 (3.5) 3.3 (4.2) 2.7 (3.6)
Delay discounting $100 (−13.7–4.7) −3.8 (2.9) −3.7 (3.1) −4.2 (3.0) −3.6 (2.6)
$1000 (−13.7–4.7) −4.9 (3.4) −5.2 (3.4) −4.9 (3.2) −4.5 (3.6)
Barratt Impulsivene ss Scale-11 Attentional impulsiveness (825) 14.5 (3.6) 14.3 (3.8) 14.5 (4.2) 14.6 (2.8)
Motor impulsiveness (1336) 21.6 (4.0) 21.2 (3.7) 22.9 (4.9) 21.5 (3.7)
Non-planning impulsiveness (1138) 25.5 (4.7) 24.7 (4.9) 26.2 (5.4) 26.3 (3.9)
Total (36–99) 61.6 (9.9) 60.1 (9.8) 63.6 (12.5) 62.3 (8.1)
TPQ Future-Oriented 67.2 (121) 65.0 (52) 78.9 (30) 62.9 (39)
Self-Control Good self-control 71.4 (12.1) 70.8 (12.7) 73.2 (9.2) 71.2 (12.9)
Scale (34–90)
Future time perspective (12–35) 26.7 (5.6) 26.0 (5.7) 28.3 (4.5) 26.5 (5.9)
Problem solving (16–40) 32.4 (6.0) 32.5 (6.3) 32.4 (5.1) 32.4 (6.2)
Self-reinforcement (4–15) 12.3 (2.5) 12.3 (2.7) 12.5 (2.4) 12.3 (2.5)
Poor self-control (21–85) 44.4 (13.1) 42.9 (11.1) 44.2 (13.6) 46.6 (14.9)
Distractibility (630) 11.1 (4.5) 10.5 (4.4) 11.1 (4.0) 10.5 (4.4)
Delay of gratification (8–40) 18.8 (6.4) 18.3 (5.7) 18.8 (6.7) 19.3 (7.2)
Present time perspective (7–35) 14.6 (5.3) 14.1 (4.3) 14.3 (5.9) 15.4 (6.1)
Treatment utilization Patch use Yes 95.4 (167) 95.0 (76) 91.7 (33) 98.3 (58)
Patch, number4 1–146 40.8 (23.3) 42.3 (25.0) 38.5 (24.4) 40.1 (20.4)
Number of treatment sessions attended 1–6 5.7 (0.9) 5.8 (0.6) 5.5 (1.2) 5.7 (1.0)
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*

p<.05,

**

p< .01,

***

p<.001;

Means with the same letter superscript indicate the location of a significant difference;

^

married or living with significant other;

1

Of partnered respondents;

2

Of employed respondents;

3

In the past month other than regular job;

4

Of those who used patches; SUDs= Substance use disorders; FTND= Fagerstrom Test for Nicotine Dependence; MSPSS= Multidimensional Scale of Perceived Social Support; PANAS = Positive and Negative Affect scale; CES-D=Center for Epidemiologic Studies Depression Scale; SCQ=Smoking Consequences Questionnaire; PSS-4 = Perceived Stress Scale – 4 items; LOCS = Rotters Locus of Control Scale; BRCI = Brief Religious Coping Inventory

A broad definition of recovery was used because few individuals with SUDs receive formal diagnoses or treatment for SUDs (Substance Abuse and Mental Health Services Administration, 2014). Recovery status was self-identified. That is, recovery status was assessed by asking at baseline: “Do you consider yourself in recovery from drugs or alcohol?” If the response was yes, length of recovery was assessed with the question: “How many months have you been in recovery now?” STR was defined as ≤ 12 months of most recent recovery; LTR was defined >12 months of most recent recovery.

Psychosocial measures

Standard tobacco use and psychosocial information was collected during the baseline assessment. Motivation, self-efficacy and concern about weight gain were measured on a scale of 0–10 with 0=”not at all” and 10=”the most ever” and the questions: “How much do you want to quit smoking?”, “How confident are you that you can quit using tobacco and stay quit for good?”, and “How concerned are you about gaining weight after you quit?” (McCarthy et al., 2010; McKee et al., 2005; Moolchan et al., 2003; Perkins et al., 2001)

The MacArthur Scale of Subjective Social Status (SSS) was used to assess participants’ perception of social status. Participants reported where they stood on a 10-rung ladder relative to others. Higher rungs represented higher social status (more money and education, better jobs) and lower rungs represented lower social status. SSS has been found to predict abstinence from smoking independently of socioeconomic status (SES) (Adler et al., 2000; Reitzel et al.). Deprivation of basic needs was measured with the question, “In the last six months, to what degree did you feel that your basic needs such as healthy food and safe housing are being met?” Responses were measured on a scale of 0–10 with 0 = “not at all” and 10 = “the most possible.”

The Fagerström Test for Nicotine Dependence (FTND) is a 6-item measure assessing dependence level. Scores range from 0–10 with greater values indicating greater dependence and a lesser likelihood of attaining abstinence (Fagerstrom et al., 1996; Heatherton et al., 1991).

The Smoking Consequences Questionnaire-Adult (SCQ-A) is a 55-item measure comprised of 10 subscales assessing the positive and negative outcome expectancies of smoking. Responses are rated on a scale of 0–9 where 0 = “completely unlikely” and 9 = “completely likely.” Higher scores indicate a greater expectation that the consequence will occur because of smoking (Copeland et al., 1995).

Smoking policies at work and at home were assessed with the following options: a) no smoking anywhere inside or outside, b) no smoking inside, but smoking is allowed outside, c) smoking is allowed in certain areas inside, or d) smoking is allowed anywhere inside. Policies were considered smoke-free when a) or b) were endorsed. Smoke-free policies in the home are associated with increased odds of long-term abstinence (Messer et al., 2008).

The Epworth Sleepiness Scale (ESS) is an 8-item measure assessing the likelihood of falling asleep during daily activities on a 4-point scale where 0= “never” and 3 = “high chance of dozing.” Higher scores indicate poor quality sleep (Johns, 1991). Poor sleep is associated with higher levels of stress, poverty, and ethnic minority status (Grandner et al., 2010; Patel et al., 2010).

Physical Activity was assessed with items from Behavioral Risk Factor Surveillance System assessing physical activity in the past month. Greater physical activity is associated with better sleep, mood, and judgment skills (Centers for Disease Control and Prevention, 2015; Penedo and Dahn, 2005).

The Multidimensional Scale of Perceived Social Support (MSPSS) is 12-item measure assessing levels of social support from family, friends, and significant other on a 7-point scale where 1 = “very strongly disagree” and 7 = “very strongly agree”(Dahlem et al., 1991; Zimet et al., 1988). Greater values indicate greater levels of support. More support is associated with improved abstinence outcomes (Fiore et al., 2008).

The Positive and Negative Affect Schedule (PANAS) is a 20-item measure composed of two scales assessing positive and negative affect on a 5-point scale where 1= “very slightly or not at all” and 5 = “extremely” (Watson et al., 1988). Higher scores indicate higher levels of positive or negative affect. Higher negative affect scores are associated with poorer abstinence outcomes (Kenford et al., 2002).

The Center for Epidemiologic Studies Depression scale (CES-D) is 20-item measure assessing past week distress and depressive symptomatology on a 4-point scale where 0 = “rarely” and 3 = “most of the time.” Higher scores indicate higher levels of distress and depressive symptoms (Radloff, 1977).

The Perceived Stress Scale - 4 (PSS-4) is a 4-item measure of stress assessed on a 4 point scale ranging from “never” to “very often.” Higher scores indicate greater stress levels and a lesser likelihood of abstinence (Cohen and Lichtenstein, 1990).

Rotter’s Locus of Control (RLOCS) is a 29-item measure assessing perceived control over events. Scores range from 0–29 with greater values indicating a more externally-focused locus of control (Rotter, 1966). An externally-focused locus of control is associated with more impulsiveness and a lesser likelihood of achieving abstinence in treatment (Sheffer et al., 2012a).

The Life Event Checklist (LEC) is 17-item measure of lifetime exposure to traumatic events. Response options include: “happened to me” “witnessed it” “learned about it” “not sure” and “doesn’t apply.” A score of 1 is assigned if the event was directly experienced or witnessed and 0 for any other option (Gray et al., 2004). Higher scores indicate more exposure to trauma.

The Life Event Stress Scale (LESS) is a 43-item checklist of past year life events from the Social Readjustment Rating Scale (Holmes and Rahe, 1967; Masuda and Holmes, 1978). Events have scores corresponding to severity (e.g., death of a spouse=100, Christmas season=12). Higher scores indicate higher susceptibility to stress-related illness (Masuda and Holmes, 1978).

The William’s Everyday Discrimination Scale (WEDS) is a 9-item measure assessing the frequency of experiencing interpersonal mistreatment on a 4-point scale where 0 = “never” and 3 = “often.” Higher scores are associated with poorer physical health (Williams et al., 1997).

The Perceived Discrimination Scale (PDS) is a 3-item measure assessing the experience of discrimination on a 5-point scale (1= “strongly disagree,” 5 = “strongly agree”). Greater values indicate greater perceived discrimination (Fuller-Rowell et al., 2012a; Fuller-Rowell et al., 2012b).

The Coping Self-Efficacy Scale (CSE) is a 26-item measure assessing confidence in performing coping behaviors on an 11-point scale (0= “cannot do at all” to 10= “certain can do”). Coping behaviors are grouped into three groups: problem-focused coping, stopping unpleasant emotions and thoughts, and getting support from friends/family. Higher scores indicate higher confidence in ability to cope (Chesney et al., 2006).

The Brief Religious Coping Inventory (BRCI) is a 14-item measure of positive and negative religious coping on a 4-point scale (0= “not at all” to 3= “a great deal”). Higher negative religious coping scores are associated with spiritual struggles (Pargament et al., 2011).

Delay discounting is the degree to which rewards are modulated by the delay to their receipt (Logue et al., 1986). Participants completed the adjusting amount computerized delay discounting tasks assessing two magnitudes ($100 and $1,000) of hypothetical rewards. Tasks require participants to make a series of choices among smaller sooner versus larger later amounts for seven time delays (1 day, 1 week, 6 months, 1 year, 5 years and 25 years). The final value at the end of the series is the indifference point for that magnitude. The indifference point is the value of subjective equality between the smaller, sooner and larger, later amount. Indifference points are then fit to Mazur’s (1987) hyperbolic discounting model to determine k and expressed as the natural logarithm of k (lnk). Higher k values indicate a greater preference for the smaller sooner reward and lower values indicate a preference for the larger, later reward (Mazur, 1987). A preference for larger later rewards is linked with improved tobacco dependence treatment outcomes (Sheffer et al., 2014; Sheffer et al., 2012a).

The Barratt Impulsiveness Scale-11 (BIS) is a 30-item measure assessing general impulsiveness and includes three subscales: motor impulsiveness (acting without thinking), cognitive or attentional impulsiveness (making quick cognitive decisions), and non-planning impulsiveness (lack of concern about the future) (Reise et al., 2013). Items are scored on a 4-point scale (0= “rarely/never,” 3= “almost always/always”). Greater values indicate more impulsiveness and poorer tobacco dependence treatment outcomes (Sheffer et al., 2012a).

The Time Perspective Questionnaire (TPQ) is a single-item measure of future orientation assessed on a 5-point scale (strongly disagree to strongly agree) by the question: “How much do you agree with the following statement: You spend a lot of time thinking about how what you do today will affect your life in the future.” Strongly agree or agree responses are considered “future-oriented” (Fong and Hall, 2003; Hall, 2012; Sansone et al., 2013).

The Self-Control Scale (SCS) is a 39-item measure composed of two scales assessing good and poor self-control on a 5-point scales (“not at all true” to “very true” or “never” to “usually”). Good self-control includes three subscales: future time perspective (seven items), problem solving (eight items), and self-reinforcement (three items). Poor self-control includes three subscales: present time perspective (seven items), delay of gratification (eight items), and distractibility (six items). Higher scores reflect higher levels of good or poor self-control (Pearson et al., 2013).

Treatment utilization was assessed with three measures: Nicotine patch use (yes/no); number of nicotine patches used; and number of treatment sessions attended. Daily nicotine patch use was assessed using the timeline follow-back (TLFB) procedure (Sobell and Sobell, 1992), a validated self-report measure.

Smoking relapse

Days to first smoking relapse, the outcome variable, was defined as any smoking for seven consecutive days following the quit day (Hughes, 2003), thus including participants who had no days of abstinence (n = 5). This was assessed with the TLFB procedure (Sobell and Sobell, 1992), an accurate and reliable procedure for recalling the daily number of cigarettes smoked over the last 30 days (Toll et al., 2005).

Statistical Analysis

Descriptive analyses (i.e., frequencies, means and standard deviations) were conducted followed by comparisons using one-way analysis of variance (ANOVA) and χ2 analyses as appropriate. Bonferroni post-hoc tests were used for multiple comparisons. Adjusted p-values are presented in Table 1. Differences among participants who responded to the recovery question and those who did not (responded versus non-respondents) were evaluated to examine the potential for selection bias.

A cox proportional hazards survival analysis (CPH) model, including hazard ratios (HR) and associated confidence intervals (CI), was used to evaluate the relative risk of smoking relapse (in days) between participants in recovery from SUDs and those who were not. Survival data were right censored (i.e., censored if the occurrence of an event/relapse was unknown). Variables that differed significantly by recovery status (i.e., significant variables in Table 1: work status, health insurance status, smoking policy at home, perceived discrimination, level of support from friends and family, positive PANAS, and positive religious coping) were included as covariates in the model. Treatment condition was included in the model to control for the effects of treatment condition. No gross violations of the proportional hazards assumption were apparent when evaluating Schoenfeld residuals.

We examined differences in the relative risk of relapse among those not in recovery from SUDs, those in short-term (≤ 12 months) recovery from SUDs, and those in long-term (> 12 months) recovery from SUDs, with another right censored CPH model. This model included all significant variables from the previous CPH model (i.e., treatment condition, insurance status, positive religious coping).

RESULTS

Participants

Of the 227 participants who attended at least one session of treatment in the clinical trial, 79.3% (n=180) responded to the recovery status question and were included in this analysis. Table 1 describes these participants overall and by SUD recovery status. Participants were primarily male, middle aged, identified as racial or ethnic minorities, and were of lower income and education level. About three-quarters (73.9%) reported household incomes of less than $15,000. Mean years of education was 12.3 (SD 2.2). Over half of participants (57.8%) were unemployed and 90.6% were enrolled in Medicaid and/or Medicare. Over half reported that they were in recovery from SUDs (55.6%; n=100), 38.0% (n=38) of whom reported STR and 62.0% (n=62) reported LTR.

Several differences were found between those who responded to the recovery question (respondents, n=180) and those who did not (non-respondents, n=47; see Table 2). Non-respondents characteristics were generally indicative of more difficulty quitting, although respondents’ deprivation of basic needs was greater. Non-respondents appeared to have a bit more difficulty with impulsiveness, self-control, social support, and coping. Non-respondents mean CES-D scores reached the 16 point cut-off for probable depression while respondent’s scores were about 3 points lower. Non-respondents attended 5.3 sessions versus 5.7 sessions of treatment and used the nicotine patches for 2 fewer weeks than respondents, which suggests less engagement, more difficulty adhering to the protocol, or more perhaps more challenges quitting that might not be reflected in the number of days to relapse. Consequently, even though the respondents who comprised this sample have many characteristics that make cessation challenging in common with non-respondents, they are likely to be more adherent and engaged than the non-respondents.

Table 2.

Characteristics of participants who did and did not self-report recovery status

Variable Respondents n=180 M (SD)/% Non-respondents n=47 M (SD)/%
Demographic and Psychosocial Characteristics Education, y** 12.3 (2.2) 11.3 (1.7)
Deprivation of basic needs ** 7.0 (2.9) 5.7 (3.4)
CES-D* 13.2 (8.8) 16.5 (8.8)
Barratt Impulsiveness Scale-11 Attentional Impulsiveness * 14.5 (3.6) 15.7 (3.3)
Self-Control Scale/Good self-control* 71.4 (12.1) 67.2 (14.8)
Self-Control Scale/Self reinforcement ** 12.3 (2.5) 11.3 (3.0)
Self-Control Scale/Distractibility* 12.8 (5.6) 11.1 (4.5)
MSPSS family support* 19.9 (6.1) 17.7 (6.8)
MSPSS total** 20.5 (6.9) 17.3 (7.9)
Coping Self-Efficacy Scale* 43.4 (11.8) 39.1 (14.2)
Treatment- related characteristics Number of Sessions Attended*** 5.7 (0.9) 5.3 (1.4)
Nicotine Patch Use** 96 83.3
Attended 6 month outcome assessment*** 95.6 59.6
Number of days to relapse 60.1 (69.2) 62.5 (67.8)
Total number of days abstinent 75.0 (66.3) 74.5 (64.2)
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*

p<.05,

**

p≤.01,

***

p≤.001

Differences among participants in self-reported recovery and not in recovery from SUDs

Several demographic and psychosocial differences were found between the STR, LTR, and NR groups, shown in Table 1. Fewer participants in STR and LTR reported working part-time or full-time compared to NR (part-time: 7.9% and 6.5% vs. 21.2%; full-time: 2.6% and 6.5% vs. 16.2%; (χ2=23.7, df=10, p=.01). More participants in STR reported smoke-free policies in their homes (81.6% vs. 51.6% (LTR) vs. 48.8% (NR); χ2=12.3, df=2, p=.002).

Significant differences were found in the levels of positive affect on the PANAS (F(2,177)=4.46, p=.01); positive religious coping on the RCOPE (F(2,177)=3.17, p=.05); perceived discrimination on the PDS (F(2,177)=4.11, p=.02); and getting support from family and friends on the CSE (F(2,177)=3.00, p=.05). STR (M=38.3 (SD 6.6) reported higher levels of positive affect than NR (M=34.0 (SD 8.0), p=.02), but no differences were found between STR and LTR (M=36.5 (SD 7.8), p=.76) nor between LTR and NR (p=.17). STR (M=16.6 (SD 5.4) reported higher levels of positive religious coping than NR (M=13.6 (SD 6.0), p=.04), but no differences were found between STR and LTR (M=14.4 (SD 6.2), p=.23) nor between LTR and NR (p=1.0). STR (M=8.0 (SD 2.7)) reported higher levels of perceived discrimination than NR (M=6.5 (SD 2.7), p=.02), but no differences were found between STR and LTR (M =6.8 (SD 2.6), p=.09) nor between LTR and NR (p=1.0). LTR (M=22.4 (SD 6.2) reported higher levels of coping by getting support from family and friends than NR (M=19.5 (SD 7.8), p=.05) but no differences were found between LTR and STR (M=21.4 (SD 7.3), p=1.0) nor between STR and NR (p=.55).

No differences were found between STR, LTR, and NR in treatment utilization including the number of treatment sessions attended (M =5.5 (SD 1.2) vs. M =5.7 (SD 1.0) vs. M=5.8 (SD 0.6); F(2,177)=1.51, p=.22); nicotine patch use (91.7% vs. 98.3% vs. 95.0%, χ2=.2.3, df=2, p=.31); or the mean number of patches used (M=35.3 (SD 25.7) vs. M=39.4 (SD 20.9) vs. M=40.2 (SD 26.1), F(2,172)=.51, p=.60). Importantly, no differences were found between STR, LTR, and NR in the proportion of participants lost to follow-up (7.9% vs. 4.8% vs. 2.5%; χ2=1.8, df=2, p=.41).

Cox Proportional Hazards Models

The first CPH Model retained recovery status (Y/N), treatment condition (standard/adapted), work status (full time, part time, retired, disabled, unemployed, home maker), health insurance status (Medicaid/Medicare, none, private), home smoking policy (smoke free Y/N), perceived discrimination, support from family and friends, positive PANAS score, and positive religious coping score as covariates. Significant effects for three variables were found: treatment condition (HR: 1.68; 95% CI: 1.22, 2.33; p=.002); positive religious coping (HR: .97; 95% CI: .94, .99; p=.035); and recovery status (HR: 1.48, 95% CI: 1.05, 2.09; p=.024).

The second CPH model retained treatment condition, health insurance status, positive religious coping, and recovery status delineated into 3 groups (i.e., not in recovery from SUDs, in short-term (≤ 12 months) recovery from SUDs, and in long-term (> 12 months) recovery from SUDs) and found significant effects for treatment condition, positive religious coping, and recovery status. Table 3 shows that participants in the standard treatment condition (HR: 1.57; 95% CI: 1.15; 2.15; p=.005) had a greater risk of smoking relapse. For every one unit increase in positive religious coping, the risk of relapse decreased by 3% (HR: .97; 95% CI: .95, 1.00; p=.031). Participants in STR (HR: 1.98; 95% CI: 1.30, 3.03; p=.002) and LTR (HR: 1.44; 95% CI: 1.01, 2.05; p=.043) had a greater risk of relapse when compared to NR. No significant differences in time to smoking relapse were found between participants in short-term and long-term recovery. Figure 2 depicts the survival function of the three recovery status groups.

Table 3.

Cox Proportional Hazards Survival Analysis Results for Days to Smoking Relapse (n=180)

Variable HR 95% CI p-value
*Recovery Status
 In Long Term Recovery (LTR) 1.44 (1.01, 2.05) .043
 In Short Term Recovery (STR) 1.98 (1.30, 3.03) .002
 Not in Recovery (NR) Reference
*Treatment Condition 1.57 (1.15, 2.15) .005
Health Insurance Status
 Medicaid and/or Medicare .48 (.22, 1.04) .061
 None .43 (.15, 1.23) .115
 Private Reference
*Positive Religious Coping .97 (.95, 1.00) .031
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*

significant at p < .05

Figure 2.

Figure 2.

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Survival Curves by Recovery Status.

DISCUSSION

In this group of primarily lower SES smokers, a history of SUD was associated with increased risk of smoking relapse compared to smokers with no history of SUD. Our findings show that risk of smoking relapse for smokers in recovery was consistently higher than for smokers with no SUDs. Participants in STR had almost 2 times the risk and those in LTR 1.5 times the risk of smoking relapse compared to NR participants. These findings suggest that smokers in recovery from SUDs have more difficulty preventing relapse after quitting tobacco than individuals with no history of SUDs, even with intensive, evidence-based treatment for tobacco use. The challenges preventing relapse appeared to be more pronounced for individuals in recovery from SUDs for one year or less.

These findings are consistent with and extend previous research on smoking cessation among individuals with remitted substance use problems that suggests individuals in recovery from SUDs are at increased risk of smoking relapse (Kelly et al., 2018). Among individuals with alcohol use disorder, duration of recovery appears to be positively associated with abstinence from tobacco after group treatment for tobacco dependence (Kalman et al., 2006). Among individuals with SUDs, risk of smoking relapse appears to decrease as length of time in recovery increases (Kelly et al., 2018). This study is consistent with these findings and indicates that individuals with a history of SUDs do have more difficulty preventing relapse than individuals without a history of SUDs.

These findings provide additional insights into the potential factors associated with differences in relapse to smoking among a sample that included a high proportion of individuals categorized as lower SES, a group that demonstrates disparate tobacco dependence treatment outcomes (Sheffer et al., 2014). No significant differences between STR, LTR, and NR in household income and educational level were found in the preliminary analyses. Nonetheless, we conducted an exploratory analysis by adding SES to the CPH models. The results showed that the days to relapse HR’s were minimally altered and SES was not a significant factor. Health insurance status, a proxy for SES, was significant when evaluating recovery status as a dichotomous variable, but not when dividing those in recovery by duration of recovery. Positive religious coping slightly decreased the risk of smoking relapse in this sample, which may be related to a spiritual external locus of control, a feature of 12-step recovery programs (Li et al., 2000). Indeed, individuals with higher religiosity are less likely to use substances (Oleckno and Blacconiere, 1991) and among those in recovery, higher levels of religiosity are associated with greater optimism, stress resiliency, and perceived support (Pardini et al., 2000), characteristics often associated with successful smoking cessation outcomes.

Understanding the challenges that smokers with remitted substance use problems experience while preventing relapse from smoking is important to the development of more effective and perhaps tailored treatments for this group. The increased relapse rates among participants in recovery might be attributed to relapse with other substances (Kalman et al., 2010; Kohn et al., 2003; Sullivan and Covey, 2002) or with the management of unrecognized, undiagnosed, and/or untreated co-occurring mental health disorders (Kalman et al., 2010). Another possibility is that smoking functions as a substitute for other drug use rendering the maintenance of cessation more difficult when in recovery, an effect that may reduce over time. Further, neuroadapatations that co-occur with recovery from drug abuse (Humphreys and Bickel, 2018) might take some time to develop and be more robust and beneficial for those in LTR than in STR. Formative research is needed to ascertain the range of factors associated with relapse prevention after treatment for tobacco dependence for smokers in recovery.

The strengths of this study include a no SUDs comparison group; the assessment of duration of recovery; a detailed demographic and psychosocial assessment; and the provision of treatment of high enough intensity and efficacy to enable patterns in relapse prevention to emerge. The limitations include a definition of recovery based on self-report as opposed to diagnosis; although we sought to balance exactitude with the reality that most individuals who experience problems with SUDs never receive a formal diagnosis or treatment for SUDs (Substance Abuse and Mental Health Services Administration, 2014). In fact, many studies involving substance use recovery as a variable of interest utilize these types of self-report measures and rarely report on behavior prior to recovery (Sobell et al., 2000). Nonetheless, the selected sample may not reflect the actual population of people in recovery from SUD’s limiting generalizability. Best practices for determining recovery status are needed to help standardize and simplify future research efforts while providing consistency for comparability across studies. Limitations also include no co-morbid psychiatric diagnoses, which may affect smoking relapse, and no identification of past substances used. Risk of smoking relapse might vary by substance/s used. The primary focus of the clinical trial was not the use of other substances, so while participants using other substances were excluded at baseline, we are unable to ascertain if relapse to smoking was associated with use of other substances after baseline. Finally, this study was not powered to find differences in relapse outcomes among three groups, so actual differences might be more pronounced than reflected in the findings.

Conclusion

These findings indicate that individuals with a history of remitted SUDs are likely to have more difficulty preventing relapse to smoking after tobacco dependence treatment, compared with individuals without a history of SUDs. Among a primarily lower SES group of smokers undergoing treatment for tobacco dependence, risk of smoking relapse was 1.5 to 2 times greater for smokers in recovery than for smokers with no SUDs. The risk of relapse was greater for individuals in recovery for one year or less. More research is needed to understand the factors associated with tobacco dependence treatment outcomes among individuals with SUDs and to develop more effective treatment approaches for this vulnerable, high-risk group of smokers.

Highlights.

  • Smoking prevalence is up to 4 times higher in individuals with substance use disorder.

  • Smoking relapse rates are 45 to 60% for persons in recovery from other substances.

  • Risk of smoking relapse after tobacco dependence treatment was evaluated.

  • Individuals in short term recovery had 2 times the risk of smoking relapse.

  • Individuals in long term recovery had 1.5 times the risk of smoking relapse.

Funding

This study was supported by the National Institutes of Health, National Institute on Minority Health and Health Disparities (R01 MD007054 PI: Sheffer).

Footnotes

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Author Disclosures:

The authors have no disclosures.

Conflict of Interest Statement

The authors declare no conflicting interests.

REFERENCES

  1. Adler NE, Epel ES, Castellazzo G, Ickovics JR, 2000. Relationship of subjective and objective social status with psychological and physiological functioning: preliminary data in healthy white women. Health Psychol 19(6), 586–592. [DOI] [PubMed] [Google Scholar]
  2. Adler NE, Stewart J, 2010. Health disparities across the lifespan: meaning, methods, and mechanisms. Annals of the New York Academy of Sciences 1186, 5–23. [DOI] [PubMed] [Google Scholar]
  3. Administration, S.A.a.M.H.S., 2012. SAMHSAs working definition of recovery: 10 guiding principles of recovery, in: Administration, S.A.a.M.H.S. (Ed.). [Google Scholar]
  4. Administration, S.A.a.M.H.S., 2013. Results from the 2012 National Survey on Drug Use and Health: Summary of National Findings, in: Administration, S.A.a.M.H.S. (Ed.). Substance Abuse and Mental Health Services Administration, Rockville, MD. [Google Scholar]
  5. Baker TB, Piper ME, McCarthy DE, Majeskie MR, Fiore MC, 2004. Addiction motivation reformulated: an affective processing model of negative reinforcement. Psychol Rev 111(1), 33–51. [DOI] [PubMed] [Google Scholar]
  6. Biederman J, Monuteaux MC, Mick E, Wilens TE, Fontanella JA, Poetzl KM, Kirk T, Masse J, Faraone SV, 2006. Is cigarette smoking a gateway to alcohol and illicit drug use disorders? A study of youths with and without attention deficit hyperactivity disorder. Biol Psychiatry 59(3), 258–264. [DOI] [PubMed] [Google Scholar]
  7. Breslau N, 1995. Psychiatric comorbidity of smoking and nicotine dependence. Behav Genet 25(2), 95–101. [DOI] [PubMed] [Google Scholar]
  8. Businelle MS, Kendzor DE, Reitzel LR, Costello TJ, Cofta-Woerpel L, Li Y, Mazas CA, Vidrine JI, Cinciripini PM, Greisinger AJ, Wetter DW, 2011. Mechanisms linking socioeconomic status to smoking cessation: a structural equation modeling approach. Health Psychol 29(3), 262–273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Campbell BK, Le T, Gubner NR, Guydish J, 2019. Health risk perceptions and reasons for use of tobacco products among clients in addictions treatment. Addict Behav 91(149–155). [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Centers for Disease Control and Prevention, N.C.f.C.D.P.a.H.P., Division of Population Health, 2015. BRFSS Prevalence & Trends Data [online]. in: Services, U.D.o.H.a.H. (Ed.). US Department of Health and Human Services, Centers for Disease Control and Prevention, Atlanta. [Google Scholar]
  11. Chesney MA, Neilands TB, Chambers DB, Taylor JM, Folkman S, 2006. A validity and reliability study of the coping self-efficacy scale. British journal of health psychology 11(Pt 3), 421–437. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cohen S, Lichtenstein E, 1990. Perceived stress, quitting smoking, and smoking relapse. Health psychology : official journal of the Division of Health Psychology, American Psychological Association 9(4), 466–478. [DOI] [PubMed] [Google Scholar]
  13. Copeland A, Brandon TH, Quinn EP, 1995. The Smoking Consequences Questionnaire-Adult: Measurement of smoking outcome expectancies of experienced smokers. Psychological Assessment 7(4), 10. [Google Scholar]
  14. Dahlem NW, Zimet GD, Walker RR, 1991. The Multidimensional Scale of Perceived Social Support: a confirmation study. J Clin Psychol 47(6), 756–761. [DOI] [PubMed] [Google Scholar]
  15. De Dios MA, Vaughan EL, Stanton CA, Niaura R, 2009. Adolescent tobacco use and substance abuse treatment outcomes. Journal of Substance Abuse Treatment, 37(17–24). [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Degenhardt L, Hall W, 2001. The relationship between tobacco use, substance-use disorders and mental health: results from the National Survey of Mental Health and Well-being. Nicotine Tob Res 3(3), 225–234. [DOI] [PubMed] [Google Scholar]
  17. Department of Health and Human Services, 2014. 2014 Surgeon General’s Report: The Health Consequences of Smoking - 50 Years of Progress, in: Services, D.o.H.a.H. (Ed.). U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, Atlanta, GA. [Google Scholar]
  18. Evans SD, Sheffer CE, Bickel WK, Cottoms N, Olson M, Piti LP, Austin T, Stayna H, 2015. The Process of Adapting the Evidence-Based Treatment for Tobacco Dependence for Smokers of Lower Socioeconomic Status. J Addict Res Ther 6(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Fagerstrom KO, Kunze M, Schoberberger R, Breslau N, Hughes JR, Hurt RD, Puska P, Ramstrom L, Zatonski W, 1996. Nicotine dependence versus smoking prevalence: comparisons among countries and categories of smokers. Tobacco control 5(1), 52–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Ferguson J, Bauld L, Chesterman J, Judge K, 2005. The English smoking treatment services: one-year outcomes. Addiction 100 Suppl 2, 59–69. [DOI] [PubMed] [Google Scholar]
  21. Fernander AF, Shavers VL, Hammons GJ, 2007. A biopsychosocial approach to examining tobacco-related health disparities among racially classified social groups. Addiction 102 Suppl 2, 43–57. [DOI] [PubMed] [Google Scholar]
  22. Fiore MC, Jaén CR, Baker TB, Bailey WC, Benowitz NL, Curry SJ, Dorfman SF, Froelicher EF, Goldstein MG, Healton CG, Henderson PN, Heyman RB, Koh HK, Kottke TE, Lando HA, Mecklenburg RE, Mermelstein RM, Mullen PD, Orleans CT, Robinson L, Stitzer M, Tommasello AC, Villejo L, Wewers ME, 2008. Treating tobacco use and dependence: 2008 update. Clinical practice guideline, in: Services, U.D.o.H.a.H. (Ed.). Public Health Service, Rockville, MD. [Google Scholar]
  23. Fong GT, Hall PL, 2003. Time perspective: a potentially important construct for decreasing health risk behaviors among adolescents, in: Romer D (Ed.) Reducing Adolescent Risk: Toward an Integrated Approach. Sage Publications, Thousand Oaks, CA, pp. 106–112. [Google Scholar]
  24. Forouzanfar MH, Alexander L, Anderson HR, Bachman VF, Biryukov S, Brauer M, Burnett R, Casey D, Coates MM, Cohen A, Delwiche K, Estep K, Frostad JJ, Astha KC, Kyu HH, Moradi-Lakeh M, Ng M, Slepak EL, Thomas BA, Wagner J, Aasvang GM, Abbafati C, Abbasoglu Ozgoren A, Abd-Allah F, Abera SF, Aboyans V, Abraham B, Abraham JP, Abubakar I, Abu-Rmeileh NM, Aburto TC, Achoki T, Adelekan A, Adofo K, Adou AK, Adsuar JC, Afshin A, Agardh EE, Al Khabouri MJ, Al Lami FH, Alam SS, Alasfoor D, Albittar MI, Alegretti MA, Aleman AV, Alemu ZA, Alfonso-Cristancho R, Alhabib S, Ali R, Ali MK, Alla F, Allebeck P, Allen PJ, Alsharif U, Alvarez E, Alvis-Guzman N, Amankwaa AA, Amare AT, Ameh EA, Ameli O, Amini H, Ammar W, Anderson BO, Antonio CA, Anwari P, Argeseanu Cunningham S, Arnlov J, Arsenijevic VS, Artaman A, Asghar RJ, Assadi R, Atkins LS, Atkinson C, Avila MA, Awuah B, Badawi A, Bahit MC, Bakfalouni T, Balakrishnan K, Balalla S, Balu RK, Banerjee A, Barber RM, Barker-Collo SL, Barquera S, Barregard L, Barrero LH, Barrientos-Gutierrez T, Basto-Abreu AC, Basu A, Basu S, Basulaiman MO, Batis Ruvalcaba C, Beardsley J, Bedi N, Bekele T, Bell ML, Benjet C, Bennett DA, Benzian H, Bernabe E, Beyene TJ, Bhala N, Bhalla A, Bhutta ZA, Bikbov B, Bin Abdulhak AA, Blore JD, Blyth FM, Bohensky MA, Bora Basara B, Borges G, Bornstein NM, Bose D, Boufous S, Bourne RR, Brainin M, Brazinova A, Breitborde NJ, Brenner H, Briggs AD, Broday DM, Brooks PM, Bruce NG, Brugha TS, Brunekreef B, Buchbinder R, Bui LN, Bukhman G, Bulloch AG, Burch M, Burney PG, Campos-Nonato IR, Campuzano JC, Cantoral AJ, Caravanos J, Cardenas R, Cardis E, Carpenter DO, Caso V, Castaneda-Orjuela CA, Castro RE, Catala-Lopez F, Cavalleri F, Cavlin A, Chadha VK, Chang JC, Charlson FJ, Chen H, Chen W, Chen Z, Chiang PP, Chimed-Ochir O, Chowdhury R, Christophi CA, Chuang TW, Chugh SS, Cirillo M, Classen TK, Colistro V, Colomar M, Colquhoun SM, Contreras AG, Cooper C, Cooperrider K, Cooper LT, Coresh J, Courville KJ, Criqui MH, Cuevas-Nasu L, Damsere-Derry J, Danawi H, Dandona L, Dandona R, Dargan PI, Davis A, Davitoiu DV, Dayama A, de Castro EF, De la Cruz-Gongora V, De Leo D, de Lima G, Degenhardt L, del Pozo-Cruz B, Dellavalle RP, Deribe K, Derrett S, Des Jarlais DC, Dessalegn M, deVeber GA, Devries KM, Dharmaratne SD, Dherani MK, Dicker D, Ding EL, Dokova K, Dorsey ER, Driscoll TR, Duan L, Durrani AM, Ebel BE, Ellenbogen RG, Elshrek YM, Endres M, Ermakov SP, Erskine HE, Eshrati B, Esteghamati A, Fahimi S, Faraon EJ, Farzadfar F, Fay DF, Feigin VL, Feigl AB, Fereshtehnejad SM, Ferrari AJ, Ferri CP, Flaxman AD, Fleming TD, Foigt N, Foreman KJ, Paleo UF, Franklin RC, Gabbe B, Gaffikin L, Gakidou E, Gamkrelidze A, Gankpe FG, Gansevoort RT, Garcia-Guerra FA, Gasana E, Geleijnse JM, Gessner BD, Gething P, Gibney KB, Gillum RF, Ginawi IA, Giroud M, Giussani G, Goenka S, Goginashvili K, Gomez Dantes H, Gona P, Gonzalez de Cosio T, Gonzalez-Castell D, Gotay CC, Goto A, Gouda HN, Guerrant RL, Gugnani HC, Guillemin F, Gunnell D, Gupta R, Gutierrez RA, Hafezi-Nejad N, Hagan H, Hagstromer M, Halasa YA, Hamadeh RR, Hammami M, Hankey GJ, Hao Y, Harb HL, Haregu TN, Haro JM, Havmoeller R, Hay SI, Hedayati MT, Heredia-Pi IB, Hernandez L, Heuton KR, Heydarpour P, Hijar M, Hoek HW, Hoffman HJ, Hornberger JC, Hosgood HD, Hoy DG, Hsairi M, Hu G, Hu H, Huang C, Huang JJ, Hubbell BJ, Huiart L, Husseini A, Iannarone ML, Iburg KM, Idrisov BT, Ikeda N, Innos K, Inoue M, Islami F, Ismayilova S, Jacobsen KH, Jansen HA, Jarvis DL, Jassal SK, Jauregui A, Jayaraman S, Jeemon P, Jensen PN, Jha V, Jiang F, Jiang G, Jiang Y, Jonas JB, Juel K, Kan H, Kany Roseline SS, Karam NE, Karch A, Karema CK, Karthikeyan G, Kaul A, Kawakami N, Kazi DS, Kemp AH, Kengne AP, Keren A, Khader YS, Khalifa SE, Khan EA, Khang YH, Khatibzadeh S, Khonelidze I, Kieling C, Kim D, Kim S, Kim Y, Kimokoti RW, Kinfu Y, Kinge JM, Kissela BM, Kivipelto M, Knibbs LD, Knudsen AK, Kokubo Y, Kose MR, Kosen S, Kraemer A, Kravchenko M, Krishnaswami S, Kromhout H, Ku T, Kuate Defo B, Kucuk Bicer B, Kuipers EJ, Kulkarni C, Kulkarni VS, Kumar GA, Kwan GF, Lai T, Lakshmana Balaji A, Lalloo R, Lallukka T, Lam H, Lan Q, Lansingh VC, Larson HJ, Larsson A, Laryea DO, Lavados PM, Lawrynowicz AE, Leasher JL, Lee JT, Leigh J, Leung R, Levi M, Li Y, Liang J, Liang X, Lim SS, Lindsay MP, Lipshultz SE, Liu S, Liu Y, Lloyd BK, Logroscino G, London SJ, Lopez N, Lortet-Tieulent J, Lotufo PA, Lozano R, Lunevicius R, Ma J, Ma S, Machado VM, MacIntyre MF, Magis-Rodriguez C, Mahdi AA, Majdan M, Malekzadeh R, Mangalam S, Mapoma CC, Marape M, Marcenes W, Margolis DJ, Margono C, Marks GB, Martin RV, Marzan MB, Mashal MT, Masiye F, Mason-Jones AJ, Matsushita K, Matzopoulos R, Mayosi BM, Mazorodze TT, McKay AC, McKee M, McLain A, Meaney PA, Medina C, Mehndiratta MM, Mejia-Rodriguez F, Mekonnen W, Melaku YA, Meltzer M, Memish ZA, Mendoza W, Mensah GA, Meretoja A, Mhimbira FA, Micha R, Miller TR, Mills EJ, Misganaw A, Mishra S, Mohamed Ibrahim N, Mohammad KA, Mokdad AH, Mola GL, Monasta L, Montanez Hernandez JC, Montico M, Moore AR, Morawska L, Mori R, Moschandreas J, Moturi WN, Mozaffarian D, Mueller UO, Mukaigawara M, Mullany EC, Murthy KS, Naghavi M, Nahas Z, Naheed A, Naidoo KS, Naldi L, Nand D, Nangia V, Narayan KM, Nash D, Neal B, Nejjari C, Neupane SP, Newton CR, Ngalesoni FN, Ngirabega Jde D, Nguyen G, Nguyen NT, Nieuwenhuijsen MJ, Nisar MI, Nogueira JR, Nolla JM, Nolte S, Norheim OF, Norman RE, Norrving B, Nyakarahuka L, Oh IH, Ohkubo T, Olusanya BO, Omer SB, Opio JN, Orozco R, Pagcatipunan RS Jr., Pain AW, Pandian JD, Panelo CI, Papachristou C, Park EK, Parry CD, Paternina Caicedo AJ, Patten SB, Paul VK, Pavlin BI, Pearce N, Pedraza LS, Pedroza A, Pejin Stokic L, Pekericli A, Pereira DM, Perez-Padilla R, Perez-Ruiz F, Perico N, Perry SA, Pervaiz A, Pesudovs K, Peterson CB, Petzold M, Phillips MR, Phua HP, Plass D, Poenaru D, Polanczyk GV, Polinder S, Pond CD, Pope CA, Pope D, Popova S, Pourmalek F, Powles J, Prabhakaran D, Prasad NM, Qato DM, Quezada AD, Quistberg DA, Racape L, Rafay A, Rahimi K, Rahimi-Movaghar V, Rahman SU, Raju M, Rakovac I, Rana SM, Rao M, Razavi H, Reddy KS, Refaat AH, Rehm J, Remuzzi G, Ribeiro AL, Riccio PM, Richardson L, Riederer A, Robinson M, Roca A, Rodriguez A, Rojas-Rueda D, Romieu I, Ronfani L, Room R, Roy N, Ruhago GM, Rushton L, Sabin N, Sacco RL, Saha S, Sahathevan R, Sahraian MA, Salomon JA, Salvo D, Sampson UK, Sanabria JR, Sanchez LM, Sanchez-Pimienta TG, Sanchez-Riera L, Sandar L, Santos IS, Sapkota A, Satpathy M, Saunders JE, Sawhney M, Saylan MI, Scarborough P, Schmidt JC, Schneider IJ, Schottker B, Schwebel DC, Scott JG, Seedat S, Sepanlou SG, Serdar B, Servan-Mori EE, Shaddick G, Shahraz S, Levy TS, Shangguan S, She J, Sheikhbahaei S, Shibuya K, Shin HH, Shinohara Y, Shiri R, Shishani K, Shiue I, Sigfusdottir ID, Silberberg DH, Simard EP, Sindi S, Singh A, Singh GM, Singh JA, Skirbekk V, Sliwa K, Soljak M, Soneji S, Soreide K, Soshnikov S, Sposato LA, Sreeramareddy CT, Stapelberg NJ, Stathopoulou V, Steckling N, Stein DJ, Stein MB, Stephens N, Stockl H, Straif K, Stroumpoulis K, Sturua L, Sunguya BF, Swaminathan S, Swaroop M, Sykes BL, Tabb KM, Takahashi K, Talongwa RT, Tandon N, Tanne D, Tanner M, Tavakkoli M, Te Ao BJ, Teixeira CM, Tellez Rojo MM, Terkawi AS, Texcalac-Sangrador JL, Thackway SV, Thomson B, Thorne-Lyman AL, Thrift AG, Thurston GD, Tillmann T, Tobollik M, Tonelli M, Topouzis F, Towbin JA, Toyoshima H, Traebert J, Tran BX, Trasande L, Trillini M, Trujillo U, Dimbuene ZT, Tsilimbaris M, Tuzcu EM, Uchendu US, Ukwaja KN, Uzun SB, van de Vijver S, Van Dingenen R, van Gool CH, van Os J, Varakin YY, Vasankari TJ, Vasconcelos AM, Vavilala MS, Veerman LJ, Velasquez-Melendez G, Venketasubramanian N, Vijayakumar L, Villalpando S, Violante FS, Vlassov VV, Vollset SE, Wagner GR, Waller SG, Wallin MT, Wan X, Wang H, Wang J, Wang L, Wang W, Wang Y, Warouw TS, Watts CH, Weichenthal S, Weiderpass E, Weintraub RG, Werdecker A, Wessells KR, Westerman R, Whiteford HA, Wilkinson JD, Williams HC, Williams TN, Woldeyohannes SM, Wolfe CD, Wong JQ, Woolf AD, Wright JL, Wurtz B, Xu G, Yan LL, Yang G, Yano Y, Ye P, Yenesew M, Yentur GK, Yip P, Yonemoto N, Yoon SJ, Younis MZ, Younoussi Z, Yu C, Zaki ME, Zhao Y, Zheng Y, Zhou M, Zhu J, Zhu S, Zou X, Zunt JR, Lopez AD, Vos T, Murray CJ, 2015. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 386(10010), 2287–2323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Fuller-Rowell TE, Doan SN, Eccles JS, 2012a. Differential effects of perceived discrimination on the diurnal cortisol rhythm of African Americans and Whites. Psychoneuroendocrinology 37(1), 107–118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Fuller-Rowell TE, Evans GW, Ong AD, 2012b. Poverty and health: the mediating role of perceived discrimination. Psychological science 23(7), 734–739. [DOI] [PubMed] [Google Scholar]
  27. Gellert C, Schottker B, Brenner H, 2012. Smoking and all-cause mortality in older people: systematic review and meta-analysis. Arch Intern Med 172(11), 837–844. [DOI] [PubMed] [Google Scholar]
  28. Goldstein RB, Dawson DA, Chou SP, Grant BF, 2012. Sex differences in prevalence and comorbidity of alcohol and drug use disorders: results from wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Journal of studies on alcohol and drugs 73(6), 938–950. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Goodwin RD, Pacek LR, Copeland A, Moeller SJ, Dierker L, Weinberger AH, Gbedemah M, Zvolensky MJ, Wall MM, Hasin DS, 2018. Trends in Daily Cannabis Use Among Cigarette Smokers: United States, 2002–2014. Am J Public Health 108(1), 137–142. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Goodwin RD, Pagura J, Spiwak R, Lemeshow AR, Sareen J, 2011. Predictors of persistent nicotine dependence among adults in the United States. Drug Alcohol Depend 118(2–3), 127–133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Goodwin RD, Sheffer CE, Chartrand H, Bhaskaran J, Hart CL, Sareen J, Bolton J, 2014. Drug use, abuse, and dependence and the persistence of nicotine dependence. Nicotine Tob Res 16(12), 1606–1612. [DOI] [PubMed] [Google Scholar]
  32. Grandner MA, Patel NP, Gehrman PR, Xie D, Sha D, Weaver T, Gooneratne N, 2010. Who gets the best sleep? Ethnic and socioeconomic factors related to sleep complaints. Sleep medicine 11(5), 470–478. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Gray MJ, Litz BT, Hsu JL, Lombardo TW, 2004. Psychometric properties of the life events checklist. Assessment 11(4), 330–341. [DOI] [PubMed] [Google Scholar]
  34. Guydish J, Passalacqua E, Pagano A, Martinez C, Le T, Chun J, Tajima B, Docto L, Garina D, Delucchi K, 2016. An international systematic review of smoking prevalence in addiction treatment. Addiction 111(2), 220–230. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Guydish J, Passalacqua E, Tajima B, Chan M, Chun J, Bostrom A, 2011. Smoking prevalence in addiction treatment: a review. Nicotine Tob Res 13(6), 401–411. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Gwaltney CJ, Metrik J, Kahler CW, Shiffman S, 2009. Self-Efficacy and Smoking Cessation: A Meta-Analysis. Psychology of Addictive Behaviors 23(1), 56–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Hall PA, 2012. Executive control resources and frequency of fatty food consumption: findings from an age-stratified community sample. Health Psychol 31(2), 235–241. [DOI] [PubMed] [Google Scholar]
  38. Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO, 1991. The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict 86(9), 1119–1127. [DOI] [PubMed] [Google Scholar]
  39. Holmes TH, Rahe RH, 1967. The Social Readjustment Rating Scale. Journal of psychosomatic research 11(2), 213–218. [DOI] [PubMed] [Google Scholar]
  40. Honjo K, Tsutsumi A, Kawachi I, Kawakami N, 2006. What accounts for the relationship between social class and smoking cessation? Results of a path analysis. Soc Sci Med 62(2), 317–328. [DOI] [PubMed] [Google Scholar]
  41. Hughes J, 1993. Treatment of smoking cessation in smokers with past alcohol/drug problems. Journal of Substance Abuse Treatment, 10(2), 181–187. [DOI] [PubMed] [Google Scholar]
  42. Hughes J, 2002. Do smokers with current or past alcoholism need different or more intensive treatment? Alcohol Clin Exp Res 26(12), 1934–1935. [DOI] [PubMed] [Google Scholar]
  43. Hughes JR, Kalman D, 2006. Do smokers with alcohol problems have more difficulty quitting? Drug Alcohol Depend 82(2), 91–102. [DOI] [PubMed] [Google Scholar]
  44. Hughes JR, Keely JP, Niaura RS, Ossip-Klein DJ, Richmond RL, Swan GE, 2003. Measures of abstinence in clinical trials: issues and recommendations. Nicotine Tob Res 5(1), 13–25. [PubMed] [Google Scholar]
  45. Humphreys K, Bickel WK, 2018. Toward a Neuroscience of Long-term Recovery From Addiction. JAMA Psychiatry 75(9), 875–876. [DOI] [PubMed] [Google Scholar]
  46. Hurt RD, Offord KP, Croghan IT, Gomez-Dahl L, Kottke TE, Morse RM, Melton LJ 3rd, 1996. Mortality following inpatient addictions treatment. Role of tobacco use in a community-based cohort. Jama 275(14), 1097–1103. [DOI] [PubMed] [Google Scholar]
  47. Jha P, Ramasundarahettige C, Landsman V, Rostron B, Thun M, Anderson RN, McAfee T, Peto R, 2013. 21st-century hazards of smoking and benefits of cessation in the United States. N Engl J Med 368(4), 341–350. [DOI] [PubMed] [Google Scholar]
  48. Johns MW, 1991. A new method for measuring daytime sleepiness: the Epworth Sleepiness Scale. Sleep 14(6), 540–545. [DOI] [PubMed] [Google Scholar]
  49. Kalman D, Kahler CW, Garvey AJ, Monti PM, 2006. High-dose nicotine patch therapy for smokers with a history of alcohol dependence: 36-week outcomes. J Subst Abuse Treat 30(3), 213–217. [DOI] [PubMed] [Google Scholar]
  50. Kalman D, Kim S, DiGirolamo G, Smelson D, Ziedonis D, 2010. Addressing tobacco use disorder in smokers in early remission from alcohol dependence: the case for integrating smoking cessation services in substance use disorder treatment programs. Clin Psychol Rev 30(1), 12–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Kalman D, Morissette SB, George TP, 2005. Co-morbidity of smoking in patients with psychiatric and substance use disorders. The American journal on addictions / American Academy of Psychiatrists in Alcoholism and Addictions 14(2), 106–123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Kelly JF, Greene MC, Bergman B, Hoeppner B, 2018. Smoking cessation in the context of recovery from drug and alcohol problems: Prevalence, predictors, and cohort effects in a national U.S. sample. Drug Alcohol Depend 195, 6–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Kenford SL, Smith SS, Wetter DW, Jorenby DE, Fiore MC, Baker TB, 2002. Predicting relapse back to smoking: contrasting affective and physical models of dependence. Journal of consulting and clinical psychology 70(1), 216–227. [PubMed] [Google Scholar]
  54. Kohn CS, Tsoh JY, Weisner CM, 2003. Changes in smoking status among substance abusers: baseline characteristics and abstinence from alcohol and drugs at 12-month follow-up. Drug Alcohol Depend 69(1), 61–71. [DOI] [PubMed] [Google Scholar]
  55. Koob GF, 2009. Neurobiological substrates for the dark side of compulsivity in addiction. Neuropharmacology 56 Suppl 1, 18–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Koob GF, Le Moal M, 2008a. Addiction and the brain antireward system. Annual review of psychology 59, 29–53. [DOI] [PubMed] [Google Scholar]
  57. Koob GF, Le Moal M, 2008b. Review. Neurobiological mechanisms for opponent motivational processes in addiction. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 363(1507), 3113–3123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Krentzman AR, Higgins MM, Staller KM, Klatt ES, 2015. Alexithymia, emotional dysregulation, and recovery from alcoholism: therapeutic response to assessment of mood. Qual Health Res 25(6), 794–805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Li EC, Feifer C, Strohm M, 2000. A pilot study: Locus of control and spiritual beliefs in alcoholics anonymous and smart recovery members. Addict Behav 25(4), 633–640. [DOI] [PubMed] [Google Scholar]
  60. Logue AW, Pena-Correal TE, Rodriguez ML, Kabela E, 1986. Self-control in adult humans: variation in positive reinforcer amount and delay. J Exp Anal Behav 46(2), 159–173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Manfredi C, Cho YI, Crittenden KS, Dolecek TA, 2007. A path model of smoking cessation in women smokers of low socio-economic status. Health Educ Res 22(5), 747–756. [DOI] [PubMed] [Google Scholar]
  62. Marks JL, Hill EM, Pomerleau CS, Mudd SA, Blow FC, 1997. Nicotine dependence and withdrawal in alcoholic and nonalcoholic ever-smokers. Journal of substance abuse treatment 14(6), 521–527. [DOI] [PubMed] [Google Scholar]
  63. Masuda M, Holmes TH, 1978. Life events: perceptions and frequencies. Psychosomatic medicine 40(3), 236–261. [DOI] [PubMed] [Google Scholar]
  64. Mazur JE, 1987. An adjusting procedure for studying delayed reinforcement, in: Commons ML, M. JE, Nevin JA, Rachlin H (Ed.) Qantitative Analysis of Behavior. Erlbaum, Hillsdale, NJ, pp. 55–73. [Google Scholar]
  65. McCarthy DE, Piasecki TM, Jorenby DE, Lawrence DL, Shiffman S, Baker TB, 2010. A multi-level analysis of non-significant counseling effects in a randomized smoking cessation trial. Addiction 105(12), 2195–2208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. McKee SA, O’Malley SS, Salovey P, Krishnan-Sarin S, Mazure CM, 2005. Perceived risks and benefits of smoking cessation: gender-specific predictors of motivation and treatment outcome. Addict Behav 30(3), 423–435. [DOI] [PubMed] [Google Scholar]
  67. Messer K, Mills AL, White MM, Pierce JP, 2008. The effect of smoke-free homes on smoking behavior in the U.S. Am J Prev Med 35(3), 210–216. [DOI] [PubMed] [Google Scholar]
  68. Moeller SJ, Fink DS, Gbedemah M, Hasin DS, Galea S, Zvolensky MJ, Goodwin RD, 2018. Trends in Illicit Drug Use Among Smokers and Nonsmokers in the United States, 2002–2014. Journal of Clinical Psychiatry 79(3). [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Monti P, Rohsenow D, Colby S, Abrams D, 1995. Smoking among alcoholics during and after treatment: implications for models, treatment strategies, and policy, in: Fertig J, Allen J (Eds.), Alcohol and tobacco from basic science to clinical practice. National Institutes of Health, Bethesda, MD, pp. 187–206. [Google Scholar]
  70. Moolchan ET, Berlin I, Robinson ML, Cadet JL, 2003. Characteristics of African American teenage smokers who request cessation treatment: implications for addressing health disparities. Arch Pediatr Adolesc Med 157(6), 533–538. [DOI] [PubMed] [Google Scholar]
  71. Myers MG, Kelly JF, 2006. Cigarette smoking among adolescents with alcohol and other drug use problems. Alcohol research & health : the journal of the National Institute on Alcohol Abuse and Alcoholism 29(3), 221–227. [PMC free article] [PubMed] [Google Scholar]
  72. Nestler EJ, 2005. Is there a common molecular pathway for addiction? Nature neuroscience 8(11), 1445–1449. [DOI] [PubMed] [Google Scholar]
  73. Okoli CTC, Khara M, 2014. Smoking cessation outcomes and predictors among individuals with co-occurring substance use and/or psychiatric disorders. Journal of dual diagnosis 10(1), 9–18. [DOI] [PubMed] [Google Scholar]
  74. Oleckno WA, Blacconiere MJ, 1991. Relationship of religiosity to wellness and other health-related behaviors and outcomes. Psychological Reports 80(3), 819–826. [DOI] [PubMed] [Google Scholar]
  75. Pardini DA, Plante TG, Sherman A, Stump JE, 2000. Religious faith and spirtuality in substance abuse recovery: Determining the mental health benefits. Journal of Substance Abuse Treatment, 19, 347–354. [DOI] [PubMed] [Google Scholar]
  76. Pargament K, Feuille M, Burdzy D, 2011. The Brief RCOPE: Current Psychometric Status of a Short Measure of Religious Coping. Religions 2, 51–76. [Google Scholar]
  77. Patel NP, Grandner MA, Xie D, Branas CC, Gooneratne N, 2010. “Sleep disparity” in the population: poor sleep quality is strongly associated with poverty and ethnicity. BMC Public Health 10, 475. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Pearson MR, Kite BA, Henson JM, 2013. Predictive effects of good self-control and poor regulation on alcohol related outcomes: do protective behavioral strategies mediate? Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors 27(1), 81–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Penedo FJ, Dahn JR, 2005. Exercise and well-being: a review of mental and physical health benefits associated with physical activity. Curr Opin Psychiatry 18(2), 189–193. [DOI] [PubMed] [Google Scholar]
  80. Perkins KA, Marcus MD, Levine MD, D’Amico D, Miller A, Broge M, Ashcom J, Shiffman S, 2001. Cognitive behavioral therapy to reduce weight concerns improves smoking cessation outcome in weight-concerned women. Journal of consulting and clinical psychology 69(4), 604–613. [PubMed] [Google Scholar]
  81. Prochaska JJ, Delucchi K, Hall SM, 2004. A meta-analysis of smoking cessation interventions with individuals in substance abuse treatment or recovery. Journal of consulting and clinical psychology 72(6), 1144–1156. [DOI] [PubMed] [Google Scholar]
  82. Radloff LS, 1977. The CES-D Scale : A Self-Report Depression Scale for Research in the General Population. Applied Psychological Measurement 1(3), 385–401. [Google Scholar]
  83. Reich MS, Dietrich MS, Finlayson AJ, Fischer EF, Martin PR, 2008. Coffee and cigarette consumption and perceived effects in recovering alcoholics participating in Alcoholics Anonymous in Nashville, Tennessee, USA. Alcohol Clin Exp Res 32(10), 1799–1806. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Reise SP, Moore TM, Sabb FW, Brown AK, London ED, 2013. The Barratt Impulsiveness Scale-11: reassessment of its structure in a community sample. Psychological assessment 25(2), 631–642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Reitzel LR, Businelle MS, Kendzor DE, Li Y, Cao Y, Castro Y, Mazas CA, Cofta-Woerpel L, Cinciripini PM, Wetter DW, Subjective social status predicts long-term smoking abstinence. BMC Public Health 11, 135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Richter KP, Ahluwalia HK, Mosier MC, Nazir N, Ahluwalia JS, 2002. A population-based study of cigarette smoking among illicit drug users in the United States. Addiction 97(7), 861–869. [DOI] [PubMed] [Google Scholar]
  87. Rotter JB, 1966. Generalized expectancies for internal versus external control of reinforcement. Psychol Monogr 80(1), 1–28. [PubMed] [Google Scholar]
  88. Sansone G, Fong GT, Hall PA, Guignard R, Beck F, Mons U, Potschke-Langer M, Yong HH, Thompson ME, Omar M, Jiang Y, 2013. Time perspective as a predictor of smoking status: findings from the International Tobacco Control (ITC) Surveys in Scotland, France, Germany, China, and Malaysia. BMC Public Health 13(1), 346. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Sheffer CE, Bickel WK, Franck CT, Panissidi L, Pittman JC, Stayna H, Evans S, 2017. Improving tobacco dependence treatment outcomes for smokers of lower socioeconomic status: A randomized clinical trial. Drug Alcohol Depend 181, 177–185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Sheffer CE, Christensen DR, Landes R, Carter LP, Jackson L, Bickel WK, 2014. Delay discounting rates: a strong prognostic indicator of smoking relapse. Addict Behav 39(11), 1682–1689. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Sheffer CE, Mackillop J, McGeary J, Landes R, Carter L, Yi R, Jones B, Christensen D, Stitzer M, Jackson L, Bickel WK, 2012a. Delay discounting, locus of control, and cognitive impulsiveness independently predict tobacco dependence treatment outcomes in a highly dependent, lower socioeconomic group of smokers. The American journal on addictions / American Academy of Psychiatrists in Alcoholism and Addictions 21(3), 221–232. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Sheffer CE, Stitzer M, Landes R, Brackman SL, Munn T, Moore P, 2012b. Socioeconomic disparities in community-based treatment of tobacco dependence. Am J Public Health 102(3), e8–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Sobell LC, Ellingstad TP, Sobell MB, 2000. Natural recovery from alcohol and drug problems: methodological review of the research with suggestions for future directions. Addiction 95(5), 749–764. [DOI] [PubMed] [Google Scholar]
  94. Sobell LC, Sobell MB, 1992. Timeline follow-back: A technique for assessing self-consumpton, in: Allen J, Litten RZ (Eds.), Measuring Alcohol Consumption:Psychosocial and Biological Methods. Humana Press, Totowa, NJ, pp. 41–72. [Google Scholar]
  95. Stronks K, van de Mheen HD, Looman CW, Mackenbach JP, 1997. Cultural, material, and psychosocial correlates of the socioeconomic gradient in smoking behavior among adults. Prev Med 26(5 Pt 1), 754–766. [DOI] [PubMed] [Google Scholar]
  96. Substance Abuse and Mental Health Services Administration, C.f.B.H.S.a.Q., 2014. The NSDUH Report: Substance Use and Mental Health Estimates from the 2013 National Survey on Drug Use and Health: Overview of Findings. Rockville, MD. [PubMed] [Google Scholar]
  97. Sullivan MA, Covey LS, 2002. Current perspectives on smoking cessation among substance abusers. Curr Psychiatry Rep 4(5), 388–396. [DOI] [PubMed] [Google Scholar]
  98. Thurgood SL, McNeill A, Clark-Carter D, Brose LS, 2016. A Systematic Review of Smoking Cessation Interventions for Adults in Substance Abuse Treatment or Recovery. Nicotine Tob Res 18(5), 993–1001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  99. Toll BA, Cooney NL, McKee SA, O’Malley SS, 2005. Do daily interactive voice response reports of smoking behavior correspond with retrospective reports? Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors 19(3), 291–295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Varghese M, Sheffer C, Stitzer M, Landes R, Brackman SL, Munn T, 2014. Socioeconomic disparities in telephone-based treatment of tobacco dependence. Am J Public Health 104(8), e76–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Watson D, Clark L, Tellegen A, 1988. Development and validation of brief measures of positive and negative affect. The PANAS scale. Journal of Personality and Social Psychology 54, 1063–1070. [DOI] [PubMed] [Google Scholar]
  102. Weinberger AH, Funk AP, Goodwin RD, 2016. A review of epidemiologic research on smoking behavior among persons with alcohol and illicit substance use disorders. Prev Med 92, 148–159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Weinberger AH, Pacek LR, Wall MM, Gbedemah M, Lee JT, Goodwin RD, in press. Cigarette smoking quit rates among adults with cannabis use and cannabis use disorders, 2002 to 2016: Representative data from the United States population. Tobacco control. [DOI] [PMC free article] [PubMed] [Google Scholar]
  104. Weinberger AH, Platt J, Copeland A, Goodwin RD, 2018. Is Cannabis Use Associated With Increased Risk of Cigarette Smoking Initiation, Persistence, and Relapse? Longitudinal Data From a Representative Sample of US Adults. Journal of Clinical Psychiatry 79(2). [DOI] [PMC free article] [PubMed] [Google Scholar]
  105. Weinberger AH, Platt J, Esan H, Galea S, Erlich D, Goodwin RD, 2017. Cigarette Smoking Is Associated With Increased Risk of Substance Use Disorder Relapse: A Nationally Representative, Prospective Longitudinal Investigation. The Journal of Clinical Psychiatry 78(2), e152–e160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  106. Weinberger AH, Platt J, Jiang B, Goodwin RD, 2015. Cigarette Smoking and Risk of Alcohol Use Relapse Among Adults in Recovery from Alcohol Use Disorders. Alcohol Clin Exp Res 39(10), 1989–1996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  107. Williams DR, Yan Y, Jackson JS, Anderson NB, 1997. Racial Differences in Physical and Mental Health: Socioeconomic Status, Stress and Discrimination. Journal of health psychology 2(3), 335–351. [DOI] [PubMed] [Google Scholar]
  108. Witkiewitz K, Marlatt GA, 2004. Relapse prevention for alcohol and drug problems: that was Zen, this is Tao. Am Psychol 59(4), 224–235. [DOI] [PubMed] [Google Scholar]
  109. Zimet GD, dahlem N, Zimet S, Farley G, 1988. The mutidimensional scale of perceived social support. Journal of Personality Assessment 52(1), 30–41. [DOI] [PubMed] [Google Scholar]

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