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Prediction of Smoking Abstinence in Women Living With Human Immunodeficiency Virus Infection

Kim, Sun S.; Cooley, Mary E.; Lee, Sang A; DeMarco, Rosanna F.

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doi: 10.1097/NNR.0000000000000421
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Tobacco use is the leading cause of preventable disease and death in the United States, accounting for more than 480,000 deaths every year (U.S. Department of Health and Human Services, 2014). People living with the human immunodeficiency virus (HIV) smoke nearly three times that of the general population in the United States (Mdodo et al., 2015), and smoking is the main cause of non-acquired immunodeficiency syndrome-defining cancers (e.g., Hessol et al., 2018; Sigel, Makinson, & Thaler, 2017). In this study, negative emotional state is defined as psychopathological mood or negative affect such as depression and anxiety that is often reported among women living with HIV (Balfour et al., 2017; Tsuyuki et al., 2017). The high prevalence of psychiatric comorbidity among people living with HIV may be a contributing factor to their high rates of smoking and low rates of quitting. In addition, there is evidence that people living with HIV are faster nicotine metabolizers and more heavily dependent on nicotine than people without HIV infection (Ashare et al., 2019). Therefore, smokers with HIV infection may have more nicotine withdrawal symptoms and difficulty in quitting than smokers without HIV infection.

A combination of behavioral therapy and pharmacotherapy is recommended as standard tobacco dependence treatment (Fiore et al., 2008). Behavioral therapy provides training for effective behavioral strategies dealing with nicotine withdrawal symptoms and craving for smoking cigarettes in a variety of risky situations—such as when feeling tense or anxious and after meals. There are seven first-line smoking cessation medications approved by the U.S. Food and Drug Administration; they are designed to alleviate cognitive, physiological, and psychomotor symptoms of nicotine withdrawal (Fiore et al., 2008). Among the seven medications, nicotine patch, gum, and lozenge can be purchased without a prescription, and any form of the three is equally effective.

There is substantial evidence that women experience more nicotine withdrawal symptoms after quitting than men (e.g., Allen, Allen, Widenmier, & al'Absi, 2009). A more recent study replicated the finding and reported that women manifested more postquit anxiety symptoms than men (Kaufmann et al., 2015). Faulkner et al. (2018) also found a gender difference in negative emotional states among overnight abstinent younger smokers whereas no gender differences in older smokers. On the other hand, Pang, Bello, Liautaud, Weinberger, and Leventhal (2019) reported a gender-by-race interaction effect on overnight abstinence-induced negative emotional states. Compared to non-Hispanic White men, non-Hispanic White women exhibited greater abstinence-induced increases in negative emotional states. In contrast, there was no such difference between non-Hispanic Black men and women. However, there was an age difference between the two racial groups, especially among women; the Black women were much older than the White women. The older age of the Black women in the study could be the reason for lack of gender differences found in the Black group.

This study examined whether (a) baseline negative emotional states (depression and anxiety) would predict nicotine withdrawal symptoms among treatment-seeking women living with HIV during the early phase of smoking abstinence, (b) an HIV-tailored smoking cessation intervention would be more effective in reducing postquit nicotine withdrawal symptoms than an attention-control intervention, and (c) nicotine withdrawal symptoms would predict cessation outcomes (smoking vs. abstinence) at 3-month follow-up.

The following three hypotheses were proposed:

  1. HIV-infected women who had higher levels of negative emotional states (i.e., depression and anxiety symptoms) at baseline would report more nicotine withdrawal symptoms during the first 4 weeks following quit day.
  2. HIV-infected women who received an HIV-tailored smoking cessation intervention would report less nicotine withdrawal symptoms than their counterparts who received an attention-control intervention.
  3. HIV-infected women who experienced more postquit withdrawal symptoms would be less likely to achieve cotinine-verified smoking abstinence at 3-month follow-up.


This study is a secondary analysis of data from two pilot randomized controlled trials. The first study was conducted with 49 women living with HIV who primarily resided in the states of Massachusetts and New York. The study (Kim, Darwish, Lee, Sprague, & DeMarco, 2018) compared the effect of telephone-based, video call, cessation counseling (HIV-tailored) with telephone-based, voice call (attention-control) counseling. Participants were recruited between June 2016 and July 2017. The study was completed in December 2017. The second study (Kim, Lee, Mejia, Cooley, & DeMarco, 2019) was conducted with 53 women who were recruited from across the nation (13 states). The study compared an HIV-tailored storytelling intervention using a digitized film plus video call cessation counseling to an attention-control storytelling intervention using a digitized film and video call cessation counseling. Participants were recruited between September 2017 and July 2018, and final data of the study were collected in October 2018.

Bandura’s social cognitive theory was the theoretical framework guiding the two parent studies. The theory posits that self-efficacy is the most influential determinant of the behavior (Bandura, 1977). Self-efficacy can be enhanced by the following four sources: actual accomplishment, vicarious experience, verbal persuasion, and physiological state. We postulated that HIV-tailored smoking cessation interventions and nicotine replacement therapy would increase self-efficacy by mitigating nicotine withdrawal symptoms (i.e., physiological state). Irrespective of group allocation, all participants in both studies received eight 30-minute weekly cessation counseling sessions via voice or video calls and they also received nicotine patches for the same period. The two studies were approved by the University of Massachusetts Boston Institutional Review Board.

This study included participants who provided at least two weekly assessments of nicotine withdrawal symptoms among the four scheduled. This is because it aimed to assess the change of nicotine withdrawal symptoms over time. Sixty-nine participants (29 from the first study and 40 from the second study) met this requirement and were included in this study. The total number of observations of the symptoms was 229, with an average of 3.3 per participant (range: 2–4).


Participants were women who had smoked five or more cigarettes per day on average for the past 6 months and were willing to make a quit attempt within 4 weeks from the first counseling session. Other selection criteria included (a) HIV infection; (b) the ages of 18 and 65 years; (c) English speaking; and (d) capability to use a video call app such as Skype, Facebook Messenger, and FaceTime. Individuals were excluded if they (a) were pregnant or lactating, (b) had an active skin disease or serious alcohol use problems (>26 on the Alcohol Use Disorders Identification Test; Babor, Higgins-Biddle, Saunders, & Monteiro, 2001), or (c) currently used any illegal substance except for marijuana. Because of the high prevalence of psychiatric comorbidity among people living with HIV who currently smoke cigarettes, we did not exclude individuals with depression or anxiety disorder unless they reported current suicidal ideation or a diagnosis of serious mental illness, such as schizophrenia or bipolar disorder.


Participants in the two parent studies were recruited from online and offline advertisements, referrals from healthcare providers, and snowball sampling technique. They were screened for eligibility over a telephone interview and invited into the study if they met all selection criteria. The HIV serostatus was confirmed by asking participants to provide their CD4 cell count and viral load at the time of the screening interview. During the first counseling session, participants were encouraged to select a quit day date within the next 4 weeks. They were encouraged to use a nicotine patch on or before the quit day and counseled on behavioral strategies dealing with withdrawal symptoms.

We assessed withdrawal symptoms only among those who reported abstinence or were smoking nondaily after the planned quit day because we were interested in cessation-induced withdrawal symptoms. Participants were asked to rate the extent of each withdrawal symptom (e.g., craving for cigarettes, anger/irritability/frustration, anxiety, and difficulty concentrating) as they experienced at the time—which was done just before each postquit weekly counseling session. The first assessment of the symptoms usually took place within the first 3 days of quitting and then weekly thereafter. As stated before, in this study, we included only those who participated in at least two weekly assessments of the symptoms.


Demographic characteristics, HIV-related data, and smoking history and behavior were assessed at baseline. Demographic data included race and ethnicity, age, marital status, years of education, and employment status. For smoking behavior, age at smoking onset and the number of cigarettes smoked per day on average were collected.

Nicotine dependence was assessed using the Fagerström Test for Nicotine Dependence that has six items (Heatherton, Kozlowski, Frecker, & Fagerström, 1991). Four items are dichotomous between 0 and 1, and two items range from 0 to 3. The total score is the sum of the six-item scores and ranges between 0 and 10. The higher the score is, the more serious nicotine dependence is. The Fagerström Test for Nicotine Dependence Scale is used most widely as a measure of nicotine dependence; yet, it usually yields a low Cronbach’s alpha below .70 (e.g., Bakhshaie, Zvolensky, Langdon, Leventhal, & Schmidt, 2018). It was .46 in this study, which is largely related to the violation of tau equivalence (having equal weights for all question items) that is assumed in the estimation of internal reliability (Raykov, 1997). Instead of Cronbach’s alpha, composite reliability was recommended, and its coefficient was .70 in this study.

Self-efficacy was assessed using a smoking self-efficacy questionnaire (Velicer, DiClemente, Rossi, & Prochaska, 1990) that assesses perceived confidence in resisting smoking temptation at nine high-risk situations (e.g., “When I feel tense or anxious” and “When I wake up in the morning”). Each item ranges from “1” (completely unconfident) to “5” (completely confident), and the scale score is the sum of nine item scores. A Cronbach’s alpha of .83 was obtained in the current study.

In the first study (Kim et al., 2018), depression was assessed using the Center for Epidemiologic Studies-Depression Scale (CES-D) on a 4-point (0–3) scale from “0” (rarely or none of the time) to “3” (most or all of the time; Radloff, 1977). Participants rated how often they had experienced each symptom of the 20 symptoms during the past week. Four items describe positive feelings and require a reverse coding. The total score is the sum of the 20-item scores, and higher scores indicate more depressive symptoms with the cutoff score of 16 for the determination of clinical depression. Cronbach’s alpha was .83 in this study.

To reduce participants’ burden related to 20 items of the CES-D, the Patient Health Questionnaire-9 (PHQ-9; Kroenke, Spitzer, & Williams, 2001) that has nine items was used in the second study. The questionnaire reflects depressive symptom criteria listed in the Diagnostic Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Scores of each item range from “0” (not at all) to “3” (nearly every day), with high scores being more depressed. PHQ-9 scores of 10 and higher had a sensitivity of 88% and a specificity of 88% for clinical depression (Kroenke et al., 2001). Cronbach’s alpha was .84 in this study. The raw scores of the two measures were dichotomized using the recommended cutoff score of 16 for the CES-D (Radloff, 1977) and 10 for the PHQ-9 (Kroenke et al., 2001).

Anxiety was assessed at baseline using the Generalized Anxiety Disorder 7-Item Scale (Spitzer, Kroenke, Williams, & Löwe, 2006) for both studies. Each item score ranges “0” (not at all) to “3” (nearly every day). The seven items reflect anxiety symptom criteria of generalized anxiety disorder in DSM-IV. With a cutoff score of 10, the scale had a sensitivity of 89% and a specificity of 82% compared with the DSM-IV (Spitzer et al., 2006). Cronbach’s alpha was .92 in this study.

Nicotine withdrawal symptoms were assessed using the Minnesota Nicotine Withdrawal Scale (MNWS) developed by Hughes and Hatsukami (1986). It is a 5-point Likert-type scale of eight items: craving, irritability/frustration/anger, anxiety, difficulty concentrating, restlessness, depression, increased appetite, and insomnia. Participants rated the symptoms of nicotine withdrawal as not present (0), slight (1), mild (2), moderate (3), and severe (4). Hughes and Hatsukami (1998), who developed the MNWS, recommended that craving should not be included when calculating a total withdrawal symptom score because the symptom behaves differently from other withdrawal symptoms. The total score of the seven items excluding craving ranges from 0 to 28. The MNWS has shown good validity and reliability (Hughes, Gust, Skoog, Keenan, & Fenwick, 1991). Cronbach’s alphas of the scales at four weekly assessments ranged from .81 to .87 in this study.

Smoking abstinence was assessed using 7-day point prevalence abstinence at 3-month follow-up that could be verified with a salivary cotinine test. The abstinence was defined as having not smoked a single puff during the past 7 days (Hughes et al., 2003). Cotinine is the major proximate metabolite of nicotine and has a long half-life approximately 15–19 hours (Benowitz & Jacob, 1994). Participants had used nicotine patches for approximately 8 weeks up to 2-month follow-up. Although continuous abstinence indicates a steady state, it cannot be verified with a biochemical measure. Thus, repeated 7-day point prevalence abstinence with biochemical verification was recommended to overcome some of the problems associated with continuous abstinence (Hughes et al., 2003). Most participants were using nicotine patches at 1- and 2-month follow-ups, and therefore, the salivary cotinine test was done only once at 3-month follow-up, using the NicAlert test strip (Nymox Pharmaceutical Corporation, Hasbrouck Heights, NJ). The test kit was mailed to each participant before the testing. Participants conducted the test following step-by-step instructions provided by a research assistant who remotely monitored the whole procedure through a video call.

Data Analysis

Analyses were performed using Stata 15 (StataCorp LLC, College Station, TX). At first, we performed a separate analysis of each primary study, and findings were identical. Therefore, data from the two studies were merged and analyzed together. Descriptive statistics were used to calculate means and frequencies of baseline characteristics, nicotine withdrawal symptoms, and smoking abstinence at 3-month follow-up. We used the listwise deletion method for missing variables, and the rate of missing data was 17%. Pearson’s correlations of the eight nicotine withdrawal symptoms were estimated, and a growth curve model was conducted to examine between-person differences in a within-person trend of change in postquit nicotine withdrawal symptoms. For this, we used the restricted maximum likelihood option with the “unstructured” covariance matrix to reduce small sample biases (Kenward & Roger, 1997). We first estimated the effects of participants’ negative emotional states (baseline depression and anxiety), treatment condition (experimental vs. control arms), and time (week) on craving for cigarettes and composite nicotine withdrawal symptoms. We also performed a binary logistic regression analysis to examine whether craving for cigarettes and other nicotine withdrawal symptoms would predict failure in quitting smoking at 3-month follow-up while controlling for treatment condition.

Sample Size and Power

The two parent studies were conducted to establish a preliminary effect size of an HIV-tailored smoking cessation intervention as compared to an attention-control intervention. For pilot studies, it was suggested that 24–25 subjects per arm generally yield a near accurate estimate of an effect size (Hertzog, 2008).


Baseline Characteristics

The two parent studies showed no difference in baseline characteristics except years of living with HIV infection (data not shown here). Participants in the first study had longer years of living with HIV than those in the second study, t(1, 67) = 2.03, p < .05. Nicotine withdrawal symptoms and smoking abstinence at 3-month follow-up showed no differences between the two parent studies.

Characteristics of participants who were included in this study showed no difference in any baseline characteristics by treatment condition (Table 1). Marital status showed marginal significance, indicating participants in the HIV-tailored arm were more likely to be single, whereas those in the attention-control arm were more likely to be divorced, separated, or widowed. There was no difference in the number of sessions attended and the number of patches used between the two arms. Combining the two arms, participants generally attended seven sessions (SD = 1.2) of the eight weekly counseling sessions. Forty-one participants (59.4%) were compliant with nicotine patches and used 46.8 patches (SD = 10.4) on average.

Characteristics of Participants at Baseline by Treatment Condition

Baseline Characteristics Predicting Nicotine Withdrawal Symptoms

Among eight symptoms of the MNWS, seven symptoms excluding “increased appetite” showed correlations with other symptoms (all ps < .01, data are not shown here). Among baseline characteristics listed in Table 1, none predicted craving symptoms, whereas only two variables, depression (β = 0.10, SE = 0.04, p = .01) and anxiety (β = 0.29, SE = 0.08, p < .001), showed a significant relationship with the composite score of seven nicotine withdrawal symptoms. Yet, neither of the variables were significant when they were analyzed together in a multivariate analysis because of high correlation (r = .60, p < .001) between the two variables. Therefore, depression was not entered in the final model (Table 2). Participants who had higher anxiety scores at baseline had more postquit nicotine withdrawal symptoms (β = 0.22, SE = 0.10, p = .02). The symptoms declined over time (the fixed effect of the slope, β = −1.48, SE = 0.32, p < .001), and the random effect of the change was also significant (95% CI [1.34, 2.71]).

Factors Predicting the Composite Score of Seven Nicotine Withdrawal Symptoms

The Effect of HIV-Tailored Intervention on Nicotine Withdrawal Symptoms

Treatment condition (HIV-tailored interventions vs. attention-control interventions) and time (weeks) had a high relationship with craving for cigarettes (Figure 1). Those who received an HIV-tailored intervention (experimental condition) reported less craving for cigarettes than their counterparts in the attention-control arm (β = −0.52, SE = 0.24, p = .032; Table 3). The weekly rate of changes in craving symptom (the fixed effect of the slope) was significant (β = −0.22, SE = 0.07, p = .002). The random effect of the change was also significant (95% CI [0.16, 0.60]), indicating that change varied randomly across participants.

Change in craving for cigarettes over time by treatment condition.
Effect of Treatment Condition on Craving for Cigarettes

Predictors of Short-Term Smoking Abstinence

At 3-month follow-up, 31 participants reported smoking abstinence for the past 7 days (7-day point prevalence abstinence), but only 22 were found to be abstinent when their saliva was verified with the NicAlert test strip. Univariate analyses revealed that treatment condition, craving for cigarettes, and the composite score of nicotine withdrawal symptoms were predictors of smoking versus abstinence at 3-month follow-up (Table 4). The relationship between craving and the composite score of other withdrawal symptoms showed a high correlation (r = .69, p < .001); thus, only craving was entered in a multivariate analysis (Table 4). Participants in the HIV-tailored arm were 2.4 times more likely to achieve short-term smoking abstinence compared to their counterparts in the attention-control arm. As participants had a one-unit increase in craving symptom, they were 38% less likely to achieve smoking abstinence.

Odds Ratios for the Effect of Treatment Condition and Craving on Smoking Abstinence


This study’s primary aim was to examine whether baseline negative emotional states (depression and anxiety) would predict postquit craving for cigarettes and other nicotine withdrawal symptoms and whether the symptoms would predict failure in achieving short-term smoking abstinence among women living with HIV. Consistent with prior research (Bakhshaie et al., 2018; Kaufmann et al., 2015), higher anxiety and depression levels at baseline increased overall nicotine withdrawal symptoms during the first 4 weeks of quitting. Others (e.g., Assayag, Bernstein, Zvolensky, Steeves, & Stewart, 2012; Johnson, Farris, Schmidt, Smits, & Zvolensky, 2013) reported that smokers who had high anxiety sensitivity at baseline were more likely to have high postquit withdrawal symptoms. Researchers usually include craving as part of nicotine withdrawal symptoms when they examine the relationships between negative emotional state and postquit withdrawal symptoms (e.g., Bakhshaie et al., 2018). However, Hughes and Hatsukami (1998) recommended that craving should be separately examined.

Women in this study who had more withdrawal symptoms were less likely to achieve smoking abstinence. This finding is in support of the report that postquit withdrawal symptoms—especially postquit anxiety and depression—were a strong predictor of relapse to smoking (Kaufmann et al., 2015; Levine, Marcus, Kalarchian, Houck, & Cheng, 2010). For example, Kaufmann et al. (2015) reported that those who showed greater increases in postquit anxiety withdrawal symptom had a lower rate of smoking cessation. On the other hand, Piper, Vasilenko, Cook, and Lanza (2017) reported that craving—but no other withdrawal symptoms—was a predictor of smoking abstinence at 2 and 6 months postquit. Zuo, Rabinovich, and Gilbert (2017) found both craving for cigarettes and postquit depression and anxiety symptoms predicted failure in quitting smoking at 3 months postquit.

Women in this study who received an HIV-tailored intervention reported less craving for cigarettes, and those who had less craving were more likely to achieve smoking abstinence at 3-month follow-up. It is interesting to note that neither baseline depression nor baseline anxiety had a strong relationship with craving for cigarettes or smoking abstinence at 3 months postquit. These findings may suggest that women living with HIV infection can successfully quit smoking regardless of their baseline negative emotional states if they receive an HIV-tailored smoking cessation intervention and learn how to manage postquit craving for cigarettes. These results, if they can be replicated in a larger sample, will be encouraging, given that people living with HIV have higher rates of depressive and anxiety disorders than people without the infection (Balfour et al., 2017; Tsuyuki et al., 2017).


Findings from this study should be interpreted with caution. First, the sample size is small. Nevertheless, women in this study showed characteristics that were almost identical to those in other clinical trials of smoking cessation in this population (e.g., Satterfield et al., 2018; Valera et al., 2017). Second, why and how the HIV-tailored intervention was effective in alleviating craving for cigarettes was not clear and hence need to be delineated. Third, baseline depression scores were dichotomized in this study instead of using raw scores because of the use of two different measures—which might have caused loss of power to detect a stronger relationship between baseline depression and nicotine withdrawal symptoms. Lastly, we did not assess female reproductive information that may shed light on some individual differences in the experience of nicotine withdrawal symptoms. Postmenopausal smokers showed slower nicotine metabolism than premenopausal smokers whereas no age difference among male smokers (Kosmider et al., 2018). There is substantial evidence that menstrual cycle phase at quit date and use of hormone replacement therapy affect nicotine withdrawal symptoms and smoking cessation (e.g., Allen et al., 2009; Epperson et al., 2010; Weinberger et al., 2015). Future studies should collect information on menstruation cycle around quit date, any gynecological surgeries, and hormonal replacement therapy.

Despite the limitations stated above, the study also has several strengths. To the best of our knowledge, this is the first study reporting that an HIV-tailored smoking cessation intervention reduced craving for cigarettes among women living with HIV, and those who had less craving were more likely to achieve biochemically verified abstinence at 3-month follow-up. There is a need to examine why and how the HIV-tailored interventions, such as video call cessation counseling and digital storytelling, could effectively reduce craving for cigarettes among women living with HIV. Future studies also should test the mediating effect of craving with a large sample of the group. Of note, mediation can occur even in the absence of an overall effect of treatment on the outcome (MacKinnon & Fairchild, 2009) and can provide important information about mechanisms through which interventions may influence outcomes.


Craving for cigarettes appeared to be the most important predictor of failure in quitting smoking even for short term among women living with HIV. Irrespective of their existing conditions (e.g., anxiety and depression), they may be able to quit smoking if provided with an HIV-tailored intervention, such as video call cessation counseling combined with a narrative storytelling intervention via a digitized film.


Allen A. M., Allen S. S., Widenmier J., & al'Absi M. (2009). Patterns of cortisol and craving by menstrual phase in women attempting to quit smoking. Addictive Behaviors, 34, 632–635.
Ashare R. L., Thompson M., Leone F., Metzger D., Gross R., Mounzer K., … Schnoll R. (2019). Differences in the rate of nicotine metabolism among smokers with and without HIV. AIDS, 33, 1083–1088.
Assayag Y., Bernstein A., Zvolensky M. J., Steeves D., & Stewart S. S. (2012). Nature and role of change in anxiety sensitivity during NRT-aided cognitive-behavioral smoking cessation treatment. Cognitive Behaviour Therapy, 41, 51–62.
Babor T. F., Higgins-Biddle J. C., Saunders J. B., & Monteiro M. G. (2001). AUDIT: The Alcohol Use Disorders Identification Test: Guidelines for use in primary health care (2nd ed.). World Health Organization.
Bakhshaie J., Zvolensky M. J., Langdon K. J., Leventhal A. M., & Schmidt N. B. (2018). Reduction of anxiety sensitivity in relation to nicotine withdrawal symptoms during smoking cessation: An examination among successful quitters. Cognitive Behaviour Therapy, 47, 301–314.
Balfour L., Wiebe S. A., Cameron W. D., Sandre D., Pipe A., Cooper C., … MacPherson P. A. (2017). An HIV-tailored quit smoking counseling pilot intervention targeting depressive symptoms plus nicotine replacement therapy. AIDS Care, 29, 24–31.
Bandura A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215.
Benowitz N. L., & Jacob P. III (1994). Metabolism of nicotine to cotinine studied by a dual stable isotope method. Clinical Pharmacology & Therapeutics, 56, 483–493.
Epperson C. N., Toll B., Wu R., Amin Z., Czarkowski K. A., Jatlow P., … O‘Malley S. S. (2010). Exploring the impact of gender and reproductive status on outcomes in a randomized clinical trial of naltrexone augmentation of nicotine patch. Drug and Alcohol Dependence, 112(1-2), 1–8.
Faulkner P., Petersen N., Ghahremani D. G., Cox C. M., Tyndale R. F., Hellemann G. S., & London E. D. (2018). Sex differences in tobacco withdrawal and responses to smoking reduced-nicotine cigarettes in young smokers. Psychopharmacology, 235, 193–202.
Fiore M. C., Jaén C. R., Baker T. B., Bailey W. C., Benowitz N. L., Curry S. J., … Henderson P. N. (2008). Treating tobacco use and dependence: 2008 update. Rockville, MD: U.S. Department of Health and Human Services, Public Health Service.
Heatherton T. F., Kozlowski L. T., Frecker R. C., & Fagerström K. O. (1991). The Fagerström Test for Nicotine Dependence: A revision of the Fagerström Tolerance Questionnaire. British Journal of Addiction, 86, 1119–1127.
Hertzog M. A. (2008). Considerations in determining sample size for pilot studies. Research in Nursing & Health, 31, 180–191.
Hessol N. A., Whittemore H., Vittinghoff E., Hsu L. C., Ma D., Scheer S., & Schwarcz S. K. (2018). Incidence of first and second primary cancers diagnosed among people with HIV, 1985–2013: A population-based, registry linkage study. Lancet HIV, 5, e647–e655.
Hughes J. R., Gust S. W., Skoog K., Keenan R. M., & Fenwick J. W. (1991). Symptoms of tobacco withdrawal. A replication and extension. Archives of General Psychiatry, 48, 52–59.
Hughes J. R., & Hatsukami D. K. (1986). Signs and symptoms of tobacco withdrawal. Archives of General Psychiatry, 43, 289–294.
Hughes J., & Hatsukami D. K. (1998). Errors in using tobacco withdrawal scale. Tobacco Control, 7, 92.
Hughes J. R., Keely J. P., Niaura R. S., Ossip-Klein D. J., Richmond R. L., & Swan G. E. (2003). Measures of abstinence in clinical trials: Issues and recommendations. Nicotine & Tobacco Research, 5, 13–25.
Johnson K. A., Farris S. G., Schmidt N. B., Smits J. A. J., & Zvolensky M. J. (2013). Panic attack history and anxiety sensitivity in relation to cognitive-based smoking processes among treatment-seeking daily smokers. Nicotine & Tobacco Research, 15, 1–10.
Kaufmann A., Hitsman B., Goelz P. M., Veluz-Wilkins A., Blazekovic S., Powers L., … Schnoll R. A. (2015). Rate of nicotine metabolism and smoking cessation outcomes in a community-based sample of treatment-seeking smokers. Addictive Behaviors, 51, 93–99.
Kenward M. G., & Roger J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53, 983–997.
Kim S. S., Darwish S., Lee S. A., Sprague C., & DeMarco R. F. (2018). A randomized controlled pilot trial of a smoking cessation intervention for U.S. women living with HIV: Telephone-based video call vs voice call. International Journal of Women’s Health, 10, 545–555.
Kim S. S., Lee S. A., Mejia J., Cooley M. E., & DeMarco R. F. (2019). A pilot randomized controlled trial of a digital storytelling intervention for smoking cessation in women living with HIV. Annals of Behavioral Medicine, kaz062.
Kosmider L., Delijewski M., Koszowski B., Sobczak A., Benowitz N. L., & Goniewicz M. L. (2018). Slower nicotine metabolism among postmenopausal Polish smokers. Pharmacological Reports, 70, 434–438.
Kroenke K., Spitzer R. L., & Williams J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16, 606–613.
Levine M. D., Marcus M. D., Kalarchian M. A., Houck P. R., & Cheng Y. (2010). Weight concerns, mood, and postpartum smoking relapse. American Journal of Preventive Medicine, 39, 345–351.
MacKinnon D. P., & Fairchild A. J. (2009). Current directions in mediation analysis. Current Directions in Psychological Science, 18, 16–20.
Mdodo R., Frazier E. L., Dube S. R., Mattson C. L., Sutton M. Y., Brooks J. T., & Skarbinski J. (2015). Cigarette smoking prevalence among adults with HIV compared with the general adult population in the United States: Cross-sectional surveys. Annals of Internal Medicine, 162, 335–344.
Pang R. D., Bello M. S., Liautaud M. M., Weinberger A. H., & Leventhal A. M. (2019). Gender differences in negative affect during acute tobacco abstinence differ between African American and White adult cigarette smokers. Nicotine & Tobacco Research, 21, 1072–1078.
Piper M. E., Vasilenko S. A., Cook J. W., & Lanza S. T. (2017). What a difference a day makes: Differences in initial abstinence response during a smoking cessation attempt. Addiction, 112, 330–339.
Radloff L. S. (1977). The CES-D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401.
Raykov T. (1997). Scale reliability, Cronbach’s coefficient alpha, and violations of essential tau-equivalence with fixed congeneric components. Multivariate Behavioral Research, 32, 329–353.
Satterfield J. M., Gregorich S. E., Kalkhoran S., Lum P. J., Bloome J., Alvarado N., … Vijayaraghavan M. (2018). Computer-facilitated 5A’s for smoking cessation: A randomized trial of technology to promote provider adherence. American Journal of Preventive Medicine, 55, 35–43.
Sigel K., Makinson A., & Thaler J. (2017). Lung cancer in person with HIV. Current Opinion in HIV and AIDS, 12, 31–38.
Spitzer R. L., Kroenke K., Williams J. B. W., & Löwe B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166, 1092–1097.
Tsuyuki K., Pitpitan E. V., Levi-Minzi M. A., Urada L. A., Kurtz S. P., Stockman J. K., & Surratt H. L. (2017). Substance use disorders, violence, mental health, and HIV: Differentiating a syndemic factor by gender and sexuality. AIDS and Behavior, 21, 2270–2282.
U.S. Department of Health and Human Services (2014). The health consequences of smoking—50 years of progress: A report of the Surgeon General. Retrieved from
Valera P., McClernon F. J., Burkholder G., Mugavero M. J., Willig J., O’Cleirigh C., & Cropsey K. L. (2017). A pilot trial examining African American and White responses to algorithm-guided smoking cessation medication selection in persons living with HIV. AIDS and Behavior, 21, 1975–1984.
Velicer W. F., DiClemente C. C., Rossi J. S., & Prochaska J. O. (1990). Relapse situations and self-efficacy: An integrative model. Addictive Behaviors, 15, 271–283.
Weinberger A. H., Smith P. H., Allen S. S., Cosgrove K. P., Saladin M. E., Gray K. M., … McKee S. A. (2015). Systematic and meta-analytic review of research examining the impact of menstrual cycle phase and ovarian hormones on smoking and cessation. Nicotine & Tobacco Research, 17, 407–421.
Zuo Y., Rabinovich N. E., & Gilbert D. G. (2017). Negative affect subtypes and craving differentially predict long-term success among smokers achieving initial abstinence. Psychopharmacology, 234, 761–771.

craving for cigarettes; HIV; smoking cessation

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