Abstract
Background:
With 1 in 2 adult tobacco users being highly dependent on nicotine, population-based interventions specifically designed for this group are urgently needed. This study used data from a randomized trial to evaluate whether (1) Acceptance and Commitment Therapy (ACT) delivered via a smartphone application (iCanQuit) would be more efficacious for cessation of nicotine-containing tobacco products than the US Clinical Practice (USCPG)-based application (QuitGuide) among highly nicotine-dependent adults, (2) the effect of treatment on cessation was mediated by increases in acceptance of cravings to smoke, and (3) treatment utilization and satisfaction differed by arm.
Methods:
A total of 1452 highly nicotine-dependent adults received the iCanQuit or QuitGuide application for 12-months. Cessation outcomes were self-reported complete-case 30-day abstinence of nicotine-containing tobacco products (e.g., combustible cigarettes, e-cigarettes, chewing tobacco, snus, hookahs, cigars, cigarillos, tobacco pipes, and kreteks) at 3, 6, and 12-month post-randomization timepoints, missing-as-smoking and multiple imputation analyses. Acceptance of cues to smoke and satisfaction with the applications was also reported.
Results:
Participants who received iCanQuit were significantly more likely to report 30-day abstinence of nicotine-containing tobacco products than those who received QuitGuide at 12-months (24% vs. 17%; OR=1.47 95% CI: 1.11, 1.95). iCanQuit participants utilized their application more frequently and reported greater satisfaction than those who received QuitGuide. Increases in participants’ acceptance of cues to smoke mediated the intervention effect on cessation of nicotine-containing tobacco products.
Conclusions:
Among nicotine-dependent adults, an application-delivered ACT-based intervention was more engaging and efficacious than a USCPG-based intervention for cessation of nicotine-containing tobacco products.
Keywords: Acceptance & Commitment Therapy, iCanQuit, QuitGuide, nicotine dependence, smartphone applications, smoking cessation
Introduction
Cigarette smoking remains the leading preventable cause of premature death in the United States (US).1,2 Individuals who stop smoking gain up to a decade of life expectancy.3 Continued smoking is sustained by nicotine dependence, a chronic, relapsing condition defined by a compulsive craving to use the drug, along with the symptoms of tolerance and withdrawal.4,5 Abstaining from the use of tobacco products is particularly difficult for individuals with high nicotine dependence, who experience strong physical and psychological barriers to quit.6–8 While the overall prevalence of cigarette smoking and use of nicotine-containing tobacco products in the US has declined to 14.0% and 20.8%,1 respectively, data from a nationally representative sample of 36,309 US adults showed significantly higher use of nicotine-containing tobacco products among adults with high nicotine dependence (26.9%).9–11 With 1 in 2 adult tobacco users being highly dependent on nicotine and 50% less likely to quit,9,11,12 population-based interventions specifically designed for this group are urgently needed.
The physical and psychological consequences of nicotine dependence promote heavy smoking and cause severe withdrawal symptoms and high risk of relapse.6–8 As such, nicotine dependence is a strong predictor of poor cessation outcomes, although there are certainly studies not showing evidence of this relationship.13–18 While most nicotine-dependent adults try to quit, very few are successful in any single quit attempt.19 For example, in a large multi-country cohort study among 2431 adults who smoke, it was found that for every 2-unit increase in the Fagerström Test for Nicotine Dependence (FTND) score at baseline (0 to 10 = highly dependent), there was a 16% increase in odds of relapse over a period of 6-months.20
Cravings to smoke, a hallmark of nicotine dependence,20–22 are a strong driver of relapse, making it crucial to directly address cravings in treating adults with high nicotine dependence. Although the provision of nicotine replacement therapy (NRT) and other FDA-approved cessation pharmacotherapies reduce cravings for people with high nicotine dependence and may result in quit rates ranging from 6.2% to 25.5% by 6-months or longer,23 absolute quit rates could be improved. Evidence-based behavioral therapies may hold promise by directly targeting the cravings symptom of nicotine dependence. One specific behavioral therapy that targets cravings in a novel way is Acceptance and Commitment Therapy (ACT).24 Previous studies have demonstrated that ACT-based interventions promote smoking cessation by teaching adults who smoke to accept their cravings to smoke and let these cravings pass without smoking.25–33 Compared to standard approaches for tobacco cessation, such as the US Clinical Practice Guidelines (USCPG) that teaches avoidance of cravings, ACT-based processes teaches people to observe and accept emotions and cravings that cue smoking. USCPG-based approaches to avoid cravings may be unsuitable for highly nicotine-dependence individuals who might experience strong withdrawal symptoms when attempting to quit and are more likely to relapse during cravings episodes.6,8 For example, accumulating evidence showed the promise of ACT when tested against USCPG or cognitive behavioral therapy (CBT) for smoking cessation, which have resulted in quit rates ranging from 24.6% and 31.0% in ACT versus 18.1% to 28.8% for CBT. 26,34,35 However, none of these studies have focused on adult smokers with high nicotine dependence nor on cessation of nicotine-containing tobacco products in addition to combustible cigarettes. Therefore, ACT-based interventions are a potentially efficacious treatment to help nicotine-dependent individuals abstain from nicotine-containing tobacco products because they focus on improving skills to recognize and be open to experiencing cravings. However, a major barrier to achieving high quit rates is under-utilization of evidence-based treatment interventions, which is suggested to be higher among individuals with higher levels of nicotine dependence.1,36 In fact, among those who tried quitting in the past year, only 31.2% used evidence-based cessation treatments.19
One way to reach a population of highly nicotine-dependent adults who could potentially benefit from ACT-based interventions for tobacco cessation is via digital interventions. To date, only one randomized trial with long term follow-up has examined the efficacy of a digital smartphone application-delivered intervention for helping adults quit smoking in the US.37 This full-scale randomized trial compared an ACT-based smartphone application (iCanQuit) with the National Cancer Institute USCPG-based smartphone application for smoking cessation among a racially/geographically diverse sample of 2415 adult smokers recruited from all 50 US states. Results of this trial showed that the iCanQuit ACT-based application was more efficacious than the QuitGuide USCPG-based application for cigarette smoking cessation at the 12-month follow-up (OR=1.49; 95% CI: 1.22–1.83), with similar patterns observed at the 3 and 6-month follow-ups.37 Results from this study also showed that the effect of the intervention on cigarette smoking was mediated through increase in acceptance of cravings to smoke.27 Whether the cultivation of skills to accept cravings to smoke in ACT-based interventions increases cessation of nicotine-containing tobacco products among highly nicotine-dependent adults has not been evaluated.
The size and rigorous design of the iCanQuit parent trial provide an opportunity to test a novel behavioral approach and highly accessible tool to aid cessation in this highly at-need group. Accordingly, we conducted a secondary analysis of the iCanQuit trial data to explore (1) the efficacy of iCanQuit relative to QuitGuide for cessation of nicotine-containing tobacco products (e.g., combustible cigarettes, e-cigarettes or vaping, chewing tobacco, snus, hookahs, cigars, cigarillos, tobacco pipes, and kreteks) among highly nicotine-dependent adults at 3, 6 and 12-month post-randomization timepoints; (2) whether the effect of treatment on cessation of nicotine-containing tobacco products at 12-months was mediated by increases in acceptance of cravings to smoke; and (3) whether treatment utilization and satisfaction differed by arm. The results from this secondary analysis will help generate hypotheses to inform the design and implementation of future dissemination trials tailored to the unique needs of highly nicotine-dependent adult smokers nationwide.
Methods
Overview
Data are from a randomized controlled trial (RCT) that tested the efficacy of an Acceptance and Commitment Therapy (ACT) smartphone application (iCanQuit) against a US Clinical Practice Guidelines (USCPG) smartphone application for smoking cessation among 2,415 adult smokers.37 The parent iCanQuit trial eligibility criteria and detailed methods have been previously published.37 Briefly, adults (18 years old and older) who smoked at least 5 combustible cigarettes, wanted to quit cigarette smoking (and other nicotine-containing tobacco products, if applicable), who were able to read English and were not receiving cessation treatment were randomized 1:1 to receive iCanQuit or QuitGuide for 12-months. All participants provided informed consent to participate via online forms. The Fred Hutchinson Cancer Center Institutional Review Board approved all study procedures.
Participants and recruitment
The present study included only participants with high nicotine dependence (n=1452 adults, 60.1% of the total sample). Nicotine dependence was assessed via the Fagerström Test for Nicotine Dependence (FTND)38 and high nicotine dependence was defined by FTND scores of 6 or higher (range 0–10 = highly dependent). Most highly nicotine-dependent enrolled participants were recruited via Facebook ads (1219/1452, 84%). The rest were recruited either via a survey sampling company (170/1452, 12%), a search engine (36/1452, 2%), or word of mouth (27/1452, 2%). The period of recruitment of all trial participants was May 2017 through September 2018. Data collection occurred between August 2017 through December 2019 via online self-reported study questionnaires at the 3-month, 6-month, and 12-month post-randomization timepoints. Participants were compensated for completed data collection at each timepoint, with up to $105 in total compensation per participant.
Smartphone application-based interventions
iCanQuit
Details of the iCanQuit app have been previously published.37 Briefly, participants who had access to the ACT-based iCanQuit app for 12-months received eight levels of intervention content based on two key processes of ACT: acceptance of cravings to smoke and enactment of core life values that motivate living a smoke-free life. In the “Preparing to Quit” phase, iCanQuit focuses on helping the user develop acceptance of physical sensations, emotions, and thoughts that trigger smoking, and allowing these triggers to pass without smoking via mindfulness and perspective taking. There is an “Urge Help” feature that is tailored to the type of trigger experienced by the user, as well as a tracking feature that encourages participants to track the number of cigarettes smoked and urges passed. In the “After You Quit” phase, iCanQuit focuses on helping the user stay motivated and preventing relapse.
QuitGuide
Details of the QuitGuide app have been previously published.37 Briefly, the QuitGuide app developed by the National Cancer Institute is based on the US clinical practice guidelines (USCPG) for smoking cessation.4 QuitGuide is widely available and free to the public. Similar to iCanQuit, QuitGuide provides education and skills for preparing to quit and preventing relapse, as well as education on common triggers to smoke, barriers to cessation, and FDA-approved medications to aid cessation. Contrary to iCanQuit’s focus on acceptance, QuitGuide focuses on increasing motivation to quit by using logic and expectancies (e.g., providing information on the health consequences of smoking) and teaches skills for avoiding situations that lead to wanting to smoke.
Measures
Baseline questionnaire
Participants self-reported baseline data via self-administered online surveys. This included data on age, sex, race and ethnicity, education, employment, household income, marital status, sexual orientation, and residence geographic location. Smoking behaviors data at baseline included information on (1) number of cigarettes per day, (2) level of nicotine dependence via the Fagerström Test for Nicotine Dependence (FTND), (3) used of e-cigarettes or any other nicotine-containing tobacco products other than combustible cigarettes, (4) quit attempts, (5) confidence in being smoke-free, and (6) friends or family members who were smokers too. Data on alcohol consumption was also collected via the Quick Drinking Screen.39 Participants were also screened for mental health conditions, including (1) depression via the Center for Epidemiological Studies-Depression scale, (2) panic disorder via the Autonomic Nervous System Questionnaire, and posttraumatic syndrome disorder via the PTSD checklist.40–42
Abstinence Rates
The primary endpoint was self-reported complete-case 30-day point prevalence abstinence (PPA) of nicotine-containing tobacco products, including combustible cigarettes, e-cigarettes, vaping, chewing tobacco, snus, hookahs, cigars, cigarillos, tobacco pipes, and kreteks (unfiltered cigarettes made with a blend of tobacco, cloves, and other flavors) at the 12-month post-randomization timepoint. Secondary assessments included missing-as-smoking and multiple imputation analysis of 30-day PPA of nicotine-containing tobacco products at the 3-month, 6-month, and 12-month post-randomization timepoints. Smoking status was the self-reported response to the question “When was the last time you smoked, or even tried, a cigarette?” Response choices were “Earlier today”, “24 hours ago”, “2–7 days ago”, “8–30 days ago”, and “Over 30 days ago”. Participants who responded, “Over 30 days ago” were considered abstinent for the 30-day PPA outcome and those who responded “8–30 days ago” and “Over 30 days ago” were considered abstinent for the 7-day PPA outcome. Other outcomes included cessation of cigarette smoking alone (i.e., not in combination with other tobacco products) as measured by self-reported complete-case 30- and 7-day PPA at all timepoints, missing-as-smoking and multiple imputation, and prolonged abstinence at the 12-month post-randomization timepoint (no smoking since the 3-month post-randomization timepoint, using self-reported date of last cigarette, at the 12-month post-randomization timepoint).
Acceptance and Commitment Therapy (ACT) Processes and Mediation
The ACT-based acceptance of cues to smoke was assessed via the Avoidance and Inflexibility Scale (AIS-27),43 which has been validated among treatment seeking adults who smoke.44,45 Scores are derived using the mean of the three 9-item subscales that assess one’s willingness to experience sensations, emotions, and thoughts that cue smoking without smoking. The items are rated on a 5-point scale from (1) “Not at all” to (5) “Very willing” and averaged, with higher scores indicating greater acceptance. A sample sensation item is “How willing are you to notice these bodily sensations without smoking?”, and items from the emotions and thoughts subscales are similar, substituting “feelings” or “thoughts” for “bodily sensations”.
Utilization and satisfaction with the assigned application-based intervention
Google Analytics objectively measured treatment utilization of the smartphone applications. This included (1) number of times the application was opened, (2) the time spent per session, and (3) the number of unique days of use. To assess satisfaction with the assigned applications, participants completed an adapted satisfaction survey previous research,26,46 at 3-months only.
Statistical analysis
Participants’ socio-demographic characteristics and smoking behaviors were summarized by mean and standard deviation for continuous variables and frequency and percentages for categorical variables. To explore the efficacy of iCanQuit relative to QuitGuide for cessation nicotine-containing tobacco products, logistic regression models were used to compare reported 30-day abstinence from nicotine-containing tobacco products at the 12-month post-randomization timepoint across study arms. Multiple imputation sensitivity analyses, with ten imputed datasets, were used to estimate effect size and standard errors for the 30-day abstinence from nicotine-containing tobacco products at 12-months.47 Second, generalized linear models were used to compare utilization of the applications across arms, and negative binomial models were used for any right-skewed count utilization data. Third, logistic regression models were used to compare how satisfied participants were with their assigned applications and outcome data retention rates across study arms. Finally, to evaluate whether increases in willingness to experience cues to smoke (e.g., acceptances of cues to smoke) without smoking mediated the effect of the intervention on reported 30-day abstinence from nicotine-containing tobacco products at the 12-month post-randomization timepoint, Hayes’s PROCESS macro for SAS was used.48 Using this method with 5,000 bootstrapped samples, indirect effects were estimated and considered statistically significant when their bias-corrected 95% CI did not include zero. We also calculated the effect size statistics of the change from baseline to 3-months in ACT-based process via Cohen’s d.49 The R software version 4.0.3. was used for all other statistical analyses, including R libraries ‘MASS’, ‘mice’, and ‘psych’ for negative binomial regression, multiple imputation, and Cohen’s d analyses, respectively.50–53 All statistical tests were 2-sided, with α=.05.
Results
Sample characteristics
Figure 1 illustrates the geographical location of trial participants recruited from 49 US states (all but Hawaii), with 25% residing in rural areas. Study retention was 86%, 88%, and 87% of study participants who completed data collection at the 3-month, 6-month, and 12-month timepoints, respectively. Retention rates did not differ between arms at any time point (all p > 0.05). On average, participants were 38 years old, 30% male, 7% Hispanic (Table 1). About half (52%) were employed and 39% had annual household incomes <$20,000. Almost half (48%) of study participants screened positive for depression, 28% screened positive for panic disorder, and 45% screened positive for PTSD. Baseline characteristics were very similar between treatment arms.
Figure 1.
Geographic locations of trial participants with high nicotine dependence
Table 1.
Baseline socio-demographic characteristics of trial participants with high nicotine dependence
| No. (%) or Mean (SD) | ||||
|---|---|---|---|---|
|
| ||||
| Characteristic | n | Overall (N =1452) |
QuitGuide (n = 716) |
iCanQuit (n = 736) |
| Age, mean (SD), y | 1452 | 38.3 (10.6) | 38.3 (10.5) | 38.4 (10.7) |
| Male | 1452 | 439 (30%) | 218 (30%) | 221 (30%) |
| Race | ||||
| White | 1430 | 1018 (71%) | 504 (71%) | 514 (71%) |
| Black or African American | 1430 | 271 (19%) | 130 (18%) | 141 (19%) |
| Multiracial | 1430 | 100 (7%) | 53 (8%) | 47 (6%) |
| American Indian or Alaska Native | 1430 | 35 (2%) | 17 (2%) | 18 (2%) |
| Asian | 1430 | 3 (<1%) | 0 (0%) | 3 (<1%) |
| Native Hawaiian or Pacific Islander | 1430 | 3 (<1%) | 2 (<1%) | 1 (<1%) |
| Hispanic or Latino ethnicity | 1452 | 98 (7%) | 52 (7%) | 46 (6%) |
| Education | ||||
| Less than GED or high school education | 1452 | 139 (10%) | 74 (10%) | 65 (9%) |
| GED | 1452 | 193 (13%) | 88 (12%) | 105 (14%) |
| High school diploma | 1452 | 317 (22%) | 153 (21%) | 164 (22%) |
| Some college, no degree | 1452 | 526 (36%) | 265 (37%) | 261 (35%) |
| College degree or higher | 1452 | 277 (19%) | 136 (19%) | 141 (19%) |
| Employment status | ||||
| Employed | 1452 | 748 (52%) | 372 (52%) | 376 (51%) |
| Unemployed | 1452 | 182 (13%) | 75 (10%) | 107 (15%) |
| Disabled | 1452 | 241 (17%) | 122 (17%) | 119 (16%) |
| Out of labor force | 1452 | 281 (19%) | 147 (21%) | 134 (18%) |
| Income | ||||
| <$20,000/year | 1452 | 560 (39%) | 266 (37%) | 294 (40%) |
| $20,000 - $54,999/year | 1452 | 676 (47%) | 344 (48%) | 332 (45%) |
| ≥$55,000/year | 1452 | 216 (15%) | 106 (15%) | 110 (15%) |
| Married | 1452 | 449 (31%) | 235 (33%) | 214 (29%) |
| LGBT | 1452 | 226 (16%) | 107 (15%) | 119 (16%) |
| Rural residence | 1452 | 358 (25%) | 181 (55%) | 177 (24%) |
|
| ||||
| Mental health positive screening results | ||||
|
| ||||
| Depressiona | 1445 | 695 (48%) | 338 (47%) | 357 (49%) |
| Panic disorderb | 1422 | 399 (28%) | 213 (30%) | 186 (26%) |
| Posttraumatic stress disorderc | 1438 | 654 (45%) | 317 (45%) | 337 (46%) |
|
| ||||
| Alcohol use | ||||
|
| ||||
| No. of drinks/drinking day, mean (SD) | 1409 | 1.6 (3.6) | 1.4 (2.9) | 1.8 (4.1) |
| Heavy drinkerd | 1409 | 173 (12%) | 80 (12%) | 93 (13%) |
|
| ||||
| Smoking behavior | ||||
|
| ||||
| No. of cigarettes smoked per day, mean (SD) | 1452 | 22.8 (15.8) | 23.4 (17.3) | 22.2 (14.1) |
| FTND score, mean (SD) | 1452 | 7.2 (1.1) | 7.2 (1.1) | 7.2 (1.1) |
| Time to first cigarette within 5 min of waking | 1452 | 1139 (78%) | 561 (78%) | 578 (79%) |
| Smokes more than one-half pack/d | 1452 | 1298 (89%) | 650 (91%) | 648 (88%) |
| Smokes more than 1 pack/d | 1452 | 452 (31%) | 219 (31%) | 233 (32%) |
| Smoked for ≥10 years | 1452 | 1243 (86%) | 617 (86%) | 626 (85%) |
| Used e-cigarettes at least once in past month | 1452 | 335 (23%) | 165 (23%) | 170 (23%) |
| Used any other tobacco productse | 1452 | 261 (18%) | 125 (17%) | 136 (18%) |
| Quit attempts in past 12-months, mean (SD) | 1363 | 1.0 (5.6) | 1.2 (7.8) | 0.8 (1.9) |
| Confidence to quit smoking, mean (SD)f | 1452 | 63.5 (27.5) | 64.3 (26.8) | 62.7 (28.1) |
| Friend and partner smoking | ||||
| Close friends who smoke, mean (SD) | 1452 | 2.8 (1.7) | 2.8 (1.7) | 2.8 (1.8) |
| No. of housemates who smoke, mean (SD) | 1452 | 1.5 (0.9) | 1.5 (1.0) | 1.5 (0.8) |
| Living with partner who smokes | 1452 | 533 (37%) | 260 (36%) | 273 (37%) |
Abbreviations: FTND, Fagerström Test for Nicotine Dependence; GED, General Education Development; LGBT, lesbian, gay, bisexual, or transgender; PTSD, Posttraumatic Stress Disorder.
Positive screening results for depression via the Center for Epidemiological Studies Depression Scale (CESD-20; cutoff ≥16).
Positive screening results for panic disorder via the 5-item Autonomic Nervous System Questionnaire (ANSQ; reporting ≥1 panic attack within the past month indicates a positive screen).
Positive screening results for PTSD via the PTSD Checklist (PCL-6; scores of ≥14 indicate a positive screen).
Heavy drinking is defined as 4 or more drinks on a typical drinking day for women and 5 or more drinks on a typical drinking day for men within the past 30 days.
Any other tobacco products (other than combustible cigarettes) include any kind of chewing tobacco, snus, hookahs, cigars, cigarillos, tobacco pipes, and kreteks
Range, 0–100, where 0 indicates not at all confident and 100 indicates extremely confident.
Avoidance and Inflexibility Scale. Range is 1 to 5. Higher scores indicate greater acceptance.
Smoking behaviors at baseline
At baseline, participants smoked a mean of 22.8 cigarettes (SD = 15.8) per day and were highly dependent on nicotine with FTND mean score of 7.3 (SD = 1.1) (range 0–10 = highly dependent). The majority (78%) reported having their first cigarette within 5 minutes of waking and having smoked for 10 years or longer (86%). Only 23% reported having used e-cigarettes at least once in the past month. On average, participants reported making one attempt to quit during the past 12-months while more than a third (37%) reported living with a partner who smokes. Smoking behaviors were very similar between treatment arms.
Abstinence outcomes
Participants in the iCanQuit vs. QuitGuide arm had greater odds of abstaining from nicotine-containing tobacco products at all timepoints (Table 2). The self-reported complete-case 30-day abstinence from nicotine-containing tobacco products was 24% for iCanQuit vs. 17% for QuitGuide at the 12-month post-randomization timepoint assessment (OR=1.47 95% CI: 1.11, 1.95), 21% vs. 11% at 6-months (OR=2.22 95% CI: 1.61, 3.06), and 14% vs. 6% at 3-months (OR=2.67 95% CI: 1.78, 4.01). Similar 12-month quit rates of nicotine-containing tobacco products were found for the missing-as-smoking (20% iCanQuit vs. 15% QuitGuide, OR=1.41 95% CI: 1.07, 1.86) and the multiple imputation sensitivity analyses (24% iCanQuit vs. 17% QuitGuide, OR=1.49 95% CI: 1.14, 1.93). The 30-day PPA for cigarette smoking were also significantly higher in the iCanQuit relative to the QuitGuide arm at all timepoints with similar findings for the missing-as-smoking and multiple imputation at 12-months. Overall, the mean (SD) number of days of abstinence, from date of last cigarette to the 12-month post-randomization timepoint was 183 (108.6) days. The mean (SD) number of days of abstinence for QuitGuide participants was 147.1 (98.9) days compared with 206.4 (108.6) for iCanQuit participants.
Table 2.
| No. (%) | |||||
|---|---|---|---|---|---|
| Outcome variable | Overall (N =1452) | QuitGuide (n = 716) | iCanQuit (n = 736) | OR (95% CI) | p value |
| Cessation of all nicotine-containing tobacco products c | |||||
|
| |||||
| 12-months outcomes | |||||
| 30-d PPA | 259/1262 (21%) | 109/633 (17%) | 150/629 (24%) | 1.47 (1.11, 1.95) | 0.007 |
| 30-d PPA, missing-as-smoking | 259/1452 (18%) | 109/716 (15%) | 150/736 (20%) | 1.41 (1.07, 1.86) | 0.016 |
| 30-d PPA, multiple imputation | 2985/14520 (21%) | 1236/7160 (17%) | 1749.7360 (24%) | 1.49 (1.14, 1.93) | 0.003 |
| 6-months outcomes | |||||
| 30-d PPA | 205/1275 (16%) | 71/640 (11%) | 134/635 (21%) | 2.22 (1.61, 3.06) | <0.001 |
| 3-months outcomes | |||||
| 30-d PPA | 125/1254 (10%) | 37/624 (6%) | 88/630 (14%) | 2.67 (1.78, 4.01) | <0.001 |
|
| |||||
| Cessation of combustible cigarette smoking only | |||||
|
| |||||
| 12-months outcomes | |||||
| 30-d PPA | 310/1261 (25%) | 132/632 (21%) | 178/629 (28%) | 1.46 (1.12, 1.90) | 0.005 |
| 30-d PPA, missing-as-smoking | 310/1452 (21%) | 132/716 (18%) | 178/736 (24%) | 1.42 (1.09, 1.85) | 0.008 |
| 30-d PPA, multiple imputation | 3574/14520 (25%) | 1488/7160 (21%) | 2086/7360 (28%) | 1.52 (1.16, 1.98) | 0.002 |
| 7-d PPA | 394/1261 (31%) | 178/632 (28%) | 216/629 (34%) | 1.33 (1.05, 1.69) | 0.019 |
| Prolonged abstinenced | 101/1007 (10%) | 30/499 (6%) | 71/508 (14%) | 2.51 (1.61, 3.94) | <0.001 |
| 6-months outcomes | |||||
| 30-d PPA | 251/1276 (20%) | 91/640 (14%) | 160/636 (25%) | 2.03 (1.52, 2.70) | <0.001 |
| 7-d PPA | 364/1276 (29%) | 145/640 (23%) | 219/636 (34%) | 1.79 (1.39, 2.29) | <0.001 |
| 3-months outcomes | |||||
| 30-d PPA | 160/1254 (13%) | 55/624 (9%) | 105/630 (17%), n=630 | 2.12 (1.50, 3.01) | <0.001 |
| 7-d PPA | 274/1254 (22%) | 100/624 (16%) | 174/630 (28%), n=630 | 2.02 (1.53, 2.67) | <0.001 |
Abbreviations: OR, odds ratio; PPA, point prevalence abstinence.
All models include the following covariates: education (high school diploma or less), cigarette smoking frequency (>20 cigs/day), minority race or ethnicity and depression symptoms (CESD-20 ≥16).
All outcomes are complete case (i.e., exclusion of participants lost to assessments at each time point), except where noted.
Nicotine-containing tobacco products include, combustible cigarettes, any kind of e-cigarettes or vaping, chewing tobacco, snus, hookahs, cigars, cigarillos, tobacco pipes, and kreteks.
Defined as no smoking since 3-months post-randomization, using self-reported data of last cigarette.
Change in Acceptance and Commitment Therapy (ACT) processes and mediation
Indirect effects of the treatment on cessation of nicotine-containing tobacco products through ACT-based processes are shown in Table 3. Participants with high nicotine dependence who received the iCanQuit ACT-based intervention application reported significantly greater increases in acceptance of physical sensations, emotions (from baseline to 3-months), and thoughts that cue smoking, as well as mean acceptance than those who received the QuitGuide USCPG-based application. Increases in acceptance were in turn associated with greater odds of quitting nicotine-containing tobacco products by 12-months. Increases in acceptance of sensations (indirect effect: 0.07; 95% CI: 0.01, 0.14; p<0.05), and emotions (indirect effect: 0.14; 95% CI: 0.06, 0.25; p<0.05), but not acceptance of thoughts that cue smoking mediated the treatment effect on cessation of nicotine-containing tobacco products at 12-months. Mean acceptance significantly mediated the relationship between treatment and cessation of nicotine-containing tobacco products at 12-months (indirect effect: 0.28; 95% CI: 0.17, 0.41; p<0.05). We further explored mediation by utilization of the application, objectively measured via number of unique days of use at the 3-month timepoint assessment. We found that the number of days of use mediated the effect of the intervention on point prevalence abstinence of nicotine-containing tobacco products at the 12-month post-randomization timepoint (indirect effect: 0.11; 95% CI: 0.03, 0.21; p<0.05).
Table 3.
Change in ACT-based acceptance processes from baseline to 3-months as mediators of the effect of treatment on cessation of nicotine-containing products at the 12-month post-randomization timepointa,b,c,d
| Mean (SD) | |||||||
|---|---|---|---|---|---|---|---|
|
| |||||||
| Mediator | n | Overall (N =1452) | QuitGuide (n = 716) | iCanQuit (n = 736) | Point estimate for difference (95% CI) | p value | Estimate of mediation effect (95% CI)e |
| Acceptance of internal cues to smoke f | |||||||
| Physical sensations | |||||||
| Baseline | 1432 | 3.0 (0.6) | 3.0 (0.6) | 3.0 (0.6) | |||
| 3-months | 1200 | 3.2 (0.7) | 3.1 (0.6) | 3.3 (0.7) | |||
| Change from baseline to 3-months | 1187 | 0.2 (0.7) | 0.1 (0.7) | 0.2 (0.8) | 0.2 (0.1, 0.2) | <0.001 | 0.07 (0.01, 0.14)* |
| Emotions | |||||||
| Baseline | 1439 | 2.9 (0.5) | 2.9 (0.5) | 2.8 (0.5) | |||
| 3-months | 1213 | 3.0 (0.6) | 2.9 (0.6) | 3.0 (0.6) | |||
| Change from baseline to 3-months | 1205 | 0.1 (0.7) | 0.0 (0.6) | 0.2 (0.7) | 0.1 (0.1, 0.2) | <0.001 | 0.14 (0.06, 0.25)* |
| Thoughts | |||||||
| Baseline | 1442 | 2.8 (0.4) | 2.8 (0.4) | 2.8 (0.4) | |||
| 3-months | 1213 | 2.9 (0.6) | 2.9 (0.5) | 3.0 (0.6) | |||
| Change from baseline to 3-months | 1207 | 0.1 (0.6) | 0.0 (0.6) | 0.2 (0.7) | 0.1 (0.1, 0.2) | <0.001 | 0.07 (−0.001, 0.16) |
| Mean score | |||||||
| Baseline | 1426 | 2.9 (0.4) | 2.9 (0.4) | 2.9 (0.4) | |||
| 3-months | 1194 | 3.0 (0.5) | 3.0 (0.5) | 3.1 (0.6) | |||
| Change from baseline to 3-months | 1178 | 0.1 (0.6) | 0.0 (0.5) | 0.2 (0.6) | 0.2 (0.1, 0.2) | <0.001 | 0.28 (0.17, 0.41)* |
Abbreviations: ACT, Acceptance and Commitment Therapy.
Nicotine-containing tobacco products include, combustible cigarettes, any kind of e-cigarettes or vaping, chewing tobacco, snus, hookahs, cigars, cigarillos, tobacco pipes, and kreteks.
All models include the following covariates: education (high school diploma or less), cigarette smoking frequency (>20 cigs/day), minority race or ethnicity and depression symptoms (CESD-20 ≥16).
All changes in acceptance scores calculated as value at 3-months minus baseline value.
The effect size statistics, estimated by Cohen’s d,49 are as follows for change from baseline to 3-months in ACT-based processes: (1) physical sensations d = 0.24, 95% CI: 0.13, 0.36; (2) emotions d = 0.24, 95% CI: 0.13, 0.35; (3) thoughts d = 0.26, 95% CI: 0.14, 0.37; and (4) mean score d = 0.30, 95% CI: 0.19, 0.42
Estimation of mediation effect (indirect effect) for the PROCESS macro is greater than 0.05 if the bootstrap CI contains 0.
Avoidance and Inflexibility Scale. Range in change is −4 to 4. Positive scores indicate higher acceptance at timepoint assessments.
p<0.05
Utilization of intervention applications and satisfaction
Data on the utilization of intervention applications and reported satisfaction with the applications can be found in Table 4. Participants who received the iCanQuit ACT-based intervention application utilized their assigned application more frequently than those who received the QuitGuide USCPG-based intervention application, as objectively measured by (1) number of logins (26.0 vs. 8.7, p<0.001), (2) time spent using the application per session (4.2 vs. 2.5 minutes, p<0.001), and (3) total number of unique days using the application (16.3 vs. 6.5, p<0.001). Overall, 93% of study participants used their assigned application at least once (i.e., received the intervention) prior to the 12-month post-randomization assessment. Participants who received the iCanQuit ACT-based intervention application were significantly more satisfied with their assigned application than those who received the QuitGuide USCPG-based intervention application (87% iCanQuit vs. 80% QuitGuide, p=0.002). iCanQuit participants were also more likely to report that their application was useful for quitting (81% iCanQuit vs. 72% QuitGuide, p<0.001), were more likely to recommend the application for quitting (84% vs. 73%, p<0.001), and more likely to report they felt the application was made for them (82% vs. 70%, p<0.001).
Table 4.
Treatment utilization and satisfaction of the assigned smartphone applicationa
| Mean (SD) or No. (%) | ||||||
|---|---|---|---|---|---|---|
|
| ||||||
| Variable | n | Overall (N =1452) | QuitGuide (n = 716) | iCanQuit (n = 736) | IRR, point estimate or Odds Ratio (95% CI) | p value |
| Treatment utilization at 6-monthsb | ||||||
| Number of logins, mean (SD) | 1431 | 17.5 (46.0)c | 8.7 (29.6)d | 26.0 (56.3)e | IRR: 3.04 (2.64, 3.51) | <0.001 |
| Time spent per session, mean (SD), min | 1288 | 3.4 (4.2) | 2.5 (2.5) | 4.2 (5.2) | Point estimate: 1.7 (1.2, 2.1) | <0.001 |
| Number of unique days of use, mean (SD) | 1431 | 11.4 (22.3)f | 6.5 (9.9)f | 16.3 (29.0)c | IRR: 2.51 (2.20, 2.87) | <0.001 |
| Satisfaction at 3-months, No. (%) | ||||||
| Satisfied with assigned application | 1185 | 988 (83%) | 477 (80%) | 511 (87%) | 1.64 (1.20, 2.24) | 0.002 |
| Application was useful for quitting | 1188 | 908 (76%) | 429 (72%) | 479 (81%) | 1.67 (1.27, 2.19) | <0.001 |
| Would recommend assigned application | 1213 | 956 (79%) | 445 (73%) | 511 (84%) | 1.90 (1.43, 2.53) | <0.001 |
| Felt application was made for me | 1172 | 894 (76%) | 412 (70%) | 482 (82%) | 1.96 (1.48, 2.59) | <0.001 |
Abbreviations: IRR, incident rate ratio; OR, odds ratio; PE, point estimate.
All models include the following covariates: education (high school diploma or less), heavy smoking (>20 cigs/day), minority race or ethnicity and depression symptoms (CESD-20 ≥16).
A full 6-months of utilization data from Google Analytics were available for n=1431/1452, 99%.
median = 5
median = 4.5
median = 7
median = 4
Discussion
The present study showed that an Acceptance and Commitment Therapy-based application-delivered intervention may be efficacious for helping adult tobacco users with high nicotine dependence abstain from the use of nicotine-containing tobacco products. Across all timepoints (3, 6, and 12-month post-randomization timepoints), participants in the iCanQuit vs. QuitGuide arm had greater odds of abstaining from nicotine-containing tobacco products with odds ratios (ORs) ranging from 1.41 to 2.67. The self-reported complete-case 30-day abstinence rate was 24% for iCanQuit vs. 17% for QuitGuide at 12-months. Greater mean acceptance of cravings to smoke mediated the intervention effect on cessation of nicotine-containing tobacco products. iCanQuit participants utilized their application more frequently and were more satisfied than QuitGuide participants.
To put the quit rates of this study in context, the only known prior study of a technology intervention among adults with high nicotine dependence is a randomized controlled trial by Masaki et al.54 That study tested the augmentation of in-person counseling and pharmacotherapy treatment for smoking cessation with a smartphone application among 584 adults with nicotine dependence (FTND mean score of 5.3 (SD = 2.1)) in Japan. Although overall smoking cessation rates were high, considering that both groups received in-person counseling and pharmacotherapy, the authors attribute an additional 13.4% in quit rates to the use of the smartphone application. In comparison, the current study’s 21% cessation of all nicotine-containing tobacco products at 6-months in the iCanQuit arms is almost double that rate, even though this sample had higher nicotine dependence (FTND mean (SD) score of 7.2 (1.1)). These results are noteworthy, considering that as a stand-alone technology intervention, iCanQuit would cost less to deliver if disseminated broadly to adults with high nicotine dependence.
The fact that this study resulted in long-term higher quit rates among iCanQuit relative to QuitGuide participants without the provision of human-delivered coaching or pharmacotherapy is noteworthy. Exploratory analyses showed that the reported use of NRT at 12-months was not significantly different between arms (27% iCanQuit vs. 30% QuitGuide, p=0.168), which provides partial support for the interpretation that the effect of iCanQuit vs. QuitGuide on quit rates was unrelated to use of NRT. An important next step is to test, in highly nicotine-dependent tobacco users, the efficacy of iCanQuit alone versus in combination with pharmacotherapy.
Quit rates were higher at the 12-month post-randomization timepoint than at the 3-month or 6-month timepoints. The total length of time participants used the application is unlikely to explain this fully, since participants used the application for M (SD) 78 (110) days, and 66% of participants did not use it after 3-months (data not shown). Another possibility is whether the number of 24-hour quit attempts increased at each timepoint assessment, because it may take many quit attempts to finally be successful 55. Indeed, we found that the mean number of quit attempts at the 3, 6, and 12-month timepoints were 7.0 (12.3), 9.5 (21.0), and 13.6 (53.5), respectively. Future studies on the nature of the association between quit attempts and quit success are needed.
In accordance with ACT theoretical model, mediation analyses confirmed that greater acceptance of cravings to smoke mediated the effect of the intervention on cessation of nicotine-containing tobacco products (Indirect effect = 0.28, 95% CI: 0.17, 0.41); p<0.05). This is in agreement with what was found in the full sample of the iCanQuit parent trial for cigarette smoking.27 Learning how to observe and allow cravings to come and go by applying mindfulness techniques and perspective taking may have contributed to achieving and maintaining long-term cessation in the iCanQuit relative to the QuitGuide arm. Future studies could further evaluate whether this key ACT processes of acceptance could help other highly nicotine-dependent tobacco users offset withdrawal symptoms, thereby improving long-term abstinence, including the growing population of vapers.
This study showed that iCanQuit participants engaged significantly more with their application than those who received QuitGuide as measured by the number of logins (26.0 vs. 8.7). While the specific reasons why iCanQuit was more engaging are beyond the scope of this paper, we posit that the appeal of the content (e.g., ACT skills modules) using audio and videos in addition to text, as well as the structure of the content (e.g., content is unlocked in a sequential manner) of the iCanQuit application may have contributed to higher engagement as compared with QuitGuide. This level of engagement may have in turn contributed to a greater likelihood of smoking cessation through activation of key psychological processes targeted in the intervention.27
There are several strengths of this study. First, the potential for broad reach is demonstrated by the racial/ethnic and geographic diversity of the sample of highly nicotine-dependent adults recruited from 49 US states (29% minority race/ethnicity, 25% rural residence). Second, retention rate was high (87%) with most study participants completing data collection at the 12-month post-randomization timepoint. Third, results showed high utilization of and satisfaction with the application-based interventions, thereby also demonstrating high acceptability of digital interventions in this population. Lastly, iCanQuit’s high cessation rates of nicotine-containing tobacco products were achieved without provision of any pharmacotherapy or human coaching,27 which makes the intervention lower cost and logistically easier to disseminate.
This study also has limitations. First, post hoc subgroup analysis can be biased by chance results and thus should be viewed with caution.56,57 Second, the selection of a sample of participants with high nicotine dependence as measured by FTND scores may be limited to high dependence to cigarette smoking, and therefore, the results may not be representative of the full population of adult tobacco users. Third, the iCanQuit application was designed to help adults quit cigarette smoking (rather than all nicotine-containing products) and some of the application features were designed to address cigarette smoking specifically. Nonetheless, this study is an important step in identifying an existing tool that could be easily adapted for cessation of all nicotine-containing products and tested in a future trial. Finally, self-reported abstinence from cigarette smoking was not biochemically verified given the methodological challenges associated with biochemical confirmation of smoking status in large population-based studies. And while the external validity of the cessation outcome remains unknown, given the randomized design, we see no compelling reason that the validity of the results would differ between study arms. Future studies could consider utilizing novel approaches for remote confirmation of smoking status that are showing great promise.58,59 Finally, this study is meant to generate hypotheses and provides the necessary preliminary results to inform the design and implementation of a future dissemination trial focusing on highly nicotine-dependent adult smokers nationwide
Conclusions
This study provides new evidence that the cultivation of skills to accept cravings to smoke in ACT theory-based application-delivered intervention may be more efficacious than the USCPG-based approach for helping adult with high nicotine dependence abstain from the use of nicotine-containing tobacco products, thereby enhancing the potential for long-term abstinence in this population.
Acknowledgements.
We appreciate the tireless contributions of the entire study staff, most notably, Eric Meier, Eric Strand, Carolyn Ehret, Alanna Boynton, the design services of Ayogo, Inc., and the development services of Moby, Inc. We are very appreciative of the study participants.
Funding.
This study was supported by the National Cancer Institute under grant R01 CA192849 awarded to Dr. Bricker. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
Declaration of interest statement. None of the authors have a financial interest in the iCanQuit application. Nancy A Rigotti has been a consultant for Achieve Life Sciences and received funds from the company to conduct a clinical trial of an investigational smoking cessation medication (cytisine).
Data availability statement.
The code for the data analysis underlying this article will be shared on reasonable request to Jonathan B. Bricker at jbricker@fredhutch.org.
References
- 1.U.S. Department of Health and Human Services. Atlanta, GA: 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 2020. [Google Scholar]
- 2.Ng M, Freeman MK, Fleming TD, et al. Smoking prevalence and cigarette consumption in 187 countries, 1980–2012. JAMA 2014;311(2):183–192. [DOI] [PubMed] [Google Scholar]
- 3.U.S. Department of Health and Human Services. Smoking Cessation: A Report of the Surgeon General—Executive Summary. Atlanta, GA: 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: 2020. [Google Scholar]
- 4.Fiore MC. US public health service clinical practice guideline: treating tobacco use and dependence. Respir Care 2000;45(10):1200–1262. [PubMed] [Google Scholar]
- 5.U.S. Department of Health and Human Services. Smoking Cessation: A Report of the Surgeon General—Executive Summary. Atlanta, GA: 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 2020. [Google Scholar]
- 6.Potter LN, Haaland BA, Lam CY, et al. A time-varying model of the dynamics of smoking lapse. Health Psychol 2021;40(1):40–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Robinson JD, Li L, Chen M, et al. Evaluating the temporal relationships between withdrawal symptoms and smoking relapse. Psychol Addict Behav 2019;33(2):105–116. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Martinez-Vispo C, Lopez-Duran A, Senra C, Becona E. Specific Relapse Predictors: Could Cognitive-Behavioral Treatment for Smoking Cessation Be Improved? Int J Environ Res Public Health 2020;17(12). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Snell M, Harless D, Shin S, Cunningham P, Barnes A. A longitudinal assessment of nicotine dependence, mental health, and attempts to quit Smoking: Evidence from waves 1–4 of the Population Assessment of Tobacco and Health (PATH) study. Addict Behav 2021;115:106787. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Muscat JE, Ahn K, Richie JP, Jr., Stellman SD. Nicotine dependence phenotype and lung cancer risk. Cancer 2011;117(23):5370–5376. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Grant BF, Shmulewitz D, Compton WM. Nicotine Use and DSM-IV Nicotine Dependence in the United States, 2001–2002 and 2012–2013. Am J Psychiatry 2020;177(11):1082–1090. [DOI] [PubMed] [Google Scholar]
- 12.Lin H, Chen M, Yun Q, Zhang L, Chang C. Tobacco dependence affects determinants related to quitting intention and behaviour. Sci Rep 2021;11(1):20202. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Baker TB, Piper ME, McCarthy DE, et al. Time to first cigarette in the morning as an index of ability to quit smoking: implications for nicotine dependence. Nicotine Tob Res 2007;9 Suppl 4:S555–570. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Piper ME, McCarthy DE, Bolt DM, et al. Assessing dimensions of nicotine dependence: an evaluation of the Nicotine Dependence Syndrome Scale (NDSS) and the Wisconsin Inventory of Smoking Dependence Motives (WISDM). Nicotine Tob Res 2008;10(6):1009–1020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Ashe ML, Wilson SJ. Very Light Daily Smoking in Young Adults: Relationships Between Nicotine Dependence and Lapse. Nicotine Tob Res 2021;23(2):327–333. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Borland R, Yong HH, O’Connor RJ, Hyland A, Thompson ME. The reliability and predictive validity of the Heaviness of Smoking Index and its two components: findings from the International Tobacco Control Four Country study. Nicotine Tob Res. 2010;12 Suppl:S45–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Frost-Pineda K, Muhammad-Kah R, Rimmer L, Liang Q. Predictors, indicators, and validated measures of dependence in menthol smokers. J Addict Dis 2014;33(2):94–113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Le Grande M, Borland R, Yong HH, et al. Predictive Power of Dependence Measures for Quitting Smoking. Findings From the 2016 to 2018 ITC Four Country Smoking and Vaping Surveys. Nicotine Tob Res 2021;23(2):276–285. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Babb S, Malarcher A, Schauer G, Asman K, Jamal A. Quitting smoking among adults—United States, 2000–2015. MMWR Morb Mortal Wkly Rep 2017;65(52):1457–1464. doi: 10.15585/mmwr.mm6552a1.2017. [DOI] [PubMed] [Google Scholar]
- 20.Zhou X, Nonnemaker J, Sherrill B, Gilsenan AW, Coste F, West R. Attempts to quit smoking and relapse: factors associated with success or failure from the ATTEMPT cohort study. Addict Behav 2009;34(4):365–373. [DOI] [PubMed] [Google Scholar]
- 21.Canterberry M, Hanlon CA, Hartwell KJ, et al. Sustained reduction of nicotine craving with real-time neurofeedback: exploring the role of severity of dependence. Nicotine Tob Res 2013;15(12):2120–2124. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Reese ED, Veilleux JC. Relationships Between Craving Beliefs and Abstinence Self-Efficacy are Mediated by Smoking Motives and Moderated by Nicotine Dependence. Nicotine Tob Res 2016;18(1):48–55. [DOI] [PubMed] [Google Scholar]
- 23.Patnode CD, Henderson JT, Coppola EL, Melnikow J, Durbin S, Thomas RG. Interventions for Tobacco Cessation in Adults, Including Pregnant Persons: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA 2021;325(3):280–298. [DOI] [PubMed] [Google Scholar]
- 24.Hayes SC, Levin ME, Plumb-Vilardaga J, Villatte JL, Pistorello J. Acceptance and commitment therapy and contextual behavioral science: examining the progress of a distinctive model of behavioral and cognitive therapy. Behav Ther 2013;44(2):180–198. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.McCallion EA, Zvolensky MJ. Acceptance and Commitment Therapy (ACT) for smoking cessation: a synthesis. Curr Opin Psychol 2015;2:47–51. [Google Scholar]
- 26.Bricker JB, Bush T, Zbikowski SM, Mercer LD, Heffner JL. Randomized trial of telephone-delivered acceptance and commitment therapy versus cognitive behavioral therapy for smoking cessation: a pilot study. Nicotine Tob Res 2014;16(11):1446–1454. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Bricker JB, Levin M, Lappalainen R, Mull K, Sullivan B, Santiago-Torres M. Mechanisms of Smartphone Apps for Cigarette Smoking Cessation: Results of a Serial Mediation Model From the iCanQuit Randomized Trial. JMIR Mhealth Uhealth 2021;9(11):e32847. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Santiago-Torres M, Mull KE, Sullivan BM, et al. Efficacy and utilization of an acceptance and commitment therapy-based smartphone application for smoking cessation among Black adults: secondary analysis of the iCanQuit randomized trial. Addiction 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Santiago-Torres M, Mull KE, Sullivan BM, et al. Efficacy and Utilization of Smartphone Applications for Smoking Cessation among American Indians and Alaska Natives: Results from the iCanQuit Trial. Nicotine Tob Res 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Mak YW, Loke AY, Leung DYP. Acceptance and Commitment Therapy versus Social Support for Smoking Cessation for People with Schizophrenia: A Randomised Controlled Trial. J Clin Med 2021;10(19). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Karekla M, Savvides SN. Smoking cessation avatar-led Acceptance and Commitment Therapy digital intervention: feasibility and acceptability in young adults. Transl Behav Med 2021;11(1):198–205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Mak YW, Leung DYP, Loke AY. Effectiveness of an individual acceptance and commitment therapy for smoking cessation, delivered face-to-face and by telephone to adults recruited in primary health care settings: a randomized controlled trial. BMC Public Health 2020;20(1):1719. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Vilardaga R, Rizo J, Ries RK, et al. Formative, multimethod case studies of learn to quit, an acceptance and commitment therapy smoking cessation app designed for people with serious mental illness. Transl Behav Med 2019;9(6):1076–1086. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.McClure JB, Bricker J, Mull K, Heffner JL. Comparative Effectiveness of Group-Delivered Acceptance and Commitment Therapy versus Cognitive Behavioral Therapy for Smoking Cessation: A Randomized Controlled Trial. Nicotine Tob Res 2020;22(3):354–362. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Bricker JB, Sullivan BM, Mull KE, Torres AJ, Carpenter KM. Full-scale randomized trial comparing Acceptance and Commitment Therapy (ACT) telephone-delivered coaching with standard telephone-delivered coaching among Medicare/uninsured quitline callers. Nicotine Tob Res 2022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Pacek LR, McClernon FJ, Bosworth HB. Adherence to Pharmacological Smoking Cessation Interventions: A Literature Review and Synthesis of Correlates and Barriers. Nicotine Tob Res 2018;20(10):1163–1172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Bricker JB, Watson NL, Mull KE, Sullivan BM, Heffner JL. Efficacy of Smartphone Applications for Smoking Cessation: A Randomized Clinical Trial. JAMA Intern Med 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO. The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict 1991;86(9):1119–1127. [DOI] [PubMed] [Google Scholar]
- 39.Roy M, Dum M, Sobell LC, et al. Comparison of the quick drinking screen and the alcohol timeline followback with outpatient alcohol abusers. Subst Use Misuse 2008;43(14):2116–2123. [DOI] [PubMed] [Google Scholar]
- 40.Radloff LS. The CES-D Scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1977;1(3):385–401. [Google Scholar]
- 41.Stein MB, Roy-Byrne PP, McQuaid JR, et al. Development of a brief diagnostic screen for panic disorder in primary care. Psychosom Med 1999;61(3):359–364. [DOI] [PubMed] [Google Scholar]
- 42.Lang AJ, Wilkins K, Roy-Byrne PP, et al. Abbreviated PTSD Checklist (PCL) as a guide to clinical response. Gen Hosp Psychiatry 2012;34(4):332–338. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Gifford EV, Kohlenberg BS, Hayes SC, et al. Acceptance-based treatment for smoking cessation. Behavior Therapy 2004;35(4):689–705. [Google Scholar]
- 44.Farris SG, Zvolensky MJ, DiBello AM, Schmidt NB. Validation of the Avoidance and Inflexibility Scale (AIS) among treatment-seeking smokers. Psychol Assess 2015;27(2):467–477. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Farris SG, DiBello AM, Heggeness LF, et al. Sustained smoking abstinence is associated with reductions in smoking-specific experiential avoidance among treatment-seeking smokers. J Behav Ther Exp Psychiatry 2016;51:51–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Bricker JB, Mull KE, McClure JB, Watson NL, Heffner JL. Improving quit rates of web-delivered interventions for smoking cessation: full-scale randomized trial of WebQuit.org versus Smokefree.gov. Addiction 2018;113(5):914–923. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Rubin DB. Multiple imputation for nonresponse in surveys New York: ;: Wiley; 1987. [Google Scholar]
- 48.Hayes AF. Introduction to mediation, moderation, and conditional process analysis: a regression-based approach 2nd ed. New York, NY: Guilford Press.2018. [Google Scholar]
- 49.Cohen J. Statistical Power Analysis for the Behavioral Sciences 2nd ed: Lawrence Erlbaum Associates; 1988. [Google Scholar]
- 50.R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org/. [Google Scholar]
- 51.Venables WN, Ripley BD. Modern Applied Statistics with S 4th ed. Springer; 2002. doi: 10.1007/978-0-387-21706-2. [DOI] [Google Scholar]
- 52.mice: multivariate imputation by chained equations in R. J Stat Softw 2011. [Google Scholar]
- 53.psych: Procedures for Personality and Psychological Research [computer program]. Version 2.0.9. Northwestern University, Evanston, Illinois, USA. 2020. [Google Scholar]
- 54.Masaki K, Tateno H, Nomura A, et al. A randomized controlled trial of a smoking cessation smartphone application with a carbon monoxide checker. NPJ Digit Med 2020;3:35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Chaiton M, Diemert L, Cohen JE, et al. Estimating the number of quit attempts it takes to quit smoking successfully in a longitudinal cohort of smokers. BMJ Open 2016;6(6):e011045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Assmann SF, Pocock SJ, Enos LE, Kasten LE. Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet 2000;355(9209):1064–1069. [DOI] [PubMed] [Google Scholar]
- 57.Pocock SJ, Assmann SE, Enos LE, Kasten LE. Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Stat Med 2002;21(19):2917–2930. [DOI] [PubMed] [Google Scholar]
- 58.McClure EA, Tomko RL, Carpenter MJ, Treiber FA, Gray KM. Acceptability and compliance with a remote monitoring system to track smoking and abstinence among young smokers. Am J Drug Alcohol Abuse 2018;44(5):561–570. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Thrul J, Meacham MC, Ramo DE. A novel and remote biochemical verification method of smoking abstinence: Predictors of participant compliance. Tob Prev Cessat 2018;4. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The code for the data analysis underlying this article will be shared on reasonable request to Jonathan B. Bricker at jbricker@fredhutch.org.