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. Author manuscript; available in PMC: 2023 Apr 1.
Published in final edited form as: Prev Med. 2022 Mar 4;157:107008. doi: 10.1016/j.ypmed.2022.107008

Efficacy of an acceptance and commitment therapy-based smartphone application for helping rural populations quit smoking: Results from the iCanQuit randomized trial

Margarita Santiago-Torres a,*, Kristin E Mull a, Brianna M Sullivan a, Amy K Ferketich b, Jonathan B Bricker a,c
PMCID: PMC9793445  NIHMSID: NIHMS1855398  PMID: 35257698

Abstract

Limited access to evidence-based smoking cessation interventions among rural populations contributes to high rates of cigarette smoking and poor cessation outcomes. Yet, accessible digital interventions for cessation focusing on rural populations are lacking. In a secondary analysis, we determined the acceptability and efficacy of an Acceptance and Commitment Therapy (ACT)-based smartphone application (iCanQuit) relative to a U.S. Clinical Practice Guidelines (USCPG)-based smartphone application (QuitGuide) for smoking cessation among rural participants enrolled in the two-arm randomized iCanQuit trial. Participants were enrolled between May 2017 and September 2018 and randomized to either receive iCanQuit or QuitGuide for 12-months. Rural residence was determined by sub-county level Rural-Urban Commuting Area codes. A total of 550 rural participants were recruited from 43 U.S. states. Self-reported complete-case 30-day point-prevalence abstinence was 15% (33/226) for iCanQuit vs. 9% (22/253) for QuitGuide at 3-months (OR = 1.83; 95% CI: 1.03, 3.25) and 29% (66/231) for iCanQuit vs. 25% (64/288) for QuitGuide at 12-months (OR = 1.19 95% CI: 0.80, 1.79). Retention rate was 89% at 12-months and did not differ by arm. iCanQuit vs. QuitGuide participants were significantly more engaged and satisfied with the iCanQuit application. Increased acceptance of internal cues to smoke mediated the effect of treatment on cessation. Findings suggest that iCanQuit had significantly higher short-term quit rates, descriptively higher long-term quit rates, and operated through its hypothesized mechanisms of action relative to QuitGuide. Future larger studies are needed to further evaluate the efficacy of and methods for disseminating the iCanQuit application for smoking cessation among U.S. rural adults nationwide.

Trial registration

ClinicalTrials.gov Identifier: NCT02724462

Keywords: ACT, Acceptance & commitment therapy, Rural smokers, Digital interventions, Health disparities, iCanQuit, QuitGuide, Smartphone applications, Smoking cessation

1. Introduction

Despite the overall decreasing trends in cigarette smoking in the United States (U.S.), major disparities in smoking prevalence persist by education, income, race/ethnicity, sexual orientation, mental health, and active military status (U.S. Department of Health and Human Services. Smoking Cessation: A Report of the Surgeon General—Executive Summary, 2020). Rural populations are further affected by geographic disparities that limit their access to healthcare, including access to evidence-based smoking cessation programs (Drope et al., 2018; Cepeda-Benito et al., 2018). Rural populations have higher rates of cigarette smoking (Doogan et al., 2017; Roberts et al., 2016), are less likely to quit smoking than their urban counterparts, and are more likely to face the adverse health consequences of smoking (Lum et al., 2020). Considering that 19.3% of the U.S. population live in a rural area (Ratcliffe et al., 2016a), there are a substantial number of medically underserved individuals who may be at risk of smoking-related health disparities.

There are myriad reasons for rural individuals’ limited access to evidence-based smoking cessation programs, including lack of knowledge of available programs, lack of healthcare insurance or inadequate insurance coverage, and transportation-related barriers (e.g., distance to medical facilities, limited public transportation) (Drope et al., 2018; Buettner-Schmidt et al., 2019). For instance, rural Medicare beneficiaries are more likely to travel farther to receive medical care and spend more time traveling to access treatment than their urban counterparts (Larson et al., 2021a). Rural individuals who smoke represent a medically underserved population in great need of more accessible and convenient evidence-based smoking cessation programs.

The use of telemedicine for the delivery of smoking cessation programs has been evaluated as one way to help meet the unique needs of individuals living in rural areas (Mussulman et al., 2014; Richter et al., 2015). Telephone-based smoking cessation programs such as state quitlines can be generally effective, but these approaches are limited by low levels of population reach (Matkin et al., 2019). Moreover, little is known about their efficacy for rural populations. And while tobacco control policies and media campaigns could effectively reduce smoking at the population level (Buettner-Schmidt et al., 2019), the high rates of smoking in rural areas suggest that rural populations are benefiting less from these initiatives as compared to their urban counterparts, further widening the smoking disparity in this group (Cepeda-Benito et al., 2018; Doogan et al., 2017; Roberts et al., 2016).

Digital interventions, such as smartphone interventions, that are freely available, accessible, and proven to be efficacious in helping adults quit smoking can provide a timely solution for rural communities, thereby helping reduce smoking-related disparities in this group. While there may be concerns about access to smartphone interventions among rural populations, 85% of U.S. adults overall and 80% residing in rural areas reported that they own a smartphone (Pew Research Center, 2021). Moreover, reliance on smartphones for internet access is especially common among rural individuals (Pew Research Center, 2021). Fortunately, there are existing digital smoking cessation programs (Bricker et al., 2018; Taylor et al., 2017; Whittaker et al., 2019; Vidrine et al., 2019), including smartphone interventions (Bricker et al., 2020; Danaher et al., 2019; BinDhim et al., 2018), that have already demonstrated potential for population-level reach, but none, to our knowledge, have focused on the rural population of smokers in the U.S.

In addition to limited access to smoking cessation programs, rural communities have a higher density of tobacco retailers (Adibe et al., 2019) and tobacco advertisement, more permissive smoking control policies (Coughlin et al., 2020), and a higher social acceptability of smoking (Nemeth et al., 2018; Roberts et al., 2020) than urban settings (Cruz et al., 2019). All of these external factors can make quit attempts more difficult for rural individuals because they can cue internal factors that lead to smoking. Evidence-based smoking cessation treatments can provide specific skills to help offset both external and internal barriers to quit smoking by teaching smokers to accept sensations, emotions, and thoughts that trigger smoking via distancing from thoughts about smoking, mindfulness skills, and flexible perspective taking.

One such treatment is Acceptance and Commitment Therapy (ACT) (Bricker et al., 2014a; Hayes et al., 2013). In ACT, acceptance means making room for sensations, emotions, and thoughts that trigger smoking while allowing them to come and go. Commitment in ACT means articulating what is deeply meaningful to individuals (e.g., family, spirituality) to motivate stopping smoking. ACT is an evidence-based behavioral approach that has shown promise in smoking cessation as evidenced by fifteen randomized clinical trials published that compared ACT to U.S. Clinical Practice Guidelines (USCPG)-based interventions for smoking cessation (Bricker et al., 2018; Bricker et al., 2014a; Bricker et al., 2014b; Bricker, 2022), and thus could address the need for more efficacious interventions for rural adults. Importantly, this teaching of acceptance of cravings is conceptually distinct from USCPG-based approaches that teach avoidance of cravings (Fiore, 2000). This distinction is relevant here because the avoidance of cravings may be impractical in rural communities with high density of tobacco retailers, permissive smoking policies, and high social acceptability of smoking. Therefore, ACT-based interventions for smoking cessation could help individuals in rural areas to effectively cope with these external factors because they focus on increasing one’s ability to recognize and be opened to experiencing discomfort associated with cravings.

iCanQuit is an ACT-based smartphone application that is accessible and may be highly acceptable among rural populations. In a two-arm randomized clinical trial (RCT), the efficacy of iCanQuit was tested against a USCPG-based smartphone application (QuitGuide) among 2415 smokers (Bricker et al., 2020). Compared to QuitGuide, iCanQuit was found to be 1.49 times more efficacious (OR = 1.49; 95% CI: 1.22–1.83) in helping adults quit cigarette smoking by 12-months. iCanQuit is downloaded on participants’ phones, after which no internet access is required to use all components of the intervention. This has the potential to benefit rural populations. Unknown is whether iCanQuit is also efficacious in helping rural individuals who smoke quit.

In response to the complete absence of evidence-based digital interventions for smoking cessation that focus on rural individuals, this study aimed to determine the efficacy of iCanQuit relative to QuitGuide for smoking cessation among rural adults who smoke enrolled in the iCanQuit parent trial; compare treatment arms on retention rates and changes in ACT-based processes; and determine the extent to which the effect of the treatment on smoking cessation is mediated by changes in ACT-based processes. We hypothesized that compared to the QuitGuide arm, rural participants in the iCanQuit arm would have higher rates of smoking cessation, utilization and satisfaction, and mediation by greater changes in ACT-based processes.

2. Methods

2.1. Design

Data were from the iCanQuit parent trial, a two-arm RCT that tested the efficacy of an ACT-based smartphone application (iCanQuit) against a USCPG-based smartphone application for smoking cessation among 2415 daily adult cigarette smokers recruited from all 50 U.S. states (Bricker et al., 2020). Participants were randomized 1:1 to receive iCanQuit or QuitGuide for 12-months. Neither research staff nor study participants had access to randomized group assignments. For blinding, each application was branded as “iCanQuit” and did not mention either ACT or QuitGuide. Eligibility criteria included smartphone access, 5 or more cigarettes smoked per day for the past year, wanting to quit smoking in the next 30 days, and being able to read English. Exclusion criteria included receiving smoking cessation treatment, having used QuitGuide in the past, or having a household member already enrolled in the study. The use of e-cigarettes or smokeless tobacco was not part of the exclusion criteria. Study procedures were approved by the Fred Hutchinson Cancer Research Center Institutional Review Board. For each follow-up, participants received $25 for completing the follow-up survey and an additional $10 if the online survey was completed within 24 h of the email invitations to complete the survey.

The selected population in this secondary analysis consisted of daily smokers enrolled in the iCanQuit parent RCT with U.S. rural residence determined by Rural-Urban Commuting Area (RUCA) codes that classify U.S. Census tracts based on population density, urbanization, and daily commuting (US Department of Agriculture, Economic Research Service, 2020). RUCA codes distinguish metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. RUCA uses whole numbers from 1 to 10 to represent transition from metropolitan area core (1) to micropolitan area core (4), to small town core (7), and finally to rural area (10). RUCA codes of 1–3 were considered urban, while RUCA codes of 4–10 were considered rural (Larson et al., 2021b; Ratcliffe et al., 2016b; Chen et al., 2019; Unger et al., 2018). Using this definition of rural residence, a total of 550 rural daily adult smokers were included in the present study (550/2415, 22.8%). Trial participants with rural residence were recruited via Facebook ads (456/550, 83%), a survey sampling company (77/550, 14%), search engine (11/550, 2%), and word of mouth (6/550, 1%).

2.2. Interventions

2.2.1. iCanQuit

The iCanQuit smartphone application (version 1.2.1) teaches ACT skills for coping with smoking urges, staying motivated, and preventing relapse (Bricker et al., 2020). The content is delivered in eight levels. The first four levels contain content and exercises designed to prepare the users for their chosen quit day. Level One, ‘Becoming an Urge Expert’, introduces the main features of the application and introduces a fictional tobacco treatment “guide” who specializes in helping people quit smoking. The guide navigates the user through the application and teaches ACT skills for accepting cravings to smoke. Levels Two to Four contain exercises teaching skills to accept triggers to smoke. The last four levels contain content and exercises designed to help the user stay smoke-free after their quit date. All levels contain at least one “user story” (testimonial) presented by fictitious individuals who quit smoking, about how they overcome common challenges, and how quitting has helped them live more meaningful lives. The program is self-paced, and content is unlocked in a sequential manner. If a participant lapses, the program encourages them to set a new quit date and return to the first levels for preparation. iCanQuit did not contain content specifically tailored to rural smokers.

2.2.2. QuitGuide

The USCPG-based QuitGuide smartphone application (version 1.2.2) focuses on increasing motivation to quit by using reason and logic and providing information on the health consequences of smoking. The application helps users develop a quit plan, identify smoking behaviors, triggers, and reasons for being smoke-free, and identify sources of social support for quitting. QuitGuide teaches skills for avoiding cravings to smoke and presents tips to stay smoke-free and skills for coping with slips. Full details on both interventions have been previously published (Bricker et al., 2020).

Both interventions provided information on U.S. Food and Drug Administration-approved medications for quitting smoking but did not provide any pharmacotherapy. No coaching, or other interventions, were provided in either arm. Similar to real-world use of smartphone applications, participants could reach out to our staff for technical support though this occurred very rarely. Both applications primarily use text to explaini concepts and teach coping skills and provide “push” notifications that the user could turn on and off depending on their preferences. For the interested reader, see Table 1 of the iCanQuit trial’s main outcome paper for a comparison of the two applications in terms of their modes of communicating information (Bricker et al., 2020).

Table 1.

Baseline socio-demographic characteristics of rural trial participants.

Characteristic n No. (%) or mean (SD)
 P value
Overall (N = 550) QuitGuide (n = 288) iCanQuit (n = 262)
Age, mean (SD) 550 38.4 (10.8) 38.1 (10.9) 38.8 (10.8) 0.443
Male 550 128 (25%) 65 (23%) 73 (28%) 0.183
Race 547 0.569
 White 547 419 (77%) 218 (76%) 201 (77%)
 Black or African American 547 66 (12%) 34 (12%) 32 (12%)
 Multiracial 547 39 (7%) 22 (8%) 17 (7%)
 American Indian or Alaska Native 547 20 (4%) 12 (4%) 8 (3%)
 Asian 547 2 (<1%) 0 (0%) 2 (1%)
 Native Hawaiian or Pacific Islander 547 1 (<1%) 1 (<1%) 0 (0%)
Hispanic or Latino ethnicity 550 28 (5%) 13 (5%) 15 (6%) 0.652
Income 0.629
 <$20,000/year 550 204 (37%) 103 (36%) 101 (39%)
 $20,000 – $54,499/year 550 270 (49%) 147 (49%) 123 (47%)
 ≥$55,000/year 550 76 (14%) 38 (13%) 38 (15%)
Education 0.582
 Less than GED or high school education 550 42 (8%) 26 (9%) 16 (6%)
 GED 550 80 (15%) 39 (14%) 41 (16%)
 High school diploma 550 125 (23%) 67 (23%) 58 (22%)
 Some college, no degree 550 199 (36%) 106 (37%) 93 (35%)
 College degree or higher 550 104 (19%) 50 (17%) 54 (21%)
Employment status 0.175
 Employed 550 289 (53%) 155 (54%) 134 (51%)
 Unemployed 550 63 (11%) 25 (9%) 38 (15%)
 Disabled 550 88 (16%) 46 (16%) 42 (16%)
 Out of labor force 550 110 (20%) 62 (22%) 48 (18%)
Marrieda 550 191 (35%) 99 (34%) 92 (35%) 0.926
LGBT 550 75 (14%) 41 (14%) 34 (13%) 0.760
Mental health positive screening results
 Depressionb 548 274 (50%) 147 (51%) 127 (49%) 0.608
 Panic disorderc 539 145 (27%) 84 (30%) 61 (24%) 0.167
 Posttraumatic stress disorderd 548 254 (46%) 132 (46%) 122 (47%) 0.928
Alcohol use
 Heavy drinkere 535 79 (15%) 39 (14%) 40 (16%) 0.627
 No. of drinks/drinking day, mean (SD) 535 1.8 (4.2) 1.4 (3.1) 2.1 (5.1) 0.058
Smoking behavior
 FTND score, mean (SD) 550 6.1 (2.0) 6.0 (2.1) 6.2 (1.8) 0.170
 High nicotine dependence (FTND score ≥6) 550 358 (65%) 181 (63%) 177 (68%) 0.286
 Smokes more than one-half pack/d 550 456 (83%) 240 (83%) 216 (82%) 0.870
 Smokes more than 1 pack/d 550 144 (26%) 76 (26%) 68 (26%) 0.985
 First cigarette within 5 min of waking 550 313 (57%) 152 (53%) 161 (61%) 0.049
 Smoked for ≥10 years 550 468 (85%) 240 (83%) 228 (87%) 0.274
 Used e-cigarettes at least once in past month 550 124 (23%) 64 (22%) 60 (23%) 0.930
 Quit attempts in past 12-months, mean (SD) 525 1.2 (4.6) 1.2 (2.8) 1.2 (6.0) 0.933
 Confidence to quit smoking, mean (SD)f 550 64.1 (26.2) 64.3 (26.2) 64.0 (26.2) 0.856
 Friend and partner smoking
  Close friends who smoke, mean (SD) 550 2.9 (1.8) 2.8 (1.7) 3.0 (1.8) 0.100
  No. of housemates who smoke, mean (SD) 550 1.5 (0.8) 1.5 (0.9) 1.5 (0.7) 0.987
  Living with partner who smokes 550 238 (43%) 117 (41%) 121 (46%) 0.220
ACT-based measures
 Acceptanceg, mean (SD)
  Physical sensations 547 3.1 (0.6) 3.1 (0.6) 3.1 (0.5) 0.648
  Emotions 549 2.9 (0.5) 2.9 (0.5) 2.9 (0.4) 0.462
  Thoughts 550 2.9 (0.5) 2.9 (0.5) 2.8 (0.4) 0.689
  Acceptance mean score 527 2.9 (0.4) 3.0 (0.4) 2.9 (0.4) 0.523
Valued livingh, mean (SD)
 Progressi 549 18.8 (7.5) 18.9 (7.5) 18.8 (7.4) 0.852
 Obstructionj 546 11.8 (8.5) 11.6 (8.6) 12.0 (8.3) 0.620
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Abbreviations: ACT, Acceptance and Commitment Therapy; FTND, Fagerström Test for Nicotine Dependence; GED, General Education Development; LGBT, lesbian, gay, bisexual, or transgender; PTSD, Posttraumatic Stress Disorder.

a

“Married” refers to the current legal marital status.

b

Positive screening results for depression via the Center for Epidemiological Studies Depression Scale (CESD-20; cutoff ≥16).

c

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).

d

Positive screening results for PTSD via the six-item PTSD checklist (PCL-6; scores of ≥14 indicate a positive screen).

e

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.

f

Range, 0–100, where 0 indicates not at all confident and 100 indicates extremely confident.

g

Avoidance and inflexibility scale. Range is 1 to 5. Higher scores indicate greater acceptance.

h

Valuing questionnaire.

i

Range is 0 to 30. Higher scores indicate greater progression towards one’s values.

j

Range is 0 to 30. Higher scores indicate greater obstruction of one’s values.

2.3. Measures

Participants’ baseline characteristics were collected via online questionnaires. Socio-demographic data included information on geographic location, age, gender, race/ethnicity, education, employment, income, marital status, and sexual orientation. Positive screening results for depression, panic, and posttraumatic stress disorders were assessed via the Center for Epidemiological Studies Depression Scale (cutoff ≥16) (Radloff, 1977a), the 5-item Autonomic Nervous System Questionnaire (reporting ≥1 panic attack within the past month indicates a positive screen) (Stein et al., 1999), and the six-item PTSD Checklist (scores of ≥14 indicate a positive screen), respectively (Lang et al., 2012). Alcohol consumption was assessed via the Quick Drinking Screen (Roy et al., 2008). Smoking behavior variables included nicotine dependence as measured by the Fagerström Test for Nicotine Dependence (FTND) (Heatherton et al., 1991), number of cigarettes smoked per day, years smoking, use of e-cigarettes at least once in past month, quit attempts during the past 12-months, confidence in quitting smoking, and close relationships with other smokers.

Smoking cessation outcomes were measured at the 3, 6 and 12-month follow-ups. The primary smoking cessation outcome was specified a priori as self-reported complete-case 30-day point-prevalence abstinence (PPA) at 12-months. Secondary smoking cessation outcomes were: 7- and 30-day PPA at the 3, 6 and 12-months, intent-to-treat missing as smoking 30-day PPA, and prolonged abstinence and cessation of all nicotine/tobacco products including e-cigarettes and smokeless tobacco at 12-months only.

ACT-based processes, including acceptance of internal cues to smoke and valued living, were measured using the Avoidance and Inflexibility Scale (AIS) (Farris et al., 2015) and Valuing Questionnaire (Smout et al., 2014), respectively. The AIS includes three subscales that assess willingness to experience sensations, emotions, and thoughts that cue smoking. The 27 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. The Valuing Questionnaire (Smout et al., 2014) assesses the extent of personal values enactment. Each item is rated on a 7-point scale ranging from (0) “Not at all true” to (6) “Completely true.” Items were summed with higher scores indicating greater progress or greater obstruction towards valued living.

Treatment engagement was objectively measured by Google Analytics (number of times the application was opened, the time spent per session, and the number of unique days of use). An 11-item measure of satisfaction with the intervention, adapted from previous research (Bricker et al., 2018; Bricker et al., 2014a), was completed at the 3-month follow-up.

2.4. Statistical analysis

Baseline characteristics were compared between arms using Fisher’s exact tests for categorical variables and t-tests for continuous variables. Zip codes were tied to geographic location using the R library ‘zipcode’ (zipcode: U.S., 2012) and were categorized as urban or rural using RUCA codes (US Department of Agriculture, Economic Research Service, 2020). Logistic regression models were used to compare binary smoking cessation outcomes, retention rates, and satisfaction outcomes between arms. Generalized linear models were used to compare changes from baseline to 3-months in ACT-based processes and time spent using the app. Right-skewed count outcomes (e.g., number of logins) were compared using negative binomial models. All models were adjusted for factors used in stratified randomization to avoid losing power and obtaining incorrect 95% confidence intervals (Kernan et al., 1999) including daily smoking frequency (≤20 vs. ≥21 cigarettes/day), non-White race, and Hispanic or Latino ethnicity, education level (≤high school vs. ≥some college), and positive screening for depression (CESD-20 score ≤15 vs. ≥16) (Radloff, 1977b). Hayes’ PROCESS macro for SAS was used to test mediation of the effect of treatment on cessation by changes in ACT-based processes between baseline and 3-months (Hayes, 2018). Indirect effects were estimated with 5000 bootstrapped samples and were considered statistically significant when bias-corrected 95% confidence intervals did not include zero. All statistical tests were 2-sided, with α = 0.05. Regression analyses were completed using R, version 4.0.3, and library ‘MASS’ for negative binomial regression (R Core Team, 2020; Venables and Ripley, 2002).

3. Results

3.1. Enrollment and data retention

Of 12,881 screened, 6559 were eligible to participate and 3470 provided informed consent (Fig. 1). Of those, 1945 were urban residents. Among 558 randomized rural residents, 293 received the iCanQuit application and 265 received the QuitGuide application for 12-months. Of 558 individuals randomized, 8 (1.4%) were further excluded because another household member was already enrolled in the study, or they enrolled twice (duplicate). Retention was high, with 87%, 89%, and 89% of trial participants residing in rural areas providing study data at the 3, 6, and 12-month follow-ups, respectively, and no differential rates between arms (all p > 0.05).

Fig. 1.

Fig. 1.

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CONSORT Diagram.

aTo increase enrollment of racial/ethnic minorities and men, some nonminorities and women that were eligible for study enrollment were randomly selected to be excluded.

3.2. Participants characteristics

The proportion of participants with rural residence in the iCanQuit parent study (550/2415, 22.8%) was slightly higher than the U.S. population at 19.3% (Ratcliffe et al., 2016c). Rural trial participants were recruited from 43 U.S. states as shown in Fig. 2. Participants were an average 38.4 (SD = 10.8) years old and 25% were male (Table 1). More than half of participants reported some college or higher education (55%), annual income of >$20,000 (63%) and being employed (53%). Half of participants (50%) had elevated symptoms for depression, 27% for panic disorder and 46% for PTSD. Most participants (85%) reported smoking for at least 10 years and 43% reported living with a partner who smokes. More iCanQuit vs. QuitGuide participants reported having a cigarette within 5 min of waking (p = 0.049). As this baseline factor was not predictive of the primary outcome, it was not included in covariate-adjusted analyses (Pocock et al., 2015). Furthermore, a more comprehensive measure of nicotine dependence, indicated by an FTND score of 6 or higher, did not significantly differ between arms (iCanQuit 68% vs. QuitGuide 63%).

Fig. 2.

Fig. 2.

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Geographic Locations of Rural Trial Participants.

3.3. Engagement and satisfaction

Treatment engagement and satisfaction results are shown in Table A.2. Full engagement data up to 12-months was not available due to a technical error by Google Analytics. For this reason, we report engagement for participants with full 6 months of data (98%). iCanQuit vs. QuitGuide participants opened the application on more occasions (32.1 vs. 7.7 times, p < 0.001), spent more time per session (4.4 vs. 2.6 min, p < 0.001), and used the application on more days (18.6 vs. 5.9 days, p < 0.001). Treatment satisfaction was significantly higher among iCanQuit vs. QuitGuide participants (88% vs. 75%, p < 0.001). iCanQuit vs. QuitGuide participants found the application more useful for quitting (80% vs. 69%, p = 0.006), and were more likely to recommend the application (83% vs. 74%, p = 0.023), and to report they felt the application was made for them (83% vs. 67%, p < 0.001).

3.4. Quit rates

The self-reported, complete-case 30-day PPA was 29% (66/231) for iCanQuit vs. 25% (64/261) for QuitGuide participants at 12-months (OR = 1.19 95% CI: 0.80, 1.79), 25% vs. 18% at 6-months (OR = 1.47 95% CI: 0.94, 2.28), and 15% vs. 9% at 3-months (OR = 1.83 95% CI: 1.03, 3.25) (Table 2). The results for 7-day PPA followed a similar pattern at all timepoints. Rates of prolonged abstinence at 12-months were 15% for iCanQuit vs. 10% for QuitGuide (OR = 1.66; 95% CI: 0.90, 3.06). The 12-month 30-day PPA for cessation from all tobacco products, including e-cigarettes and smokeless tobacco, was 26% for iCanQuit vs. 19% for QuitGuide (OR = 1.47 95% CI: 0.96, 2.27). Missing as smoking quit rates are presented in Table A.1 of the Appendix A.

Table 2.

Smoking cessation outcomes by follow-up time pointa,b.

Variable No. (%)
 OR (95% CI) p value
Overall (N = 550) QuitGuide (n = 288) iCanQuit (n = 262)
12-months outcomes
 30-d PPA 130/492 (26%) 64/261 (25%) 66/231 (29%) 1.19 (0.80, 1.79) 0.391
 7-d PPA 162/492 (33%) 80/261 (31%) 82/231 (35%) 1.22 (0.83, 1.78) 0.308
 Prolonged abstinencec 49/398 (12%) 21/213 (10%) 28/185 (15%) 1.66 (0.90, 3.06) 0.105
 30-d PPA of all tobacco productsd 111/492 (23%) 50/261 (19%) 61/231 (26%) 1.47 (0.96, 2.27) 0.079
6-months outcomes
 30-d PPA 103/488 (21%) 46/256 (18%) 57/232 (25%) 1.47 (0.94, 2.28) 0.089
 7-d PPA 151/488 (31%) 70/256 (27%) 81/232 (35%) 1.41 (0.96, 2.08) 0.081
3-months outcomes
 30-d PPA 55/479 (11%) 22/253 (9%) 33/226 (15%) 1.83 (1.03, 3.25) 0.041
 7-d PPA 100/479 (21%) 42/253 (17%) 58/226 (26%) 1.79 (1.14, 2.81) 0.012
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Abbreviations: OR, odds ratio; PPA, point prevalence abstinence.

a

All models include the following covariates: Education (high school diploma or less), heavy smoking (>20 cigs/day), non-White race, Hispanic or Latino ethnicity and depression symptoms (CESD-20 ≥16).

b

All outcomes are complete case (i.e., exclusion of participants lost to followup) was specified a priori as the primary outcome, except where noted. Missing as smoking quit rates are presented in Table A.1 of the Appendix A.

c

Defined as no smoking since 3-months post-randomization, using selfreported data of last cigarette.

d

Including e-cigarettes and smokeless tobacco.

3.5. Mediation of acceptance of cues to smoke

Mediation results of treatment effect on cessation are shown in Table 3. Baseline to 3-month increases in the mean acceptance score, including sensations, emotions, and thoughts that cue smoking, were significantly greater among iCanQuit vs. QuitGuide participants. Subscales for acceptance of sensations and emotions were significantly greater among iCanQuit vs. QuitGuide participants (p < 0.05 both). Acceptance of thoughts did not differ between arms, nor did change in progress and obstruction of valued living (p >0.05 for all). Baseline to 3-month increases in acceptance of sensations (indirect effect = 0.19; 95% CI: 0.05, 0.36; p < 0.001), emotions (indirect effect = 0.24; 95% CI: 0.04, 0.48; p < 0.001), and mean acceptance (indirect effect = 0.22; 95% CI: 0.03, 0.44; p < 0.001) mediated the relationship between treatment and cessation at 12 months. There was no evidence for mediation by acceptance of thoughts that cue smoking.

Table 3.

Mediation (indirect effect) of change in ACT-based processes from baseline to 3-months of the effect of treatment on cessation outcomesa,b.

Mediators n Change from baseline to 3-months Mean (SD)
 Estimate of indirect effect (95% CI) for cessation outcomed
Overall (N = 550 QuitGuide (n = 288 iCanQuit (n = 262 Point Estimatec (95% CI) p value
Acceptance of internal cues to smokee
 Sensations 465 0.15 (0.70) 0.07 (0.67) 0.24 (0.72) 0.15 (0.04, 0.26) 0.008 0.19 (0.05, 0.36)*
 Emotions 469 0.16 (0.66) 0.09 (0.56) 0.23 (0.74) 0.12 (0.02, 0.23) 0.021 0.24 (0.04, 0.48)*
 Thoughts 472 0.13 (0.63) 0.13 (0.56) 0.13 (0.69) 0.02 (−0.08, 0.12) 0.692 0.05 (−0.09, 0.20)
 Mean score 464 0.14 (0.57) 0.09 (0.50) 0.20 (0.63) 0.10 (0.01, 0.20) 0.028 0.22 (0.03, 0.44)*
Valued livingf
 Progressg 472 −0.48 (7.49) −0.59 (7.62) −0.36 (7.36) 0.20 (−0.98, 1.37) 0.744 0.01 (−0.04, 0.08)
 Obstructionh 466 0.36 (8.41) 0.20 (8.20) 0.54 (8.65) 0.68 (−0.60, 1.95) 0.300 −0.01 (−0.06, 0.02)
Open in a new tab

Abbreviations: ACT, Acceptance and Commitment Therapy.

a

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).

b

All changes in acceptance and valued living scores calculated as follow-up at 3-months minus baseline.

c

Point estimate is for the difference between treatment arms in the change from baseline to 3-month follow-up.

d

Estimation of mediation effect (indirect effect) for the PROCESS macro is greater than 0.05 if the bootstrap CI contains 0.

e

Avoidance and inflexibility scale. Range in change is −4 to 4. Positive scores indicate higher acceptance at follow-up.

f

Valuing questionnaire. Range in change is −30 to 30.

g

Positive scores of valued living progress indicate improvement.

h

Positive scores valued living obstruction indicate worse condition.

*

P < 0.05.

4. Discussion

This study provides evidence on the acceptability and efficacy of smartphone applications for smoking cessation among U.S. rural smokers who wanted to quit smoking in the next 30 days. Participants in the iCanQuit arm were significantly more engaged and satisfied with the iCanQuit application relative to the QuitGuide arm. Complete-case quit rates were 29% for iCanQuit vs. 25% for QuitGuide participants at 12-months, and 25% vs. 18% at 6-months. Although quit rates were descriptively higher for iCanQuit vs. QuitGuide participants at 12 and 6-months, they did not significantly differ between arms (p > 0.05 for both). The 12-month quit rates are comparable to the iCanQuit parent RCT (28% iCanQuit vs 21% QuitGuide) (Bricker et al., 2020). Cessation of all tobacco products, including e-cigarettes and smokeless tobacco trended higher at 6-months and quit rates at 3-months were higher for iCanQuit vs. QuitGuide participants (15% vs. 9%, p = 0.04). Increased acceptance of internal cues to smoke mediated the effect of treatment on cessation.

This is the first study, to our knowledge, to report on the acceptability and efficacy of smartphone applications for smoking cessation focused on rural individuals. For the general population of smokers, iCanQuit is the only smartphone application to date that has proven efficacy for smoking cessation in a full-scale randomized trial with long term follow-up (Bricker et al., 2020). For rural smokers, while iCanQuit’s 29% 12-month quit rate was not significantly higher than the 25% 12-month quit rate of QuitGuide, the absolute difference of 4% could have significant public health impact when scaled across a large population of rural adults who smoke: small clinical effects can have large population-level effects especially when an intervention can be broadly scaled (West, 2007).

To put these quit rates in context of prior technology interventions for smoking cessation among rural individuals, the only known comparison is a study that tested telemedicine vs. telephone coaching for smoking cessation among 566 rural smokers (Richter et al., 2015). The 12-month biochemically confirmed quit rates were 9.8% for telemedicine vs. 12% for telephone coaching (p > 0.05). Although difficult to compare because of iCanQuit’s self-reported outcome, the current trial’s 29% 12-month quit was more than double than that observed in this trial’s telephone coaching arm, and as a stand-alone technology intervention, iCanQuit would cost less to deliver if disseminated to rural residents who smoke (Cobos-Campos et al., 2021; Smit et al., 2013; Chen et al., 2012).

Consistent with the theoretical model underlying ACT, increases in ACT-based acceptance of internal cues to smoke mediated the effect of treatment on cessation. These analyses showed that ACT-based processes in the iCanQuit arm helped rural individuals quit smoking via increases in acceptance of internal sensations and emotions, but not acceptance of thoughts that cue smoking. Although acceptance of thoughts is an important ACT-based theoretical pathway of smoking cessation, in this study it did not mediate the effect of treatment on cessation. If replicated, these findings suggest a potential point for theory refinement in applying the ACT model to smoking cessation in future trials. Results also showed that valued living measures did not mediate the effect of treatment on cessation. Since the Valuing Questionnaire (Smout et al., 2014) used in the study is not a smoking-specific measure, it is possible that it lacks the specificity to capture changes in values related to quitting smoking. Future trials should examine values with validated tools specific to the given behavioral content area. These findings suggest that providing acceptance skills to help offset both environmental and interpersonal barriers to quit smoking might help adults living in rural areas successfully quit smoking.

There are several study strengths. First, this is the first U.S. study, to our knowledge, to test the acceptability and efficacy of smartphone applications for smoking cessation among rural individuals. Second, the study showed high acceptability of smartphone applications for smoking cessation with potential for broad reach in rural populations, as demonstrated by high treatment engagement and satisfaction in a nationwide sample of rural adults from 43 U.S. states. Third, retention rates were high, with 89% of participants retained at 12-months. Fourth, smokeless tobacco use, which is highly prevalent among rural males (Roberts et al., 2017), was assessed and accounted for in the use of all tobacco products cessation outcome. Finally, the sample demographics were highly comparable to national samples of U.S. rural smokers (Cepeda-Benito et al., 2018; Doogan et al., 2017) and reflective of the high percentage of low-income non-Hispanic Whites residing in the rural U.S. (James et al., 2017)

The study also has limitations. First, the applications were not tailored to rural smokers. Second, the results may not be representative of the general population of adults who smoke cigarettes, as the current trial included a higher proportion of women and participants with mental health conditions. However, the higher fraction of women is consistent with them being more interested in quitting smoking and more likely to use counseling when trying to quit (Babb et al., 2017). Moreover, both women and people with mental health problems are high priorities for intervention development given their low quit smoking success rates (Smith et al., 2016; Cougle et al., 2010; Lasser et al., 2000; McCabe et al., 2004). Third, smoking status was not biochemically verified. As previously reported, the self-reported outcome in the parent trial was prespecified based on methodological issues with remote biochemical verification: (1) high attrition, (2) difficulty with identifying the person providing the sample, and (3) high-cost relative to the prospect of falsifying abstinence in low-contact interventions (Herbec et al., 2019; Thrul et al., 2018). Furthermore, previous population-based studies have validated self-reported data against biochemically verified smoking status (van der Aalst and de Koning, 2016; Wong et al., 2012), and expert consensus suggests that biochemical verification is unnecessary in remote population-based studies (Benowitz et al., 2020). However, it is also important to acknowledge the existing evidence on discordance between self-reported and biochemical validation of smoking status (Patrick et al., 1994). Given the double blinding of the trial, there is no compelling reason why rates of falsifying abstinence would differ between arms. Lastly, full utilization data up to 12-months was not available due to an error by Google Analytics. Because participants were unaware of the error, the missing data are unlikely to change the validity of the results (Graham, 2009).

5. Conclusion

This study provided evidence on the acceptability and efficacy of smartphone applications for smoking cessation among rural smokers in the U.S. Findings suggest that iCanQuit was more engaging and satisfying, had significantly higher short-term quit rates than QuitGuide, descriptively higher long-term quit rates, and operated through its hypothesized mechanisms of action. The findings suggest ACT-based smartphone interventions have the potential for broad reach among rural smokers, and thereby potential for making an impact in underserved rural populations. Future larger studies are needed to further evaluate the efficacy of and methods for disseminating the iCanQuit smartphone application for smoking cessation among U.S. rural smokers nationwide.

Supplementary Material

Supplementary data

Acknowledgments

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 sources

This study was funded by grant R01CA192849, awarded to Dr. Bricker, from the National Cancer Institute and registered in ClinicalTrials.gov (NCT02724462).

Abbreviations:

ACT

Acceptance and commitment therapy

CI

95% confidence interval

FTND

Fagerström test for nicotine dependence

GED

General education development

LGBT

Lesbian, gay, bisexual, or transgender

OR

Odds ratio

PPA

Point-prevalence abstinence

PTSD

Posttraumatic stress disorder

RCT

Randomized clinical trial

RUCA

Rural-urban commuting area

USCPG

United States clinical practice guidelines

U.S.

United States

Footnotes

Declaration of Competing Interest

No financial disclosures were reported by the authors of this paper.

Financial disclosures

No financial disclosures were reported by the authors of this paper. None of the authors have a financial interest in the iCanQuit smartphone application.

CRediT authorship contribution statement

Margarita Santiago-Torres: Conceptualization, Writing – original draft. Kristin E. Mull: Conceptualization, Formal analysis, Writing – review & editing. Brianna M. Sullivan: Writing – review & editing. Amy K. Ferketich: Writing – review & editing. Jonathan B. Bricker: Conceptualization, Writing – review & editing, Funding acquisition.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ypmed.2022.107008.

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