Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Aug 1.
Published in final edited form as: Addict Behav. 2025 Apr 9;167:108354. doi: 10.1016/j.addbeh.2025.108354

Exploring relationships among smoking cessation app use, smoking behavioral outcomes, and pharmacotherapy utilization among individuals who smoke cigarettes

Schuyler C Lawson a,*, Karin Kasza b, RLorraine Collins c, Richard J O’Connor b, Gregory G Homish c
PMCID: PMC12128583  NIHMSID: NIHMS2081979  PMID: 40209664

Abstract

Introduction:

Most individuals who smoke cigarettes are interested in quitting, but many are unable to quit. Fewer than one-third of individuals who smoke cigarettes attempt to quit using FDA-approved cessation methods, such as nicotine replacement therapy (NRT) and prescription medications. Smoking cessation apps (SCAs) provide individuals with personalized quit plans, information about smoking cessation treatments, craving management strategies, and other features. However, their relationship to NRT/prescription medication utilization and quit attempts is understudied.

Methods:

We conducted a longitudinal secondary data analysis using a subset of adults who smoked at least 100 cigarettes in their lifetime, currently smoked every day or some days, and planned to quit within a year. This subset was drawn from the Population Assessment of Tobacco and Health (PATH) Study, a nationally representative cohort study. We utilized Generalizing Estimating Equation models to examine the longitudinal associations between SCA use initiation and the following outcomes across 2014–2019: NRT, prescription medications, and quit attempts.

Results:

SCA use initiation was associated with greater odds of prescription medication utilization (AOR = 2.43, 95 % CI: 1.63, 3.64; p < 0.05). Likewise, SCA use initiation was associated with greater odds of making a quit attempt (AOR = 1.38, 95 % CI: 1.09, 1.76; p < 0.01), but not NRT utilization.

Conclusion:

Among adults who regularly smoked cigarettes and had plans to quit, SCA use initiation was associated with prescription medication utilization and quit attempts but not NRT utilization. SCAs may have utility as a population-level intervention but specific features needed to be studied further.

Keywords: Smoking cessation apps, NRT, Prescription medications, PATH, Longitudinal

1. Introduction

Despite decades of declining smoking prevalence since the landmark publication of the 1964 Surgeon General’s report, smoking continues to be the leading cause of preventable death in the United States (HHS, 2020). It is estimated that approximately 480,000 deaths per year are attributable to cigarette smoking and secondhand smoke exposure (HHS, 2020; Rostron, 2013). Over a third of those deaths are people with mental illnesses, who tend to have a high prevalence of smoking relative to the general population (Prochaska et al., 2017). According to the Surgeon General’s Office, the economic toll of cigarette smoking exceeds $170 billion in the United States each year (HHS, 2020).

Most individuals who smoke are interested in quitting (Babb et al., 2017). Still, fewer than one-third of individuals who smoke cigarettes attempt to quit using FDA-approved smoking cessation pharmacotherapies (e.g., nicotine replacement therapy and prescription medications) (Babb et al., 2017; HHS, 2020). Notably, the most common quitting method is ‘cold turkey’ (i.e., unaided quit attempts), even though fewer than one in 10 individuals who smoke achieve smoking cessation (i.e., having discontinued smoking for one day or longer with the intention of quitting; (Babb et al., 2017; HHS, 2020). This is worrisome because smoking cessation confers health benefits to all individuals who smoke, regardless of age (HHS, 2020). Furthermore, the underutilization of FDA-approved smoking cessation pharmacotherapies is inconsistent with the US Public Health Service’s Clinical Practice Guideline on Treating Tobacco Use and Dependence, which recommends improving smoking cessation rates via evidence-based treatment modalities (Fiore et al., 2008; HHS, 2020). While substantial evidence suggests these smoking cessation medications are associated with increasing quit rates in individuals who smoke, bolstering their utilization remains an unrealized public health goal (Cox et al., 2011; HHS, 2020; Schlam & Baker, 2013). From a health equity standpoint, this is also concerning given that the rates of smoking cessation pharmacotherapy utilization are even lower among people from marginalized populations, such as African Americans, Hispanic/Latinx Americans, and American Indians/Alaska Natives relative to White Americans due to factors such as financial barriers and medical mistrust (Fu et al., 2007; Fu et al., 2008; Fu et al., 2014; Fu et al., 2005; Scharff et al., 2010). Consequently, these populations experience disproportionately higher rates of smoking-related morbidity and mortality relative to White Americans (Agaku et al., 2020; Braveman et al., 2010; Gone & Trimble, 2012; HHS, 2020; Martell et al., 2016; Mowery et al., 2015).

Smoking cessation apps (SCAs) are computer programs available on mobile devices, such as tablets and smartphones, designed to aid in smoking cessation (Abroms et al., 2013). SCAs are heterogeneous, variously incorporating features such as cigarette trackers, geofencing, educational materials, behavioral therapies, and gamification to maintain smoking cessation progress (Abroms et al., 2013; Abroms et al., 2011; Portelli et al., 2022; Robinson et al., 2020; Seo et al., 2022). Specifically, 29 % of SCAs are only informational (i.e., similar to an electronic book or e-Book), 30 % are multifunctional (i.e., they have assistive features to facilitate smoking cessation), and 42 % are a combination of informational and multifunctional (Seo et al., 2022).

In the past decade, there has been an influx of SCAs in the marketplace (Abroms et al., 2013; Abroms et al., 2011; Seo et al., 2022). As of 2020, over one hundred SCAs were available in the Google Play and iPhone App Stores (Seo et al., 2022); 62 % were free, 24 % were initially free with in-app purchases for additional features, and 14 % required upfront payment (Seo et al., 2022). In addition to the commercial options, the National Cancer Institute created two free SCAs (QuitGuide and quitSTART) integrated with their Smokefree.gov initiative (Prutzman et al., 2021). SCAs are a potentially accessible method of disseminating smoking cessation interventions at the population level, given that American smartphone usage is increasingly commonplace (Heffner & Mull, 2017; Smith et al., 2015).

Feasibility/pilot studies of specific SCAs have shown high ratings in usability, acceptability, engagement, and likability (Bricker et al., 2017; Gowarty et al., 2021; Iacoviello et al., 2017; Marler et al., 2019). However, randomized clinical trial (RCT) data regarding the association between SCA utilization and smoking abstinence are mixed. Some studies noted higher rates of smoking abstinence relative to various comparators (e.g., usual care, self-help booklets, web-based interventions, other apps, smoking cessation counseling, or placebos that lack functionality) (BinDhim et al., 2018; Bricker et al., 2022; Bricker et al., 2020; Carrasco-Hernandez et al., 2020; Houston et al., 2022; Masaki et al., 2020; Webb et al., 2022), whereas other studies did not find any significant differences (Affret et al., 2020; Baskerville et al., 2018; Etter & Khazaal, 2022; Garrison et al., 2020). A recent systematic review/meta-analysis of 9 RCTs conducted between 2017 and 2023, which were described as high-quality by Guo and colleagues (2023), did not support the effectiveness of SCAs as standalone interventions. Still, it noted that their effectiveness was augmented when used with pharmacotherapies (Guo et al., 2023). Specifically, participants using SCAs with pharmacotherapy (3 studies, N = 1342) reported greater rates of smoking abstinence relative to participants who only received pharmacotherapy. SCAs have the potential to be utilized as a population-level intervention, but a lack of population-level studies limits the current literature. Population-level studies are important to consider given that RCTs tend to be limited by samples that are not always generalizable due stringent eligibility criteria and other barriers that prevent underrepresented populations from enrolling in the studies (King et al., 2011; Scharff et al., 2010; Webb Hooper et al., 2019). This limitation must be addressed to more clearly understand if this type of intervention is applicable to the general population, especially people from marginalized populations. Furthermore, many apps available in the iPhone and Android marketplace remained unstudied.

Using population-level data, the present study aimed to examine relationships among SCA use initiation, pharmacotherapy utilization, and quit attempts longitudinally in individuals who regularly smoke cigarettes. The specific aims are to longitudinally examine, among individuals who regularly smoke cigarettes, the relationship between 1) SCA use initiation and NRT utilization, 2) SCA use initiation and prescription medication utilization, and 3) SCA use initiation and quit attempts.

2. Methods

2.1. Study design

The Population Assessment of Tobacco and Health (PATH) Study is a nationally representative, longitudinal cohort study of US adults (18 years and older) and youth (12–17 years old) designed to examine tobacco use and health to inform tobacco control policies (USDHHS, 2022). This study utilizes data from the publicly available Wave 2 (October 2014-October 2015), Wave 3 (October 2015-October 2016), Wave 4 (December 2016-January 2018), and Wave 5 (December 2018-November 2019) surveys from a weighted sample of 13,862 adult participants. The weighting is designed to account for different probabilities of non-response, selection, and possible limitations in the sampling frame (e.g., underrepresentation of certain populations). PATH Study participants were recruited using an address-based, area-probability sampling approach. This recruitment method also included using an in-person household screener to select adults from households that oversampled adult tobacco users, young adults, and African American adults. Sample weighting procedures were employed to adjust for oversampling and nonresponse, enabling estimates to be representative of the non-institutionalized civilian US population.

After obtaining participant consent, data were collected via Audio-Computer Assisted Self-Interviews provided in English or Spanish. The PATH Study protocol, methodological information, interviewing procedures, and response rates are available elsewhere: the Population Assessment of Tobacco and Health (PATH) Study Series (umich.edu) (Hyland et al., 2017; Tourangeau et al., 2019; United States Department of Health and Human Services, 2022). The University at Buffalo’s Institutional Review Board approved the current analysis.

2.2. Participants

Our study used a subsample of participants from the PATH Study. These participants reported smoking 100 cigarettes in their lifetime, currently smoked every day or some days (i.e., smoking regularly), and indicated a plan to quit smoking/tobacco products within a year. Of the 13,862 available participants, our analysis sample included n = 3,378 individuals and n = 12,409 observations of those individuals across multiple waves. This sample also included individuals who used other types of tobacco products.

2.3. Independent variable

2.3.1. SCA use initiation

At Waves 2–5 (2014/2015–2018/2019), SCA use initiation was assessed by asking participants, “Have you ever used an app on your tablet computer or smartphone to help you quit using tobacco?”. This variable was recoded from a lifetime (i.e., ever use) variable to a dichotomous variable that captured the initiation of past 12-month use at each wave (i.e., never use vs. new past 12-month use).

2.4. Dependent variables

2.4.1. NRT utilization

At Wave 2 (2014/2015), all adult participants were asked, “In the past 12 months, have you used a nicotine patch, gum, inhaler, nasal spray, lozenge or pill?” to assess their NRT utilization. At Waves 3–5 (2015/2016–2018/2019), this question was limited to adult participants who used non-electronic tobacco products regularly, experimentally, or discontinued them recently. Also, the wording regarding “pill” use was removed from the PATH Study at Wave 5. Participants who did not receive this question were marked as missing or inapplicable and excluded from the analysis of the respective wave.

2.4.2. Prescription medication utilization

At Waves 2–5 (2014/2015, 2015/2016, 2016/2018/2018/2019), all adult participants who used non-electronic tobacco products and were past year quitters or quit attempters were asked, “Did you use Chantix, varenicline, Wellbutrin, Zyban, or bupropion to quit smoking/using tobacco products completely?” to assess their prescription medication utilization. Participants who did not receive this question were marked as a missing or “no” depending on if they met the criteria of our subsample (see section 2.2.).

2.4.3. Quit attempts

At Waves 2–5 (2014/2015–2018/2019), all adult participants who used tobacco products regularly (excluding e-cigarettes) were asked, “In the past 12 months, have you tried to quit [tobacco products/specific product]” to assess their quit attempts. This variable was recoded to also include individuals who recently quit smoking at follow-up.

2.5. Covariates

2.5.1. Race

At their baseline interview, participants were asked, “What is your race? Choose all that apply.” Due to our use of the publicly available dataset, the only racial categories included are Non-Hispanic Black, Non-Hispanic White, and Other. The other racial/ethnic groups in the “Other” category are available in the restricted dataset. We recoded this variable to include Hispanic participants by adding them to a new variable if they identified as Hispanic at the PATH Study baseline interview.

2.5.2. Sex

At their baseline interview, participants were asked, “What is your sex?” with the options being male or female.

2.5.3. Age

Participants’ age was a categorical variable derived from their baseline interview. The categories were the following: 18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, 65 to 74, and 75 years old or older. At Wave 4, the 75 years or older category was removed from the PATH Study in favor of 65 or more years old. Due to the small sample sizes in those two categories, we transformed this variable into one with six age categories (18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, and 65 years old or older).

2.5.4. Income

At Waves 2–5 (2014/2015–2018/2019), participants were asked, “Which of the following categories best describes your total household income in the past 12 months?” (Less than $10,000, $10,000 to $24,999, $25,000 to $49,999, $50,000 to $99,999, and $100,000 or more).

2.5.5. Nicotine Dependence

Participant nicotine dependence was assessed using a 16-item composite scale of questions from the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) measure for Impaired Control (American Psychiatric Association, 2013), the Nicotine Dependence Syndrome Scale (NDSS) (Shiffman et al., 2004), and the Wisconsin Inventory of Smoking Dependence Motives (WISDM) (Piper et al., 2004). This combined scale was developed in previous research to provide a validated standard instrument for measuring nicotine dependence among users of various tobacco products, with scores ranging from 15 to 76 (Strong et al., 2017). Participants received an average score at each wave, with higher scores indicative of higher nicotine dependence (Liu et al., 2017; Strong et al., 2017). To increase the interpretability of this continuous variable, we transformed it into a dichotomous variable in which scores less than or equal to the sample mean were classified as low nicotine dependence, and scores higher than the sample mean were classified as high nicotine dependence (Snell et al., 2021).

2.5.6. Mental health: internalizing problems

The Global Appraisal of Individual Needs assessed mental health problems – Short Screener (GAIN-SS), which was modified for the PATH Study (Dennis et al., 2006). This validated measure is used to identify individuals at risk for mental health and substance use disorders using a continuous measure of severity per the number of items endorsed. This study assessed internalizing problems (e.g., anxiety, depression, obsessive–compulsive disorder, and other disorders that involve high levels of negative affectivity) using 4-items. The reliability of the modified GAIN-SS subscales has been reported elsewhere (Conway et al., 2018). The number of responses endorsed for lifetime mental health problems was summed for each subscale. Complete data for subscale components were required with a range of 0–4. Participants were categorized into none/low symptoms (0–1), moderate symptoms (2–3), and high symptoms (4) to indicate their severity levels. Participants who endorsed high symptom severity levels indicated a high likelihood of a lifetime occurrence of a disorder with a need for treatment services (Dennis et al., 2006).

2.6. Statistical analyses

Generalized estimating equation (GEE) models were used to evaluate the primary outcomes. GEE models are ideal for evaluating correlated data from longitudinal analyses where repeated measures from the same individual are correlated (Muthén, 2006; Singer et al., 2003). Additionally, GEE models can examine various dependent variable outcomes such as continuous, binary, and count outcomes (e.g., Poisson, negative binomial) (Hardin & Hilbe, 2012). This statistical approach enables the inclusion of transitions from all three periods in a single analysis while statistically controlling for interdependence among observations contributed by the same individuals (Hardin & Hilbe, 2012; Liang & Zeger, 1986). We conducted GEE logistic regression models to evaluate SCA use initiation between baseline and follow-up and its association with treatment utilization and quit attempts assessed at follow-up, W2-W3 (2014/2015–2015/2016), W3-W4, (2015/2016–2016/2017) and W4-W5 (2016/2018–2018/2019).

Each primary outcome was examined using three GEE logistic regression models: an unadjusted model, an adjusted model with sociodemographic covariates, and an adjusted model with sociodemographic and mental health covariates. These analyses included GEE logistic regression models with specified unstructured covariance, within-person correlation matrices, and binomial distribution of dependent variables using the logit link function. Analyses were weighted using the wave 5 “all waves” weights to produce nationally representative estimates, and variances were computed using the balanced repeated replication method with Fay’s adjustment set to 0.3 (Judkins, 1990). All analyses were conducted using Stata version 17 software (StataCorp., 2019). Demographic covariates (i.e., race, sex, age, and income) were included in each adjusted model. Estimates with a relative standard error > 30 or a denominator < 50 were suppressed since these estimates may provide unreliable precision.

3. Results

The sample was predominantly female (56 %), 25 to 34 years and 55 or older (24 % & 25 % respectively), non-Hispanic White (71 %), with incomes of $10,000 to $24,999 and $25,000 to $49,999 (27 % & 25 %), and high nicotine dependence (62 %). Overall, 4 % of the sample reported the initiation of SCA use. The descriptive characteristics of this sample are shown in Table 1.

Table 1.

PATH Study population characteristics: W2-W5 (2014/2015–2018/2019).

W2 (n = 3,378)% (n) W3 (n = 3,424)% (n) W4 (n = 3,212)% (n) W5 (n = 2,888)% (n)
SCA Use Initiation
 Yes 4 % (123) 4 % (134) 4 % (133) 5 % (158)
 No 96 % (3255) 96 % (3290) 96 % (3079) 95 % (2730)
Sex
 Male 46 % (1556) 48 % (1633) 46 % (1477) 45 % (1297)
 Female 54 % (1822) 52 % (1789) 54 % (1734) 55 % (1590)
Age
 18 to 24 years 17 % (558) 14 % (417) 11 % (352) 6 % (181)
 25 to 34 years 24 % (807) 24 % (695) 25 % (798) 25 % (724)
 35 to 44 years 20 % (681) 20 % (594) 19 % (620) 21 % (586)
 45 to 54 years 20 % (676) 19 % (575) 19 % (619) 18 % (520)
 55 to 64 years 14 % (492) 16 % (470) 26 % (592) 21 % (589)
 65 years or older 5 % (164) 7 % (195) 7 % (241) 9 % (263)
Race
 Non-Hispanic White 63 % (2093) 61 % (1780) 62 % (1956) 60 % (1682)
 Non-Hispanic Black 18 % (588) 19 % (556) 19 % (603) 21 % (581)
 Hispanic 10 % (341) 10 % (287) 10 % (321) 10 % (284)
 Other 9 % (302) 10 % (273) 9 % (286) 9 % (270)
Income
 Less than $10,000 22 % (724) 24 % (672) 20 % (632) 19 % (524)
 $10,000 to $24,999 27 % (867) 27 % (762) 27 % (845) 27 % (739)
 $25,000 to $49,999 25 % (788) 25 % (695) 26 % (788) 25 % (691)
 $50,000 to $99,999 20 % (632) 18 % (515) 20 % (635) 21 % (580)
 $100,000 or more 6 % (204) 6 % (181) 7 % (214) 8 % (228)
Nicotine Dependence
 Low 37 % (1233) 37 % (1067) 38 % (1220) 39 % (1091)
 High 63 % (2092) 63 % (1828) 62 % (1963) 61 % (1738)
Internalizing Disorders
 None/Low Symptoms 48 % (1618) 52 % (1503) 50 % (1596) 51 % (1452)
 Moderate Symptoms 26 % (865) 23 % (673) 24 % (775) 22 % (625)
 High Symptoms 26 % (856) 25 % (726) 26 % (834) 26 % (734)
NRT Utilization
 Yes 14 % (464) 14 % (314) 13 % (310) 13 % (334)
 No 86 % (2912) 86 % (2011) 87 % (2053) 87 % (2297)
Prescription Medication Utilization
 Yes 7 % (112) 7 % (88) 6 % (113) 8 % (107)
 No 93 % (2910) 93 % (2535) 94 % (2767) 92 % (2440)
Past Year Quit Attempt
 Yes 32 % (1086) 47 % (1246) 46 % (1198) 44 % (1191)
 No 68 % (2284) 53 % (1380) 54 % (1419) 56 % (1498)
Open in a new tab

The data reported in the table are based on weighted data.

3.1. Past 12-month NRT utilization as a function of SCA use initiation

We did not observe significant differences in NRT utilization between individuals who used SCAs compared to individuals who did not (Table 2). Results were consistent across the unadjusted and adjusted models.

Table 2.

GEE Models of Past 12 Month NRT Utilization and SCA Use Initiation.

Past 12 Month NRT Utilization OR
(95 % CI)
(an = 7,319)
(Model 1)		 Past 12 Month NRT Utilization AOR
(95 % CI)
(an = 6,788)
(Model 2)		 Past 12 Month NRT Utilization AOR
(95 % CI)
(an = 6,741)
(Model 3)
SCA Use Initiation 1.39 (0.92, 2.09) 1.45 (0.96, 2.19) 1.42 (0.95, 2.15)
Sex
 Male N/A Referent Referent
 Female 1.22 (0.99, 1.51) 1.18 (0.96, 1.45)
Age
 18 to 24 years old N/A Referent Referent
 25 to 34 years old 1.38 (0.91, 2.10) 1.42 (0.94, 2.17)
 35 to 44 years old 2.00** (1.32, 3.02) 2.10*** (1.40, 3.17)
 45 to 54 years old 2.16*** (1.47, 3.19) 2.30*** (1.57, 3.37)
 55 to 64 years old 2.98*** (2.01, 4.41) 3.23*** (2.18, 4.78)
 65 years or older 3.27*** (2.12, 5.04) 3.63*** (2.36, 5.60)
Race
 Non-Hispanic White N/A Referent Referent
 Non-Hispanic Black 0.85 (0.66, 1.10) 0.89 (0.69, 1.14)
 Hispanic 0.54** (0.37, 0.81) 0.54** (0.36, 0.79)
 Other 1.08 (0.80, 1.44) 1.07 (0.80, 1.44)
Income
 Less than $10,000 N/A Referent Referent
 $10,000 to $24,999 0.87 (0.67, 1.13) 0.88 (0.68, 1.16)
 $25,000 to $49,999 0.89 (0.67, 1.17) 0.91 (0.69, 1.21)
 $50,000 to $99,999 0.98 (0.74, 1.29) 1.03 (0.78, 1.36)
 $100,000 or more 0.88 (0.59, 1.32) 0.97 (0.65, 1.46)
Nicotine Dependence
 Low Nicotine Dependence N/A Referent Referent
 High Nicotine Dependence 2.47*** (1.96, 3.11) 2.37*** (1.90, 2.97)
Internalizing Disorders
 None/low Symptoms N/A N/A Referent
 Moderate Symptoms 1.21 (0.97, 1.49)
 High Symptoms 1.45*** (1.19, 1.76)
Open in a new tab
*

p < 0.05;

**

p < 0.01;

***

p < 0.001.

AOR > 1.00 = the case group has higher odds of the outcome than the referent group, controlling for the other covariates in the model.

AOR < 1.00 = the case group has lower odds of the outcome than the referent group, controlling for the other covariates in the model.

The varying population sizes for each of the regression models are attributable to the following: missing data due to data removal per respondent request, “don’t know” responses, “refused” responses and individuals who did not receive the questions due to branching logic restrictions.

a

Due to the longitudinal design of the study, this refers to number of observations as opposed to number of participants.

3.2. Prescription medication utilization as a function of SCA use initiation

We observed that SCA users reported significantly greater odds of prescription medication utilization than individuals who did not use SCAs (AOR = 2.43, 95 % CI: 1.63, 3.64; p < 0.05). Results were consistent across the unadjusted and adjusted models (Table 3).

Table 3.

GEE models prescription medication and SCA use initiation.

Prescription Medication Utilization OR
(95 % CI)
(an = 11,072)
(Model 1)		 Prescription Medication Utilization AOR
(95 % CI)
(an = 10,510)
(Model 2)		 Prescription Medication Utilization AOR
(95 % CI)
(an = 10,422)
(Model 3)
SCA Use Initiation 2.09*** (1.43, 3.05) 1.62* (1.04, 2.54) 2.43*** (1.63, 3.64)
Sex
 Male N/A Referent Referent
 Female 1.08 (0.85, 1.35) 1.06 (0.83, 1.34)
Age
 18 to 24 years old N/A Referent Referent
 25 to 34 years old 1.49 (0.64, 3.47) 1.49 (0.64, 3.48)
 35 to 44 years old 2.90* (1.26, 6.64) 2.87* (1.25, 6.59)
 45 to 54 years old 4.31*** (1.88, 9.91) 4.49*** (1.94, 10.38)
 55 to 64 years old 3.85** (1.59, 9.31) 4.05** (1.65, 9.93)
 65 years old or older 6.40*** (2.69, 15.23) 6.93*** (2.86, 16.76)
Race
 Non-Hispanic White N/A Referent Referent
 Non-Hispanic Black 0.73 (0.49, 1.09) 0.76 (0.51, 1.14)
 Hispanic 0.46* (0.24, 0.88)b 0.47* (0.25, 0.91)b
 Other
Income
 Less than $10,000 N/A Referent Referent
 $10,000 to $24,999 0.95 (0.67, 1.34) 0.97 (0.68, 1.39)
 $25,000 to $49,999 0.90 (0.60, 1.34) 0.94 (0.62, 1.43)
 $50,000 to $99,999 0.91 (0.59, 1.40) 0.97 (0.63, 1.50)
 $100,000 or more 1.44 (0.91, 2.27) 1.56 (0.98, 2.47)
Nicotine Dependence
 Low Nicotine Dependence N/A Referent Referent
 High Nicotine Dependence 1.51** (1.12, 2.05) 1.48* (1.08, 2.02)
Internalizing Disorders
 None/low Symptoms N/A N/A Referent
 Moderate Symptoms 1.37* (1.04, 1.81)
 High Symptoms 1.21(0.91, 1.62)
Open in a new tab
*

p < 0.05;

**

p < 0.01;

***

p < 0.001.

AOR > 1.00 = the case group has higher odds of the outcome than the referent group, controlling for the other covariates in the model.

AOR < 1.00 = the case group has lower odds of the outcome than the referent group, controlling for the other covariates in the model.

The varying population sizes for each of the regression models are attributable to the following: missing data due to data removal per respondent request, “don’t know” responses, “refused” responses and individuals who did not receive the questions due to branching logic restrictions.

a

Due to the longitudinal design of the study, this refers to number of observations as opposed to number of participants.

b

Estimate could not be computed due to low cell count.

3.3. Past 12-month quit attempts as a function of SCA use initiation

We observed that SCA users reported significantly greater odds of past 12-month quit attempts than individuals who did not use SCAs (AOR = 1.38, 95 % CI: 1.09, 1.76; p < 0.01). Results were consistent across the unadjusted and adjusted models (Table 4).

Table 4.

GEE Models of Past 12 Month Quit Attempts and SCA Use Initiation.

Past 12 Month Quit Attempts OR
(95 % CI)
(an = 7,932)
(Model 1)		 Past 12 Month Quit Attempts AOR
(95 % CI)
(an = 7,394)
(Model 2)		 Past 12 Month Quit Attempts AOR
(95 % CI)
(an = 7,338)
(Model 3)
SCA Use Initiation 1.30* (1.05, 1.59) 1.37* (1.07, 1.73) 1.35* (1.06, 1.71)
Sex
 Male N/A Referent Referent
 Female 0.98 (0.87, 1.10) 0.97 (0.86, 1.09)
Age
 18 to 24 years old N/A Referent Referent
 25 to 34 years old 0.83 (0.66, 1.04) 0.84 (0.67, 1.06)
 35 to 44 years old 0.84* (0.63, 0.98) 0.81 (0.65, 1.00)
 45 to 54 years old 0.77* (0.61, 0.97) 0.79* (0.62, 0.99)
 55 or 64 years old 0.99 (0.79, 1.24) 1.02 (0.82, 1.27)
 65 years or older 1.29 (0.99, 1.69) 1.33* (1.01, 1.75)
Race
 Non-Hispanic White N/A Referent Referent
 Non-Hispanic Black 1.11 (0.95, 1.30) 1.13 (0.96, 1.32)
 Hispanic 1.35* (1.06, 1.72) 1.36* (1.06, 1.73)
 Other 1.16 (0.94, 1.45) 1.18 (0.94, 1.47)
Income
 Less than $10,000 N/A Referent Referent
 $10,000 to $24,999 0.84* (0.71, 0.98) 0.85* (0.72, 0.99)
 $25,000 to $49,999 0.81** (0.69, 0.94) 0.82** (0.70, 0.95)
 $50,000 to $99,999 0.83* (0.69, 0.99) 0.84* (0.70, 1.01)
 $100,000 or more 0.81(0.62, 1.05) 0.82 (0.63, 1.08)
Nicotine Dependence
 Low Nicotine Dependence N/A Referent Referent
 High Nicotine Dependence 0.95(0.84, 1.09) 0.94 (0.82, 1.07)
Internalizing Disorders
 None/low Symptoms N/A N/A Referent
 Moderate Symptoms 1.10 (0.96, 1.25)
 High Symptoms 1.11 (0.96, 1.28)
Open in a new tab
*

p < 0.05;

**

p < 0.01;

***

p < 0.001.

AOR > 1.00 = the case group has higher odds of the outcome than the referent group, controlling for the other covariates in the model.

AOR < 1.00 = the case group has lower odds of the outcome than the referent group, controlling for the other covariates in the model.

The varying population sizes for each of the regression models are attributable to the following: missing data due to data removal per respondent request, “don’t know” responses, “refused” responses and individuals who did not receive the questions due to branching logic restrictions.

a

Due to the longitudinal design of the study, this refers to number of observations as opposed to number of participants.

4. Discussion

In the present study, we used data from the PATH Study to longitudinally examine the relationship between SCA use initiation, NRT utilization, prescription medication utilization, and quit attempts in individuals who regularly smoked cigarettes and planned to quit within a year. The results showed an association between past 12-month SCA use initiation and prescription medication utilization at follow-up. However, we did not observe this relationship for past 12-month NRT utilization. Additionally, we observed an association between past 12-month SCA use initiation and past 12-month quit attempts at follow-up. Notably, these findings persisted even when controlling for socioeconomic status and internalizing mental health factors, both of which have a significant association with smoking. This study extends the literature by providing a longitudinal population-level analysis of SCAs.

Inconsistent with previous SCA studies, we did not observe an association between SCA use initiation and NRT utilization (Masaki et al., 2020; Webb et al., 2022). This is notable given that, unlike other studies focusing exclusively on the nicotine patch and gum, our study included all NRT modalities (Masaki et al., 2020; Webb et al., 2022). These discrepant findings may be attributable to whether some SCAs emphasized other treatment modalities instead of NRT (Abroms et al., 2013). Furthermore, some participants may have been skeptical of the effectiveness of NRT relative to prescription medications since the former contains nicotine, especially some racial/ethnic minority participants (Carpenter et al., 2011; Fu et al., 2007; Fu et al., 2008; Fu et al., 2014; Fu et al., 2005). This may partly explain why we observed an association between SCA use initiation and prescription medication utilization. These findings are consistent with the results of a previous longitudinal SCA study that examined the relationship between an SCA and varenicline utilization to see if it predicted smoking cessation (Carrasco-Hernandez et al., 2020). SCA users’ prescription medication utilization may be related to the informational components of their respective apps, which dispel misconceptions about the commercially available medications. While SCAs cannot address the financial barriers that often prevent prescription medication utilization, they may contribute to less negative attitudes about their safety and effectiveness (HHS, 2020; Zeng et al., 2011). Given this potential explanation for the findings, SCAs may be a novel intervention for increasing positive attitudes about pharmacotherapies among marginalized populations, who have tended to underutilize them (Fu et al., 2007; Fu et al., 2008; Fu et al., 2005; HHS, 2020). Furthermore, the SCAs can be utilized to provide information about pharmacotherapies that are available for free via state-sponsored quitlines (Prutzman et al., 2021). However, there remains the need to study attitudes among marginalized populations regarding the efficacy and safety of SCAs as a smoking cessation intervention.

Overall, these findings partially correspond with the recent meta-analysis, which noted that combining SCAs and pharmacotherapies (i.e., NRT and prescription medications) may be more effective than standalone SCA utilization (Guo et al., 2023). Specifically, SCA users are likely to benefit from app features such as providing accurate and accessible information about pharmacotherapies, which leads to their utilization (Heffner et al., 2015; Hoeppner et al., 2015). Given that pharmacotherapies are heavily underutilized by individuals who smoke, SCAs may be a population-level intervention that merits further investigation. Furthermore, the informational components of SCAs may also be related to increased pharmacotherapy utilization by minoritized groups that have historically underutilized them due to systemic barriers as well as misconceptions about their effectiveness, but it may not be effectively addressing NRT misconceptions (Avila et al., 2022; Fagan et al., 2004; Fu et al., 2007; HHS, 2020).

Some studies have shown that smoking abstinence and quit attempts are linked to SCA utilization (Bricker et al., 2020; Carrasco-Hernandez et al., 2020; Danaher et al., 2019; Houston et al., 2022; Masaki et al., 2020; Webb et al., 2022). However, systematic review/meta-analysis research suggest this relationship may depend on the inclusion of pharmacotherapies (Guo et al., 2023). Our study, however, supports that notion of using SCAs as a standalone intervention is associated with quit attempts in individuals who regularly smoked cigarettes, regardless of their race/ethnicity, sex, or mental health status. Furthermore, earlier research has noted that using an acceptance and commitment therapy-based SCA was associated with higher odds of quitting smoking relative to a comparator app, even in marginalized groups (e.g., Black, Hispanic/Latinx, and low-income adults) (Hayes et al., 2009; Santiago-Torres, Mull, Sullivan, Kendzor, et al., 2022; Santiago-Torres, Mull, Sullivan, Zvolensky, et al., 2022; Santiago-Torres et al., 2022). However, some studies have not found differences in smoking abstinence rates or quit attempts between an SCA group and a control group (Affret et al., 2020; Baskerville et al., 2018; Etter & Khazaal, 2022; Garrison et al., 2020). The discrepancies in results could be due to differences in the study’s sample characteristics, SCA type examined, or study duration. Our study used data from a nationally representative cohort study, while three of the four studies just mentioned (Baskerville et al., 2018; Etter & Khazaal, 2022; Garrison et al., 2020) did not use nationally representative samples. The study that included a nationally representative sample (Affret et al., 2020) was conducted in France, which has a different tobacco regulatory environment than the United States. Additionally, the four other studies (Affret et al., 2020; Baskerville et al., 2018; Etter & Khazaal, 2022; Garrison et al., 2020) each focused on a specific type of SCA, whereas our study examined all types of SCAs. Finally, our study had a longer timeframe than these four RCT studies due to the prospective cohort design of the PATH Study (Affret et al., 2020; Baskerville et al., 2018; Etter & Khazaal, 2022; Garrison et al., 2020; Hyland et al., 2017).

5. Strengths and limitations

Our study had several noteworthy strengths. To our knowledge, it is the first study to investigate use of SCAs in a nationally representative sample from the United States. Many studies on SCAs tend to have small sample sizes of minoritized participants, whereas our study had a substantial number of such participants. Moreover, the study was bolstered by a longitudinal design that spanned five years. Another aspect of our study that differed from the existing literature is that we evaluated SCAs more broadly instead of focusing on a specific type, making our findings more applicable to the real-world experiences of SCA users.

Along with the strengths, we also note some limitations of the present study. We measured past 12-month SCA use initiation without posing any specific questions about the type of SCAs used by the participants. Therefore, we cannot determine what types of SCAs, such as cognitive-behavioral therapy-based, acceptance and commitment therapy-based, and others, are driving the relationship between the primary outcomes. This limitation is notable because many commercially available SCAs are not supported by scientific data, thus raising concerns about their efficacy. (Abroms et al., 2013; Heffner et al., 2015). Additionally, there is the possibility that some participants may be unknowingly using pro-tobacco apps developed by the tobacco industry (BinDhim et al., 2014, 2015). Another limitation of our study is that it only focused on individuals who regularly smoked cigarettes and planned to quit within a year. As such, it may not generalize to people who smoke cigarettes infrequently and are less certain about their quit plans. Our pharmacotherapy treatment utilization variable included Wellbutrin, Zyban, and Bupropion, which are medications used for the treatment of smoking as well as treatment of depression. This variable also included Chantix, which is exclusively used for smoking cessation. Given that depression and smoking are correlated, it may be difficult to tease apart who is taking the medication to treat depression vs. smoking cessation. However, this issue is partly addressed by our sample being limited to people who indicated a plan to quit smoking/tobacco products within a year. Lastly, we did not examine sex differences in relation to the outcome variables. These analyses may have yielded notable differences given that prior research has noted that men and women have disparate quit rates depending on what type of smoking cessation treatment is utilized (Smith, Bessette, et al., 2016; Smith, Weinberger, et al., 2016).

6. Conclusion

In our study, we longitudinally examined the relationship between SCA use initiation, NRT/prescription medication utilization, and quit attempts using a nationally representative sample of individuals who regularly smoked and planned to quit smoking within a year. Our findings suggest that SCA use initiation is associated with prescription medication utilization as well as quit attempts. Future studies should examine which types of SCAs drive this relationship and which sociodemographic factors relate to their use, and if they relate to quitting smoking. Qualitative research is also needed to better understand the relationship between SCA use initiation and prescription medication utilization.

Acknowledgements

The authors would like to acknowledge the staff, investigators, and participants from the PATH Study. This research would not be possible without their contributions. This work was supported by the National Institute of Health’s Initiative for Maximizing Student Development Porgram (T32, 5R25GM095459-10) to Dr. Margarita L. Dubocovich; Centers for Disease Control and Prevention (R01, CE003144) to Drs. Linda S. Kahn and Gregory G. Homish; the National Institute on Drug Abuse (R01-DA034072) to Dr. Gregory G. Homish; and the National Center for Advancing Translational Sciences (UL1TR001412) to Dr. Timothy Murphy.

Abbreviations:

SCA

Smoking Cessation App

NRT

Nicotine Replacement Therapy

Footnotes

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Schuyler C. Lawson: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. Karin Kasza: Writing – review & editing, Supervision, Methodology, Conceptualization. R.Lorraine Collins: Writing – review & editing, Supervision, Conceptualization. Richard J. O’Connor: Data curation. Gregory G. Homish: Writing – review & editing, Supervision, Methodology, Funding acquisition, Conceptualization.

Ethics approval

This study was classified as “Not Human Research” by the University at Buffalo Institutional Review Board (STUDY00006947). The data were not collected by the authors and were deidentified when extracted from a publicly available online source.

Data availability

Data will be made available on request.

References

  1. Abroms LC, Lee Westmaas J, Bontemps-Jones J, Ramani R, & Mellerson J (2013). A content analysis of popular smartphone apps for smoking cessation. American Journal of Preventive Medicine, 45(6), 732–736. 10.1016/j.amepre.2013.07.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Abroms LC, Padmanabhan N, Thaweethai L, & Phillips T (2011). iPhone apps for smoking cessation. American Journal of Preventive Medicine, 40(3), 279–285. 10.1016/j.amepre.2010.10.032 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Affret A, Luc A, Baumann C, Bergman P, Le Faou A-L, Pasquereau A, Arwidson P, Alla F, & Cambon L (2020). Effectiveness of the e-Tabac Info Service application for smoking cessation: A pragmatic randomised controlled trial. BMJ Open, 10(10), Article e039515. 10.1136/bmjopen-2020-039515 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Agaku IT, Odani S, Okuyemi KS, & Armour B (2020). Disparities in current cigarette smoking among US adults, 2002–2016. Tobacco Control, 29(3), 269–276. [DOI] [PubMed] [Google Scholar]
  5. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (Vol. 5). American psychiatric association; Washington, DC. [Google Scholar]
  6. Avila JC, Sokolovsky AW, Nollen NL, Lee J, Schmid CH, & Ahluwalia JS (2022). The effect of race/ethnicity and adversities on smoking cessation among U.S. Adult smokers. Addictive Behaviors, 107332. 10.1016/j.addbeh.2022.107332 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Babb S, Malarcher A, Schauer G, Asman K, & Jamal A (2017). Quitting smoking among adults—United States, 2000–2015. Morbidity and Mortality Weekly Report, 65 (52), 1457–1464. [DOI] [PubMed] [Google Scholar]
  8. Baskerville NB, Struik LL, Guindon GE, Norman CD, Whittaker R, Burns C, Hammond D, Dash D, & Brown KS (2018). Effect of a mobile phone intervention on quitting smoking in a young adult population of smokers: randomized controlled trial. JMIR Mhealth Uhealth, 6(10), Article e10893. 10.2196/10893 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. BinDhim NF, Freeman B, & Trevena L (2014). Pro-smoking apps for smartphones: The latest vehicle for the tobacco industry? Tobacco Control, 23(1), e4. [DOI] [PubMed] [Google Scholar]
  10. BinDhim NF, Freeman B, & Trevena L (2015). Pro-smoking apps: Where, how and who are most at risk. Tobacco Control, 24(2), 159–161. 10.1136/tobaccocontrol-2013-051189 [DOI] [PubMed] [Google Scholar]
  11. BinDhim NF, McGeechan K, & Trevena L (2018). Smartphone Smoking Cessation Application (SSC App) trial: A multicountry double-blind automated randomised controlled trial of a smoking cessation decision-aid ‘app’. BMJ Open, 8(1), Article e017105. 10.1136/bmjopen-2017-017105 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Braveman PA, Cubbin C, Egerter S, Williams DR, & Pamuk E (2010). Socioeconomic disparities in health in the United States: What the patterns tell us. American Journal of Public Health, 100(S1), S186–S196. 10.2105/ajph.2009.166082 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Bricker JB, Copeland W, Mull KE, Zeng EY, Watson NL, Akioka KJ, & Heffner JL (2017). Single-arm trial of the second version of an acceptance & commitment therapy smartphone application for smoking cessation. Drug and Alcohol Dependence, 170, 37–42. 10.1016/j.drugalcdep.2016.10.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Bricker JB, Mull KE, Santiago-Torres M, Miao Z, Perski O, & Di C (2022). Smoking Cessation smartphone app use over time: predicting 12-month cessation outcomes in a 2-arm randomized trial. Journal of Medical Internet Research, 24(8), Article e39208. 10.2196/39208 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Bricker JB, Watson NL, Mull KE, Sullivan BM, & Heffner JL (2020). Efficacy of smartphone applications for smoking cessation: A randomized clinical trial. JAMA Internal Medicine, 180(11), 1472–1480. 10.1001/jamainternmed.2020.4055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Carpenter MJ, Ford ME, Cartmell K, & Alberg AJ (2011). Misperceptions of nicotine replacement therapy within racially and ethnically diverse smokers. Journal of the National Medical Association, 103(9–10), 885–896. 10.1016/s0027-9684(15)30444-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Carrasco-Hernandez L, Jódar-Sánchez F, Núñez-Benjumea F, Moreno Conde J, Mesa González M, Civit-Balcells A, Hors-Fraile S, Parra-Calderón CL, Bamidis PD, & Ortega-Ruiz F (2020). A mobile health solution complementing psychopharmacology-supported smoking cessation: Randomized controlled trial. JMIR mHealth and uHealth, 8(4), Article e17530. 10.2196/17530 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Conway KP, Green VR, Kasza KA, Silveira ML, Borek N, Kimmel HL, Sargent JD, Stanton CA, Lambert E, Hilmi N, Reissig CJ, Jackson KJ, Tanski SE, Maklan D, Hyland AJ, & Compton WM (2018). Co-occurrence of tobacco product use, substance use, and mental health problems among youth: Findings from wave 1 (2013–2014) of the population assessment of tobacco and health (PATH) study. Addictive Behaviors, 76, 208–217. 10.1016/j.addbeh.2017.08.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Cox LS, Okuyemi K, Choi WS, & Ahluwalia JS (2011). A review of tobacco use treatments in U.S. ethnic minority populations. American Journal of Health Promotion, 25(5_suppl), S11–S30. 10.4278/ajhp.100610-lit-177 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Danaher BG, Tyler MS, Crowley RC, Brendryen H, & Seeley JR (2019). Outcomes and device usage for fully automated internet interventions designed for a smartphone or personal computer: The MobileQuit smoking cessation randomized controlled trial. Journal of Medical Internet Research, 21(6), Article e13290. 10.2196/13290 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Dennis ML, Chan YF, & Funk RR (2006). Development and validation of the GAIN Short Screener (GSS) for internalizing, externalizing and substance use disorders and crime/violence problems among adolescents and adults. The American Journal on Addictions, 15, s80–s91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Etter JF, & Khazaal Y (2022). The Stop-tabac smartphone application for smoking cessation: A randomized controlled trial. Addiction, 117(5), 1406–1415. 10.1111/add.15738 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Fagan P, King G, Lawrence D, Petrucci SA, Robinson RG, Banks D, Marable S, & Grana R (2004). Eliminating tobacco-related health disparities: Directions for future research. American Journal of Public Health, 94(2), 211–217. 10.2105/ajph.94.2.211 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Fiore MC, Jáen CR, Baker TB, Bailey WC, Benowitz N, Curry S, Dorfman S, Froelicher E, Goldstein M, & Healton C (2008). Clinical practice guideline. Rockville, MD: US Department of health and Human Services. Public Health Service, 1196–1199. [Google Scholar]
  25. Fu SS, Burgess D, Van Ryn M, Hatsukami DK, Solomon J, & Joseph AM (2007). Views on smoking cessation methods in ethnic minority communities: A qualitative investigation. Preventive Medicine, 44(3), 235–240. 10.1016/j.ypmed.2006.11.002 [DOI] [PubMed] [Google Scholar]
  26. Fu SS, Kodl MM, Joseph AM, Hatsukami DK, Johnson EO, Breslau N, Wu B, & Bierut L (2008). Racial/ethnic disparities in the use of nicotine replacement therapy and quit ratios in lifetime smokers ages 25 to 44 years. Cancer Epidemiology, Biomarkers & Prevention, 17(7), 1640–1647. 10.1158/1055-9965.epi-07-2726 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Fu SS, Rhodes KL, Robert C, Widome R, Forster JL, & Joseph AM (2014). Designing and evaluating culturally specific smoking cessation interventions for american indian communities. Nicotine & Tobacco Research, 16(1), 42–49. 10.1093/ntr/ntt111 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Fu SS, Sherman SE, Yano EM, Van Ryn M, Lanto AB, & Joseph AM (2005). Ethnic disparities in the use of nicotine replacement therapy for smoking cessation in an equal access health care system. American Journal of Health Promotion, 20(2), 108–116. 10.4278/0890-1171-20.2.108 [DOI] [PubMed] [Google Scholar]
  29. Garrison KA, Pal P, O’Malley SS, Pittman BP, Gueorguieva R, Rojiani R, Scheinost D, Dallery J, & Brewer JA (2020). Craving to quit: a randomized controlled trial of smartphone app–based mindfulness training for smoking cessation. Nicotine & Tobacco Research, 22(3), 324–331. 10.1093/ntr/nty126 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Gone JP, & Trimble JE (2012). American Indian and Alaska Native mental health: Diverse perspectives on enduring disparities. Annual review of clinical psychology, 8, 131–160. [DOI] [PubMed] [Google Scholar]
  31. Gowarty MA, Aschbrenner KA, & Brunette MF (2021). Acceptability and usability of mobile apps for smoking cessation among young adults with psychotic disorders and other serious mental illness. Frontiers in Psychiatry, 12, Article 656538. 10.3389/fpsyt.2021.656538 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Guo Y-Q, Chen Y, Dabbs AD, & Wu Y (2023). The effectiveness of smartphone app–based interventions for assisting smoking cessation: Systematic review and meta-analysis. Journal of Medical Internet Research, 25, Article e43242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Hardin JW, & Hilbe JM (2012). Generalized estimating equations. CRC Press. [Google Scholar]
  34. Hayes SC, Strosahl KD, & Wilson KG (2009). Acceptance and commitment therapy. DC: American Psychological Association Washington. [Google Scholar]
  35. Heffner JL, & Mull KE (2017). Smartphone ownership among US adult cigarette smokers: 2014 health information national trends survey (HINTS) data. Journal of Medical Internet Research, 19(8), e305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Heffner JL, Vilardaga R, Mercer LD, Kientz JA, & Bricker JB (2015). Feature-level analysis of a novel smartphone application for smoking cessation. The American Journal of Drug and Alcohol Abuse, 41(1), 68–73. 10.3109/00952990.2014.977486 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. HHS. (2020). Smoking Cessation: A Report of the Surgeon General (U.S. Department of Health and Human Services, Issue. https://www.cdc.gov/tobacco/sgr/2020-smoking-cessation/index.html. [PubMed] [Google Scholar]
  38. Hoeppner BB, Hoeppner SS, Seaboyer L, Schick MR, Wu GWY, Bergman BG, & Kelly JF (2015). How smart are smartphone apps for smoking cessation? A content analysis. Nicotine & Tobacco Research, 18(5), 1025–1031. 10.1093/ntr/ntv117 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Houston TK, Chen J, Amante DJ, Blok AC, Nagawa CS, Wijesundara JG, Kamberi A, Allison JJ, Person SD, Flahive J, Morley J, Conigliaro J, Mattocks KM, Garber L, & Sadasivam RS (2022). Effect of technology-assisted brief abstinence game on long-term smoking cessation in individuals not yet ready to quit. JAMA Internal Medicine, 182(3), 303. 10.1001/jamainternmed.2021.7866 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Hyland A, Ambrose BK, Conway KP, Borek N, Lambert E, Carusi C, Taylor K, Crosse S, Fong GT, Cummings KM, Abrams D, Pierce JP, Sargent J, Messer K, Bansal-Travers M, Niaura R, Vallone D, Hammond D, Hilmi N, & Compton WM (2017). Design and methods of the population assessment of tobacco and health (PATH) study. Tobacco Control, 26(4), 371–378. 10.1136/tobaccocontrol-2016-052934 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Iacoviello BM, Steinerman JR, Klein DB, Silver TL, Berger AG, Luo SX, & Schork NJ (2017). Clickotine, A personalized smartphone app for smoking cessation: Initial evaluation. JMIR mHealth and uHealth, 5(4), e56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Judkins DR (1990). Fay’s method for variance estimation. Journal of Official Statistics, 6(3), 223–239. [Google Scholar]
  43. King AC, Cao D, Southard CC, & Matthews A (2011). Racial differences in eligibility and enrollment in a smoking cessation clinical trial. Health Psychology, 30 (1), 40–48. 10.1037/a0021649 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Liang K-Y, & Zeger SL (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 13–22. [Google Scholar]
  45. Liu G, Wasserman E, Kong L, & Foulds J (2017). A comparison of nicotine dependence among exclusive E-cigarette and cigarette users in the PATH study. Preventive Medicine, 104, 86–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Marler JD, Fujii CA, Utley DS, Tesfamariam LJ, Galanko JA, & Patrick H (2019). Initial assessment of a comprehensive digital smoking cessation program that incorporates a mobile app, breath sensor, and coaching: Cohort study. JMIR mHealth and uHealth, 7(2), Article e12609. 10.2196/12609 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Martell BN, Garrett BE, & Caraballo RS (2016). Disparities in adult cigarette smoking—United States, 2002–2005 and 2010–2013. Morbidity and Mortality Weekly Report, 65(30), 753–758. [DOI] [PubMed] [Google Scholar]
  48. Masaki K, Tateno H, Nomura A, Muto T, Suzuki S, Satake K, Hida E, & Fukunaga K (2020). A randomized controlled trial of a smoking cessation smartphone application with a carbon monoxide checker. NPJ Digital Medicine, 3(1). 10.1038/s41746-020-0243-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Mowery PD, Dube SR, Thorne SL, Garrett BE, Homa DM, & Nez Henderson P (2015). Disparities in smoking-related mortality among American Indians/Alaska natives. American Journal of Preventive Medicine, 49(5), 738–744. 10.1016/j.amepre.2015.05.002 [DOI] [PubMed] [Google Scholar]
  50. Muthén B (2006). The potential of growth mixture modelling. Infant and Child Development, 15(6), 623. [Google Scholar]
  51. Piper ME, Piasecki TM, Federman EB, Bolt DM, Smith SS, Fiore MC, & Baker TB (2004). A multiple motives approach to tobacco dependence: The Wisconsin Inventory of Smoking Dependence Motives (WISDM-68). Journal of Consulting and Clinical Psychology, 72(2), 139. [DOI] [PubMed] [Google Scholar]
  52. Portelli P, Bogolyubova O, Lopez BT, & Sainz-Maza A (2022). A Quality review of smart-phone applications for smoking cessation. Journal of Consumer Health on the Internet. 10.1080/15398285.2021.2021372 [DOI] [Google Scholar]
  53. Prochaska JJ, Das S, & Young-Wolff KC (2017). Smoking, mental illness, and public health. Annual Review of Public Health, 38(1), 165–185. 10.1146/annurev-publhealth-031816-044618 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Prutzman YM, Wiseman KP, Grady MA, Budenz A, Grenen EG, Vercammen LK, Keefe BP, & Bloch MH (2021). Using digital technologies to reach tobacco users who want to quit: evidence from the national cancer institute’s smokefree.gov initiative. Am J Prev Med, 60(3 Suppl 2), S172–S184. 10.1016/j.amepre.2020.08.008 [DOI] [PubMed] [Google Scholar]
  55. Robinson CD, Seaman EL, Grenen E, Montgomery L, Yockey RA, Coa K, Prutzman Y, & Augustson E (2020). A content analysis of smartphone apps for adolescent smoking cessation. Translational Behavioral Medicine, 10(1), 302–309. 10.1093/tbm/iby113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Rostron B (2013). Smoking-attributable mortality by cause in the United States: Revising the CDC’s data and estimates. Nicotine & Tobacco Research, 15(1), 238–246. 10.1093/ntr/nts120 [DOI] [PubMed] [Google Scholar]
  57. Scharff DP, Mathews KJ, Jackson P, Hoffsuemmer J, Martin E, & Edwards D (2010). More than Tuskegee: Understanding mistrust about research participation. Journal of Health Care for the Poor and Underserved, 21(3), 879. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Schlam TR, & Baker TB (2013). Interventions for Tobacco Smoking. Annual Review of Clinical Psychology, 9(1), 675–702. 10.1146/annurev-clinpsy-050212-185602 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Seo S, Cho S-I, Yoon W, & Lee CM (2022). Classification of smoking cessation apps: Quality review and content analysis. JMIR mHealth and uHealth, 10(2), Article e17268. 10.2196/17268 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Shiffman S, Waters AJ, & Hickcox M (2004). The nicotine dependence syndrome scale: A multidimensional measure of nicotine dependence. Nicotine & Tobacco Research, 6(2), 327–348. [DOI] [PubMed] [Google Scholar]
  61. Singer JD, Willett JB, & Willett JB (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford university press. [Google Scholar]
  62. Smith A, McGeeney K, & Duggan M (2015). U.S. smartphone use in 2015 [Report]. Pew Research Center. https://apo.org.au/node/59004. [Google Scholar]
  63. Snell M, Harless D, Shin S, Cunningham P, & Barnes A (2021). 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. Addictive Behaviors, 115, Article 106787. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. StataCorp. (2019). Stata Statistical Software. Release 17.[Software]. College Station, TX. [Google Scholar]
  65. Strong DR, Pearson J, Ehlke S, Kirchner T, Abrams D, Taylor K, Compton WM, Conway KP, Lambert E, & Green VR (2017). Indicators of dependence for different types of tobacco product users: Descriptive findings from Wave 1 (2013–2014) of the Population Assessment of Tobacco and Health (PATH) study. Drug and Alcohol Dependence, 178, 257–266. [DOI] [PubMed] [Google Scholar]
  66. Tourangeau R, Yan T, Sun H, Hyland A, & Stanton CA (2019). Population Assessment of Tobacco and Health (PATH) reliability and validity study: Selected reliability and validity estimates. Tobacco Control, 28(6), 663–668. [DOI] [PubMed] [Google Scholar]
  67. Webb Hooper M, Asfar T, Unrod M, Dorsey A, Correa JB, Brandon KO, Simmons VN, Antoni MA, Koru-Sengul T, Lee DJ, & Brandon TH (2019). Reasons for exclusion from a smoking cessation trial: An analysis by race/ethnicity. Ethnicity & Disease, 29(1), 23–30. 10.18865/ed.29.1.23 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Webb J, Peerbux S, Ang A, Siddiqui S, Sherwani Y, Ahmed M, MacRae H, Puri H, Majeed A, & Glasner S (2022). Long-term effectiveness of a clinician-assisted digital cognitive behavioral therapy intervention for smoking cessation: secondary outcomes from a randomized controlled trial. Nicotine & Tobacco Research, 24(11), 1763–1772. 10.1093/ntr/ntac113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Zeng F, Chen C-I, Mastey V, Zou KH, Harnett J, & Patel BV (2011). Effects of copayment on initiation of smoking cessation pharmacotherapy: An analysis of varenicline reversed claims. Clinical Therapeutics, 33(2), 225–234. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data will be made available on request.

RESOURCES