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Nondaily smoking is an increasingly prevalent pattern of smoking, which despite perceptions to the contrary, results in substantial health detriments. Currently, 25% of all US adults who smoke do so less frequently than daily []. This prevalence has increased by 27% in the last decade []. Formerly believed to be a transient pattern of smoking [,], research has established that nondaily smoking is a persistent pattern [-]. Nondaily smoking is more prevalent among Black and Latinx populations [-] and is increasingly prevalent among people with serious mental health issues []. People who smoke less than daily and have never smoked daily are younger than those who smoke daily or have previously smoked daily []. More than 10 years ago, nondaily smoking was highlighted as an important public health issue []. This call to action has produced compelling evidence on the substantial negative impact of nondaily smoking on health [], observed for smoking as few as 6 to 10 cigarettes per month [].
Despite the prevalence of nondaily smoking, the US Clinical Practice Guidelines for smoking cessation [] offer no guidance on how to support people who smoke less than daily in smoking cessation due to a lack of evidence for efficacious approaches. People who smoke less than daily do not view themselves as “smokers” who need “treatment” and thus are challenging to engage in traditional smoking cessation treatments [-]. They do, however, report high motivation to quit smoking, more so than people who smoke daily [,,], which manifests in more recent and planned cessation efforts [,-]. To date, only 2 trials have tested interventions for nondaily smoking cessation. Both focused on pharmacological treatments (ie, nicotine replacement therapy) and both failed to show efficacy in achieving smoking abstinence [,]. This lack of efficacy is in line with perceptions by people who smoke less than daily that withdrawal is not a barrier to their smoking cessation [,]. Behavioral intervention approaches may be more effective. Smartphone apps are a highly sought after source of behavioral support, as demonstrated by >33 million downloads that smoking cessation apps have generated []. Moreover, this technology-facilitated approach invokes less treatment resistance among people who smoke less than daily than more traditional smoking cessation treatments [].
Using the Smiling instead of Smoking Smartphone App to Support People Who Smoke Less Than Daily in Quitting SmokingBuilding on research that found that people who smoke less than daily prioritized having positive self-identity and wellness [], our team built a smartphone app that focused on fostering the experience of positive emotions, the Smiling instead of Smoking (SiS) app [,]. As detailed in the protocol paper matching this outcome report [], the treatment approach of the SiS app was inspired by the development of positive psychotherapy for smoking cessation [,]. Typically, during smoking cessation, positive affect decreases temporally, following a U-shaped trajectory consistent with a withdrawal effect []. However, research has shown that having high positive affect is beneficial to quitting in that it is related to increased self-efficacy to abstain from smoking [], decreased desire to smoke [,], and greater readiness to process self-relevant health information []. While positive psychotherapy for smoking cessation was originally developed for daily smokers, research with people who smoke less than daily has highlighted the impact of positive affect on reducing craving []. As many other constructs related to smoking cessation are less salient to people who smoke less than daily [], we chose “fostering positive affect” as the therapeutic goal of the SiS app.
To develop the SiS app, we used an iterative, staged process, consisting of 3 studies. This paper reports on the outcomes of the third study (NCT04672239). The 2 prior studies demonstrated the app’s ability to engage people who smoke less than daily when onboarded in-person and when onboarded remotely [,]. User experiences by participants in both studies were used to further adapt and develop the app, as described previously [,]. In both studies, within-person changes were observed in line with our conceptual model [,]. Moreover, in a feature-level analysis of data obtained during the second study (NCT03951766), the number of days participants used the app significantly predicted 30-day point prevalence abstinence (PPA) at the end of treatment and 6-month follow-up []. Further analyses indicated that this effect was primarily due to interacting with the positive psychology components of the SiS app, rather than the app’s tools dedicated to smoking cessation guidance.
ObjectivesIn this paper, we present the outcomes of the first randomized trial testing the SiS app, using a parallel, unblinded, randomized design. As described in the protocol paper [], this study is a proof-of-concept randomized controlled trial (RCT), where we targeted increased self-efficacy by treatment end as a proximal, conceptual proof-of-concept indicator of treatment efficacy. We chose self-efficacy as our primary, proximal outcome variable because of its prominence in theoretical models of health behavior change [-] and because of prior demonstrable effects. Namely, a large-scale efficacy trial of an SMS text messaging intervention for smoking cessation identified self-efficacy as the primary mediator of treatment on conferring benefit for smoking cessation [].
In this trial, we tested the SiS app against 2 control conditions to allow us to choose the more rigorous control condition for a subsequent, large-scale efficacy RCT. Both control conditions use smoking cessation materials developed and disseminated by the National Cancer Institute (NCI): the NCI’s smartphone app QuitGuide (QG) and the NCI’s smoking cessation brochure “Clearing the Air” (CtA), respectively. Secondary goals of this proof-of-concept RCT were to compare treatment acceptability and feasibility across groups, describe smoking cessation outcomes, and test for differences on secondary proof-of-concept efficacy outcomes. In line with our conceptual model, we chose positive affect, craving, and attitudes toward smoking as these secondary conceptual markers of efficacy.
Participants were adults who smoke less than daily and were interested in using support materials to help them quit smoking (recruitment period: February 25, 2021, to June 29, 2022). Study recruitment information was displayed on Craigslist, Facebook, Reddit, Smokefree.gov, ClinicalTrials.gov, a study recruitment website at Massachusetts General Hospital, and a study specific website, and was shown to people interacting with study recruitment websites (ie, Clinical Connection and Wayturn). To be eligible, participants had to be above the age of 18 years, own an Android or iPhone smartphone, smoke cigarettes at least weekly but no more than 25 out of the past 30 days, have a lifetime history of having smoked >100 cigarettes, be willing to make a quit attempt as part of the study, and currently reside in the United States.
For this proof-of-concept RCT, we selected a sample size that would allow us to detect group differences on our primary outcome variable (ie, self-efficacy) []. A large RCT testing a mobile health intervention for smoking cessation found group differences of Cohen d=0.66 on self-efficacy 1 month after quitting []. We conservatively chose to power the trial to detect Cohen d=0.50, as our primary end point was further out (ie, 6 weeks after quitting).
ProcedureThe study was conducted entirely remotely. Participants were recruited on the web nationwide within the United States, using the tagline “are you smoking nondaily and want to quit?.” Interested participants were phone screened, where they learned more about the study (ie, that the study involves randomization, that some but not all groups would use a smartphone app, and that there would be web-based survey spanning 6 months). Following this phone screen, interested people were emailed a screening survey. This survey contained check items (eg, “Please indicate ‘strongly agree’”) to assess participants’ ability to successfully interact with the web-based surveying platform, REDCap (Research Electronic Data Capture; Vanderbilt University) []. Screening participants who completed this survey were invited to an enrollment phone call and asked to provide contact information for family or friends who would be available to the study staff to help contact participants, in case the participants changed their contact information during the course of the study. Participants were advised that the enrollment phone call needed to be scheduled to occur 1 week before their quit date, but that they could choose their quit day, and the enrollment call would be scheduled to accommodate their chosen quit day. If their chosen quit date occurred before the study closed for enrollment, their preference was accommodated. On average, 19 (SD 15) days elapsed between participants completing the screening survey and their chosen quit day.
During the enrollment phone call, the study fact sheet was reviewed, smoking status was reconfirmed via self-report, and participants provided verbal consent to enroll in the study. Following consent, while on the call, study staff emailed participants a link to download their assigned app (for both apps, links to the GooglePlay and iStore listings were sent; both apps were freely available to the public at no cost) or the PDF for CtA, as chosen by randomization using a 1:1:1 allocation; randomization occurred via randomization sheet, with staff looking up a participant’s group assignment within the 24 hours before the enrollment visit to prepare for the onboarding. Staff then engaged participants in a scripted 15-minute onboarding dialogue based on their assigned treatment condition. Staff did not specify which treatment condition was the intervention of treatment versus the control conditions, but rather presented each assigned smoking cessation material as potentially useful to help support smoking cessation. The onboarding script length was matched between randomized groups. This scripted dialogue systematically led participants through their assigned smoking cessation materials, with study staff asking participants to read out loud the text displayed by their assigned app or brochure and asking participants to share answers to questions their smoking materials asked them to think about.
At the end of onboarding, study staff instructed participants to use their assigned app or brochure for a period of 7 weeks (1 week before quitting and 6 weeks after quitting) and to complete follow-up surveys on the web 2, 6, 12, and 24 weeks after their initially chosen quit date. Participants were told that they could change their quit date as needed, but that this initially chosen quit date would be the anchor date for surveying.
In total, we screened 1268 individuals over phone. Of these, 41.4% (525/1268) of the individuals were found ineligible during the phone screening (primarily due to not smoking less than daily; 497/525, 94.7%), and 15.3% (194/1268) of the individuals decided against the study (most commonly due to having lost interest in the study; 56/194, 28.9%) or not liking the study as described (22/194, 11.3%), but also due to study logistics, such as needing to provide their social security number for payment by check (36/194, 18.6%) or having to wait for checks to be mailed (26/194, 13.4%). The remaining 43.3% (549/1268) of the individuals were emailed the screening survey. Of these, 175 (31.9%) chose not to complete the survey, 86 (15.7%) failed the check items embedded in the survey, and 2 (0.4%) completed the survey after the study had closed enrollment. The remaining 52.1% (286/549) of the individuals were invited to the enrollment phone call. Of these, 28 (9.8%) participants did not show up for the enrollment phone call, 20 (7%) participants decided against the study at this point, and 1 (0.3%) participant could not be reached to schedule the enrollment. The remaining 82.9% (237/289) of the individuals started the enrollment phone call. During this phone call, 2.8% (8/237) of the individuals were found ineligible (ie, n=5 did not smoke less than daily, n=2 did not want to quit smoking, and n=1 did not reside in the United States). The remaining 96.6% (229/237) of the individuals were enrolled and proceeded to onboarding. During onboarding, after having learned about their group assignment, 3 participants (one in each treatment group) decided against the study, resulting in a final sample size of 226 participants who were enrolled and successfully onboarded to their smoking cessation materials.
Survey responses were obtained from 95.1% (215/226), 89.4% (202/226), 82.3% (186/226), and 81.9% (185/226) of the participants at 2, 6, 12, and 24 weeks after the initially chosen quit day, respectively; these included participants who only completed partial surveys (12/226, 5.3%; 8/226, 3.5%; 7/226, 3.1%; and 9/226, 4%, respectively) and participants who completed the survey but incorrectly responded to 2+ check items (6/226, 2.7%; 6/226, 2.7%; 7/226, 3.1%; and 2/226, 0.9%, respectively). Obtaining survey responses did not differ between groups (all P>.07 at all assessments).
Treatment Condition: SiS Smartphone AppParticipants in the treatment condition were asked to use the SiS app every day for 7 weeks. As described in more detail elsewhere [], the SiS app provides smoking cessation tools within the framework of positive psychology. Thus, app users were asked to engage in activities that foster positive affect and in activities that focus on smoking cessation (). To enhance prescriptive clarity [], a tool called “Today’s Tasks” listed all tasks to be completed that day; clicking on the task brought the app user directly to that task in the app.
Figure 1. Summary of the content provided in the Smiling instead of Smoking (SiS) app. The positive psychology content was derived from positive psychology findings, as summarized previously []. App users were asked to complete a positive psychology habit-building exercise every day to develop a habit of noticing and savoring the positive experiences app users encountered in everyday life. For moments of low positive affect, the SiS app offered a variety of happiness boost activities. The rationale for engaging in positive affect fostering activities was reinforced throughout the app: the overall rationale was provided in a section called “why work on happiness”; “why” buttons were linked to all exercises, which provided information about the rationale for each specific exercise; and push notifications, called “Owl Wisdoms,” were sent every 2-3 days to provide positive psychology science findings relevant to the tasks the app assigned at that time.
The smoking cessation content built directly on the materials provided by the NCI’s Smokefree.gov resources, which are in line with United States Clinical Practice Guidelines []. The smoking cessation content consisted of tools to engage app users in tracking their cigarette use, understanding their triggers for smoking, learning about benefits of quitting smoking, and reflecting on personal reasons to quit smoking. Guidance to engage with these tools was provided via time-anchored push notifications, called “Behavioral Challenges,” which asked app users to complete a specific task within the app on that specific day.
During the course of this trial, the SiS app, as hosted on hospital servers, experienced three downtimes, lasting from 1 hour to 2 days, where the app did not record its use, but users could still interact with the app in its intended way. The downtimes were noted by the research team at Massachusetts General Hospital; the app programmers (ie, PreviewLabs Inc) then implemented the needed changes in the programming to overcome outages. No changes were made to the overall functionality of the app during the duration of this study.
Control Condition 1: NCI QG Smartphone AppParticipants in control condition 1 were asked to use the QG app every day for 7 weeks. As described in more detail elsewhere [], the QG app provided many tools analogous to the smoking cessation content provided in the SiS app, just designed differently. Specifically, functionality exists to set the quit date, track mood and craving, read about best practices for quitting smoking, log cigarettes, see graphs about smoking patterns based on one’s cigarette log, set reminders to stay smoke free (time- and location-based), and receive tips in response to reporting craving or negative affect. QG is frequently used as a comparison app in smartphone app smoking cessation studies [,-] because it controls for time, attention, and modality of delivery of smoking cessation support.
Control Condition 2: NCI CtA BrochureParticipants in control condition 2 were asked to use the CtA brochure every day for 7 weeks. As described in more detail elsewhere [], this 36-page brochure [] provided analogous smoking cessation information as the QG app. It is composed of both text and worksheets to engage the reader in reflecting on reasons to quit, learning about the benefits of quitting, logging smoking and its triggers, and formulating strategies for dealing with triggers. This booklet has been used as a “treatment as usual” comparison condition in past RCTs evaluating phone-based and other mHealth smoking cessation technologies [-].
MeasuresTwo sources of data were collected: self-report answers to web-based surveys and passively recorded app use data. The primary end point for this study was treatment end, which occurred 6 weeks after the originally chosen quit date.
Primary OutcomeThe primary outcome for this proof-of-concept RCT was self-efficacy to abstain from smoking, as measured using the Self-Efficacy Questionnaire, assessed at baseline, and 2, 6, and 12 weeks after the initially chosen quit day. This 12-item scale asks participants to rate their confidence on a 0 to 100 slider scale in their ability to abstain from smoking when facing internal stimuli (eg, feeling depressed) and external stimuli (eg, being with people who smoke). Scale scores are reported as mean scores (range 0-100), where higher scores indicate greater self-efficacy to abstain from smoking. The primary outcome variable was the total Self-Efficacy Questionnaire score. Subscale scores (ie, internal stimuli and external stimuli) were also calculated to provide more nuanced insight and are presented on the same mean score scale. The internal consistency (Cronbach α) of the total score ranged from 0.89 at baseline to 0.95 at the 6-month follow-up, for the internal subscale from 0.86 at baseline to 0.95 at the 6-month follow-up, and for the external subscale from 0.80 at week 2 to 0.91 at the 6-month follow-up in this sample.
Secondary Outcomes: Treatment AcceptabilityTreatment acceptability was assessed at treatment end using the Client Satisfaction Questionnaire (CSQ) [] to assess satisfaction with the received smoking cessation support, the System Usability Scale (SUS) [] to assess acceptability of the technology used to provide smoking cessation support, and 2 single item measures to assess overall ratings of treatment likeability and satisfaction. The CSQ scores are reported as total scores that can range from 0 to 27, where higher scores indicate higher client satisfaction; the internal consistency of CSQ scores was 0.95 in this sample. The SUS scale scores are transformed to a 0 to 100 range, where higher scores mean greater system usability. Interpreted on a letter grading system, SUS scores in the range from 78.9 to 80.7 correspond to an A-, 80.8 to 84.0 to an A, and 84.1 to 100 to an A+ []. The internal consistency of SUS scale scores was 0.88 in this study. Both single item measures were rated on 5-point Likert scales ranging from 1=“I strongly disliked using the app” to 5=“I strongly liked using the app” for likeability and from 1=“very unsatisfied” to 5=“very satisfied” for satisfaction.
Secondary Outcomes: Treatment FeasibilityTreatment acceptability was assessed at multiple time points. For participants randomized to use apps, acceptability was measured by their actual engagement with their assigned apps (ie, number of days participants used their assigned apps) and their subjective appraisal of how much (in minutes per week) they used their assigned apps at midtreatment (week –1 to week 2 after the quit date) and at the end of treatment (week 3 to 6 after the quit date). All participants, regardless of whether they were assigned a smartphone app, were asked to reflect at treatment end on how much time they spent applying content learned through the smoking cessation support materials (in minutes per week), to report on recommended smoking cessation strategies they used, and to rate the perceived impact of their assigned smoking cessation treatment. Smoking cessation strategies used were evaluated with an 8-item survey rated on 5-point Likert scales ranging from 1=“strongly disagree” to 5=“strongly agree,” and aggregate scores were presented as mean scores, where higher scores indicate a higher use of multiple cessation strategies. The internal consistency of this Use of Smoking Cessation Strategies scale was 0.80 in this sample. Because the SiS app uses a positive psychology approach to smoking cessation, we also included items that addressed positive psychology approaches to smoking cessation in this list (eg, “I focused on feeling as happy as possible while I was quitting smoking” and “I focused on the good things that happened each day”), as used in prior research on positive psychotherapy for smoking []. These items were summarized into a similar mean score of Use of Positive Psychology Strategies (same range and interpretation as the Use of Smoking Cessation Strategies) and had an internal consistency of 0.88 in this sample. The perceived impact of assigned smoking cessation treatments was evaluated with a 17-item survey, as used in prior studies on the SiS app [,], (eg, [The assigned treatment] “...made me think that it was worthwhile for me to quit.”, “...gave me the feeling I could get trusted advice at any time.”) rated on a 5-point Likert scale ranging from 1=“strongly disagree” to 5=“strongly agree.” Aggregate scores were presented as mean scores, where higher scores indicated a higher perceived impact of the assigned treatment on participants’ smoking cessation support. The internal consistency of the Perceived Impact scale was 0.95 in this sample.
Secondary Outcomes: Exploratory Treatment Effectiveness OutcomesWhile this trial was not powered to detect differences in smoking cessation, we did assess smoking cessation outcomes for descriptive purposes. Specifically, we assessed 30-day PPA, as self-reported smoking status 6, 12, and 24 weeks after the initially chosen quit day. Participants who did not report on smoking status were presumed to be smoking. We also assessed smoking reduction (ie, the difference in cigarettes smoked in the past week, assessed at baseline vs the end of treatment).
Secondary Outcomes: Secondary Proof-of-Concept MarkersIn addition to the primary outcome, we assessed additional constructs in line with our conceptual model. Specifically, we used the positive affect subscale of the Positive and Negative Affect Schedule [] to assess positive affect within the past week and the Brief Questionnaire of Smoking Urges (Brief-QSU) [] to assess in the moment craving. The 10-item positive affect subscale of the Positive and Negative Affect Schedule is reported as a total score that can range from 10 to 50, with higher scores representing higher levels of positive affect; the internal consistency of this scale ranged from 0.91 at week 2 to 0.94 at the 6-month follow-up. The 10-item Brief-QSU scale is reported as a total score that can range from 7 to 70, with higher scores indicating greater cigarette craving. In this sample, the internal consistency of the Brief-QSU ranged from 0.92 at baseline to 0.96 at the 6-month follow-up. To assess attitudes toward smoking, we used the 3 subscales of the Attitudes Toward Smoking [] scale, and the Decisional Balance Inventory for Smoking short form (DCB-SF) []. The Attitudes Toward Smoking subscales have different numbers of items, so we present each subscale score as a mean score with a possible range from 1=“strongly disagree” to 5=“strongly agree,” so that higher scores indicate stronger agreement with statements about the Adverse Effects of Smoking (10 items), the Psychoactive Benefits of Smoking (4 items), and the Pleasure of Smoking (4 items) for the respective subscales. The internal consistencies of all 3 subscales ranged from 0.82 for the Psychoactive Benefits of Smoking at baseline to 0.94 for the Adverse Effects of Smoking at the 3-month follow-up. The DCB-SF is a 6-item scale that is rated on 0 to 100 slider scales. Three DCB-SF items evaluate the positive smoking expectancies (pros) and 3 items evaluate the negative smoking expectancies (cons); item scores are averaged within each subscale for a score range of 0 to 100, where higher scores indicate greater agreement with the pros or cons of smoking, respectively. The internal consistency of the subscales ranged from 0.74 to 0.85 for pros and 0.68 to 0.78 for cons across the assessments.
Participant Descriptors: Demographics, Smoking Characteristics, and Clinical CharacteristicsAt baseline, participants reported on demographic information, smoking characteristics, and clinical characteristics. Most items were stand-alone multiple-choice items. We used validated scales to assess nicotine dependence, depression severity, anxiety severity, and capacity to experience pleasure. Specifically, we used the Fagerström Test for Cigarette Dependence [], the Center of Epidemiologic Studies Depression Scale [], the Generalized Anxiety Disorder Screener [], and the Snaith-Hamilton Pleasure Scale [], respectively. For cigarette dependence, Fagerström Test for Cigarette Dependence total scores can range from 0 to 10, where higher scores indicate greater dependence. To summarize clinical characteristics, we used a Center of Epidemiologic Studies Depression Scale cutoff score of ≥10 to indicate the risk of depression [], a Generalized Anxiety Disorder Screener cutoff score of ≥10 to indicate moderate anxiety [], and a Snaith-Hamilton Pleasure Scale cutoff score of >2 to indicate anhedonia [], an inability to feel pleasure.
Analytic StrategyData PreparationBefore analysis, we reviewed all check items participants completed as part of their web-based surveys. Data were set to missing for a specific scale if the participant incorrectly responded to the check item embedded in that scale. Data were set to missing for the entire time point, if the participant responded incorrectly to ≥2 check items. Scale scores were then calculated; if an item was left blank, the scale score was calculated using the average of the remaining items (after accounting for reverse coding, as applicable), so long as ≥80% of the items were completed.
Analysis of Primary OutcomeTo test if randomized group assignment was significantly related to treatment outcome, we used a generalized linear mixed model, where repeated observations per person were modeled using an unstructured covariance matrix. Predictors included in the model were GROUP (ie, SiS vs QG vs CtA), TIME (ie, baseline, week 2, week 6, week 12, and week 24), and the GROUP*TIME interaction effect. A contrast statement was used to derive the test statistic for the GROUP comparison at week 6, our primary endpoint, given the overall longitudinal model. The contrast statement tested the null hypothesis that SiS=QG=CtA. If this null hypothesis was rejected (P<.05) for the primary outcome, we followed this up with pair-wise follow-up tests to compare the SiS app treatment to each of the specific control groups (ie, SiS vs QG, SiS vs CtA); for secondary outcomes, we provided these secondary pairwise comparisons for all outcomes, regardless of statistical significance of the overall effect. Between-group effect sizes were calculated using Cohen d, where effect sizes are interpreted such that 0.2 is small, 0.5 is medium, and 0.8 is large []. Details about the significance tests and pairwise comparisons are available in Tables S1 and S2 in .
Analysis of Secondary OutcomesThe same analytic strategy was used for secondary outcomes, but adjusted for fewer assessments (ie, no TIME predictor, if outcome was only assessed once), and the distribution of the variable of interest (ie, logit model used for the binary outcome 30-day point prevalence smoking abstinence). Some outcomes (including change in past week cigarettes, time spent applying content, app use, and app use) were heavily skewed to the right; for these outcomes, we used the nonparametric Wilcoxon rank sum test to analyze group differences (Table S3 in ). Effect sizes for these outcomes were reported as r=Z/sqrt(N), where Z is the test statistic from the Wilcoxon rank sum test and N is the total number of observations (ie, participants), and which are interpreted such that 0.1 is small, 0.3 is medium, and 0.5 is large []. Effect sizes for the logit models are presented as odds ratios with 95% CIs. Because these secondary outcomes cover distinctly different domains and are interpreted marginally to give a fuller picture of the participant experience, we did not correct for multiple testing []. We calculated effect sizes to provide insight into the relative strength of these effects. All analyses were performed using SAS System for Windows (version 9.4; IBM Corp).
Ethical ConsiderationsThe study was approved by the Mass General Brigham institutional review board (2020P003466) and registered on ClinicalTrials.gov (NCT04672239). All participants provided informed consent; specifically, they provided verbal consent after reviewing the study fact sheet and engaging in a true or false knowledge test of the content of the study fact sheet in conversation with study staff. Surveys were collected via Health Insurance Portability and Accountability Act compliant technology (ie, REDCap), which was only accessible to staff trained in the conduct of research. Smartphone data were stored on password protected Mass General Brigham servers and linked to survey data via study-generated app ID number. Participants received US $25 for completed surveys or US $10 for incomplete surveys or surveys with failed check items. They received US $50 for the week 6 survey (end of treatment), which was longer than the other surveys. Participants provided their social security number to enable remuneration by check.
Study participants (; CONSORT-eHEALTH checklist is provided in ) lived in urban, suburban, and rural areas, located in 42 out of 50 US states (Northeast region: 51/226, 22.6%; Midwest region: 31/226, 13.7%; South region: 97/226, 42.9%; West region: 47/226, 20.8%). They were largely (151/226, 66.8%) people who had smoked daily previously, who smoked an average of 3.4 (SD 2.7) cigarettes per smoking day on 14.9 (SD 4.9) days out of the past 30 days. Many had made a previous quit attempt; many had tried e-cigarettes; few were using them at the time of the study ().
Figure 2. Flow of participants throughout the study from phone screening to the end of 6-month follow-up. NCI: National Cancer Institute; PI: principal investigator; SiS: Smiling instead of Smoking. Table 1. Baseline characteristics of study participants.CharacteristicSiS3a (n=80)QuitGuide (n=75)Clearing the Air (n=71)Total (N=226)DemographicsaSiS3: version 3 of the Smiling instead of Smoking app.
bFTCD: Fagerström Test for Cigarette Dependence (range: 0-10, where higher scores indicate greater nicotine dependence).
cCES-D-10: Center of Epidemiologic Studies Depression Scale, 10-item version (yes=total score of ≥10, indicating risk of depression).
dGAD-7: Generalized Anxiety Disorder Screener (yes=total score of ≥10, indicating moderate or more severe anxiety).
eSHAPS: Snaith-Hamilton Pleasure Scale (yes=total score >2, indicating mild or more severe anhedonia, an inability to feel pleasure).
Scores on validated scales indicated presence of significant depressive symptoms for 47.8% (108/226) and moderate anxiety symptoms for 26.1% (59/226) of the participants. One-third of the participants (69/226, 30.5%) reported having been diagnosed with a mental health condition in their lifetime. Capacity to experience pleasure was in the normal range for most participants (180/226, 79.6%). A quarter of participants exceeded drinking guidelines [] during the 30 days before the study.
Primary Outcome: Self-EfficacyAnalyses indicated a significant difference between randomized groups at treatment end (F2,198=4.32; P=.01), where pair-wise follow-up tests indicated higher self-efficacy for participants randomized to SiS compared to QG (t198.8=2.31; P=.02; Cohen d=0.40) and CtA (t199.7=2.72; P=.007; Cohen d=0.50; ; Tables S1 and S2 in ). The effect was consistent with the effect observed in subscale specific analyses, with effect sizes ranging from Cohen d=0.34 to 0.50 across the pairwise comparisons.
Table 2. Study outcomes at end of treatment (6 weeks after initially chosen quit date).OutcomeSiS3a appQGb appCtAcEffect size and P value, SiS3 versusaSiS3: version 3 of the Smiling instead of Smoking app.
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