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Feature-level analysis of a novel smoking cessation program integrating app-based behavioral therapy and an electronic cigarette

BMC Digital Health volume 3, Article number: 7 (2025) Cite this article

Abstract

Background

Smoking cessation apps and electronic cigarettes have emerged as potentially effective tools for smokers motivated to quit. Nuumi, a novel intervention, integrates app-based behavioral therapy and an electronic cigarette. Using secondary data from a single-arm pilot trial evaluating the intervention, we investigated the degree to which users engaged with the program’s features, and the relationships between feature utilization and smoking abstinence during the first 8 weeks of the trial. Features included (1) behavioral therapy lessons, (2) meditation audios, (3) a toolbox, (4) progress tracking, and (5) an electronic cigarette. Outcomes were self-reported 7-day point prevalence abstinence from smoking 4 weeks and 8 weeks after program initiation. Self-reported smoking abstinence data and app utilization data of 62 participants were analyzed. Multiple univariate logistic regressions were performed with total program engagement and feature utilization as predictor variables, and 4-week and 8-week post-baseline smoking abstinence rates as dependent variables.

Results

Four weeks and eight weeks after program initiation, 35% (n = 22) of participants reported abstinence. Higher total engagement predicted higher likelihood of abstinence at 4 weeks (OR = 2.73, 95% CI [1.53, 5.70], p = 0.002), and 8 weeks follow-up (OR = 1.52, 95% CI [1.10, 2.22], p = 0.017). Four weeks after program initiation, significant feature-level predictors of smoking cessation included the number of therapy lessons completed (OR = 1.13, 95% CI [1.04, 1.24], p = 0.010), meditations completed (OR = 1.54, 95% CI [1.11, 2.30], p = 0.025), the number of times the progress tracking feature was accessed OR = 1.02, 95% CI [1.00, 1.03], p = 0.036), and the number of days the electronic cigarette was used (OR = 1.30, 95% CI [1.21, 1.58], p = 0.010). Eight weeks after initiation, only electronic cigarette use was predictive of smoking abstinence (OR = 1.06, 95% CI [1.02, 1.12], p = 0.048).

Conclusion

The intervention may have potential to support smokers motivated to quit. With respect to program features, use of the electronic cigarette may promote sustained cigarette abstinence; however, the association between behavioral therapy features and smoking abstinence may disappear over time. Measures to increase user engagement should be explored, and future research should test the intervention using a randomized controlled trial.

Trial registration

German Clinical Trials Register DRKS00032652, registered prospectively 09/15/2023.

Peer Review reports

Background

Smoking presents a pervasive global threat to public health, constituting a leading cause of preventable illness and death [1]. Technological advancements offer promising avenues for aiding smoking cessation efforts. Among these innovations, mobile applications (apps) have emerged as accessible and scalable tools for delivering interventions for individuals wanting to quit smoking [2,3,4,5]. Examples of features commonly used in smoking cessation apps include setting a quit date, self-tracking of cigarettes smoked or money saved, mindfulness-informed or Acceptance and Commitment Therapy (ACT)-based information materials or activities, as well as distraction features, and behavioral coaching via push notifications [6, 7]. Findings on the effectiveness of smartphone app use for smoking cessation are mixed. A recent meta-analysis of 39 randomized controlled trials (RCTs) found that mobile health (mHealth) interventions could promote smoking cessation, but that their effectiveness may diminish over time [8]. For an app-based intervention to be effective, users need to engage with the therapeutic content [9, 10]; however, due to generally low adherence to mHealth smoking cessation interventions [8, 11, 12], apps may fail to achieve long-term cessation rates [8, 13].

Results on the standalone efficacy of mHealth applications vary, but some findings suggest that combining these digital interventions with pharmacological treatment may increase efficacy. A systematic review and meta-analysis of 9 RCTs using a sample consisting of over 12,000 adult smokers, demonstrated that studies examining the integration of smartphone applications with pharmacotherapy interventions reported significantly larger effect sizes [13]. Apps combined with pharmacological interventions typically include features such as information on nicotine replacement therapy (NRT), tracking NRT usage, or reminders to use NRT via push notifications [2, 14]. This aligns with general findings that smoking cessation efforts are more effective when behavioral and pharmacotherapy interventions like NRT are combined [15, 16]. NRT may alleviate cravings and withdrawal symptoms by providing nicotine to the user, thus easing the transition from cigarette smoking to abstinence and increasing the likelihood of long-term cessation maintenance [17]. In recent years, electronic cigarettes (ECs) have emerged as potential cessation tools which may provide smokers with nicotine during quit attempts [18]. ECs are battery-powered devices that simulate some aspects of cigarette smoking and are comprised of a reservoir holding a liquid solution, a power source, and a heating element to vaporize the liquid [18]. The liquid solution contains solvents such as vegetable glycerin and/or propylene glycol, flavorings, and may contain nicotine [18]. While their long-term safety remains a subject of ongoing research [19], some evidence suggests that ECs may help smokers quit more effectively than NRT [20,21,22]. While ECs are recommended by medical guidelines as smoking cessation tools in some countries, such as the United Kingdom [21], they have not been approved to date as effective and/or safe for smoking cessation and are sold as consumer products in other countries, such as Germany [23].

To our knowledge, there is currently no mHealth program available that incorporates an EC. To address this gap, the funder of this study, Sanos Group GmbH (Berlin, Germany), developed a digital intervention comprising app-based behavioral therapy and an EC connected to the app via Bluetooth, nuumi. The EC component was hypothesized to provide users with nicotine to address withdrawal symptoms, while the app-based behavioral therapy component was theorized to modify individuals’ smoking-related thoughts, beliefs, and behaviors, promoting long-term behavior change. We conducted a feature-level app usage analysis of the nuumi intervention.

Research is limited in this area, with only two studies having previously examined the relationship between feature utilization of a smoking cessation app and cessation outcomes. First, an analysis of the SmartQuit app showed that the use of three features, namely reviewing an individualized quit plan, tracking the practice of letting urges pass, and tracking of ACT skill practice significantly predicted 30-day smoking point-prevalence abstinence (PPA) at the 60-day follow-up [24]. Second, a feature-level analysis of the Smiling Instead of Smoking app demonstrated that the number of days the app was used significantly predicted 30-day PPA at the end of treatment and at the 6-month follow-up [25]. Further, it was found that the number of days participants engaged with the positive psychology content of the app predicted smoking abstinence at the end of treatment and at the 6-month follow-up, an effect that was not observed for the smoking cessation content of the app [25]. Investigating the utilization and effectiveness of mHealth smoking cessation program features allows for the identification of factors that drive user engagement and potentially contribute to cessation success. The integration of an EC with app-based behavioral therapy content represents a novel and comprehensive potential smoking cessation tool. In-depth investigation of the utilization and effectiveness of the program’s individual features is needed.

The goal of this research was (1) to investigate feature utilization of the nuumi program in the first 8 weeks after program initiation, and (2) to examine the relationship between overall program engagement and feature utilization and smoking abstinence (i.e. 7-day PPA) four and eight weeks after intervention initiation.

Methods

Participants

We conducted a secondary analysis of app utilization data from a sample of 62 adult daily smokers. The sample is a sub-sample of a single-arm trial evaluating the nuumi program (total sample size n = 71, German Clinical Trial Register DRKS00032652). The methods of the parent trial, which the present analysis is part of, have been previously described in detail [26]. Eligible participants were aged 18–65 years, reported having smoked ≥ 5 cigarettes per day (CPD) for ≥ 12 months, were motivated to stop smoking (Motivation To Stop Scale (MTSS; [27]) > 4 points), had daily access to their own smartphone, resided in Germany, had access to a personal email account, and reported being able to read and write in German. Ineligibility criteria are specified elsewhere (see German Clinical Trial Register DRKS00032652). The screening survey is attached in Appendix A.

Procedures

Ethics Committee/Institutional Review Board (IRB) approval for the trial was obtained in September 2023 (Ethics committee of Witten/Herdecke University: 123/2023). Recruitment for the trial took place from November 2023 to January 2024 via online advertisements, flyers, and a study website. After screening, informed consent was collected digitally by asking participants to select “I consent” via a checkbox (see Appendix B). Participants then filled out the baseline survey; upon completion, participants received instructions to download the nuumi app and were prompted to download a voucher. Participants were instructed to use the voucher when ordering the nuumi EC, which they were then able to order at no cost from the manufacturer’s website. Participants were asked to complete follow-up online surveys 4 weeks (t1), 8 weeks (t2), 12 weeks (t3) and 24 (t4) weeks post-baseline. Each time, participants were sent an email invitation with a link to complete the follow-up survey online, and were sent two additional reminders within the following two weeks. At the end of the trial period, participants received €10 financial compensation for each completed follow-up survey.

The present analyses were conducted using data from the follow-up surveys conducted at t1 and t2, and app utilization data collected by Sanos Group GmbH at t1 and t2. App utilization data was available for 62 individuals who had participated in the trial. For the 62 participants, response rates were 95% at t1, and 89% at t2.

Description of the intervention

The intervention included app-based behavioral training, and an EC. Participants received a kit including an EC device, a charger, a power bank, and a number of pods, the latter of which was calculated by the manufacturer based on each individual’s self-reported cigarette consumption.

The EC device was developed and manufactured by Sanos Group GmbH, and was a closed system device, i.e., empty pods must be replaced with prefilled pods obtained through the manufacturer. Additionally, in order to use any pod, an individual had to unlock it via the app prior to using.

When ordering the EC, participants could choose between two tobacco flavors (“Tobacco No. 1” and “Red Galliant”) that differed only in tobacco flavor intensity. The EC was powered by a 450 mAh battery, puff activated, and the settings cannnot be modified by the user. Participants received pods containing a liquid solution with a nicotine strength from 20 mg/ml to 0 mg/ml, with nicotine concentration decreasing in steps of 2 mg/ml. In total, an average of 65 pods were sent to each participant. On average, 9 pods were provided per participant containing liquid with nicotine strengths of 20 mg/ml, 18 mg/ml, 16 mg/ml, 14 mg/ml, and 12 mg/ml; and with approximately 4 pods containing liquid with nicotine strengths of 10 mg/ml, 8 mg/ml, 6 mg/ml, 4 mg/ml, 2 mg/ml and 0 mg/ml. The specific number of pods issued per participant varied depending on their self-reported number of CPD at baseline. The provided number of pods was intended to last participants for around 16 weeks based on the manufacturer's calculations, depending on the individual EC usage behavior of the participants. Participants were prompted to start out with the 20 mg/ml pods and gradually use pods containing lower nicotine strength (week 1–2: 20 mg/ml, week 3–4: 18 mg/ml, week 5–6: 16 mg/ml, week 7–8: 14 mg/ml, week 9–10: 12 mg/ml, week 11: 10 mg/ml, week 12: 8 mg/ml, week 13: 6 mg/ml, week 14: 4 mg/ml, week 15: 2 mg/ml, week 16: 0 mg/ml). A gradual reduction in nicotine strength was selected as previous research findings on combustible cigarettes suggested that reducing nicotine content may decrease their dependence potential, with no evidence of compensatory puffing [28]. The EC could be paired with the nuumi app via Bluetooth, allowing individuals to track their EC use patterns, e.g., number of daily puffs (see Fig. 1 (a)). Participants were asked to manually enter the number of CPD using a tracking function in the app. For each cigarette that was entered into the app, 12 puffs were added to the number of puffs taken that day. Participants were informed via the app that one cigarette was considered to consist of 12 puffs. This number of puffs was based on an estimate made by the manufacturer, as the number of puffs taken per cigarette varies between individuals due to differences in smoking behavior, such as puff duration and volume, and has been shown to range between 7 and 20 puffs per cigarette [29, 30]. Two weeks after program start, participants were provided with a daily puff budget. The two-week period before the implementation of the puff budget was used to gather data on daily EC puffs and self-reported cigarette consumption. These data were used to calculate an average of numbers of puffs taken per day, which served as the initial puff budget. Participants were encouraged not to exceed the allotted number of puffs. Throughout the day, participants received push notifications to inform them of the number of puffs remaining in their daily budget, and were notified when the budget was exhausted. If participants exceeded the budget, additional puff counts were shown in the app and the color of the budget interface changed from green to red. Limiting the number of puffs was hypothesized to function as a measure to discourage compensatory puffing [31] in response to the gradual nicotine reduction. Participants were asked each week via the app whether they wished to reduce their puff budget by a certain percentage. An app feature allowed participants to reduce or increase their puff budgets any time according to participants’ perceived needs.

Fig. 1
figure 1

Display of the features of the nuumi intervention. The figures were used with permission from Sanos Group GmbH

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Additionally, the app featured a behavior change program consisting of an established health promotion and stress management course featuring content from four core areas, including behavior, exercise, relaxation, and nutrition (BERN, [32, 33]). Originally offered in-person and certified by the Central Prevention Testing Center (Zentrale Prüfstelle Prävention, ZPP) of the German statutory health insurance system, the course was digitalized and modified to meet the needs of smokers. During the program digitalization process, educational content from the course manual was audio recorded by a professional voice actor; the recording was made available via the app. Graphical content was uploaded as images in the app. Exercises from the manual were adapted for digital format (e.g. check boxes, text boxes). Psychoeducational content on the mechanisms of dependence was added, and exercises were adapted to include evidence-based strategies for smoking cessation, such as implementation intentions [34], goal setting [35], or urge surfing [36]. The course featured cognitive behavioral therapy (CBT)-based techniques [37, 38] used to modify maladaptive thinking patterns, beliefs, and behaviors associated with smoking [38]. The course featured mindfulness-informed components aimed to teach individuals to intentionally direct their attention toward physical sensations, emotions, and thoughts to increase awareness and foster nonjudgmental acceptance [39, 40] instead of resorting to smoking [41]. The adapted content consisted of a total of 11 modules encompassing 80 behavioral therapy lessons; content was delivered via educational audio recordings, interactive exercises, and quizzes (see Fig. 1 (b)); (for detailed content of the modules see Table A of Appendix C). Participants received daily push notifications containing motivational and informative text messages based on the lessons currently in progress. Brief summaries of the coping techniques introduced in the modules were made accessible within the Toolbox section of the app (see Fig. 1 (c)) which could be accessed after module 3 had been completed; participants were advised to apply these coping techniques (e.g. “urge surfing” [36]) when dealing with cravings, stress, negative thoughts, and emotions. In addition, the app featured a meditation library with 32 guided meditation audios (see Fig. 1 (d)). The meditations, recorded by a professional voice actor, were split into eight categories (relaxation, thoughts and feelings, sleep, body and movement, focus, communication, compassion and gratitude, happiness) and were accompanied by audio recordings of binaural beats [42]. Additionally, the nuumi app included a dashboard displaying participants’ progress within each area of the program; i.e. nicotine concentration of the pod currently in use, number of daily puffs, therapy modules, and minutes meditated (see Fig. 1 (e)). Integrating these features, nuumi draws from neurological and behavioral economic models of behavioral change [35, 43].

Outcomes

App usage

Utilization metrics collected by Sanos Group GmbH included (1) the number of behavioral therapy lessons completed, (2) the number of meditations completed, (3) the number of toolbox exercises completed, (4) the number of times the progress tracking feature was accessed, and (5) the number of days the EC was used.

Smoking cessation

The primary smoking cessation outcomes were 7-day PPA from smoking at the 4-week and 8-week post-baseline follow-ups of the parent trial. Smoking abstinence was self-reported (“Have you smoked cigarettes in the past 7 days? [Yes/No]”, see Appendix B). Smoking status was not biochemically verified during the parent trial. As part of every survey, participants were reminded to report whether they still smoked cigarettes or not, and were asked to provide truthful information. Participants were informed that there were no "right" or "wrong" answers when answering the survey, and that their responses were confidential and would solely be used for the research purposes stated in the study description and the consent form.

Other variables

For the present secondary analysis, we have included the following baseline variables from the parent trial: age, gender, higher education, CPD, and self-efficacy to abstain from smoking (Smoking Self-Efficacy Questionnaire; SEQ-12 [44, 45], see Appendix B), with permission from the first author to use the SEQ-12. Additionally, we included participants’ self-reported data on whether they were using the nuumi EC at the 4-week and 8-week follow-ups.

Statistical analyses

As indicators for feature utilization, we analyzed (1) the number of completed behavioral therapy lessons, (2) the number of meditation audios completed and/or repeated, (3) the number of toolbox exercises completed and/or repeated, (4) the number of times the progress tracking feature was accessed, (5) the number of days the nuumi EC was used.

To index general engagement with the intervention across all program components, a total engagement score was calculated. We performed a tertile split for frequency of the use of each of the five features (scores ranging between 1 indicating “lowest tertile utilization” and 3 indicating “highest tertile utilization”), resulting in 5 tertile split scores. These 5 scores were summed up, resulting in one single score (with possible scores ranging from 1 to 15). Two logistic regressions were performed, each with total program engagement as the predictor variable, and 7-day PPA from smoking at 4-week and 8-week follow-up as the dependent variables (with 0 indicating “non-abstinent” and 1 indicating “abstinent”).

To examine the relationship between feature utilization and smoking cessation, we aimed to conduct a total of 10 univariate logistic regressions for each of the five features to predict 7-day PPA from smoking at 4-week and 8-week follow-up. The predictor variable was the utilization of each of the five app features, and 7-day PPA served as the dependent variable. To control for multiple comparisons, we applied Benjamini-Hochberg (BH) adjustments which control the False Discovery Rate (FDR), i.e. the expected proportion of false positives among the declared significant results [46].

To control for confounding effects on the relationship between feature utilization and smoking cessation outcomes in our regression models, we examined which baseline characteristics (age, gender, higher education, CPD, self-efficacy) significantly differed by smoking status at t1, t2, or both. To test for significant differences, we conducted independent samples t-tests for continuous variables, and chi-square tests for categorical variables.

Participants were assumed to be currently smoking if they did not respond to the follow-up survey at 4-weeks post-baseline, 8-weeks post-baseline, or both. Statistical significance was evaluated using an alpha level of 0.05. All analyses were conducted using the statistical software R, version 4.4.0 (R Core Team, 2024).

Results

Sample characteristics and smoking status

The average age of participants was 38.55 years (SD = 10.17). Approximately two thirds of participants (68%) identified as women. Twenty-five participants (40%) held a college degree. On average, participants smoked 17.52 cigarettes per day (SD = 6.50), and had been smoking for 20.39 years (SD = 9.84). Data on smoking status was missing for n = 3 individuals at 4 weeks follow-up; these individuals were assumed to have resumed smoking [47]. Smoking status data at 8 weeks follow-up were complete for all n = 62 participants.

At both t1 and t2, 35% (n = 22) of participants reported they did not smoke any cigarette in the past 7 days, respectively. Baseline variables CPD (i.e. the self-reported number of CPD at baseline) and higher education (college degree) significantly differed between smoking abstinent and non-abstinent individuals at t1, and baseline CPD significantly differed between smoking abstinent and non-abstinent individuals at t2 (see Table 1); we controlled for these variables in our regression analyses.

Table 1 Baseline characteristics by smoking status at t1 and t2
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App feature utilization

Table 2 shows frequency of utilization of each program feature. At t1, participants had completed an average of 12.24 (SD = 9.76) of the available 80 lessons (15%), and 1.32 (SD = 2.01) meditations. None of the participants had completed any toolbox exercises at t1. On average, participants had accessed the progress tracking feature 59.74 (SD = 51.59) times, and used the nuumi EC an average of 11.31 days (SD = 6.22).

Table 2 Nuumi program feature utilization
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At t2, participants had completed an average of 19.18 lessons (SD = 16.60; 24% of total lessons), and had completed 1.84 (SD = 2.92) meditations. None of the participants had completed any toolbox exercises at t2. On average, participants had accessed the progress tracking feature 94.18 (SD = 105.3) times at t2, and had used the nuumi EC on 22.69 days (SD = 14.18). Utilization of the lessons, meditations, progress tracking feature and EC increased significantly from t1 to t2 (see Table 2).

At 4-week follow-up, 89% (n = 55) of participants self-reported having used the nuumi EC in the past 7 days. Thirty-five percent of participants (n = 22) self-reported cigarette abstinence and nuumi EC use, and 53% (n = 33) self-reported use of both products (dual use). At 8-week follow-up, 70% (n = 43) of participants self-reported having used the nuumi EC in the past 7 days. Twenty-seven percent of participants (n = 17) self-reported cigarette abstinence and nuumi EC use, and 42% (n = 26) self-reported use of both products (dual use).

Relationship of program utilization and smoking abstinence

Tertile split regressions showed that total program engagement was predictive of smoking abstinence both at 4-week follow-up (p = 0.002) and at 8-week follow-up (p = 0.017; see Table 3).

Table 3 Results of the logistic regressions
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At t1, number of completed lessons (p = 0.010), number of completed meditations (p = 0.025), number of times the progress tracking feature was accessed (p = 0.036), and number of days of EC use (p = 0.010) significantly predicted smoking abstinence (see Table 3). At t2, only the number of days of EC use significantly predicted smoking abstinence (p = 0.048; see Table 3). As the toolbox feature had not been accessed by any participant, no regression analyses were conducted for this feature.

Discussion

This secondary data analysis of utilization metrics of a novel smoking cessation intervention combining app-based behavioral therapy and an EC sought to investigate whether general program engagement and specific program features predicted smoking status 4 weeks and 8 weeks after program initiation. The observed abstinence rates of 35% at both the 4-week and the 8-week follow-up indicates that nuumi may help some smokers quit. Findings from this study should be interpreted with caution, as long-term cessation rates were not assessed, and no causal inferences can be drawn given the design of the study. Additionally, some compliance issues were observed that may have limited the intervention’s effectiveness. In line with previous analyses of digital smoking cessation programs [24, 25], we found that overall greater program engagement predicted a higher likelihood of self-reported smoking abstinence four weeks and eight weeks after program initiation. These findings indicate that the nuumi program may hold some potential as a therapeutic intervention for smokers motivated to quit.

Findings revealed that utilization of behavioral therapeutic features of the program, i.e., therapy lessons, meditations, and the progress feature, as well as the EC were associated with smoking cessation four weeks after program initiation; however, only utilization of the EC was associated with smoking abstinence at the 8-week follow-up. These findings suggest that in the early stages of smoking cessation, abstinence from cigarette smoking may be related to increased use of the nuumi program’s behavioral features and the EC, but that later on, the EC may play a more critical role in achieving and maintaining smoking abstinence.

There are several reasons that may explain our finding that behavioral features were associated with smoking cessation only in the shorter term. First, participants could have acquired the necessary knowledge and skills to remain abstinent from cigarettes during their initial utilization of these features. Once participants were able to apply their newly acquired skills independently, they may have no longer perceived the need to rely on the app content and may have stopped using the features altogether. Second, participants’ decreasing engagement over time may reflect their fading interest and/or may suggest that participants stopped using these features due to a lack of perceived or actual helpfulness in participants’ attainment of sustained abstinence.

Even though lesson completion rates still increased from the 4-week to the 8-week follow-up, the majority of lessons were completed in the first period after program initiation, and only 24% of total lessons had been completed by the sample at the 8-week follow-up, with the toolbox feature not having been used at all. It is noteworthy that the toolbox could only be accessed after module 3 had been completed, meaning that participants who did not complete this module were not able to access the toolbox. Engagement with the meditation audios was generally low; on average, participants had completed fewer than two meditations at 8 weeks after program initiation, with more than half of the sample having not accessed the meditation feature at all at both time points. As interest in meditation was not an inclusion criterion for the parent trial, the limited interaction may suggest a lack of interest in meditation exercises in general and/or limited interest in the intervention-specific meditation exercises. Like behavioral therapy lessons and meditation audios, utilization of the progress tracking feature was linked to smoking cessation four weeks, but not eight weeks after program initiation. This result is in line with previous findings suggesting that gamification features of a smoking cessation app such as monitoring self-progress positively predict smoking abstinence in the short-term [48].

In general, our observations on decreasing user engagement with the app features over time, and low rates of compliance, align with previous findings showing that medians of 30-day app retention rates of mental health apps often fall below 5% [12]. However, regular engagement with the therapeutic content is necessary to increase the likelihood of sustained effects on behavioral change [9, 10]. Several measures could be taken to increase users’ engagement with the nuumi app. Adding more gamification elements beyond the progress tracking function in its current form could increase engagement; options could include badges, achievements, or point systems [35, 48]. Rooted in the theoretical framework of approach motivation, such features leverage the psychological and neurobiological processes associated with positive, hedonic experiences [35, 49]. Gamification features can act as reinforcers by providing users with positive feedback, allowing them to experience a sense of accomplishment and empowerment, which may in turn increase user engagement and users’ belief in their own ability to successfully quit smoking, i.e., self-efficacy; both user engagement and self-efficacy have been shown to increase intervention effectiveness [50]. One option for further development of the nuumi program could be the integration of a behavior change feature in the form of a single, comprehensive progress score across all behaviors tracked via the app, i.e. EC puffing behavior, nicotine reduction, completion of behavioral therapy lessons, meditation library usage. For example, when considering the low engagement in the area of meditation in our study, integrating a comprehensive score reflecting progress made in all areas could reward users for making progress overall even when only little to no progress is made in a specific area. Another strategy to enhance users’ engagement with the app could involve incorporating social support features such as community forums or peer-to-peer messaging [51], which may increase participants’ sense of community and belonging [52], and may subsequently increase the likelihood of successful cessation [53]. Personalizing the intervention by delivering content that is customized to the individual characteristics of each user, such as tailored counseling messages, could boost engagement as well [13, 51]. Moreover, integrating digital human coaching to offer more intensive support has been demonstrated to significantly increase user engagement in other mental health applications [54]. Compliance-related issues highlight the need for gaining insight on what factors of the present program participants perceived as helpful or hindering to smoking cessation, and what aspects of the program should be improved to make nuumi a helpful cessation tool. Qualitative research was conducted as part of the parent trial, and findings may provide valuable insights into how to enhance the intervention.

Unlike utilization of the behavioral therapy features, the number of days the EC was used was associated with smoking cessation eight weeks after program initiation. Nicotine dependence is a primary driver of smoking behavior [55], and some ECs alleviate withdrawal symptoms during cigarette cessation attempts [56]. Further, ECs mimic the physical act of smoking, thus addressing behavioral aspects of smoking [20]. At the follow-ups, individuals may have still struggled with nicotine cravings, withdrawal symptoms, or the need to engage in the physical aspects of smoking, and the continued use of the EC may have supported abstinence for some participants. Although 89% of participants had reported using the EC at 4-week follow-up, and 70% had reported doing so at 8-week follow-up, on average, participants had used the EC for only 23 days in a time frame of eight weeks. We cannot be certain whether EC use rates reflect a compliance issue or whether participants increasingly felt they no longer needed to use the EC, for example because the program had progressed far enough that some individuals may have no longer experienced cravings at that point. Adding an ecological momentary assessment (EMA) component could provide insight into the mechanisms contributing to EC use or lack thereof at different time points. For example, using an EMA component, future research could assess the severity of cigarette cravings at various times throughout the day and investigate whether and to what degree EC use occurs in response.

Participants reporting cravings but not engaging in EC use in response to these cravings may reflect EC-related noncompliance; reasons for noncompliance should be investigated as these could include the ECs inability to provide users with sufficient nicotine, or an aversion to the taste or smell of the EC. These findings could in turn inform steps to increase compliance, for example, by taking steps to improve device components or the liquid solution.

Previous research has identified that behavioral support focusing on adherence and sending tailored messages addressing beliefs, concerns, or a lack of knowledge can increase adherence to NRT and medications for tobacco dependence [57, 58]. Similar strategies could be applied to increase EC adherence in the framework of the nuumi intervention.

Although ECs are likely to be less harmful than tobacco cigarettes, concerns remain regarding long-term safety [19]. Meta-analysis data suggest that approximately 70% of individuals who underwent EC-supported smoking cessation report ongoing EC use at six months or longer [59]. Smokers utilizing ECs to quit smoking should also receive support to quit EC use once smoking cessation has been attained. The nuumi program incorporates such support by providing users with the opportunity to taper off nicotine consumption gradually, and users are prompted to stay within their daily puff budget which has been hypothesized to prevent compensatory puffing. However, compensatory puffing does not only include taking a greater number of puffs, but can also include taking longer or deeper puffs to achieve desired levels of nicotine intake [31] which was not accounted for by any program features. Participants may have still compensated for the decrease in nicotine, thus potentially offsetting the purpose of the puff budget. Future research should investigate the usefulness of features such as a puff budget for the tapering of EC use.

Overall, we found that the number of CPD reported at baseline was a significant covariate in all regression analyses of feature utilization and smoking cessation outcomes, with a higher number of baseline CPD being linked to a lower likelihood of cessation. These findings align with previous studies showing that a higher number of CPD decreases the likelihood of successful quit attempts [60, 61]. Poor cessation outcomes among individuals who smoke more cigarettes mirror barriers to quitting that disproportionately affect this particularly vulnerable group; heavy smoking is especially prevalent among individuals from lower socioeconomic backgrounds and is linked to demographic factors such as limited education, substance use disorders and other mental health conditions, which can further complicate cessation efforts [62]. Given the multitude of factors placing individuals who smoke more cigarettes at risk for unsuccessful quitting attempts and/or relapse, their needs may exceed the scope of this intervention. Adding more features to increase user engagement in the nuumi app as described above may have the potential to address the needs of heavier smokers.

Although our findings suggest that both the behavioral therapy features and use of the EC were associated with smoking cessation in the short-term, only EC was linked to cessation in the longer term; thus, the respective relationship of these features with smoking behavior seems to vary over time. The potentially more critical role of the EC may be attributed to its ability to address the persistent physiological and behavioral components of cigarette dependence that challenge sustained abstinence. While app-delivered behavioral therapy offers knowledge and coping strategies for quitting, this component may not fully compensate for the physical and sensorimotor aspects of smoking addiction that the EC may simulate. These findings underscore the importance of integrating multiple intervention components that address both psychological and physiological aspects of nicotine addiction for longer-term success in smoking cessation efforts.

Limitations

Our study has several limitations. The design of our study does not allow for causal inferences on the relationship between nuumi program utilization and smoking cessation, and between feature utilization and smoking cessation. Further research is required to investigate causal relationships between the utilization of the program as a whole as well as feature utilization and smoking outcomes, e.g., by conducting an RCT. Further, we performed a series of univariate logistic regressions to analyze the effect of feature utilization on cessation outcomes, which does not account for interactions between features.

The present analysis is part of the exploratory phase of research on the nuumi program. Our primary goal was to identify potential relationships between nuumi program participation and smoking cessation outcomes, and generate hypotheses for future research. Conducting multiple univariate analyses helped provide some insight into which app features may show potential associations with smoking cessation outcomes, which can in turn help inform targeted, hypothesis-driven research. Moreover, we only analyzed the relationships between feature utilization and smoking abstinence rates four weeks and eight weeks after program initiation. Analyzing these outcomes does not capture long-term effects of the app on smoking cessation. Smoking cessation is a complex process, and relapses are common [63]; longer follow-up periods are necessary to understand the sustained impact utilization of the investigated program. For the assessment of self-reported smoking abstinence, we adopted the phrasing used in other smoking cessation studies conducted in Germany (e.g. [64]); however, by using this operationalization of 7-day PPA, some participants may have reported abstinence despite having smoked one single cigarette or a few puffs. Importantly, self-reported data are subject to social desirability bias [65], and misreporting is common [66]. Future research should consider incorporating biochemical verification methods to enhance the validity and reliability of smoking abstinence assessments [66]. Another potential limitation of our study is that we assumed the three participants who did not report any smoking status at 4-week follow-up to have continued smoking. However, assuming that non-responders still smoke is a common approach in smoking cessation studies [47], supported by evidence showing that non-responders are more likely to be smokers [67]. While this practice is a conservative approach to handling missing data, it may induce bias. If non-responders included abstinent individuals, treating them as non-abstinent could have influenced the regression coefficients. Another limitation concerning the EC device included in the nuumi program is that, prior to the start of the parent trial, neither the nicotine delivery of the EC nor its effects on cravings to smoke had been tested in clinical lab studies; therefore, it is unclear if and to what degree users were able to derive sufficient nicotine from the EC to suppress their cravings. If an EC is not able to suppress cravings effectively, cigarette smoking may continue [68, 69]. Also, the effects of gradually reducing nicotine over time in decrements of 2 mg/ml or any decrements, e.g. on compensatory puffing [31], have not been tested previously, and research is needed to examine whether and to what degree this approach may aid smoking cessation. However, potential future findings on the effects of nicotine reduction using specific decrements should be interpreted with caution given that ECs constitute a diverse class of products [18]. Therefore, findings derived from research involving a specific device may not be generalizable to other devices, as nicotine delivery is impacted not only by nicotine concentration, but also by device power and user experience [70, 71]. Another limitation of our study is that the generalizability of our findings may also be limited given that the nuumi program was provided to study participants at no cost. Although this approach may have had the benefit of reducing the risk of inadvertent exclusion of individuals with limited socioeconomic resources from this study, receiving a program that would otherwise need to be purchased as part of a research study could have been perceived as a financial incentive by our sample. There is high-certainty evidence available that incentives in general improve smoking cessation outcomes, and that financial incentives may continue to affect sustained cessation, even after they have ended [65]. Thus, it is possible that self-reported smoking cessation rates would have been lower if participants had not received the program for free.

Conclusions

Higher engagement with the nuumi program is positively linked to smoking cessation. To explore these preliminary results more thoroughly, we recommend conducting an RCT. Our findings suggest that the EC integrated in the nuumi program may support sustained cigarette abstinence for some smokers, as observed within the present study’s timeframe. More research is needed to explore EC utilization patterns in smoking cessation attempts beyond the time frame investigated in this study, and on how to support individuals in achieving abstinence also from the EC in later stages of their quit smoking attempts. App-based behavioral therapy features of the nuumi program may also potentially support smokers in achieving abstinence in the short term, and more research is needed to gain a deeper understanding of user engagement patterns, including barriers and facilitators associated with the use of these features. Based on our exploratory findings, future studies should investigate additional nuumi program features that may increase user engagement and investigate causal relationships between app feature utilization and smoking cessation with a longer follow-up period to assess potential long-term effects, and to ultimately inform development of technology-advanced interventions.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ACT:

Acceptance and commitment therapy

BH:

Benjamini-Hochberg

CBT:

Cognitive-behavioral therapy

CPD:

Cigarettes per day

EC:

Electronic cigarette

EMA:

Ecological Momentary Assessment

FDR:

False discovery rate

mHealth:

Mobile health

Mobile applications:

Apps

NRT:

Nicotine replacement therapy

PPA:

Point prevalence abstinence

RCT:

Randomized controlled trial

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Funding

Open Access funding enabled and organized by Projekt DEAL. IGVF at Witten/Herdecke University received funding to perform this trial from Sanos Group GmbH, the manufacturer of nuumi. Sanos Group GmbH is financially supported by the European Union’s Fund for Regional Development and Investitionsbank Berlin for its technological innovation and social impact by the funding programs "Pro FIT – Early Stage Financing" and “Pro FIT – Project Financing”.

The funder’s responsibilities included provision of a recruitment website and approval of the final study design. The University of Witten/Herdecke’s research team’s responsibilities included participant screening, data collection, data management, data analyses, interpretation of results, and writing manuscripts. During the first phase of participant recruitment (10–11/2023), HS was employed by Sanos Group, and TE held shares of Sanos Group during recruitment, data collection, analysis and preparation of this manuscript (11/2023–07/2024).

Contact information of the funder:

Sanos Group GmbH.

Luetzowstrasse 102.

10,785 Berlin.

Germany.

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Authors and Affiliations

  1. Institute for Integrative Health Care and Health Promotion, Faculty of Health/School of Medicine, Witten/Herdecke University, Alfred-Herrhausen-Straße 50, Witten, 58455, Germany

    Helen Schiek, Tobias Esch & Cosima Hoetger

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Contributions

H.S. developed the research design, methods, and study materials in close collaboration with C.H. and T.E. Data analysis was conducted by H.S., advised by C.H. This manuscript was prepared by H.S. and reviewed and edited by C.H. and T.E. All authors approved of the submitted version.

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Correspondence to Helen Schiek.

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Ethics approval and consent to participate

Ethical approval for this research has been obtained from the Ethics Committee of Witten/Herdecke University in September 2023 (123/2023). All individuals filled out an informed consent form before participation in this study.

Consent for publication

Participants received an informed consent document via e-mail. Written informed consent was collected digitally by asking participants to select “I consent” via a checkbox in an online form. The informed consent document contained detailed information about the intervention, study procedures, potential risks and benefits of study participation, data handling and data protection, as well as on analysis of the collected data and publication of the study results.”

Competing interests

HS was a salaried employee at Sanos Group GmbH from March 2022 until November 2023. TE held shares in Sanos Group GmbH from 05/2022 until 07/2024. CH declares that she has no competing interests.

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Schiek, H., Esch, T. & Hoetger, C. Feature-level analysis of a novel smoking cessation program integrating app-based behavioral therapy and an electronic cigarette. BMC Digit Health 3, 7 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s44247-025-00147-7

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