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Decreasing trends in the number of individuals accessing face-to-face support are leaving a significant gap in the treatment options for smokers seeking to quit. Face-to-face behavioral support and other interventions attempt to target psychological factors such as the self-efficacy and motivation to quit of smokers, as these factors are associated with an increased likelihood of making quit attempts and successfully quitting. Although digital interventions, such as smoking cessation mobile apps, could provide a promising avenue to bridge the growing treatment gap, little is known about their impact on psychological factors that are vital for smoking cessation.
This study aims to better understand the possible impact of smoking cessation mobile apps on important factors for successful cessation, such as self-efficacy and motivation to quit. Our aim is to assess the self-efficacy and motivation to quit levels of smokers before and after the use of smoking cessation mobile apps.
Smokers seeking to quit were recruited to participate in a 4-week app-based study. After screening, eligible participants were asked to use a mobile app (Kwit or Quit Genius). The smoking self-efficacy questionnaire and the motivation to stop smoking scale were used to measure the self-efficacy and motivation to quit, respectively. Both were assessed at baseline (before app use), midstudy (2 weeks after app use), and end-study (4 weeks after app use). Paired sample two-tailed
A total of 116 participants completed the study, with the majority being male (71/116, 61.2%), employed (76/116, 65.6%), single (77/116, 66.4%), and highly educated (87/116, 75.0%). A large proportion of participants had a low to moderate dependence on nicotine (107/116, 92.2%). A statistically significant increase of 5.09 points (95% CI 1.83-8.34) from 37.38 points at baseline in self-efficacy was found at the end of the study. Statistically significant increases were also found for the subcomponents of self-efficacy (intrinsic and extrinsic self-efficacies). Similarly, a statistically significant increase of 0.38 points (95% CI 0.06-0.70) from 5.94 points at baseline in motivation to quit was found at the end of the study. Gender, age, and nicotine dependence were not statistically significantly associated with changes in self-efficacy and motivation to quit.
The assessed mobile apps positively impacted the self-efficacy and motivation to quit of smokers making quit attempts. This has important implications on the possible future use of digitalized interventions and how they could influence important psychological factors for quitting such as self-efficacy and motivation. However, further research is needed to assess whether digital interventions can supplement or replace traditional forms of therapy.
Smoking is a significant risk factor for many health problems, including lung cancer, heart disease, stroke, and asthma [
In the context of smoking cessation, self-efficacy is defined as a smoker’s confidence in their ability to refrain from smoking when faced with internal (intrinsic self-efficacy) and external stimuli (extrinsic self-efficacy) [
Despite the effectiveness of face-to-face support targeting important psychological factors such as self-efficacy and motivation, not all individuals are willing or able to access face-to-face support. One study showed that the number of individuals accessing smoking cessation services provided by the National Health Service in the United Kingdom has been continuously declining in part because of budget cuts; a similar trend has been observed in other European countries [
The provision of digital solutions has gathered increased interest in the field of public health, concurrently with the declining use of behavioral support for smoking cessation. With increased ownership and use of smartphones, smoking cessation interventions delivered via smartphones could be a promising and cost-effective avenue to bridge this gap. One study even reported that in 2015, 400 smoking cessation mobile apps were available in the various app markets, and this number has most likely risen over the past years [
Specifically, the literature on whether and how smoking cessation mobile apps can affect psychological success factors such as self-efficacy and motivation to quit is sparse. The few studies that had attempted to investigate this had relatively small sample sizes and/or were purely qualitative [
Smokers seeking to quit were recruited to participate in a 4-week web-based study with no face-to-face contact. Study recruitment and data collection were conducted from June 2019 to July 2020. After an initial screening, eligible participants were asked to use 1 of 2 smoking cessation mobile apps, Kwit or Quit Genius. Participants were asked to complete questionnaires at 3 study time points: baseline (before using the app), midstudy (2 weeks after using the app), and end-study (4 weeks after using the app). A follow-up questionnaire was sent 8 weeks after using the app. Participants were incentivized to participate in the study by providing them with free access to smoking cessation apps with all premium features and the chance to win a £50 (US $68) Amazon voucher.
Participants were recruited via social media and posters across public places in London. Interested participants were sent a screening questionnaire to assess their eligibility. Participants who were 18 years or older, proficient in English, current smokers (at least 100 cigarettes smoked in their lifetime and smoked at least one cigarette a day), trying and willing to quit, not using other forms of smoking cessation treatments (including mobile apps), not using or had never used the apps Quit Genius or Kwit, and were not diagnosed with a mental health condition were eligible to participate.
The research presented in this paper is part of a broader study exploring the use of gamification in smoking cessation mobile apps. Therefore, the sample size was calculated based on a previous study that investigated the impact of gamification elements in a fitness mobile app [
Participants were asked to use 1 of the 2 smoking cessation mobile apps based on their assigned participant identification number (PID). PIDs were assigned on receipt of informed consent and completion of eligibility screening. Participants with even-numbered PIDs were assigned to the mobile app Quit Genius, and participants with odd-numbered PIDs were assigned to the mobile app Kwit. As the analysis presented in this paper is from a broader study investigating the impact of gamification, both apps were chosen based on their embedment of gamification features and adherence to cessation guidelines in the United Kingdom [
Quit Genius is a gamified smartphone mobile app targeted for smokers seeking to quit smoking and/or maintain their quit status [
Screenshots of Quit Genius.
Kwit is a gamified and evidence-based smoking cessation mobile app that helps smokers quit and maintain their quit status [
Screenshots of Kwit.
At baseline, participants were asked about general sociodemographic characteristics such as age, gender, marital status, and education. Participants were also asked about their current smoking habits, past quit attempts, self-efficacy, and motivation to quit. After app use, an assessment was conducted at 2 weeks (midstudy) and at 4 weeks (end-study). During both the midstudy and end-study assessments, participants were asked about their self-efficacy and motivation to quit. A follow-up questionnaire was sent at 8 weeks to assess app use, self-efficacy, and motivation to quit.
Several sociodemographic factors were assessed at baseline. These included age in years (18-29, 30-41, 42-53, or 54-65), gender (male or female), marital status (single, married, or civil partnered), residence categorized based on World Health Organization regions (Western Pacific, Americas, Southeast Asia, Europe, Africa, and Eastern Mediterranean) [
The 6-item Fagerström test was used to measure a participant’s tolerance and degree of dependence on nicotine [
Participants were asked how many serious attempts to stop smoking they made over the past 12 months. Participants were also asked if they used nicotine replacement products, prescribed medications, or mobile apps to help them quit during their past quit attempts in the previous year.
The smoking self-efficacy questionnaire is a 12-item scale used to measure an individual’s confidence in their ability to refrain from smoking on a 5-point Likert scale with the following responses: not at all sure (1), not very sure (2), more or less sure (3), fairly sure (4), and absolutely sure (5) [
Motivation to quit smoking is the level of importance a smoker places on quitting and the level of determination a smoker has to quit successfully at a given quit attempt [
The analyses were conducted using STATA 13.1 (StataCorp). Descriptive statistics were used to present general participant characteristics, including current smoking habits and information on past quit attempts. Mean values of self-efficacy and motivation to quit were calculated at baseline, midstudy, and end-study. Owing to a low response rate of 40% to the follow-up questionnaire at 8 weeks, data from this questionnaire are not presented. Two-tailed paired sample
The flowchart in
Overview of participant numbers from expression of interest to study completion.
General characteristics, smoking habits, and past use of quitting aids (n=116).
General characteristics | Respondents, n (%) | |
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18-29 | 49 (42.2) |
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30-41 | 41 (35.3) |
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42-53 | 15 (12.9) |
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54-65 | 11 (9.5) |
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Male | 71 (61.2) |
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Female | 45 (38.8) |
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Low (primary school) | 8 (6.9) |
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Medium (secondary school) | 21 (18.1) |
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High (university or college degree) | 87 (75.0) |
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Single | 77 (66.4) |
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Married or civil partnered | 39 (33.6) |
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Employed | 76 (65.5) |
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Nonemployed | 31 (26.7) |
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Unemployed | 6 (5.2) |
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Prefer not to answer | 3 (2.6) |
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Western Pacific | 4 (3.4) |
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Americas | 10 (8.6) |
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Southeast Asia | 16 (13.8) |
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Europe | 67 (57.8) |
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Africa | 17 (14.7) |
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Eastern Mediterranean | 2 (1.7) |
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≤10 | 63 (54.3) |
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11-20 | 43 (37.1) |
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21-30 | 8 (6.9) |
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≥31 | 2 (1.7) |
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Low (0-4) | 62 (53.4) |
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Moderate (5-7) | 45 (38.8) |
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High (8-10) | 9 (7.8) |
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<15 | 30 (25.9) |
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16-29 | 82 (70.7) |
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≥30 | 4 (3.4) |
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No | 64 (55.2) |
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Yes | 52 (44.8) |
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No | 95 (81.9) |
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Yes | 21 (18.1) |
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No | 108 (93.1) |
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Yes | 8 (6.9) |
Mean self-efficacy and motivation to quit scores across different study time points (n=116).
Characteristics | Baseline, mean (SD) | Midstudy, mean (SD) | End-study, mean (SD) | Baseline versus midstudy, mean difference (95% CI) | Baseline versus end-study, mean difference (95% CI) | Midstudy versus end-study, mean difference (95% CI) | |||||||
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Intrinsic (6-30) | 18.4 (7.2) | 20.6 (5.6) | 21.0 (6.1) | 2.2 (0.8 to 3.7) | 2.7 (1.0 to 4.4) | 0.5 (−0.7 to 1.6) | ||||||
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Extrinsic (6-30) | 19.0 (7.0) | 20.8 (6.2) | 21.4 (6.3) | 1.8 (0.2 to 3.4) | 2.4 (0.7 to 4.1) | 0.6 (−0.6 to 1.8) | ||||||
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Overall (12-60) | 37.4 (13.3) | 41.4 (10.5) | 42.5 (11.5) | 4.0 (1.2 to 6.8) | 5.1 (1.8 to 8.3) | 1.1 (−1.0 to 3.2) | ||||||
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Importance (1-4) | 3.0 (0.8) | 3.2 (0.8) | 3.2 (0.8) | 0.2 (0.0 to 0.3) | 0.2 (0.0 to 0.4) | 0.1 (−0.1 to 0.2) | ||||||
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Determination (1-4) | 2.9 (0.8) | 3.0 (0.9) | 3.1 (0.9) | 0.1 (−0.1 to 0.3) | 0.2 (0.0 to 0.4) | 0.1 (−0.1 to 0.2) | ||||||
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Overall (2-8) | 5.9 (1.4) | 6.2 (1.5) | 6.3 (1.7) | 0.2 (0.0 to 0.5) | 0.4 (0.1 to 0.7) | 0.1 (−0.1 to 0.3) |
The results of the linear regression models are shown in
Linear regression models examining factors associated with change in self-efficacy and change in motivation to quit between end-study and baseline (n=116).
Variables | Change in overall self-efficacy (end-study vs baseline), |
Change in overall motivation (end-study vs baseline), |
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Age (years) | −.07 (−.39 to .24) | −.02 (−.05 to .01) | |||
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Male | Reference | Reference | ||
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Female | −.91 (−8.08 to 6.26) | .17 (−.54 to .87) | ||
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Low | Reference | Reference | ||
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Moderate | −.01 (−7.07 to 7.04) | −.11 (−.81 to .58) | ||
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High | 1.87 (−10.99 to 14.74) | .13 (−1.14 to 1.40) | ||
Constant | 7.82 (−3.33 to 18.97) | .96 (−.14 to 2.06) |
We found that self-reported self-efficacy and motivation to quit among participants using the assigned apps increased after app use. However, this increase largely occurred during the first 2 weeks of app use and then plateaued. We also found that age, gender, and nicotine dependence were not associated with changes in overall self-efficacy and motivation to quit between end-study and baseline.
The observed statistically significant increase in overall self-efficacy from baseline to midstudy and baseline to end-study implies that smokers seeking to quit smoking experienced an increase in perceived confidence in their ability to refrain from smoking. The same was found when examining intrinsic and extrinsic self-efficacy, suggesting that participants experienced increased confidence in their ability to refrain from smoking when faced not only with internal stimuli such as their feelings and cravings but also with external stimuli such as socializing with other smokers and drinking alcohol. This finding is important for smokers seeking to quit, as several studies have shown that high self-efficacy is associated with better smoking cessation outcomes [
The increase in self-efficacy after app use was generally consistent with previous studies. For example, one study examining the impact of a mobile app promoting smoking cessation in hospitalized patients found positive changes in self-efficacy among patients after app use [
We also noted that the majority of the increase in self-efficacy was found between baseline and midstudy, after which self-efficacy levels stabilized. This could mean that the mobile apps may have a saturated effect by the midstudy point; therefore, the first 2 weeks act as a
Similar to self-efficacy, a statistically significant difference in determination, importance, and overall motivation to quit was found between baseline and end-study. This shows that participants experienced an increase in their perceived determination to quit smoking and how important they felt it was to quit at this attempt after using the app compared with baseline. Both apps contained several features that could have led to increased motivation. For example, possible features embedded in the Kwit app that could have led to higher motivation include unlocking achievements, motivation cards, trackers, and craving management tools. On the other hand, Quit Genius includes features such as achievement badges, cravings management, and a quit coach. Comparing the 2 apps and/or understanding which app-specific features were associated with changes in self-efficacy and motivation to quit was beyond the scope of our analysis.
The statistically significant increase in motivation to quit found in our analysis is in line with another study investigating the impact of Quit Genius, which also reported that participants had higher motivation to quit after using the app [
Furthermore, we did not find a statistically significant association between age, gender, and nicotine dependence with change in overall self-efficacy and motivation to quit. This suggests that the mobile apps had a similar effect on participants’ self-efficacy and motivation to quit at the end of the study compared with baseline, regardless of age, gender, and level of nicotine dependence. However, this might not be generalizable, as our sample had a majority of male, educated, and employed participants with low to moderate nicotine dependence. It may also have been underpowered to detect nuanced differences in effectiveness. A more diverse sample could allow for stronger inference on benefits of these apps across users of all demographics.
The positive impact of the mobile apps on the self-efficacy and motivation to quit of smokers highlights the importance and role of these psychological factors during a quit attempt. According to Elshatarat et al [
Future research should continue to build upon our understanding of how mobile app solutions can positively impact psychological factors, such as self-efficacy, motivation to quit, and empowerment, which are found to be vital for successful cessation. For example, certain features or design elements may be more effective than others in influencing important psychological factors, promoting health behavior change, and improving quit rates. App developers and tobacco cessation and behavior change specialists could benefit from working together to develop effective digital cessation programs that contain features targeted at improving and enhancing psychological factors that may play a role in the quitting process. Finally, as past studies have shown that internet-based interventions can help the disadvantaged more, the possibility of providing effective digitalized interventions could help reduce health inequalities [
One of the limitations of our study is that the majority of participants had low dependence on nicotine, which could affect baseline self-efficacy and/or motivation to quit. Future research could replicate the research on high-dependency smokers to see whether the results are generalizable. Similarly, unlike our study, future studies could also include participants with mental health conditions to ensure that the findings are generalizable to this population subgroup as well. Another limitation is the reliance on only self-reported data, which can lead to biases such as social desirability bias and, therefore, may not always be the most reliable. Moreover, our study had some methodological limitations. For example, participants were assigned to 1 of 2 mobile apps to ensure accurate data collection before and after app use. However, in reality, smokers would naturally self-select interventions on their app stores. It could also be that our study consisted of individuals with higher motivation than the general population, causing some volunteer bias. Finally, not enough follow-up data were collected because of low response rates at 8 weeks to be able to comment on long-term impact. Therefore, future studies could investigate whether the effects of mobile apps are sustained in the long term and how this can be compared with face-to-face behavioral cessation programs. Despite these limitations, this study develops a better understanding of the impact of smoking cessation apps and could provide a basis for future randomized controlled trials.
In conclusion, we found that smoking cessation mobile apps could have a positive impact on important psychological factors associated with better cessation outcomes. This has important implications for the development and use of mobile apps as evidence-based support for smoking cessation. Although this research might provide insights for the development of future apps, further research is required to enhance our understanding of how digitalized interventions could positively impact self-efficacy and other psychological factors vital for successful cessation. The limitations of our study methodology highlight the issues that future research can address differently. More rigorous and evidence-based research is vital to determine whether digital interventions can supplement or replace traditional forms of therapy.
participant identification number
The study was conducted in accordance with the recommendations for physicians involved in research on human subjects adopted by the 18th World Medical Assembly, Helsinki 1964, and later versions. Ethical approval was obtained from the Joint Research Imperial College London Research Ethics Committee before the beginning of the study (reference 19IC5158). This research did not receive a grant or funding from any agencies that are public, commercial, or within the not-for-profit sector. Quit Genius and Kwit provided free access to the study participants but had no other financial or material input.
NBR, NM, and FTF worked together to design the study and develop the study protocol. NBR acted as the study coordinator to collect the necessary data and handle the day-to-day management of the study. NBR performed the statistical analysis with guidance provided by NM and FTF. NM and FTF reviewed and revised the manuscript. All authors read and approved the final manuscript.
None declared.