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The proportion of smokers making quit attempts and the proportion of smokers successfully quitting have been decreasing over the past few years. Previous studies have shown that smokers with high self-efficacy and motivation to quit have an increased likelihood of quitting and staying quit. Consequently, further research on strategies that can improve the self-efficacy and motivation of smokers seeking to quit could lead to substantially higher cessation rates. Some studies have found that gamification can positively impact the cognitive components of behavioral change, including self-efficacy and motivation. However, the impact of gamification in the context of smoking cessation and mobile health has been sparsely investigated.
This study aims to examine the association between perceived usefulness, perceived ease of use, and frequency of use of gamification features embedded in smoking cessation apps on self-efficacy and motivation to quit smoking.
Participants were assigned to use 1 of the 2 mobile apps for a duration of 4 weeks. App-based questionnaires were provided to participants before app use and 2 weeks and 4 weeks after they started using the app. Gamification was quantitatively operationalized based on the Cugelman gamification framework and concepts from the technology acceptance model. The mean values of perceived frequency, ease of use, and usefulness of gamification features were calculated at midstudy and end-study. Two linear regression models were used to investigate the impact of gamification on self-efficacy and motivation to quit.
A total of 116 participants completed the study. The mean self-efficacy increased from 37.38 (SD 13.3) to 42.47 (SD 11.5) points and motivation to quit increased from 5.94 (SD 1.4) to 6.32 (SD 1.7) points after app use.
Gamification embedded in mobile apps can have positive effects on self-efficacy and motivation to quit smoking. The findings of this study can provide important insights for tobacco control policy makers, mobile app developers, and smokers seeking to quit.
Smoking is the second leading risk factor for early death and disability worldwide, with approximately 8 million deaths annually [
Motivation to quit refers to the level of determination and importance placed by an individual on quitting smoking [
One strategy that has been frequently applied across both physical and remote interventions for behavioral change is gamification, also known as the use of game elements in a nongame context [
Although gamification seems promising, the impact of gamification in the field of health behavior change has focused primarily on improving physical activity levels [
Exploring whether gamification in the context of mHealth can increase the self-efficacy and motivation of smokers could lead to the design of more tailored interventions, which, in turn, could improve cessation rates and reduce the health burden of tobacco consumption. The findings of this study could also provide insights into the effective design of mobile apps. Moreover, because of the wide reach and low cost of mHealth solutions, understanding the effects of gamification on mHealth could have considerable effects on helping disadvantaged groups and reducing health inequalities [
On the basis of an a priori analysis using a power level of 1−
Participants were required to be at least 18 years old and current smokers (at least one cigarette a day and 100 cigarettes smoked in their lifetime) to be eligible. Moreover, to take part in the study, individuals had to report that they were trying or willing to quit smoking in the next 30 days and were not using other forms of smoking cessation treatments. Individuals diagnosed with mental health conditions were excluded from the study.
A 4-week observational study assessing the association between gamification, self-efficacy, and motivation to quit was conducted from June 2019 to July 2020. No face-to-face contact was required, and the study was conducted on the internet. Participants were recruited via social media, and posters were displayed in public places in London. Initially, participants who expressed interest in the study (N=326) were screened to assess their eligibility. Eligible participants provided informed consent (n=170) and were assigned a participant identification number (PID). They were then requested to complete a baseline questionnaire that asked about general demographics (age, gender, education, marital status, education, country of residence, etc), smoking habits (number of cigarettes smoked, nicotine dependence, etc), self-efficacy, and motivation to quit.
In total, 154 participants completed the baseline assessment and were provided instructions on how to download and start using the app. Even-numbered PIDs were assigned to the mobile app Quit Genius, and odd-numbered PIDs were assigned to the mobile app Kwit. This deterministic method was used to ensure an equal split of participants between the 2 apps. Participants were asked to use the assigned mobile app for a total of 4 weeks. A midstudy questionnaire after 2 weeks of using the app and an end-study questionnaire after 4 weeks of using the app were given to participants.
Of those participants who completed the baseline assessment, 138 installed the app and 116 completed all 4 weeks of the study. Midstudy and end-study assessments included questions regarding gamification, self-efficacy, and motivation to quit. Participants were incentivized via free access to all features of the app and a chance to win a £50 (US $68) Amazon voucher. An overview of the number of participants at each stage of the study is presented in
Mobile apps for the study were selected based on a mobile app review that found these 2 apps to have a high embedment of gamification features and a high adherence to smoking cessation guidelines in the United Kingdom [
Kwit is a smoking cessation mobile app that helps individuals starting their quit journey and individuals trying to stay quit [
Quit Genius is a gamified smoking cessation mobile app based on CBT [
Common sociodemographic factors were assessed at baseline. Age in years was categorized as 18-29, 30-41, 42-53, or 54-65 years, and gender was categorized as male or female. Marital status was categorized as single or married or civil partnered. Education was based on United Nations Educational, Scientific and Cultural Organization’s classification into 3 categories: low if primary school was completed, medium if secondary school was completed, and high if a college or university degree was attained [
The Fagerström test with 6 items was used to measure participants’ tolerance of and dependence on nicotine [
The self-efficacy of a participant was measured using a 12-item scale called The Smoking Self-Efficacy Questionnaire [
Participants were asked 2 items frequently used in cessation studies to measure their motivation to quit smoking [
Gamification features for each app were identified using Cugelman framework for gamification strategies and tactics and are displayed in
Participants were also asked how frequently they engaged with each gamification element or feature during their quit attempt. Participants were provided with 5-point Likert scale responses: almost always, often, sometimes, rarely, and never. Responses were assigned points ranging from 1 to 5 for each gamification feature. A pooled mean was calculated for all features, with a higher mean (from 1 to 5) indicating greater overall engagement with gamification.
The statistical software STATA 13.1 (StataCorp), was used for the analyses. Box and whisker plots were created to present an overview of self-efficacy and motivation to quit levels of participants at various study time points. The mean values of perceived frequency, ease of use, and usefulness of gamification features were calculated at midstudy and end-study. Two-way paired sample
As shown in
Sociodemographic and general characteristics of participants (n=116).
Characteristics | Respondents, n (%) | |
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Kwit | 58 (50) |
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Quit Genius | 58 (50) |
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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) |
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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 (65.6) |
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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 or less | 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 or more | 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) |
Self-efficacy and motivation to quit at baseline, midstudy, and end-study (n=116).
The linear regression model results presented in
Overview of perceived frequency of use, ease of use, and usefulness of gamification features embedded in the Kwit and Quit Genius apps (n=116).
Gamification features | Midstudy, mean (SD) | End-study, mean (SD) | |||||
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Perceived usefulnessa | Perceived ease of usea | Perceived frequency of usea | Perceived usefulnessa | Perceived ease of usea | Perceived frequency of usea | |
Logging diaries | 3.78 (0.99) | 4.08 (0.95) | 3.13 (1.21) | 3.85 (0.98) | 3.85 (0.97) | 3.19 (1.20) | |
Achievements and badges | 3.64 (1.11) | 3.91 (0.96) | 2.90 (1.27) | 3.78 (1.06) | 3.97 (1.07) | 2.96 (1.23) | |
Progress tracking | 3.91 (0.94) | 4.07 (0.86) | 3.23 (1.11) | 4.04 (0.93) | 4.07 (0.96) | 3.30 (1.21) | |
Unlocking levels or competing stages | 3.93 (0.89) | 4.01 (0.94) | 3.03 (1.02) | 3.94 (0.93) | 4.18 (0.79) | 3.09 (1.08) | |
Sharing feature | 3.08 (1.15) | 3.72 (0.87) | 1.86 (1.13) | 3.28 (1.17) | 3.72 (0.95) | 1.93 (1.16) | |
Motivation cardsb | 3.64 (0.95) | 4.10 (0.79) | 2.95 (1.26) | 3.71 (1.12) | 4.08 (0.98) | 3.14 (1.23) | |
Goal settingc | 4.14 (0.85) | 4.31 (0.71) | 2.64 (0.91) | 4.14 (0.80) | 4.36 (0.81) | 2.97 (1.03) | |
Overall | 3.71 (0.75) | 4.00 (0.64) | 2.83 (0.80) | 3.80 (0.78) | 4.04 (0.72) | 2.92 (0.87) |
aRange: 1-5.
bOnly applicable to Kwit.
cOnly applicable to Quit Genius.
Linear regression model examining the association between perceived usefulness, ease of use, and frequency of use of gamification features with change in self-efficacy and change in motivation to quit (n=116).
Variables | Change in self-efficacy | Change in motivation to quit | |||
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95% CI |
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95% CI | |
Age (years) | −.05 | −0.26 to 0.17 | −.01 | −0.04 to 0.02 | |
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Male (referent) | Reference | Reference | Reference | Reference |
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Female | 1.89 | −2.48 to 6.26 | .19 | −0.37 to 0.76 |
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Low (referent) | Reference | Reference | Reference | Reference |
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Moderate | −1.02 | −5.50 to 3.46 | −.08 | −0.66 to 0.50 |
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High | 5.93 | −2.15 to 14.02 | .42 | −0.61 to 1.46 |
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Low (referent) | Reference | Reference | Reference | Reference |
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Medium | −4.95 | −15.01 to 5.16 | −1.31a | −2.60 to −0.01 |
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High | −8.01 | −16.63 to 0.61 | −1.21a | −2.32 to −0.10 |
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Single (referent) | Reference | Reference | Reference | Reference |
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Married | −.10 | −5.35 to 5.17 | −.03 | −0.70 to 0.65 |
Mean frequency of gamification use | 3.35a | 0.31 to 6.40 | .54a | 0.15 to 0.94 | |
Mean ease of use of gamification | −1.21 | −5.16 to 2.74 | −.03 | −0.54 to 0.48 | |
Mean usefulness of gamification | 1.63 | −2.53 to 5.79 | .51 | −0.03 to 1.04 | |
Baseline self-efficacy | −1.06a | −1.22 to −0.90 | −.02 | −0.04 to −0.00 | |
Baseline motivation to quit | 1.14 | −0.44 to 2.71 | −.69a | −0.90 to −0.49 | |
Constant | 35.79a | 15.68 to 53.89 | 3.19 | 0.73 to 5.65 |
a
We found that the use of Kwit and Quit Genius was associated with increased self-efficacy and motivation to quit levels 4 weeks after app use compared with baseline. Our study also found that the perceived frequency of use of gamification features was associated with an increase in self-efficacy and motivation to quit. Finally, higher baseline self-efficacy and motivation to quit were both associated with smaller increases in self-efficacy and motivation to quit levels 4 weeks after using the mobile apps compared with preapp use.
The key finding from our analyses showed that the frequency of gamification use was associated with increased levels of self-efficacy and motivation to quit after app use compared with before app use. One possible reason for this could be that the frequency of gamification use has an effect on the overall user engagement with the app, which in turn influences the self-efficacy and motivation to quit levels. Some studies in the existing literature have found positive effects of gamification on user engagement. For example, Othman et al [
Although not explicitly investigated in our study, it is also possible that the frequency of gamification use had an effect on user enjoyment, which in turn affected the motivation to quit and self-efficacy levels. Higher levels of user engagement could intrinsically influence motivation levels, as the use of the app could be rewarding or enjoyable for the user regardless of the final outcome. The theory of flow suggests that people can experience the state of
The association between perceived frequency of use of gamification features and increases in motivation to quit and self-efficacy can have important implications for the use of gamification and game design principles in nongame contexts such as health behavior change. We found progress tracking to be the most frequently used gamification feature after 4 weeks of app use. According to a review of smoking cessation mobile apps, this feature was found to be most commonly integrated into apps by app developers [
Our study also found some indications that the average perceived usefulness of gamification was associated with increased levels of self-efficacy and motivation to quit after 4 weeks of app use compared with baseline. This finding is in accordance with previous studies that have explored the impact of gamification on motivation to quit. For example, Pløhn and Aalberg [
In addition to our findings on gamification, a general finding of our analyses was the increase in self-efficacy between baseline and 4 weeks after app use. This implies that participants experienced increased levels of confidence to refrain from smoking not only when faced with both internal stimuli such as cravings and emotions but also when faced with external stimuli such as being surrounded by other smokers or social situations that trigger smoking cravings. Likewise, the increase in motivation to quit between baseline and end-study suggests that participants experienced higher determination to quit and placed greater importance in successfully quitting on the current quit attempt. The association between high self-efficacy and motivation to quit with better cessation outcomes has been established in a large number of previous studies [
It is important to note that the majority of increases in both self-efficacy and motivation to quit are evident during the first 2 weeks of app use. This could imply that the gamified mobile apps have a saturated effect after an initial period of using the app, after which they help participants maintain their self-efficacy and motivation levels. Past research has found that an increase in self-efficacy during the course of an intervention can lead to greater likelihood of long-term success [
Finally, our analyses also showed that having higher education, baseline self-efficacy, and motivation to quit were associated with smaller improvements in self-efficacy and motivation to quit. This suggests that the gamified mobile apps in our study have a greater benefit for individuals with lower levels of confidence in their ability to quit, individuals with lower determination to quit, and individuals with lower education levels or individuals with lower socioeconomic status. As socioeconomic differences are present in both smoking prevalence and successful cessation, this finding could be used to inform future interventions to help disadvantaged groups and thereby reduce inequalities.
By examining the impact of gamified smoking cessation mobile apps quantitatively, we attempt to address a gap in the existing literature. However, to do so, we developed a questionnaire to quantitatively assess gamification that requires participant self-report. Self-reporting may have led to different or inaccurate perceptions, particularly when answering questions regarding frequency of use. Moreover, the developed questionnaire has not yet been scientifically validated. Future research could test the validity and reliability of the questionnaire developed to assess gamification more rigorously. Another drawback of our research was that the majority of participants reported having a low to moderate dependence on nicotine. It could be that the findings differed among individuals with high nicotine dependence. Therefore, future research could explore the differences between the various types of smokers. Similarly, participants with mental health conditions were not eligible to participate in the study, and it could be that the findings are not generalizable to all members of the population. Finally, our study comprised motivated volunteers, which could subject the findings to volunteer bias.
To address the natural limitations of this study design, future research could consider running randomized controlled trials with 2 smoking cessation apps that are as similar as possible, differing only in the number of gamification elements or, more importantly, the type of gamification elements to robustly test the impact of gamification. Our study was underpowered to investigate the impact of individual game elements; therefore, we were unable to explore the effects of and differences between game elements. Future studies could try to isolate and test individual game elements within behavior change interventions, as not all gamification elements may have the same impact or function in the same way. It could be that certain gamification elements interact with other elements or with individual dispositions, situational circumstances, and the characteristics of particular target activities differently than others [
In conclusion, our research found that more frequent engagement with gamification features in smoking cessation apps was associated with higher self-efficacy and motivation to quit. The findings of this study provide a good platform for further investigation into the role of gamification in improving important cognitive factors essential for the quitting process of smokers. Future studies should continue to explore the impact and usefulness of gamification in the context of mHealth. On the basis of our findings and existing literature, we recommend that mobile app developers collaborate with behavior change specialists to develop more tailored, evidence-based, and theory-driven interventions. At the same time, app developers should be encouraged to work together with scientists to explore and test strategies, such as gamification, that could target vital psychological components of behavior change while possibly improving engagement with the app.
Study participant flowchart.
Screenshots of Quit Genius and Kwit.
Gamification features and tactics by Cugelman embedded in the Kwit and Quit Genius apps.
The t tests statistically examining the mean differences in self-efficacy and motivation to quit scores between study time points (n=116).
cognitive behavioral therapy
Imperial College London Research Ethics Committee
mobile health
participant identification number
Ethical approval was obtained from the Joint Research Imperial College London Research Ethics Committee (ICREC) before the beginning of the study (ICREC 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.
The 3 authors (NBR, NM, and FTF) jointly developed and designed the study. NBR handled day-to-day activities to manage the study and therefore conducted data collection. The statistical analyses presented in this paper were performed by NBR with assistance and guidance from NM and FTF. All authors reviewed the manuscript and approved the final version.
None declared.