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Impact of Gamification on the Self-Efficacy and Motivation to Quit of Smokers: Observational Study of Two Gamified Smoking Cessation Mobile Apps

Impact of Gamification on the Self-Efficacy and Motivation to Quit of Smokers: Observational... Background: 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. Objective: 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. Methods: 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. Results: 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. Goal setting was perceived to be the most useful gamification feature, whereas sharing was perceived to be the least useful. Participants self-reported that they used the progress dashboards the most often, whereas they used the sharing feature the least often. The average perceived frequency of gamification features was statistically significantly associated with change in self-efficacy (β=3.35; 95% CI 0.31-6.40) and change in motivation to quit (β=.54; 95% CI 0.15-0.94) between baseline and end-study. Conclusions: 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. (JMIR Serious Games 2021;9(2):e27290) doi: 10.2196/27290 KEYWORDS gamification; smoking cessation; mobile applications; self-efficacy; motivation to quit; mHealth; mobile phone https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al dominant theory of motivation, suggests that gamification Introduction elements such as points and badges serve as informational feedback instilling a sense of intrinsically motivating Smoking is the second leading risk factor for early death and competence in the user [21]. Similarly, goal-setting theory, disability worldwide, with approximately 8 million deaths another prominent theory of motivation, has been associated annually [1,2]. In the United Kingdom, 16% of all deaths in with gamification; for example, elements such as leaderboards 2016 were attributed to smoking [3]. Despite most smokers and levels provide individuals with smaller, more immediate wanting to quit, research shows that the number of smokers in goals that can improve task performance and, in turn, increase the United Kingdom who tried to quit in the past year dropped one’s confidence in their ability to complete tasks [21,22]. by 7% between 2008 and 2017 [4]. Among the smokers making attempts, only 3%-5% are successful in staying quit after a year Although gamification seems promising, the impact of [5]. Exploring methods that can improve the success rates of gamification in the field of health behavior change has focused long-term quitting can lead to a decreased prevalence of chronic primarily on improving physical activity levels [23-25]. More diseases and premature mortality. Previous studies have shown specifically, there is room to further study the use of that high motivation to quit and self-efficacy, 2 factors often gamification in the context of other behaviors, such as smoking. integrated into face-to-face behavioral support interventions, With the proliferation of smartphone use and the increased have been found to increase the likelihood of attempting to quit availability of digitalized interventions, it is vital to investigate and successfully quitting [6-9]. novel strategies, such as gamification, that complement mHealth solutions and are in line with current digitalization trends. The Motivation to quit refers to the level of determination and majority of studies that have explored the impact of gamification importance placed by an individual on quitting smoking [10]. in the context of smoking cessation have been purely qualitative On the other hand, self-efficacy, a theoretical construct first and consequently have not operationalized gamification termed by Bandura [11], in the context of smoking cessation quantitatively. For example, Pløhn and Aalberg [26] interviewed refers to one’s confidence in their ability to refrain from smoking participants after they used a digital smoking cessation when faced with internal and external stimuli [11,12]. Several intervention with gamification features and found positive strategies are adopted by health behavior change interventions perceptions of gamification as an important motivational factor to influence self-efficacy or motivation to quit. For example, to aid quitting. However, the smoking cessation intervention motivational interviewing is often used to help smokers explore was not delivered via a mobile app. Another study by El-Hilly reasons for quitting and “make them feel more willing and able et al [27] found promising results on the effect of gamification to stop smoking” [13]. Similarly, vicarious experience and on the motivation and engagement of smokers within the context performance attainment are strategies to influence self-efficacy. of mHealth. Similar to the study by Pløhn and Aalberg [26], Vicarious experience involves “exposing the individual to the study by El-Hilly et al [27] was also qualitative and had a successful behaviour performances or gaining experience small sample size (n=16), hindering the external validity of the through practice” [14]. In the context of smoking cessation, this findings. On the other hand, Lin et al [28] investigated could mean showing smokers examples of other smokers who gamification quantitatively and found that program progress or successfully quit after attending cessation programs. step completion as a gamification element in a smoking Performance attainment refers to having successful experiences cessation mobile app had a positive impact on psychological [11,14]; for a smoker trying to quit, this could mean staying factors such as user well-being, inspiration, and empowerment abstinent for a day and recognizing this as a success. Such [28]. It would be worthwhile to investigate whether gamification strategies have been integrated into both face-to-face and digital in smoking cessation apps can also positively impact essential interventions, such as mobile health (mHealth) solutions, which cognitive factors such as self-efficacy and motivation to quit. have become increasingly important to improve access, knowledge, and behavior across different contexts and Exploring whether gamification in the context of mHealth can population groups [15]. increase the self-efficacy and motivation of smokers could lead to the design of more tailored interventions, which, in turn, One strategy that has been frequently applied across both could improve cessation rates and reduce the health burden of physical and remote interventions for behavioral change is tobacco consumption. The findings of this study could also gamification, also known as the use of game elements in a provide insights into the effective design of mobile apps. nongame context [16]. Some examples of game elements include Moreover, because of the wide reach and low cost of mHealth achievement badges, goal setting, progress tracking, sharing solutions, understanding the effects of gamification on mHealth progress, and levels [17]. Past studies have found that could have considerable effects on helping disadvantaged groups gamification can positively impact cognitive components of and reducing health inequalities [29]. On the basis of the behavioral change, including self-efficacy and motivation. For limitations of prior research, this study’s aim of exploring example, self-efficacy was the most cited advantage of gamified gamified smoking cessation apps can also help enhance the classrooms, as it improved the confidence levels of students current understanding of the effectiveness of mHealth [18]. Similarly, Thorsteinsen et al [19] found that gaming interventions for behavior change and extend our knowledge elements significantly increased the motivation of individuals of novel methods to promote healthy lifestyle changes. to engage in physical activity. The use of gamification has Specifically, our study aims to quantitatively assess the gradually become more popular as it appears to share key association between overall perceived usefulness, ease of use, components with several behavioral change theories and techniques [17,20]. For example, self-determination theory, a https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al and frequency of use of gamification features and the Mobile Apps self-efficacy and motivation of smokers seeking to quit. Mobile apps for the study were selected based on a mobile app review that found these 2 apps to have a high embedment of Methods gamification features and a high adherence to smoking cessation guidelines in the United Kingdom [33,34]. Screenshots of both Sampling and Eligibility apps are shown in Multimedia Appendix 2. On the basis of an a priori analysis using a power level of Kwit 1−β=.80 and a significance level α=.05, we aimed to recruit 140 participants. The results of a previous study examining the Kwit is a smoking cessation mobile app that helps individuals impact of gamification elements in a fitness app on perceived starting their quit journey and individuals trying to stay quit competence, a proxy for self-efficacy, indicated that 112 [35]. The app includes several features such as a calculator, a participants were required to complete the study [25]. Similar smoking diary that helps smokers log and analyze cravings and to previous mobile app studies, we considered a dropout rate triggers, motivation cards, social media sharing, levels, and of approximately 20% [30-32], which resulted in the recruitment achievement cards. The app is based on cognitive behavioral of 140 participants. The sample size calculation also assumes therapy (CBT) and gamification strategies. The versions of Kwit that both apps are similar based on a prior mobile app review used during the study period included those released from June [33]. 2019 (v.4.1) to July 2020 (v.4.4). Participants were required to be at least 18 years old and current Quit Genius smokers (at least one cigarette a day and 100 cigarettes smoked Quit Genius is a gamified smoking cessation mobile app based in their lifetime) to be eligible. Moreover, to take part in the on CBT [36]. It delivers personalized support to individuals study, individuals had to report that they were trying or willing seeking to quit smoking and helps quitters maintain their quit to quit smoking in the next 30 days and were not using other status. It includes several features such as a tracker, a daily diary forms of smoking cessation treatments. Individuals diagnosed that allows quitters to log their cravings and triggers, a quitting with mental health conditions were excluded from the study. toolbox, a goal-setting feature, achievement badges, stages of information that build upon each other, and a quit coach who Study Design provides continuous personalized support. The versions A 4-week observational study assessing the association between downloaded by participants were those released from June 2019 gamification, self-efficacy, and motivation to quit was conducted (v.1.1) to July 2020 (v.1.9). from June 2019 to July 2020. No face-to-face contact was required, and the study was conducted on the internet. Measures Participants were recruited via social media, and posters were Sociodemographic Factors displayed in public places in London. Initially, participants who expressed interest in the study (N=326) were screened to assess Common sociodemographic factors were assessed at baseline. their eligibility. Eligible participants provided informed consent Age in years was categorized as 18-29, 30-41, 42-53, or 54-65 (n=170) and were assigned a participant identification number years, and gender was categorized as male or female. Marital (PID). They were then requested to complete a baseline status was categorized as single or married or civil partnered. questionnaire that asked about general demographics (age, Education was based on United Nations Educational, Scientific gender, education, marital status, education, country of and Cultural Organization’s classification into 3 categories: low residence, etc), smoking habits (number of cigarettes smoked, if primary school was completed, medium if secondary school nicotine dependence, etc), self-efficacy, and motivation to quit. was completed, and high if a college or university degree was attained [37]. Employment status was categorized as In total, 154 participants completed the baseline assessment and unemployed (individuals who are willing and able to work but were provided instructions on how to download and start using have no employment), employed, or nonemployed (individuals the app. Even-numbered PIDs were assigned to the mobile app who are unable to work, including students and homemakers). Quit Genius, and odd-numbered PIDs were assigned to the Residence was categorized based on the World Health mobile app Kwit. This deterministic method was used to ensure Organization regions: Western Pacific, Americas, Southeast an equal split of participants between the 2 apps. Participants Asia, Europe, Africa, and Eastern Mediterranean [38]. 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 Nicotine Dependence an end-study questionnaire after 4 weeks of using the app were The Fagerström test with 6 items was used to measure given to participants. participants’ tolerance of and dependence on nicotine [36]. On the basis of the responses, participants were categorized into 3 Of those participants who completed the baseline assessment, levels: low (0-4 points), moderate (5-7 points), and high (8-10 138 installed the app and 116 completed all 4 weeks of the points) [39,40]. study. Midstudy and end-study assessments included questions regarding gamification, self-efficacy, and motivation to quit. Self-Efficacy Participants were incentivized via free access to all features of The self-efficacy of a participant was measured using a 12-item the app and a chance to win a £50 (US $68) Amazon voucher. scale called The Smoking Self-Efficacy Questionnaire [12]. An overview of the number of participants at each stage of the The scale assesses an individual’s confidence in their ability to study is presented in Multimedia Appendix 1. https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al refrain from smoking when faced with internal and external higher mean (from 1 to 5) indicating greater overall engagement stimuli. Response options included not at all sure, not very sure, with gamification. more or less sure, fairly sure, and absolutely sure. A total score Statistical Analysis ranging from 12 to 60 was computed for each participant, with The statistical software STATA 13.1 (StataCorp), was used for higher scores signifying higher self-efficacy. the analyses. Box and whisker plots were created to present an Motivation to Quit overview of self-efficacy and motivation to quit levels of Participants were asked 2 items frequently used in cessation participants at various study time points. The mean values of studies to measure their motivation to quit smoking [10,41,42]. perceived frequency, ease of use, and usefulness of gamification The first item asked was as follows: How important is it to you features were calculated at midstudy and end-study. Two-way to give up smoking altogether at this attempt? Responses paired sample t tests were used to test whether differences in included the following: desperately important, very important, self-efficacy and motivation to quit at various time points of quite important, and not all that important. The second item the study were statistically significant. In addition, we explored asked was as follows: How determined are you to give up various linear regression models to examine whether smoking at this attempt? Response options included the gamification was associated with changes in self-efficacy and following: extremely determined, very determined, quite motivation to quit. On the basis of an iterative process that determined, and not all that determined. A total score ranging considered the fit of the data with our model (ie, comparing the from 2 to 8 was calculated for each participant, with higher Akaike information criterion and Bayesian information scores signifying higher motivation. criterion), 2 linear regression models were performed. The first tested the association between perceived ease of use, frequency Gamification of use, and usefulness of gamification with change in Gamification features for each app were identified using self-efficacy, and the second tested the association between Cugelman framework for gamification strategies and tactics perceived ease of use, frequency of use, and usefulness of and are displayed in Multimedia Appendix 3 [17]. For each gamification with change in motivation to quit. Both models identified gamification feature, participants were asked how controlled for age, gender, education, marital status, nicotine useful and easy to use they found it during their quit attempt. dependence, baseline self-efficacy, and baseline motivation to Participants were provided with 5-point Likert scale responses: quit. Significance at the 5% level (0.05), along with 95% CIs strongly agree, agree, neither agree nor disagree, disagree, and for all included coefficients, is presented in the Results section. strongly disagree. Perceived usefulness and ease of use are 2 vital components of the technology acceptance model, which Results has been widely used in existing literature to better understand user acceptance and attitudes toward mobile apps and app Study Participants features [43,44]. As shown in Table 1, there was an equal split of participants who used the apps Kwit (58/116, 50%) and Quit Genius (58/116, Participants were also asked how frequently they engaged with 50%). The majority of participants were male (71/116, 61.2%), each gamification element or feature during their quit attempt. highly educated (87/116, 75%), single (77/116, 66.4%), Participants were provided with 5-point Likert scale responses: employed (76/116, 65.6%), and living in Europe (67/116, almost always, often, sometimes, rarely, and never. Responses 57.8%). More than half of the participants smoked 10 or fewer were assigned points ranging from 1 to 5 for each gamification cigarettes on a daily basis (63/116, 54.3%), and the majority feature. A pooled mean was calculated for all features, with a had low to moderate dependence on nicotine (107/116, 92.2%). https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al Table 1. Sociodemographic and general characteristics of participants (n=116). Characteristics Respondents, n (%) Assigned mobile app Kwit 58 (50) Quit Genius 58 (50) Age (years) 18-29 49 (42.2) 30-41 41 (35.3) 42-53 15 (12.9) 54-65 11 (9.5) Gender Male 71 (61.2) Female 45 (38.8) Education Low (primary school) 8 (6.9) Medium (secondary school) 21 (18.1) High (university or college degree) 87 (75) Marital status Single 77 (66.4) Married or civil partnered 39 (33.6) Employment status Employed 76 (65.6) Nonemployed 31 (26.7) Unemployed 6 (5.2) Prefer not to answer 3 (2.6) World Health Organization regions Western Pacific 4 (3.4) Americas 10 (8.6) Southeast Asia 16 (13.8) Europe 67 (57.8) Africa 17 (14.7) Eastern Mediterranean 2 (1.7) Daily smoking (number of cigarettes) 10 or less 63 (54.3) 11-20 43 (37.1) 21-30 8 (6.9) 31 or more 2 (1.7) Fagerström nicotine dependence Low (0-4) 62 (53.4) Moderate (5-7) 45 (38.8) High (8-10) 9 (7.8) https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al 41.37 points after 2 weeks of using the app and then to 42.47 Self-Efficacy and Motivation to Quit points after 4 weeks of using the app. Median self-efficacy (and Figure 1 shows that the mean motivation to quit increased from IQR) at baseline was 37.00 (IQR 27-39), which increased to 5.94 at baseline to 6.20 after 2 weeks of app use and to 6.32 42.50 points (IQR 33-50) at 2 weeks and increased further to points after 4 weeks of app use. The median motivation to quit 44.00 (IQR 35-51) at 4 weeks. Paired t tests found that increases (and IQR) at baseline was 6.00 (IQR 5-7), 6.00 (IQR 5-8) at 2 in mean self-efficacy and motivation to quit between baseline weeks, and 7.00 (IQR 5-8) at 4 weeks. Similarly, the mean and midstudy and baseline and end-study were statistically self-efficacy score increased from 37.38 points at baseline to significant (Multimedia Appendix 4). Figure 1. Self-efficacy and motivation to quit at baseline, midstudy, and end-study (n=116). ease of use and usefulness of gamification were not associated Gamification with changes in self-efficacy. In addition, a 1-point increase in Table 2 displays the average midstudy and end-study perceived baseline self-efficacy was associated with a 1.06-point decrease usefulness, ease of use, and frequency of use of overall in self-efficacy between baseline and end-study (β=−1.06; 95% gamification and specific gamification features embedded in CI −1.22 to −0.90). Gender, marital status, nicotine dependence, the apps. At midstudy and end-study, goal setting was perceived baseline motivation, average perceived ease of use, and to be the most useful gamification feature (4.14 score out of 5), usefulness of gamification features were not statistically whereas sharing was perceived to be the least useful feature significantly associated with changes in self-efficacy from (3.72 at midstudy and 3.28 at end-study out of 5). Participants baseline to end-study. The second linear regression model also perceived goal setting to be the easiest to use feature at presented in Table 3 shows that individuals with medium or both midstudy and end-study (4.31 and 4.36, respectively). In high education had, on average, a 1.31 point (95% CI −2.60 to terms of frequency of use, participants self-reported that they −0.01) and 1.21 point (95% CI −2.32 to −0.10) lower motivation used the progress dashboards the most often during both the to quit than individuals with a low level of education. Moreover, midstudy and end-study assessments (3.23 and 3.30, a 1-point increase in average perceived frequency of use of respectively). The feature reported to be used the least frequently gamification features was statistically significantly associated was the sharing feature, as not many participants shared their with a 0.54-point increase in motivation to quit at end-study progress or results with others. compared with baseline (β=.54; 95% CI 0.15-0.94). Similarly, there was some indication that the average usefulness of The linear regression model results presented in Table 3 show gamification and baseline self-efficacy are associated with that a 1-point increase in average perceived frequency of change in motivation to quit. Finally, a 1-point increase in gamification features was statistically significantly associated baseline motivation to quit was statistically significantly with a 3.35-point increase in self-efficacy from baseline to associated with a 0.69-point decrease in motivation to quit end-study (β=3.35; 95% CI 0.31-6.40). The average perceived (β=−.69; 95% CI −0.90 to −0.49). https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al Table 2. 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) Perceived Perceived Perceived fre- Perceived Perceived Perceived fre- a a a a a a usefulness ease of use quency of use usefulness ease of use quency of use 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) 3.64 (0.95) 4.10 (0.79) 2.95 (1.26) 3.71 (1.12) 4.08 (0.98) 3.14 (1.23) Motivation cards 4.14 (0.85) 4.31 (0.71) 2.64 (0.91) 4.14 (0.80) 4.36 (0.81) 2.97 (1.03) Goal setting 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) Range: 1-5. Only applicable to Kwit. Only applicable to Quit Genius. Table 3. 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 β 95% CI β 95% CI Age (years) −.05 −0.26 to 0.17 −.01 −0.04 to 0.02 Gender Male (referent) Reference Reference Reference Reference Female 1.89 −2.48 to 6.26 .19 −0.37 to 0.76 Nicotine dependence Low (referent) Reference Reference Reference Reference Moderate −1.02 −5.50 to 3.46 −.08 −0.66 to 0.50 High 5.93 −2.15 to 14.02 .42 −0.61 to 1.46 Education Low (referent) Reference Reference Reference Reference Medium −4.95 −15.01 to 5.16 −2.60 to −0.01 −1.31 High −8.01 −16.63 to 0.61 −2.32 to −0.10 −1.21 Marital status Single (referent) Reference Reference Reference Reference Married −.10 −5.35 to 5.17 −.03 −0.70 to 0.65 a a Mean frequency of gamification use 0.31 to 6.40 0.15 to 0.94 3.35 .54 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 −1.22 to −0.90 −.02 −0.04 to −0.00 −1.06 Baseline motivation to quit 1.14 −0.44 to 2.71 −0.90 to −0.49 −.69 Constant 15.68 to 53.89 3.19 0.73 to 5.65 35.79 P<.05. https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al badges, level advancements, and progress tracking, gamification Discussion may help fulfill competence needs and enhance self-efficacy. On the other hand, elements such as the sharing feature could Principal Findings help support and enhance the feeling of relatedness and in turn We found that the use of Kwit and Quit Genius was associated boost motivation levels. with increased self-efficacy and motivation to quit levels 4 The association between perceived frequency of use of weeks after app use compared with baseline. Our study also gamification features and increases in motivation to quit and found that the perceived frequency of use of gamification self-efficacy can have important implications for the use of features was associated with an increase in self-efficacy and gamification and game design principles in nongame contexts motivation to quit. Finally, higher baseline self-efficacy and such as health behavior change. We found progress tracking to motivation to quit were both associated with smaller increases be the most frequently used gamification feature after 4 weeks in self-efficacy and motivation to quit levels 4 weeks after using of app use. According to a review of smoking cessation mobile the mobile apps compared with preapp use. apps, this feature was found to be most commonly integrated The key finding from our analyses showed that the frequency into apps by app developers [52]. However, we also found that of gamification use was associated with increased levels of one of the gamification features that users interacted with self-efficacy and motivation to quit after app use compared with frequently (unlocking levels or completing stages) was also the before app use. One possible reason for this could be that the feature that was adopted by only 20% of the smoking cessation frequency of gamification use has an effect on the overall user apps investigated in the review. It could be valuable for app engagement with the app, which in turn influences the developers to investigate the impact of such gamification self-efficacy and motivation to quit levels. Some studies in the features as they are not often integrated into mobile apps but existing literature have found positive effects of gamification could have the potential to improve user engagement and thereby on user engagement. For example, Othman et al [45] found that self-efficacy and motivation to quit. This also highlights the gamification had a positive impact on user engagement with importance and need for collaboration between mobile app Play4fit, a fitness smartphone app. Similarly, Looyestyn et al developers, researchers, and behavior change specialists to [46] found that gamification was effective in increasing create interventions that can effectively target and influence engagement levels with app-based programs. Therefore, higher vital cognitive factors via strategies such as gamification. overall app engagement as a result of gamification could have Our study also found some indications that the average perceived increased smokers’ confidence in their ability to quit and the usefulness of gamification was associated with increased levels level of motivation to quit. Moreover, as higher engagement of self-efficacy and motivation to quit after 4 weeks of app use has been found to be positively associated with intervention compared with baseline. This finding is in accordance with effectiveness [47,48], it is possible that apps with gamification previous studies that have explored the impact of gamification features that influence the overall engagement levels are on motivation to quit. For example, Pløhn and Aalberg [26] associated with better cessation outcomes than those without found that participants who managed to quit smoking after using gamification features. a gamified app-based cessation program reported the Although not explicitly investigated in our study, it is also effectiveness of gamification as a motivational factor [26]. possible that the frequency of gamification use had an effect on Similarly, a study of 16 participants found that individuals who user enjoyment, which in turn affected the motivation to quit engaged in a gamified cessation intervention had higher levels and self-efficacy levels. Higher levels of user engagement could of motivation than those who engaged with a nongamified intrinsically influence motivation levels, as the use of the app cessation intervention [27]. Our findings, supported by other could be rewarding or enjoyable for the user regardless of the studies, highlight the value of further investigating the usefulness final outcome. The theory of flow suggests that people can of specific gamification features to better design mHealth experience the state of flow in which they are highly involved solutions geared toward facilitating health behavior change. in an activity because it is so enjoyable that they would engage In addition to our findings on gamification, a general finding in it even at a cost [49]. Research shows that gamification of our analyses was the increase in self-efficacy between elements can enhance the level of enjoyment experienced, baseline and 4 weeks after app use. This implies that participants leading to higher levels of motivation [49]. Another possible experienced increased levels of confidence to refrain from theory-based explanation for why the frequency of gamification smoking not only when faced with both internal stimuli such use was found to be associated with increased self-efficacy as cravings and emotions but also when faced with external levels could be that gamification may influence a user’s stimuli such as being surrounded by other smokers or social competence and confidence. Certain gamification features such situations that trigger smoking cravings. Likewise, the increase as providing immediate feedback on performance, incremental in motivation to quit between baseline and end-study suggests levels, and providing badges of achievement could have that participants experienced higher determination to quit and provided a low risk way to attempt a task while also increasing placed greater importance in successfully quitting on the current confidence levels that their set goals are attainable [50]. quit attempt. The association between high self-efficacy and According to the self-determination theory, the fulfillment of motivation to quit with better cessation outcomes has been 3 types of psychological needs (autonomy, competence, and established in a large number of previous studies [6-9]. Although relatedness) can foster motivation [51]. By providing immediate we did not assess quit outcomes in our study, the evidence that feedback on performance through game elements such as increased self-efficacy and motivation to quit can lead to better https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al quitting rates is an encouraging finding for gamified smoking the validity and reliability of the questionnaire developed to cessation apps. Such apps could be considered as possible assess gamification more rigorously. Another drawback of our cessation interventions available to smokers seeking to quit. research was that the majority of participants reported having Increased use of mobile apps for smoking cessation could also a low to moderate dependence on nicotine. It could be that the have wide-reaching consequences for alleviating health findings differed among individuals with high nicotine inequalities, as mHealth solutions are able to reach a large dependence. Therefore, future research could explore the number of people at low cost [15]. differences between the various types of smokers. Similarly, participants with mental health conditions were not eligible to It is important to note that the majority of increases in both participate in the study, and it could be that the findings are not self-efficacy and motivation to quit are evident during the first generalizable to all members of the population. Finally, our 2 weeks of app use. This could imply that the gamified mobile study comprised motivated volunteers, which could subject the apps have a saturated effect after an initial period of using the findings to volunteer bias. app, after which they help participants maintain their self-efficacy and motivation levels. Past research has found that To address the natural limitations of this study design, future an increase in self-efficacy during the course of an intervention research could consider running randomized controlled trials can lead to greater likelihood of long-term success [53]. Another with 2 smoking cessation apps that are as similar as possible, study showed that participants who experienced an increase in differing only in the number of gamification elements or, more self-efficacy during the first 2 weeks of a 12-week smoking importantly, the type of gamification elements to robustly test cessation intervention were significantly more likely to stay the impact of gamification. Our study was underpowered to quit after treatment [54]. The study also sheds light on the investigate the impact of individual game elements; therefore, importance of promoting a smoker’s early sense of confidence we were unable to explore the effects of and differences between in their ability to quit to increase the odds of successfully game elements. Future studies could try to isolate and test quitting long after the intervention is completed [54]. These individual game elements within behavior change interventions, findings could have possible implications for future smoking as not all gamification elements may have the same impact or cessation interventions to adopt strategies to raise self-efficacy function in the same way. It could be that certain gamification and motivation levels over the course of the intervention, elements interact with other elements or with individual especially early on. dispositions, situational circumstances, and the characteristics of particular target activities differently than others [20]. Finally, our analyses also showed that having higher education, Consequently, it is vital for future research to focus on baseline self-efficacy, and motivation to quit were associated theory-driven studies that investigate how individual with smaller improvements in self-efficacy and motivation to gamification elements work. quit. This suggests that the gamified mobile apps in our study have a greater benefit for individuals with lower levels of Conclusions confidence in their ability to quit, individuals with lower In conclusion, our research found that more frequent engagement determination to quit, and individuals with lower education with gamification features in smoking cessation apps was levels or individuals with lower socioeconomic status. As associated with higher self-efficacy and motivation to quit. The socioeconomic differences are present in both smoking findings of this study provide a good platform for further prevalence and successful cessation, this finding could be used investigation into the role of gamification in improving to inform future interventions to help disadvantaged groups and important cognitive factors essential for the quitting process of thereby reduce inequalities. smokers. Future studies should continue to explore the impact and usefulness of gamification in the context of mHealth. On Limitations the basis of our findings and existing literature, we recommend By examining the impact of gamified smoking cessation mobile that mobile app developers collaborate with behavior change apps quantitatively, we attempt to address a gap in the existing specialists to develop more tailored, evidence-based, and literature. However, to do so, we developed a questionnaire to theory-driven interventions. At the same time, app developers quantitatively assess gamification that requires participant should be encouraged to work together with scientists to explore self-report. Self-reporting may have led to different or inaccurate and test strategies, such as gamification, that could target vital perceptions, particularly when answering questions regarding psychological components of behavior change while possibly frequency of use. Moreover, the developed questionnaire has improving engagement with the app. not yet been scientifically validated. Future research could test Acknowledgments 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. https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al Authors' Contributions 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. Conflicts of Interest None declared. Multimedia Appendix 1 Study participant flowchart. [DOCX File , 46 KB-Multimedia Appendix 1] Multimedia Appendix 2 Screenshots of Quit Genius and Kwit. [DOCX File , 646 KB-Multimedia Appendix 2] Multimedia Appendix 3 Gamification features and tactics by Cugelman embedded in the Kwit and Quit Genius apps. [DOCX File , 13 KB-Multimedia Appendix 3] Multimedia Appendix 4 The t tests statistically examining the mean differences in self-efficacy and motivation to quit scores between study time points (n=116). [DOCX File , 13 KB-Multimedia Appendix 4] References 1. GBD 2013 Risk Factors Collaborators, Forouzanfar MH, Alexander L, Anderson HR, Bachman VF, Biryukov S, et al. . [doi: 10.1016/S0140-6736(15)00128-2] [Medline: 26364544] 2. Tobacco. World Health Organisation. 2020. URL: https://www.who.int/en/news-room/fact-sheets/detail/tobacco [accessed 2021-01-05] 3. Statistics on Smoking, England - 2019. National Health Service. 2019. URL: https://digital.nhs.uk/data-and-information/ publications/statistical/statistics-on-smoking/statistics-on-smoking-england-2019/ part-1-smoking-related-ill-health-and-mortality [accessed 2020-11-05] 4. 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Addict Behav 2011 Jan;36(1-2):144-147 [FREE Full text] [doi: 10.1016/j.addbeh.2010.08.024] [Medline: 20869812] Abbreviations CBT: cognitive behavioral therapy ICREC: Imperial College London Research Ethics Committee mHealth: mobile health PID: participant identification number https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 12 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al Edited by N Zary; submitted 20.01.21; peer-reviewed by J Moon, M Schmidt-Kraepelin; comments to author 12.02.21; revised version received 24.02.21; accepted 02.04.21; published 27.04.21 Please cite as: Rajani NB, Mastellos N, Filippidis FT Impact of Gamification on the Self-Efficacy and Motivation to Quit of Smokers: Observational Study of Two Gamified Smoking Cessation Mobile Apps JMIR Serious Games 2021;9(2):e27290 URL: https://games.jmir.org/2021/2/e27290 doi: 10.2196/27290 PMID: ©Nikita B Rajani, Nikolaos Mastellos, Filippos T Filippidis. Originally published in JMIR Serious Games (https://games.jmir.org), 27.04.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Serious Games, is properly cited. The complete bibliographic information, a link to the original publication on https://games.jmir.org, as well as this copyright and license information must be included. https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 13 (page number not for citation purposes) XSL FO RenderX http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JMIR Serious Games JMIR Publications

Impact of Gamification on the Self-Efficacy and Motivation to Quit of Smokers: Observational Study of Two Gamified Smoking Cessation Mobile Apps

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Abstract

Background: 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. Objective: 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. Methods: 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. Results: 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. Goal setting was perceived to be the most useful gamification feature, whereas sharing was perceived to be the least useful. Participants self-reported that they used the progress dashboards the most often, whereas they used the sharing feature the least often. The average perceived frequency of gamification features was statistically significantly associated with change in self-efficacy (β=3.35; 95% CI 0.31-6.40) and change in motivation to quit (β=.54; 95% CI 0.15-0.94) between baseline and end-study. Conclusions: 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. (JMIR Serious Games 2021;9(2):e27290) doi: 10.2196/27290 KEYWORDS gamification; smoking cessation; mobile applications; self-efficacy; motivation to quit; mHealth; mobile phone https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al dominant theory of motivation, suggests that gamification Introduction elements such as points and badges serve as informational feedback instilling a sense of intrinsically motivating Smoking is the second leading risk factor for early death and competence in the user [21]. Similarly, goal-setting theory, disability worldwide, with approximately 8 million deaths another prominent theory of motivation, has been associated annually [1,2]. In the United Kingdom, 16% of all deaths in with gamification; for example, elements such as leaderboards 2016 were attributed to smoking [3]. Despite most smokers and levels provide individuals with smaller, more immediate wanting to quit, research shows that the number of smokers in goals that can improve task performance and, in turn, increase the United Kingdom who tried to quit in the past year dropped one’s confidence in their ability to complete tasks [21,22]. by 7% between 2008 and 2017 [4]. Among the smokers making attempts, only 3%-5% are successful in staying quit after a year Although gamification seems promising, the impact of [5]. Exploring methods that can improve the success rates of gamification in the field of health behavior change has focused long-term quitting can lead to a decreased prevalence of chronic primarily on improving physical activity levels [23-25]. More diseases and premature mortality. Previous studies have shown specifically, there is room to further study the use of that high motivation to quit and self-efficacy, 2 factors often gamification in the context of other behaviors, such as smoking. integrated into face-to-face behavioral support interventions, With the proliferation of smartphone use and the increased have been found to increase the likelihood of attempting to quit availability of digitalized interventions, it is vital to investigate and successfully quitting [6-9]. novel strategies, such as gamification, that complement mHealth solutions and are in line with current digitalization trends. The Motivation to quit refers to the level of determination and majority of studies that have explored the impact of gamification importance placed by an individual on quitting smoking [10]. in the context of smoking cessation have been purely qualitative On the other hand, self-efficacy, a theoretical construct first and consequently have not operationalized gamification termed by Bandura [11], in the context of smoking cessation quantitatively. For example, Pløhn and Aalberg [26] interviewed refers to one’s confidence in their ability to refrain from smoking participants after they used a digital smoking cessation when faced with internal and external stimuli [11,12]. Several intervention with gamification features and found positive strategies are adopted by health behavior change interventions perceptions of gamification as an important motivational factor to influence self-efficacy or motivation to quit. For example, to aid quitting. However, the smoking cessation intervention motivational interviewing is often used to help smokers explore was not delivered via a mobile app. Another study by El-Hilly reasons for quitting and “make them feel more willing and able et al [27] found promising results on the effect of gamification to stop smoking” [13]. Similarly, vicarious experience and on the motivation and engagement of smokers within the context performance attainment are strategies to influence self-efficacy. of mHealth. Similar to the study by Pløhn and Aalberg [26], Vicarious experience involves “exposing the individual to the study by El-Hilly et al [27] was also qualitative and had a successful behaviour performances or gaining experience small sample size (n=16), hindering the external validity of the through practice” [14]. In the context of smoking cessation, this findings. On the other hand, Lin et al [28] investigated could mean showing smokers examples of other smokers who gamification quantitatively and found that program progress or successfully quit after attending cessation programs. step completion as a gamification element in a smoking Performance attainment refers to having successful experiences cessation mobile app had a positive impact on psychological [11,14]; for a smoker trying to quit, this could mean staying factors such as user well-being, inspiration, and empowerment abstinent for a day and recognizing this as a success. Such [28]. It would be worthwhile to investigate whether gamification strategies have been integrated into both face-to-face and digital in smoking cessation apps can also positively impact essential interventions, such as mobile health (mHealth) solutions, which cognitive factors such as self-efficacy and motivation to quit. have become increasingly important to improve access, knowledge, and behavior across different contexts and Exploring whether gamification in the context of mHealth can population groups [15]. increase the self-efficacy and motivation of smokers could lead to the design of more tailored interventions, which, in turn, One strategy that has been frequently applied across both could improve cessation rates and reduce the health burden of physical and remote interventions for behavioral change is tobacco consumption. The findings of this study could also gamification, also known as the use of game elements in a provide insights into the effective design of mobile apps. nongame context [16]. Some examples of game elements include Moreover, because of the wide reach and low cost of mHealth achievement badges, goal setting, progress tracking, sharing solutions, understanding the effects of gamification on mHealth progress, and levels [17]. Past studies have found that could have considerable effects on helping disadvantaged groups gamification can positively impact cognitive components of and reducing health inequalities [29]. On the basis of the behavioral change, including self-efficacy and motivation. For limitations of prior research, this study’s aim of exploring example, self-efficacy was the most cited advantage of gamified gamified smoking cessation apps can also help enhance the classrooms, as it improved the confidence levels of students current understanding of the effectiveness of mHealth [18]. Similarly, Thorsteinsen et al [19] found that gaming interventions for behavior change and extend our knowledge elements significantly increased the motivation of individuals of novel methods to promote healthy lifestyle changes. to engage in physical activity. The use of gamification has Specifically, our study aims to quantitatively assess the gradually become more popular as it appears to share key association between overall perceived usefulness, ease of use, components with several behavioral change theories and techniques [17,20]. For example, self-determination theory, a https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al and frequency of use of gamification features and the Mobile Apps self-efficacy and motivation of smokers seeking to quit. Mobile apps for the study were selected based on a mobile app review that found these 2 apps to have a high embedment of Methods gamification features and a high adherence to smoking cessation guidelines in the United Kingdom [33,34]. Screenshots of both Sampling and Eligibility apps are shown in Multimedia Appendix 2. On the basis of an a priori analysis using a power level of Kwit 1−β=.80 and a significance level α=.05, we aimed to recruit 140 participants. The results of a previous study examining the Kwit is a smoking cessation mobile app that helps individuals impact of gamification elements in a fitness app on perceived starting their quit journey and individuals trying to stay quit competence, a proxy for self-efficacy, indicated that 112 [35]. The app includes several features such as a calculator, a participants were required to complete the study [25]. Similar smoking diary that helps smokers log and analyze cravings and to previous mobile app studies, we considered a dropout rate triggers, motivation cards, social media sharing, levels, and of approximately 20% [30-32], which resulted in the recruitment achievement cards. The app is based on cognitive behavioral of 140 participants. The sample size calculation also assumes therapy (CBT) and gamification strategies. The versions of Kwit that both apps are similar based on a prior mobile app review used during the study period included those released from June [33]. 2019 (v.4.1) to July 2020 (v.4.4). Participants were required to be at least 18 years old and current Quit Genius smokers (at least one cigarette a day and 100 cigarettes smoked Quit Genius is a gamified smoking cessation mobile app based in their lifetime) to be eligible. Moreover, to take part in the on CBT [36]. It delivers personalized support to individuals study, individuals had to report that they were trying or willing seeking to quit smoking and helps quitters maintain their quit to quit smoking in the next 30 days and were not using other status. It includes several features such as a tracker, a daily diary forms of smoking cessation treatments. Individuals diagnosed that allows quitters to log their cravings and triggers, a quitting with mental health conditions were excluded from the study. toolbox, a goal-setting feature, achievement badges, stages of information that build upon each other, and a quit coach who Study Design provides continuous personalized support. The versions A 4-week observational study assessing the association between downloaded by participants were those released from June 2019 gamification, self-efficacy, and motivation to quit was conducted (v.1.1) to July 2020 (v.1.9). from June 2019 to July 2020. No face-to-face contact was required, and the study was conducted on the internet. Measures Participants were recruited via social media, and posters were Sociodemographic Factors displayed in public places in London. Initially, participants who expressed interest in the study (N=326) were screened to assess Common sociodemographic factors were assessed at baseline. their eligibility. Eligible participants provided informed consent Age in years was categorized as 18-29, 30-41, 42-53, or 54-65 (n=170) and were assigned a participant identification number years, and gender was categorized as male or female. Marital (PID). They were then requested to complete a baseline status was categorized as single or married or civil partnered. questionnaire that asked about general demographics (age, Education was based on United Nations Educational, Scientific gender, education, marital status, education, country of and Cultural Organization’s classification into 3 categories: low residence, etc), smoking habits (number of cigarettes smoked, if primary school was completed, medium if secondary school nicotine dependence, etc), self-efficacy, and motivation to quit. was completed, and high if a college or university degree was attained [37]. Employment status was categorized as In total, 154 participants completed the baseline assessment and unemployed (individuals who are willing and able to work but were provided instructions on how to download and start using have no employment), employed, or nonemployed (individuals the app. Even-numbered PIDs were assigned to the mobile app who are unable to work, including students and homemakers). Quit Genius, and odd-numbered PIDs were assigned to the Residence was categorized based on the World Health mobile app Kwit. This deterministic method was used to ensure Organization regions: Western Pacific, Americas, Southeast an equal split of participants between the 2 apps. Participants Asia, Europe, Africa, and Eastern Mediterranean [38]. 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 Nicotine Dependence an end-study questionnaire after 4 weeks of using the app were The Fagerström test with 6 items was used to measure given to participants. participants’ tolerance of and dependence on nicotine [36]. On the basis of the responses, participants were categorized into 3 Of those participants who completed the baseline assessment, levels: low (0-4 points), moderate (5-7 points), and high (8-10 138 installed the app and 116 completed all 4 weeks of the points) [39,40]. study. Midstudy and end-study assessments included questions regarding gamification, self-efficacy, and motivation to quit. Self-Efficacy Participants were incentivized via free access to all features of The self-efficacy of a participant was measured using a 12-item the app and a chance to win a £50 (US $68) Amazon voucher. scale called The Smoking Self-Efficacy Questionnaire [12]. An overview of the number of participants at each stage of the The scale assesses an individual’s confidence in their ability to study is presented in Multimedia Appendix 1. https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al refrain from smoking when faced with internal and external higher mean (from 1 to 5) indicating greater overall engagement stimuli. Response options included not at all sure, not very sure, with gamification. more or less sure, fairly sure, and absolutely sure. A total score Statistical Analysis ranging from 12 to 60 was computed for each participant, with The statistical software STATA 13.1 (StataCorp), was used for higher scores signifying higher self-efficacy. the analyses. Box and whisker plots were created to present an Motivation to Quit overview of self-efficacy and motivation to quit levels of Participants were asked 2 items frequently used in cessation participants at various study time points. The mean values of studies to measure their motivation to quit smoking [10,41,42]. perceived frequency, ease of use, and usefulness of gamification The first item asked was as follows: How important is it to you features were calculated at midstudy and end-study. Two-way to give up smoking altogether at this attempt? Responses paired sample t tests were used to test whether differences in included the following: desperately important, very important, self-efficacy and motivation to quit at various time points of quite important, and not all that important. The second item the study were statistically significant. In addition, we explored asked was as follows: How determined are you to give up various linear regression models to examine whether smoking at this attempt? Response options included the gamification was associated with changes in self-efficacy and following: extremely determined, very determined, quite motivation to quit. On the basis of an iterative process that determined, and not all that determined. A total score ranging considered the fit of the data with our model (ie, comparing the from 2 to 8 was calculated for each participant, with higher Akaike information criterion and Bayesian information scores signifying higher motivation. criterion), 2 linear regression models were performed. The first tested the association between perceived ease of use, frequency Gamification of use, and usefulness of gamification with change in Gamification features for each app were identified using self-efficacy, and the second tested the association between Cugelman framework for gamification strategies and tactics perceived ease of use, frequency of use, and usefulness of and are displayed in Multimedia Appendix 3 [17]. For each gamification with change in motivation to quit. Both models identified gamification feature, participants were asked how controlled for age, gender, education, marital status, nicotine useful and easy to use they found it during their quit attempt. dependence, baseline self-efficacy, and baseline motivation to Participants were provided with 5-point Likert scale responses: quit. Significance at the 5% level (0.05), along with 95% CIs strongly agree, agree, neither agree nor disagree, disagree, and for all included coefficients, is presented in the Results section. strongly disagree. Perceived usefulness and ease of use are 2 vital components of the technology acceptance model, which Results has been widely used in existing literature to better understand user acceptance and attitudes toward mobile apps and app Study Participants features [43,44]. As shown in Table 1, there was an equal split of participants who used the apps Kwit (58/116, 50%) and Quit Genius (58/116, Participants were also asked how frequently they engaged with 50%). The majority of participants were male (71/116, 61.2%), each gamification element or feature during their quit attempt. highly educated (87/116, 75%), single (77/116, 66.4%), Participants were provided with 5-point Likert scale responses: employed (76/116, 65.6%), and living in Europe (67/116, almost always, often, sometimes, rarely, and never. Responses 57.8%). More than half of the participants smoked 10 or fewer were assigned points ranging from 1 to 5 for each gamification cigarettes on a daily basis (63/116, 54.3%), and the majority feature. A pooled mean was calculated for all features, with a had low to moderate dependence on nicotine (107/116, 92.2%). https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al Table 1. Sociodemographic and general characteristics of participants (n=116). Characteristics Respondents, n (%) Assigned mobile app Kwit 58 (50) Quit Genius 58 (50) Age (years) 18-29 49 (42.2) 30-41 41 (35.3) 42-53 15 (12.9) 54-65 11 (9.5) Gender Male 71 (61.2) Female 45 (38.8) Education Low (primary school) 8 (6.9) Medium (secondary school) 21 (18.1) High (university or college degree) 87 (75) Marital status Single 77 (66.4) Married or civil partnered 39 (33.6) Employment status Employed 76 (65.6) Nonemployed 31 (26.7) Unemployed 6 (5.2) Prefer not to answer 3 (2.6) World Health Organization regions Western Pacific 4 (3.4) Americas 10 (8.6) Southeast Asia 16 (13.8) Europe 67 (57.8) Africa 17 (14.7) Eastern Mediterranean 2 (1.7) Daily smoking (number of cigarettes) 10 or less 63 (54.3) 11-20 43 (37.1) 21-30 8 (6.9) 31 or more 2 (1.7) Fagerström nicotine dependence Low (0-4) 62 (53.4) Moderate (5-7) 45 (38.8) High (8-10) 9 (7.8) https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al 41.37 points after 2 weeks of using the app and then to 42.47 Self-Efficacy and Motivation to Quit points after 4 weeks of using the app. Median self-efficacy (and Figure 1 shows that the mean motivation to quit increased from IQR) at baseline was 37.00 (IQR 27-39), which increased to 5.94 at baseline to 6.20 after 2 weeks of app use and to 6.32 42.50 points (IQR 33-50) at 2 weeks and increased further to points after 4 weeks of app use. The median motivation to quit 44.00 (IQR 35-51) at 4 weeks. Paired t tests found that increases (and IQR) at baseline was 6.00 (IQR 5-7), 6.00 (IQR 5-8) at 2 in mean self-efficacy and motivation to quit between baseline weeks, and 7.00 (IQR 5-8) at 4 weeks. Similarly, the mean and midstudy and baseline and end-study were statistically self-efficacy score increased from 37.38 points at baseline to significant (Multimedia Appendix 4). Figure 1. Self-efficacy and motivation to quit at baseline, midstudy, and end-study (n=116). ease of use and usefulness of gamification were not associated Gamification with changes in self-efficacy. In addition, a 1-point increase in Table 2 displays the average midstudy and end-study perceived baseline self-efficacy was associated with a 1.06-point decrease usefulness, ease of use, and frequency of use of overall in self-efficacy between baseline and end-study (β=−1.06; 95% gamification and specific gamification features embedded in CI −1.22 to −0.90). Gender, marital status, nicotine dependence, the apps. At midstudy and end-study, goal setting was perceived baseline motivation, average perceived ease of use, and to be the most useful gamification feature (4.14 score out of 5), usefulness of gamification features were not statistically whereas sharing was perceived to be the least useful feature significantly associated with changes in self-efficacy from (3.72 at midstudy and 3.28 at end-study out of 5). Participants baseline to end-study. The second linear regression model also perceived goal setting to be the easiest to use feature at presented in Table 3 shows that individuals with medium or both midstudy and end-study (4.31 and 4.36, respectively). In high education had, on average, a 1.31 point (95% CI −2.60 to terms of frequency of use, participants self-reported that they −0.01) and 1.21 point (95% CI −2.32 to −0.10) lower motivation used the progress dashboards the most often during both the to quit than individuals with a low level of education. Moreover, midstudy and end-study assessments (3.23 and 3.30, a 1-point increase in average perceived frequency of use of respectively). The feature reported to be used the least frequently gamification features was statistically significantly associated was the sharing feature, as not many participants shared their with a 0.54-point increase in motivation to quit at end-study progress or results with others. compared with baseline (β=.54; 95% CI 0.15-0.94). Similarly, there was some indication that the average usefulness of The linear regression model results presented in Table 3 show gamification and baseline self-efficacy are associated with that a 1-point increase in average perceived frequency of change in motivation to quit. Finally, a 1-point increase in gamification features was statistically significantly associated baseline motivation to quit was statistically significantly with a 3.35-point increase in self-efficacy from baseline to associated with a 0.69-point decrease in motivation to quit end-study (β=3.35; 95% CI 0.31-6.40). The average perceived (β=−.69; 95% CI −0.90 to −0.49). https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al Table 2. 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) Perceived Perceived Perceived fre- Perceived Perceived Perceived fre- a a a a a a usefulness ease of use quency of use usefulness ease of use quency of use 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) 3.64 (0.95) 4.10 (0.79) 2.95 (1.26) 3.71 (1.12) 4.08 (0.98) 3.14 (1.23) Motivation cards 4.14 (0.85) 4.31 (0.71) 2.64 (0.91) 4.14 (0.80) 4.36 (0.81) 2.97 (1.03) Goal setting 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) Range: 1-5. Only applicable to Kwit. Only applicable to Quit Genius. Table 3. 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 β 95% CI β 95% CI Age (years) −.05 −0.26 to 0.17 −.01 −0.04 to 0.02 Gender Male (referent) Reference Reference Reference Reference Female 1.89 −2.48 to 6.26 .19 −0.37 to 0.76 Nicotine dependence Low (referent) Reference Reference Reference Reference Moderate −1.02 −5.50 to 3.46 −.08 −0.66 to 0.50 High 5.93 −2.15 to 14.02 .42 −0.61 to 1.46 Education Low (referent) Reference Reference Reference Reference Medium −4.95 −15.01 to 5.16 −2.60 to −0.01 −1.31 High −8.01 −16.63 to 0.61 −2.32 to −0.10 −1.21 Marital status Single (referent) Reference Reference Reference Reference Married −.10 −5.35 to 5.17 −.03 −0.70 to 0.65 a a Mean frequency of gamification use 0.31 to 6.40 0.15 to 0.94 3.35 .54 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 −1.22 to −0.90 −.02 −0.04 to −0.00 −1.06 Baseline motivation to quit 1.14 −0.44 to 2.71 −0.90 to −0.49 −.69 Constant 15.68 to 53.89 3.19 0.73 to 5.65 35.79 P<.05. https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al badges, level advancements, and progress tracking, gamification Discussion may help fulfill competence needs and enhance self-efficacy. On the other hand, elements such as the sharing feature could Principal Findings help support and enhance the feeling of relatedness and in turn We found that the use of Kwit and Quit Genius was associated boost motivation levels. with increased self-efficacy and motivation to quit levels 4 The association between perceived frequency of use of weeks after app use compared with baseline. Our study also gamification features and increases in motivation to quit and found that the perceived frequency of use of gamification self-efficacy can have important implications for the use of features was associated with an increase in self-efficacy and gamification and game design principles in nongame contexts motivation to quit. Finally, higher baseline self-efficacy and such as health behavior change. We found progress tracking to motivation to quit were both associated with smaller increases be the most frequently used gamification feature after 4 weeks in self-efficacy and motivation to quit levels 4 weeks after using of app use. According to a review of smoking cessation mobile the mobile apps compared with preapp use. apps, this feature was found to be most commonly integrated The key finding from our analyses showed that the frequency into apps by app developers [52]. However, we also found that of gamification use was associated with increased levels of one of the gamification features that users interacted with self-efficacy and motivation to quit after app use compared with frequently (unlocking levels or completing stages) was also the before app use. One possible reason for this could be that the feature that was adopted by only 20% of the smoking cessation frequency of gamification use has an effect on the overall user apps investigated in the review. It could be valuable for app engagement with the app, which in turn influences the developers to investigate the impact of such gamification self-efficacy and motivation to quit levels. Some studies in the features as they are not often integrated into mobile apps but existing literature have found positive effects of gamification could have the potential to improve user engagement and thereby on user engagement. For example, Othman et al [45] found that self-efficacy and motivation to quit. This also highlights the gamification had a positive impact on user engagement with importance and need for collaboration between mobile app Play4fit, a fitness smartphone app. Similarly, Looyestyn et al developers, researchers, and behavior change specialists to [46] found that gamification was effective in increasing create interventions that can effectively target and influence engagement levels with app-based programs. Therefore, higher vital cognitive factors via strategies such as gamification. overall app engagement as a result of gamification could have Our study also found some indications that the average perceived increased smokers’ confidence in their ability to quit and the usefulness of gamification was associated with increased levels level of motivation to quit. Moreover, as higher engagement of self-efficacy and motivation to quit after 4 weeks of app use has been found to be positively associated with intervention compared with baseline. This finding is in accordance with effectiveness [47,48], it is possible that apps with gamification previous studies that have explored the impact of gamification features that influence the overall engagement levels are on motivation to quit. For example, Pløhn and Aalberg [26] associated with better cessation outcomes than those without found that participants who managed to quit smoking after using gamification features. a gamified app-based cessation program reported the Although not explicitly investigated in our study, it is also effectiveness of gamification as a motivational factor [26]. possible that the frequency of gamification use had an effect on Similarly, a study of 16 participants found that individuals who user enjoyment, which in turn affected the motivation to quit engaged in a gamified cessation intervention had higher levels and self-efficacy levels. Higher levels of user engagement could of motivation than those who engaged with a nongamified intrinsically influence motivation levels, as the use of the app cessation intervention [27]. Our findings, supported by other could be rewarding or enjoyable for the user regardless of the studies, highlight the value of further investigating the usefulness final outcome. The theory of flow suggests that people can of specific gamification features to better design mHealth experience the state of flow in which they are highly involved solutions geared toward facilitating health behavior change. in an activity because it is so enjoyable that they would engage In addition to our findings on gamification, a general finding in it even at a cost [49]. Research shows that gamification of our analyses was the increase in self-efficacy between elements can enhance the level of enjoyment experienced, baseline and 4 weeks after app use. This implies that participants leading to higher levels of motivation [49]. Another possible experienced increased levels of confidence to refrain from theory-based explanation for why the frequency of gamification smoking not only when faced with both internal stimuli such use was found to be associated with increased self-efficacy as cravings and emotions but also when faced with external levels could be that gamification may influence a user’s stimuli such as being surrounded by other smokers or social competence and confidence. Certain gamification features such situations that trigger smoking cravings. Likewise, the increase as providing immediate feedback on performance, incremental in motivation to quit between baseline and end-study suggests levels, and providing badges of achievement could have that participants experienced higher determination to quit and provided a low risk way to attempt a task while also increasing placed greater importance in successfully quitting on the current confidence levels that their set goals are attainable [50]. quit attempt. The association between high self-efficacy and According to the self-determination theory, the fulfillment of motivation to quit with better cessation outcomes has been 3 types of psychological needs (autonomy, competence, and established in a large number of previous studies [6-9]. Although relatedness) can foster motivation [51]. By providing immediate we did not assess quit outcomes in our study, the evidence that feedback on performance through game elements such as increased self-efficacy and motivation to quit can lead to better https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al quitting rates is an encouraging finding for gamified smoking the validity and reliability of the questionnaire developed to cessation apps. Such apps could be considered as possible assess gamification more rigorously. Another drawback of our cessation interventions available to smokers seeking to quit. research was that the majority of participants reported having Increased use of mobile apps for smoking cessation could also a low to moderate dependence on nicotine. It could be that the have wide-reaching consequences for alleviating health findings differed among individuals with high nicotine inequalities, as mHealth solutions are able to reach a large dependence. Therefore, future research could explore the number of people at low cost [15]. differences between the various types of smokers. Similarly, participants with mental health conditions were not eligible to It is important to note that the majority of increases in both participate in the study, and it could be that the findings are not self-efficacy and motivation to quit are evident during the first generalizable to all members of the population. Finally, our 2 weeks of app use. This could imply that the gamified mobile study comprised motivated volunteers, which could subject the apps have a saturated effect after an initial period of using the findings to volunteer bias. app, after which they help participants maintain their self-efficacy and motivation levels. Past research has found that To address the natural limitations of this study design, future an increase in self-efficacy during the course of an intervention research could consider running randomized controlled trials can lead to greater likelihood of long-term success [53]. Another with 2 smoking cessation apps that are as similar as possible, study showed that participants who experienced an increase in differing only in the number of gamification elements or, more self-efficacy during the first 2 weeks of a 12-week smoking importantly, the type of gamification elements to robustly test cessation intervention were significantly more likely to stay the impact of gamification. Our study was underpowered to quit after treatment [54]. The study also sheds light on the investigate the impact of individual game elements; therefore, importance of promoting a smoker’s early sense of confidence we were unable to explore the effects of and differences between in their ability to quit to increase the odds of successfully game elements. Future studies could try to isolate and test quitting long after the intervention is completed [54]. These individual game elements within behavior change interventions, findings could have possible implications for future smoking as not all gamification elements may have the same impact or cessation interventions to adopt strategies to raise self-efficacy function in the same way. It could be that certain gamification and motivation levels over the course of the intervention, elements interact with other elements or with individual especially early on. dispositions, situational circumstances, and the characteristics of particular target activities differently than others [20]. Finally, our analyses also showed that having higher education, Consequently, it is vital for future research to focus on baseline self-efficacy, and motivation to quit were associated theory-driven studies that investigate how individual with smaller improvements in self-efficacy and motivation to gamification elements work. quit. This suggests that the gamified mobile apps in our study have a greater benefit for individuals with lower levels of Conclusions confidence in their ability to quit, individuals with lower In conclusion, our research found that more frequent engagement determination to quit, and individuals with lower education with gamification features in smoking cessation apps was levels or individuals with lower socioeconomic status. As associated with higher self-efficacy and motivation to quit. The socioeconomic differences are present in both smoking findings of this study provide a good platform for further prevalence and successful cessation, this finding could be used investigation into the role of gamification in improving to inform future interventions to help disadvantaged groups and important cognitive factors essential for the quitting process of thereby reduce inequalities. smokers. Future studies should continue to explore the impact and usefulness of gamification in the context of mHealth. On Limitations the basis of our findings and existing literature, we recommend By examining the impact of gamified smoking cessation mobile that mobile app developers collaborate with behavior change apps quantitatively, we attempt to address a gap in the existing specialists to develop more tailored, evidence-based, and literature. However, to do so, we developed a questionnaire to theory-driven interventions. At the same time, app developers quantitatively assess gamification that requires participant should be encouraged to work together with scientists to explore self-report. Self-reporting may have led to different or inaccurate and test strategies, such as gamification, that could target vital perceptions, particularly when answering questions regarding psychological components of behavior change while possibly frequency of use. Moreover, the developed questionnaire has improving engagement with the app. not yet been scientifically validated. Future research could test Acknowledgments 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. https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al Authors' Contributions 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. Conflicts of Interest None declared. Multimedia Appendix 1 Study participant flowchart. [DOCX File , 46 KB-Multimedia Appendix 1] Multimedia Appendix 2 Screenshots of Quit Genius and Kwit. [DOCX File , 646 KB-Multimedia Appendix 2] Multimedia Appendix 3 Gamification features and tactics by Cugelman embedded in the Kwit and Quit Genius apps. [DOCX File , 13 KB-Multimedia Appendix 3] Multimedia Appendix 4 The t tests statistically examining the mean differences in self-efficacy and motivation to quit scores between study time points (n=116). [DOCX File , 13 KB-Multimedia Appendix 4] References 1. GBD 2013 Risk Factors Collaborators, Forouzanfar MH, Alexander L, Anderson HR, Bachman VF, Biryukov S, et al. . [doi: 10.1016/S0140-6736(15)00128-2] [Medline: 26364544] 2. Tobacco. World Health Organisation. 2020. URL: https://www.who.int/en/news-room/fact-sheets/detail/tobacco [accessed 2021-01-05] 3. Statistics on Smoking, England - 2019. National Health Service. 2019. URL: https://digital.nhs.uk/data-and-information/ publications/statistical/statistics-on-smoking/statistics-on-smoking-england-2019/ part-1-smoking-related-ill-health-and-mortality [accessed 2020-11-05] 4. 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Addict Behav 2011 Jan;36(1-2):144-147 [FREE Full text] [doi: 10.1016/j.addbeh.2010.08.024] [Medline: 20869812] Abbreviations CBT: cognitive behavioral therapy ICREC: Imperial College London Research Ethics Committee mHealth: mobile health PID: participant identification number https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 12 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Rajani et al Edited by N Zary; submitted 20.01.21; peer-reviewed by J Moon, M Schmidt-Kraepelin; comments to author 12.02.21; revised version received 24.02.21; accepted 02.04.21; published 27.04.21 Please cite as: Rajani NB, Mastellos N, Filippidis FT Impact of Gamification on the Self-Efficacy and Motivation to Quit of Smokers: Observational Study of Two Gamified Smoking Cessation Mobile Apps JMIR Serious Games 2021;9(2):e27290 URL: https://games.jmir.org/2021/2/e27290 doi: 10.2196/27290 PMID: ©Nikita B Rajani, Nikolaos Mastellos, Filippos T Filippidis. Originally published in JMIR Serious Games (https://games.jmir.org), 27.04.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Serious Games, is properly cited. The complete bibliographic information, a link to the original publication on https://games.jmir.org, as well as this copyright and license information must be included. https://games.jmir.org/2021/2/e27290 JMIR Serious Games 2021 | vol. 9 | iss. 2 | e27290 | p. 13 (page number not for citation purposes) XSL FO RenderX

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Published: Apr 27, 2021

Keywords: gamification; smoking cessation; mobile applications; self-efficacy; motivation to quit; mHealth; mobile phone

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