Player Types and Gamification in Adolescent Tobacco Prevention: Insights from Game-Based Interventions

Abstract

Background:

Game-based tobacco prevention programs have demonstrated the potential to reduce adolescents’ intention to vape. This study investigated the influence of gamification player types on the effectiveness of two programs: A Smoking Prevention Interactive Experience (ASPIRE), a computer-based intervention with videos and activities, and Storm-Heroes, a social game emphasizing interaction and action-based gameplay.

Methods:

Using the HEXAD typology, which categorizes players into six types (Philanthropists, Socializers, Free Spirits, Achievers, Players, and Disruptors), this study examined player type distributions, their relationship to intention to vape, and the impact of distraction during gameplay.

Results:

A randomized trial involving 175 adolescents aged 11–18 from after-school programs evaluated player types, distraction levels, and intention to vape at baseline and 1.5 months post-intervention. Results revealed that participants in the Storm-Heroes group scored higher on player, disruptor, achiever, and free spirit types than those in the ASPIRE group. A tendency to be socializers was positively associated with higher vaping intention, while a tendency to be players predicted lower vaping intention. In addition, a significant three-way interaction indicated that disruptors in the Storm-Heroes group reduced vaping intention over time. Distraction effects varied by player type and program. Disruptors in Storm-Heroes were less affected by distraction, whereas socializers and players in ASPIRE experienced heightened distraction effects.

Conclusion:

These findings highlight the importance of tailoring interventions to specific player types to enhance engagement and effectiveness. The study supports the potential of gamified interventions to reduce adolescent vaping intentions. Future research should explore larger, more diverse populations and examine the broader impact of gamification on tobacco prevention outcomes.

Keywords

gamification player types game-based interventions tobacco prevention adolescents behavioral intervention

Introduction

Critical to today’s health behaviors among youths, adolescent nicotine vaping has been associated with significant health risks, including immediate effects on the pulmonary, cardiovascular, and immune systems, and increases the risk of nicotine exposure, dependence, and combustible cigarette use.¹ These concerns highlight the need for innovative strategies to reduce youth vaping, especially during critical developmental stages.

Game-based interventions have shown success in preventing tobacco use among adolescents.^2,3 By engaging youth through dynamic experiences, games can deliver persuasive messages, educate about tobacco risks, and encourage healthy behavior change.⁴ Previous studies have shown how game elements such as interactivity and entertainment can reduce adolescents’ intention to use tobacco, including conventional tobacco use and e-cigarette vaping.⁵ However, while these elements are important drivers of outcomes, the players’ gaming styles in health-focused games remain underexplored.⁵

Gaming research has introduced player typologies that help tailor game experiences to individual needs. Player typologies like Bartle’s player types and Yee’s five motivations to play are validated gaming typology examples.^6,7 Of particular interest is the HEXAD User Types framework developed by Marczewski, categorizing players into six types: philanthropists, socializers, free spirits, achievers, players, and disruptors.⁸ Philanthropists are driven by the purpose of helping others.⁸ Socializers seek connection.⁸ Free Spirits value autonomy.⁸ Achievers pursue goals.⁸ Players aim for rewards.⁸ Disruptors seek system change.⁸ Understanding how these player types interact with game elements can help explain why interventions might vary in effectiveness when aiming to promote healthy attitudes and behaviors.⁹ We hypothesize that socializers and players would respond differently to game-based prevention, informed by self-determination theory and the HEXAD framework.^10,11 Players may engage more with goal-oriented content in structured games, while socializers may benefit from peer-based formats like Storm-Heroes.

In addition to the potential role of player types in games for health, attention and distraction during gameplay may impact gaming success. Distraction involves selective attention during which players find difficulty focusing on the gaming activities and pay attention to external stimuli.¹² Distraction tends to impede media presence experienced in the gaming environment and ultimately disrupts immersion in media activities.^13,14 One study examining student learning through games found that attention to gaming was more critical than skills-building, challenge confrontation, and peer interactions for the success of game-based education interventions.¹⁵ Another study of tobacco prevention for adolescents revealed that greater attention and lower distraction were associated with higher tobacco knowledge by the end of the trial.¹⁶

To investigate the potential linkage between gaming styles, engagement, and distraction in tobacco-related outcomes, we focused on two game-based tobacco prevention programs: A Smoking Prevention Interactive Experience (ASPIRE) Program⁵ and Storm-Heroes. ASPIRE is a computer-based tobacco prevention intervention that uses entertaining videos and various gaming activities to communicate tobacco risks.⁵ On the other hand, Storm Heroes is a social game-based program that leverages action-based gameplay and social interaction to promote a tobacco-free lifestyle. These programs differ in their approach to engagement and interaction, providing an opportunity to study how player types interact with and respond to game-based tobacco prevention interventions.

The objective of this study is to identify player type distributions and examine their relationship with adolescents’ intention to vape after playing ASPIRE or Storm-Heroes. We present the main hypotheses for the current study in Table 1. Building on previous work validating HEXAD as a six-type framework in English and Spanish^10,17 and the current intervention design, we focused on testing hypotheses for two types that align most closely with the socially and personally engaging aspects.

Table 1.

List of Hypotheses

Type of hypothesis	Hypotheses
Participants’ player types	The tendency to be of particular player types moderates the effectiveness of the interventions in reducing intention to vape.
Moderating role of player types	The tendency to be a socializer and the tendency to be a player are associated with lower intention to vape than other player types.
Engagement-related experience	Distraction during game play weakens the effectiveness of the game-based programs on reducing intention to vape.

Material and Methods

Study design

For the current study, we conducted a secondary analysis of data stemming from a cluster-randomized comparative trial conducted in after-school programs in Florida. The original study compared adolescents who received ASPIRE with those who received Storm-Heroes with respect to their intention to vape and their intention to use conventional tobacco. Questionnaires assessed demographics, psychosocial drivers of tobacco use, intention to use tobacco products, and participants’ experience with the interventions. The current study focused on data pertaining to participants’ player types, their intention to vape, and their distraction during gameplay.

Interventions

Storm-Heroes integrated social interaction with gameplay, where teams of three to six participants engaged in a collaborative gaming environment, incorporating approximately 50% of the ASPIRE content, combined with team-based social interactive activities (Figure 1b). On the other hand, participants who received ASPIRE engaged in individually delivered video-based content on computer screens without social interaction (Figure 1a). Both programs addressed a wide range of tobacco-related health topics, including product composition, effects on the body and brain, environmental impact, and strategies for prevention and advocacy. Additional information about each game-based intervention is detailed elsewhere, following the Template for Intervention Description and Replication (TIDieR) checklist.¹⁶ A Storm-Heroes activity description is also included in Supplementary Figure S1 to present intervention features.

FIG. 1.

Overview of the two intervention arms. (a) ASPIRE condition: participants viewed the full ASPIRE video-based program individually. (b) Storm-Heroes condition: participants viewed a portion of the ASPIRE video, followed by team-based gameplay using the Storm-Heroes board game. ASPIRE, A Smoking Prevention Interactive Experience.

Participants

Participants were adolescents aged 11–18 years old, recruited from 10 after-school programs in Northeast Florida, specifically in the Gainesville, Orlando, and Jacksonville metropolitan areas. Participants were deemed eligible if they were students in a middle school or high school and needed to be comfortable using a computer. All participants were included regardless of prior or current vaping behavior.

Ethical considerations

Ethical approval for the study was granted by the University of Florida Institutional Review Board (IRB201903082). Interested adolescents and their parents were informed of the study’s purpose and procedure offline at the after-school program, and they signed an assent form and a parental consent form. During the data collection periods, all participants completed the surveys in a private classroom with supervision, and participants were asked to keep their own identity and the identity of other participants confidential. Participants were told that they could quit the study at any time. The data collected from the participants were also deidentified and kept anonymous during the data analysis process.

Procedures

After obtaining consent from 179 adolescents and their legal guardians, 10 after-school programs were randomly assigned to receive ASPIRE or Storm-Heroes using an algorithm generated by the statistical team. After-school sites were randomized by matching them based on the site size and its location. The intervention started 3–7 days after the participants completed the baseline survey. Trained team members administered the ASPIRE and Storm-Heroes programs at the sites, maintaining participant anonymity. A study team member was available on-site for supervision, ensured protocol adherence, recorded attendance, and addressed questions. In addition, a volunteer site staff trained in youth engagement was present to ensure participants were following the expected procedures. Both intervention arms used similar computers installed in private classrooms. Adolescents in both conditions engaged in five to seven intervention sessions, and each session lasted about 45 minutes. Adolescents in the ASPIRE condition engaged individually in human–computer interaction, without peer-to-peer communication. In contrast, Storm-Heroes participants were purposely grouped into 34 teams by using friendship network data, pairing at-risk adolescents with lower-risk friends and adjusting for absences. As a result, groups may have involved participants from common schools. For both conditions, we asked participants to complete surveys on-site and under supervision, at baseline, immediately after intervention completion, and 1.5 months after the intervention.

Study measures

The primary outcome of this study was the intention to vape, assessed at baseline and 1.5 months post-intervention. This measure consisted of five questions related to perceptions of vaping risk and the likelihood of engaging in vaping behaviors. Responses were rated on a 4-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree). Participants also completed a 10-item adapted version of the HEXAD Gamification User Types Questionnaire at baseline.¹⁰ This scale assesses players based on their gaming tendencies and the extent to which they exhibit characteristics associated with six player types.¹⁰ Each item was rated on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Internal consistency (Cronbach’s alpha) for the adapted scale was 0.92. While the full 24-item scale is the standard, we used a shortened version due to constraints on survey length. The 10 items were picked based on previous factor analysis of validity testing conducted by Tondello and colleagues.¹⁰ The specific study measures and the time of assessment were included in Supplementary Appendix SA2, with sample questions for each player type in the HEXAD scale.

Distraction was measured post-intervention using a self-reported question in the questionnaire, “When I was going through the program Storm-Heroes, I was distracted by activity around me,” rated on a Likert scale from 0 (not at all) to 10 (very much). Although this is a single-item measure, it was chosen to reduce respondent burden and aligns with prior pilot work on distraction.¹⁸

Statistical analysis

We conducted statistical analyses using Stata (version 18.0). We first performed descriptive analyses to compare baseline characteristics between the two intervention groups, focusing on player types and demographic information. One-way analysis of variance tests were used to assess differences in player types between the groups.

We used mixed-effects linear models to examine the relationship between player types and the intention to vape at the 1.5-month follow-up, adjusting for baseline intention to vape and accounting for site-level random effects due to the cluster-randomized design. Our confirmatory hypotheses focused on the effects of the Socializer and Player types, which were pre-specified based on theory and prior evidence. Exploratory analyses examined the roles of other player types (e.g., disruptor), distraction, and interaction terms, including player type × intervention group and player type × distraction interactions. Three-way interaction models (e.g., player type × intervention group × time) were also tested to assess whether changes over time varied by player profile and intervention condition. No adjustments were made for multiple comparisons, given the exploratory nature of several subgroup analyses. As a secondary analysis and the first to examine player types in relation to intention to vape, the expected effect size was unknown; therefore, sample size calculations were based on the primary hypothesis predicting intention to vape. Using mixed-effects models (power = 0.95, effect size = 0.15, α = 0.05),^5,19 the estimated sample size was 148 participants, which increased to 180 to account for an anticipated 18% dropout rate.

Results

Participant characteristics

Demographic and baseline characteristics of participants are presented in Table 2. All 10 after-school programs with 175 participants completed the study. The participants were on average 13.1 (standard deviation [SD] 1.5) years of age, with a majority of the participants (81.5%) self-identified as Black or African. On average, 23.5% of the participants reported tobacco use in their family. In terms of gamification player types, participants showed the highest average score for players (Mean = 3.6, SD = 1.35) and lowest average score for disruptors (Mean = 2.49, SD = 1.25). Adolescents in the Storm-Heroes group scored significantly higher in players (F = 7.60, P = 0.0061), disruptors (F = 7.99, P = 0.0049), achievers (F = 5.99, P = 0.0148), and Free Spirits (F = 3.92, P = 0.0482) compared with those in the ASPIRE group.

Table 2.

Characteristics of Study Participants at Baseline by Groups

Characteristics	Overall	ASPIRE	Storm heroes
Age at study
Mean (SD)	13.1 (1.5)	13.2 (1.6)	12.9 (1.3)
Sex at birth
Female	85 (49.4%)	50 (49.5%)	35 (49.3%)
Male	87 (50.6%)	51 (50.5%)	36 (50.7%)
Race
American Indian or Alaska Native	4 (2.3%)	1 (1.0%)	3 (4.2%)
Asian	2 (1.2%)	0 (0.0%)	2 (2.8%)
Black or African American	141 (81.5%)	83 (81.4%)	58 (81.7%)
Native Hawaiian or Other pacific Islander	3 (1.7%)	2 (2.0%)	1 (1.4%)
Other	19 (11.0%)	14 (13.7%)	5 (7.0%)
White	4 (2.3%)	2 (2.0%)	2 (2.8%)
Being Hispanic/Latino	21 (12.1%)	9 (8.8%)	12 (16.9%)
Gamification player types
Player, M(SD)	3.60 (1.35)	3.46 (1.38)	3.81 (1.28)
Socializer, M(SD)	3.33 (1.30)	3.29 (1.26)	3.38 (1.35)
Achiever, M(SD)	3.26 (1.29)	3.14 (1.28)	3.44 (1.29)
Free Spirit, M(SD)	3.24 (1.26)	3.15 (1.29)	3.38 (1.21)
Philanthropist, M(SD)	3.05 (1.48)	3.07 (1.47)	3.03 (1.52)
Disruptor, M(SD)	2.59 (1.42)	2.43 (1.39)	2.81 (1.44)

ASPIRE, A Smoking Prevention Interactive Experience.

Player types and intention to vape

We first tested our confirmatory hypotheses, focusing on socializers and players. A regression model adjusting for site randomization (n = 89), adjusting for site randomization and baseline intention, found that a higher score in socializer was positively associated with intention to vape (B = 0.39, P < 0.001) and a lower score in player (B = −0.15, P = 0.034) were associated with an intention to vape by 1.5-month follow-up.

We then tested exploratory three-way interaction effects involving time, intervention group, and player types. In a repeated-measures mixed-effect regression model, we observed a three-way interaction between being in the Storm-Heroes group, being a disruptor, and time in predicting intention to vape (B = −0.83, P = 0.033; Table 3). Separately for each intervention program, being disruptors was not significantly related to intention to vape over time (Supplementary Appendix SA3). The results in Figure 2a show a reduction in intention to vape among disruptors in the Storm-Heroes group and non-disruptors in the ASPIRE group.

FIG. 2.

The adjusted prediction of intention to vape over time for different gamification player types and intervention groups. (a) Intention to vape over time by the intervention group and being a disruptor. (b) Intention to vape over time by intervention group and being a socializer. (c) Intention to vape over time by intervention group and being a player.

Table 3.

A Three-Way Interaction Effect Model Between Intervention Group, Being Disruptors, Socializers, and Players, and Time Predicting Intention to Vape (n = 228)

	Model 1		Model 2		Model 3
	B (SE)^a	P value	B (SE) ^a	P value	B (SE) ^a	P value
Time	−0.35 (0.08)	<0.001	−0.50 (0.13)	<0.001	−0.40 (0.15)	0.010
Being in the Storm-Heroes group	−0.14 (0.14)	0.300	−0.13 (0.15)	0.395	−0.39 (0.10)	<0.001
Time by being in the Storm-Heroes group	0.49 (0.29)	0.093	0.07 (0.20)	0.717	0.65 (0.43)	0.130
Being a disruptor	0.17 (0.17)	0.311	—	—	—	—
Time and Being a disruptor	0.26 (0.24)	0.284	—	—	—	—
Being a disruptor by intervention group	0.33 (0.34)	0.325
Time by being a disruptor by intervention group	−0.83 (0.39)	0.033
Being a socializer	—	—	−0.33 (0.22)	0.126	—	—
Time and Being a socializer	—	—	0.56 (0.11)	<0.001	—	—
Being a socializer by intervention group	—	—	0.42 (0.30)	0.160	—	—
Time by being a socializer by intervention group	—	—	−0.11 (0.23)	0.640	—	—
Being a player	—	—	—	—	−0.49 (0.09)	<0.001
Time and being a player	—	—	—	—	0.33 (0.19)	0.082
Being a player by intervention group	—	—	—	—	0.91 (0.29)	0.002
Time by being aplayer by intervention group	—	—	—	—	−1.15 (0.41)	0.005
Wald x²	1083.26	<0.001	974.25	<0.001	899.47	<0.001

Time includes baseline and 1.5-month follow-up. Being a disruptor was treated as a binary variable based on the median score for the tendency to be a disruptor.

^aSE stands for standard error, and B coefficients are not standardized.

As noted in Table 3, a three-way interaction effect was not observed between being in the Storm-Heroes group, being a socializer, and time when predicting intention to vape (B = −0.11, P = 0.640). Also, being socializers significantly predicted lower intention to vape over time (socializer-by-time effect: B = 0.56, P < 0.001). Separately for each intervention program (Supplementary Appendix SA3), we found that non-socializers were more likely to decrease in their intention to vape over time among adolescents who received ASPIRE (B = 0.55, P < 0.001) and adolescents who received Storm-Heroes (B = 0.44, P = 0.044). These results are further depicted in Figure 2b.

As shown in Table 3, a three-way interaction effect was observed between the intervention group, player, and time when predicting intention to vape (B = −0.84, P = 0.031). In the Storm-Heroes group, participants who were players were more likely to decrease their intention to vape over time (B = −0.84, P = 0.031). In the ASPIRE group, being players was not significantly related to intention to vape (Supplementary Appendix SA3). These results are depicted in Figure 2c.

Distraction and intention to vape

With two mixed-effect models, we found that players (B = −0.03, P = 0.014) and disruptors (B = −0.04, P = 0.022) weakened the effect of distraction on intention to vape (Table 4). We examined the role of distraction and player types for participants who received ASPIRE and those who received Storm-Heroes separately (Supplementary Appendix SA4). For those who received ASPIRE, socializers (B = 0.007, P < 0.001) and players (B = 0.004, P < 0.001) strengthened the effect of distraction on intention to vape. For those who received Storm-Heroes, disruptors weakened the effect of distraction on intention to vape (B = −0.07, P = 0.039).

Table 4.

Mixed Effect Models with Being Players and Disruptors and Level of Distraction Predicting Intention to Vape at 1.5 Months After Program Completion (n = 56)

	Model 1		Model 2
	B (SE)^a	P value	B (SE)^a	P value
Being in the Storm-Heroes group	0.36 (0.19)	0.063	0.40 (0.22)	0.074
Level of distraction	0.13 (0.06)	0.033	0.14 (0.06)	0.019
Tendency to be a player	−0.02 (0.06)	0.731	—	—
Tendency to be a player by level of distraction	−0.03 (0.01)	0.014	—	—
Tendency to be a disruptor	—	—	0.09 (0.12)	0.502
Tendency to be a disruptor by level of distraction	—	—	−0.04 (0.02)	0.022
Intention to vape at baseline	0.30 (0.12)	0.011	0.29 (0.12)	0.016
Wald x²	41.45	<0.001	383.90	<0.001

SE stands for standard error, and B coefficients are not standardized.

Discussion

The objective of this study was to identify player type distributions and investigate the relationship between different player types and adolescents’ intention to vape after playing ASPIRE and Storm-Heroes. Our findings show that HEXAD profiles were distributed differently across the two groups and that player types interacted meaningfully with the intervention format to influence outcomes. To our knowledge, this is among the first tobacco prevention game studies to use a gamification user typology to test whether player motivation profiles shape intervention effects.

Socializer tendencies were associated with higher overall intention to vape, while participants with lower socializer scores were more likely to decrease their intention across both interventions. This finding is consistent with previous research demonstrating the role of peer influence and perceived social norms in vaping behavior.²⁰ It also underscores findings from qualitative research on the role of social learning through games for health.⁴ Although we hypothesized that socializers would benefit from Storm-Heroes’ collaborative format, the results suggest that social interaction may not be sufficient and it may undermine tobacco messaging when peer norms are not consistently health-promoting. This finding highlights the need for more purposefully structured social interactions that reinforce healthy norms. Future interventions may test how peer networks can be leveraged more deliberately to support social engagement while protecting against risk reinforcement.

Notably, we did not observe a three-way interaction for socializers, indicating that Storm-Heroes was not especially effective for this group over time. Nonetheless, the consistently higher intention to vape among socializers suggests a vulnerability to peer distraction and a social reinforcement that may decrease traditional health message uptake. These findings align with prior work emphasizing that peer-based dynamics can both enhance or hinder health interventions depending on how norms are framed.²¹ Further research is needed to determine the reasons behind these results and study whether more socially engaging gamified elements could be introduced, such as competitive or cooperative gameplay.

We identified a three-way interaction effect involving receiving Storm-Heroes, being a disruptor, and time, suggesting that Storm-Heroes may be particularly effective for adolescents characterized as disruptors. While disruptor traits alone did not predict vaping intention, the intervention context is crucial. Previous studies have focused on disruptors as a challenge,^17–22 given their tendency to resist rules or conventional structures. However, our results suggest that tailored gamified approaches may effectively engage this group through novel features, such as rules that encourage disruption to illustrate the consequences of vaping behavior. Future work can investigate how to better design game-based interventions to engage adolescent disruptors in tobacco prevention.

Participants characterized as players showed a significant reduction in intention to vape over time in the Storm-Heroes group but not in the ASPIRE group, suggesting that Storm-Heroes’ design may be more engaging for reward-oriented adolescents. Game mechanics that emphasize goals and achievements are known to enhance gamified health engagement.^23–25 The goal-oriented features likely appealed to players, enhancing the intervention’s effectiveness and contributing to a noticeable shift in their intention to vape. While previous studies have focused primarily on peer pressure or psychological drivers of vaping, our findings suggest that an alignment between player type and game mechanics may also influence behavioral intentions.²⁶ This study adds to prior work by identifying player type as a meaningful moderator of gamified intervention effectiveness.^25,27 Future qualitative research can clarify which specific reward structures of Storm-Heroes are most appealing to players.

Exploratory analyses showed that players and disruptors weakened the impact of distraction on vaping intentions. This finding indicates that certain player types may be more resilient to external distractions and driven by intrinsic motivation or certain game mechanics, potentially influencing their health-related decisions. Although rarely examined in tobacco prevention research, studies on games for health show that lower distraction through spatial presence can enhance engagement and promote health behaviors.^28,29 Future research can explore how gaming features, such as playful virtual environments, can improve spatial presence, mitigate distractions, and enhance behavioral interventions.

Overall, this study contributes new evidence that HEXAD player types moderate adolescents’ responses to tobacco prevention games and that these effects depend on the interaction between game design and individual motivational profiles. Applying HEXAD in a tobacco prevention game context adds a gaming motivation context that typical tobacco prevention interventions would not capture. These findings align with a recent review emphasizing the importance of adaptive vaping prevention interventions.³⁰ To broaden potential effectiveness across HEXAD profiles, prevention games could be designed with optional engagement pathways so the same core prevention content can be accessed through different motivational routes (e.g., prosocial contribution opportunities for Philanthropists, autonomy and customization for Free Spirits, and goal-oriented progression with clear feedback for Achievers).

Strengths and limitations

Several limitations must be noted. First, the sample of 175 participants limits the power to detect small effects or to fully examine player type differences across more diverse populations. In addition, the predominance of Black or African American adolescents may limit the generalizability to other populations. In addition, recruiting participants exclusively from after-school programs may further constrain applicability, and informal peer interactions during group play might influence engagement or distraction despite the lack of direct in-game communication. We also included adolescents regardless of prior vaping history, which might have affected post-intervention intentions. Although we accounted for group randomization, unmeasured confounders such as socioeconomic status, peer influence, or environmental factors were not controlled for. Although participation occurred in groups, gameplay was primarily digital with no direct in-game communication implemented. Informal verbal interactions were permitted, which may have influenced distraction or engagement levels. Future studies could address this limitation by conducting larger studies. Moreover, measurement constraints should be considered: the HEXAD scale was shortened from 24 to 10 items to reduce burden, which may affect construct validity. Lastly, distraction was measured with a single self-report item, limiting the assessment of attentional complexity.

Despite these limitations, this study has notable strengths. The randomized comparative design adds rigor to the findings, allowing for more reliable inferences, and the longitudinal approach allows for the observation of changes in intention over time. Importantly, this study is one of the few that investigates the relationship between player types and their impact on engagement and the effectiveness of games for health, offering insights into tailoring gamification for adolescents and improving behavioral interventions. Although mentioned as one limitation, the predominance of Black or African American adolescents in this sample is also a strength, as it increases the relevance of these findings for an often-underrepresented group in tobacco prevention research.³¹

Conclusions

The findings have important public health implications, suggesting that tailoring interventions to player types may enhance engagement and outcomes. The study provides early evidence that HEXAD player types can inform how adolescents engage with games for health, supporting the development of personalized digital prevention strategies. By demonstrating that players and socializers, this work extends HEXAD applications to behavioral health and contributes to the literature on adaptive gamification. Future research should further examine distraction, consider a larger sample size, and explore these mechanisms in the context of combustible tobacco products to better address the multifaceted drivers of adolescent tobacco behavior.

Authors’ Contributions

B.Z.: Conceptualization, methodology, data curation, formal analysis, writing—original draft, writing—review and editing, and project administration. G.E.K.: Conceptualization, supervision, validation, and writing—review and editing.

Footnotes

Acknowledgments

The authors thank everyone who contributed indirectly to this study. Research reported in this publication was supported by the National Institute on Drug Abuse of the National Institutes of Health under Award Number R00DA044277 (Principal Investigator: Georges Khalil, MPH, PhD). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this article.

Supplemental Material

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