The study protocol was approved by the Medical Ethics Committee of the Children’s Hospital Affiliated with Nanjing Medical University (approval number 202505033-1). Administrative permission was obtained from the participating school prior to study initiation. Informed consent was obtained from all parents or legal guardians. Child assent was also secured verbally from each participant immediately before activities. To protect privacy and adhere to data minimization principles, only essential nonidentifiable variables were retained for analysis: gender, class, and grade. Student IDs were temporarily used solely to match paired responses across the 3 phases and were permanently deleted upon completion of matching, replaced with anonymized participant codes. All analyses were conducted using this deidentified dataset. No compensation was provided to participants or their families for taking part in the study. The manuscript and supplementary materials contain no images that could identify individual participants. All game screenshots are generic and do not include any personal information.

This methodological study comprised 3 independent, sequential phases (Figure 1) to develop, validate, and demonstrate the application of a novel gamified assessment tool for child medication literacy.

Phase 1 aimed to establish the tool’s psychometric properties using an independent sample, without any prior intervention, including content validity through a 2-round expert review, construct validity via Rasch analysis, and test-retest reliability with a 1-month interval.

Phase 2 used a randomized crossover design with a separate sample, also without any prior intervention, to methodologically compare the new tool against a traditional paper-based questionnaire, testing its equivalence and superiority (data quality and participant experience). Participants were randomly allocated to 1 of 2 administration sequences and completed 2 assessments: sequence A (gamified → paper-based) and sequence B (paper-based → gamified), with a 10-minute washout period between sessions, which corresponded to the regular break between 2 class sessions, chosen to minimize disruption to the school schedule while providing a brief period to reduce short-term memory of responses.

Phase 3 applied the validated tool in a longitudinal, independent pre-post study with a sample distinct from those in prior phases to assess its practical responsiveness (generalized linear mixed models [GLMMs]) in detecting changes following a school-based medication literacy intervention. We implemented a compact pre-post assessment using the validated gamified tool, with participants completing the assessment immediately before and after the intervention, to maximize sensitivity to change while minimizing confounding. The intervention was designed as a single 40-minute in-person session, which included a 20-minute interactive educational component delivered by trained pharmacists, with the remaining time used for the pretest and posttest. To ensure age-appropriateness, we adapted core components from the Chinese Citizens’ Health Literacy—Basic Knowledge and Skills (2024), focusing on medication literacy relevant to general child health rather than adult-centric topics. The selected components centered on three core principles: (1) distinguishing dietary supplements from medicines, (2) rational medication use principles, and (3) science-based and rational health care practices. To translate these principles into an engaging format, interactive educational material titled “The Great Journey of a Little Medicine” was created, which adopts a first-person narrative from the perspective of a medicine describing its metabolic journey. The storyline translates these principles into four specific educational objectives: (1) dietary supplements are not medicine substitutes, (2) preference for oral over infusion, (3) no unilateral discontinuation for side effects, and (4) no alarm for drug-induced urine discoloration. To ensure consistency, the session was delivered in a standardized manner by the same trained pharmacist, following a three-part format: (1) engaging storytelling centered on the narrative, enhanced with animated demonstrations, (2) explicit emphasis on core medication literacy points, and (3) interactive question-and-answer and experience-sharing to facilitate immediate application, clarify concepts, and promote personal engagement.

This phased, independent-sample design ensured that psychometric properties and methodological advantages were established prior to and uncontaminated by intervention effects, aligning with best practices for novel measurement tool validation.

The study included elementary school students in grades 4 to 6. Students present on the scheduled assessment days with parental consent and child assent were included; no additional exclusion criteria were applied.

Participants in psychometric validation were recruited from 2 classes in grades 4 and 6. The total sample for this phase consisted of 81 students (40/81, 49.4% female). Methodological comparison involved a separate cohort from 2 classes in grades 5 and 6. A total of 85 students (42/85, 49.4% female) participated in the randomized crossover study. Intervention application recruited a third independent cohort from 2 classes in grades 5 and 6, comprising 85 students (41/85, 48.2% female).

The study was conducted in a single public elementary school in Nanjing, China. Data collection for the 3 phases took place between September and December 2025. Using cluster random sampling, distinct intact classes from grades 4 to 6 were randomly selected as separate, nonoverlapping cohorts for each phase. The overall study targeted grades 4 to 6, as children in these grades (approximate age 9-12 years) have the cognitive and literacy skills needed to understand medication-related concepts and complete the gamified assessment independently. This choice was supported by the elementary school teachers who participated in our content validity review. We used grade level rather than chronological age because age within the same grade is inconsistent (birthdays occur at different times, and some families use the traditional Chinese nominal age system). In contrast, grade provides a relatively uniform educational background, allowing students to be reasonably treated as a single group for analysis.

The sample size was determined based on the practical capacity of the health care professional team and the feasibility of implementation within the school’s routine. Given a maximum class size of 40 to 45 students, and to match the health care professionals’ workload, minimize disruption to normal school schedules, and align with available devices, we planned to recruit 2 intact classes for each phase of the study. This provided sufficient participants for each phase while allowing for occasional absences. Because of the pilot and methodological nature of the study, formal a priori power calculations were not performed. Instead, sample size adequacy was assessed against methodological literature. For Rasch analysis, simulation studies have shown that item calibration remains stable even with small samples (n≤50) [34]; our phase 1 sample (n=81) exceeds this threshold, supporting reliable parameter estimation. For the GLMMs with clustered data, methodological research indicates that GLMMs remain a consistent analytical approach when the number of clusters is small [35], and using appropriate small-sample corrections can control type I error rates [36]. In our study, the within-class sample size (n=40-45 per class) together with the Satterthwaite approximation (default in the lmerTest package, detailed in the Analytic Strategy section) supports the reliability of the GLMM results.

We developed a novel gamified digital assessment, which was structured around the integrated KAP-TPB framework and comprised 4 core dimensions: knowledge, attitudes, PBC, and behavioral intention. Detailed descriptions of the scoring rules and measurement procedures are provided in the Measures and Covariates section.

The game content was directly derived from the key learning objectives of the planned medication literacy intervention. Although our assessment employed gamified interactions rather than traditional paper-based items, we adapted the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) framework [37] to report on user recruitment, engagement metrics, and data completeness, as these principles remain applicable to digital survey modalities.

To reduce abstraction and enhance measurability, we designed the game to generate contextualized, ordered responses rather than rely on abstract self-report. Each dimension was operationalized through a distinct game mechanic (Figure 2): knowledge was assessed via a 5-item “target practice” game where children selected answers by clicking on targets. Each item had 1 correct option; an “I do not know” choice was included to minimize guessing. Responses were scored 1 (correct) or 0 (incorrect/I do not know). Attitude was measured using a 4-item “block-hitting” game. Children jumped a character to hit blocks corresponding to their agreement with statements on a 5-point Likert scale (1=strongly disagree to 5=strongly agree), with both positively and negatively worded items. PBC was evaluated through a 4-item “speech preparation” drag-and-drop task. For each topic, children selected from 2 correct arguments (+1 each), 2 incorrect arguments (–1 each), and an “I do not know” option (0). The total score (–2 to 2) was then mapped onto a 5-point Likert scale. Behavioral intention was gauged via 4 interactive “scenario simulation” role plays. Children faced medication-related dilemmas in a continuous storyline that provides a realistic and coherent decision-making context and chose among 5 predefined behavioral responses, each prescored 1 to 5 based on theoretical alignment with rational medication literacy.

To mitigate testing anxiety and reduce social desirability bias, we replaced traditional numerical scores with a performance-based digital badge system. Participants earned a unique badge upon completing each game dimension. Crucially, badges were not titled with explicit performance tiers (eg, no “gold” or “silver”) to minimize peer comparison. The complete badge system, including score ranges and design rationale, is presented in Table S2 in Multimedia Appendix 1. All badges were aggregated and displayed on a final “badge collection” screen (Figure 2). This approach was designed to elicit more authentic, low-pressure assessments of children’s knowledge, attitudes, PBC, and behavioral intention.

To transform the assessment into a learning-reinforcement tool, we provided immediate corrective feedback in the posttest of intervention application to capitalize on the “teachable moment.” This feedback differed by dimension (Figure 2). For knowledge and PBC, a perfect score triggered an audio cue (“great”) and unlocked the next level. An imperfect score prompted “think again,” followed by display of the correct answers. For attitude and behavioral intention, a score ≥4 (on a 5-point scale) elicited positive audio feedback (“good choice”); a lower score triggered “think again,” followed by a review of key intervention content. No feedback was provided during the psychometric validation, methodological comparison, and baseline pretest of intervention application to ensure measurement purity. Of note, the feedback mechanism did not influence scoring, as scores were calculated prior to feedback delivery. The workflow of this gamified assessment and feedback system is illustrated in Figure 3.

Data quality was operationalized as the overall validity of each completed assessment, coded as a binary outcome (valid=1, invalid=0) based on predefined criteria.

In the methodological comparison study, participant experience (enjoyment and preference) was evaluated through self-reported ratings via a 5-point Likert scale.

In the longitudinal pre-post intervention study, the independent variable was time, reflecting the measurement occasion relative to the intervention (pretest vs posttest). The dependent variables were the scores of the 4 dimensions from the gamified tool. Covariates included in the analyses were class and gender.

Data were collected through 2 primary modes: the gamified assessment automatically recorded responses on the devices, while the paper-based questionnaire was completed manually.

Data for phase 1 were collected at baseline and again at a 1-month follow-up. Data for phase 2 were collected using both the gamified and paper-based formats according to participants’ assigned sequences. Participant experience measures were collected immediately after this first assessment. Data for phase 3 were collected immediately before and after the intervention. Participants completed a 10-minute baseline pretest (T0), followed by the intervention session, and then a 10-minute posttest (T1) with the immediate feedback mechanism.

Initial data entry and basic cleaning were performed in Microsoft Excel 2024. At this stage, all negatively worded items were reverse-coded to ensure uniform scoring direction across the tool.

No formal training or reliability assessment was conducted for data collectors, as the gamified tool was fully automated and the paper-based questionnaire was administered following a standardized protocol. Repeated observations were not used.

No masking was performed because the gamified and paper-based formats were visually distinct and the intervention was delivered in an open classroom setting.

A systematic 2-round expert review was conducted to establish the content validity of the gamified tool, ensuring relevance, clarity, and age-appropriateness for elementary school students. An initial review by 5 senior pharmacists (2 chief pharmacists and 3 associate chief pharmacists; all holding master’s degrees, with >10 years of experience, and representing pharmacy administration, clinical pharmacy, and outpatient pharmacy services within a tertiary children’s hospital) focused on medical accuracy and relevance, ensuring item clarity for subsequent reviewers. A subsequent review by 5 experienced elementary school teachers (>10 years of teaching) then evaluated child-appropriateness, with a focus on the comprehensibility of language and the realism of scenarios for the target age group. In both rounds, experts rated each item on a 4-point Likert scale (1=irrelevant, unclear, or inappropriate; 4=highly relevant, very clear, or very appropriate). The item-level content validity index (I-CVI) and the scale-level content validity index/average (S-CVI/Ave) were calculated. Given that each panel comprised 5 experts, an I-CVI of 1.00 (unanimous agreement) was required for item retention, resulting in the overall S-CVI/Ave also having to reach the maximum value of 1.00.

We examined the internal structure and measurement properties of the gamified tool by assessing construct validity using Rasch analysis. Item fit was assessed by examining the weighted (infit) and unweighted (outfit) mean-square (MNSQ) statistics for each item, with values between 0.5 and 1.5 considered indicative of acceptable model conformity. The essential unidimensionality of each scale was tested through a principal component analysis of the standardized residuals; support for unidimensionality required the eigenvalue of the first residual component to be below 3.0, coupled with a ratio of the second to first eigenvalue of less than 0.5. The discrimination power of each item was assessed via its point-measure correlation, where a value greater than 0.30 was taken to confirm the item’s positive alignment with the intended latent construct. Finally, the precision and reliability of each dimension’s measure were evaluated using the person separation reliability coefficient. A coefficient exceeding 0.70 was deemed acceptable, indicating that the tool possessed adequate power to distinguish between participants of different ability levels within the sample.

To evaluate test-retest reliability, we quantified the degree of absolute agreement between the 2 time points for each dimension by calculating the intraclass correlation coefficient (ICC). An ICC value of ≥0.70 was considered the threshold for acceptable temporal stability. Then, we complemented this by examining systematic score drift through 2-tailed paired t tests for each dimension. A nonsignificant result (P>.05) would indicate no meaningful average change in scores over the 1-month period, further supporting the stability of the measurement.

To ensure data integrity, clear validity criteria were applied to all collected questionnaires. For the paper-based questionnaire, manual completeness checks were performed, and any questionnaire with blank items was excluded. For the gamified assessment, the design enforced completion of each item before proceeding, eliminating data missingness at the item level. All responses from both formats were screened for patterned or nondifferentiated responding, which also led to exclusion. For pre-post comparisons, only participants with a complete pair of responses were considered.

Missing data were handled using complete-case analysis (listwise deletion). We also evaluated individual person-response validity by flagging participants whose infit or outfit statistics fell outside the 0.5 to 1.5 range, suggesting potential aberrant response patterns. When the estimated class-level variance was 0 (resulting in a singular fit), the ICC was set to 0.00. Model diagnostics were examined for linear models.

No additional statistical outliers were defined or excluded beyond the prespecified validity criteria. No formal distributional tests were performed as the planned analyses (GLMMs) are robust to moderate deviations from normality; residual diagnostics were examined for linear models. No additional data transformations were applied beyond those described in the Data Collection section.

All statistical analyses were conducted using R software (version 4.5.2; R Core Team) with packages dplyr (version 1.1.4), tidyr (version 1.3.1), and ggplot2 (version 4.0.1) for data cleaning, transformation, and visualization, and dedicated packages for specific analyses as detailed below. All statistical tests were 2-sided with a significance level of α=.05.

In phase 1, construct validity analyses were conducted on the baseline data within the Rasch measurement framework. Given the different response formats across dimensions, we applied the dichotomous Rasch model to the binary-scored knowledge items and the partial credit model to the polytomous items measuring attitude, PBC, and behavioral intention. All models were estimated via the marginal maximum likelihood method, using robust convergence criteria (maximum iterations=1000, increment factor=1.05) using the TAM package (version 4.3-25), complemented by the psych package (version 2.5.6). Regarding item performance, we calibrated item difficulty/location parameters and their standard errors. Test-retest reliability was quantified by comparing data from the 2 time points using a 2-way mixed-effects model for absolute agreement (ICC 3-1) via the psych package (version 2.5.6). Paired t tests (base R) were used to examine systematic score drift over the 1-month interval.

In phase 2, measurement equivalence was evaluated through a multimethod approach using data from both assessment sessions. To ensure the validity of pooling data from the crossover design, we tested for order effects using a 2 × 2 mixed-design ANOVA (base R), with sequence (A or B) as the between-subjects factor and modality (gamified or paper) as the within-subjects factor. A nonsignificant sequence × modality interaction (P>.05) was taken to indicate the absence of a meaningful order effect, permitting data pooling. With order effects assessed, absolute agreement between the 2 modalities was quantified using ICC(3,1). We used paired t tests to examine systematic mean differences, with Cohen d (effsize package, version 0.8.1) quantifying the effect size. Concurrently, Bland-Altman analysis (BlandAltmanLeh package, version 0.3.1) was applied to visualize individual-level differences, estimate mean bias, and calculate 95% limits of agreement. Finally, to assess the consistency of categorical decisions made from the 2 tools, scores for each dimension were dichotomized into “high” and “low” groups based on the median. We then computed both Cohen κ coefficient (irr package, version 0.84.1) and simple percentage agreement to evaluate the classification consistency between modalities. To ensure a fair comparison of initial exposure and minimize carryover effects in the crossover design, the superiority analysis used only the data from the first assessment session (gamified assessment from sequence A; paper-based assessment from sequence B). We compared the proportion of valid responses between the 2 modalities using a chi-square test (base R) and calculated the validity rate with its 95% CI for each. Participant experience was compared using independent-samples t tests (base R) with Cohen d as the effect size.

In phase 3, baseline comparability was assessed using only the pretest data. We used 1-way ANOVA (base R) to compare class-level baseline scores for all 4 dimensions: knowledge (percentage correct, 0%-100%), attitude, PBC, and behavioral intention (each a Likert sum score ranging from 4 to 20). To quantify between-class clustering at baseline, we estimated the ICC(1) for each dimension using a 1-way random-effects model with a random intercept for class (lme4 package, version 1.1-38) and extracted the ICC (performance package, version 0.15.3). Responsiveness was evaluated using GLMMs on paired pre-post test data using the lme4 (version 1.1-38), lmerTest (version 3.2-0), and emmeans (version 2.0.1) packages. Considering the distinct measurement scales, different model families were specified: a Poisson mixed model with a log link for the count-type knowledge score, and linear mixed models for the continuous attitude, PBC, and behavioral intention scores. Each model included time (T0/T1), class, gender, and the class × time interaction as fixed effects, and incorporated a random intercept per participant to account for repeated measurements. The class × time interaction was tested using likelihood-ratio tests (Poisson model) or F tests (linear models). If the interaction was significant, simple-effects analyses with Tukey-adjusted P values were performed to clarify differential change across classes. To quantify the magnitude of within-class change, the effect size was estimated as Cohen d for paired data, derived from the model-estimated marginal means and pooled SD.

The flowchart of participant enrollment is detailed in Figure 4. In phase 1, the full sample (n=81) provided data for the assessment of construct validity. For test-retest reliability analysis, 77 of the original 81 participants (39/77, 50.6% female) completed both assessments. Four participants were excluded due to nonresponse at the second time point (1-month follow-up), resulting in 4 missing data points (4/81, 4.9%). In phase 2, all 85 participants provided data for the analysis of methodological superiority, which used first-assessment data only. For the sequence effects and equivalence analyses, after confirming no significant order effect, data from participants with valid responses at both time points were pooled across sequences, yielding a final analytic sample of 76 paired observations (39/76, 51.3% female). Nine participants were excluded because at least 1 of their 2 assessments was invalid (9/85, 10.6%). In phase 3, the entire sample with complete preintervention and postintervention data was used in the longitudinal GLMM analysis to assess the tool’s responsiveness. Detailed sample sizes for each analysis are presented in Table 1.

The primary objective of this study was to develop and validate a gamified assessment tool for measuring medication literacy in elementary school children, to compare it with a traditional paper-based format, and to evaluate its responsiveness to an educational intervention. The results across the 3 phases collectively show that the tool has adequate psychometric properties, is measurement-equivalent to the paper-based questionnaire, is preferred by children, and can detect changes associated with the intervention consistently across classrooms. The psychometric validation results indicate that the newly developed gamified assessment tool demonstrates adequate psychometric properties for research use with elementary school students. The perfect content validity indices confirm that the gamified content is not only theoretically sound but also contextually appropriate and comprehensible for elementary school students [38]. Rasch analysis supported the structural validity of the 4 hypothesized constructs within the gamified format. The essential unidimensionality and acceptable item fit of all dimensions indicate that children’s interactions with the game-like interface produced response data consistent with the underlying measurement models [39,40]. The point-measure correlations exceeding the criterion further verify that each item effectively discriminated along its intended latent trait [40]. The person separation reliability coefficients of attitude, PBC, and behavioral intention were in the acceptable range for group-level analysis [40]. The slightly lower value for knowledge is not surprising, given that binary items typically yield lower person separation reliability than polytomous items with a comparable number of items [41]. Overall, the tool provides reasonable precision for distinguishing between cohorts with different ability levels across all dimensions. These Rasch-based findings are consistent with the methodological approach used in the validation of other child-focused assessment tools [42], supporting the appropriateness of this framework for pediatric samples. The test-retest results showed strong temporal stability, indicating that the engaging format did not introduce response instability or significant practice effects upon readministration [43]. In summary, phase 1 establishes that the methodological innovation of gamification can yield measurements that meet rigorous psychometric standards, providing a reliable and valid means to assess the target constructs. This lays the foundation for its subsequent comparison against traditional methods in phase 2 and its application in evaluating intervention effects in phase 3.

Turning to the methodological comparison, the results indicate that the 2 modalities produce effectively equivalent measurements, while the gamified version addresses a key limitation of traditional paper-based questionnaires by significantly enhancing participant engagement. The equivalence analysis confirmed that the 2 formats measure the same constructs [44]. After establishing the absence of order effects, pooled data revealed high ICCs across all dimensions [45]. This was consistent with other metrics: no systematic mean differences were detected, Bland-Altman plots showed minimal bias [46], and decision consistency was high [47]. These convergent results across multiple indices support measurement equivalence, satisfying a fundamental prerequisite for considering the gamified tool a valid alternative to the paper-based format [44]. Where the tools meaningfully diverge is in the participant experience—the core value proposition of this methodological innovation. While both yielded data of comparable quality—reflected in the high and nonsignificantly different validity rates—this surface-level equivalence in completion likely reflects the strong subjective norms of the school setting [32], which can ensure task compliance even for less engaging formats. The gamified tool, in contrast, was rated substantially higher in both enjoyment and preference, consistent with previous reports that gamified assessments are more engaging for children [27]. This shift proactively addresses a core challenge to the validity of child self-report data [23]. It moves beyond reliance on external compliance by transforming the assessment into a more motivationally compatible activity—a transformation that fosters greater willingness to engage and reduces inattentive responding [48]. In conclusion, this comparative phase demonstrates that the gamified tool successfully bridges a common gap in assessment methodology. It achieves measurement equivalence with the paper-based format while simultaneously enhancing the engagement necessary for high-quality data collection from children. This achievement validates it as a methodologically robust instrument appropriate for the target population, well-suited for application in the subsequent intervention study.

In terms of intervention responsiveness, the gamified assessment tool demonstrated satisfactory responsiveness [49], detecting significant changes across all 4 medication literacy domains following a targeted educational intervention. The moderate to large effect sizes indicated that the tool captured substantial changes across the 4 domains [50]. Notably, the pre-post changes were consistent across classrooms for all dimensions, despite a significant baseline difference in knowledge scores. This finding carries practical importance: students with lower baseline knowledge showed comparable improvements to those with higher baseline scores. This pattern aligns with recent evidence that game-based interventions can improve outcomes equitably regardless of baseline disparities [51]. More broadly, school-based health literacy interventions have been recognized as a promising strategy for reducing health inequalities [52]. The tool’s ability to detect uniform changes across classrooms may be useful for evaluating health education initiatives in diverse settings [53,54]. Taken together, these results confirm that the tool is capable of capturing meaningful pre-post changes in children’s medication literacy, supporting its utility as an outcome measure for school-based health promotion initiatives.

Several limitations should be acknowledged. The intervention used a single-group pre-post design without a control condition. While the primary aim was to evaluate the tool’s responsiveness rather than intervention efficacy, the absence of a comparison group limits causal inference regarding the observed changes. Therefore, our results should be interpreted as evidence that the tool can detect changes associated with the intervention, not as proof that the intervention caused those changes. Moreover, the immediate posttest administration may have introduced short-term recall effects, potentially overestimating immediate improvements relative to long-term retention. This study did not examine whether the observed changes were sustained over time, leaving the question of their long-term sustainability unanswered. Future studies should consider incorporating a control arm and longer-term follow-up assessments to strengthen causal conclusions and evaluate the durability of the measured changes.

All 3 phases recruited independent samples from a single elementary school—a decision driven by practical considerations of conducting research in real-world school settings, though it may constrain generalizability to other educational environments or age groups. Replicating these findings across multiple schools with diverse demographics would help establish the tool’s broader applicability. The sample sizes are acceptable but modest; the Rasch sample approaches the lower bound for stable calibration, and the small number of classes (2 per phase) limits the precision of random-effect estimates. While the absence of significant order effects indicates that the 10-minute washout period was adequate for this study, longer intervals (eg, 24 hours) could be considered in future crossover designs to further eliminate any potential recall of responses. Although the missing data proportion was low (4/81, 4.9% in phase 1, none in phase 3) and the exclusions in phase 2 (9/85, 10.6%) were based on questionnaire validity, complete-case analysis may introduce some bias. We allocated 10 minutes for each assessment session based on pilot estimates, but individual completion times were not systematically recorded. Future studies should collect such feasibility data to better inform practical implementation in school settings.

We conclude that a gamified assessment tool can be both psychometrically rigorous, engaging for elementary school children, and responsive to detecting intervention-associated changes. This study demonstrates the use of a fully gamified format to target children’s medication literacy, directly compares it against a paper-based questionnaire using a randomized crossover design, and shows consistent pre-post changes across classrooms with different baseline knowledge levels. Unlike many existing child health measures that rely on adult proxy reports or adapted adult scales, our tool is child-centered, directly validated with students, and designed to enhance engagement. Beyond providing an effective alternative to traditional paper-based questionnaires, the tool may offer a practical solution for evaluating school-based medication literacy programs. The ability to detect uniform changes across classrooms points to its potential to support health equity. We suggest that as digital health interventions become more common in schools, validated gamified assessments like this one may provide practical outcome measures for evaluating their effects. Future research should extend this tool to other health domains and test its utility in longitudinal, multisite studies, ideally with larger samples (eg, ≥150 participants) and more clusters (eg, ≥4 schools) to improve generalizability.