Design and Evaluation of a Gamification-Based Mobile Application for Reporting Adverse Drug Reactions by Patients and Healthcare Professionals: A Pilot Usability Study in Diabetes Care

Abstract

Background

Adverse drug reactions (ADRs) represent a significant challenge in pharmacovigilance, particularly for patients with chronic conditions like diabetes. Traditional reporting methods are often cumbersome and suffer from substantial underreporting. Mobile health applications with gamification elements offer a promising approach to enhance ADR reporting engagement.

Objective

This study aimed to design, develop, and evaluate a gamified mobile application (RxFeedback) for ADR reporting among diabetic patients and healthcare professionals in Iran, with gamification mechanics (points, leaderboards, challenges) as the core engagement strategy.

Methods

This analytical cross-sectional pilot study employed a user-centered design methodology in three phases: 1) Requirements gathering through literature review and stakeholder surveys (5 patients, 4 professionals); 2) Application development using Flutter framework with Dart programming language for Android platform; 3) Evaluation using Mobile Application Rating Scale (MARS) by 5 experts and Questionnaire for User Interaction Satisfaction (QUIS) by 20 patients.

Results

The RxFeedback application incorporated comprehensive ADR reporting features, medication management, and gamification elements (points system, leaderboards, challenges). Expert evaluation yielded a mean MARS score of 3.65/5, with highest scores in functionality (4.05) and lowest in aesthetics (3.40). Patient satisfaction assessment showed an overall QUIS score of 6.14/10, with highest satisfaction in application features (6.8) and lowest in terminology (5.7), though the limited sample size (n=20 patients, n=5 experts) and focus on usability should be noted as preliminary study constraints.

Conclusion

RxFeedback demonstrated acceptable usability and quality (MARS: 3.65/5; QUIS: 6.14/10) in this pilot study. However, effectiveness in improving actual ADR reporting rates was not assessed. These findings establish preliminary acceptability as a foundation for future experimental studies.

Introduction

In the complex landscape of healthcare management, patients with chronic conditions such as diabetes face numerous challenges, particularly concerning medication safety and adherence.1 Adverse drug reactions (ADRs) emerge as a critical concern that significantly impacts treatment outcomes and patient safety worldwide.2 Despite global efforts to improve pharmacovigilance systems, traditional methods of reporting medication side effects remain cumbersome and time-consuming, contributing to substantial underreporting—estimated at over 90% in some settings.3,4 This gap is particularly pronounced in low-resource and middle-income regions where healthcare infrastructure may be limited.5

The rapid proliferation of smartphones and mobile technologies presents a transformative opportunity to revolutionize patient engagement in pharmacovigilance.6,7 Recent studies have demonstrated the potential of mobile health applications to simplify ADR reporting processes, with several countries successfully implementing national reporting apps that significantly reduce reporting time and increase patient participation.4,8 However, many healthcare systems still lack dedicated digital tools for patient-centered ADR monitoring.

The ability of mobile apps to diversify ADR reporting is supported by Montastruc et al,9 who showed that VigiBIP^® captures more patient reports and non-serious ADRs compared to classical methods. Similarly, Kadima et al5 demonstrated in Rwanda that even basic mobile phone technology (calls) achieved a high contact rate (87.5%) and substantial ADR reporting (48.57%). This finding underscores that high mobile penetration (over 80% in many Sub-Saharan African countries) presents a significant opportunity to engage patients in pharmacovigilance outside clinical settings. Unlike traditional hospital-based systems such as the UK’s Yellow Card Scheme, which rely primarily on healthcare professionals (doctors, pharmacists, nurses) to report ADRs, mobile-based approaches can capture ADRs directly from patients in community settings. This is particularly valuable for detecting mild or moderate ADRs that do not lead to hospital visits but still affect quality of life, medication adherence, and treatment outcomes. In low-resource settings where healthcare infrastructure is limited and patients have limited access to providers, mobile-based reporting may complement or even partially substitute for traditional systems, thereby providing a more complete picture of drug safety.

Gamification, which is defined as the strategic integration of game-design elements into non-gaming contexts, has emerged as a promising approach to enhance user engagement in health applications.10,11 Evidence from various healthcare settings indicates that well-designed gamified systems can improve adherence to treatment protocols and increase participation in health monitoring activities.8 When applied to ADR reporting, gamification offers the potential to transform a traditionally bureaucratic process into an engaging, user-friendly experience that encourages sustained participation. The positive association between mobile app use and increased ADR reporting is evident across multiple implementations.8 For instance, a mobile app introduced at the University of Chicago significantly increased monthly reporting rates by healthcare professionals. The WEB-RADR applications in the UK, Netherlands, and Croatia attracted substantial patient reporting without compromising report quality. Similarly, the ADR PvPI app in India increased reporting rates from 3.55% to 96.45% within one year of launch.

The absence of a dedicated national mobile reporting application in Iran, much like the situation in Portugal highlighted by Parracha et al,4 represents a significant gap in the country’s pharmacovigilance infrastructure. While Iran has a well-established spontaneous reporting system managed by the Iran Food and Drug Administration (IFDA), the lack of a patient-centered mobile tool limits public engagement and contributes to underreporting. The recommendation by Parracha et al4 for a country-specific version of an app like WEB-RADR strongly aligns with the potential of RxFeedback to serve as a model for Iran’s pharmacovigilance system.

Our research addresses this crucial intersection between patient engagement, technology, and medication safety by developing a novel mobile application specifically designed for chronic disease patients, with initial focus on diabetes management. Diabetes was specifically selected for three reasons: (1) its high and rising prevalence in Iran, (2) the complexity of polypharmacy in diabetic patients (often requiring 5–10 concurrent medications), which increases ADR risk, and (3) the critical need for continuous monitoring of ADRs such as hypoglycemia, which is often underreported due to patients mistaking it for disease progression rather than a drug reaction. By leveraging evidence-based gamification techniques and user-centered design principles, we aim to create a platform that not only simplifies ADR reporting but also motivates patients to actively participate in medication safety monitoring.

The motivation behind this study stems from several key observations identified in recent literature: the persistent underutilization of traditional ADR reporting systems despite their importance for public health; the demonstrated success of mobile reporting applications in various international contexts; and the growing evidence supporting gamification as an effective engagement strategy in healthcare applications.4,5,8

Accordingly, the primary research objectives of this study are threefold: (1) to design an intuitive, gamified mobile application for ADR reporting using a user-centered approach; (2) to evaluate the application’s usability and user satisfaction as key preliminary indicators of its potential for adoption; and (3) to lay the necessary groundwork for subsequent longitudinal studies assessing the causal impact of gamification on actual ADR reporting behavior and pharmacovigilance effectiveness in chronic disease management.

By combining technological innovation with insights from successful international implementations, this study contributes to the growing body of knowledge on digital pharmacovigilance solutions.4,8 The resulting application represents a scalable approach that can potentially transform how adverse drug reactions are documented and understood, ultimately contributing to improved patient safety and healthcare outcomes across diverse healthcare settings.

Materials and Methods

This study presents a method for developing and evaluating a gamified mobile application designed to improve the reporting of adverse drug reactions among diabetic patients and healthcare professionals. The methodology is structured into a sequential process comprising three primary phases: requirements gathering, application design and development, and evaluation. This approach ensures the final product is both user-centered and effectively incorporates gamification elements to enhance engagement and usability. This methodology was selected to first validate the tool’s design and user experience before progressing to complex behavioral outcome assessments in a subsequent, larger-scale study. This process is illustrated in Figure 1.

Figure 1.

Flow diagram of the study methodology: Phase 1—Requirements gathering; Phase 2—Application design and development; Phase 3—Evaluation. This figure was generated using Napkin AI tool (version 1.0). The authors confirm the originality and accuracy of the content, have checked the terms of use for the AI tool used, and confirm suitability for publication. The authors have the right to publish the images generated and take full responsibility for the integrity of the whole content, including accuracy of references.

Phase 1: Requirements Gathering

The initial phase focused on identifying the structural and content requirements for the application through a comprehensive information collection process. This began with a literature review using keywords such as “adverse drug reactions”, “digital health”, and “gamification” and their synonyms across major databases including ScienceDirect, Scopus, Elsevier, ProQuest, and PubMed. Following the literature review, a cross-sectional survey questionnaire (Figure 2) was administered to both diabetic patients and healthcare professionals to evaluate their opinions on potential prerequisites, functionalities, and perceived barriers related to a mobile application for ADR reporting. The questionnaire was administered to five diabetic patients and four healthcare professionals. The custom questionnaire was pilot-tested with two diabetic patients and one endocrinologist (not included in the final sample) to check for clarity, relevance, and face validity. Minor wording adjustments were made based on their feedback. The sample size of 9 stakeholders (5 patients, 4 professionals) was determined based on conventions for qualitative needs assessment in user-centered mHealth research. Similar studies have used 5–21 participants for initial requirements gathering to capture diverse stakeholder perspectives and identify key functional and non-functional requirements.12 As this phase focused on gathering stakeholder feedback to inform the initial prototype design, the sample size was considered sufficient for a pilot study, consistent with the principle that pilot trials should be sized based on their specific feasibility objectives rather than power calculations for efficacy testing.13 Patients were recruited from the endocrinology outpatient clinic of a teaching hospital in Tehran using convenience sampling. Experts were purposively sampled from the university’s medical informatics and nursing faculties based on their expertise in pharmacovigilance, diabetes care, or mHealth evaluation. All provided written informed consent. Participants were asked to rate the usefulness of proposed features on a Likert scale and to provide open-ended feedback on the optimal procedural implementation of the app and any potential obstacles. The insights gathered from both the literature and the surveys were then synthesized to document the core objectives and derive the final functional and non-functional requirements and gamification features for the application.

Figure 2.

The custom questionnaire used for stakeholder requirements gathering. It included 6 functional requirement items, 7 gamification mechanic items, and 4 non-functional requirement items, each rated on a 5-point Likert scale, plus open-ended questions for qualitative feedback.

Phase 2: Design and Development

Upon defining the essential data elements and structural requirements, the design and development phase commenced. A prototyping model was employed to iteratively identify user needs and define application requirements, with the goal of creating a tangible and visible prototype for user feedback.

The development team prioritized efficient server communication to minimize load and ensure stability during concurrent use. The application features a user authentication process where users initially enter their mobile number on a landing page to receive an activation code via SMS, facilitated by the SMS.ir service. The application was developed for the Android platform using the Dart language and the Flutter framework, chosen for its high performance, interoperability, and compact runtime library, which contributes to faster application startup times. User data is stored in local SQLite database tables. The application interface was developed in Persian as the primary language to cater to the target users.

Crucially, gamification elements were integrated into the application based on the analysis of similar systems and literature.

The description of the application’s design and development process aimed to adhere to the core principles of comprehensive reporting for digital health interventions, as outlined in guidelines such as mHealth Evidence Reporting and Assessment (mERA),14 by providing detailed information on context, functionality, and testing.

Phase 3: Evaluation

The evaluation of the developed application was conducted in two distinct stages, focusing on establishing its foundational usability and acceptability as a prerequisite for future behavioral studies.

The quality of the RxFeedback application was evaluated by five specialists (three medical informatics experts and two diabetes care specialists) using the Mobile App Rating Scale (MARS) questionnaire. The MARS tool contains 23 questions categorized into five key quality domains: engagement, functionality, aesthetics, information, and subjective quality. Each question is rated on a 5-point Likert scale. For the MARS expert evaluation, 5 experts meet the recommended minimum for heuristic evaluation.15 They were briefed on the questionnaire in a one-hour session, and the original English version of MARS was used due to the panel’s proficiency.

The second stage focused on evaluating the user satisfaction of the application by the end-users. This was performed using the validated Persian version of the Questionnaire for User Interaction Satisfaction (QUIS), which has a demonstrated Cronbach’s alpha of 0.94.16 The QUIS comprises 30 questions across six sections: demographic information, overall user reaction, screen design, terminology and system information, learnability, and system capabilities. Responses are recorded on a 10-point Likert scale.17 For the QUIS patient evaluation, a sample of 20 patients is sufficient to detect approximately 80% of usability problems in a pilot study.18 These sample sizes are consistent with similar mHealth pilot usability studies. Diabetic patients who had internet and Android operating system proficiency and were using at least five different medications were invited to use the application for a period of 10 days, after which they completed the QUIS questionnaire. Patients were recruited from the endocrinology outpatient clinic of a teaching hospital in Tehran using convenience sampling. All provided written informed consent.

Data Analysis

Data from the MARS and QUIS questionnaires were entered into Microsoft Excel 2025. Only descriptive statistics (mean, standard deviation, and 95% confidence intervals) were calculated for all items. Confidence intervals were computed using the standard formula: CI = mean ± (1.96 × standard error). Results are presented as mean ± SD [95% CI] where appropriate. Inferential statistics (eg, t-tests, ANOVA) were not performed due to the exploratory pilot nature of this study and the small sample sizes (n=5 for MARS, n=20 for QUIS). This limitation is acknowledged in the Discussion section.

Results

Requirements Gathering

Functional Requirements

Based on the cross-sectional survey administered to 9 stakeholders (5 diabetic patients and 4 healthcare professionals), participants rated the usefulness of six proposed functional requirements using a 5-point Likert scale (1=not at all useful to 5=extremely useful). The results are summarized in Table 1.

Table 1.

Mean Usefulness Scores for Functional Requirements (N=9)

Functional Requirements	Description	Mean	SD
User registration with comprehensive demographic and medical history	Capture user age, gender, medical history, duration of diabetes, education level, height, and weight to establish a baseline profile.	4.67	0.50
ADR recording lifecycle managed by the user	Enable the user to record, edit, and delete ADRs with metadata such as onset date, severity, and accompanying symptoms	4.89	0.33
Patient-managed medication list	Allow users to create, modify, and remove the list of medications they are consuming, including dosing information and start/end dates.	4.78	0.44
Medication reminder management	Provide functionality to create, edit, schedule, and delete medication reminders, including repeat intervals and notification methods.	4.44	0.73
ADR inquiries during medication use	Support user-initiated inquiries about the occurrence of ADRs during the course of medication usage, with options to document responses or clarifications.	4.33	0.71
Preservation of user-entered history	Maintain a persistent history of entered ADRs, medications, reminders, and related interactions to support retrospective analysis and reporting.	4.56	0.53

Abbreviations: ADR, Adverse Drug Reaction; SD, Standard Deviation.

All six functional requirements received mean scores above 4.3 (out of 5), indicating high perceived usefulness. Recording, editing, and deleting ADRs received the highest mean score (4.89 ± 0.33), with 8 out of 9 participants rating it as “extremely useful” (score 5). The lowest-rated requirement was daily interactive questions about ADRs (4.33 ± 0.71), though this still indicates favorable acceptance. Based on these results, all six functional requirements were included in the RxFeedback application design.

Gamification Features

Following the requirement analysis, gamification mechanics were integrated into the application to enhance user engagement. These mechanics were categorized into five groups: rewards and points, social network, challenges, leaderboards, and progress mapping. Each activity within these categories was assigned a specific point value. Point values were adapted from Self-Determination Theory (SDT) and previous gamified health interventions.19,20 For example, daily ADR reporting (40 points with morning bonus) was weighted higher than medication logging (10 points) to prioritize pharmacovigilance over adherence. Penalties for incomplete reports (0 points) were designed to discourage partial submissions without demotivating users, following the “loss aversion” principle from behavioral economics. Examples of these gamification elements are presented in Appendix.Box1.

The gamification features were designed not merely as a points system but to target fundamental psychological needs as outlined in SDT. The points and leaderboards (eg, “top performer”) were intended to foster a sense of competence. The social features, such as asking and answering questions, were designed to create relatedness and a sense of community. Finally, the flexibility in profile completion and the ability to self-initiate ADR reports supported user autonomy. This theory-informed approach increases the likelihood that the gamification will lead to sustained intrinsic motivation, moving beyond superficial engagement.

Non-Functional Requirements

Based on the cross-sectional survey of 9 stakeholders (5 patients, 4 professionals) using a 5-point Likert scale, security and confidentiality received the highest importance score (4.89 ± 0.33), with 8/9 participants rating it as “extremely important”. Ease of use followed closely (4.78 ± 0.44; 7/9 rated extremely important), with one patient noting: “If the app is complicated, I will not use it”. Speed and performance scored 4.44 ± 0.53 (5/9 extremely important), while compatibility with Android versions received the lowest score (4.11 ± 0.78; 4/9 extremely important), primarily from users with older devices. All four requirements were prioritized in development, with particular emphasis on security and usability (Table 2).

Table 2.

Mean Importance Scores for Non-Functional Requirements (N=9)

Non-Functional Requirement	Mean Score (1–5)	SD
Security & Confidentiality	4.89	0.33
Ease of Use (Usability)	4.78	0.44
Speed & Performance	4.44	0.53
Compatibility (Android versions)	4.11	0.78

Abbreviation: SD, Standard Deviation.

Open-Ended Feedback Analysis

The analysis of open-ended feedback from stakeholders revealed that the demand for a simple medication reminder was not solely for adherence, but also to create a contextual trigger for ADR reflection— “Did I experience any side effects after taking this pill?”. Similarly, the desire for a social forum was driven by patients’ feelings of isolation and a need for peer validation regarding their experiences, suggesting that gamification’s “social” layer addresses a profound need for community support.

Application Design and Development

The application was successfully implemented as designed. Figure 3 illustrates the final user interface of RxFeedback.

Figure 3.

A visual overview of the designs created using Adobe XD, showing the entry screen of the application (A), the user profile screen (B), the home screen (C), the adverse drug reaction reporting input screen (D), the medication reminder section (E), and the social network section (F). This figure illustrates the overall UI structure and the layout of the main components developed in the prototype.

As illustrated in Figure 2, the application features distinct interfaces for patients and specialists. Key functionalities include user profile management, ADR reporting, medication reminders, a social network section (including a discussion forum and FAQs), a leaderboard, and challenge participation. The patient application flow begins with login via SMS, profile completion, and leads to a home screen featuring a central progress circle for daily reporting, navigation menus for medications, social features, leaderboard, and education.

Evaluation Results

Quality Evaluation by Experts (MARS)

The expert panel had a mean age of 35.2 years (±3.2 SD), consisting of 60% females and 40% males. The results indicated that the Functionality domain received the highest mean score (4.05 ± 0.18), while Aesthetics received the lowest (3.40 ± 0.45). The final mean quality score of the application, calculated from the four main MARS domains (excluding subjective quality), was 3.65 ± 0.2, indicating an acceptable evaluation from the experts. Detailed results are shown in Table 3.

Table 3.

Results of the MARS Questionnaire Completed by Experts (N=5)

Expert	Engagement	Functionality	Aesthetics	Information	Subjective Quality	Final Mean
1	4.00	4.25	3.00	4.00	4.00	3.80
2	3.00	3.75	3.33	3.00	3.00	3.20
3	4.00	4.00	3.00	3.00	3.00	3.50
4	4.25	4.25	3.99	3.25	4.00	3.90
5	3.75	4.00	4.00	4.00	3.75	3.90
Mean	3.80	4.05	3.40	3.45	3.55	3.66
SD	0.43	0.18	0.45	0.45	0.45	0.205
95% CI	[3.42–4.18]	[3.89–4.21]	[3.01–3.79]	[3.06–3.84]	[3.16–3.94]	[3.48–3.84]

Notes: The final mean is the average of the four main domains (Engagement, Functionality, Aesthetics, Information) of the MARS tool. CI (Confidence Interval) = mean ± (1.96 × SD/√n), where n=5.

Abbreviations: SD, Standard Deviation; MARS, Mobile Application Rating Scale.

Usability Evaluation by Patients (QUIS)

User satisfaction was evaluated by 20 diabetic patients who used the application for one week. The validated Persian version of the QUIS was used. The participating patients had a mean age of 43.2 years (±1.4 SD); 65% were female. Most participants (60%) had a diabetes history of 1–5 years, and 75% had type 2 diabetes. Fifty-five percent reported taking 5–10 medications daily.

The findings revealed that users rated the application as acceptable, with an overall mean score of 6.14. The “Overall Application Features” section received the highest mean score (6.78 ± 0.33), while the “Terminology and System Information” section received the lowest (5.68 ± 0.90). The results are summarized in Table 4.

Table 4.

Results of the QUIS Questionnaire Completed by Patients (N=20)

Evaluation Dimension	Mean	SD	95% CI
Screen Design	5.82	0.70	[5.52–6.12]
Terminology and System Information	5.68	0.88	[5.30–6.06]
Learnability	6.55	0.60	[6.28–6.82]
Overall Application Features	6.78	0.34	[6.63–6.93]
Overall Impression	5.76	0.52	[5.53–5.99]
Overall Mean Score	6.12	0.41	[5.93–6.31]

Abbreviations: SD, Standard Deviation; CI, Confidence Interval; QUIS, Questionnaire for User Interaction Satisfaction.

Discussion

This study successfully designed, developed, and evaluated RxFeedback, a gamified mobile application for reporting ADRs among diabetic patients and healthcare professionals. The findings, when contextualized within the broader literature, provide critical insights and reinforce the importance of user-centered design and engagement strategies in mHealth applications for pharmacovigilance.

This study adds several novel contributions to the existing literature. Unlike previous descriptive studies of ADR reporting apps,4,8 which primarily focused on app availability and feature comparison, our study provides empirical usability data collected from both patients and experts using validated instruments (MARS and QUIS). Furthermore, we present a context-specific design tailored for Iran, a middle-income country that currently lacks a dedicated national ADR reporting application. Finally, our study introduces a detailed gamification point system grounded in Self-Determination Theory, which has not been previously reported for ADR reporting applications. These contributions extend the existing knowledge from mere feature description to evidence-based usability evaluation.

The favorable reception of RxFeedback, evidenced by its high quality and satisfaction scores, aligns with findings from Mongkhonmath et al, who demonstrated that well-designed mobile applications for ADR reporting can achieve high user satisfaction and report quality.21 This study’s emphasis on simplicity and a user-friendly interface resonates with research by Kiguba et al,7 where training and intuitive design were key facilitators of adoption among health workers in Uganda.7 Furthermore, the preference for straightforward digital tools is consistent with observations by Wakob et al,22 who noted that both patients and general practitioners value ease of use and low complexity.22

The application’s overall design, which included gamification features, was well-received by participants. While this study did not specifically evaluate the isolated impact of gamification elements, the observed user engagement with the application aligns with findings from other mHealth studies. Miller et al19 highlighted that well-designed digital interfaces can sustain user motivation and improve adherence.19 Similarly, Fukushima et al23 identified that interactive features and two-way communication enhance reporting rates in digital platforms.23 These broader trends in digital health engagement provide a useful context for understanding our application’s acceptance, though further research would be needed to determine the specific contribution of individual engagement elements.

When situated within the global landscape of ADR reporting apps as mapped by Parracha et al4 and Leskur et al,8 RxFeedback demonstrates characteristics consistent with successful international implementations. As reviewed by Leskur et al,8 multiple international implementations have demonstrated positive associations between mobile app introduction and increased ADR reporting rates. The high functionality score of RxFeedback (4.05) underscores the importance of intuitive navigation—a strength shared by established apps such as MedWatcher, VigiBIP, and the WEB-RADR family (Yellow Card, Bijwerking, Halmed). These apps have proven effective in reducing reporting time—for instance, MedWatcher cut average reporting time to 11.4 minutes—thereby addressing a key barrier to spontaneous reporting: lack of time among healthcare professionals.4,8,22

Benchmarking against recent systematic reviews, the mean MARS score of 3.65/5 in our study is comparable to the average score of 3.51 reported in a meta-analysis of 5920 mobile health apps from 215 studies.24 Our functionality sub score (4.05) exceeded the average functionality score of 3.98, while our aesthetics score (3.40) was slightly below the average of 3.52. Similarly, Cheah et al25 reported an overall mean MARS score of 3.2 for 42 chronic disease management apps, with functionality scoring highest (3.9) and engagement lowest (2.9). A comparison of MARS scores between RxFeedback and previous ADR reporting app studies is presented in Table 5.

Table 5.

Comparison of MARS Scores Between RxFeedback and Previous Studies

Study	Application	MARS Score (Mean)	Key Findings
Current study	RxFeedback	3.65	Functionality scored highest (4.05); aesthetics lowest (3.40)
Lef et al (2024)24	Meta-analysis of 5920 apps	3.51	Functionality average 3.98; aesthetics average 3.52
Cheah et al (2024)25	42 chronic disease apps	3.20	Functionality highest (3.9); engagement lowest (2.9)

The slightly lower score in the Aesthetics domain (3.40) and terminology-related usability challenges identified in RxFeedback mirror observations from Parracha et al4 and Leskur et al,8 who noted that apps allowing free-text input (like those in the WEB-RADR project) were preferred by users who found medical terminology confusing. This highlights a critical design consideration also raised by Kiguba et al, where health workers struggled with medical terms in ADR reporting apps.7

The lower satisfaction score in the “Terminology and System Information” domain reported by diabetic patients highlights a key design challenge: medical terminology can be confusing for patients, necessitating simpler language in future iterations. This tension is inherent in many clinical tools. Rather than a flaw, this finding provides a clear directive for the next design iteration: to employ more layperson language, predictive text, and visual aids to minimize cognitive load while maintaining the reporting detail necessary for pharmacovigilance analysis.

RxFeedback could serve as a model for Iran’s pharmacovigilance system. However, future research should focus on (1) integrating such an app with Iran’s electronic health record (EHR) systems (eg, the Sib platform), and (2) testing its impact on actual ADR reporting rates using a randomized controlled trial design before claiming broader digital health transformation benefits.

This study had several limitations. First, the evaluation focused on establishing the application’s usability, quality, and user satisfaction as essential preliminary metrics. However, the chosen methodology did not assess the application’s impact on actual ADR reporting behavior, which represents a key direction for future research. Second, the sample sizes were relatively small (n=5 for MARS expert evaluation, n=20 for QUIS patient evaluation, n=9 for requirements gathering), which limits the generalizability of the findings. These sample sizes were underpowered for hypothesis testing. Therefore, we present only descriptive statistics. This limits our ability to generalize findings or detect small but meaningful differences between subgroups (eg, by age or diabetes type). Future studies should recruit larger samples to enable multivariate analysis. Third, additional limitations include the single-country setting (Iran), which limits cross-cultural generalizability, and potential selection bias toward tech-savvy patients, as participants were required to own and use an Android smartphone. These factors may affect the applicability of findings to older adults or patients in other countries with lower digital literacy. Fourth, additional limitations included the limited specialist participation, variable digital literacy among older patients, and the absence of objective ADR report quality assessment using standardized checklists (eg, WHO-UMC criteria). These limitations are consistent with challenges noted in previous studies regarding age and technical affinity,22 and variable digital literacy across user groups.23 Fifth, the evaluation period of 7–10 days is relatively short and may not capture long-term engagement patterns or the novelty effect of gamification. Future longitudinal studies should assess usability and reporting behavior over 3–6 months. Sixth, considerations such as network availability and toll-free options should also be addressed in future implementations to ensure equitable access. Seventh, a key limitation is that we did not isolate the effect of gamification from general app usability. Future research should employ a randomized controlled trial comparing the full gamified version against a non-gamified control (identical features but without points, leaderboards, or challenges) to quantify the specific contribution of gamification to engagement and reporting behavior.

Conclusion

In conclusion, this study presents RxFeedback as a foundational step toward a gamified, user-centered platform for ADR reporting. The results confirm that the application demonstrated acceptable usability and quality (MARS: 3.65/5; QUIS: 6.14/10) in this pilot study. These findings establish preliminary acceptability as a prerequisite for influencing user behavior. However, it is important to emphasize that this study evaluated usability and satisfaction, not the actual impact on ADR reporting behavior. Therefore, no causal claims can be made regarding the application’s effectiveness in improving ADR reporting rates. RxFeedback demonstrates potential to support ADR reporting, though causal inference requires future experimental studies (eg, randomized controlled trials or longitudinal analyses) to directly quantify the application’s efficacy in increasing reporting rates, improving report quality, and sustaining user engagement over time. Such studies will be crucial for translating this technological potential into tangible public health benefits.

Funding Statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this work, the authors used DeepSeek (DeepSeek-V3) for translation assistance, Napkin (Napkin AI, Version 1.0) for creating Figure 1, and Claude (Claude 3.5 Sonnet) for enhancing the quality of figures. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. All outputs generated by AI tools were thoroughly checked and verified by the authors to ensure accuracy and validity.

Ethical Consideration

This study adhered to ethical standards by ensuring patient privacy, obtaining informed consent, maintaining honesty in data use, avoiding conflicts of interest, and reporting findings without manipulation. Furthermore, this study was conducted in accordance with the principles of the Declaration of Helsinki. The research was conducted in accordance with the ethical guidelines of the hosting institution, Research Ethics Committees of Tarbiat Modares University, and ethics approval code IR.MODARES.REC.1402.078 was issued. All participants provided informed consent, personal data were de-identified and stored securely, data integrity was maintained through transparent collection and analysis, no conflicts of interest were declared, and findings were reported accurately without selective disclosure.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.