Background: Recent studies suggest that eye movements during tasks reflect cognitive processes and that analysis of eye movements using eye-tracking devices can identify developmental impairments in young children. Maintaining engagement during eye-tracking assessments in young children is challenging and often results in data loss due to distractions. This leads to incomplete recordings and repeated measurements, which can be taxing for young children. Gamification of eye-tracking procedures for cognitive diagnosis might increase engagement and help mitigate these problems, but its effects should be studied and quantified.
Objective: This study compares a standard eye-tracking test battery designed by us with a gamified cartoon version to evaluate the effectiveness of gamification in reducing data loss. The gamified test incorporated child-friendly visuals to provide context for the stimuli presented. The study has two objectives: (1) to compare the data quality between the two versions of the test and (2) to investigate whether, by applying a dynamic stopping criterion to both tests, the higher data quality of the gamified procedure allows earlier test termination.
Methods: Data were collected in a cohort of 25 children born preterm aged 5 years. We measured data quality using a metric derived from robustness, which we defined as the lost data index (LDI), along with task completion rates and feedback from participants. Data analyses were performed as follows: (1) direct comparison of the LDI for the two tests and (2) demonstrating that, although the base gamified test is longer, applying a stopping criterion results in comparable durations. The stopping criterion was based on the number of tasks with an LDI value below a predefined threshold.
Results: The gamified version demonstrated a significant reduction in average LDI compared with the standard version in the first (P<.001, Mann-Whitney U test) and second (P=.01, U test) quarters of the test. In addition, a lower rate of missing values, concentrated at the beginning of the tests, allowed the cartoon test to be stopped after fewer tasks. This, together with the longer tasks of the cartoon test, resulted in comparable test lengths for all thresholds measured by area under the curve (P=.50, U test) and at the chosen threshold of 0.2 LDI (P=.21, U test). Increased engagement was further supported by positive feedback, with 79% (11/14) of the participants who provided feedback preferring the gamified version.
Conclusions: These findings highlight the potential of serious games in eye-tracking-based cognitive assessments for 5-year-old children born preterm. Specifically, gamification might reduce missing values and increase participant engagement, leading to higher retention rates and more effective tests, without significantly lengthening testing procedures.
Background: Musculoskeletal disorders (MSDs) are a significant health concern in the workplace, and while ergonomic interventions are commonly used, their long-term effectiveness is often questioned. Serious games (SGs), designed to go beyond entertainment, have emerged as a promising tool that may address some of the limitations of traditional interventions, such as the need for sustained impact and greater worker engagement.
Objective: This review aims to identify and analyze the key characteristics of SGs-including design, gameplay, and expected outcomes-that have been developed for the prevention or mitigation of work-related MSDs. Additionally, it explores the documented effects of SG implementation, assessing their potential contribution to MSD prevention and intervention strategies.
Methods: A scoping review was conducted across 6 scientific databases (APA PsycInfo, Web of Science, Science Direct, MEDLINE, IEEE Xplore, and Google Scholar) to identify relevant studies published up to 2025. The selection process involved a multistep screening, including title and abstract review, followed by full-text assessment by 2 independent reviewers. Studies included were original research articles in English addressing MSD prevention and mitigation. Exclusions applied to studies on nonwork-related MSDs, limited content, duplicates, or repurposed entertainment games or gamification solutions. Data extraction was performed using a standardized form to capture key study characteristics. A 2-level analysis was applied: descriptive analysis, categorizing studies based on study characteristics and primary focus (design, evaluation, or both), and content-based analysis, examining game design, gameplay, expected outcomes, and evaluation methods to provide a structured synthesis of findings.
Results: The initial search identified 2700 records, with 15 studies meeting the inclusion criteria. These studies explored diverse applications of SGs for MSD prevention, focusing either on game design and development or on educational impact assessment. Notably, only 2 studies comprehensively addressed both design methodology and educational evaluation. Findings revealed considerable variability in design approaches, technological platforms, gameplay mechanics, and expected outcomes. Additionally, the literature exhibited significant inconsistencies in evaluating SG effectiveness, with methodological limitations affecting comparability. While some studies targeted rehabilitation or occupational health and safety, only a few explicitly focused on MSD prevention, with a predominant emphasis on physical risk factors, whereas psychosocial and organizational aspects remained largely underexplored.
Conclusions: This review highlights the need for standardized protocols and criteria for the design and evaluation of SGs to enable further synthesis and impact measuremen
Background: The quality and effectiveness of health care service delivery are significantly influenced by the engagement and motivation of health care workers. Integrating gamified digital tools (GDTs) into health care workers' workflows presents a promising approach to enhancing them. However, there is currently a lack of evidence supporting the implementation of such interventions.
Objective: This scoping review aims to summarize existing evidence on the influence of GDTs on the daily tasks of health care workers.
Methods: A scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews. We conducted a comprehensive search across different databases (PubMed, EMBASE, International Journal of Serious Games, Cochrane Library, and Google Scholar) for peer-reviewed studies and (OpenAlex, GreyNet, and IEEE Xplore) for gray literature published between January 2010 and January 2024. Eligibility criteria, developed using the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, and Research) framework, included qualitative, quantitative, and mixed methods studies involving health care workers using GDTs for daily tasks. Studies in English, French, Spanish, or Italian were eligible. Keywords and medical subject headings related to gamification and health care workers were used. The studies were screened, eligibility was assessed, and data were extracted. A narrative synthesis was used to summarize and interpret the findings.
Results: Of 5844 studies, 12 met the inclusion criteria and were included in the analysis. These studies exhibited considerable heterogeneity in the application of gamification. Feedback, competition, and dashboard features were the most common gamification elements identified. The implementation of these elements led to enhanced engagement, increased motivation, improved task completion, and promoted healthy competition among staff across various health care settings.
Conclusions: Integrating GDTs into health care workers' tasks holds significant potential to enhance engagement and motivation. However, empirical evidence is still lacking. Comparative studies are needed to gain comprehensive insights into the benefits and limitations of gamification in health care.
Background: Children with Down syndrome (DS) often experience cognitive and adaptive challenges that affect their ability to acquire and retain critical life skills, including those needed for effective response during emergencies. Traditional training methods used to prepare children for crises are frequently static, noninteractive, and insufficiently tailored to the unique learning profiles of children with DS. These limitations contribute to reduced engagement, poor knowledge retention, and inadequate real-world preparedness. Recent advancements in game-based learning, particularly serious games, have demonstrated potential for enhancing education and skill development among individuals with cognitive impairments.
Objective: This study aimed to design, implement, and evaluate Risk Resist, an adaptive serious game developed to improve emergency preparedness in children with DS. The game incorporates a dynamic difficulty adjustment algorithm that personalizes the learning experience by dynamically modifying game difficulty based on real-time behavioral performance metrics. The study also assessed whether this adaptive game-based learning approach leads to superior learning gains and engagement compared to conventional teacher-led training.
Methods: A quasi-experimental, between-group design was used with 18 children diagnosed with DS, aged 8 to 12 years. Participants were randomly assigned to either an experimental group (n=9), which played Risk Resist, or a control group (n=9), which received traditional instruction on emergency scenarios. Learning outcomes were assessed using pre- and postintervention knowledge tests composed of 5 emergency-related questions. Engagement levels were measured through a structured 5-point Likert scale questionnaire completed by observing teachers. The game used a machine learning-driven dynamic difficulty adjustment model, specifically a Random Forest Regressor, which adjusted difficulty in response to individual performance indicators such as success rate, response time, and behavioral patterns during gameplay.
Results: The experimental group achieved significantly higher learning gains (mean 3.6, SD 0.5) than the control group (mean 2.0, SD 0.5; P<.001). Engagement levels were also significantly greater in the game-based group (mean 4.54, SD 0.4) compared to the control group (mean 4.01, SD 0.32; P=.002). A strong positive correlation was identified between engagement and learning gain (r=0.85; P<.001), indicating that higher engagement contributed directly to improved knowledge acquisition.
Conclusions: The results support the effectiveness of Risk Resist in enhancing both engagement and learning outcomes for children with DS. The integration of adaptive difficulty algorithms provides a personalized, responsive learning experience, positioning serious games as a viable and impactful tool for emergenc
Background: The processing speed index (PSI) of the Korean Wechsler Intelligence Scale for Children-Fifth Edition (K-WISC-V) is highly correlated with symptoms of attention-deficit/hyperactivity disorder (ADHD) and is an important indicator of cognitive function. However, restrictions on the frequency of testing prevent short-term PSI assessments. An accessible, objective technique for predicting PSI scores would enable better short-term monitoring and intervention for children with ADHD.
Objective: To enable objective and accessible monitoring of cognitive function beyond traditional clinical assessments, this study aimed to develop a machine learning model that predicts the PSI scores of children with ADHD using behavioral data from serious games.
Methods: Sixty-eight children (6-13 y of age) with ADHD were recruited, and after excluding incomplete data, 59 participants were included in the final analysis. The participants completed an initial PSI assessment using the K-WISC-V followed by 25 minutes of engagement with serious game content. Data from the game sessions were used to train machine learning models, and the models' performance in predicting PSI scores was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE), with K-fold cross-validation (k=4) applied to ensure robustness.
Results: Among the individual machine learning models, support vector regression (SVR) had the best performance, with the lowest RMSE of 11.288, MAE of 7.874, and MAPE of 7.375%. The best overall performance was achieved by the ensemble integrating AdaBoost, Elastic Net, and SVR, which recorded the lowest RMSE of 10.072, MAE of 6.798, and MAPE of 6.611%. The predictive accuracy of this ensemble model was highest for PSI scores near the mean value of 100, demonstrating its reliability for clinical applications.
Conclusions: The developed PSI prediction model has the potential to serve as an objective and accessible tool for monitoring cognitive function in children with ADHD. As a complement to traditional assessments, this approach allows continuous tracking of symptom changes and can support more personalized treatment planning in both clinical and everyday settings, which may improve accessibility and adherence. However, the findings need to be validated in larger, more diverse populations, and the long-term feasibility of using serious games in clinical and educational settings must be further examined.
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