Serious Games for Preventing Musculoskeletal Disorders in Occupational Settings: Scoping Review
Bibliographic record
Abstract
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 measurement. The integration of MSD prevention into SGs remains limited and is often approached indirectly through related themes such as workplace safety or rehabilitation. Future research should focus on developing and validating more comprehensive SG-based interventions and exploring their potential as effective tools in occupational health. The findings indicate a substantial gap in empirical evidence regarding the effectiveness of SGs for MSD prevention, largely due to the disparity in experimental approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".