Effects of Digital Serious Games on Nursing Education: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

doi:10.21203/rs.3.rs-4921602/v1

Background

Advancements in digital technologies and the coronavirus disease pandemic have rapidly shifted educational practices towards engaging digital methods, such as serious games, significantly influencing healthcare education. Few high-quality systematic reviews and meta-analyses were focused exclusively on randomized controlled trials (RCTs) of digital serious games to determine their effects in nursing education.

Objective

To evaluate the effects of digital serious games on nursing education through a systematic review and meta-analysis using the Kirkpatrick model to evaluate learning and behavioral changes.

Methods

A systematic review and meta-analysis of RCTs was performed. Six databases were searched for articles published before April 2024. Risk of bias was assessed using the Revised Cochrane Risk of Bias tool for randomized trials (Rob 2.0). A sensitivity analysis was performed. Outcome variables were categorized according to the Kirkpatrick model. Effect sizes were determined using Hedges’ g in a random-effects model. Subgroup analysis was performed.

Results

Eleven studies were included in the systematic review, and eight studies, in the meta-analysis. The intervention group showed significant improvements in knowledge (Hedges’ g = 0.74, 95% confidence interval (CI) = [0.27, 1.22], p = .002, I² = 90.51%), confidence (Hedges’ g = 0.73, 95% CI = [0.23, 1.24], p = .005, I² = 82.71%), and performance (Hedges’ g = 0.49, 95% CI = [0.17, 0.80], p = .003, I² = 56.60%). Subgroup analysis showed a significant improvement in knowledge when the intervention period exceeded 2 weeks (Hedges’ g = 0.53, 95% CI = [0.32, 0.74], p < .001, I² = 25.41%).

Conclusion

This study demonstrates that digital serious games significantly enhance knowledge, performance, and confidence of nursing students. Therefore, they provide a practical and valuable alternative to traditional learning methods, meeting the modern demands of healthcare education and equipping nursing students with essential clinical competencies.

Digital technology

Education

Nursing

Gamification

Meta-analysis

Background

Innovations in information technology in the fields of big data, augmented reality, mixed reality, simulation, and artificial intelligence are transforming both educational and healthcare systems. The coronavirus disease (COVID-19) pandemic directly restricted face-to-face education in classrooms, resulting in a significant increase in online learning and accelerated changes in the healthcare education environment [1, 2]. Various digital technologies, such as e-learning, virtual reality, serious games, and podcasts, have gained renewed interest and have come into the spotlight [3].

Modern students influenced by the digital era are increasingly drawn to engaging and motivating learning experiences. This shift has led to the widespread use of digital games, particularly serious games, in education [4]. There are several perspectives on the definition of serious games, but the most common is that they are games whose primary purpose is not entertainment, enjoyment, or fun [5]. Based on this definition, serious games can be distinguished from traditional video games by their design objectives, which extend beyond entertainment to include educational, training, and other practical goals, as recognized by both academia and industry [6].

The key elements of games include points (rewarding participants for completing tasks), social interaction, leaderboards (displaying participant standing), progress status, levels, immediate feedback, narrative (including storyline or theme), badges/medals, reward systems (allowing users to exchange points/virtual currency), missions/quests, and time pressure [7, 8]. These game elements effectively enhance motivation, which is an essential part of the learning process in educational contexts [9].

Serious games in education, encompassing both digital and non-digital formats, offer a high-quality and cost-effective approach proven to enhance knowledge, skills, and satisfaction more effectively than conventional teaching methods [10], despite the lack of a definite academic stance on their digital content. When serious games incorporate digital elements, they utilize various media, such as text, graphics, animation, audio, and haptics [6]. In nursing education, serious games have been found to be effective in enhancing learning outcomes, including the learners’ knowledge, skills, and attitudes [11–13].

The effectiveness of serious games in nursing education has been investigated extensively. Previous systematic literature reviews have often included studies that ranked low in the evidence hierarchy and analyzed both digital and non-digital serious games without quantitative synthesis [14–19]. A previous study [20] performed a systematic review and meta-analysis incorporating randomized controlled trials (RCTs) and quasi-experimental studies published until July 2021 with samples comprising nursing students and nurses. This illustrates the robust and ongoing interest in this area and highlights the need for research to provide a clearer understanding of relevant findings. Consequently, there is a distinct need for high-quality meta-analyses that focus exclusively on RCTs of digital serious games to better determine their educational impact in the field of nursing.

Therefore, this study aimed to conduct a systematic review and meta-analysis focusing exclusively on RCTs that examined interventions involving digital serious games targeting nursing students. By doing so, this study aimed to enhance the quality of nursing education and contribute to the training of nursing students by equipping them with the competencies required in actual clinical settings.

Study purpose

The purpose of this study was to conduct a comprehensive systematic review and meta-analysis of the literature to evaluate the effectiveness of digital serious games for nursing students by quantifying the effect sizes of these interventions and applying the Kirkpatrick model to categorize the outcomes of digital serious games. The specific objectives of this study were as follows:

To systematically identify research studies involving digital serious games targeted at nursing students

To categorize the outcomes according to the Kirkpatrick model’s four levels

To calculate the effect sizes of digital serious games on the learning outcomes of nursing students using meta-analytical techniques

Study design

This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement [21].

Inclusion and exclusion criteria

The literature selection criteria for this study were established according to the Participants, Intervention, Comparison, Outcome, Study Design (PICOS) format recommended by the PRISMA group [22]. The specific format was as follows:

1. Participants: nursing students
2. Intervention: interventions involving digital serious games
3. Comparison: no restrictions on the comparison group
4. Outcome: no restrictions on the outcomes
5. Study Design: pretest and posttest design RCTs

The exclusion criteria for this study were as follows: 1) studies that directly compared different digital serious games; 2) studies utilizing a posttest-only RCT; 3) gray literature, such as unpublished theses; 4) studies in which the full text was unavailable; and 5) studies published in languages other than Korean or English.

Search strategy and study selection

The literature search was conducted on April 4, 2024, and utilized a total of six databases. PubMed, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Web of Science were used as international databases. The Research Information Sharing Service (RISS), Korean Studies Information Service System (KISS), and Database Periodical Information Academic (DBpia) were used as Korean databases. Search terms included (“students, nursing” OR “student, nursing” OR “nursing student*” OR “student nurse*” OR “nurse student*”) OR (“education, nursing” OR “nursing education” OR “nurse education”) AND (“game*” OR “gaming” OR “serious gam*” OR “gamification” OR “game based learning” OR “edutainment” OR “edugame” OR “educational game”). There were no restrictions on publication year for the literature search.

The bibliographic information of the selected studies was uploaded to a web-based software platform designed to facilitate the creation of systematic reviews (Covidence Systematic Review Software; Veritas Health Innovation, Melbourne, Australia). This platform automatically eliminated duplicate studies, and titles and abstracts were screened to identify studies that failed to satisfy the inclusion criteria. Subsequently, the full texts of the remaining studies were assessed for final inclusion.

The entire study selection process was independently performed by two researchers, and any disputes were adjudicated by a third researcher through a joint review of the original articles until a consensus was reached.

Data extraction and risk of bias assessment

Data extraction encompassed the identification of author(s), publication year, country, research design, sample size, specifics of the intervention (such as content, utilized platform, session duration, and overall duration), timing of measurements, intervention of comparison groups, outcome variables or measures, and statistical significance.

Given that this study exclusively focused on RCTs, the Revised Cochrane Risk of Bias tool for randomized trials (Rob 2.0) was employed to evaluate the potential biases in the selected studies [23]. The complete procedure for data extraction and assessment of risk of bias was independently conducted by two researchers. Discrepancies were resolved by a third researcher who, through a collaborative review of the original articles with the other researchers, facilitated a consensus.

Data synthesis and analysis

In this study, the outcome variables were analyzed according to the Kirkpatrick evaluation model [24]. The analysis was segmented into four levels. The first level evaluated participants’ reactions and their satisfaction with the training, focusing on its value, engagement level, and relevance. The second level evaluated the gains in knowledge and skills and enhancements in confidence and attitudes as a result of the training. The third level evaluated how well the participants apply what they have learned in their workplaces. The fourth level evaluated the impact of the training on organizational outcomes [24].

Statistical analyses were performed using the Comprehensive Meta-Analysis (CMA) 4.0 software (Biostat, Englewood, NJ, USA). A two-tailed p-value of < .05 was established as the criterion for statistical significance. Given the variability in intervention techniques, measurement instrument and timing, and duration between studies, assuming a unified treatment effect size was challenging. Consequently, a random-effects model [25] was adopted to evaluate the standardized mean difference (SMD) in continuous outcomes between the intervention and control groups. Hedges’ g, an unbiased estimator of the population SMD, which is typically favored over Cohen’s d or Glass’s ∆ in population studies [26], was selected for this analysis. The effect size was interpreted based on the 95% confidence interval (CI), deemed statistically significant if it excluded “0.” Effect sizes of 0.2, 0.5, and 0.8 were categorized as small, medium, and large, respectively [27]. Heterogeneity was visually inspected using a forest plot and quantified using Higgins I² statistics. An I² of 0% was considered to indicate no heterogeneity, 50% indicated moderate heterogeneity, and 75% was interpreted as high heterogeneity [28]. For six or more studies involving continuous variables, further analysis is advised [29]; therefore, we performed subgroup analyses based on intervention duration. A sensitivity analysis was performed to assess the robustness of the synthesized findings, and publication bias was examined using funnel plots.

Ethical approval

This study was granted an exemption from requiring ethics approval by the Institutional Review Board (IRB) of the researcher’s affiliated university (No. ****-****-****).

Study selection

The selection process for the studies included in the final review is depicted in the PRISMA 2020 flow diagram [21]. Initially, 1,237 studies were retrieved from the six databases, from which 401 duplicates were removed. Subsequent screening of the titles and abstracts led to the exclusion of 781 studies. After reviewing the full text of the remaining 55 articles, 44 were excluded, leaving 11 studies for the final review. The final selected studies are shown in Appendix A, and the study selection process is illustrated in Figure 1.

Characteristics of the included studies

The general characteristics of 11 studies analyzed in this review are shown in Table 1. The publication years ranged from 2017 to 2024, with eight of the studies [A3-A11] being published after 2020, indicating that the majority were published within the last 5 years. The sample sizes were as follows: five studies [A2, A3, A5, A8, A11] included 40-100 participants, five studies [A1, A4, A6, A7, A10] involved 100-200 participants, and one study [A9] included more than 200 participants, resulting in a total of 1,358 participants.

Regarding the characteristics of the interventions, seven studies [A1, A2, A4, A6, A8, A10, A11] involved games covering specific nursing skills (blood transfusion, tracheostomy care, basic and advanced life support, venous catheter flushing/locking, neonatal resuscitation training, venous blood specimen collection, and stoma care/colostomy irrigation), while four studies [A3, A5, A7, A9] focused on broad course matter (pathophysiology, health assessment, endocrine course, fundamentals of nursing, and diet/nutrition). Six studies [A2, A4, A6, A9, A10, A11] utilized mobile applications as gaming platforms, whereas five [A1, A3, A5, A7, A8] used websites. The most common intervention period was 1 week, as reported in five studies [A2, A3, A5, A6, A10], and three studies [A7, A9, A11] involved intervention periods longer than 2 weeks. All the studies measured the effects before and after the intervention was implemented. In five studies [A1, A3, A4, A7, A11], additional follow-up surveys were conducted at least 2 weeks and up to 7 weeks later.

The most frequent control group interventions were lectures and laboratory classes (four studies [A1, A2, A5, A11]), followed by video training (three studies [A6, A8, A10]), lectures (two studies [A4, A9]), case discussion [A3], and flipped classroom [A7].

Risk of bias

The quality appraisal results for the 11 selected studies are shown in Figure 2. All included studies were evaluated using RoB 2.0, which assesses bias across five domains: biases arising from the randomization process, biases due to deviations from intended interventions, biases due to missing outcome data, biases in measurement of the outcome, and biases in selection of the reported result. The evaluation revealed that all the studies exhibited a low risk of bias across these domains. Consequently, all the studies were included in the analysis.

Effects of digital serious games

The outcomes of the studies analyzed according to the Kirkpatrick evaluation model are presented in Table 2. The analyzed studies predominantly assessed Levels 1 and 2—Reaction and Learning, respectively, whereas Levels 3 and 4—Behavior and Results—were not evaluated. The assessments included the game experience [A4], usability [A5], and satisfaction [A8]. In the learning domain, knowledge [A1, A2, A3, A4, A7, A8, A9, A11] and performance [A1, A2, A3, A6, A7, A8, A10, A11] were the most frequently evaluated; each outcome was covered in eight studies. In addition, four studies evaluated confidence [A1, A5, A7, A8], whereas anxiety [A5], intensity of preparation [A7], motivation [A7], and perception [A1] were each examined in a single study.

A meta-analysis was conducted using eight studies selected, which provided the mean, standard deviation, and sample size for their outcome variables. We requested original data from the authors of the three studies that did not provide the mean or standard deviation; however, we were unable to obtain this information. Despite the use of diverse outcome variables and measurement instruments across these studies, separate meta-analyses were conducted for common variables, such as knowledge, performance, and confidence, with the findings displayed in forest plots (Figure 3).

A meta-analysis of six studies examining the effects of digital serious games on nursing students’ knowledge revealed a significant large effect size with high heterogeneity (Hedges’ g = 0.74, 95% CI = [0.27, 1.22], p = .002, I² = 90.51%). A meta-analysis of six studies examining the effects of digital serious games on confidence among nursing students revealed a significant large effect size with high heterogeneity (Hedges’ g = 0.73, 95% CI = [0.23, 1.24], p = .005, I² = 82.71%). A meta-analysis of four studies examining the effects of digital serious games on nursing students’ performance revealed a significant medium effect size with moderate heterogeneity (Hedges’ g = 0.49, 95% CI = [0.17, 0.80], p = .003, I² = 56.60%).

On excluding each included study individually from the sensitivity analyses of knowledge, confidence, and performance among nursing students after a digital serious game intervention, the pooled mean scores remained largely unchanged. This finding suggests that the results of the meta-analysis are relatively stable (Appendix B). Additionally, a funnel plot was used to evaluate publication bias. The funnel plot indicated asymmetry in the knowledge, performance, and confidence variables. However, when fewer than ten studies were analyzed, confirming the existence of publication bias was challenging (Appendix C).

Subgroup analyses

Subgroup analyses were conducted based on the duration of the digital serious game intervention (Figure 4). A meta-analysis of three studies investigating the effects of digital serious game interventions spanning ≥2 weeks on knowledge demonstrated a significant medium effect size with low heterogeneity (Hedges’ g = 0.53, 95% CI = [0.32, 0.74], p<.001, I² = 25.41%). Conversely, a meta-analysis of three studies examining the effects of digital serious game interventions spanning <2 weeks on knowledge showed a non-significant effect size with high heterogeneity (p = .128, I² = 95.59%).

In this study, digital serious games were found to focus more on specific nursing skills than on broad theoretical courses, aligning with findings of previous research confirming the effectiveness of digital game-based learning in medical education [30]. In addition, the meta-analysis revealed that these games significantly enhanced skill performance among nursing students, producing results similar to those of a prior study [20] that included registered nurses, although the effect size in this study (Hedges' g = 0.49) was slightly larger than earlier findings (SMD = 0.38). Considering these results, digital serious games can be utilized more effectively as supplementary educational tools in practical courses than in theoretical courses. Furthermore, they have significant potential for integration into integrated practice education because they can consistently deliver specific nursing skills training across diverse educational settings, ensuring that students gain proficiency in essential procedures. This approach effectively bridges the gap between theoretical knowledge and practical applications of various nursing skills.

In digital serious games, platform choices can vary based on the educational content and goals. The mobile platforms used in six studies [A2, A4, A6, A9, A10, A11] are preferred for procedural training, such as that in tracheostomy care and venous catheter management, offering portability and support for microlearning. Web-based platforms used in five studies [A1, A3, A5, A7, A8] are suited for theoretical subjects, such as pathophysiology and nursing fundamentals, because of their larger displays and stable connectivity, which facilitate detailed simulations and learner-content interaction. This difference in platform use emphasizes the importance of choosing the right technology to enhance educational outcomes. The alignment of technology with specific educational content needs is crucial for optimizing the effectiveness of learning interventions [31].

The meta-analysis results of this study confirm that digital serious games significantly improve nursing students' knowledge levels, which is consistent with the findings in previous studies involving nurses [20], although the effect size in previous research (SMD = 1.30) was larger than that in this study (Hedges' g = 0.75). Similar outcomes have been observed in the case of medical education [30], in which the effect size in previous studies (SMD = 0.98) was also larger. This study also found that digital serious games positively affect the learners’ confidence. However, research in the healthcare education field is limited, with most studies focusing on the enhancement of learner confidence through serious games in foreign language education [32, 33]. These findings support the existing evidence that digital serious games enhance learners' knowledge and confidence. These games accomplish this by positively influencing the learners’ mood and increasing their engagement, thereby playing a vital role in expanding their interest in achieving the game's goals and improving their cognitive skills and motivation across various disciplines [34, 35]. Serious games also promote self-confidence by allowing students to repeatedly attempt tasks until they find a solution free from the fear of peer judgment [34].

Game-based learning has been shown to effectively enhance knowledge retention, and this study confirms the positive short-term effect on knowledge retention observed in previous studies [36, 37]. However, the results regarding long-term effects were mixed [18], and although it was challenging to evaluate outcomes after the immediate post-intervention period owing to varying timelines, the analyzed studies conducted follow-up assessments 2-7 weeks later. Additionally, the studies analyzed in this review focused primarily on the immediate responses and learning outcomes associated with digital serious games. Future studies should explore the long-term impact of digital serious games on behavior and outcomes, and learning strategies should be developed not only to assess the immediate effects but also to understand the long-term effects.

Subgroup analysis revealed that digital serious game interventions affected learners' knowledge differently based on duration. Interventions longer than 2 weeks significantly improved knowledge, whereas those shorter than 2 weeks did not. This aligns with the findings of a prior study on adolescent health promotion, indicating that the effectiveness of serious games varies with intervention length, and short durations have a limited impact [38]. However, this contrasts with the findings of research on K-12 and higher education, which found the greatest effect on learning achievement with durations of <1 week [39]. These differences may arise from participant groups or outcome variables. Therefore, further research is needed to elucidate the mechanisms of action, optimize design elements, and determine the appropriate intervention duration [40].

The findings of this study, along with those of previous research, confirm that digital serious games have a positive impact on learners in various ways. However, there are significant challenges associated with implementing these games in education, including ensuring the availability of adequate equipment for students and teachers and designing the game to align with pedagogical objectives while effectively simplifying complex subjects [34]. To effectively use digital serious games in education, it is crucial to address factors, such as their perceived usefulness, ease of use, and goal clarity. In addition, integrating these games into educational strategies requires aligning game mechanics with learning attributes to cater to individual learner’s needs. This approach would ensure that serious games are seamlessly integrated into educational plans and learning processes, thereby enhancing educational effectiveness [35]. In nursing education, integrating digital serious games effectively entails designing game mechanics that enhance learners' abilities to apply clinical skills in real-life scenarios. This approach supports the understanding and management of complex clinical situations while aligning with educational objectives, thereby increasing student confidence in safe and efficient patient care environments.

The high heterogeneity observed in the data necessitates a cautious interpretation of the results. This variability may be attributed to the use of diverse measurement tools across studies, which can significantly influence the outcomes. Variability in the implementation of digital serious games across studies can also influence the consistency of the outcomes. The short duration of the interventions limits insights into the long-term retention of skills and knowledge. Moreover, most studies were focused primarily on immediate learning outcomes, with less attention paid to behavioral changes and long-term effects. Therefore, future research should aim to address these gaps by focusing on the long-term effects and behavioral changes to fully assess the efficacy of digital serious games in nursing education.

This study underscores the significant effects of digital serious games on nursing education and highlights their effectiveness in enhancing the knowledge, performance, and confidence of nursing students. The integration of digital serious games, especially involving content based on practical nursing skills, provides a robust alternative to traditional methods and aligns closely with the demands of modern healthcare education. By focusing on practical applications, these digital serious games offer a promising pathway for enriching learning experiences and improving educational outcomes in nursing training programs. This approach not only enhances the quality of nursing education but also contributes to the training of nursing students by equipping them with the competencies required in actual clinical settings, thereby preparing them to meet the evolving challenges of healthcare effectively.

Ethics approval and consent to participate

This study was granted an exemption from requiring ethics approval by the Institutional Review Board (IRB) of the researcher’s affiliated university (No. ewha-202407-0017-01).

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2023R1A2C2006838)

Authors' contributions

Minjae Lee and Eunmin Hong wrote the main manuscript text and Minjae Lee prepared all figures. Sujin Shin, Miji Lee, and Eunmin Hong reviewed the manuscript.

Acknowledgements

Not applicable

Aristovnik A, Karampelas K, Umek L, Ravšelj D. 2023. Impact of the COVID-19 pandemic on online learning in higher education: a bibliometric analysis. Front Educ. 2023;8:1225834. https://doi.org/10.3389/feduc.2023.1225834
Frenk J, Chen LC, Chandran L, Groff EOH, King R, Meleis A, et al. Challenges and opportunities for educating health professionals after the COVID-19 pandemic. Lancet. 2022;400(10362):1539–56. https://doi.org/10.1016/S0140-6736(22)02092-X
Wang MC, Tang JS, Liu YP, Chuang CC, Shih CL. Innovative digital technology adapted in nursing education between Eastern and Western countries: a mini-review. Front Public Health. 2023;11:1167752. https://doi.org/10.3389/fpubh.2023.1167752
Anastasiadis T, Lampropoulos G, Siakas K. Digital game-based learning and serious games in education. Int J Adv Sci Res Engineering. 2018;4(12):139–44. https://doi.org/10.31695/IJASRE.2018.33016
Michael DR, Chen S. Serious games: games that educate, train and inform. 2nd ed. Boston: Thomson Course Technology; 2006
Laamarti F, Eid M, El Saddik A. An overview of serious games. Int J Computer Games Technol. 2014;2014(1):358152. https://doi.org/10.1155/2014/358152
Garett R, Young SD. Health care gamification: a study of game mechanics and elements. Technol Knowl Learn. 2019;24:341–53. https://doi.org/10.1007/s10758-018-9353-4
Toda AM, Oliveira W, Klock AC, Palomino PT, Pimenta M, Gasparini I, et al. A taxonomy of game elements for gamification in educational contexts: proposal and evaluation. 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), Maceio, Brazil; 2019. http://doi.org/10.1109/ICALT.2019.00028
Leitão R, Maguire M, Turner S, Guimarães L. A systematic evaluation of game elements effects on students’ motivation. Educ Inf Technol. 2022;22:1081–103. https://doi.org/10.1007/s10639-021-10651-8
Gentry SV, Gauthier A, Ehrstrom BLE, Wortley D, Lilienthal A, Car LT, et al. Serious gaming and gamification education in health professions: systematic review. J Med Internet Res. 2019;21(3):e12994. https://doi.org/10.2196/12994
Fijačko N, Creber RM, Metličar Š, Strnad M, Greif R, Štiglic G, et al. Effects of a serious smartphone game on nursing students’ theoretical knowledge and practical skills in adult basic life support: randomized wait list-controlled trial. JMIR Serious Games. 2024;12:e56037. https://doi.org/10.2196/56037
McConnell H, Duncan D, Stark P, Anderson T, McMahon J, Creighton L, et al. Enhancing COVID-19 knowledge among nursing students: a quantitative study of a digital serious game intervention. Healthcare (Basel, Switzerland). 2024;12(11):1066. https://doi.org/10.3390/healthcare12111066
Sarvan S, Efe E. The effect of neonatal resuscitation training based on a serious game simulation method on nursing students’ knowledge, skills, satisfaction and self-confidence levels: a randomized controlled trial. Nurse Educ Today. 2022;111:105298. https://doi.org/10.1016/j.nedt.2022.105298
Ozdemir EK, Dinc L. Game-based learning in undergraduate nursing education: a systematic review of mixed-method studies. Nurse Educ Pract. 2022;62:103375. https://doi.org/10.1016/j.nepr.2022.103375
Min A, Min H, Kim S. Effectiveness of serious games in nurse education: a systematic review. Nurse Educ Today. 2022;108:105178. https://doi.org/10.1016/j.nedt.2021.105178
Nascimento KGD, Ferreira MBG, Felix MMDS, Nascimento JDSG, Chavaglia SRR, Barbosa MH. Effectiveness of the serious game for learning in nursing: systematic review. Rev Gaucha Enferm. 2021;4: e20200274. https://doi.org/10.1590/1983-1447.2021.20200274
Nasiri M, Amirmohseni L, Mofidi A, Paim CPP, Shamloo MBB, Asadi M. Educational games developed for students in perioperative nursing: a systematic review and appraisal of the evidence. Nurse Educ Pract. 2019;37:88–96. https://doi.org/10.1016/j.nepr.2019.05.002
Tavares N. The use and impact of game-based learning on the learning experience and knowledge retention of nursing undergraduate students: a systematic literature review. Nurse Educ Today. 2022;117:105484. https://doi.org/10.1016/j.nedt.2022.105484
Xu Y, Lau Y, Cheng LJ, Lau ST. Learning experiences of game-based educational intervention in nursing students: a systematic mixed-studies review. Nurse Educ Today. 2021;107:105139. https://doi.org/10.1016/j.nedt.2021.105139
Thangavelu DP, Tan AJQ, Cant R, Chua WL, Liaw SY. Digital serious games in developing nursing clinical competence: a systematic review and meta-analysis. Nurse Educ Today. 2022;113:105357. https://doi.org/10.1016/j.nedt.2022.105357
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. https://doi.org/10.1136/bmj.n71
Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097
Kim S, Park D, Seo H, Shin S, Lee S, Jang B, et al. NECA’s Guidance for Assessing Tools of Risk of Bias. Seoul: National Evidence-based Healthcare Collaborating Agency; 2021.
Kirkpatrick JD, Kirkpatrick WK. Kirkpatrick’s Four Levels of Training Evaluation. Alexandria: Association for Talent Development; 2016.
Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane Handbook for Systematic Reviews of Interventions. 2nd ed. Hoboken: Wiley-Blackwell; 2019.
Fu R, Gartlehner G, Grant M, Shamliyan T, Sedrakyan A, Wilt TJ, et al. Conducting quantitative synthesis when comparing medical interventions: AHRQ and the effective health care program. J Clin Epidemiol. 2011;64(11):1187-97. https://doi.org/10.1016/j.jclinepi.2010.08.010
Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. New York: Routledge; 2013.
Hedges LV, Olkin I. Statistical Methods for Meta-Analysis. Orlando: Academic Press; 2014.
Agency for Healthcare Research and Quality. Comparative Effectiveness Review Methods: Clinical Heterogeneity. Charleston: CreateSpace Independent Publishing Platform; 2013.
Zhao J, Zhou K, Ding Y. Digital games-based learning pedagogy enhances the quality of medical education: a systematic review and meta-analysis. Asia-Pac Educ Res. 2022;31(4):451–62. https://doi.org/10.1007/s40299-021-00587-5
Oermann MH, De Gagne JC, Phillips BC. Teaching in Nursing and Role of the Educator, 3rd ed. New York: Springer Publishing; 2022.
Li R. Does game-based vocabulary learning APP influence Chinese EFL learners’ vocabulary achievement, motivation, and self-confidence? SAGE Open. 2021;11(1):215824402110030. https://doi.org/10.1177/21582440211003092
Tsymbal SV. Enhancing students’ confidence and motivation in learning English with the use of online game training sessions. Inf Technol Learn Tools. 2019;71(3):227–35. https://doi.org/10.33407/itlt.v71i3.2460
El Rhayami M, El Mhouti A, El Borji Y. Serious games, a tool for consolidating learning outcomes. In: Motahhir S, Bossoufi B, editors. Digital technologies and applications. Cham: Springer; 2023. https://doi.org/10.1007/978-3-031-29857-8_4
Zhonggen Y. A meta-analysis of use of serious games in education over a decade. Int J Computer Games Technol. 2019;2019(1):4797032. https://doi.org/10.1155/2019/4797032
Morrell BLM, Eukel HN. Escape the generational gap: a cardiovascular escape room for nursing education. J Nurs Educ. 2020;59(2):111–5. https://doi.org/10.3928/01484834-20200122-11
Zehler A, Musallam E. Game-based learning and nursing students’ clinical judgment in postpartum hemorrhage: a pilot study. J Nurs Educ. 2021;60(3):159–64. https://doi.org/10.3928/01484834-20210222-07
Andrew L, Barwood D, Boston J, Masek M, Bloomfield L, Devine A. Serious games for health promotion in adolescents—a systematic scoping review. Educ Inf Technol. 2023;28(5):5519–5550. https://doi.org/10.1007/s10639-022-11414-9
Wang LH, Chen B, Hwang GJ, Guan JQ, Wang YQ. Effects of digital game-based STEM education on students’ learning achievement: a meta-analysis. Int J STEM Educ 2022;9(1):26. https://doi.org/10.1186/s40594-022-00344-0
Damaševičius R, Maskeliūnas R, Blažauskas T. Serious games and gamification in healthcare: a meta-review. Inf. 2023;14:105. https://doi.org/10.3390/info14020105

Table 1. Characteristics of the included studies

No	Author (Year, Country)	Sample size (n)	Intervention				Comparison	Outcome variable (effect)
No	Author (Year, Country)	Sample size (n)	Game contents	Game platform	Duration (times)	Measurement	Comparison	Outcome variable (effect)
A1	Tan et al. (2017, Singapore)	exp (57) con (46)	Blood transfusion	Website	30 min (once)	Repeated measure (before, after, and 2 weeks after intervention)	Lecture and laboratory class	Knowledge (+) Confidence (+) Performance (-) Perception (NA)
A2	Bayram & Caliskan (2019, Turkey)	exp (43) con (43)	Tracheostomy care	Mobile application	1 week (NI)	Before and after intervention	Lecture and laboratory class	Knowledge (-) Performance (+)
A3	Ignacio & Chen (2020, Singapore)	exp (23) con (26)	Pathophysiology and health assessment	Website	1 week (NI)	Repeated measure (before, after, and 6-7 weeks after intervention)	Case discussion	Knowledge (-) Performance (-)
A4	Gutiérrez-Puertas et al. (2021, Spain)	exp (92) con (92)	Basic and advanced life support	Mobile application	90 min (once)	Repeated measure (before, after, and 3 weeks after intervention)	Lecture	Knowledge (+) Game experience (NA)
A5	Calik & Kapucu (2022, Turkey)	exp (30) con (30)	Endocrine course	Website	1 week (NI)	Before and after intervention	Lecture and laboratory class	Usability (NA) Anxiety (-) Confidence (+)
A6	Gu et al. (2022, China)	exp (77) con (77)	Venous catheter flushing and locking with pre-filled saline syringes	Mobile application	1 week (anytime, anyplace)	Before and after intervention	Video training	Performance (+)
A7	Elzeky et al. (2022, Egypt)	exp (64) con (64)	Fundamentals of nursing	Website	6 weeks (NI)	Repeated measure (before, after, and 6 weeks after intervention)	Flipped classroom	Motivation (+) Knowledge (+) Performance (-) Confidence (+) Intensity of preparation (+)
A8	Sarvan & Efe (2022, Turkey)	exp (45) con (45)	Neonatal resuscitation training	Website	5 days (5 times)	Before and after intervention	Video training	Knowledge (-) Performance (+) Satisfaction (-) Confidence (-)
A9	Rosa-Castillo et al. (2023, Spain)	exp 1 (83) exp 2 (60) con 1 (82) con 2 (62)	Diet and nutrition	Mobile application	4 weeks (NI)	Before (exp 1, con 1) and after intervention (all)	Lecture	Knowledge (+)
A10	Tang et al. (2023, China)	exp (53) con (52)	Venous blood specimen collection	Mobile application	1 week (anytime, anyplace)	Before and after intervention	Video training	Performance (+)
A11	Kulakaç & Çilingir (2024, Turkey)	exp (45) con (47)	Stoma care and colostomy irrigation	Mobile application	2 weeks (anytime, anyplace)	Repeated measure (before, after, and 4 weeks after intervention)	Lecture and laboratory class	Knowledge (+) Performance (+)
Con, control group; Exp, experimental group; NA, not applicable; NI, no information

Table 2. Outcomes of digital serious game interventions according to Kirkpatrick’s levels

Kirkpatrick's levels	Outcome	Study No
Level 1	Game experience	A4
	Satisfaction	A8
	Usability	A5
Level 2	Knowledge	A1, A2, A3, A4, A7, A8, A9, A11
	Performance	A1, A2, A3, A6, A7, A8, A10, A11
	Anxiety	A5
	Confidence	A1, A5, A7, A8
	Intensity of preparation	A7
	Motivation	A7
	Perception	A1
Level 3		-
Level 4		-

No competing interests reported.

Appendices0814.docx

Effects of Digital Serious Games on Nursing Education: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

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Abstract

Background

Objective

Methods

Results

Conclusion

Figures

Introduction

Background

Study purpose

METHODS

Study design

Inclusion and exclusion criteria

Search strategy and study selection

Data extraction and risk of bias assessment

Data synthesis and analysis

Results

Discussion

Conclusions

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1