This paper is in the following e-collection/theme issue:

Digital Health Reviews (1742) Games and Gamification for Health (784) Hypertension Prevention and Treatment (300) Exergames, Active Games and Gamification of Physical Activity (245) Parent and Child Education on Physical Activity (167)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/75000, first published .
Role of Active Video Games in Blood Pressure Management Among Children and Young Adults: Systematic Review and Meta-Analysis

Role of Active Video Games in Blood Pressure Management Among Children and Young Adults: Systematic Review and Meta-Analysis

Role of Active Video Games in Blood Pressure Management Among Children and Young Adults: Systematic Review and Meta-Analysis

Authors of this article:

Hao Zhu1 Author Orcid Image ;   Keith Tsz-Suen Tung1 Author Orcid Image ;   Hung-kwan So1 Author Orcid Image ;   Parco M Siu2 Author Orcid Image ;   Ian Chi Kei Wong3 Author Orcid Image ;   Jason C Yam4 Author Orcid Image ;   Joanna Yuet-ling Tung1, 5 Author Orcid Image ;   Yih-kuen Jan6 Author Orcid Image ;   Li He7 Author Orcid Image ;   Patrick Ip1, 5 Author Orcid Image
Article Authors Cited by (1) Tweetations (4) Metrics

    1Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Room 115, 1/F, New Clinical Building, 102 Pokfulam Road, Queen Mary Hospital, Hong Kong SAR, China (Hong Kong)

    2Division of Kinesiology, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China (Hong Kong)

    3Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China (Hong Kong)

    4Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong SAR, China (Hong Kong)

    5Department of Paediatrics and Adolescent Medicine, Hong Kong Children’s Hospital, Hong Kong SAR, China (Hong Kong)

    6Department of Health and Kinesiology, University of Illinois Urbana-Champaign, Urbana, IL, United States

    7School of Physical Education and Sports, Beijing Normal University, Beijing, China

    Corresponding Author:

    Patrick Ip, MD


    Background: The significant association between blood pressure (BP) in children and young adulthood and risks of cardiovascular diseases in adulthood highlights the critical need for early BP control. While lifestyle modifications such as increased physical exercise have proven effective, traditional exercise forms always suffer from low motivation and adherence. Active video games (AVGs), combining exercise with engaging gameplay, may present a promising alternative for managing BP in children and young adults.

    Objective: This study aims to evaluate the effectiveness of AVGs in managing BP among the population aged 6 to 25 years.

    Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline, this study retrieved and screened publications archived in the 4 databases (Web of Science, Cochrane Library, PubMed, and Embase) and the registration (ClinicalTrials.gov) up to December 30, 2024. Eligible studies were defined as interventional trials involving participants aged 6 to 25 years, using AVGs as one of the intervention protocols, and reporting BP outcomes. Studies were excluded if they involved participants with heart diseases, combined AVGs protocol with other intervention components, limited outcomes to immediate postgame BP, or included only control groups that received additional physical activity interventions. Depending on the heterogeneity among included trials, random-effects or fixed-effects models were selected to pool the effect sizes of individual trials, with 95% CIs. The risk of bias was assessed using the Cochrane Risk of Bias tool for controlled trials and the Methodological Index for Non-Randomized Studies for prepost design. Sensitivity analyses were performed to evaluate result robustness, while Egger tests investigated publication bias.

    Results: A total of 17 trials from 16 studies, involving 503 participants who are normotensive, were included in this study. The analysis showed that AVGs significantly reduced systolic blood pressure (standardized mean difference=−0.50, P<.001, 95% CIs −0.80 to −0.20) and increased diastolic blood pressure (standardized mean difference=0.23, P=.03, 95% CIs 0.02 to 0.44) in children younger than 18 years, with the GRADE (Grading of Recommendations Assessment, Development and Evaluation) indicating the certainties of evidence as low for systolic blood pressure and moderate for diastolic blood pressure.

    Conclusions: These findings shed light on the cardiovascular benefits of AVGs in children younger than 18 years, underscoring their potential to improve vascular elasticity while maintaining organ perfusion. However, considering the limitations arising from small sample sizes, as well as inadequate allocation concealment and blinding in the included studies, these findings should be interpreted with caution.

    Trial Registration: PROSPERO CRD42025639976; https://www.crd.york.ac.uk/PROSPERO/view/CRD42025639976

    J Med Internet Res 2025;27:e75000

    doi:10.2196/75000

    Keywords

    active video games; systolic blood pressure; diastolic blood pressure; children; young adults; PRISMA; Preferred Reporting Items for Systematic Reviews and Meta-Analyses 


    Background

    Cardiovascular diseases (CVDs) remain a leading cause of morbidity and mortality worldwide, with blood pressure (BP) being a major modifiable risk factor. The increasing prevalence of elevated BP and hypertension among the younger generation underscores that BP management is no longer solely a concern for older adults [1,2]. Emerging evidence suggests that BP in children and young adulthood is strongly associated with the risk of developing CVDs in adulthood [3,4]. Cumulative exposure to high BP from adolescence to adulthood has been linked to myocardial dysfunction [5], accelerated vascular aging [6], and ultimately leads to target organ damage in later life [7]. This association highlights the critical importance of intervention strategies to manage BP effectively in early life. Early management can not only reduce immediate health risks but also help prevent long-term cardiovascular complications and break the intergenerational cycle of CVDs [8,9].

    Lifestyle modifications, such as increased physical activity, dietary changes, and weight management, have always been the first-line treatment for managing BP in early life stages [10,11]. Among these, physical exercise has been widely recognized for its efficacy in improving cardiovascular health and reducing BP [12]. An increasing variety of exercise forms has been found to induce beneficial cardiovascular changes and help regulate BP [13-16]. However, these traditional means of exercise intervention are confronted with inherent problems including low exercise motivation and high dropout rates [17,18]. Repeated exposure to unappealing exercise content and formats can trigger emotional disengagement and lower exercise adherence in participants [19], ultimately leading to the loss of initial health benefits gained from exercise [20].

    Current Evidence

    Against this backdrop, active video games (AVGs), also known as exergames, have emerged as a novel and innovative approach [21]. Previous studies have demonstrated that AVGs can increase habitual physical activity [22], promote energy expenditure [23], enhance cognitive flexibility [24], improve physical fitness [25], and achieve better weight management [26]. Unlike the negative cardiovascular impacts of sedentary-based video games [27], AVGs have also shown potential in improving cardiovascular health [28]. Some studies have found that AVGs can lower total cholesterol levels [29], improve microcirculation and vascular endothelial function [30], and show potential for regulating BP [31]. By integrating physical exertion into immersive video games, AVGs have captured the attention and engagement of the largest gaming demographic—children and young adults [32,33], making them a promising intervention option for managing BP that could be seamlessly integrated into the digital-centric lifestyles of today’s young people [34].

    Despite the growing interest in AVGs, their specific effects on BP regulation remain inadequately explored. Some studies suggest that AVGs can effectively lower BP [21,30,31,35-38], while others have observed no significant BP reduction [28,29,39-44], and some even report BP elevation following AVGs [45]. These inconsistent findings highlight the necessity for a comprehensive review of the evidence. The lack of a quality systematic review will inevitably limit the applicability of AVGs in clinical practice and daily life.

    Objectives

    This study aims to address this gap by systematically evaluating the current evidence on the effectiveness of AVGs in managing BP among individuals aged 6 to 25 years. This population consists of school-aged children (<18 years) and college young adults (18 to 25 years), who share homogeneous living environments due to their structured daily routines in educational settings. Additionally, the high receptivity to AVGs among this cohort of digital natives enables the findings to be directly translated into school-based health programs, establishing a “research-practice closed loop” for the seamless implementation of evidence-based strategies in educational contexts [32]. By comprehensively evaluating the resting systolic blood pressure (SBP) and diastolic blood pressure (DBP) responses after AVGs engagement, this study aims to explore the diverse effects of AVGs on BP during different phases of the cardiac cycle, thereby facilitating understanding of their potential value in vascular elasticity and organ perfusion.


    Search Strategies and Selection Criteria

    In this systematic review and meta-analysis, we developed search strategies for 4 selected databases (Web of Science, Cochrane Library, PubMed, and Embase) based on the PICOS (Participants, Intervention, Comparison, Outcome, and Study Design) framework and prior research [46,47]. Detailed search terms are presented in the Appendix S1 (P1 - 5) in Multimedia Appendix 1 [29,47-69]. Two researchers (HZ and Jingyi Zhou, MEd) independently searched these databases from their inception to December 30, 2024, while also searching records from registration (ClinicalTrials.gov) and references from relevant systematic reviews and meta-analyses (the information detailed in Appendix S1, P5 - 10 in Multimedia Appendix 1).

    Retrieved studies were imported into EndNote X9 (Clarivate Analytics) software, and duplicates were removed. Subsequently, 2 researchers independently screened titles and abstracts and conducted full-text screening of the remaining studies based on the inclusion and exclusion criteria outlined in Table 1 to identify eligible studies. Discrepancies between the 2 researchers were resolved through discussion with another independent researcher (KTST).

    Table 1. Inclusion and exclusion criteria for publications in this study.
    CriteriaInclusion criteriaExclusion criteria
    Population
    • The mean age of participants was older than 6 years but younger than 25 years
    • Participants were afflicted with heart diseases
    Intervention
    • The intervention protocols encompassed AVGsa
    • The AVGs’ protocol was combined with additional intervention components
    Control
    • For controlled trials: intervention strategies for the control group were clearly described
    • For prepost trials, baseline and postintervention data were reported
    • All control groups received additional physical activity interventions
    Outcome
    • Outcome measurements included at least one of SBPb or DBPc
    • Outcome measurements were limited to immediate postintervention blood pressure
    • The measurement procedures were not clearly described
    Study design
    • Interventional studies
    • Interventional studies reporting outcomes qualitatively
    Additional criteria
    • No language restrictions
    • Publication date: from database inception to December 30, 2024
    • Studies with incomplete data after contacting authors for clarification

    aAVG: active video game.

    bSBP: systolic blood pressure.

    cDBP: diastolic blood pressure.

    Data Extraction and Processing

    The 2 primary outcomes of interest in this study were changes in resting SBP and DBP. For studies where BP was the primary outcome, all reported BP measurements were extracted directly from the results section. For studies where BP was a secondary or exploratory outcome, the methods section was carefully reviewed by the 2 researchers. Studies that did not clearly describe the BP measurement process were excluded from the analysis. After raw data from included trials were uniformly converted to mean (SD) using previously validated methods [70], the required changes were calculated according to the following formula [71,72]. For controlled trials, effect sizes were calculated by comparing changes between intervention and control groups. For prepost trials, effect sizes were represented by the changes in means and SDs from baseline to postintervention. In subsequent sections, we conducted a reanalysis of all controlled trials after excluding prepost trials as part of the sensitivity analyses to validate the robustness of the results.

    Meanchange=Meanpostexercise−Meanbaseline  (1)
    SDchange= SDbaseline2+SDpostexercise2−2R×SDbaseline×SDpostexercise(2)

    In these 2 formulas, meanchange and SDchange represent the changes in the mean and SD before and after intervention, respectively. Meanpostexercise and meanbaseline refer to the means after the intervention and at baseline. SDbaseline and SDpostexercise indicate the SDs before and after intervention, respectively. As none of the included studies provided correlation data on BP before and after AVGs, we adopted the R of 0.5, following the methodology established by Follmann et al [73]. This approach has been widely applied in many studies to investigate the effects of various exercises on BP [74-77].

    Furthermore, the characteristics of studies were recorded, including basic information (first author’s name, publication year, country or region, and study design), participant demographics (sample size, gender, age, and BMI), and intervention characteristics (condition, duration, frequency, and time). For studies with missing data, their corresponding authors would be contacted via email at least 3 times. The data extraction and information entry above were independently performed and cross-verified 3 times by 2 researchers (HZ and Jingyi Zhou, MEd). Any persistent disagreements would be resolved through discussion with another independent researcher (KTST).

    Data Analysis

    The statistical analyses were performed using STATA (version 18.0; Stata). Given the variation in measurement units and the relatively small sample sizes across the included studies, we used the standardized mean difference (SMD) and 95% CIs, corrected by Hedges g, to pool the overall effect size of all individual trials. Hedges g introduces a correction factor to Cohen d, which allows it to provide a more accurate effect size in situations involving small sample sizes [78].

    Furthermore, the Q statistic and I² statistic were used to assess heterogeneity. If I² >50% or the P value for the Q statistic was <.05, heterogeneity was considered present, and the random-effects model was used. Otherwise, the fixed-effects model was used. Subgroup analyses were conducted to explore the sources of heterogeneity. The sensitivity analysis based on the leave-one-out method was performed to verify the robustness of the results. Publication bias was assessed using Egger tests and by visually examining the symmetry of funnel plots. If the P value from the Egger tests <.05 or the funnel plots show significant asymmetry, publication bias was considered present, and the trim-and-fill method was then used [79].

    Assessment of Bias Risk and Certainty of Evidence

    Two researchers (HZ and Jingyi Zhou, MEd) independently assessed the risk of bias for included trials. For controlled trials, Cochrane Risk of Bias tool 2.0 was used [80], while the Methodological Index for Non-Randomized Studies tool was used for prepost trials [81]. For detailed bias risk assessment information, refer to Appendix S2 to S3 (P1 - 2) in Multimedia Appendix 2.

    The certainty of evidence for the 2 primary outcomes was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach [82]. GRADE starts by assuming high evidence quality, which researchers then evaluate for potential downgrading based on 5 domains: study limitations, inconsistency of results, indirectness of evidence, imprecision, and publication bias. The final certainty of evidence is categorized as high, moderate, low, or very low. For detailed information on evidence certainty assessment, refer to Appendix S4 (P1-2) in Multimedia Appendix 3.

    This study was registered in PROSPERO (CRD42025639976) and follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting [83]. The PRISMA checklist has been uploaded as Checklist 1.


    Screening Results and Characteristics of Studies

    We retrieved a total of 482 publications from 4 databases and identified 56 records from clinical trial registration and 21 records from the reference lists of related systematic reviews and meta-analyses. After removing duplicates and conducting initial screening, 68 studies were selected for full-text screening. Ultimately, 16 studies comprising 17 trials were included in this systematic review and meta-analysis. Figure 1 outlines the detailed screening procedure, including the number of studies excluded at each stage and the corresponding reasons for exclusion.

    The characteristics of the included trials were summarized in Table 2. These trials involved 503 participants aged 6 to 25 years, of whom 322 were children younger than 18 years (from 11 trials), and 181 were young adults older than 18 years (from 6 trials). In the 11 trials with children, the average age of participants in 9 of the 11 trials was around 10 years or older, while the remaining 2 trials reported average ages of 8.25 (SD 1.5) and 7.53 (SD 0.5) years. In the 6 trials involving young adults, the average age of participants in 5 trials was approximately 23 years, with the remaining trial reporting an average age of 20.36 (SD 1.57) years. Among the 322 children, 196 were overweight or obese, while 126 had a normal BMI. In contrast, among the young adults, 29 were overweight or obese, while 152 had a normal BMI. Most trials were conducted in laboratory settings (14 trials), with 3 pediatric trials implemented in nonlaboratory settings (eg, at home). Of the 17 trials, 11 were medium to long-term AVGs with a mean duration of 10.09 (SD 6.99) weeks, spanning from 4 weeks to 30 weeks, while the remaining trials were single-session AVGs. The risk of bias assessment indicated that 2 controlled studies were high risk, 6 raised some concerns, and 2 were low risk, while the prepost studies had a mean Methodological Index for Non-Randomized Studies score of 8.43 (SD 1.4; Appendix S2 to S3, P1 to 2 in Multimedia Appendix 2).

    ‎
    Figure 1. PRISMA flowchart for systematic review and meta-analysis screening process. PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
    Table 2. Characteristics of the included 17 trials in this systematic review and meta-analysis.
    Basic informationParticipants characteristicsIntervention characteristics
    Author, yearCountry or regionStudy designSample size (sex)Age range, mean (SD)BMI category, mean (SD or percentile)ConditionsDurationFrequencyTime (each time)
    Barbosa et al [21], 2021BrazilPrepost trial14 (5 female and 9 male)13.3 (2.1)Normal, 21.48 (4.22)Laboratory8 weeks3 times per week50 minutes
    Bethea et al [42], 2012AmericaPrepost trial28 (10 female and 18 male)9.9 (0.7)Normal, 19.8 (3.9)Nonlaboratory30 weeks3 days per week at school, without restriction at home30 minutes
    van Biljon et al [41], 2021South AfricaParallel controlled trial31 (not reported)11.40 (0.86)Overweight or obesity (more than the 85th percentile)Laboratory6 weeks3 times per week30 minutes
    Carrasco et al [43], 2013BrazilPrepost trial4 (4 male)8.25 (1.5)Overweight or obesity, 23.01 (1.9)Laboratory3 weeks3 times per week60 minutes
    Maloney et al [45], 2008AmericaParallel controlled trial60 (30 female and 30 male)7.53 (0.5)Normal, 17.47 (2.73)Nonlaboratory10 weeksEncouraged 4 times per weekNot applicable
    Murphy et al [29], 2009AmericaParallel controlled trial35 (17 female and 18 male)10.21 (1.6)Overweight or obesity (more than the 85th percentile)Nonlaboratory12 weeksEncouraged 5 days per weekNot applicable
    Ramos et al [30], 2023BrazilParallel controlled trial61 (33 female and 28 male)10 to 16Overweight or obesity (not reported)Laboratory8 weeks3 times per week50 minutes
    Rauber et al [36], 2013BrazilCrossover-controlled trial8 (not reported)9.8 (0.5)Normal, 17.4 (4.7)Laboratory1 visitonce30 minutes
    Rauber et al [39], 2014BrazilCrossover-controlled trial16 (8 female and 8 male)9.3 (0.5)Normal, 18.4 (3.7)Laboratory1 visitonce30 minutes
    Staiano et al [35], 2017AmericaParallel controlled trial41 (41 female)15.6 (1.3)Overweight or obesity (more than the 85th percentile)Laboratory12 weeks3 times per week60 minutes
    Park et al [28], 2015South KoreaParallel controlled trial24 (8 female and 16 male)15 (0.4)Overweight or obesity, 25 (4.6)Laboratory1 visitonce60 minutes
    de Brito-Gomes et al (a) [38], 2019BrazilPrepost trial8 (not reported)23 (6)Overweight or obesity, 23.7 (1.9)Laboratory1 visitonce30 minutes
    de Brito-Gomes et al (b) [38], 2018BrazilPrepost trial8 (not reported)23 (6)Overweight or obesity, 23.7 (1.9)Laboratory4 weeks2 times per week30 minutes
    de Brito-Gomes et al [37], July 2019BrazilPrepost trial14 (7 female and 7 male)23 (5)Normal, 22.85 (1.16)Laboratory1 visitonce30 minutes
    de Brito-Gomes et al [31], 2021BrazilCrossover-controlled trial10 (3 female and 7 male)24.9 (7.5)Normal, 21.5 (2)Laboratory1 visitonce30 minutes
    Huang et al [40], 2017Taiwan, ChinaParallel controlled trial117 (67 female and 50 male)22.67 (2.05)Normal, 22.2 (not reported)Laboratory12 weeks3 times per week30 minutes
    Roopchand-Martin et al [44], 2015JamaicaPrepost trial24 (24 female)20.36 (1.57)Normal and overweight (normal: 20.66 [1.77] and overweight: 30.51 [5.18])Laboratory6 weeksFirst 2 weeks: 5 times per week, third and fourth weeks: 4 times per week, and last 2 weeks: 3 times per weekFirst 2 weeks: 30 minutes; third and fourth weeks: 45 minutes, and last 2 weeks: 60 minutes

    Meta-Analysis and Subgroup Analyses Results in Children and Young Adults (6-25 Years)

    Figures 2 and 3, respectively, illustrate the effects of AVGs on SBP and DBP in participants aged 6 to 25 years, as well as subgroup analyses results based on age groups, intervention conditions, intervention durations, and BMI.

    ‎
    Figure 2. Meta-analysis and subgroup effects of active video games on systolic blood pressure in children and young adults (6–25 years). Subgroup analyses were stratified by age (<18 years [children] vs. ≥18 years [young adults]), intervention setting (laboratory vs. nonlaboratory), duration (single session vs. medium to long term), and body mass index (BMI; normal vs. overweigh or obesity). Diamonds represent subgroup mean effect sizes (indicated by Standardized Mean Difference [SMD]), with horizontal bars indicating 95% confidence intervals (CIs); the vertical dashed line denotes the null effect (SMD=0). Green diamonds indicate statistically significant effects (P<.05), while red diamonds denote non-significant results (P≥.05). Additionally, because the study by Roopchand-Martin et al [44] includes both participants with normal BMI and those with overweight or obesity, and the authors reported effect sizes for these two subgroups separately, the number of trials in the BMI subgroups is 9 vs. 9.

    Figure 2 shows significant heterogeneity among the 17 trials assessing AVGs’ effect on SBP (I²=40.85%, P=.02). The results from the random-effects model indicate that AVGs can significantly reduce SBP in participants aged 6 to 25 (SMD=−0.37, 95% CI −0.59 to −0.15, P<.001). Furthermore, subgroup analyses show that: (1) AVGs significantly reduce SBP in children younger than 18 years (SMD=−0.50, 95% CI −0.80 to −0.20, P<.001), but have no significant effect on SBP in young adults older than 18 years (SMD=−0.09, 95% CI−0.36 to 0.18, P=.53), with a significant difference between the 2 age groups (P=.04); (2) AVGs in laboratory settings significantly reduce SBP (SMD=−0.43, 95% CI−0.68 to −0.17, P<.001), while no significant effect is observed in nonlaboratory settings (SMD=−0.18, 95% CI −0.52 to 0.15, P=.29), but there is no significant difference between the groups (P=.27); (3) both single-session and medium to long-term AVGs significantly reduce SBP (single session: SMD=−0.40, 95% CI −0.74 to −0.07, P=.02; medium to long-term interventions: SMD=−0.39, 95% CI −0.72 to −0.06, P=.02); and (4) AVGs demonstrate significant effects in reducing SBP among both normal weight and overweight or obese populations (normal: SMD=−0.22, 95% CI −0.43 to −0.01, P=.04; overweight or obesity: SMD=−0.44, 95% CI −0.88 to 0.00, P=.05).

    Figure 3 shows no significant heterogeneity among the 17 trials evaluating the effect of AVGs on DBP (I²=31.91%, P=.10). The results from the fixed-effects model indicate that AVGs significantly increase DBP in participants aged 6 to 25 years (SMD=0.15, 95% CI 0.01 to 0.30, P=.049). Subgroup analyses further reveal that: (1) AVGs can significantly increase DBP in children younger than 18 years (SMD=0.23, 95% CI 0.02 to 0.44, P=.03), while no significant effect is found for young adults aged 18 year sand older (SMD=0.02, 95% CI −0.23 to 0.28, P=.85); and (2) medium to long term AVGs can exert a significant effect in increasing DBP (SMD=0.23, 95% CI 0.05 to 0.41, P=.01).

    ‎
    Figure 3. Meta-analysis and subgroup effects of active video games on diastolic blood pressure in children and young adults (6–25 years). Subgroup analyses were stratified by age (<18 years [children] vs. ≥18 years [young adults]), intervention setting (laboratory vs. nonlaboratory), duration (single session vs. medium to long term), and body mass index (BMI; normal vs. overweight or obesity). Diamonds represent subgroup mean effect sizes (indicated by Standardized Mean Difference [SMD]), with horizontal bars indicating 95% confidence intervals (CIs); the vertical dashed line denotes the null effect (SMD=0). Green diamonds indicate statistically significant effects (P<.05), while red diamonds denote non-significant results (P≥.05). Additionally, because the study by Roopchand-Martin et al [44] includes both participants with normal BMI and those with overweight or obesity, and the authors reported effect sizes for these two subgroups separately, the number of trials in the BMI subgroups is 9 vs. 9.

    Meta-Analysis and Subgroup Analyses Results in Children (<18 Years)

    The aforementioned subgroup analyses show that AVGs have more pronounced effects on SBP and DBP in children younger than 18 years (SBP, SMD:−0.50, P<.001; DBP, SMD: 0.23, P=.03) compared to young adults older than 18 years (SBP, SMD:−0.09, P=.53; DBP, SMD: 0.02, P=.85). We therefore conduct further analyses on 11 trials only involving children.

    As observed in Figure 4, significant heterogeneity was found in the effects on SBP across the 11 trials (I²=45.37%, P=.04), thus the random-effects model is used. In contrast, Figure 5 shows no significant heterogeneity in the effects on DBP across the 11 trials (I²=30.54%, P=.16), so the fixed-effects model is used.

    Figure 4 and Figure 5 indicate that AVGs in laboratory settings significantly reduce SBP (SMD=−0.66, 95% CI −1.01 to −0.31, P<.001) and increase DBP (SMD=0.33, 95% CI 0.07 to 0.59, P=.01) in children younger than 18 years. However, AVGs outside laboratory settings do not induce reductions in SBP or increases in DBP. Additionally, medium to long-term AVGs were found to significantly reduce SBP (SMD=−0.53, 95% CI −0.91 to −0.14, P=.01) and increase DBP (SMD=0.30, 95% CI 0.07 to 0.53, P=.01) in children, whereas single-session AVGs did not produce significant changes in SBP and DBP. In overweight or obese children, we found that AVGs could reduce SBP (SMD=−0.59, 95% CI −1.11 to −0.08, P=.02) and increase DBP (SMD=0.47, 95% CI 0.17 to 0.76, P<.001). In contrast, among children with normal BMI, AVGs only significantly reduce SBP (SMD=−0.38, 95% CI −0.67 to −0.08, P=.01), with no significant changes in DBP (SMD=−0.001, 95% CI −0.30 to 0.29, P=.97).

    ‎
    Figure 4. Meta-analysis and subgroup effects of active video games on systolic blood pressure in children <18 years. Subgroup analyses were stratified by intervention setting (laboratory vs. nonlaboratory), duration (single session vs. medium to long term), and body mass index (BMI; normal vs. overweight or obesity). Diamonds represent subgroup mean effect sizes (indicated by Standardized Mean Difference [SMD]), with horizontal bars indicating 95% confidence intervals (CIs); the vertical dashed line denotes the null effect (SMD=0). Green diamonds indicate statistically significant effects (P<.05), while red diamonds denote non-significant results (P≥.05).
    ‎
    Figure 5. Meta-analysis and subgroup effects of active video games on diastolic blood pressure in children <18 years. Subgroup analyses were stratified by intervention setting (laboratory vs. nonlaboratory), duration (single session vs. medium to long term), and body mass index (BMI; normal vs. overweight or obesity). Diamonds represent subgroup mean effect sizes (indicated by Standardized Mean Difference [SMD]), with horizontal bars indicating 95% confidence intervals (CIs); the vertical dashed line denotes the null effect (SMD=0). Green diamonds indicate statistically significant effects (P<.05), while red diamonds denote non-significant results (P≥.05).

    Results of Sensitivity Analyses, Egger Tests, and GRADE Assessments

    This study performs 3 sensitivity analyses to validate the robustness of the results. First, we reanalyzed all controlled trials after excluding 7 prepost trials, revealing that AVGs significantly reduce SBP and increase DBP (Figure S1 to Figure S2 in Multimedia Appendix 4). Second, after excluding 3 trials with BP measurements taken in nonlaboratory settings, the effect sizes of the remaining trials are repooled. The results still indicate that the effect of AVGs on reducing SBP and increasing DBP remains significant (Figure S3 to Figure S4 in Multimedia Appendix 4). Lastly, sensitivity analyses based on the leave-one-out method are also used in this study. The results demonstrate that AVGs have a stable effect in reducing SBP (Table 3). However, the effects of AVGs on increasing DBP might be influenced by a single trial, leading to nonsignificant outcomes (Table 3).

    Furthermore, results of Egger tests and the funnel plots indicate publication bias in the effect of AVGs on the SBP of participants aged 6 to 25 years (Table 3 and Appendix S5 in Multimedia Appendix 5). After applying the trim-and-fill method, the effect size changes from −0.37 (95% CI −0.59 to −0.15) to −0.19 (95% CI −0.47 to 0.10; Appendix S6 in Multimedia Appendix 6), suggesting that further studies are required to consolidate the evidence. Based on the information, the GRADE assessments indicate that the certainties of evidence for SBP in participants aged 6 to 25 years and children younger than 18 years were rated as low, while those for DBP were rated as moderate (Appendix S4, P1 - 2 in Multimedia Appendix 3).

    Table 3. Results of sensitivity analyses and Egger tests for blood pressure outcomes.
    Populations and outcomesEffect sizes (P value)Sensitivity analysesPublication bias
    SMDa (range)P value (range)P value for Egger tests
    Children and young adults (6 to 25 years)
    SBPb−0.37 (<.001)−0.42 to −0.24<.001 to .006.03d
    DBPc0.15 (.049)0.12 to 0.18d.03 to .21d.08
    Children (<18 years)
    SBP−0.50 (<.001)−0.58 to −0.35<.001 to .004.22
    DBP0.23 (.03)0.17 to 0.31d.007 to .13d.86

    aSMD: standardized mean difference

    bSBP: systolic blood pressure

    cDBP: diastolic blood pressure

    dIndicates failure to pass sensitivity analysis or Egger test.


    Principal Results

    To our knowledge, this is the first systematic review and meta-analysis to evaluate the effects of AVGs on BP. The findings demonstrate that AVGs significantly reduce SBP and increase DBP in children younger than 18 years.

    First, the finding that AVGs can help reduce SBP is notable, particularly given that even a marginal elevation of 1 to 2 mm Hg in SBP may significantly increase the risk of CVDs [84-86]. SBP levels during youth are considered important predictors of hypertension risk in adulthood [3]. Our findings support previous studies suggesting that AVGs can help regulate SBP [21,30,31,35-38]. Additionally, DBP, another critical indicator of cardiovascular health, demonstrates a significant increase following AVGs intervention, contrasting with nonsignificant trends observed in previous studies [30,39,40,42]. AVGs can simultaneously reduce SBP while increasing DBP in participants due to distinct physiological mechanisms. Slow-paced, relaxing games lower SBP by reducing the sympathetic tone and cardiac output [87,88], while rhythm games may further decrease SBP through controlled breathing and parasympathetic activation [89,90]. Conversely, prolonged standing and mental stress during gaming trigger peripheral vasoconstriction (via adrenaline or noradrenaline release), elevating vascular resistance and DBP [91]. Additionally, isometric muscle tension from gripping controllers tightly can compress blood vessels, further increasing DBP [92]. This divergence highlights how physical effects differentially influence SBP (cardiac-driven) and DBP (vascular resistance-driven). The combined effect of reduced SBP and increased DBP can lead to decreased pulse pressure (PP=SBP–DBP) while partially offsetting SBP-driven reductions in mean arterial pressure (mean arterial pressure=1/3 SBP +2/3 DBP). This indicates that AVGs may improve vascular elasticity while maintaining stable organ perfusion pressure. Reduced PP can prevent excessive arterial stretch, delay arterial fatigue, and avoid the fracture of elastic elements, reducing the risk of intimal injury that leads to atherosclerosis and thrombosis [93]. Meanwhile, stable mean arterial pressure can help preserve normal organ perfusion, tissue oxygenation, and circulatory homeostasis [94].

    However, further subgroup analysis demonstrated that these promising effects of AVGs were only found in children younger than 18 years and not in young adults older than 18 years. The discrepancy may be attributed to age-related differences in endothelial function. As Holder et al [95] reported, systemic endothelial function—measured by flow-mediated dilation—inevitably declines with age. The higher endothelial function in children is an important factor against CVDs and may also explain their heightened susceptibility to BP control through AVGs [96]. Endothelial function in children may help manage BP in ways lost to adults, indicating that AVGs targeted at a younger age may have more beneficial effects on cardiovascular health. As suggested by the American Heart Association [97], integrating AVGs into lifestyle routines during the critical developmental period may provide favorable conditions for the development and improvement of the cardiovascular system.

    Among the included trials, 3 carried out AVGs under nonlaboratory conditions. Despite participants showing 90% adherence in these trials [45], the results indicated no significant effects of AVGs on SBP and DBP in a nonlaboratory setting [29,42,45]. The significant disparity in BP responses between laboratory and nonlaboratory settings may be attributed to variations in intervention fidelity and physiological stressor intensity. Structured laboratory environments ensure consistent exercise duration and intensity, which is difficult to achieve in nonlaboratory settings. In such an unregulated setting, children may struggle to sustain attention during prolonged AVGs participation and may not engage in them regularly and systematically to achieve the “threshold dose” [97,98]. Moreover, the absence of initial movement skills instruction and goal-setting among home-based AVG users may further diminish their effectiveness [99,100]. To translate the BP benefits of AVGs observed in laboratory conditions into broader practice, future AVGs should consider integrating digital strategies (such as streak counters, weekly goals, and rewards such as unlocking new levels for completion rate) and wearable devices (such as heart rate and calorie monitors) into the systems.

    From the perspective of intervention duration, medium to long-term AVGs demonstrated significant effects in reducing SBP and increasing DBP. In fact, most lifestyle interventions need a long time to induce beneficial changes [101]. The human body is a highly complex and sophisticated system where physiological functions interact dynamically to maintain balance [102]. When health interventions are applied to it, they do not merely affect a single indicator but require multiple systems to gradually adapt and adjust [103]. In the cardiovascular system, BP regulation involves the coordinated functioning of the heart, blood vessels, and neuroendocrine systems [11]. AVGs may require time to interact with these physiological mechanisms, shifting the original balance to a new and healthier equilibrium. Moreover, the new equilibrium needs to be maintained to achieve the expected health benefits [104], which highlights the importance of integrating interventions into daily life. Given that playing video games is a common modern pastime, cultivating habits of sustained participation in engaging AVGs may be both feasible and promising [97].

    Furthermore, greater changes in SBP and DBP were observed in overweight or obese participants. This finding may be attributed to the amelioration of endothelial dysfunction typically present in this population [28,31] following engagement with AVGs. Previous studies have supported a significant correlation between obesity and endothelial dysfunction [105]. Endothelial cells play a critical role in BP regulation by dynamically modulating vascular tone through the secretion of vasoactive substances such as nitric oxide, prostacyclin, and endothelium-derived hyperpolarizing factors [106]. Participation in AVGs has been shown to enhance nitric oxide bioavailability and augment brachial artery flow-mediated dilation, thereby mitigating endothelial dysfunction [28], which may contribute to the greater BP effects observed in overweight or obese participants. In addition to the aforementioned mechanism, the increase in physical activity induced by AVGs is believed to enhance insulin sensitivity and reduce insulin resistance in overweight and obese populations [107,108]. Improved insulin sensitivity may benefit BP management in this population by regulating sympathetic nervous system activity and reducing renal sodium retention [109,110].

    Limitations

    The findings of this study need to be interpreted with the following limitations. First, the participants included in this study had an age range of 6 to 25 years, which may introduce heterogeneity. Future studies should adopt narrower age bands or developmental stages to reduce confounding. Second, GRADE assessments indicate low certainty of evidence for SBP changes. The heterogeneity across trials and limited sample sizes suggests that further research is needed to solidify the evidence on the health significance of AVGs. Furthermore, the inclusion of prepost trials in this study may limit the causal inferences regarding AVGs’ specific effect on BP. Prepost studies, by lacking a control group, are unable to rule out confounding variables that may influence BP outcomes. Although the reanalysis of the controlled trials confirmed the robustness of the results, caution is still needed when interpreting them. Future research using randomized controlled trials with parallel control groups will be critical to strengthen causal claims about AVGs’ effects on BP. Lastly, many studies did not report exercise intensity in detail, limiting our ability to explore the optimal exercise prescription of AVGs for managing BP. Future studies are encouraged to develop and report AVGs protocols following the FITT (frequency, intensity, time, and type) principle to derive more detailed dose-response relationships and strengthen the translational value of AVGs as an evidence-based intervention.

    Conclusion

    Our research suggests that AVGs may serve as a promising strategy for pediatric BP management, demonstrating significant effects in reducing SBP and increasing DBP. More high-quality trials are called for to validate their cardiovascular benefits and clinical value.

    Acknowledgments

    This study was funded by the Collaborative Research Fund at the University of Hong Kong (C7149-20G). The authors would like to thank Mr. Jingyi Zhou, an external independent contributor, for his contributions to literature search, data processing, and other related tasks.

    Data Availability

    The data extracted during the analysis process can be obtained from the corresponding author upon reasonable request.

    Authors' Contributions

    HZ, KTST, HkS, and PI conceived and designed this study. HZ and KTST conducted literature search and data extraction. HZ, KTST, and HkS conducted the statistical analysis and wrote the first draft of this paper, which was critically revised by HZ, HkS, PMS, ICKW, JCY, JYlT, YkJ, LH, and PI. All authors gave final approval of the final version.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Search strategies for different sources.

    DOCX File, 26 KB
    Multimedia Appendix 2

    Cochrane Risk of Bias tool 2.0 for the included controlled trials.

    DOCX File, 108 KB
    Multimedia Appendix 3

    Details of evidence certainty assessment using GRADE. GRADE: Grading of Recommendations Assessment, Development and Evaluation.

    DOCX File, 19 KB
    Multimedia Appendix 4

    Sensitivity analysis results from controlled trials and those from trials with blood pressure measurements taken in laboratory settings.

    DOCX File, 233 KB
    Multimedia Appendix 5

    Funnel plots for publication bias.

    DOCX File, 273 KB
    Multimedia Appendix 6

    The funnel plot and effect size after using the trim and fill method.

    DOCX File, 95 KB
    Checklist 1

    The PRISMA checklists for the main text and abstract.

    DOCX File, 35 KB
    1. Song P, Zhang Y, Yu J, et al. Global prevalence of hypertension in children: a systematic review and meta-analysis. JAMA Pediatr. Dec 1, 2019;173(12):1154-1163. [CrossRef] [Medline]
    2. Chen L, Zhang Y, Ma T, et al. Prevalence trend of high normal blood pressure and elevated blood pressure in Chinese Han children and adolescents aged 7-17 years from 2010 to 2019. Zhonghua Yu Fang Yi Xue Za Zhi. Feb 28, 2023;57:49-57. [CrossRef] [Medline]
    3. Bao W, Threefoot SA, Srinivasan SR, Berenson GS. Essential hypertension predicted by tracking of elevated blood pressure from childhood to adulthood: the Bogalusa heart study*. Am J Hypertens. Jul 1995;8(7):657-665. [CrossRef]
    4. Urbina EM, Khoury PR, Bazzano L, et al. Relation of blood pressure in childhood to self-reported hypertension in adulthood. Hypertension. Jun 2019;73(6):1224-1230. [CrossRef] [Medline]
    5. Kishi S, Teixido-Tura G, Ning H, et al. Cumulative blood pressure in early adulthood and cardiac dysfunction in middle age: the CARDIA study. J Am Coll Cardiol. Jun 30, 2015;65(25):2679-2687. [CrossRef] [Medline]
    6. Olsen MH, Angell SY, Asma S, et al. A call to action and a lifecourse strategy to address the global burden of raised blood pressure on current and future generations: the Lancet Commission on hypertension. Lancet. Nov 26, 2016;388(10060):2665-2712. [CrossRef] [Medline]
    7. Jones DW, Clark DC. Hypertension (blood pressure) and lifetime risk of target organ damage. Curr Hypertens Rep. Sep 2, 2020;22(10):75. [CrossRef] [Medline]
    8. Muchira JM. Maternal-child cardiovascular health: the pathway to reducing the early onset and intergenerational burden of cardiovascular disease. J Cardiovasc Nurs. 2024;39(4):297-301. [CrossRef] [Medline]
    9. Allen NB, Khan SS. Blood pressure trajectories across the life course. Am J Hypertens. Apr 2, 2021;34(3):234-241. [CrossRef] [Medline]
    10. Khoury M, Urbina EM. Hypertension in adolescents: diagnosis, treatment, and implications. Lancet Child Adolesc Health. May 2021;5(5):357-366. [CrossRef] [Medline]
    11. Flynn JT, Kaelber DC, Baker-Smith CM, et al. Clinical practice guideline for screening and management of high blood pressure in children and adolescents. Pediatrics. Sep 2017;140(3):e20171904. [CrossRef] [Medline]
    12. Leung LCK, Sung RYT, So HK, et al. Prevalence and risk factors for hypertension in Hong Kong Chinese adolescents: waist circumference predicts hypertension, exercise decreases risk. Arch Dis Child. Sep 2011;96(9):804-809. [CrossRef] [Medline]
    13. Ewart CK, Young DR, Hagberg JM. Effects of school-based aerobic exercise on blood pressure in adolescent girls at risk for hypertension. Am J Public Health. Jun 1998;88(6):949-951. [CrossRef] [Medline]
    14. Popowczak M, Rokita A, Koźlenia D, Domaradzki J. The high-intensity interval training introduced in physical education lessons decrease systole in high blood pressure adolescents. Sci Rep. Feb 7, 2022;12(1):1974. [CrossRef] [Medline]
    15. Sieverdes JC, Mueller M, Gregoski MJ, et al. Effects of hatha yoga on blood pressure, salivary α-amylase, and cortisol function among normotensive and prehypertensive youth. J Altern Complement Med. Apr 2014;20(4):241-250. [CrossRef] [Medline]
    16. Zhu H, He L, Guo J, Huang B, Elliott J, Jan YK. Effects of neuromuscular fatigue induced by various modes of isometric handgrip exercise on post-exercise blood pressure responses. J Sports Med Phys Fitness. Apr 2025;65(4):571-582. [CrossRef] [Medline]
    17. Annesi J. Effects of a cognitive behavioral treatment package on exercise attendance and drop out in fitness centers. Eur J Sport Sci. Apr 2003;3(2):1-16. [CrossRef]
    18. Ice R. Long-term compliance. Phys Ther. Dec 1985;65(12):1832-1839. [CrossRef] [Medline]
    19. Jekauc D. Enjoyment during exercise mediates the effects of an intervention on exercise adherence. Psych. 2015;06(1):48-54. [CrossRef]
    20. Mujika I, Padilla S. Detraining: loss of training-induced physiological and performance adaptations. Part I. Sports Med. 2000;30(2):79-87. [CrossRef]
    21. Barbosa MLM, Barbosa LFM, Vieira CDJ, Costa A, Lima LD, Medeiros ML. Physical activity program using active video games with sedentary adolescents. RSD. 2021;10(16):e467101624066. [CrossRef]
    22. LeBlanc AG, Chaput JP, McFarlane A, et al. Active video games and health indicators in children and youth: a systematic review. PLoS One. 2013;8(6):e65351. [CrossRef] [Medline]
    23. Peng W, Lin JH, Crouse J. Is playing exergames really exercising? A meta-analysis of energy expenditure in active video games. Cyberpsychol Behav Soc Netw. Nov 2011;14(11):681-688. [CrossRef] [Medline]
    24. Sanchez-Martinez J, Tapia-Tapia D, Villagra-Ortega A, Villegas-Arriagada J, Monteiro-Junior R. Effect of active video games on cognitive functions in healthy children and adolescents. Systematic review of randomized controlled studies. Journ M Health. 2023;20(1). [CrossRef]
    25. Liu W, Zeng N, McDonough DJ, Gao Z. Effect of active video games on healthy children’s fundamental motor skills and physical fitness: a systematic review. Int J Environ Res Public Health. Nov 9, 2020;17(21):8264. [CrossRef] [Medline]
    26. Trost SG, Sundal D, Foster GD, Lent MR, Vojta D. Effects of a pediatric weight management program with and without active video games a randomized trial. JAMA Pediatr. May 2014;168(5):407-413. [CrossRef] [Medline]
    27. Goldfield GS, Kenny GP, Hadjiyannakis S, et al. Video game playing is independently associated with blood pressure and lipids in overweight and obese adolescents. PLoS One. 2011;6(11):e26643. [CrossRef] [Medline]
    28. Park SH, Yoon ES, Lee YH, et al. Effects of acute active video games on endothelial function following a high-fat meal in overweight adolescents. J Phys Act Health. Jun 2015;12(6):869-874. [CrossRef] [Medline]
    29. Murphy ECS, Carson L, Neal W, Baylis C, Donley D, Yeater R. Effects of an exercise intervention using Dance Dance Revolution on endothelial function and other risk factors in overweight children. Int J Pediatr Obes. 2009;4(4):205-214. [CrossRef] [Medline]
    30. Ramos TDA, Medeiros CCM, Figueiroa JN, de Carvalho DF, Gusmão TME, Alves JGB. Effects of exergaming on the microcirculation of adolescents with overweight or obesity-a clinical trial efficacy. Appl Physiol Nutr Metab. May 1, 2023;48(5):379-385. [CrossRef] [Medline]
    31. de Brito Gomes JL, Vancea DMM, de Araújo RC, Soltani P, de Sá Pereira Guimarães FJ, da Cunha Costa M. Cardiovascular and enjoyment comparisons after active videogame and running in type 1 diabetes patients: a randomized crossover trial. Games Health J. Oct 2021;10(5):339-346. [CrossRef] [Medline]
    32. O’Loughlin EK, Dutczak H, Kakinami L, Consalvo M, McGrath JJ, Barnett TA. Exergaming in youth and young adults: a narrative overview. Games Health J. Oct 2020;9(5):314-338. [CrossRef] [Medline]
    33. Song M, Carroll DD, Lee SM, Fulton JE. Active gaming among high school students--United States, 2010. Games Health J. Aug 2015;4(4):325-331. [CrossRef] [Medline]
    34. Kemp C. 'Exergaming’ gets kids who prefer digital technology off the couch. AAP News. 2011;32(12):2. [CrossRef]
    35. Staiano AE, Marker AM, Beyl RA, Hsia DS, Katzmarzyk PT, Newton RL. A randomized controlled trial of dance exergaming for exercise training in overweight and obese adolescent girls. Pediatr Obes. Apr 2017;12(2):120-128. [CrossRef] [Medline]
    36. Rauber SB, Carvalho FO, Sousa ID, et al. Variáveis cardiovasculares durante e após a prática do VÍDEO GAME ativo “Dance Dance Revolution” e televisão [Article in Portuguese]. Motriz: Rev Educ Fis. Jun 2013;19(2):358-367. [CrossRef]
    37. de Brito-Gomes JL, dos Santos Oliveira L, Vancea DMM, da cunha Costa M. Do 30 minutes of active video games at a moderate-intensity promote glycemic and cardiovascular changes. Conscientiae Saúde. 2019;18(3):389-401. [CrossRef]
    38. de Brito-Gomes J, Oliveira LDS, de Souza A, Brito ADF, do Monte R, da Cunha Costa M. Does a virtual functional training induce cardiovascular responses in normotensive adults after a single session and over weeks? Hum Mov. 2019;20(2):25-33. [CrossRef]
    39. Rauber SB, Boullosa DA, Carvalho FO, et al. Traditional games resulted in post-exercise hypotension and a lower cardiovascular response to the cold pressor test in healthy children. Front Physiol. 2014;5:235. [CrossRef] [Medline]
    40. Huang HC, Wong MK, Lu J, Huang WF, Teng CI. Can using exergames improve physical fitness? A 12-week randomized controlled trial. Comput Human Behav. May 2017;70:310-316. [CrossRef]
    41. van Biljon A, Longhurst G, Shaw I, Shaw BS. Role of exergame play on cardiorespiratory fitness and body composition in overweight and obese children. Asian J Sports Med. 2021;12(1):e106782. [CrossRef]
    42. Bethea TC, Berry D, Maloney AE, Sikich L. Pilot study of an active screen time game correlates with improved physical fitness in minority elementary school youth. Games Health J. Feb 2012;1(1):29-36. [CrossRef]
    43. Carrasco A, Kerppers II, Brunetti A, Pires J. Assessment of functional capacity and body composition of overweight children after an aerobic exercise program using the Nintendo Wii console: a pilot study. Med Sci Tech. 2013;54:93-98. [CrossRef]
    44. Roopchand-Martin S, Nelson G, Gordon C, Sing SY. A pilot study using the XBOX Kinect for exercise conditioning in sedentary female university students. Technol HEALTH CARE. 2015;23(3):275-283. [CrossRef] [Medline]
    45. Maloney AE, Bethea TC, Kelsey KS, et al. A pilot of a video game (DDR) to promote physical activity and decrease sedentary screen time. Obesity (Silver Spring). Sep 2008;16(9):2074-2080. [CrossRef] [Medline]
    46. Hassan MA, Zhou W, Ye M, He H, Gao Z. The effectiveness of physical activity interventions on blood pressure in children and adolescents: a systematic review and network meta-analysis. J Sport Health Sci. Sep 2024;13(5):699-708. [CrossRef] [Medline]
    47. Lourenço CLM, Barbosa AR, Meneghini V, Gerage AM. Dropout rates in controlled trials with exergames for blood pressure management: a systematic review and meta-analysis protocol. Rev Bras Ativ Fís Saúde. 2022;27:1-8. [CrossRef]
    48. Daley AJ. Can exergaming contribute to improving physical activity levels and health outcomes in children? Pediatrics. Aug 2009;124(2):763-771. [CrossRef] [Medline]
    49. Penko AL, Barkley JE. Motivation and physiologic responses of playing a physically interactive video game relative to a sedentary alternative in children. Ann Behav Med. May 2010;39(2):162-169. [CrossRef] [Medline]
    50. Mellecker RR, McManus AM. Energy expenditure and cardiovascular responses to seated and active gaming in children. Arch Pediatr Adolesc Med. Sep 2008;162(9):886-891. [CrossRef] [Medline]
    51. Perron RM, Graham CA, Hall EE. Comparison of physiological and psychological responses to exergaming and treadmill walking in healthy adults. Games Health J. Dec 2012;1(6):411-415. [CrossRef] [Medline]
    52. Straker L, Abbott R. Effect of screen-based media on energy expenditure and heart rate in 9- to 12-year-old children. Pediatr Exerc Sci. Nov 2007;19(4):459-471. [CrossRef] [Medline]
    53. Gao Z, Xiang P. Effects of exergaming based exercise on urban children’s physical activity participation and body composition. J Phys Act Health. Jul 2014;11(5):992-998. [CrossRef] [Medline]
    54. Azevedo LB, Watson DB, Haighton C, Adams J. The effect of dance mat exergaming systems on physical activity and health-related outcomes in secondary schools: results from a natural experiment. BMC Public Health. Sep 12, 2014;14(1):951. [CrossRef] [Medline]
    55. de Brito-Gomes J, Perrier-Melo RJ, de F Brito A, da CCosta M. Active videogames promotes cardiovascular benefits in young adults? Randomized controlled trial. Rev Bras Ciênc Esporte. Jan 2018;40(1):62-69. [CrossRef]
    56. Warburton DER, Bredin SSD, Horita LTL, et al. The health benefits of interactive video game exercise. Appl Physiol Nutr Metab. Aug 2007;32(4):655-663. [CrossRef] [Medline]
    57. Moholdt T, Weie S, Chorianopoulos K, Wang AI, Hagen K. Exergaming can be an innovative way of enjoyable high-intensity interval training. BMJ Open Sport Exerc Med. 2017;3(1):e000258. [CrossRef] [Medline]
    58. Best JR. Exergaming in youth: effects on physical and cognitive health. Z Psychol. Apr 1, 2013;221(2):72-78. [CrossRef] [Medline]
    59. Gao Z, Chen S, Pasco D, Pope Z. A meta-analysis of active video games on health outcomes among children and adolescents. Obes Rev. Sep 2015;16(9):783-794. [CrossRef] [Medline]
    60. Moller AC, Sousa CV, Lee KJ, Alon D, Lu AS. Active video game interventions targeting physical activity behaviors: systematic review and meta-analysis. J Med Internet Res. May 16, 2023;25:e45243. [CrossRef] [Medline]
    61. Gao Z, Lee JE, Pope Z, Zhang D. Effect of active videogames on underserved children’s classroom behaviors, effort, and fitness. Games Health J. Oct 2016;5(5):318-324. [CrossRef] [Medline]
    62. Pamela BR, Joseph P, Andrew T, James LV. The heart rate response to Nintendo Wii Boxing in young adults. Cardiopulm Phys Ther J. Jun 2012;23(2):13-29.
    63. Bock BC, Dunsiger SI, Ciccolo JT, et al. Exercise videogames, physical activity, and health: Wii Heart Fitness: a randomized clinical trial. Am J Prev Med. Apr 2019;56(4):501-511. [CrossRef] [Medline]
    64. Duncan MJ, Birch S, Woodfield L, Hankey J. Physical activity levels during a 6-week, school-based, active videogaming intervention using the gamercize power stepper in British children. Medicina Sportiva. Jun 1, 2011;15(2):81-87. [CrossRef]
    65. Fu Y, Burns RD. Effect of an active video gaming classroom curriculum on health-related fitness, school day step counts, and motivation in sixth graders. J Phys Act Health. Sep 1, 2018;15(9):644-650. [CrossRef] [Medline]
    66. Gao Z, Lee JE, Zeng N, Pope ZC, Zhang Y, Li X. Home-based exergaming on preschoolers’ energy expenditure, cardiovascular fitness, body mass index and cognitive flexibility: a randomized controlled trial. JCM. 8(10):1745. [CrossRef]
    67. Howie EK, Campbell AC, Straker LM. An active video game intervention does not improve physical activity and sedentary time of children at-risk for developmental coordination disorder: a crossover randomized trial. Child Care Health Dev. Mar 2016;42(2):253-260. [CrossRef] [Medline]
    68. Maddison R, Foley L, Ni Mhurchu C, et al. Effects of active video games on body composition: a randomized controlled trial. Am J Clin Nutr. Jul 2011;94(1):156-163. [CrossRef] [Medline]
    69. Maloney AE, Threlkeld KA, Cook WL. Comparative effectiveness of a 12-week physical activity intervention for overweight and obese youth: exergaming with “Dance Dance Revolution”. Games Health J. Apr 2012;1(2):96-103. [CrossRef] [Medline]
    70. Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. Dec 19, 2014;14:135. [CrossRef] [Medline]
    71. Cumpston M, Li T, Page MJ, et al. Updated guidance for trusted systematic reviews: a new edition of the Cochrane Handbook for Systematic Reviews of Interventions. Cochrane Database Syst Rev. Oct 3, 2019;10(10):ED000142. [CrossRef] [Medline]
    72. Fritz CO, Morris PE, Richler JJ. Effect size estimates: current use, calculations, and interpretation. J Exp Psychol Gen. Feb 2012;141(1):2-18. [CrossRef] [Medline]
    73. Follmann D, Elliott P, Suh I, Cutler J. Variance imputation for overviews of clinical trials with continuous response. J Clin Epidemiol. Jul 1992;45(7):769-773. [CrossRef] [Medline]
    74. Whelton SP, Chin A, Xin X, He J. Effect of aerobic exercise on blood pressure: a meta-analysis of randomized, controlled trials. Ann Intern Med. Apr 2, 2002;136(7):493-503. [CrossRef] [Medline]
    75. Cornelissen VA, Fagard RH. Effect of resistance training on resting blood pressure: a meta-analysis of randomized controlled trials. J Hypertens (Los Angel). Feb 2005;23(2):251-259. [CrossRef]
    76. Cornelissen VA, Fagard RH, Coeckelberghs E, Vanhees L. Impact of resistance training on blood pressure and other cardiovascular risk factors. Hypertension. Nov 2011;58(5):950-958. [CrossRef]
    77. Li Y, Hanssen H, Cordes M, Rossmeissl A, Endes S, Schmidt-Trucksäss A. Aerobic, resistance and combined exercise training on arterial stiffness in normotensive and hypertensive adults: a review. Eur J Sport Sci. 2015;15(5):443-457. [CrossRef] [Medline]
    78. Grissom RJ, Kim JJ. Effect Sizes for Research: A Broad Practical Approach. Lawrence Erlbaum Associates Publishers; 2005. ISBN: 0805850147
    79. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. Sep 13, 1997;315(7109):629-634. [CrossRef] [Medline]
    80. Higgins JPT, Altman DG, Gøtzsche PC, et al. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ. Oct 18, 2011;343(oct18 2):d5928. [CrossRef] [Medline]
    81. Slim K, Nini E, Forestier D, Kwiatkowski F, Panis Y, Chipponi J. Methodological index for non-randomized studies (minors): development and validation of a new instrument. ANZ J Surg. Sep 2003;73(9):712-716. [CrossRef] [Medline]
    82. Guyatt G, Oxman AD, Akl EA, et al. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol. Apr 2011;64(4):383-394. [CrossRef] [Medline]
    83. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Mar 29, 2021;372:n71. [CrossRef] [Medline]
    84. Sim JJ, Shi J, Kovesdy CP, Kalantar-Zadeh K, Jacobsen SJ. Impact of achieved blood pressures on mortality risk and end-stage renal disease among a large, diverse hypertension population. J Am Coll Cardiol. Aug 12, 2014;64(6):588-597. [CrossRef] [Medline]
    85. Bundy JD, Li C, Stuchlik P, et al. Systolic blood pressure reduction and risk of cardiovascular disease and mortality: a systematic review and network meta-analysis. JAMA Cardiol. Jul 1, 2017;2(7):775-781. [CrossRef] [Medline]
    86. Lewington S, Clarke R, Qizilbash N, Peto R, Collins R, Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. Dec 14, 2002;360(9349):1903-1913. [CrossRef] [Medline]
    87. Young D. Analysis of cardiac output control in response to challenges. In: Control of Cardiac Output. Morgan & Claypool Life Sciences; 2010:59-85. [CrossRef] [Medline]
    88. Porter AM, Goolkasian P. Video games and stress: how stress appraisals and game content affect cardiovascular and emotion outcomes. Front Psychol. 2019;10:967. [CrossRef] [Medline]
    89. Chang Q, Liu R, Shen Z. Effects of slow breathing rate on blood pressure and heart rate variabilities. Int J Cardiol. Oct 25, 2013;169(1):e6-e8. [CrossRef] [Medline]
    90. Jia T, Ogawa Y, Miura M, Ito O, Kohzuki M. Music attenuated a decrease in parasympathetic nervous system activity after exercise. PLoS One. 2016;11(2):e0148648. [CrossRef] [Medline]
    91. Ketelhut S, Nigg CR. Heartbeats and high scores: esports triggers cardiovascular and autonomic stress response. Front Sports Act Living. 2024;6:1380903. [CrossRef] [Medline]
    92. Kounoupis A, Papadopoulos S, Galanis N, Dipla K, Zafeiridis A. Are blood pressure and cardiovascular stress greater in isometric or in dynamic resistance exercise? Sports (Basel). Mar 28, 2020;8(4):32231128. [CrossRef] [Medline]
    93. Nichols WW, O’Rourke M, Edelman ER, Vlachopoulos C. McDonald’s Blood Flow in Arteries: Theoretical, Experimental and Clinical Principles. CRC press; 2022. [CrossRef] ISBN: 1351253751
    94. DeMers D, Wachs D. Physiology, Mean Arterial Pressure. StatPearls Publishing; 2019. [Medline]
    95. Holder SM, Bruno RM, Shkredova DA, et al. Reference intervals for brachial artery flow-mediated dilation and the relation with cardiovascular risk factors. Hypertension. May 5, 2021;77(5):1469-1480. [CrossRef] [Medline]
    96. Hopkins ND, Dengel DR, Stratton G, et al. Age and sex relationship with flow-mediated dilation in healthy children and adolescents. J Appl Physiol (1985). Oct 15, 2015;119(8):926-933. [CrossRef] [Medline]
    97. Lieberman DA, Chamberlin B, Medina E Jr, et al. The power of play: Innovations in Getting Active Summit 2011: a science panel proceedings report from the American Heart Association. Circulation. May 31, 2011;123(21):2507-2516. [CrossRef] [Medline]
    98. Staiano AE, Calvert SL. Wii Tennis play for low-income african american adolescents’ energy expenditure. Cyberpsychology (Brno). Jul 1, 2011;5(1):24058381. [Medline]
    99. Goodway JD, Crowe H, Ward P. Effects of motor skill instruction on fundamental motor skill development. Adapt Phys Activ Q. 2003;20(3):298-314. [CrossRef]
    100. McEwan D, Harden SM, Zumbo BD, et al. The effectiveness of multi-component goal setting interventions for changing physical activity behaviour: a systematic review and meta-analysis. Health Psychol Rev. 2016;10(1):67-88. [CrossRef] [Medline]
    101. Pingree S, Hawkins R, Baker T, duBenske L, Roberts LJ, Gustafson DH. The value of theory for enhancing and understanding e-health interventions. Am J Prev Med. Jan 2010;38(1):103-109. [CrossRef] [Medline]
    102. Boyd CAR, Noble D. The Logic of Life: The Challenge of Integrative Physiology. Oxford : Oxford University Press; 1993. [CrossRef] ISBN: 0192624172
    103. Lipsitz LA. Dynamics of stability: the physiologic basis of functional health and frailty. J Gerontol A Biol Sci Med Sci. Mar 2002;57(3):B115-B125. [CrossRef] [Medline]
    104. Dunn AL, Andersen RE, Jakicic JM. Lifestyle physical activity interventions. History, short- and long-term effects, and recommendations. Am J Prev Med. Nov 1998;15(4):398-412. [CrossRef] [Medline]
    105. Bruyndonckx L, Hoymans VY, Lemmens K, Ramet J, Vrints CJ. Childhood obesity-related endothelial dysfunction: an update on pathophysiological mechanisms and diagnostic advancements. Pediatr Res. Jun 2016;79(6):831-837. [CrossRef] [Medline]
    106. Cahill PA, Redmond EM. Vascular endothelium - gatekeeper of vessel health. Atherosclerosis. May 2016;248:97-109. [CrossRef] [Medline]
    107. Jo EA, Han HR, Wu SS, Park JJ. Effects of acute exergame on glucose control after glucose ingestion in individuals with pre- and type 2 diabetes. Asian J Kinesiol. Jan 2023;25(1):11-18. [CrossRef]
    108. Calcaterra V, Verduci E, Vandoni M, et al. The effect of healthy lifestyle strategies on the management of insulin resistance in children and adolescents with obesity: a narrative review. Nutrients. Nov 6, 2022;14(21):4692. [CrossRef] [Medline]
    109. Sowers JR, Frohlich ED. Insulin and insulin resistance: impact on blood pressure and cardiovascular disease. Med Clin North Am. Jan 2004;88(1):63-82. [CrossRef] [Medline]
    110. Sinaiko AR, Steinberger J, Moran A, Prineas RJ, Jacobs DRJ. Relation of insulin resistance to blood pressure in childhood. J Hypertens (Los Angel). Mar 2002;20(3):509-517. [CrossRef]

    ‎
    AVG: active video game
    BP: blood pressure
    CVD: cardiovascular disease
    DBP: diastolic blood pressure
    FITT: frequency, intensity, time, and type
    GRADE: Grading of Recommendations Assessment, Development, and Evaluations
    PICOS: Participants, Intervention, Comparison, Outcome, and Study Design
    PP: pulse pressure
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    SBP: systolic blood pressure
    SMD: standardized mean difference

    Edited by Andrew Coristine; submitted 26.03.25; peer-reviewed by Bin Pan, Shan Jiang, Yikun Yin; final revised version received 02.06.25; accepted 02.06.25; published 19.08.25.

    Copyright

    ©Hao Zhu, Keith Tsz-Suen Tung, Hung-kwan So, Parco M Siu, Ian Chi Kei Wong, Jason C Yam, Joanna Yuet-ling Tung, Yih-kuen Jan, Li He, Patrick Ip. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.8.2025.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.