Effects of Active Video Games on Energy Expenditure and Weight Management Among Young Adults: A Systematic Review

Abstract

Obesity and overweight among young adults are becoming public health concerns. Active video games (AVGs) have been demonstrated by previous studies as a healthy and enjoyable exercise, which may assist young people in weight management (WM). This review aims to critically assess the literature on the effects of AVGs on young adults in terms of energy expenditure (EE) and WM. Five international databases (PubMed, Scopus, ScienceDirect, Cochrane, and Web of Science) were searched with keywords up to 2025. A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and randomized controlled trial (RCT) studies investigating the effects of AVGs on EE and WM in young adults aged 18–25 were included. Data from all studies were extracted using a preprepared structured form, and pre- and postintervention differences were compared between the AVG intervention groups and the control/comparison groups. Thousand one hundred twenty-nine articles were retrieved, of which 14 RCT studies (n = 668, 47.5% female) met the inclusion criteria. Ten studies measured EE while playing AVGs, and nine of these found that playing AVGs could achieve moderate physical activity. Four parallel-design RCTs measured body weight or body mass index (BMI), with only one of these found a significant reduction (−0.29 kg/m², P = 0.043) in BMI in the AVG group. The differences in study design and methodological quality among the included literature make it challenging to simply summarize the results, and the findings need to be interpreted with caution. Overall, AVGs could achieve moderate physical activity and serve as an effective alternative to traditional exercise. However, the results related to WM are mixed. Future research should adhere to more rigorous methodological standards, such as larger sample sizes and stricter dietary controls, to investigate the long-term effects of AVGs on body composition.

Introduction

Overweight and obesity represent significant global health concerns. In 2016, 39% of the global adult population was classified as overweight, with 13% classified as obese.¹ Currently, the prevalence of overweight and obesity among college students is notably high,² despite existing evidence suggesting that higher levels of education are generally associated with a reduced likelihood of being overweight or obese.³ Obesity constitutes a major public health threat, contributing significantly to the global burden of noncommunicable diseases, including type II diabetes, cardiovascular disease, hypertension, and various cancers.^4,5 Furthermore, the biomechanical issues associated with substantial weight gain, such as osteoarthritis and sleep apnea, can adversely impact individuals’ quality of life.⁶ Recent findings indicate that obese patients are at an elevated risk of developing severe COVID-19 symptoms compared with those of normal weight.^7–9 Obesity not only affects physiological health but also intensifies psychological health issues and diminishes quality of life due to metabolic disorders.¹⁰ Research has identified associations between obesity and mental disorders such as anxiety and depression.^11,12 Additionally, the prevalence of physical dissatisfaction among individuals with overweight and obese has increased.¹³

Obesity is typically attributed to intricate interactions among dietary habits, physical activity levels, socioeconomic status, environmental influences, and genetic factors.¹⁴ Among college students, unhealthy lifestyles significantly contribute to the prevalence of overweight and obesity.^15,16 Video games are particularly popular within this demographic,¹⁷ and the substantial amount of time spent engaging in gaming is often associated with sedentary behavior, digital addiction, and a lack of physical activity.^18,19

Active video games (AVGs), also known as exergames, are video games that require individuals to engage in physical activities to interact with the gaming console.^3,20 These consoles detect the player’s physical movements through technologies such as cameras or hand controls, enabling interaction with the game.²¹ Many studies have demonstrated that AVGs offer significant health benefits to children and adolescents,^22–24 as well as older adults.^25–27 Some studies have found that AVGs can also bring positive effects on the physical^28–30 and mental health^31–33 of young people. Although there are a few systematic reviews^20,34 on AVGs and college students or young adults, to our knowledge, there is no review specifically focusing on the outcomes related to energy expenditure (EE) and weight management (WM) in AVGs. This review aims to systematically evaluate the application characteristics of AVGs among college students and young adults by synthesizing existing randomized controlled trials (RCTs), with a primary focus on the exercise intensity and EE of AVGs, as well as their potential to aid in WM.

Methods

This review adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines,³⁵ incorporating the PICOS (Population, Intervention, Comparison, Outcome, Study Design) framework for data extraction.³⁶ Additionally, it registered in the PROSPERO (registration ID: CRD42024564539), also conformed to the ethical standards established in sports and exercise science research.³⁷

Data sources and search strategy

Five databases, PubMed, Scopus, ScienceDirect, Cochrane, and Web of Science, were utilized for conducting a comprehensive literature search in this systematic review. The search covered literature published from 2007 to 2025. The search strategy was #1: “active video gam*” OR “active videogam*” OR exergam* OR “interactive video gam*” OR “interactive videogam*” OR “exercise video gam*” OR “exercise videogam*” OR “fitness gam*” OR “virtual reality exercise” OR “virtual reality gam*” OR Nintendo OR Wii OR Xbox OR Kinect OR Playstation OR “Ring Fit” OR Eyetoy; #2: “college student*” OR “university student*” OR “undergraduate student*” OR “graduate student*” OR “young adult*” OR young OR youth OR “college aged”; #3: “energy expenditure” OR “energy metabolism*” OR “oxygen uptake” OR VO2 OR “oxygen consumption” OR VO2peak OR “maximum oxygen uptake” OR VO2max OR “heart rate” OR HR OR HRmax OR “metabolic equivalent” OR MET* OR “exercise intensity” OR “weight loss*” OR “weight reduction*” OR “weight reducing” OR BMI OR “body mass index” OR “waist circumference” OR “body fat percentage”; #1 AND #2 AND #3. We further refined the search strategies to align with the text retrieval formats of various databases. Additionally, for comprehensive coverage of relevant literature, we also scrutinized the reference lists of pertinent articles.

Eligibility criteria

The inclusion criteria of this systematic review were defined using the PICOS model framework (Table 1).

Table 1.

Population, Intervention, Comparison, Outcome, Study Design Eligibility Criteria

PICOS	Detailed information
Population	College-age adults or college students (18–25 years old)
Intervention	AVGs
Comparison	Exercise without active video games or no exercise (control group)
Outcome	At least one indicator related to EE (EC, METs, HR, VO₂) or WM (weight, BMI, WC, BFP) was measured
Study design	RCT (crossover or parallel group designs)

BFP, body fat percentage, proportion of fat mass to total body weight (%); BMI, body mass index, weight/height² (kg/m²); EC, energy cost (kcal/min); EE, energy expenditure; HR, heart rate, beats per minute (bpm); METs, metabolic equivalents, 1 MET corresponds to the resting metabolic rate (3.5 mL O₂/kg/min or 1 kcal/kg/h); RCT, randomized controlled trial; VO₂, oxygen consumption, volume of oxygen utilized (mL/kg/min); WC, waist circumference (cm); WM, weight management.

Additionally, studies meeting any of the following criteria will be excluded: (1) unable to access the full text; (2) not published in English; (3) not a journal article; and (4) participants with disease (except obesity).

Study selection

First, the retrieved studies from the database are imported into Zotero software for management, and duplicate items were removed using the software’s deduplication function. Second, two independent reviewers (W.F. and H.A.Y.) conduct primary screening of the deduplicated literature based on title and abstract. Upon downloading the literature, they determined which articles to include based on predetermined criteria. The two independent reviewers (W.F. and H.A.Y.) worked separately and resolved any discrepancies through discussion with a third researcher (N.Z.A.).

Data extraction

After screening the studies, the following essential data were obtained from eligible studies in a preformulated extraction format: (1) publication years and authors; (2) aims of studies; (3) design of RCTs and sets of groups; (4) sample characteristics such as size, age, gender, and weight status; (5) frequency and duration of interventions sessions; (6) time points and indicators of measurement; and (7) main findings. The data extraction process was conducted by the first researcher manually using a standardized form (Microsoft Excel, 2008), and the information was cross-checked by the second researcher.

Data synthesis

The descriptive characteristics of the included studies, such as geographical location, participant demographics, publication year, study designs, study objectives, and intervention characteristics, were summarized to provide an overview of the application of AVGs. Additionally, the efficacy of AVG-based interventions was evaluated through the analysis of RCTs. A meta-analysis was not conducted due to significant heterogeneity across studies, including variations in intervention approaches, participant profiles (e.g., individuals with healthy weight vs. overweight/obese), and measurement outcomes, which rendered the aggregation of data impractical for meaningful interpretation.

Risk of bias in individual studies

As shown in Table 2, the differences in EE- or WM-related outcomes between the exergaming intervention group and the control/comparison group are categorized into six levels: (1) +E indicates a significant difference in EE-related outcomes favoring the exergaming group; (2) +W indicates a significant difference in WM-related outcomes favoring the exergaming group; (3) 0E indicates no significant difference in EE-related outcomes between the groups; (4) 0W indicates no significant difference in WM-related outcomes between the groups; (5) −E indicates a significant difference in EE-related outcomes favoring the control/comparison group; and (6) −W indicates a significant difference in WM-related outcomes favoring the control/comparison group. Specifically, based on previous literature,^52,53 the authors independently assessed the quality of the study design for each study using a 10-item scale (see Table 2). Each item was rated as positive (+) or negative (−).⁵² A design quality score ranging from 0 to 10 was calculated by summing the positive ratings. We also modified the original scoring criteria Item 4 “pre- and postintervention measurements” and Item 10 “follow-up” based on the Cochrane Risk of Bias (RoB) 2 tool’s requirements for assessing crossover trials,⁵⁴ to make them applicable to both parallel-design and crossover-design RCTs, thereby ensuring comparability in bias risk assessment between the two trial designs. Studies with scores achieving 7 were classified as low risk, and those scoring <7 were categorized as having some concerns regarding potential bias. Additionally, studies that did not adhere to the principle of random allocation were considered as high risk.

Table 2.

Design Quality Analysis for the Active Video Game Intervention Studies

Articles	1 randomization	2 control	3 isolate exergame	4 appropriate time points	5 retention	6 baseline	7 missing data	8 power analysis	9 validity measure	10 follow-up/washout	Score	Effectiveness
Warburton et al.,³⁸ 2007	＋	＋	－	＋	＋	＋	＋	－	＋	－	7	0W
Douris et al.,³⁹ 2012	＋	＋	＋	＋	＋	－	－	＋	＋	－	7	＋E
Johnston et al.,⁴⁰ 2012	－	＋	－	＋	＋	＋	＋	－	＋	－	6	0W
Scanlan et al.,⁴¹ 2013	＋	＋	＋	＋	＋	－	＋	－	＋	－	7	＋E /－E
Perusek et al.,⁴² 2014	＋	＋	＋	＋	＋	－	－	－	＋	－	6	0E
Howe et al.,⁴³ 2015	＋	＋	＋	＋	＋	－	＋	－	＋	－	7	＋E
McDonough et al.,⁴⁴ 2018	＋	＋	＋	＋	＋	－	＋	－	＋	－	7	－E
Hwang and Lu,⁴⁵ 2018	＋	＋	＋	＋	＋	＋	－	－	＋	－	7	＋E
Nani et al.,⁴⁶ 2019	＋	＋	＋	＋	＋	＋	－	－	＋	－	7	0W
Roure et al.,⁴⁷ 2019	＋	＋	＋	＋	＋	＋	－	－	＋	－	7	＋E
Pérez-López et al.,⁴⁸ 2022	－	＋	＋	＋	＋	＋	＋	－	＋	－	7	＋W
Gallou-Guyot et al.,⁴⁹ 2023	＋	＋	－	＋	＋	－	＋	－	＋	－	6	0E
Gu et al.,⁵⁰ 2023	＋	＋	＋	＋	＋	＋	＋	－	＋	－	8	0E
Philippe et al.,⁵¹ 2024	＋	＋	＋	＋	＋	－	＋	－	＋	－	7	－E

1, randomization procedures were adequately described and carried out; 2, research design allowed for comparison between the exergame intervention group and the control/comparison group; 3, research design allowed for test of effectiveness of the exergames alone (not combined with other exercises) as compared with the control/comparison group; 4, outcome variables were measured at appropriate time points (e.g., pre-/postintervention in parallel RCTs or per-phase in crossover RCTs); 5, dropouts were described and did not exceed 30%; 6, groups were comparable at baseline on key outcome variables through statistical analyses; 7, data analyses were conducted while considering missing data; 8, power analysis was conducted to determine the appropriate sample size; 9, the reliability and validity of the measures were provided; 10, washout period was adequate (e.g., ≥5 half-lives) or follow-up was documented (for parallel RCTs).

Results

Study selection

The initial database search revealed 1129 potentially relevant articles. After removing duplicates, 897 articles were screened by title and abstract. In total, 69 articles were analyzed by full text, and 14 were included (Fig. 1).

FIG. 1.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart of study selection.

Descriptive analyses

The main features of the studies are summarized in Table 3. The publication dates of these 14 articles range from 2007³⁸ to 2024⁵¹ and came from Canada,³⁸ Australia,⁴¹ Greece,⁴⁶ Spain,⁴⁸ China,⁵⁰ Switzerland,⁴⁷ France,⁴⁹ and United States.^{39,40,42–45,51} Among the 14 RCTs, six used a parallel design^{38,40,45–48} and 8 used a crossover design.^{39,41–44,49–51} Four parallel-design RCTs compared AVG interventions with control groups in assessing WM-related outcomes postintervention, with sample sizes of 14³⁸ to 115,⁴⁰ and intervention durations varying between 6 weeks³⁸ and 14 weeks.⁴⁸ Additionally, 10 studies evaluated EE by comparing AVG with either traditional exercise or sedentary video game (SVG).^{39,41–45,47,49–51} Among these, eight studies utilized crossover designs incorporating within-subject repeated measurements.^{39,41–44,49–51} All of the participants of included studies were college/graduate students^{39–44,46–51} or college-age adults.^38,45 All studies used AVG to complete exercise in the experimental group and at least one non-AVG traditional physical exercise or SVG in the comparison group. Most interventions used commercially available technology, four studies used the Nintendo Wii,^39,41,42,46 four studies used the Xbox 360 Kinect,^43–45,50 one study used the Sony Playstation,³⁸ and one study used the HADO AR,⁵¹ whereas the remaining three studies utilized self-developed games.^40,47,49 Warburton³⁸ and Pérez-López⁴⁸ incorporated auxiliary equipment (stationary bicycle) and contextual elements (movie narrative) alongside AVGs in their intervention. This design limitation obscures the ability to determine whether stand-alone AVG use confers superior EE or weight reduction benefits compared with traditional exercise or sedentary games.

Table 3.

Characteristics of Included Studies

Authors, year, countries	Aim	Research design		Population	Frequency and duration	Measurement	Main findings
Warburton et al.,³⁸ 2007, Canada	To evaluate the effectiveness of interactive video games on health-related physical fitness and exercise adherence in comparison with traditional aerobic training.	Parallel design RCT, AVG: Sony Playstation linked with a stationary cycling; Comparison: Traditional aerobic training (stationary cycling alone).		N = 14, Male = 14, AVG group = 7, Comparison group = 7, Age: 22.5 ± 3.69 (18–25), BMI: 27.5 ± 5.79	A recommended exercise training regime for all subjects: moderate intensity exercise, 30 minutes per day, 3 days per week, and lasting for 6 weeks.	(1) Measurements were taken at baseline and after 6 weeks of training. (2) Attendance was monitored throughout the 6-week program.	A training program links interactive video games to cycle exercise results in greater improvements in health-related physical fitness than that seen after traditional cycle exercise training.
Douris et al.,³⁹ 2012, United States	To compare the physiological and psychological responses of college students playing Nintendo Wii Fit versus an equal duration of moderate-intensity brisk walking.	Crossover design RCT, AVG: Nintendo Wii Fit “Free Run”; Comparison: Walking exercise on a treadmill.		N = 21, Male = 9, Female = 12, Age: 23.2 ± 1.8, BMI: 23.7 ± 3.7	(1) A repeated-measures design. (2) 30 minutes per day for 2 days (1 week apart).	(1) Every session was measured at 5-minute intervals for HR, RR, BP, and RPE. (2) After each exercise condition, measurements of HR, BP, RPP, RPE, and SEES were recorded.	Wii Fit Free Run could serve as a viable alternative to conventional moderate-intensity aerobic exercise.
Johnston et al.,⁴⁰ 2012, United States	To examine the impact of an ARG intervention on PA within the college student population.	Parallel design RCT, AVG: The Skeleton Chase (self-designed); Comparison: Fitness lab with exercise equipment.		N = 115, Male = 51, Female = 64, AVG group = 42, Comparison group = 73, Age: 18.4 ± 0.6, BMI: 23.21 ± 3.28, BFP: 20.9 ± 8.8	ARG group: 50 minutes per week, lasted for 7 weeks. Fitness lab group: Same as ARG group.	(1) Pre- and postintervention measures included BW, WC, BMI, BFP, MPA, VPA, and PA (steps/week).	A game intervention can positively influence PA within the college student population.
Scanlan et al.,⁴¹ 2013, Australia	To compare the activity intensities experienced by young adults while playing active tennis gaming with conventional sedentary gaming and tennis gameplay.	Crossover design RCT, AVG: Nintendo Wii; Comparison: (1) Sedentary video gaming; (2) Singles tennis gameplay.		N = 10, Male = 4, Female = 6, Age: 20.2 ± 0.4, BMI: 22.36 ± 1.34	(1) A repeated-measures within-subject study design. (2) 4 testing sessions on separate days. All trials were completed within 10–14 days for all subjects.	(1) HR and METs were averaged across 5-minute and 10-minute intervals, and the entire 20-minute bout within each condition.	Activity intensities elicited by active gaming were greater than sedentary gaming but less than tennis gameplay and insufficient to contribute toward promoting and maintaining good health in young adults.
Perusek et al.,⁴² 2014, United States	To compare energy expenditure during “Wii Boxing” versus heavy bag boxing in young adults.	Crossover design RCT, AVG: Wii Boxing; Comparison: Heavy bag boxing.		N = 29 Male = 15, Female = 14, Mean age: 25.6, Mean BMI: 24.47	Wii Boxing: 30 minutes per session and consisted of 10 3-minute exercise bouts. Heavy bag boxing: same with Wii Boxing group.	(1) Measurements of HR, RPE, and EE obtained from indirect calorimetry. A paired-sample test was used to analyze the results.	The Nintendo Wii may be a sufficient replacement for traditional target HR zone activities, especially for less fit individuals.
Howe et al.,⁴³ 2015, United States	To compare the physical activity energy expenditure and intensity of active video games to traditional seated video games.	Crossover design RCT, AVG: (1) Kinect Sports Football; (2) Kinect Adventures Reflex Ridge; (3) Dance Central 2; (4) Zumba Fitness; Comparison: Sedentary games: (1) Madden NFL 12; (2) Sonic Racer.		N = 53, Male = 26, Female = 27, Age: 21.8 ± 1.9 (19–28), BMI: 23.8 ± 2.7 (19.6–32.8)	(1) A repeated-measures within-subject study design. (2) 6–10 minutes of gameplay, with at least 4 minutes rest in between. (3) All trials were completed in 8 months for all subjects.	(1) The accelerator is used to measure the intensity of physical activity, portable metabolic analyzer was used to measure RMR, and the Oxycon Mobile was used to measure total oxygen consumption.	The newest AVGs do qualify as MVPA and can contribute to the recommended dose of MVPA for weight management in young adults.
McDonough et al.,⁴⁴ 2018, United States	To evaluate the effects of exergaming on college students’ EE, MVPA, LPA, RPE, and enjoyment compared with traditional treadmill exercise and sex differences.	Crossover design RCT, AVG: (1) Xbox 360 Kinect Just Dance; (2) Xbox 360 Kinect Reflex Ridge; Comparison: Treadmill walking at 4.0 mph.		N = 60, Male = 30, Female = 30, Age: 23.6 ± 4.1, BMI: 23.9 ± 4.0	(1) A repeated-measures design. (2) Three 20-minute exercise sessions with 10-minute breaks in between.	PA intensity was measured using accelerometers. (1) RPE was measured every 4 minutes during each of the three 20-minute exercise sessions.	College students find exergaming more enjoyable and report lower RPE compared to traditional treadmill exercise, though not yet matching the moderate physiological intensity level.
Hwang and Lu,⁴⁵ 2018, United States	To examine the separate and additive effects of narrative and AVGs on physical activity and cognitive function (working memory, sustained attention) in young adults, compared with sedentary video games (SVGs).	4-arm parallel RCT, AVG: (1) Narrative-AVG (N-AVG); (2) AVG without narrative (AVG); Comparison: (1) Narrative-SVG (N-SVG); (2) SVG without narrative (SVG).		N = 100, Male = 55, Female = 45, Twenty-five/group, Age: 21.3 ± 2.14 (18–25), BMI: 23.3 ± 3.93	Single 30-minute session per group. AVG groups: Played Kung-Fu Kinect (Xbox One) with/without narrative. SVG groups: Played Street Fighter V (PlayStation 4) with/without narrative.	PA metrics (Step counts, HR, time in MVPA) were measured via accelerometers.	Adding narrative to AVG as a motivational component increased physical activity, which was the primary factor in the improvement of overall working memory.
Nani et al.,⁴⁶ 2019, Greece	To investigate the effect of exergames (Wii Nintendo Sports) on psychological and physiological parameters of young adults.	Parallel design RCT, AVG: Nintendo Wii; Comparison: control group: No training program.		N = 20, Male = 7, Female = 13, AVG group = 10, Control group = 10, Age: 22.06 ± 1.32 (20–25), BMI: 22.39 ± 2.99	Wii group: 30 minutes per session, 3 times per week, lasted for 10 weeks.	(1) Outcomes were examined before and after the intervention program.	Exergames are effective tools for achieving better subjective vitality in young adults.
Roure et al.,⁴⁷ 2019, Switzerland	To identify the impact of a design-based bike exergame (Greedy Rabbit) on young adults’ physical activity metrics and situational interest.	Parallel design RCT, AVG: A design-based bike exergame (Greedy Rabbit); Comparison: Played a placebo version of Greedy Rabbit.	N = 60, Male = 31, Female = 29, AVG group = 41, Control group = 19, Age: 20.8 ± 1.3 (18–25)		One session consisting of 4 sets of 8 stages (32 stages total). Intensity levels: easy (130W), medium (180W), hard (230W), based on 65%–85% of individual maximal aerobic power (MAP).	Pretest: Incremental cycling test to determine VO₂ max, MAP, and HR max Posttest: Measured during exergame (cadence, %HR max, %VO₂ max) and after (situational interest via 19-item scale).	A design-based bike exergame might be a good option to enhance players’ health-related physical activity outcomes and situational interest.
Pérez-López et al.,⁴⁸ 2022, Spain	To examine the effects of a gamification-based teaching program, including the use of a game-based mobile app on body composition in college students.	Parallel design RCT, AVG: The narrative of ‘‘STAR WARS’’ and a mobile app; Comparison: Traditional teaching methodology.		N = 112, Male = 83, Female = 29, AVG group = 56, Traditional group = 56, Age: 21.22 ± 2.55, BMI: 23.50 ± 2.48, BFP: 21.25 ± 6.87	A 14-week gamification-based teaching intervention.	(1) A portable eight-polar bioelectrical impedance analysis was used to obtain body composition outcomes.	Gamification and the use of interactive mobile app are powerful teaching strategies in higher education to motivate students toward healthier lifestyles that lead to body composition benefits.
Gallou-Guyot et al.,⁴⁹ 2023, France	To compare objectively measured and perceived exercise intensity during exergame, cognitive-motor dual-task, and single-task (ST) training sessions in healthy young adults.	Crossover design RCT, AVG: Virtual Carpet; Comparison: (1) ST: physical exercises; (2) physical exercises with concurrent cognitive tasks.		N = 16, Male = 6, Female = 10, Age: 24.6 ± 3.1, BMI: 23.1 ± 3.2	(1) One session per training type, spaced at least 24 hours apart. (2) 30 minutes per session (effective activity: ∼24 minutes). Sessions included eight 3-minute exercise sequences with 30-second rests.	(1) Mean and peak HR during training were measured with Polar H10 monitors. (2) Perceived exertion (modified Borg scale) was measured after each session.	Exergame can be considered to be as challenging as more traditional physical training.
Gu et al.,⁵⁰ 2023, China	To investigate the effects of a dance-based exergaming on Chinese college students’ energy expenditure, self-efficacy, and enjoyment in comparison with the traditional aerobic dance exercise.	Crossover design RCT, AVG: Xbox 360 Kinect Just Dance-Just Sweat around the World); Comparison: Traditional aerobic dance led by an experienced instructor.		N = 40, Male = 7, Female = 33, Age: 21.55 ± 2.06 (18–25), BMI: 20.86 ± 2.06	(1) A repeated-measures within-subject study design. (2) Two separate 20-minutes dance sessions with 10-minutes rest between.	(1) An ActiGraph Link GT9X accelerometer (ActiGraph, Pensacola, FL) on the right hip to measure energy expenditure. (2) After each exercise session, participants responded to a three-item self-efficacy survey and a five-item enjoyment survey, both using a 5-point Likert-type scale.	Young adults could be more inclined to take part in gaming-based exercise if they expend the same amount of energy as they would in traditional aerobic exercise.
Philippe et al.,⁵¹ 2024, United States	To examine the use of an exergame in augmented reality for aerobic training in healthy young adults.	Crossover design RCT, AVG: AR dodgeball playing; Comparison: Classical dodgeball playing.		N = 18, Male = 13, Female = 5, Age: 19.8 ± 1.4 (18–23)	(1) A repeated-measures within-subject study design. (2) Participants were divided into groups of 6 people who had to participate in three 1-hout PA sessions spread over three consecutive weeks. (3) 3 consecutive weeks × 3 for the 3 groups of 6 participants.	(1) participants had to report the RPE of the session on a CR-10 RPE scale. (2) Objective PA load was measured with tri-axis accelerometers GT3X. (3) HRrest, HRmax, and HRmean were examined as physiological parameters.	An aerobic state can be attained through both physical gameplay and its augmented reality equivalent and was associated to a high level of enjoyment among healthy young adults.

ARG, alternate reality game; BC, body composition; BW, body weight; FMI, fat mass index; HRmax, maximum heart rate; HRmean, mean heart rate; LPA, light physical activity; MVPA, moderate-to-vigorous physical activity; PA, physical activity; PAEE, physical activity energy expenditure; PWB, positive well-being; RPE, rating perceived exertion; RPP, rate pressure product; RR, respiratory rate; RBP, resting blood pressure; RMR, resting metabolic rate; RSBP, resting systolic blood pressure; VO₂ max, maximum oxygen uptake.

Effectiveness of AVGs

EE

Table 4 presents all statistical data related to EE in the included studies. Ten studies measured EE-related indicators while playing AVGs.^{39,41–45,47,49–51} Among these, five studies found that the AVG group had significantly greater (all P < 0.01) EE level than comparisons, with higher energy cost (EC),⁴³ metabolic equivalents (METs),^41,43 heart rate (HR),^{39,41,43,45,47} and oxygen consumption (VO₂)^43,47 values. However, three studies found no statistically significant differences (all P > 0.05) between the AVGs and comparisons in EC,^42,50 HR,⁴⁹ or VO₂.⁴² Notably, four studies even reported significantly lower (all P < 0.01) METs^41,44 and HR^41,42,51 values in the AVG group compared with comparisons. Nine studies demonstrated that AVG play enabled participants to achieve MPA intensity as defined by the American College of Sports Medicine⁵⁵ (64%–76% HRmax/3.0–5.9 METs).^{39,40,42–45,47,49,51} Specifically, one study noted that although AVG (Xbox 360 Kinect) could reach MPA, its intensity was lower than that of treadmill walking.⁴⁴ Moreover, one study demonstrated that although active tennis video game (1.4 ± 0.1 METs) significantly (P < 0.001) exceeded sedentary gaming (1.1 ± 0.1 METs) in EE, its intensity was only 28% of real tennis gameplay (5.0 ± 0.2 METs). This implies that while AVGs may help break sedentary habits, they are insufficient for meeting recommended physical activity guidelines (≥3.0 METs for MPA).⁴¹

Table 4.

Quantitative Results of Energy Expenditure-Related Outcomes

Authors, year, countries	Groups for comparison	Indicator 1: EC (kcal/min) (MD [95% CI], P value, ES, tendency)	Indicator 2: METs (MD [95% CI], P value, ES, tendency)	Indicator 3: HR (beats/minute) (MD [95% CI], P value, ES, tendency)	Indicator 4: VO₂ (mL/kg/min) (MD [95% CI], P value, ES, tendency)	Time point	Analysis method	RoB
Douris et al.,³⁹ 2012	Wii Fit versus Treadmill	—	—	+19.2 [11.2–27.2], P = 0.001, *d = 1.09, ↑	—	Post-30min	Paired t-test	Low
Scanlan et al.,⁴¹ 2013	Active Gaming versus Sedentary Gaming	—	+0.3 [0.1–0.5], P = 0.001, *d = 1.50, ↑	+11.2 [4.6–17.8], P = 0.001, *d = 1.07, ↑	—	Post-20min	RMANOVA	Low
Scanlan et al.,⁴¹ 2013	Active Gaming versus Tennis Gameplay	—	−3.6 [−4.0 to −3.2], P < 0.001, *d = −12.0, ↓	−43.4 [−54.1 to −32.7], P < 0.001, *d = −3.14, ↓	—	Post-20min	RMANOVA	Low
Perusek et al.,⁴² 2014	Wii Boxing versus Heavy Bag Boxing	−0.9 [−2.23, 0.43], P = 0.078, d = −0.35, ↔	—	−18.0 [−29.3 to −6.7], P = 0.001, *d = −0.86, ↓	−1.6 [−4.5 to +1.3], P = 0.197, d = −0.29, ↔	Post-30 minutes	Paired t-test	SC
Howe et al.,⁴³ 2015,	Active Games versus Sedentary Games	+4.5 [4.11, 4.89], P < 0.001, *d = 6.16, ↑	+4.3 [3.89 to 4.71], P < 0.001, *d = 1.99, ↑	+41.8 [41.1–42.5], P < 0.001, *d = 23.9, ↑	+13.1 [13.0–13.2], P < 0.001, *d = 45.2, ↑	Post-6–10 minutes	RMANOVA	Low
McDonough et al.,⁴⁴ 2018	Kinect Reflex Ridge versus Treadmill	—	−2.6 [−3.5 to −1.7], P < 0.001, *d = −1.35, ↓	—	—	Post-20 minutes	RMANOVA	Low
McDonough et al.,⁴⁴ 2018	Kinect Just Dance versus Treadmill	—	−3.3 [−4.3 to −2.3], P < 0.001, *d = −1.65, ↓	—	—	Post-20 minutes	RMANOVA	Low
Hwang and Lu,⁴⁵ 2018	AVG with narrative versus SVG with narrative	—	—	+57.96 [50.12–65.80], P < 0.001, *d = 2.95, ↑	—	Post-30 minutes	RMANOVA	Low
	AVG with narrative versus SVG without narrative			+51.81 [44.25–59.37], P < 0.001, *d = 2.67, ↑
	AVG without narrative versus SVG with narrative	—	—	+50.73 [43.55–57.91], P < 0.001, *d = 2.89, ↑	—
	AVG without narrative versus SVG without narrative			+44.58 [37.62–51.54], P < 0.001, *d = 2.55, ↑
	AVG with narrative versus AVG without narrative	—	—	+7.23 [−1.62 to 16.08], P = 0.107, d = 0.35, ↔	—
Roure et al.,⁴⁷ 2019	Greedy Rabbit exergame versus Placebo gaming	—	—	%HRmax (%): +8.10 [7.12–9.08], P < 0.001, *d = 4.97, ↑	%VO₂ max (%): +11.13 [9.42–12.84], P < 0.001, *d = 4.43, ↑	Postintervention	RMANOVA	Low
Gallou-Guyot et al.,⁴⁹ 2023	AVG versus Traditional exercise	—	—	−8.3 [−16.92 to +0.32], P = 0.072, d = −0.58, ↔	—	Post-30 minutes	Paired t-test	SC
	AVG versus Traditional exercise with cognitive tasks	—	—	−4.0 [−12.32 to +4.32], P = 0.323, d = −0.26, ↔	—
	Traditional exercise versus Traditional exercise with cognitive tasks	—	—	+4.3 [−4.02 to +12.62], P = 0.298, d = 0.29, ↔	—
Gu et al.,⁵⁰ 2023	Exergaming Dance versus Traditional Dance	−2.62 [−15.12, +9.88], P = 0.65, d = −0.08, ↔	—	—	—	Post-20 minutes	Paired t-test	Very low
Philippe et al.,⁵¹ 2024	AR Dodgeball versus Classical Dodgeball	—	—	−11.5 [−19.32 to −3.68], P = 0.006, *d = −0.74, ↓	—	Post-36 minutes	Paired t-test	Low

95% CI, 95% confidence interval; ES, effect size (Cohen’s d); MD, mean difference; RMANOVA, repeated-measures analysis of variance; RoB, risk of bias; SC, some concerns regarding potential bias; ↑, the AVG group showed a significant increase compared with the control/comparison group; ↓, the AVG group showed a significant decrease compared with the control/comparison group; ↔, no significant difference between AVG group and control/comparison group; ^*, there was a statistically significant difference between groups (95% CI excluding null, P < 0.05).

WM

As shown in Table 5, four parallel-design RCTs investigating AVG interventions assessed WM-related outcomes through pre–post measurements.^38,40,46,48 One study reported significant reductions in body mass index (BMI) (−0.9 kg/m²), waist circumference (WC) (−1.70 cm), and body fat percentage (BFP) (−1.41%) following a 14-week AVG-based instructional intervention compared with traditional teaching (all P < 0.05).⁴⁸ However, this finding requires cautious interpretation due to its high risk of bias (score: 7/10, nonrandomized design). Three additional studies detected no significant differences between AVG and control groups in weight, BMI, WC, or BFP (all P > 0.3).^38,40,46 Notably, unlike the other two studies that employed active controls (traditional cycling³⁸ and fitness lab sessions⁴⁰), Nani et al.⁴⁶ utilized a passive control group (no scheduled physical activity), yet still found no significant improvements in weight (−1.49 kg, P = 0.758) or BMI (−0.41 kg/m², P = 0.747).

Table 5.

Quantitative Results of Working Memory-Related Outcomes

Authors, year, countries	Groups for comparison	Indicator 1: weight (kg) (MD [95% CI], P value, ES, tendency)	Indicator 2: BMI (kg/m²) (MD [95% CI], P value, ES, tendency)	Indicator 3: WC (cm) (MD [95% CI], P value, ES, tendency)	Indicator 4: BFP (%) (MD [95% CI], P value, ES, tendency)	Intervention duration	Analysis method	RoB
Warburton et al.,³⁸ 2007	AVG cycling versus Traditional cycling	−2.0 [−7.1 to +3.1], P = 0.412, d = −0.12, ↔	+1.0 [−1.9 to +3.9], P = 0.689, d = 0.17, ↔	—	—	6 weeks	ANCOVA	Low
Johnston et al.,⁴⁰ 2012	Gaming versus Fitness lab	+0.3 [−3.6 to +4.2], P = 0.881, d = 0.03, ↔	+0.21 [−1.1 to +1.5], P = 0.749, d = 0.06, ↔	+1.4 [−1.7 to +4.5], P = 0.382, d = 0.17, ↔	+0.9 [−2.4 to +4.2], P = 0.597, d = 0.10, ↔	9 weeks	ANCOVA	High
Nani et al.,⁴⁶ 2019	Nintendo Wii versus Control	−1.49 [−11.5 to +8.5], P = 0.758, d = −0.14, ↔	−0.41 [−3.0 to +2.2], P = 0.747, d = −0.15,↔	—	—	10 weeks	ANCOVA	Low
Pérez-López et al.,⁴⁸ 2022	AVG-based teaching versus Traditional teaching	—	−0.29 [−0.56 to −0.01], P = 0.043, * d = −0.39, ↓	−1.70 [−2.42 to −0.98], P < 0.001, *d = −0.89, ↓	−1.41 [−2.36 to −0.43], P = 0.005, *d = −0.54, ↓	14 weeks	ANCOVA	High

ANCOVA, analysis of covariance; ↓, the AVG group showed a significant decrease compared to the control/comparison group; ↔, no significant difference between AVG group and control/comparison group; ^*, there was a statistically significant difference between groups (95% CI excluding null, P < 0.05).

Other findings

In addition to the indicators related to EE and WM, several studies have also examined the psychological effects of AVG interventions. Six studies assessed participants’ RPE,^{39,42,44,45,49,51} three studies found that the AVG group exhibited lower RPE compared with traditional exercise,^42,44,51 while two studies reported the opposite,^39,45 and one found no significant difference between groups.⁴⁹ One study observed higher subjective vitality during AVG play,⁴⁶ three studies assessed enjoyment levels during AVG interventions, with divergent findings: two studies demonstrated significantly higher enjoyment in the experimental group,^47,50 while the other reported contradictory results.⁵¹ Additionally, Warburton et al.³⁸ combined stationary cycling with AVG as the intervention group, resulting in significantly higher attendance rates compared with single stationary cycling, and this increased attendance mediated greater improvements in VO₂ max (r² = 0.69).³⁸

Discussion

The 14 studies included in this systematic review demonstrated significant methodological heterogeneity in terms of study design and experimental protocols, which contributed to inconsistent conclusions regarding EE and WM outcomes. Our critical synthesis of the literature suggests these discrepancies may originate from the following several key factors.

Selection of AVGs and design of comparison groups

The current methods for measuring EE mainly include indirect calorimetry and HR measurement, with VO₂ and HR being directly related to EE.⁴² Additionally, METs relate to the rate of the body’s VO₂ for a given activity as a multiple of resting VO₂, directly reflecting the intensity and EE level of activity.⁵⁶ Therefore, this review selected the above three indicators that are directly related to EE. These results reflect solely the intensity of a single gaming session and are typically measured in real time. Specifically, when the exercise intensity of the AVG playing is inconsistent with the comparison group, the immediate EE measured will inevitably differ. Therefore, we posit that distinct motion types elicited by different AVGs may lead to varying levels of exercise intensity and EE.

Among the 10 studies that measured EE, eight employed active control groups (traditional exercise/SVG). The findings exhibited substantial heterogeneity: only two studies reported higher EE in AVG compared with traditional exercise,^39,47 whereas four studies demonstrated the opposite pattern.^41,42,44,51 We posit that these discrepancies stem primarily from the differing AVG and control interventions implemented across studies. For instance, among the two studies demonstrating significantly higher EE in AVG compared with traditional exercise, the AVG interventions comprised self-paced Wii Fit jogging and interactive chase-based cycling, whereas the control conditions consisted of 3.5 mph treadmill walking and self-paced stationary cycling, respectively.^39,47 According to the 2024 Adult Compendium of Physical Activities,⁵⁷ although 3.5 mph treadmill walking (4.5 METs) and self-paced stationary cycling (4.0 METs) meet the MPA threshold (3.0–6.0 METs) defined by the Harvard T.H. Chan School of Public Health,⁵⁸ self-paced jogging (7.5 METs) and chase-based cycling (>10 METs) substantially exceeded these values, surpassing the VPA threshold (≥6.0 METs). Conversely, in the four studies reporting lower EE for AVG than traditional exercise, the AVG interventions employed moderate-intensity exergames, whereas the control groups involved tennis play, heavy bag boxing, 4.0 mph treadmill walking, and classical dodgeball. The 2024 Compendium indicates that these traditional activities correspond to 5.8–8.0 METs, while moderate-intensity AVG averaged only 4.0 METs.⁵⁷ Similarly, the lack of significant EE differences between AVG and traditional exercise in the remaining two studies^49,50 can also be explained by referencing the MET values listed in the 2024 Compendium. Notably, across all studies employing SVG as control conditions, AVG consistently demonstrated significantly higher EE. This finding also aligns with MET values documented in the 2024 Compendium, wherein SVG activities (1.5–2.3 METs) were substantially lower than AVG plays (4.0 METs). In summary, the comparative analysis reveals distinct intensity profiles attributable to the fundamental differences between AVG and control exercise modalities.

Currently, AVG development has reached a mature stage, with diverse gameplay modalities available. Although most AVG programs allow players to select different intensity levels, it is undeniable that substantial variations exist in the overall exercise intensity across different AVG products.⁵⁹ Previous studies have confirmed that AVGs can achieve MPA levels^60,61; however, this does not necessarily imply that AVGs consistently surpass traditional exercise in intensity. When considering AVGs as potential substitutes for conventional exercise, it is critical to evaluate both the gameplay mechanics and exercise intensity of specific AVG interventions to ensure appropriate intensity matching.

Use of supplementary equipment and contextual elements

Most of the included studies chose mainstream AVGs for their intervention experiments. It is noteworthy that, apart from gaming equipment, most studies did not employ additional auxiliary devices. However, Warburton et al.³⁸ utilized a stationary bicycle as an adjunct to AVG. Although participants in the AVG group were not required to reach moderate intensity and could exercise at their preferred level, their VO₂ max demonstrated a significant improvement (P < 0.05) postintervention (11.0 ± 5.1%), whereas the traditional stationary bicycle group showed no such enhancement (3.4 ± 5.6%). Roure et al.⁴⁷ observed that interactive elements (e.g., chasing and rewards) in screen-connected stationary cycling elicited significantly higher %HRmax and %VO₂ max (all P < 0.001) than basic stationary cycling. Some studies have also confirmed that using auxiliary devices may be an effective means to increase the intensity of AVG playing. For instance, the study by Rodrigues et al.⁶² found that playing Nintendo Wii Fit Free Run on a mini-trampoline significantly increased %VO₂ max (+15.5%), %HRmax (+9.1%), and METs (+1.7) compared with playing on a hard flat surface (all P < 0.05). Furthermore, the incorporation of supplementary contextual elements in AVGs may potentially contribute to enhanced exercise intensity and improved user adherence. Pérez-López et al.⁴⁸ employed a narrative based on the movie Star Wars as the scenario for their AVG intervention, also exhibited significantly lower BMI (−0.29 kg/m², P = 0.043), WC (−1.7 cm, P < 0.01), and BFP (−1.41%, P = 0.005) compared with the control group. Hwang et al.⁴⁵ found that players engaged in significantly higher (P = 0.047) %time spent of moderate-to-vigorous physical activity during narrative-enhanced AVG play compared with nonnarrative AVG play.

Attendance

This review primarily focuses on EE and WM. The measurement of EE is instantaneous and unrelated to attendance, whereas WM is a slow process directly related to the total exercise volume resulting from attendance.⁶³ A diabetes prevention program targeting older adults demonstrated an approximately linear relationship between session attendance and weight loss. For each additional weekly session attended, participants lost an average of 0.72 pounds (95% confidence interval: 0.67–0.77).⁶⁴ Of the four studies examining postintervention body weight in this review, attendance rates were documented in only one,³⁸ and this study demonstrated that, following the 6-week intervention period, the AVG group exhibited a markedly higher attendance rate (78 ± 18%) compared with the control group (48 ± 29%), despite the absence of statistically significant between-group differences in weight change. Postintervention results showed that the AVG cycling had significant improvements in VO₂ max and vertical jump compared with the traditional cycling. Analysis indicated that ∼69% of the variance in VO₂ max changes and 44% of the variance in vertical jump changes could be explained by attendance.³⁸ Given that higher attendance likely contributed to increased exercise volume, the weight-reducing benefits of AVG cycling may diminish if its attendance advantage over traditional cycling is statistically controlled.

Limitations and future studies

The advantage of this study is that it adopts a strict systematic review method and follows PRISMA guidelines, which ensures the transparency and repeatability of the study. At the same time, all the studies included in this review are RCTs, which enhances the statistical effectiveness of the results. However, some limitations should also be noted. For instance, this review incorporates some dated studies and may have missed unpublished or non-English publications. It is also noteworthy that methodological flaws in some studies might lead to a high RoB. In particular, merely 1³⁹ out of 14 studies performed sample size estimation, and this critical omission suggests most included trials (93%) are at high risk of type II errors due to inadequate statistical power, compromising result reliability. All crossover designs lacked demonstrated washout adequacy, compromising the assumption of phase independence due to possible fatigue and learning confounders.^{39,41–44,49–51} Additionally, only one studies recorded attendance,³⁸ and almost all studies did not strictly control for diet. Among the 14 studies, one did not conduct an active control group but used a control group with no intervention as the comparator,⁴⁶ which undermines the credibility of the comparative analysis.

Existing AVG studies on EE or WM in young adults employ lab-based intervention models, limiting evaluation of its long-term health benefits. We recommend implementing AVG interventions in instructional or home settings to further investigate their positive effects in real-world scenarios. Furthermore, considering the rising incidence of psychological disorders among young individuals, the enjoyable and motivating nature of AVGs could potentially contribute to enhancing their mental health and health-related quality of life. Therefore, future research should also prioritize exploring the psychological responses of young individuals, particularly those who are overweight or obese, to AVG interventions.

Conclusions

Overall, the studies included in this review show mixed results regarding AVGs’ effects on EE and WM, largely due to differences in study designs and research quality. These findings should be interpreted carefully. Most studies confirm that AVGs can provide MPA—more vigorous than sedentary video gaming, though less intense than some traditional moderate-to-vigorous exercises. Importantly, AVGs’ entertaining nature may help young adults stick with exercise routines better, potentially aiding weight loss. We recommend more high-quality studies comparing AVGs with conventional exercises across different settings.

Footnotes

Author Disclosure Statement

The authors have no other conflicts of interest to disclose.

Funding Information

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

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