Energy Expenditure of Esports Athletes During Gameplay: A Systematic Review

Abstract

Background

As competitive gaming continues its global rise, understanding the energy expenditure of esports athletes is essential for evaluating both performance demands and potential health risks. Esports—defined as organised, competitive video gaming—have rapidly expanded and are increasingly recognised as a legitimate form of sport, although debates about their classification remain.

Objective

This systematic review aimed to evaluate the energy expenditure of esports athletes during gameplay and identify factors influencing metabolic responses.

Methods

A comprehensive search across multiple databases, including PubMed, Embase, SPORTDiscus, Web of Science, and Scopus, yielded 176 records. Studies were screened according to predefined PECO-based eligibility criteria and were critically appraised for methodological quality. The review is registered in PROSPERO (CRD42025644801) and follows the PRISMA 2020 guidelines.

Results

Five studies were included in the review. The included studies assessed energy expenditure in various esports genres, such as multiplayer online battle arena, first-person shooter, and sports simulations, and involved both amateur and professional esports athletes. Measurement methods ranged from indirect calorimetry to wearable heart rate monitors. Despite the predominantly sedentary nature of esports, findings revealed modest increases in energy expenditure and heart rate during gameplay compared to resting conditions. Notably, one study reported that amateur esports athletes expended approximately 40% more energy during competitive sessions, while another found only minimal changes in oxygen consumption among amateur esports athletes. Variation in energy expenditure was linked to player expertise, game genre, and gameplay intensity and duration. Professional esports athletes generally exhibited greater energy expenditure and more pronounced physiological responses than amateurs, suggesting that competitive pressure and cognitive demand contribute to increased metabolic output. However, considerable heterogeneity in study protocols and measurement techniques limited cross-study comparability.

Conclusion

Overall, the review indicates that while esports elicit a clear sympathetic nervous system response, as evidenced by elevated heart rate and stress biomarkers, the overall metabolic demands remain relatively low, consistent with light physical activity. These insights have important implications for understanding the health risks associated with prolonged sedentary behaviour in esports athletes. The review underscores the need for standardised methodologies in future research to accurately assess energy expenditure in esports and to further explore the long-term health effects of competitive gaming.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40798-026-01055-4.

Key Points

• Competitive video gaming increases energy use slightly, but overall demands remain low.

• Some evidence suggests that more experienced players may exhibit stronger physiological responses during competitive gameplay, although findings remain inconsistent, partly reflecting methodological differences between studies and likely depending on match format (e.g. solo-queue ranked play versus team-based tournament settings) and on players' adaptation to competitive stress.

• Quantifying energy expenditure in esports provides a basis for designing compensatory physical activity programmes and individualised nutritional recommendations that may help offset the cardiometabolic risks of prolonged sedentary behaviour and competitive psycho-emotional stress.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40798-026-01055-4.

Introduction

Esports, defined as organised competitive video gaming [1], has rapidly emerged as a global phenomenon. The evolution of esports is characterised by its digital origins and rapid global expansion, a trend accelerated by the rise of the internet and the advent of online multiplayer gaming at the turn of the millennium [2, 3]. Since the early 2010s, the industry has experienced exponential growth, transforming into a major social and competitive force, albeit one that is relatively young compared to traditional sports with centuries-long histories [4–7]. With its growth and similarities of sports competition, debates continue about whether esports should be classified as a sport, largely due to the contrast between the minimal engagement of large muscle groups and the high cognitive demands required for competitive performance [8–12]. Nevertheless, many national governments—including those of China, Germany, France, New Zealand, and South Africa—have formally recognised esports as a sport.

Dialogue regarding the inclusion of esports as a sport and medal event within the Olympic Games continues, with interest further spurred by the announcement of the inaugural Olympic Esports Games to be held in 2027 in Riyadh, Saudi Arabia. In parallel with these developments, researchers in sports science and medicine have begun to examine the physiological characteristics of esports athletes, particularly in relation to health, performance, and training load.

Understanding the energy expenditure (EE) of esports athletes is crucial for elucidating the physiological demands of competitive gaming [13, 14]. As esports athletes devote substantial time to practise, competition, and training, questions have emerged regarding the intensity and nature of these demands, and how they compare to those in more traditional sports. Although esports are mainly sedentary, characterised by prolonged periods of sitting, there is growing concern regarding potential health risks associated with this inactivity [15, 16]. Physiologically, esports is marked by an overall sedentary profile punctuated by intense local movements, particularly involving the hands and forearms. These repetitive micro-movements, essential for precise control of gaming interfaces, can lead to musculoskeletal complaints, including pain in the hands, wrists, neck, and back [11, 16]. Although some professional esports athletes incorporate compensatory physical training into their routines [11, 15, 17], overall levels of physical activity among esports athletes remain variable, with some cohorts exceeding World Health Organisation recommendations and others displaying low activity levels [17].

Preliminary evidence suggests that EE during esports may exceed that of typical sedentary behaviours, while remaining within the range associated with light-intensity activity [14]. This distinction is particularly relevant given the ongoing debate regarding the classification of esports as a sport, in which the physiological demands of competitive gameplay represent one of the central points of contention. However, the reported EE values vary considerably across studies, reflecting differences in game genres, player expertise, and measurement methodologies. Game genre may play a meaningful role, as multiplayer online battle arena (MOBA), first-person shooter (FPS), and sports simulation titles impose distinct cognitive and motor demands that could translate into different patterns of physiological activation. Player expertise has likewise been suggested as a relevant moderator, with professional and expert players exposed to greater training loads and competitive pressure than amateurs, potentially amplifying both cognitive and physiological responses during gameplay [11, 14, 17]. Despite these indications, the existing evidence has not yet been systematically synthesised, which highlights the need for a systematic review to better understand the physiological demands of competitive gaming.

At the same time, accurately measuring EE in esports poses unique challenges. Conventional methodologies—such as heart rate monitoring, heart rate variability analysis, and metabolic testing—may not adequately account for the influence of psychological stress on physiological responses during gameplay [17, 18]. For instance, increases in heart rate observed during competition may predominantly reflect cognitive and emotional stress rather than metabolic EE [19, 20]. Furthermore, discrepancies between controlled laboratory conditions and real-world competitive environments, as well as the potential interference of monitoring devices with natural gameplay, complicate the application of standard measurement techniques [21–25]. Lastly, physiological data collection during competition is limited due to policies surrounding the use of Bluetooth devices during tournament competition. The use of advanced methods, such as the doubly labelled water (DLW) technique, while promising for determining total EE, also introduces logistical constraints in tournament settings [19, 24].

This systematic review aimed to provide a comprehensive evaluation of EE in esports athletes by addressing the following key questions:

1. What is the estimated EE during gameplay?

2. How does EE differ across various esports genres, such as multiplayer online battle arenas (MOBA), first-person shooters (FPS), and sports simulations?

3. How does EE vary between esports athletes of different skill levels (e.g. professional vs. amateur)?

4. How do the intensity and duration of gameplay affect EE?

5. Finally, are there significant differences in EE between esports sessions and other sedentary or physically active controls?

Ultimately, the insights gained will guide future research and inform targeted interventions in this emerging field.

Methods

This systematic review was registered in PROSPERO (ID: CRD42025644801) and conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines updated in 2020 [26].

Eligibility Criteria

Eligibility was defined using the PECO framework. The population (P) consisted of esports athletes across various genres, including MOBA, FPS, and sports simulation games. The exposure (E) was participation in esports sessions, such as training, scrims, and competitive matches, with an emphasis on the specific intensity and duration of gameplay. The comparator (C) included comparisons across different game genres (e.g. FPS, MOBA, sports simulations), levels of player expertise (e.g. professional vs. amateur esports athletes), and comparisons with other activities such as rest, other sedentary behaviours, or traditional sports. The outcome (O) was defined as the measured or estimated EE, expressed in units such as kcal/min or metabolic equivalents (METs). Exclusion criteria included non-English language studies, review articles, conference papers, books, and book chapters, as well as studies that focused on overall daily EE.

Data Sources and Search Strategy

A comprehensive search was conducted on 29 January 2025 across the following databases: PubMed, Embase, SPORTDiscus, Web of Science, and Scopus, without publication date restrictions. No language or article-type limits were applied at the search stage. The full database-specific search strategies, including the search fields/modes, applied limits, and numbers of records retrieved from each database, are provided in Supplementary Table S2.

The search strategy combined keywords with Boolean operators as follows:

(“esports” OR “e-sports” OR “electronic sports” OR “competitive gaming” OR “video game competition” OR “online gaming” OR “digital sports” OR “competitive video game”) AND (“energy expenditure” OR “caloric burn” OR “caloric expenditure” OR “energy consumption” OR “metabolic rate” OR “metabolic equivalents” OR METs OR “oxygen consumption” OR “energy output” OR TDEE OR BMR OR “energy metabolisms” OR “energy expenditures” OR Metabolism OR Metabolisms OR Bioenergetics OR Bioenergetic OR “caloric expenditures”).

Study Selection and Data Extraction

All retrieved articles (n = 176; PubMed n = 34, Embase n = 10, SPORTDiscus n = 36, WoS n = 48, Scopus n = 48) were imported into the Rayyan systematic review software [27]. The study selection process included the removal of duplicates (n = 63), exclusion of non-English articles (n = 12), and elimination of publications with an inappropriate publication type n ( = 16). Blinded abstract screening was then conducted by reviewers JK and EK, leading to further exclusion of studies that were missing relevant keywords (n = 76) or did not address EE during esports sessions (n = 4). Ultimately, five studies were included for data extraction, which was independently performed by both JK and EK using a standardised extraction form. Despite the blinded screening process, a high level of agreement was achieved between the reviewers, with a 100% inter-rater agreement during the abstract screening phase. The study selection process is summarised in Fig. 1, the PRISMA flow diagram.

Fig. 1.

PRISMA flow diagram of the study selection process (template by Page et al. [26])

Quality Assessment

The methodological quality of the included studies was assessed by two researchers (JK and EK) using the Downs and Black [28] checklist, a validated tool for evaluating both randomised and non-randomised studies across key methodological domains. The checklist comprises 27 items spanning five domains: reporting, external validity, internal validity (bias and confounding), and statistical power. Each item was individually scored based on predefined criteria. Discrepancies between the two reviewers were resolved by a third author (MK). Detailed item-by-item scoring for each study is provided in Supplementary Table S1.

Results

A total of five studies met the inclusion criteria and were included in the systematic review. The studies varied considerably in participant demographics, esports genres, and comparator conditions. For instance, MOBA (e.g. League of Legends, DOTA2) was examined in three studies [13, 14, 29], FPS (e.g. Paladins Champions, CS:GO, Overwatch) in three studies [14, 30, 31], and sports simulations (e.g. FIFA) in two studies [29, 31]. Comparator conditions also varied; one study used cycling ergometer exercise [30], while others compared esports sessions to resting or other sedentary behaviours [13, 14, 29, 31].

The methodological quality of the included studies was generally acceptable, with studies demonstrating strengths in the clarity of aims, participant descriptions, intervention reporting, and statistical analyses. Consistent limitations across studies included the absence of randomisation, lack of participant and assessor blinding, and limited external validity due to small or non-random samples. These sampling limitations may partly reflect the practical challenges of recruiting esports players for studies involving physiological monitoring during gameplay. Notably, the study by Haupt et al. [30] employed a single-case design, which, while offering valuable individual-level insights, inherently limits generalisability and statistical power. Identified strengths and limitations for each study are summarised in Table 1.

Table 1.

Methodological quality of included studies

Study	Strengths identified	Limitations identified
Haupt et al. [30]	Clear aims and methods; detailed physiological assessment; innovative design	Single-case design; absence of randomisation and blinding; limited generalisability
Ketelhut & Nigg [29]	Robust sample size; comprehensive reporting of outcomes; multiple esports genres examined	Lack of blinding; potential selection bias
Nicholson et al. [14]	Inclusion of expert players; detailed outcome measures; appropriate statistical analyses	Lack of randomisation procedures; absence of blinding
Zimmer et al. [31]	Good intervention and participant description; physiological monitoring	Limited control for confounders; moderate sample size
Kocak [13]	Clear outcome reporting; focus on amateur players; appropriate comparators	Lack of randomisation; potential selection bias; absence of blinding

Beyond the general quality appraisal, the included studies differed considerably in their adherence to established best-practice procedures for indirect calorimetry, as outlined by Compher et al. [32]. Nicholson et al. [14] and Ketelhut and Nigg [29] fully complied with these recommendations, including explicit steady-state verification. Zimmer et al. [31] partially adhered to these procedures but reported a shorter fasting period (3 h) and did not describe steady-state criteria. Kocak [13] showed more substantial deviations, including a fasting period of only 2 h and no reported control of caffeine intake or steady-state verification. The case report by Haupt et al. [30] did not report pre-measurement controls for fasting, caffeine, alcohol, or prior physical activity. These inconsistencies in adherence to standard indirect calorimetry procedures likely contribute to the variability in EE values reported across studies and should be considered alongside the broader methodological heterogeneity described below.

The included studies exhibited considerable heterogeneity in terms of sample sizes, esports genres, and methodological approaches. Sample sizes ranged from a single-case study [30] to groups of 11 to 30 participants (e.g. Kocak [13]; Zimmer et al. [31]), with participant ages ranging from 18 to 32 years and BMI values spanning from 17.7 to approximately 24–24.1 kg/m². The investigations focused on various esports genres, including MOBA (e.g. League of Legends examined in Ketelhut and Nigg [29]; Nicholson et al. [14]; Kocak [13]), FPS (e.g. Paladins Champions in Haupt et al. [30]; CS:GO in Zimmer et al. [31]; Nicholson et al. [14]), and sports simulations (e.g. FIFA 21/20 in Ketelhut and Nigg [29]; Zimmer et al. [31]). Although most studies recruited amateur esports athletes, Nicholson et al. [14] uniquely investigated expert competitors. Competitive or ranked sessions were typically conducted over 30–45 min, with control conditions including cycling ergometer exercise or seated rest. EE was primarily measured via indirect calorimetry (e.g. METALYZER 3B), complemented by heart rate monitoring (e.g. Polar H7, Polar RS800 CX^®) and additional assessments such as blood analyses, HRV monitoring, and cardiovascular testing (Table 2).

Table 2.

Description of methods used and study characteristics

Study identification		Population/Participants					Esports				Comparator		Measurement tools
Reference	Country	Sample size	Age (years)	BMI (kg/m2)	Average gaming hours per week or day	Height (m); Weight (kg); Waist-to-Height Ratio; Body Fat (%)	Game title	Level of expertise	Session type	Duration (min)	Description of control condition (type)	Duration of comparison (min)
Haupt et al. [30]	Germany	1	32	17.72*	10—15 (W)	H 1.84; W 60	Paladins champions	Amateur	Competitive	30	Cycling Ergometer (HR adjusted)	30	IC (METALYZER 3B); HR (Polar H7); Blood (BIOSEN S-Line)
Ketelhut & Nigg [29]	Switzerland	27; FIFA: 14, LOL: 13	23 ± 2.8	24 ± 3	9.7 ± 5.9 (W)	H 1.79 ± 0.05; W 76.4 ± 10.0; WHtR 0.45 ± 0.04; BF 18.2 ± 5.7	FIFA 21, LOL	Amateur	Competitive	LOL 20.28* ± 3.00* FIFA 14.40* ± 0.92*	Rest (seated, quiet)	10	Ant. (Tanita RD-545); Exer. Test (Ergometrics800s); IC (METALYZER 3B); HR/HRV (Polar RS800 CX®); BP (Mobil-O-Graph)
Kocak [13]	Turkey	11	21.73 ± 3.50	23.11 ± 3.67	4.09 ± 1.04 (D)	H 1.81 ± 0.06; W 76 ± 12.51	DOTA2, LOL	Amateur	Ranked	DOTA2 53.00 ± 18.17; LOL 34.71 ± 1.89	Rest (seated, quiet)	15	IC (Quark CPET); APM (Desktop APM 1.17)
Nicholson et al. [14]	Australia	13	20.7 ± 2.69	24.6 ± 5.89	N/A	H 1.83 ± 0.08; W 82.1 ± 18.6	LOL, OW, RL, CS:GO, VL	Expert	Ranked	LOL 25–45; RL > 5; CS:GO 35; VL 25	Rest (seated, quiet)	10	IC (EGAIC); ECG (Custo-Cardio 300); HRV (Kubios)
Zimmer et al. [31]	Germany	30	23.1 ± 3.0	24.12*	12.3 ± 4.5(W)	H 1.80 ± 0.09; W 78.5 ± 13.3	CS:GO, FIFA 20	Amateur	Competitive	30	Rest (seated)	10 + 10	IC (METALYZER 3B); CV (PhysioFlow); Blood (BIOSEN); Cortisol (Salivette)

^*Calculated values indicated by an asterisk. These include BMI values recalculated from reported height and weight, durations converted from seconds to minutes (e.g. in Ketelhut and Nigg [29]), and EE values converted from kcal/kg/day to kcal/min using the formula: kcal/min = (kcal/kg/day × body weight in kg) / 1440

Ant Anthropometry, APM  Actions per Minute, BF  Body Fat, BMI  Body Mass Index (kg/m²), BP  Blood Pressure, CS:GO Counter-Strike: Global Offensive, CV  Cardiovascular, D Day, DOTA2 Defense of the Ancients 2, ECG Electrocardiography, EE Energy Expenditure, Exer. Test  Exercise Test, FIFA Fédération Internationale de Football Association (video game series), H Height, HR Heart Rate, HRV Heart Rate Variability, IC Indirect Calorimetry, LOL League of Legends, OW Overwatch, RL Rocket League, VL Valorant, W Weight, WHtR Waist-to-Height Ratio

In terms of cardiovascular responses, all studies consistently reported elevated heart rates during esports sessions. For instance, Haupt et al. [30] documented an increase from approximately 85 to 137 bpm within the first 10 min, with similar HR elevations observed in other studies, indicating robust sympathetic activation during competitive gameplay. In parallel, stress markers, such as increased plasma glucose, elevated cortisol levels, and reduced HRV (evidenced by lower RMSSD and shortened R–R intervals), were consistently observed, reflecting a pronounced psycho-emotional stress response. In contrast, changes in EE were more variable: studies like Kocak [13] and Ketelhut and Nigg [29] reported a significant EE increase (up to a 40% rise compared to rest) and, notably, Kocak [13] identified a positive correlation between actions per minute (APM) and MET values. Additionally, Nicholson et al. [14] demonstrated a statistically significant EE increase of approximately 17%; others [30, 31] found only modest or negligible EE changes. These key findings are summarised in Table 3.

Table 3.

Description of results

Study identification	Main outcomes			Other outcomes
Reference	Energy expenditure	Energy expenditure (kcal/min)	Additional outcomes	Heart rate (bpm)	Stress markers	Motor performance (Action per minute)	Other physiological parameters (e.g. Blood Pressure, Muscle Strength)
Haupt et al. [30]	Lower in eSports (1.38 kcal/min**) vs Cycling (3.55 kcal/min)	Lower in eSports (1.38) vs. Cycling (3.55)	N/A	Higher in eSports (137) vs rest (85)	Glucose: Higher in eSports vs Cycling	N/A	VO₂: Lower in eSports vs. Cycling
Ketelhut & Nigg [29]	↑↑ LOL (1.86 kcal/min) vs rest (1.51 kcal/min) ↑↑ FIFA (2.06 kcal/min) vs rest (1.63 kcal/min)	↑↑ LOL (1.86) vs. rest (1.51) ↑↑ FIFA (2.06) vs. rest (1.63)	≈FIFA vs. LOL	↑↑FIFA (mean: 77) vs rest (mean: 69); ↑↑LOL (mean: 81) vs rest (mean: 74)	RMSSD: ↓eSports vs. rest	N/A	pSBP: ↑↑eSports vs. rest; pDBP: ↑eSports vs. rest; cSBP: ↑eSports vs. rest; cDBP: ↑eSports vs. rest; PWV: ↑eSports vs. rest;
Kocak [13]	↑ eSports (46.18 kcal/kg/d) vs rest (33.12 kcal/kg/d)	↑ eSports (2.44*) vs. rest (1.75*)	Correlation between APM and METs	N/A	N/A	median: 121 (108–135)	N/A
Nicholson et al. [14]	↑ eSports (1.45 kcal/min) vs rest (1.28 kcal/min)	↑ eSports (1.45) vs. rest (1.28)	N/A	↑eSports (median: 87.1) vs rest (median: 84.5)	R-R interval: ↓eSports vs. rest	N/A	N/A
Zimmer et al. [31]	≈eSports (1.99 kcal/min) vs rest (1.98 kcal/min)	≈eSports (1.99) vs. rest (1.98)	≈CS:GO vs. FIFA	eSports (at 0.5 min: 82 ± 11) vs ↓↓rest (at 10 min: 74 ± 13)	Cortisol: eSports vs ↓rest; Glucose: eSports vs ↓rest	N/A	SV: ≈eSports vs. rest

^*Calculated values indicated by an asterisk. EE values originally reported in kcal/kg/day were converted to kcal/min using the formula: kcal/min = (kcal/kg/day × body weight in kg)/1440

^**The abstract of the study incorrectly reports the EE value as 1.38 kJ/min. Based on the values reported in the results section (three gaming phases: 1.52, 1.26, 1.37 kcal/min), the correct average is 1.38 kcal/min. This correction has been made accordingly

↑ = statistically significant increase (P < 0.05)

↑↑ = highly significant increase (P < 0.001)

↓ = statistically significant decrease (P < 0.05)

↓↓ = highly significant decrease (P < 0.001)

≈ = non-significant differences

APM Actions per Minute, cDBP Central Diastolic Blood Pressure, cSBP Central Systolic Blood Pressure, CS:GO Counter-Strike: Global Offensive, EE Energy Expenditure, FIFA Fédération Internationale de Football Association (video game series), HR Heart Rate (bpm), LOL League of Legends, pDBP Peripheral Diastolic Blood Pressure, pSBP Peripheral Systolic Blood Pressure, PWV Pulse Wave Velocity, RMSSD Root Mean Square of Successive Differences, SV Stroke Volume, VO₂ Oxygen Consumption

The five included studies examined a range of physiological outcomes during esports gameplay. The following subsections summarise the reported findings according to the main variables assessed: cardiovascular and autonomic responses (heart rate and heart rate variability), EE and metabolic equivalents, oxygen consumption and respiratory parameters, and the relationship between in-game activity and stress responses.

Heart Rate and Autonomic Response

All studies measuring heart rate reported increases during gameplay, although the magnitude varied. Haupt et al. [30] observed an increase from approximately 85 bpm at rest to 137 bpm within the first 10 min of gaming, with elevated heart rate sustained throughout the session. Similarly, Ketelhut and Nigg [29] found significant time effects for heart rate, alongside changes in HRV (RMSSD). However, Nicholson et al. [14] found no significant correlation between HRV and EE during gameplay. In contrast, Zimmer et al. [31] reported a decrease in heart rate towards the end of a 30-min gaming session, suggesting habituation or reduced engagement in amateur esports athletes.

EE and MET Values

Across studies, EE during esports gameplay was consistently higher than rest, but generally remained within the range associated with sedentary to light-intensity activity. Kocak [13] reported an increase in EE from 33 kcal/kg/d at rest to 46 kcal/kg/d during gaming, with MET values rising accordingly. Ketelhut and Nigg [29] reported gameplay MET values of 1.6–2.0, which, when adjusted for individual resting EE, aligned more closely with sedentary behaviour. Nicholson et al. [14] found a 17% increase in EE during play, while Haupt et al. [30] observed minimal changes in EE relative to rest. Zimmer et al. [31] similarly found no significant changes in EE across rest, gameplay, and post-game phases in amateur esports athletes.

Oxygen Consumption and Respiratory Parameters

Kocak [13] and Nicholson et al. [14] both reported increases in VO₂ and VCO₂ during gameplay, though these remained modest compared to values observed during traditional exercise. Haupt et al. [30] found that VO₂ remained unchanged during gaming, in contrast to a 0.6 L/min increase during cycle ergometer exercise. Zimmer et al. [31] reported no significant changes in VO₂ or VCO₂ over the course of gameplay. Notably, ventilation increased by approximately 50% during gaming in the Haupt et al. [30] study, and Kocak [13] observed increases in respiratory frequency.

Correlates of In-Game Activity and Stress Responses

Only one study explored the relationship between gameplay intensity and physiological load. Kocak [13] identified a significant positive correlation between actions per minute (APM) and MET values, indicating that greater in-game activity may be associated with higher energy demands. Haupt et al. [30] reported elevated plasma glucose and free fatty acid levels, consistent with a non-specific stress response to psycho-emotional arousal during gameplay. In contrast, Zimmer et al. [31] did not observe an acute stress response in amateur esports athletes, suggesting possible variability based on player experience or game intensity.

Discussion

This systematic review aimed to provide a comprehensive evaluation of the EE of esports athletes during their gameplay. Of the five research questions posed in this review, only two were directly and consistently addressed across the included studies: (1) the estimation of EE during gameplay, and (2) the influence of player skill level. Findings suggest that while EE is generally low, expert esports athletes may experience slightly higher metabolic demands. However, other questions—such as differences in EE across esports genres, the role of gameplay intensity and duration, and comparisons with traditional sedentary or physically active tasks—were either only partially addressed or not explored at all. This was largely attributable to the limited number of eligible studies (n = 5), as well as methodological inconsistencies in measurement and reporting. The small article pool underscores the early stage of research in this field and highlights the need for more targeted, genre-specific, and ecologically valid investigations.

A substantial heterogeneity in EE outcomes was reported across studies. Some studies, such as those by Kocak [13] and Nicholson et al. [14], demonstrated a significant increase in EE during gameplay. For instance, Kocak [13] found that amateur esports athletes expended roughly 40% more energy while playing compared to resting, with this increase positively correlated with the number of APM. This suggests that enhanced cognitive and motor activity may drive higher energy demands. Similarly, Nicholson et al. [14] reported that expert esports athletes experienced a notable rise in median EE during competitive play compared to rest (1.28 kcal/min vs. 1.45 kcal/min, p = 0.02), accompanied by elevated oxygen consumption and carbon dioxide production.

In contrast, other studies present subtler changes. The case study by Haupt et al. [30] recorded only a low EE (approximately 1.38 kcal/min) in an amateur player, significantly lower than that observed during dynamic exercise at an equivalent heart rate. Moreover, Zimmer et al. [31] observed that a 30-min esports intervention among amateurs did not significantly alter metabolism or EE, and even failed to induce a discernible stress response. These discrepancies highlight that mental stress during gameplay, at least among amateur competitors, might not necessarily translate into substantial increases in EE.

These conflicting findings underscore the importance of considering multiple factors that could modulate EE during esports. Notably, player expertise appears to be a potentially relevant determinant. While studies involving expert esports athletes (e.g. Nicholson et al. [14]) suggest that high levels of competitive drive and performance optimisation may lead to elevated cognitive and physiological demands, investigations focusing on amateurs (e.g. Haupt et al. [30]; Zimmer et al. [31]) report far less pronounced effects. However, these apparent differences must be interpreted with considerable caution, as they may also reflect substantial disparities in methodological quality and in the control of pre-measurement variables across studies. As illustrated in the quality appraisal, studies that fully adhered to best-practice procedures for indirect calorimetry [32] reported more pronounced physiological responses, whereas studies with incomplete control of fasting, caffeine intake, or steady-state verification may have underestimated the true physiological load. Direct comparisons between expert and amateur players based on the current evidence are, therefore, limited, and firm conclusions regarding the role of expertise cannot yet be drawn. This underscores the importance of examining physiological responses under genuine competitive conditions among elite esports athletes using standardised, methodologically rigorous protocols. In high-stakes tournaments—where the pressures of expectation, competition, and public scrutiny converge—the stress response and resulting physiological demands are likely to be more pronounced than those observed in controlled laboratory settings [29, 31]. Indeed, research by Troubat et al. [33] demonstrated that even cognitively taxing activities like chess can provoke measurable physiological changes, hinting at a similar potential in esports.

The heart rate during esports sessions is markedly elevated even without vigorous physical activity. For instance, Haupt et al. [30] reported that heart rate surged from approximately 85 bpm during the resting phase to 137 bpm within the first ten minutes of gameplay—a pattern reminiscent of high-stress situations like competitive driving or challenging examinations. This activation of the sympathetic nervous system aligns with our earlier discussion on the intense cognitive and emotional demands inherent to esports, suggesting that even minimal physical exertion can trigger a robust stress response. The magnitude of this response, however, is likely to vary considerably between players, and recent psychological research in esports offers a framework for understanding this variability. Trotter et al. [34] demonstrated that higher-ranked players display significantly better self-regulation than lower-ranked players, and that self-regulation mediates the relationship between stress appraisal and in-game performance, with more skilled players tending to appraise competitive demands as challenges rather than threats. Related work by Poulus et al. [35] has shown that esports players with higher mental toughness perceive greater control over stressors and make more frequent use of problem- and emotion-focused coping strategies. However, these adaptive psychological mechanisms are likely to be challenged under the more ecologically demanding conditions of live competition, where the additional pressures of expectation, audience, and consequence may exceed the regulatory capacity developed through routine training and ranked play.

Against this psychological background, the recurrent finding of increased heart rate during esports gameplay, consistently documented across the reviewed studies [14, 29–31], is notable for being decoupled from any corresponding increase in EE, highlighting the primarily mental character of the stress response. Whereas experienced players may show attenuated autonomic indices such as HRV [14] or a slight decline in HR during prolonged sessions [31], less experienced players tend to exhibit more pronounced cardiovascular responses [30]. This variability may thus reflect differences in self-regulation and stress appraisal rather than in the underlying stressor itself, consistent with convergent observations outside the reviewed studies. For instance, Andre et al. [19] documented sustained HR elevations of similar magnitude among collegiate players during a live tournament, despite the absence of physical exertion, suggesting that the psycho-physiological profile of competitive gameplay is relatively consistent across settings but is modulated by individual psychological characteristics.

Turning to metabolic demand, despite these clear cardiovascular responses, the metabolic consequences of esports gameplay appear modest. Even in studies reporting measurable metabolic indicators [13, 14], the overall metabolic load remains substantially lower than that observed in traditional physical activities such as cycling, and several studies reported only minimal changes in VO₂ [30, 31]. An elevated heart rate during esports, therefore, does not consistently translate into a proportional rise in metabolic output, reinforcing the dissociation between cardiovascular and metabolic responses that characterises competitive gameplay.

Beyond this cardiovascular-metabolic dissociation, a notable aspect particularly highlighted by Haupt et al. [30] is the concurrent rise in plasma glucose and free fatty acid levels during gameplay, which may reflect increased activity of stress-related hormones such as catecholamines or cortisol. This hormonal response underscores a non-specific stress reaction induced by psycho-emotional factors, further distinguishing the cardiovascular response in esports from that seen in dynamic physical activities [11, 29–31].

In summary, these findings indicate that while esports consistently trigger a marked activation of the sympathetic nervous system, as evidenced by elevated heart rates and stress hormone levels, the resulting metabolic demands remain low, positioning esports more closely with sedentary behaviour or light physical activity. This systematic analysis emphasises that a comprehensive understanding of the physiological demands of esports is essential not only for optimising gaming performance but also for preventing long-term health risks associated with a predominantly sedentary lifestyle and intense mental stress.

The low number of eligible studies limits the strength of the available evidence and highlights that the EE of esports athletes remains an underexplored topic. Beyond this, the current literature exhibits substantial heterogeneity in methodological approaches across studies. Variations in the definition of resting conditions and in the calculations of EE undermine the reliability of the findings and complicate direct comparisons across studies. A further limitation relates to the characteristics of the study samples. Recruiting esports athletes, particularly professional or expert players, poses specific challenges, including restricted availability due to training and competitive schedules, contractual obligations with teams, and restrictions on the use of external monitoring devices during official tournaments. These constraints likely contribute to the predominance of small, non-randomised samples and to the scarcity of studies involving elite competitors, thereby limiting both statistical power and the generalisability of current findings. Accordingly, the differences observed between expert and amateur players in the current literature should be interpreted with caution, as they may reflect not only expertise itself but also the competitive context and match format in which the data were collected [14, 29].

Given these limitations, future research should focus on adopting standardised protocols for measuring EE and other physiological parameters, including consistent reporting of pre-measurement controls such as food intake, caffeine consumption, physical activity, and sleep [32]. Further studies should extend beyond amateur samples to include elite players under ecologically valid conditions, such as live tournament settings, where physiological responses are expected to be most pronounced [19]. Systematic comparisons across game genres, levels of player expertise, gameplay intensity and duration, and different match formats (for example, solo-queue ranked play versus team-based competition) are also needed to clarify the factors that shape physiological demands during competitive gaming.

Conclusion

This systematic review offers a comprehensive, multi-layered perspective on the physiological impacts of esports. Although esports consistently elicit a marked activation of the sympathetic nervous system, as reflected by increased heart rates and elevated stress hormone levels, the overall EE during gameplay remains relatively low, typically ranging between 1.3 and 2.5 kcal/min, resembling that of light physical activity or even sedentary behaviour. This disconnect between cardiovascular activation and metabolic response highlights the predominantly psycho-emotional nature of the stress response in esports athletes. Moreover, the observed heterogeneity, which tentatively suggests that studies involving expert esports athletes indicate a more pronounced increase in EE than those focusing on amateurs, points to the potential influence of factors such as skill level, game genre, and competitive context, although differences in methodological quality across studies limit the strength of these comparisons.

If confirmed by future studies, this pattern could reflect a trend of increasing energy demands with escalating cognitive and emotional intensity, especially in more competitive scenarios. However, the current research lacks the methodological tools to reliably capture these acute physiological shifts during high-stress, real-world matches, where the highest metabolic responses may occur but remain undocumented.

These insights bear significant implications for understanding the long-term health effects of esports. While gameplay itself does not satisfy the criteria for physical activity with beneficial health outcomes, the repeated and prolonged psycho-emotional stress associated with esports may pose risks to cardiovascular health and overall well-being. Therefore, it is imperative to develop targeted interventions and training strategies that minimise potential negative impacts while promoting overall physical fitness among esports athletes.

Abbreviations

APM

Actions per minute

BF

Body fat

BMI

Body mass index

BP

Blood pressure

cDBP

Central diastolic blood pressure

cSBP

Central systolic blood pressure

CS:GO

Counter-strike:Global offensive

CV

Cardiovascular

DLW

Doubly labelled water

DOTA2

Defense of the ancients 2

ECG

Electrocardiography

EE

Energy expenditure

FPS

First-person shooter

HR

Heart rate

HRV

Heart rate variability

IC

Indirect calorimetry

LOL

League of legends

METs

Metabolic equivalents

MOBA

Multiplayer online battle arena

OW

Overwatch

pDBP

Peripheral diastolic blood pressure

pSBP

Peripheral systolic blood pressure

PWV

Pulse wave velocity

RL

Rocket league

RMSSD

Root mean square of successive differences

RR

Respiratory rate

SV

Stroke volume

VL

Valorant

VO₂

Oxygen consumption

WHtR

Waist-to-height ratio

Author Contributions

All authors contributed to the study conception and design. Jiří Kotas and Edita Krejzová performed a literature search, data extraction, and independent methodological quality assessment. Michal Kumstát adjudicated discrepancies in the quality assessment. Jiří Kotas wrote the first draft of the manuscript. All authors commented on and critically revised previous versions of the manuscript. All authors read and approved the final article.

Funding

This work was supported by the Specific University Research Grant provided by the Ministry of Education, Youth and Sports of the Czech Republic (number MUNI/A/1475/2024).

Data Availability

All data generated or analysed during this study are included in this published article and its supplementary information files.

Declarations

Ethics Approval and Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

The authors declare that they have no competing interests.