Yona T¹, Lindberg L², Østergaard LD³, Lyng KD⁴, Rathleff MS^3,4,5, Straszek CL^2,3,4.

Affiliations:
¹ The Israeli Physiotherapy Society, Tel-Aviv, Israel
² Department of Physiotherapy, University College of Northern Denmark (UCN), Aalborg, Denmark.
³ Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
⁴ Center for General Practice in Aalborg, Department of Clinical Medicine, Aalborg University
⁵ Department of Physiotherapy and Occupational Therapy, Aalborg University Hospital

Corresponding author:
Tomer Yona

Twitter: @Tomer_PT

Email: tomeryona@gmail.com

Phone: +972547841186

Esports- rapidly evolving and highly demanding

Esports is a new and rapidly evolving sports discipline with a growing number of athletes engaged in structured practice and competitive tournaments with extensive money prizes. Esports has many similarities with traditional sports, like football, handball, or athletics [1]. It includes interaction with other members or competitors, centered around competition, and requires high concentration and a specific skillset. Notably, the lack of physical activity is what makes esports stand out compared to more traditional sports. Nonetheless, many esports athletes experience musculoskeletal pain.

Becoming a skilled esports athlete requires extensive practice, and several studies suggest that professional esports athletes, on average, practice between 3.4-5.2 hours/day, corresponding to 25-35 hours/ week [2,3]. This number may increase with up to 10 hours/day before a competition, with some tournaments consisting of up to three hours non-stop, intensive gaming [4]. Such high workloads are likely to have an impact on adolescents engaged in esports.

Musculoskeletal pain in esports

Research regarding esports is rapidly evolving – In the most extensive cross-sectional study to date, Lindberg et al. (2020) surveyed 188 Danish esports athletes, aged 15 to 35, and participating in structured esports, and found that 42% of esports athletes suffer from musculoskeletal pain [3]. Previous smaller reports support this surprisingly high prevalence by showing approximately 2 out of 5 (40%) esports athletes suffer from either back or neck pain[4]. Not surprisingly, upper limb pain is also common, with approximately 30-36% reporting shoulder or wrist pain [3,4]. Preliminary research suggests that long gaming-session may be one of the risk factors for musculoskeletal pain in adolescents [5,6].

How does the high prevalence of musculoskeletal pain in esports impact young athletes? A body of literature document that adolescents reporting pain also describe decreased quality of life [7], reduced sleep time and quality of sleep [8,9], and suffers from depression and anxiety [10,11]. Specifically, esports athletes with musculoskeletal pain report reduced sleep time and quality; 26% of them sought medical attention, and 16.3% used analgesics, resulting in 5.6 fewer hours of training per week [3].

Pain during youth is also associated with a variety of issues presenting later in life, including; smoking, obesity, poor mental health, poor sleep, and physical inactivity. Of particular concern is the current uncertainty regarding treatment methods for musculoskeletal pain in adolescents [12].

These numbers suggest that the high prevalence of pain in esports has an impact and should not be neglected. With the rapid growth in the number of esports athletes, health professionals can only expect an increase in adolescents’ consultations due to esports-related pain complaints.

An office work sport?

The physical demands of esports may be somewhat similar to that of office-based work (with an added stress of competitive sport). Prolonged use of a stationary work is associated with a higher prevalence of wrist and hand symptoms [13], and prolonged sitting for 2 hours has shown to increase discomfort in all body areas [14] and increase the number of creative problem-solving errors when performing computer work [14]. Previous research suggests that exercise might be the first line of treatment for work-related musculoskeletal disorders and can reduce pain [15], along with new episodes of neck [16,17] and shoulder [17] pain.

Understanding the work demands of office-based work may be one of the key elements to understand the pain complaints among esports athletes and could be a promising approach to prevent and manage pain associated with esports. However, additional research is required to understand this in the context of esports. Currently, the knowledge regarding specific demands of popular esports games, specific medical needs of the young athletes, and the impact of pain on their performance is scarce.

A Call for Action

Our results [3] constitute an unanswered need; as the popularity of esports continues to evolve into a massive phenomenon, and the number of professional and amateur athletes increases rapidly, it is paramount to provide proper medical attention to esports athletes. However, the knowledge concerning specific and personalized management strategies for this sport is still lacking [18].

Specific exercise and physical activity interventions are known to be one of the first-line treatments for office-based workers with musculoskeletal complaints [15–17]. The question remains if this approach can be tailored and implemented in the esports community.

Together with the athletes, coaches, and parents, we need to address the specific [19] needs of esports, e.g., assessing common injuries for different types of games and gaming consoles, the different modes of play, and different demands during the season (Figure 1).

Figure 1

Based on this knowledge, we can start to develop evidence-inspired strategies to support esports athletes to decrease the risk of musculoskeletal pain, and support those with musculoskeletal pain.

Until further evidence arises, we suggest that clinicians working with esports athletes consider the specific demands of esports, ergonomics during play, and encourage physical activities among this population.

References
1. Rosell Llorens M. eSport Gaming: The Rise of a New Sports Practice. Sport Ethics Philos. 2017;
2. Kari T, Karhulahti VM. Do e-athletes move? A study on training and physical exercise in elite e-sports. Int J Gaming Comput Simulations. 2016;
3. Lindberg L, Nielsen SB, Damgaard M, Sloth OR, Rathleff MS, Straszek CL. MUSCULOSKELETAL PAIN IS COMMON IN COMPETITIVE GAMING: A CROSS-SECTIONAL STUDY AMONG 188 DANISH ESPORT ATHLETES. Manuscript submitted for publication.
4. Difrancisco-Donoghue J, Balentine J, Schmidt G, Zwibel H. Managing the health of the eSport athlete: An integrated health management model. BMJ Open Sport Exerc Med. 2019;
5. Sekiguchi T, Hagiwara Y, Momma H, Tsuchiya M, Kuroki K, Kanazawa K, et al. Excessive game playing is associated with musculoskeletal pain among youth athletes: a cross-sectional study in Miyagi prefecture. J Sports Sci. 2018;
6. Sekiguchi T, Hagiwara Y, Yabe Y, Tsuchiya M, Itaya N, Yoshida S, et al. Playing video games for more than 3 hours a day is associated with shoulder and elbow pain in elite young male baseball players. J Shoulder Elb Surg. 2018;
7. Gonçalves TR, Mediano MFF, Sichieri R, Cunha DB. Is health-related quality of life decreased in adolescents with back pain? Spine (Phila Pa 1976). 2018;
8. Palermo TM, Law E, Churchill SS, Walker A. Longitudinal course and impact of insomnia symptoms in adolescents with and without chronic pain. J Pain. 2012;
9. Andreucci A, Campbell P, Richardson E, Chen Y, Dunn KM. Sleep problems and psychological symptoms as predictors of musculoskeletal conditions in children and adolescents. Eur J Pain (United Kingdom). 2020;
10. McLaren N, Kamper SJ, Hodder R, Wiggers J, Wolfenden L, Bowman J, et al. Increased substance use and poorer mental health in adolescents with problematic musculoskeletal pain. J Orthop Sports Phys Ther. 2017;
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Sport Sciences – Performance and Technology, Department of Health Science and Technology, Aalborg University, Niels Jernes vej 12, 9220 Aalborg East, Denmark. Tel: 00 45 99402411. Email: afsamani@hst.aau.dk

Even at force levels of mostly below 10% of the maximum muscle force (e.g. computer work in the context of e-sport), prolonged low load physical activity may contribute to the development of musculoskeletal disorders (MSD). Fatigue is often regarded as a precursor of developing MSD. However, identifying fatigue development and designing effective interventions to impede fatigue in such activities have been under-studied. The problem becomes even more complex when the physical activity is concomitant with cognitive loading as the interaction of cognitive and physical aspects of an activity may alter the course of fatigue development. This article summarizes a set of three interconnected experimental studies which aimed at quantifying cognitive load alterations and fatigue during a standardized computer work and outlining the design of a smart fatigue alarm based on characteristics of oculomotor system.

Background

Prolonged low load physical activity such as computer work may contribute to the development of musculoskeletal disorders [1]. Such activities may develop fatigue [2] and fatigue is known as the precursor of developing MSD [3]. Concurrent physical and cognitive loading may exacerbate the fatigability and further intensify the adverse effects of fatigue [4]. Implementing a pausing regime during physical activities may reduce the adverse effects of fatigue on the human body [5]. To intervene in the pausing regime, fatigue should be monitored and the pause breaks should be implemented with proper timing [6]. However, the typical metrics of fatigue have not been fully successful in monitoring fatigue state in low load physical activities (e.g., computer work) [2]. In a set of three interconnected experimental studies, the potentials of utilizing oculomotor system characteristics (i.e., oculometrics) to sense the altered levels of cognitive loading and fatigue during a standardized computer work were investigated [6–8]. Oculometrics are particularly of interest as they could potentially be recorded unobtrusively during physical activities. Furthermore, in the last study in this set, oculometrics were shown to be effective in implementing a smart fatigue alarm which could potentially be utilized to administer the pausing regime during physical activity [6]. Here, the results of these recent studies are summarized and discussed.

Procedures

In study I [7], the sensitivity of oculometrics (e.g., properties of fixations and saccadic eye movements) to altered cognitive loading and their between-days reliability were investigated. Thirty-eight participants took part in this study and performed an identical experimental protocol over two experimental days. The participants performed a standardized computer mouse task which was composed of a cyclic activity consisting of memorization of a dotted pattern and replication of the dotted pattern after a short wash-out period (Figure 1). At each experimental day, the participants performed the computer task in three sessions of five minutes, representing altered levels of cognitive loading, namely, low, medium and high. The cognitive loading was altered by changing the geometrical complexity of the dotted pattern to be memorized.

Figure 1. The experimental setup in study I to III and the timeline of a cycle of the standardized computer task

In study II [8], the same participant pool as in study I took part and the oculometrics, which were found reliable in study I, were studied during a prolonged session of 40 min of an identical computer work as in study I. After finishing every 20 cycles of the task (approximately 200 s ), the participants scored their level of cognitive fatigue based on the Karolinska sleepiness scale (KSS) [9]. The changes of oculometrics with time-on-task were then compared with the temporal changes of KSS.

In study III [6], a computational model of fatigue state was developed and it allowed prediction of fatigue state based on the captured oculometrics. Twenty participants performed the computer task in two experimental sessions. Each session took 30 min and after every 20 cycles of the task, the participants scored their fatigue based on the KSS. Additionally, they could potentially take a micro-break of 25 s decided based on either their own discretion (manual session) or the alarming feedback generated by the fatigue alarm (automatic session). During the micro-breaks, the participants externally rotated their shoulders while holding an elastic band between the two hands and keeping the elbows in approximately 90° flexed and the shoulders up to 45° abducted. Moreover, the micro-break was accompanied by mindful breathing.

Data recording and analysis

With a sampling rate of 360 Hz, a monocular head-mounted eye tracker (Eye-Trac 7, Applied Science Laboratories, Bedford, MA, USA) was used in all studies to record gaze position, pupil diameter and blink moments (Figure 1). A motion-capture system (Visualeyez II system set up with two VZ4000 trackers, Phoenix Technologies Inc., Canada) tracked head movements and sent its information to the eye tracker to allow accounting for head movements. A data-driven algorithm was used to extract ocular events (i.e., saccades, fixations, or blinks). Based on the yielded output of the algorithm, the oculometrics were computed [10].

In study I, a long list of relevant oculometrics were tested and reliable metrics with sensitivity to altered mental load were chosen to be used in study II. Saccade peak velocity, saccade duration, the slope of line regressing peak velocity and amplitude of saccades (i.e. main sequence), fixation duration, blink duration, blink frequency and the mean of pupil dilation range (i.e. pupillary response) were utilized in study II to reflect fatigue with time-on-task.

The overall performance (OP) of participants carrying out the task was monitored in terms of the speed and accuracy of the pattern replication. The workload of the tasks in all studies was subjectively score by each participant based on a computerized version of the National Aeronautics and Space Administration Task Load Index (NASA-TLX) [11]. Furthermore, KSS was scored on a scale from 1 to 9, corresponding to “very alert” and “very sleepy, fighting sleep,” respectively.

All three studies involved common statistical analysis. A repeated-measures analysis of variance was used to examine the effects of the three levels of mental load as the within-subject factor on oculometrics in study I and the time effect in study II. In study I, the relative and absolute reliability of the oculometrics were assessed using intra-class correlation coefficient ICC and the limits of agreement (LoA) obtained from Bland-Altman plots.

Results

In study I, saccade peak velocity, saccade duration and the main sequence decreased whereas fixation duration increased with increased cognitive load demand. These metrics also exhibited acceptable reliability scores (ICC>0.5 and normalized LOA below 20%). Metrics like pupillary response and blink frequency and duration were also highlighted as they have been shown to be reliable in a number of studies [12,13]. As expected, OP decreased and NASA-TLX increased with cognitive load. In study II, as expected KSS increased with time-on-task and this was associated with decreasing saccade peak velocity, saccade duration and increasing pupillary response and blink frequency and duration as well as fixation duration with time-on-task. Study III revealed that the computational model yielded about 70% accuracy in identifying fatigue state. OP and KSS increased with time in both automatic and manual sessions but the increase in KSS was delayed in automatic session compared with the manual session. The participants reported lower loading demand in automatic session as NASA-TLX was markedly lower in the automatic session.

Conclusion

This set of studies confirmed the feasibility of the quantification of cognitive load and fatigue based on oculometrics during a functional computer task. The most insightful finding of this set of studies for practitioners was related to the contribution of oculometrics to planning the pausing regime during physical activity and showing that such a system could be more effective than self-triggered breaks in impeding fatigue.

Overall, this set of studies may shed light to the complex relationships between oculometrics, mental load, and fatigue and provide a practical approach for using eye tracking in the prevention of associated MSD risks in low load physical activities such as e-sport.

References

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