NCT03910517

Brief Summary

Background: Musculoskeletal (MSK) pain and injuries are common in endurance sports where athletes are required to perform at high intensity for long periods of time. In the short term, MSK pain may significantly impair the athletes' performance, which can lead to unwanted time-off from practice and competitive tournaments. Previous studies found an association between training load, MSK pain and performance. These results indicate that an athlete may experience MSK pain or get injured from both too low and to high training loads. Electronic sports (E-sports), also known as competitive gaming, is defined by Hamari and Sjöblom as "a form of sports where the primary aspects of the sport is facilitated by electronic systems; the input of players and teams as well as the output of the E-sports system are mediated by human-computer interference". There are only few data on MSK pain in E-sports, however a small study with 65 participants found that 41% suffered from back or neck pain and more than 1 in 3 had pain related to the wrist. E-sports athletes have to perform for an extended period of time, similar to athletes from traditional endurance sport. As such, MSK pain in E-sports may be associated with training load like it is seen in other sports. Therefore, MSK pain in E-sports could be an unrecognised issue. To provide health professionals with and optimal starting point for managing these issues, there is a need for well-conducted studies on the prevalence of MSK pain among E-sports athletes. In addition, it is highly relevant to investigate if training loads related to E-sports and physical activity levels are different among athletes with MSK pain compared to athletes without MSK pain. Aims: The aims of this questionnaire-based cross-sectional study are to; I) investigate the prevalence of MSK pain in E-sports athletes, II) assess if training loads among athletes with MSK pain are different from athletes without MSK pain, III) investigate if physical activity levels among athletes with MSK pain are different from athletes without MSK pain and IIII) descriptively present data on participant characteristics, sleep patterns, physical activity levels and utilization of health professionals and pain medication in the study population.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
208

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Mar 2019

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

March 26, 2019

Completed
1 day until next milestone

Study Start

First participant enrolled

March 27, 2019

Completed
14 days until next milestone

First Posted

Study publicly available on registry

April 10, 2019

Completed
16 days until next milestone

Primary Completion

Last participant's last visit for primary outcome

April 26, 2019

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

April 26, 2019

Completed
Last Updated

July 9, 2019

Status Verified

May 1, 2019

Enrollment Period

1 month

First QC Date

March 26, 2019

Last Update Submit

July 8, 2019

Conditions

Keywords

Musculoskeletal PainSleepTraining loadE-sportPhysical activity

Outcome Measures

Primary Outcomes (1)

  • Prevalence of pain in the body during the previous week.

    Participants are asked if they have experienced any pain in their body during the previous week (yes/no).

    Baseline - at time of inclusion

Secondary Outcomes (14)

  • Primary pain site: questionnaire

    Baseline - at time of inclusion

  • Other pain sites: questionnaire

    Baseline - at time of inclusion

  • Pain frequency

    Baseline - at time of inclusion

  • Pain intensity: numeric pain rating scale

    Baseline - at time of inclusion

  • Pain interference

    Baseline - at time of inclusion

  • +9 more secondary outcomes

Other Outcomes (5)

  • Use of pain medicine

    Baseline - at time of inclusion

  • Use of pain medicine - type

    Baseline - at time of inclusion

  • Use of pain medicine - frequency

    Baseline - at time of inclusion

  • +2 more other outcomes

Study Arms (1)

E-sport athletes

People aged 15-35 who engage in structured E-sport (e.g. community-based, pro team or educational setting).

Other: No intervention

Interventions

No intervention

E-sport athletes

Eligibility Criteria

Age15 Years - 35 Years
Sexall
Age GroupsChild (0-17), Adult (18-64)
Sampling MethodNon-Probability Sample
Study Population

People aged 15-35 who engage in E-sport at an educational institution, a community-based team or a pro-team in Denmark.

You may qualify if:

  • Age 15-35
  • Engaged in E-sport at an educational institution, a community-based team or a pro-team.
  • Participating in E-sport through a computer-based game.

You may not qualify if:

  • Not providing written informed consent prior to enrolment.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

E-Sport clubs and/or team based in the community, at an educational institution or in a private organization i Denmark

Aalborg, 9000, Denmark

Location

Related Publications (4)

  • Johnston R, Cahalan R, O'Keeffe M, O'Sullivan K, Comyns T. The associations between training load and baseline characteristics on musculoskeletal injury and pain in endurance sport populations: A systematic review. J Sci Med Sport. 2018 Sep;21(9):910-918. doi: 10.1016/j.jsams.2018.03.001. Epub 2018 Mar 14.

    PMID: 29559317BACKGROUND
  • Gabbett TJ. The training-injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med. 2016 Mar;50(5):273-80. doi: 10.1136/bjsports-2015-095788. Epub 2016 Jan 12.

    PMID: 26758673BACKGROUND
  • Juho Hamari, Max Sjöblom, What is eSports and why do people watch it? Internet Res. 2017

    BACKGROUND
  • 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 Jan 10;5(1):e000467. doi: 10.1136/bmjsem-2018-000467. eCollection 2019.

    PMID: 30792883BACKGROUND

MeSH Terms

Conditions

Musculoskeletal PainMotor Activity

Condition Hierarchy (Ancestors)

Muscular DiseasesMusculoskeletal DiseasesPainNeurologic ManifestationsSigns and SymptomsPathological Conditions, Signs and SymptomsBehavior

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
CROSS SECTIONAL
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Assistant lecturer

Study Record Dates

First Submitted

March 26, 2019

First Posted

April 10, 2019

Study Start

March 27, 2019

Primary Completion

April 26, 2019

Study Completion

April 26, 2019

Last Updated

July 9, 2019

Record last verified: 2019-05

Data Sharing

IPD Sharing
Will not share

Locations