Validity of a Wheelchair Rugby Field Test to Simulate Physiological and Thermoregulatory Match Outcomes.
Fabian GrossmannJoelle Leonie FlueckBart RoelandsRomain MeeusenClaudio PerretPublished in: Sports (Basel, Switzerland) (2022)
The purpose of the study was to verify the criterion-validity (concurrent) of an existing and reliable, submaximal wheelchair Rugby (WCR) field test by examining the correlations of selected measures of physical performance between the field test and real games. Therefore, ten WCR athletes were observed during two WCR real games and during completing the field test two times. Total distance, mean and peak velocity, playing time, number of sprints, sprints per minute, mean and maximal heart rate, body core temperature (Tc), sweat rate, body weight loss, rate of perceived exertion and thermal sensation were measured. Values were correlated with the data observed by completing the field test two times separated by seven days. The results showed significant correlations between games and field tests for sweat rate ( r = 0.740, p < 0.001), body weight loss ( r = 0.732, p < 0.001) and the increase of Tc ( r = 0.611, p = 0.009). All other correlations were not significant. For perceptual responses Bland-Altman analysis showed data within the limits of agreement. Descriptive statistics showed similarity for mean velocity and total distance between tests and games. In conclusion the study provides the first indications that the submaximal field test seems comparable with the game outcomes in terms of increase in Tc, covered distance, mean velocity and perceptual responses. Nevertheless, more research and additional validation are required.
Keyphrases
- heart rate
- weight loss
- heart rate variability
- blood pressure
- physical activity
- bariatric surgery
- working memory
- electronic health record
- blood flow
- depressive symptoms
- squamous cell carcinoma
- roux en y gastric bypass
- type diabetes
- metabolic syndrome
- cross sectional
- adipose tissue
- machine learning
- gastric bypass
- deep learning
- rectal cancer
- glycemic control
- artificial intelligence