The relationships between training load and stress and recovery with performance, injury rate and training adherence in well-trained female collegiate UK rowers

Quantifying training is important when evaluating athletes’ responses to training intensity distribution and training load. Monitoring training load, training intensity distribution and the balance between stress and recovery can be used to optimise supercompensation, prevent overtraining, injury and inform coaches on the “readiness” of athletes to perform certain training. The main aim of this study was to investigate whether the Acute Recovery and Stress Scale (ARSS) can be used to monitor acute training load in well-trained female-student rowers. Twelve female rowers (mean ± SD, age: 20.1 ± 0.9 years; height: 178.25 ± 7.88 cm; weight: 78.43 ± 7.27 kg; body fat: 21.83 ± 3.78 %; training sessions: 11 ± 3 sessions.wk-1; training duration: 9.1 ± 2.4 hrs.decimin) were monitored over a 15-week training period. Daily training load assessed by both objective and subjective measures of exercise intensity i.e. heart rate (HRTL) and sessions rating of perceived exertion (sRPETL), respectively, were recorded in addition to volume load for resistance training, training intensity distribution below (<LT1) and above (>LT1) lactate threshold, and adherence to training. On a weekly basis, stress and recovery was assessed by the ARSS Questionnaire (Nässi et al., 2017) and power output was assessed by a 30-min bout (PO30) of rowing. Pre- and post-study athlete burnout was assessed using the Athlete Burnout Questionnaire (ABQ Raedeke and Smith, 2001). Pearson correlations indicated significant moderate relationships between training distance and sRPETL (r = 0.656, p = 0.032, 95 %CI: 0.129 – 0.952) and HRTL (r = 0.591, p = 0.043, 95 %CI: 0.084 – 0.908); HRTL and mean difference in PO30 (r = 0.606, p = 0.037, 95 %CI: 0.174 – 0.863); and a significant strong relationship between sRPETL and Physical Performance Capacity (ARSS construct) (r = 0.784, p = 0.004, 95 %CI: 0.378 – 0.967). No other significant relationships were determined between the ARSS constructs and training load measures. Paired T-tests determined that there was no significant difference (p > 0.05) for ABQ constructs. Therefore, on a group level in this population, training load can be monitored using HRTL but not sRPETL or the ARSS. Due to the moderate relationship’s other parameters (e.g. blood lactate, creatine kinase, biomarkers, etc.) may need to be included to provide an accurate reflection of training load. Results need to be investigated on an individual level to determine whether ARSS can track the acute responses to training load within Page 4 individuals. In addition, it is possible that ARSS may be more effective when used to measure chronic training load.

Grantor dc:publisher

Oxford Brookes University

Year dc:date

2019

Author dc:creator

• Coetzee, Anerida

Contributors dc:contributor

• Davey, Sarah

Statement dc:rights

• All rights reserved

Language dc:language

en

DOI dc:identifier

https://doi.org/10.24384/jfxg-fc53

OAI identifier oai:identifier

tle:b7983a1b-6770-47cd-9092-6d9dccb31e9b:d6bd9758-527a-46cd-bfe2-c433766e8fca:1