This commentary delivers a practical perspective on the current state of subjective training load (TL) monitoring, and in particular sessional ratings of perceived exertion, for performance enhancement and injury prevention. Subjective measures may be able to reflect mental fatigue, effort, stress, and motivation. These factors appear to be important moderators of the relationship TL has with performance and injury, and they also seem to differ between open and closed skill sports. As such, mental factors may affect the interaction between TL, performance, and injury in different sports. Further, modeling these interactions may be limited due to the assumption that an independent signal can adequately account for the performance or injury outcomes. An independent signal model does not accurately reflect training environments where multiple stressors (e.g., mechanical, emotional, nutritional) impact adaptations. Common issues with using subjective TL monitoring, including a lack of differentiation between biomechanical, physiological, and cognitive load, may be overcome by considering psychometric measurement best practices, finer graded scales, and differential ratings of perceived exertion. Methods of calculating TL, including different acute and chronic time periods, may also need to be individualized to different sports and potentially different individuals within the same sport. As TL monitoring is predominately a “chronic” decision-making tool, “acute” decision-making tools, e.g., subjective wellness and autonomic nervous system measures, should be combined in a bespoke multivariate model to aid sports coaches. A call to action is presented for future research on key issues associated with TL monitoring that will have relevance for practitioners in an applied setting.

This article addresses several key issues that have been raised related to subjective training load (TL) monitoring. These key issues include how TL is calculated if subjective TL can be used to model sports performance and where subjective TL monitoring fits into an overall decision-making framework for practitioners. Regarding how TL is calculated, there is conjecture over the most appropriate (1) acute and chronic period lengths, (2) smoothing methods for TL data and (3) change in TL measures (e.g., training stress balance (TSB), differential load, acute-to-chronic workload ratio). Variable selection procedures with measures of model-fit, like the Akaike Information Criterion, are suggested as a potential answer to these calculation issues with examples provided using datasets from two different groups of elite athletes prior to and during competition at the 2016 Olympic Games. Regarding using subjective TL to model sports performance, further examples using linear mixed models and the previously mentioned datasets are provided to illustrate possible practical interpretations of model results for coaches (e.g., ensuring TSB increases during a taper for improved performance). An overall decision-making framework for determining training interventions is also provided with context given to where subjective TL measures may fit within this framework and the determination if subjective measures are needed with TL monitoring for different sporting situations. Lastly, relevant practical recommendations (e.g., using validated scales and training coaches and athletes in their use) are provided to ensure subjective TL monitoring is used as effectively as possible along with recommendations for future research.

Purpose: To investigate the relationships between internal and external training load (TL) metrics with elite international women’s basketball performance. Methods: Sessional ratings of perceived exertion, PlayerLoad^™/minute, and training duration were collected from 13 elite international-level female basketball athletes (age 29.0 [3.7] y, stature 186.0 [9.8] cm, body mass 77.9 [11.6] kg) during the 18 weeks prior to the International Basketball Federation Olympic qualifying event for the 2016 Rio Olympic Games. Training stress balance, differential load, and the training efficiency index were calculated with 3 different smoothing methods. These TL metrics and their change in the last 21 days prior to competition were examined for their relationship to competition performance as coach ratings of performance. Results: For a number of TL variables, there were consistent significant small to moderate correlations with performance and significant small to large differences between successful and unsuccessful performances. However, these differences were only evident for external TL when using exponentially weighted moving averages to calculate TL. The variable that seemed most sensitive to performance was the change in training efficiency index in the last 21 days prior to competition (performance r = .47–.56, P < .001 and difference between successful and unsuccessful performance P < .001, f² = 0.305–0.431). Conclusions: Internal and external TL variables were correlated with performance and distinguished between successful and unsuccessful performances among the same players during international women’s basketball games. Manipulating TL in the last 3 weeks prior to competition may be worthwhile for basketball players’ performance, especially in internal TL.

Coyne, JOC, Coutts, AJ, Newton, RU, and Haff, GG. The influence of mental fatigue on sessional ratings of perceived exertion in elite open and closed skill sports athletes. J Strength Cond Res 35(4): 963–969, 2021—The main purpose of this investigation was to examine influence of mental fatigue on sessional ratings of perceived exertion (sRPE) over a training week in elite athletes in open skill (OS, i.e., more unpredictable and externally paced sports) and closed skill (CS, i.e., more predictable and internally paced) sports. Visual analogue scales for mental fatigue, sRPE (CR-10 scale), and training duration were collected from an OS group (n = 27) of basketball and volleyball athletes and a CS group (n = 28) of weightlifting and track and field athletes during a typical training week 5 months before the 2016 Olympic Games. These variables were then examined using repeated measure correlations and linear mixed models with the level of significance set for the study at p < 0.05. There was a small significant correlation between mental fatigue and sRPE in the OS group (r = 0.23, p < 0.01), but not in the CS group (r = −0.07, p = 0.38). Mental fatigue had trivial influence on sRPE during individual sessions, but had a moderate effect on total sRPE over a week (p < 0.001, f ² = 0.265) when accounting for type of sport, training duration, and injury/illness burden. It seems mental fatigue may not significantly influence sRPE in individual training sessions, but may potentially have a cumulative effect that may affect the sRPE over a training week. This suggests monitoring mental fatigue independently of other training load (TL) measures may be worthwhile for strength and conditioning specialists and sports coaches to manage their athletes and researchers conducting studies into TL and performance.

This study investigated the correlations with anthropometry, upper extremity strength, and surfboard paddling performance in recreational and competitive surfers. Twenty-nine recreational and competitive male surfers were assessed for height, mass, relative arm span, relative seated height, relative biacromial width, and skinfolds (Sum7). The upper extremity strength variables assessed were the relative 1RM pull up and 1RM dip. The surfboard paddling variables assessed were a 15 m sprint-paddling test and a 400 m endurance-paddling water-based test. Pearson correlation analysis and independent t-tests were used to compare differences between groups and determine the significant correlations between variables. Sum7 and relative arm span were correlated with speed in all paddling efforts meanwhile the sprint-paddling ability was correlated with the upper extremity strength. Significant differences between competitive and recreational surfers exist in relative arm span and endurance paddle performance. The strong relationships between the physical variables can be used by sports coaches and support staff to influence talent identification, training, and nutrition programs.

Purpose: Criticisms of the acute to chronic workload ratio (ACWR) have been that the mathematical coupling inherent in the traditional calculation of the ACWR results in a spurious correlation. The purposes of this commentary are (1) to examine how mathematical coupling causes spurious correlations and (2) to use a case study from actual monitoring data to determine how mathematical coupling affects the ACWR. Methods: Training and competition workload (TL) data were obtained from international-level open-skill (basketball) and closed-skill (weightlifting) athletes before their respective qualifying tournaments for the 2016 Olympic Games. Correlations between acute TL, chronic TL, and the ACWR as coupled/uncoupled variations were examined. These variables were also compared using both rolling averages and exponentially weighted moving averages to account for any potential benefits of one calculation method over another. Results: Although there were some significant differences between coupled and uncoupled chronic TL and ACWR data, the effect sizes of these differences were almost all trivial (g = 0.04–0.21). Correlations ranged from r = .55 to .76, .17 to .53, and .88 to .99 for acute to chronic TL, acute to uncoupled chronic TL, and ACWR to uncoupled ACWR, respectively. Conclusions: There may be low risk of mathematical coupling causing spurious correlations in the TL–injury-risk relationship. Varying levels of correlation seem to exist naturally between acute and chronic TL variables regardless of coupling. The trivial to small effect sizes and large to nearly perfect correlations between coupled and uncoupled AWCRs also imply that mathematical coupling may have little effect on either calculation method, if practitioners choose to apply the ACWR for TL monitoring purposes.