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Quantification of Training Load and Training Response for Improving Athletic Performance

Campbell, Bill I. PhD1; Bove, Daniel MS1; Ward, Patrick MS2; Vargas, Andres MS1; Dolan, Jeff MS1

Strength & Conditioning Journal: October 2017 - Volume 39 - Issue 5 - p 3–13
doi: 10.1519/SSC.0000000000000334
Article

ABSTRACT ONE IMPORTANT RESPONSIBILITY OF A STRENGTH AND CONDITIONING COACH IS THE DEVELOPMENT OF A TRAINING PROGRAM THAT ENHANCES PHYSIOLOGICAL QUALITIES, WITH THE GOAL OF IMPROVING ATHLETIC PERFORMANCE. TO ACHIEVE THIS GOAL, COACHES DESIGN TRAINING PROGRAMS THAT SUCCESSFULLY APPROPRIATE AND MANAGE TRAINING VOLUME AND INTENSITY DURING THE ATHLETE'S OFFSEASON, PRESEASON, AND IN-SEASON PERIODS. BY QUANTIFYING TRAINING LOADS AND RESPONSES, COACHES CAN BETTER PRESCRIBE TRAINING STIMULI WHILE ALSO MANAGING STRESS LEVELS. ATHLETE-MONITORING STRATEGIES PROVIDE A WAY FOR STRENGTH COACHES TO GATHER USEFUL INFORMATION REGARDING TRAINING DEMANDS AND ATHLETE FATIGUE.

1Exercise Science Program, University of South Florida, Tampa, Florida; and

2Seattle Seahawks, Seattle, Washington

Address correspondence to Dr. Bill I. Campbell, bcampbell@usf.edu.

Conflicts of Interest and Source of Funding: The authors report no conflicts of interest and no source of funding.

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Bill I. Campbell is an associate professor of Exercise Science and director of the Performance and Physique Enhancement Laboratory at the University of South Florida.

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Daniel Bove is a sports scientist for the Atlanta Hawks.

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Patrick Ward is a sports scientist for the Seattle Seahawks.

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Andres Vargas is a graduate assistant in the Exercise Science Program at the University of South Florida.

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Jeff Dolan is a Strength and Conditioning Coach for the Pulaski Yankees.

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INTRODUCTION

One of the most salient responsibilities of a strength and conditioning coach is the development of a periodized training program that maximizes the adaptive response leading to an increase in athletic performance. In most circumstances, a positive training adaptation is attained by assigning a workout or training regimen of greater intensity than the athlete is accustomed to (known as the overload principle) (70). This overload can be applied through an increase in training volume or intensity, which then leads to enhancements in fitness qualities and performance. As training progresses, athletes accrue fitness and fatigue, a relationship that has been explored in the scientific literature (4,8,9). However, if there is a mismatch of the fatigue that is induced and the fitness adaptations that are realized, problems may arise. For example, on an acute basis, if the intensity of the training session is too low, it will not adequately induce the desired physiological adaptations. By contrast, if the intensity is too high, it will result in fatigue and a premature end to the training session. If a mismatch of fitness and fatigue is compounded over time, fatigue may begin to mask fitness gains and lead to a state of nonfunctional overreaching. Nonfunctional overreaching is a state of extreme overreaching that results when the intensification of a training stimulus continues without adequate recovery. Such a state causes the athlete to be susceptible to injury, illness, changes in mood states (e.g., depression, anxiety, and burnout), and performance decrement (30). Thus, it is the strength and conditioning coach's job to design a program that successfully appropriates and manages training volume and intensity during the athlete's offseason, preseason, and in-season periods. Athlete-monitoring strategies may provide a way for strength coaches to gather useful information regarding training demands and athlete fatigue. Athlete-monitoring strategies can be viewed in 2 ways:

  • Strategies that quantify training load;
  • Strategies that quantify the athlete's physical status over time.

The monitoring and quantification of training load has been described as either external to the athlete or internal to the athlete (24). External training loads can be thought of as the amount of physical exercise completed by the athlete, independent of his or her internal characteristics (66). These types of loads represent the actual output of what the athlete did in the training session and may include metrics such as weight lifted, training volume, power output, speed, acceleration, time motion analysis, and neuromuscular functioning (e.g., jump testing) (24). On the contrary, internal loads are dependent on the internal characteristics of the athlete and represent how the athlete physiologically responded to the training session and previously accumulated training volume. Methods of internal load monitoring include session rating of perceived exertion (sRPE), heart rate (HR), HR to rating of perceived exertion (RPE) ratio, training impulse (TRIMP), lactate concentrations, HR variability (HRV), and HR recovery (24). Finally, the quantification of physical status over time may be influenced by the acute and chronic responses to training and external stressors not directly related to training or competition (30). For example, daily stress resulting from sleep quality and quantity (or lack thereof), occupation, and interpersonal relationships can influence the fatigue response (2). Physical status may be monitored using readiness questionnaires, which quantify the athlete's psychological state of well-being (30), or through a more recently investigated method such as HRV (24).

A consideration involved with measuring and applying training data is that a strength and conditioning coach must work with a team's head coach to adjust the training load in consideration of sport specific loading (practices, games). A strength and conditioning coach may use the previously mentioned training response measurements to track overall fatigue and compare it with basic performance models. These markers can help strength and conditioning coaches monitor overall response to their training program and their athletes' practice schedule and competitive bouts. Choosing weekly training loads will be highly influenced by the amount of games and practices in a week. This article explores many of these strategies to further understand how quantification of training load and training response can help improve athletic performance on a consistent basis.

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QUANTIFYING RESISTANCE-TRAINING LOAD

Two of the most important factors involved in monitoring the workload of a resistance-training program are volume and intensity (10,50). Generally, the volume of a session is calculated by multiplying the amount of weight used for an exercise by the number of sets and number of repetitions completed (22). This calculation yields a value referred to as total volume. Although this strategy can be successfully used to track an athlete's overall volume throughout a training cycle, it does little to quantify the intensity at which the training volume was performed. One must take into account the relative intensity with which that volume was accumulated to get a more accurate representation of how much stress the athlete has incurred. For example, an athlete who can back squat 300 pounds can accumulate 6,000 pounds of volume by squatting 120 pounds (40% 1 repetition maximum [1RM]) for 5 sets of 10 repetitions or by squatting 240 pounds (80% 1RM) for 5 sets of 5 repetitions. Accumulating that volume with 40% of 1RM will have a drastically different impact on stress and fatigue than doing so with 80% of the athlete's 1RM.

A more appropriate strategy for tracking volume is to calculate the relative volume of the training for those lifts, where a 1RM determination makes sense (back squat, deadlift, bench press, etc.) (59). This strategy involves multiplying the number of sets and repetitions of an exercise by the intensity used, and this yields a figure that is expressed in arbitrary training units (ATUs) (9). When assigning a value for the intensity of the exercise, it may seem attractive to simply use the percentage of 1RM that the athlete is using for that day. However, this still inherently involves inaccuracies in situations, where an athlete is completing a submaximal amount of repetitions with a resistance. Instead, using the percentage of the repetition range maximum may be a better strategy (22). This involves calculating the maximum weight that the athlete can theoretically handle for the prescribed number of repetitions and comparing it with the weight that is prescribed for that day (38). Reconsidering the theoretical 300 pound back squatter, a coach can estimate that their athlete can perform 5 repetitions with a maximum of 261 pounds. Using 240 pounds for the athlete's working weight will equate to effectively using 92% of their 5RM for 5 sets of 5 repetitions and thus they will have accumulated a relative volume of 23 ATU (0.92 × 5 reps × 5 sets = 23 ATU).

Although intensity is indirectly monitored in tracking relative volume, it is still important to measure the perceived intensity of the resistance training bout. This can be accomplished with the use of objective or subjective tools. One popular method for monitoring intensity is the use of RPE. The Borg 15-point RPE scale was originally developed to monitor the amount of exertion used in a medical or athletic test. Recently, however, several adaptations of the Borg scale have been developed to better assess exertion during a resistance training bout. One such adaptation called the OMNI Resistance Exercise Scale (OMNI-RES) to measure perceived exertion has been found to be effective in determining the amount of exertion put forth during an exercise bout (Figure 1) (28,35,49).

Figure 1

Figure 1

Because of its subjective nature, RPE may possess a slight limitation in its capability to accurately measure the true intensity of the resistance training bout. Some research has suggested that self-reported RPE values may be skewed when using heavier loads as compared with lighter loads (18). In other words, an athlete may report higher RPE when handling heavy loads even when completing submaximal repetitions. This may be due, in part, to the psychological impact of loading the body with heavier loads. In addition, coaches who train several athletes at 1 time may find it difficult to record RPE values for every set of every exercise. In this case, session sRPE, which involves the athlete reporting the perceived exertion of the entire training session may be a better option (51). Early research suggested that this method required a 15- to 30-minute washout period after the cessation of the resistance training bout before sRPE could be measured (33,55). However, recent evidence has shown that sRPE values obtained immediately after exercise are equally valid compared with values obtained after a washout period (11). This makes the sRPE tool far more practical for coaches with many athletes and busy schedules. sRPE has been shown by numerous sources to represent a valid quantification of training load (12,19,34,43,64). The way in which sRPE could be used by the strength coach is to track this variable consistently. If the athlete's sRPE is trending upward with similar workout volumes and intensities, the strength coach may want to consider a lowering of lifting intensity, lifting volume, or both. By contrast, if the athlete is reporting a lowered sRPE with similar prescribed volume and intensity, this may be an indication that the athlete is adapting to the training and the exercise stimulus needs to be increased.

A more objective approach to determining intensity and exertion is to monitor bar velocity during the concentric portion of an exercise. It has been descriptively reported that a negative linear relationship exists between concentric bar velocity and the relative load lifted (71). This indicates that as an athlete progresses closer to their true 1RM, the velocity of their concentric action will decrease. Furthermore, it has been established that individuals will maintain a constant minimal velocity threshold (the mean concentric velocity that would be seen during a true RM) even as their maximal strength increases (21). Knowing that these relationships exist with respect to mean concentric velocity, a coach can use velocity data to monitor the stress and fatigue being exerted on their athlete in 2 ways:

  • The coach can develop a pretraining test that assesses bar velocity during a submaximal or maximal task, which can be used to determine the athlete's global fatigue. It may also be advantageous for a coach to develop a speed profile that gauges the spectrum of velocities as an athlete progresses toward a 1RM.
  • Intraworkout bar velocity can be monitored to gain objective insight into the difficulty of the exercise session. For example, a coach can assign a training load to their athlete based on a minimum concentric velocity for the athlete to maintain throughout the workout. Exercise order and daily training load (before velocity-based training [VBT]) should be considered when comparing intersession velocities.

The monitoring of bar velocities can be used most effectively when comparisons are made over time and derived from identical (or as similar as possible) workouts. For example, if an athlete typically registers a bench press bar velocity of 1.05 m/s, and a recent assessment conducted with the same intensity (weight lifted) registered a bar velocity of 0.80 m/s, the strength coach would be alerted to the possibility that the athlete may be fatigued and an adjustment to the lifting volume and intensity may be needed.

Although VBT does seem to eliminate the possible error associated with RPE-based training, it is important to note that it does require the use of potentially expensive velocity tracking devices. As a result, it may be difficult for many to use VBT with their athletes. A recent investigation compared the validity of RPE against repetition velocity (71) and reported a strong inverse relationship (r = −0.77, p = 0.001) between RPE values and the velocity of the repetition in both experienced and novice lifters. In addition, the study found that RPE was a valid tool for assigning load and estimating exertion when compared with velocity-based methods. Given this finding, it is suggested that VBT be used whenever possible but can be effectively substituted by RPE during training. Aside from providing an RPE to quantify the athlete's perception of the training session in its entirety, more recent research suggests that RPE can be used within each training set to determine reps in reserve (RIR) (71). RIR reflects the athlete's perceptual intensity of their set relative to the amount of reps they can perform (71).

Another method of monitoring a resistance-training athlete involves gaining insight into the readiness of an athlete to train on a given day through a perceptual response questionnaire. One such perceptual response questionnaire created by McLean and colleagues has been shown to be potentially effective (40). This questionnaire provides data related to an athlete's perceived fatigue, sleep quality, muscle soreness, stress level, and psychological mood state using a Likert scale. A coach can use these data, in conjunction with the internal load data discussed up to this point, to determine the appropriate intensity of the upcoming resistance-training bout. In addition, these data can be used to assess how an athlete is responding to a training program and whether a workload reduction is necessary. Table 1 provides a summary of the advantages and disadvantages of each resistance training fatigue quantification scale.

Table 1

Table 1

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QUANTIFYING ENDURANCE TRAINING LOAD

As has been discussed, an athlete's state of fatigue is influenced by a diverse number of sources including training and general life stress. Endurance athletes perform large volumes of aerobic-based work in training, and as such, the magnitude of this load should be quantified and accounted for in managing the global fatigue of this type of athlete. Several methods of quantifying training load are widely used among endurance athletes and their coaches. These may fall into the category of external or internal training load. Calculating an external training load involves the simple measurement of work performed by the athlete, such as the distance, pace, or power. However, this approach ignores the intensity variable (just as simply calculating total volume in the weight room also ignores the intensity variable). The most useful tools to quantify how hard an endurance athlete is training are those metrics that account for both the volume and the intensity of the exercise bout. Technological devices such as power meters for cycling and global positioning system (GPS) watches for running provide data related to distance traveled and running speed to give a more detailed view of the training load. The value that is calculated from these sources is typically referred to as the Training Stress Score (TSS) (56). Cycling TSS accounts for the normalized power output during the ride and intensity in relation to the athlete's lactate threshold power level. This lactate threshold power level represents the power output that corresponds with an exponential increase in blood lactate concentration. Various formulas have been developed to quantify TSS for running (56). Most of these formulas involve the inclusion of distance, pace, terrain characteristics, and relative intensity. Figure 2 is an example of a running TSS formula. It is important to note that while the formulas (including figure 2) created for monitoring external training load have been well received within the endurance sport community, they currently lack substantial scientific validity (67).

Figure 2

Figure 2

In addition, there are numerous techniques frequently used for monitoring the internal training load of endurance athletes, which considers the athlete's responses to the training. These include HR, RPE, and TRIMP (66). The TRIMP method was originally proposed by Banister et al. (5), and uses the athlete's HR response along with the training duration to yield a dosage score for that session. The TRIMP formula is

CV

CV

D = duration of the training session

b = 1.67 for females and 1.92 for males

e = 2.71828

Δ HR Ratio = (HRexercise − HRrest)/(HRmax − HRrest), where HRrest is the average heart rate during rest and HRexercise is the average heart rate during exercise.

Using this equation gives the ability to assign a value to each training session based on its intensity and duration. A lower TRIMP score is equated with a lower internal training load of the athlete (i.e., the training session was relatively “easier” for the athlete), and a higher TRIMP score is equated with a higher internal training load for the athlete (i.e., the training session was relatively “harder” for the athlete). Tracking this over time can give a clearer picture of the acute internal load provided by 1 training session or the cumulative training load provided by several days or weeks of training. Bannister's TRIMP model is overly simplistic in that it is based on a standard lactate curve which does not consider individual differences, and it only accounts for a single mean HR for the entire session. This makes it difficult to accurately represent the training load incurred by noncontinuous or interval sessions. To rectify this problem, Edwards (1993) proposed a variation of TRIMP, which accounts for time spent in several different HR zones (15,43). This version is referred to as the Edwards TRIMP (15). Garcia-Ramos et al. (17) proposed a similar TRIMP variation for use with elite swimmers, and that formula was found to provide a more accurate quantification of training load. The reason cited for the improvement was likely due to the weighting of both exercise and recovery intervals separately for the corresponding HR-derived intensity (17). More research must be conducted to continue to design improved methods to training load quantification in endurance athletes. Table 2 provides a summary of the advantages and disadvantages of the methods discussed that attempt to quantify endurance training load.

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QUANTIFYING PRACTICE AND COMPETITION LOAD

When managing the global fatigue levels and appropriation of training for team sport athletes, a coach should consider the fact that athletes acquire training stress from multiple sources along with the strength and conditioning program—most importantly the on-field sport practices and competitions. Practices and games for team sport athletes provide a unique challenge in terms of quantifying training load. These sports are characterized as intermittent and reactive in nature because the necessary physical efforts of players at any given moment are determined by the consistently changing game environment. As such, no 2 games will demand the exact same variety of efforts from a given athlete. A multitude of factors may influence the athlete's on-field movements. First, the position the athlete plays on the field may affect the types of efforts undertaken during gameplay (69). Second, situational factors may influence the gameplay characteristics. For example, a study from Lago et al. (36) (2014) showed that Spanish soccer league players covered more distance at high intensities while their team was losing. Because of these factors, fatigue from on-field practice sessions and games is harder to quantify than from unimodal, closed environment tasks such as weightlifting. Integrated microtechnology tracking systems (GPS, accelerometer, and gyroscope) are valid and reliable tools which have made it easier for sport scientists and coaches to quantify the external training load placed on athletes during on-field activities (13,29,52,65).

GPS technology was originally developed for military use (72), but has recently been heavily used by high-level sports organizations. GPS units worn by athletes contain sensors, which communicate their position and movement through satellites, which allows for the quantification of distance and speed of movement. Triaxial accelerometers are useful in quantification of the amount of acceleration performed in all 3 planes of motion and can assess impact forces from collisions with other athletes (52). This allows for an objective quantification of all forces acting on the athlete during the game or practice, which allows for a greater understanding of the stress incurred during that session. The data from these monitoring devices can yield a total calculation of athlete stress from any given practice or game (Table 2).

Table 2

Table 2

GPS data can be analyzed by grouping efforts into movement profile classifications such as low (walk), moderate (jog), high (run), and very high (sprint) efforts. The data from the GPS unit can be analyzed to determine how much time was spent in each zone. Different velocities have been used to denote certain zones for different sports. Although there is no standard for velocity zones, the work of most authors is similar across multiple sports. Table 3 represents the compilation of data provided by Dwyer and Gabbett, who determined the ranges by “applying normal curves of the best fit to the actual average distribution curve of velocity data for each sport,” (14). The table also references data for American football provided by Wellman et al. (68).

Table 3

Table 3

It is also important to track the number of short-duration sprints that are performed during a session. Many of these efforts will not be sustained long enough to reach sprint-threshold velocity, but the maximal nature of their effort makes them very fatiguing nonetheless. Dwyer and Gabbert (14) defined the sprint accelerations as the highest 5% of accelerations performed. Wellman et al. denoted 3 classifications of accelerations and decelerations for National Collegiate Athletic Association DI football players: moderate (1.5–2.5 m·s−2), high (2.6–3.6 m·s−2), and maximal (>3.5 m·s−2) (68). If the number of sprints is tracked, the strength coach can monitor this data and adjust other aspects of the training program as needed. For example, if an athlete has accumulated several days of high numbers of sprint bouts in a short period, the strength coach could reduce the volume for lower body resistance exercise during the next scheduled training session.

When all the GPS data are analyzed from a given session, a comprehensive view of the stress incurred can be seen. This includes not only total distance covered but total distance in each zone and the number of distinct acceleration efforts. As such, the degree of load placed on the athlete during an on-field session can be objectively quantified.

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MONITORING ATHLETE RESPONSE TO TRAINING WITH HEART RATE VARIABILITY

Aside from monitoring training load, monitoring the athlete's response to training may provide more detailed information about how the athlete is adapting to the overall program. HRV has been investigated as a potential method for evaluating training adaptations through the quantification of autonomic nervous system (ANS) function (7,24,39,44,61,62).

The ANS, consisting of the parasympathetic and sympathetic branches, regulates bodily functions without volitional control. These 2 branches dually innervate the heart and serve a complimentary role in regulating HR (3,39). Parasympathetic activity involves the slowing of HR, whereas sympathetic activity increases it (3). HRV is used to monitor the interplay between these 2 branches by quantifying the beat-to-beat variability of the heart (1,3,7,60). Chronically low HRV is associated with increased sympathetic tone, impaired health, increased markers of inflammation, and a greater potential for cardiac events (42,57). Conversely, higher HRV is indicative of a greater parasympathetic state, decreased inflammatory markers, and healthy cardiac function (42,57).

In athletic populations, HRV may provide insights into aerobic fitness because it relates to endurance performance (32,48,54), musculoskeletal injury (20), illness (25), and stress recovery (41). Changes in HRV have also been investigated as a potential marker for overtraining. It is believed that reduced variability is a function of negative training adaptations, whereas increased fitness is believed to lead to larger variability and positive adaptations (25,31,37,47,61). However, understanding these changes have proven to be difficult as increases, decreases, and no change in HRV have all been reported in overtrained athletes (23,24,37,47,48). The reasons for these inconsistent findings may be due to differences between the types of indices used to analyze HRV, lack of standardized measurement techniques, differences in methodological approaches to quantifying change (e.g., daily or weekly), or an inability to differentiate between stages of overtraining (47).

Several methods of HRV analysis have been proposed with the most common techniques consisting of either time domain or frequency domain analysis (1,6,7).

Although the nomenclature of the different measurements is complex, they represent different ways to look at the variability and distribution of the HR over time and parallel the similar, and unique, uses of mean, median, and mode as descriptors of population measurements (39). A time domain representation gives the amplitudes of the signal at the instants of time during which it was sampled, and a frequency domain is used to do a more thorough analysis of the time domain signal, particularly in analyzing the signal with respect to the frequency (3). The type of analysis used may depend on the duration of collection time or the branch of the ANS to be evaluated (7). One of the most commonly used methods is the natural log of the square root of the mean sum of the squared differences between RR intervals (Ln rMSSD) on a QRS wave (7,24,47). This measure of HRV is highly applicable for practitioners working in athletic settings because it is not heavily influenced by respiration rate, does not require a long collection period (5–10 minutes is recommended; however, ultra-short periods of 60 seconds have been shown to be acceptable) and it is easy to calculate without the need for special software (7,16,47). It is important to note that although the estimation of HRV using Ln rMSSD of a QRS wave is common in practical settings, this method is inferior to Holter monitoring (which continuously records electrical heart tracings over a period of 24–72 hours).

Standardized measurement strategies may help to improve reliability between recordings because the ANS is sensitive to environmental conditions (7). Postexercise HRV (ranging from immediately postexercise up to 4 hours postexercise) may be used to measure recovery from a training session (39,53,58,60); however, it is often suggested that HRV measurement be taken on waking, in a supine position, while controlling for conditions such as excessive noise, light, and temperature to provide the most consistent measurement (7). Even with standardized approaches to collection, HRV may exhibit a high day-to-day variability producing misleading results (46,47). To combat measurement noise, Plews and colleagues (46,48) recommend using weekly or 7-day rolling averages to represent changes in the athlete's autonomic state. Their research indicates that this approach is more suggestive of overreaching in elite triathletes (47) and is highly correlated with changes in maximal aerobic speed and 10-km running performance in recreationally trained runners (46). The 7-day rolling average may be most useful when compliance is poor or obtaining daily values proves to be difficult in a practical setting. As such, a minimum of 3 recordings per week is recommended to assess the status of trained athletes (45).

Making changes to a training program based on HRV requires the practitioner to determine whether the change in HRV is meaningful for the individual. As such, the smallest worthwhile change (SWC) may be used to track changes for individual athletes over time (26,27). For performance tests, Hopkins (26) recommends that the SWC be the typical variation of an athlete's performance in competitions multiplied by a magnitude of 0.3. Similar approaches have been performed on physiological measures such as HRV by applying different sizes of magnitude (0.2–0.5) (37,46–48,63). Highly trained athletes may require a more individualized approach, specific to their unique physiological changes. As such, Plews and colleagues (45,47) used a 7-day rolling average and a SWC of 0.5 × the within-athlete coefficient of variation to detect training adaptations and potential overreaching. Although some have cautioned against using day-to-day recordings to make training decisions (46–48) others have developed daily-guided training approaches based on SWC of day-to-day readings. Vesterinen et al. (63) established the SWC as ±0.5 × within-athlete SD from the rMSSD values obtained in a 4-week preparatory period. After the preparatory period, athletes were separated into an HRV-guided or a predefined training group for an 8-week intensive training phase. Those in the HRV-guided training group only performed moderate- and high-intensity training on days, where their HRV was found to be within their individual SWC. The HRV-guided training group performed less moderate- and high-intensity sessions than the predefined training group and saw improvements in a 3,000-m running performance test compared with the predefined training group. Further research needs to be conducted to determine the most logical magnitude to apply in practice because individual variations and physiological adaptations throughout training phases may warrant different SWCs (7). Figure 3 shows a weekly average of HRV data taken 6 times per week for 12 weeks. The gray shading represents SWC and the black boxes represent anything outside of the SWC for that week.

Figure 3

Figure 3

HRV may provide practitioners with an easy, noninvasive way of obtaining data regarding training adaptations and risk of overtraining. Practitioners should make attempts to standardize collection methods, thereby limiting the noisiness of the data. When determining whether positive or negative adaptations are occurring or when looking to make changes to a training program based on the data, it is recommended that the practitioner establish meaningful thresholds by calculating the SWC. It is important to remember that HRV is not the gold standard of monitoring. HRV provides an indirect assessment of ANS function but does not provide information about other physiological processes. For this reason, practitioners should use HRV alongside other validated measures of training load, wellness, and fatigue monitoring.

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PRACTICAL APPLICATIONS OF MONITORING

The aim of this paper was to provide practitioners with an overview of methods for monitoring athletes during the training process. To succinctly describe the training process, practitioners should be aware of both external and internal load factors that may dictate the type of response or adaptation an athlete experiences from a given training program.

Not all methods within this paper should be used at once because the operationalization of them may be due to factors specific to the environment the practitioner works within. For example, a lack of financial resources may limit the practitioner's ability to obtain certain technologies (e.g., linear position transducers, GPS, and HR); however, that practitioner may find value in low-cost solutions (e.g., wellness questionnaires). In addition, the extra time spent in data collection and analysis may take away from the practitioner's ability to perform other tasks (e.g., strength and conditioning, rehabilitation, and program design). As such, the chosen methods of monitoring should be easy to collect, easy to analyze, and fit within the framework of the practitioner's daily role.

Given the aim of this paper and the limitations of monitoring discussed above, we propose that practitioners evaluate the setting they currently work in and determine 2–3 methods of monitoring that they will be able to implement without difficulty. Furthermore, it is suggested that practitioners select both internal and external load monitoring strategies because multiple methods will allow for a more complete picture of how the athlete is tolerating the training program. For example, a practitioner operating with minimal funds may choose to monitor their athletes in the following way:

  • Morning wellness questionnaire to evaluate how the athlete is handling training and nontraining stresses (life stresses, quality of the previous evening's sleep, and subjective feelings of tiredness) and their ability to cope with the program.
    • • How the practitioner could use this data: If the athlete perceives an increase in their life stresses and subjective feelings of tiredness, training could be altered with lower volume and/or intensity.
  • HR monitoring during training to measure internal training load and the physiological impact of the session.
    • • How the practitioner could use this data: If the HR data is elevated above that which would be expected (based on previous HR data obtained at similar training intensities), the remainder of that training session or upcoming training sessions could be altered accordingly.
  • A written journal of sets, reps, and training intensity (% 1RM) as a measure of external load during gym workouts.
    • • How the practitioner could use this data: Consistent tracking of the athlete's external load can be used to determine whether the external loads are related to positive or negative outcomes in the other monitoring methods used (morning wellness questionnaire, HR monitoring, and post-training RPE).
  • Post-training RPE, to quantify the athlete's perception of the training session and evaluate their RPE training for both positive and negative changes that may be taking place over time.
    • • How the practitioner could use this data: If the post-training RPE data are elevated above that which would be expected (based on previous post-training RPE data obtained at similar training intensities), a reduction in training volume and intensity may be considered. By contrast, if the post-training RPE data are lower than would be expected (based on previous post-training RPE data obtained at similar training intensities), an increase in training volume and intensity may be considered.

Even with limited funds, a practitioner can easily put together simple monitoring strategies to objectively assess the training program. This will provide an improved understanding of the changes taking place in the athlete over time, and better assist in program design to ensure continued improvement is taking place and maladaptation is minimized.

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REFERENCES

1. Achten J, Jeukendrup AE. Heart rate monitoring: Applications and limitations. Sports Med 33: 517–538, 2003.
2. Åkerstedt T, Axelsson J, Lekander M, Orsini N, Kecklund G. Do sleep, stress, and illness explain daily variations in fatigue? A prospective study. J Psychosom Res 76: 280–285, 2014.
3. Aubert AE, Seps B, Beckers F. Heart rate variability in athletes. Sports Med 33: 889–919, 2003.
4. Banister EW, Morton RH, Fitz-Clarke JR. Modeling human performance in running. J Appl Physiol 69: 1171–1177, 1990.
5. Banister EW, MacDougall JD, Wenger HA. Modeling Elite Athletic Performance: Physiological Testing of the High-performance Athlete. Campaign, IL: Human Kinetics, 1991. pp. 403–425.
6. Billman GE. Heart rate variability—A historical perspective. Front Physiol 2: 86, 2011.
7. Buchheit M. Monitoring training status with HR measures: Do all roads lead to Rome? Front Physiol 5: 73, 2014.
8. Busso T, Kkinen KH, Pakarinen A, Carasso C, Lacour JR, Komi PV, Kauhanen H. A systems model of training responses and its relationship to hormonal responses in elite weightlifters. Eur J Appl Physiol Occup Physiol 61: 48–54, 1990.
9. Calvert TW, Banister EW, Savage MV, Bach T. A systems model of the effects of training on physical performance. IEEE Trans Syst Man Cybern 6: 94–102, 1976.
10. Campos GE, Luecke TJ, Wendeln HK, Toma K, Hagerman FC, Murray TF, Ragg KE, Ratamess NA, Kraemer WJ, Staron RS. Muscular adaptations in response to three different resistance-training regimens: Specificity of repetition maximum training zones. Eur J Appl Physiol Occup Physiol 88: 50–60, 2002.
11. Christen J, Foster C, Porcari JP, Mikat RP. Temporal robustness of the session RPE. Int J Sports Physiol Perform 11: 1088–1093, 2016.
12. Clarke N, Farthing JP, Norris SR, Arnold BE. Quantification of training load in Canadian football: Application of session-RPE in collision-based team sports. J Strength Cond Res 27: 2198–2205, 2013.
13. Coutts AJ, Duffield R. Validity and reliability of GPS devices for measuring movement demands of team sports. J Sci Med Sport 13: 133–135, 2010.
14. Dwyer DB, Gabbett TJ. Global positioning system data analysis: Velocity ranges and a new definition of sprinting for field sport athletes. J Strength Cond Res 26: 818–824, 2012.
15. Edwards S, ed. High performance training and racing. In: The Heart Rate Monitor Book. Sacramento, CA: Feet Fleet Press, 1993. pp. 113–123.
16. Esco MR, Flatt AA. Ultra-short-term heart rate variability indexes at rest and post-exercise in athletes: Evaluating the agreement with accepted recommendations. J Sci Med Sport 13: 535–541, 2014.
17. Garcia-Ramos A, Feriche B, Calderon C, Iglesias X, Barrero A, Chaverri D, Schuller T, Rodriguez FA. Training load quantification in elite swimmers using a modified version of the training impulse method. Eur J Sport Sci 15: 85–93, 2015.
18. Gearhart RF, Goss FL, Lagally KM, Jakicic JM, Gallagher J, Gallagher KI, Robertson RJ. Ratings of perceived exertion in active muscle during high-intensity and low-intensity resistance exercise. J Strength Cond Res 16: 87–91, 2002.
19. Genner KM, Weston M. A comparison of workload quantification methods in relation to physiological responses to resistance exercise. J Strength Cond Res 28: 2621–2627, 2014.
20. Gisselman A, Spontelli G, Baxter D, Wright A, Hegedus E, Tumilty S. Musculoskeletal overuse injuries and heart rate variability: Is there a link? Med Hypotheses 87: 1–7, 2016.
21. González-Badillo JG, Sánchez-Medina LS. Movement velocity as a measure of loading intensity in resistance training. Int J Sports Med 31: 347–352, 2010.
22. Haff GG. Quantifying workloads in resistance training: A brief review. Strength Cond J 10: 31–40, 2010.
23. Halson SL, Jeukendrup AE. Does overtraining exist? An analysis of overreaching and overtraining research. Sports Med 34: 967–981, 2004.
24. Halson S. Monitoring training load to understand fatigue in athletes. Sports Med 2: S139–S147, 2015.
25. Hellard P, Guimaraes F, Avalos M, Houel N, Hausswirth C, Toussaint JF. Modeling the association between HR variability and illness in elite swimmers. Med Sci Sports Exerc 43: 1063–1070, 2011.
26. Hopkins WG. How to interpret changes in an athletic performance test. Sportscience 8: 1–7, 2004.
27. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 41: 3–12, 2009.
28. Irving BA, Rutkowski J, Brock DW, Davis CK, Barrett EJ, Gaesser GA, Weltman A. Comparison of Borg-and OMNI-RPE as markers of the blood lactate response to exercise. Med Sci Sports Exerc 38: 1348, 2006.
29. Johnston RD, Watsord ML, Pine MJ, Spurrs RW, Murphy AJ, Pruyn EC. The validity and reliability of 5-Hz global positioning system units to measure team sport movement demands. J Strength Cond Res 26: 758–765, 2012.
30. Kellmann M. Preventing overtraining in athletes in high-intensity sports and stress/recovery monitoring. Scand J Med Sci Sports 20: 95–102, 2010.
31. Kiviniemi AM, Tulppo MP, Hautala AJ, Vanninen E, Uusitalo ALT. Altered relationship between R-R Interval and R-R interval variability in endurance athletes with overtraining syndrome. Scand J Med Sci Sports 24: 77–85, 2014.
32. Koenig J, Jarczok MN, Wasner M, Hillecke TK, Thayer JF. Heart rate variability and swimming. Sports Med 44: 1377–1391, 2014.
33. Kraft JA, Green JM, Thompson KR. Session ratings of perceived exertion responses during resistance training bouts equated for total work but differing in work rate. J Strength Cond Res 28: 540–545, 2014.
34. Kraft JA, Green JM, Gast TM. Work distribution influences session ratings of perceived exertion response during resistance exercise matched for total volume. J Strength Cond Res 28: 2042–2046, 2014.
35. Lagally KM, Amorose AJ, Rock B. Selection of resistance exercise intensity using ratings of perceived exertion from the OMNI-RES 1. Percept Mot Skills 108: 573–586, 2009.
36. Lago C, Casais L, Dominguez E, Sampaio J. The effects of situational variables on distance covered at various speeds in elite soccer. Eur J Sport Sci 10: 103–109, 2010.
37. Le Meur Y, Pichon A, Schaal K, Schmitt L, Louis J, Gueneron J, Vidal PP, Hausswirth C. Evidence of parasympathetic hyperactivity in functionally overreached athletes. Med Sci Sports Exerc 45: 2061–2071, 2013.
38. LeSuer DA, McCormick JH, Mayhew JL, Wasserstein RL, Arnold MD. The accuracy of prediction equations for estimating 1-RM performance in the bench press, squat, and deadlift. J Strength Cond Res 11: 211–213, 1997.
39. Makivic B, Nikic MD, Willis MS. Heart rate variability (HRV) as a tool for diagnostic and monitoring performance in sport and physical activities. J Exerc Physiol Online 16: 103–131, 2013.
40. McLean BD, Coutts AJ, Kelly V, McGuigan MR, Cormack SJ. Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players. Int J Sports Physiol Perform 5: 367–383, 2010.
41. Morales J, Álamo JM, García-Massó X, Buscà B, López JL, Serra-Añó P, González LM. Use of heart rate variability in monitoring stress and recovery in judo athletes. J Strength Cond Res 28: 1896–1905, 2014.
42. Myllymaki T, Rusko H, Syvaoja HS, Juuti T, Kinnunen ML, Krolainen H. Effects of exercise intensity and duration on nocturnal heart rate variability and sleep. Eur J Appl Physiol 112: 801–809, 2012.
43. Perandini LA, Siqueira-Pereira TA, Okuno NM, Soares-Caldeira LF, Nakamura FY. Use of session RPE to training load quantification and training intensity distribution in taekwondo athletes. J Sci Sports 27: 25–30, 2012.
44. Pichot V, Rochie F, Gaspoz JM, Enjorlas F, Antoniadis A, Minini P, Costes F, Busso T, Lacour JR, Barthelemy JC. Relation between heart rate variability and training load in middle-distance runners. Med Sci Sports Exerc 32: 1729–1736, 2000.
45. Plews DJ, Laursen PB, Kilding AE, Buchheit M. Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison. Eur J Appl Physiol 112: 3729–3741, 2012.
46. Plews DJ, Laursen PB, Kilding AE, Buchheit M. Evaluating training adaptation with heart-rate measures: A methodological comparison. Int J Sports Physiol Perform 8: 688–691, 2013.
47. Plews DJ, Laursen P, Stanley J, Kilding EE, Buchheit M. Training adaptation and heart rate variability in elite endurance athletes: Opening the door to effective monitoring. Sports Med 43: 773–781, 2013.
48. Plews DJ, Laursen PB, Le Meur Y, Hausswirth C, Kilding AE, Buchheit M. Monitoring training with heart rate-variability: How much compliance is needed for valid assessment? Int J Sports Physiol Perform 9: 783–790, 2014.
49. Robertson RJ, Goss FL, Rutkowski J, Lenz B, Dixon C, Timmer J, Frazee K, Dube J, Andreacci J. Concurrent validation of the OMNI perceived exertion scale for resistance exercise. Med Sci Sports Exerc 35: 333–341, 2003.
50. Schoenfeld BJ, Ratamess NA, Peterson MD, Contreras B, Sonmez GT, Alvar BA. Effects of different volume-equated resistance training loading strategies on muscular adaptations in well-trained men. J Strength Cond Res 28: 2909–2918, 2014.
51. Scott TJ, Black CR, Quinn J, Coutts AJ. Validity and reliability of the session-RPE method for quantifying training in Australian football: A comparison of the CR10 and CR100 scales. J Strength Cond Res 27: 270–276, 2013.
52. Scott M, Scott T, Kelly VG. The validity and reliability of global positioning systems in team sport: A brief review. J Strength Cond Res 30: 1470–1490, 2016.
53. Seiler S, Haugen O, Kuffel E. Autonomic recovery after exercise in trained athletes: Intensity and duration effects. Med Sci Sports Exerc 39: 1366–1373, 2007.
54. Silva D, Fernandes D, Verri SM, Nakamura FY, Machado FA. Longitudinal changes in cardiac autonomic function and aerobic fitness indices in endurance runners: A case study with a high-level team. Eur J Sport Sci 14: 443–451, 2013.
55. Singh F, Foster C, Tod D, Mcguigan M. Monitoring different types of resistance training using session rating of perceived exertion. Int J Sports Physiol Perform 2: 34–45, 2007.
56. Skiba PF. The Triathlete's Guide to Training with Power. Tinton Falls, NJ: PhysFarm Press, 2008. pp. 1–119.
57. Soares-Miranda L, Negrao CE, Antunes-Correa LM, Nobre TS, Silva P, Santos R, Vale S, Mota J. High levels of C-reactive protein are associated with reduced vagal modulation and low physical activity in young adults. Scand J Med Sci Sports 22: 278–284, 2012.
58. Stuckey MI, Tordi N, Mourot L, Gurr LJ, Rakobowchuk M, Millar PJ, Toth R, MacDonald MJ, Kamath MV. Autonomic recovery following sprint interval exercise. Scand J Med Sci Sports 22: 756–763, 2012.
59. Testa M, Noakes TD, Desgorces FD. Training state improves the relationship between rating of perceived exertion and relative exercise volume during resistance exercises. J Strength Cond Res: 2990–2996, 2012.
60. Thayer JF, Ahs F, Fredrikson M, Sollers JJ, Wager TD. A Meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neurosci Biobehav Rev 36: 747–756, 2012.
61. Tian Y, He ZH, Zhao JX, Tao DL, Xu KY, Earnest CP, McNaughton LR. Heart rate variability threshold values for early-warning nonfunctional overreaching in elite female wrestlers. J Strength Cond Res 27: 1511–1519, 2013.
62. Vesterinen V, Hakkinen K, Hynynen E, Mikkola J, Hokka L, Nummela A. Heart rate variability in prediction of individual adaptations to endurance training in recreational endurance runners. Scand J Med Sci Sports 23: 171–180, 2013.
63. Vesterinen V, Nummela A, Heikura I, Laine T, Hynynen E, Botella J, Hakkinen K. Individual endurance training prescription with heart rate variability. Med Sci Sports Exerc 48: 1347–1354, 2016.
64. Veugelers KR, Young WB, Fahrner B, Harvey JT. Different methods of training load quantification and their relationship to injury and illness in elite Australian football. J Sci Med Sport 19: 24–28, 2016.
65. Vickery WM, Dascombe BJ, Baker JD, Higham DG, Spratford WA, Duffield R. Accuracy and reliability of GPS devices for measurement of sport-specific movement patterns related to cricket, tennis, and field-based team sports. J Strength Cond Res 28: 1697–1705, 2014.
66. Wallace LK, Slattery KM, Coutts AJ. The ecological validity and application of the session-RPE method for quantifying training loads in swimming. J Strength Cond Res 23: 33–38, 2009.
67. Wallace LK, Slattery KM, Coutts AJ. A comparison of methods for quantifying training load: Relationships between modeled and actual training responses. Eur J Appl Physiol 114: 11–20, 2014.
68. Wellman AD, Coad SC, Goulet GC, McLellan CP. Quantification of competitive game demands of NCAA Division I college football players using global positioning systems. J Strength Cond Res 30: 11–19, 2016.
69. Wehbe GM, Hartwig TB, Duncan CS. Movement analysis of Australian national league soccer players using global positioning system technology. J Strength Cond Res 28: 834–842, 2014.
70. Zatsiorsky V. Science and Practice of Strength Training. Champaign, IL: Human Kinetics, 1995. pp. 4–5. Print.
71. Zourdos MC, Klemp A, Dolan C, Quiles JM, Schau KA, Jo E, Helms E, Esgro B, Duncan S, Merino SG, Blanco R. Novel resistance training–specific rating of perceived exertion scale measuring repetitions in reserve. J Strength Cond Res 30: 267–275, 2016.
72. Zumberge JF. GPS: A military/civilian collaboration. Science 311: 1377–1378, 2006.
Keywords:

training load; training response; training impulse; fatigue; volume; intensity; heart rate variability; RPE; sport science

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