Developing Cost-Effective, Evidence-Based Load Monitoring Systems in Strength and Conditioning Practice : Strength & Conditioning Journal

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Developing Cost-Effective, Evidence-Based Load Monitoring Systems in Strength and Conditioning Practice

Clubb, Jo MSc1,2; McGuigan, Mike PhD3,4

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Strength and Conditioning Journal 40(6):p 75-81, December 2018. | DOI: 10.1519/SSC.0000000000000396



There has been a rapid evolution of technology available in the realm of sports performance and strength and conditioning. Consequently, our knowledge and execution of monitoring athletes have revolutionized decision making, training prescription, and injury management (62). Indeed, scientists and practitioners are often early adopters of integrating technology to improve sporting performance (14). As such, monitoring athletes in the professional sport setting has now become commonplace (1). Research has demonstrated that relationships exist between training load and performance (3), as well as training load and injury risk (16). Therefore, capturing measures of training load, fitness, and fatigue may assist practitioners in assessing whether an athlete is adapting to their training program or is potentially at risk of overtraining or injury (23).

When introducing such measures into the training environment, a needs analysis should be conducted to understand the resource requirements for successful implementation. If investment in technology is deemed necessary, the potential limitations of using the specific technology should be considered, such as the validity, reliability, and specificity of a product (62). This article aims to highlight the cost- and time-effective fundamentals of load monitoring and recovery, which should be used in the development of a best-practice athlete monitoring system by strength and conditioning coaches. Such a system has a wide application across the field of athletic performance and is not limited to athletes at the highest level. The principles and tools to be discussed in this review may be used to monitor the load of athletes across a range of settings, including high school, college, and professional sports.


The association between player availability and on-field success is well documented across a number of sports (22,49,65). Therefore, managing injury risk in an attempt to maximize player availability is of the utmost importance to sports support staff. Associations between training load and injury risk have been established across a range of sports (15,20,31). Consequently, monitoring training load may enable the prescription of appropriate loads in the practice setting and the assessment of how an athlete is responding to the actual loads (23).

Rating of perceived exertion (RPE) is a valid and reliable method of assessing exercise intensity that involves the athlete rating how hard a session was based on either a 6–20 (7) or 0–10 scale (6). The intensity from the 0–10 scale can then be multiplied by the duration of the session to calculate the session RPE (sRPE) (19). A large body of research has established the reliability and validity of this method across a range of training modalities (33,53,58). As well as session load, sRPE can also be used to calculate training monotony and strain, which have been associated with illness and injury (19). Large week-to-week changes in training load, measured through sRPE, are associated with a greater risk of injury in Australian Football (46,50) and rugby union (15).

As well as considering cumulative and week-to-week changes in internal load, the acute:chronic workload ratio (32) can be used to assess sRPE training load data (or using other variables). Both a low acute:chronic workload ratio, less than 0.85 (43), and a high acute:chronic workload ratio, more than 2.0 (32), have been associated with higher risks of injury. The coupling of high cumulative loads and high acute:chronic workload ratios has also been associated with a higher risk of injury in elite rugby sevens players (63). Such research highlights the potential benefit in analyzing different calculations of load alongside each other (40,64). The potential application of sRPE is not limited to injury risk because associations have also been demonstrated between internal load and match outcomes in elite Australian Football (3). The authors in this study found that measures using RPE, namely weekly load and training stress balance, were associated with match success (3). Subjective measures of internal training load have also been shown to relate better to changes in intermittent performance over a preseason in professional soccer players than heart rate–based measures (12). Literature using the sRPE method confirms that it is a valid, reliable, and cost-effective option for quantifying training stress (19,33,53). Consequently, this should be the first method used to track athletes in any sport setting.


Despite this assortment of perspectives from which to analyze internal load data (e.g., RPE and heart rate), it would be remiss not to acknowledge the importance of external load within the athlete monitoring cycle (21). Simple, cost-effective measures of external load are available to the practitioner, including match exposure (65), balls bowled in cricket (31), and pitch counts in baseball (18) and softball (54). These studies have demonstrated associations between such measures and injury risk, without the need for specialized equipment.

However, if technologies to monitor other aspects of external load are to be considered, Torres-Ronda and Schelling (62) have presented an excellent critical process for the implementation of technology in sports that can provide guidance to practitioners. One of the most common methods used to monitor external load is the use of global positioning system technology (1). Understanding the validity and reliability of the metrics captured by such technology is of the utmost importance to the practitioner, to understand the accuracy of data pertaining to external load (9,13). Reviewing the processes already in place may also be warranted, given that Akenhead and Nassis (1) have suggested, where necessary, simplifying existing monitoring processes to alleviate the constraints imposed by limited human resources.


Measuring an athlete's response to training load is an important process in the monitoring cycle (21). A systematic review by Saw et al. (52) investigated the levels of evidence for objective measures (e.g., blood markers, heart rate at rest, oxygen consumption, and heart rate responses during exercise) and subjective measures (e.g., mood and perceived stress). The authors found that such objective and subjective measures did not correlate with each other, and subjective measures were in fact more sensitive and consistent in reflecting acute and chronic training loads (52).

A study with an English Premier League soccer team also showed that subjective measures of fatigue, sleep quality, and muscle soreness collected in the morning were more sensitive to daily training loads than heart rate–derived methods (61). These measures provide inexpensive and noninvasive alternatives to physical performance assessments that can often be exhaustive and time-consuming, making them unsuitable for many sporting environments (60). A 2017 review outlined practical measures for monitoring fatigue in team sport athletes and considerations for implementing and analyzing them in the applied setting (60).


Recovery is the umbrella term for the multifaceted, restorative process that takes place over time and includes both physiological regeneration and psychological restoration (33). Because optimizing performance involves applying a suitable balance between training stress and recovery, strategies to enhance recovery have become an important focus of sports science support (4). Consequently, a wide array of methods and technologies are available to practitioners who claim to support recovery. These include, but are not limited to, active recovery, massage, foam rolling, cold-water immersion, contrast baths, compression garments, and electrical stimulation (44). Although conflicting research exists for a number of these modalities, strong evidence exists to support the use of sleep, certain nutritional interventions, and cold-water immersion in enhancing the recovery process (44).

It has been demonstrated that athletes in both team sports and individual events obtain less sleep than recommended (38). Achieving sleep extension has been shown to improve the restoration of muscle function, sprint times, and subjective reporting of stress (47), as well as specific measures of performance (41). Given this gap between recommendations and actual practice, as well as the potential improvement in recovery and athletic performance, a key focus of recovery should include designing the program to optimize sleep times, where possible, and educating athletes on sleep hygiene. Therefore, a simple starting point for practitioners can be to monitor the sleep patterns of their athletes (37). An assortment of measures can be collected subjectively, including sleep quality, sleep onset latency, and sleep quantity, and have been shown as reliable estimates of sleep-wake patterns (37,51). Because sleep hygiene includes behaviors around regular bed and rise times, capturing information on these habits may also be beneficial (2).

Self-report questionnaires have been shown to be reliable instruments to assess sleep measures, although there is a tendency for overestimation of sleep duration (11,39). Validated questionnaires available to practitioners include the Pittsburgh Sleep Quality Index (10) and the Holland Sleep Disorder Questionnaire (36). A simple 2-week sleep diary from the American Academy of Sleep Medicine is also available online ( A recent review by Simpson et al. (55) outlines a number of practical recommendations to improve sleep in athletes, which can be implemented. These include strategies pertaining to maintaining healthy sleep habits, minimizing the impact of travel, and identifying/addressing any possible sleep disorders (55). Furthermore, evidence supports the suggestion that sleep hygiene education can subsequently improve sleep indices in athletes (45).

Practical overviews for team sport athletes are available, which help guide practitioners in their prescription of nutritional strategies within a recovery program (26,48). Such reviews reinforce the importance of focusing on the inexpensive basics, first and foremost, to restore the body's functions; nutrition, rehydration, and sleep (59). Furthermore, nutritional interventions may potentially be used to enhance sleep (24). Thereafter, additional interventions may be considered depending on the sport, schedule, and individuals (30). Clearly, maximizing the impact of sleep and nutrition on athletic recovery should be in place before additional modalities and technologies are invested in. Kellman et al. (35) also highlight the considerations for the entire multidisciplinary team in introducing a successful recovery environment. Regular and open communication, commitment and agreement on strategies, as well as a supportive milieu are also important elements (35).


The evolution of technology in the sporting environment has enabled objective data collection on athletes in situ, access to real-time data, and advancements of previously invasive technologies to noninvasive alternatives, as well as the ongoing improvements in the speed, size, and quality of data collection tools (62). Despite the desire to explore innovation, practitioners should avoid the temptation of an overreliance on and/or excessive investment in technology to maintain the integrity of the sports science industry (14).

Assessments used to monitor the training process should be critically evaluated based on their reliability, usefulness, and practicality (1). This is of even greater concern given the potential for information overload and pseudoscience marketing in the sports community (25). Recent work has demonstrated that of 36 commercially available wearable technologies designed for athletes, only 9 devices have been evaluated scientifically (17). Furthermore, commonly used variables from micromechanical-electrical systems such as accelerations (9), sport-specific inertial measures (13), and high-speed movements (34) have demonstrated questionable reliability. Such research highlights the need to conduct in-house assessments of validity and reliability, as well as supporting industry pressure on technology suppliers to conduct honest and transparent assessments themselves.

A questionnaire of 41 top-level soccer clubs demonstrated perceptions of substantial barriers to training load monitoring that included numbers of staff/human resources, coach buy-in, and the validity and reliability of assessments (1). Attention should also be paid to the ethics of storing data on unsecure servers and the commercial use of such personal data (17). Halson et al. (25) outline recommendations before incorporating wearable devices into the applied environment that ensures athlete health and safety, as well as ethical considerations surrounding testing and data collection, which are at the forefront of decision making. With implantable monitoring devices and 24-hour monitoring of athletes now being discussed (56,57), we must maintain scientific rigor and ethical awareness when considering any form of technology implementation and data collection.


Thus far, this article has outlined low-cost, best-practice approaches for the load monitoring cycle in applied practice, as well as outlining considerations for implementing technology if investment is deemed necessary. These approaches, regardless of whether data are collected subjectively or objectively, require some form of data analysis. For statistical analysis of applied sports science data, the magnitude-based inference approach has been suggested as more appropriate than null hypothesis significance testing (8). This approach enables the assessment of change in individuals, magnitude of change which is often what matters most to practitioners, allows for the distinction between clear and unclear trivial effects (based on confidence limits), and improves informative data visualization (5,8,27). The smallest worthwhile change (SWC) is the magnitude of performance enhancement needed to exceed the uncertainty or noise in the test result (27):

  • For individual elite athletes, the required change in performance is 0.3 × typical variation in performance (27).
  • For individuals within a team setting, the SWC is one-fifth of the between-athlete SD (a Cohen effect size of 0.2) (27).
  • A systematic review has published variability (as a coefficient of variation percentage) of performance of elite athletes across a range of sports, through mixed modeling reliability analysis (42).

The web site provides a variety of resources to read and use in applied practice to robustly analyze data. In addition, strength and conditioning practitioners can use several spreadsheets including:

  • A Spreadsheet for Monitoring an Individual's Changes and Trend (29);
  • Spreadsheets for Analysis of Validity and Reliability (28).

Although a variety of athlete data management systems exist, with a range of financial commitment, there are low-cost options available to all practitioners to store and manage data. Microsoft Excel and Google Docs provide 2 excellent options to practitioners. Further to the resources mentioned above, a number of free excel spreadsheets are available to download for managing load monitoring data ( These resources include evidence-based approaches including the acute:chronic workload ratio (32) and exponentially weighted calculations (66). When using various resources, practitioners are encouraged to reverse engineer the calculations to understand the analyses that are being conducted. Once comfortable with the analysis, it may be that practitioners are capable of building modified versions of such resources themselves. For information on developing Excel skills relevant for sports science analysis, the reader is directed to the ExcelTricksforSports YouTube channel for over 100 free videos ( Although it can be tempting to be attracted to expensive technology and data management/analysis systems, this section highlights the variety of cost-effective resources available to every practitioner to assist them with the storage and analysis of load monitoring data.


The Table outlines an example of a low-cost monitoring system that could be implemented by a strength and conditioning practitioner working with a team sport.

Examples of monitoring variables that can be used by practitioners in team sports

Modified z-score can be calculated for these monitoring variables using the following formula:

The baseline can be determined by the practitioner over an appropriate period, for example, beginning of preseason for a 2-week period.


The evolution of technology continues to provide a seemingly endless variety of tools available to strength and conditioning coaches and sports scientists. However, cost-effective options that require minimal investment in technology exist, which could be the foundation of an effective athlete monitoring system. sRPE is a valid and reliable measure of internal load that has been associated with injury risk and performance. Data can be analyzed from a variety of perspectives that include monotony, strain, cumulative loads, week-to-week changes, and the acute:chronic workload ratio. Subjective wellness questionnaires are valid, reliable, and sensitive tools to measure responses to assess how the athletes are coping with the training load. Despite a range of recovery tools available, there remains potential for competitive advantage with the education of optimal sleep and nutrition strategies.

Should further technology be justified and within budgetary restrictions, considerations should be given for the following before implementation:

  • Athlete health and safety;
  • Validity and reliability of the technology;
  • Human resources required;
  • Data management and analysis;
  • Ethics surrounding the data use and storage.

In addition, several resources exist online that provide useful tools for practitioners for analysis and interpretation of monitoring data. In conclusion, the quality and efficiency of an athlete monitoring system is not determined by the time and budget constraints, but by the selection, implementation, and analysis of appropriate and evidence-based measures.


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load; monitoring; nutrition; recovery; sleep; technology

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