Athlete self-report measures (ASRM) can be a relatively simple and inexpensive means for determining an athlete’s training load and his or her subsequent responses to that training . In fact, using ASRM, such as wellness questionnaires, is the most common method for monitoring athlete fatigue and recovery in high performance sport in New Zealand and Australia . And for good reason, as we will soon discover.
- ASRM Can Indicate Physiological Stress And Fatigue
- ASRM Associates with Physiological Capacity to Perform and Objective Markers of Muscle Damage
- ASRM Potential Impact on Athlete Psychology
- Value of ASRM for Identifying the Unmeasured Stressors
- ASRM to Assist Practitioners and Coaches
- ASRM Interpretation and Implementation General Guidelines
- ASRM Implementation Specifics
- ASRM Common Barriers and What to Do About Them
- A Key Concept for ASRM Data Interpretation
Grey boxes are summary points
Blue boxes give more detail about key terms or subjects
ASRM Can Indicate Physiological Stress And Fatigue
A substantial amount of research has been conducted which confirms that athlete self-report measures (ASRM), such as wellness questionnaires, can indicate changes in training load/stress in elite team sport athletes [1-17, 33]. These athletes include those who play Australian Rules Football [1, 10], volleyball [2, 33], rugby , tag football , Division 1 college football [5, 17], and professional soccer [6, 7, 13-15]. Perceived wellness has been linked with both internal [1-3, 5, 7, 9, 33] and external [4, 6, 10, 13, 14, 17] stressors, as well as muscle damage biomarkers [3, 12, 13].
Athlete self-report measures (ASRM) is a method for monitoring the training response whereby the athlete records their subjective physical and/or psychosocial well-being.
Thorpe et al. (2016) found that internal training load (session-RPE) was significantly higher on match day, which coincided with 35-40% worse ratings of self-reported fatigue, sleep quality and DOMS on the post-match morning, compared with pre-match morning, in English Premier League soccer players . Internal training load (session-RPE) progressively decreased over the 3 days leading up to match day, and these reductions in training load were mirrored by improvements in perceived wellness .
In another study on English Premier League soccer players by Thorpe et al. (2017), there were associations between fluctuations in accumulated high-intensity running distance covered over the previous 2, 3, and 4 days, and fluctuations in perceived fatigue over those time periods; greater high-intensity running distance coincided with increased fatigue .
Hogarth et al. (2015) reported that perceived well-being peaked one hour before the first match and then progressively declined after each subsequent match during a 3-match tournament, in national tag football players .
According to an experiment by Flatt et al. (2017), Division I collegiate swimmers noted increased perceived fatigue and soreness in response to a training period meant to overload the body . After a lower-intensity tapering period that followed, perceived fatigue and soreness decreased back to baseline .
Similarly, Buchheit et al. (2013) found a relationship between training load and perceived wellness outcomes during an intensified period of training (a two-week training camp) in a professional an Australian Rules Football squad . The athletes reported reduced levels of perceived wellness on mornings following days that had higher daily training loads .
Clemente et al. (2018) recently observed associations between perceived well-being parameters and weekly training loads in 13 elite volleyball players during a full competitive season (nine months, 237 training sessions and 37 official matches), . The authors found moderate-to-large correlations between perceived weekly training load (session-RPE) and perceived status of muscle soreness, fatigue and stress . Higher weekly training load was associated with increased muscle soreness, fatigue, and stress .
Interestingly, Thornton et al. (2015) suggest that perceived wellness data may be used to reduce illness risk in elite team sport athletes . They found an increased risk of self-reported illness in 32 professional rugby players when there was a coinciding increase in internal training load (sRPE) and reduction in perceived well-being .
The relationship between increased training load/stress and reductions in perceived well-being has been demonstrated in various cohorts of athletes. We saw that one study suggested a correspondence between these variables and illness risk, but there are other reasons to pay attention to the relationship between training load and perceived well-being.
ASRM Associates with Physiological Capacity to Perform and Objective Markers of Muscle Damage
The research noted above indicates that elevations in acute training stress can associate with reductions in perceived wellness in elite athletes. Reductions in perceived wellness has also been associated with higher levels of muscle damage and reduced capacity to perform physically in subsequent training sessions and competitions.
For example, in a study by Freitas et al. (2014), increased perceived fatigue, reduced perceived recovery, and increased muscle damage (creatine kinase) coincided with intensification of internal training load (session-RPE), in elite Brazilian volleyball players .
Johnston et al. (2013) observed a relationship between reductions in perceived wellness, neuromuscular fatigue (via plyometric press-up and countermovement jump performance), and increased muscle damage, in seven male amateur rugby league players . Similarly, Hills and Rogerson (2018) observed concomitant reductions in subjective wellness scores and countermovement jump performance in 37 professional male rugby players .
Following an intense 3-day basketball tournament, Montgomery et al. (2008) observed a relationship between perceived muscle soreness and muscle damage, in 29 young male basketball players . Although the coincidence of muscle damage and soreness should be expected, it’s important to recognize the association between athlete perceptions and objective biomarkers, in general.
In an investigation by Varley et al. (2017), a reduction in total number of accelerations, decelerations and sprints performed during match play were strongly associated with muscle damage (creatine kinase), perceived muscle soreness, and reduced perceived wellness overall, in elite soccer players . In another study involving elite soccer players, Malone et al. (2018) observed an association between reduction in players’ pre-training perceived well-being (1 Z-Score below their normal) and decreased high speed distance and maximal velocity distance covered during the training session later that day .
Sticking with soccer, Noon et al. (2015) observed that their cohort of elite young soccer players experienced a gradual reduction in perceived wellness over the course of a season; this is a relationship that one could presume to be associated with physiological fatigue . In fact, reduced sprint performance was also evident in the later stages of the season .
In Division 1 male sprint swimmers, Flatt et al. (2017) reported that perceived fatigue and soreness increased during an induced overload training period, and later returned to baseline levels during the tapering period . Recently, Pla et al. (2019) examined the effects of polarized training versus threshold training on elite junior swimmers’ energetics, perceptions of fatigue and recovery, and performance in a 100-meter maximal swim sprinting test . When swimmers performed polarized training, an improvement in 100-meter swimming performance was observed, which coincided with lower and higher levels of reported fatigue and recovery, respectively .
Wellman et al. (2017) conducted an interesting investigation in NCAA Division 1 college football players using perceived wellness questionnaires and accelerometer/GPS-based player load metrics during practice and competition . Players who accrued less external load during games also reported lower levels of fatigue and soreness the day after games . This relationship was observed with various markers of external load, including overall load (“player load”), distances covered at low, medium, and high intensities, total distance covered, and acceleration and deceleration distance covered at all intensities. In a nutshell, players who exerted themselves less during games also reported lower levels of fatigue and soreness the following day .
Perception appears to be reality in elite athletes; reduction in perceived well-being correlates with previous training loads, internal muscle damage, and/or the capacity to perform physiologically.
ASRM Potential Impact on Athlete Psychology
Athlete psychology is an invaluable consideration that must be made prior to athlete self-report measure (ASRM) implementation. The research investigating how ASRM implementation affects the psyche of athletes is severely limited, to-date.
Young et al. (2009) had 26 intercollegiate swimmers keep a take-home daily log for 26 days and assessed whether this intervention changed the athletes’ training behaviors . During the first 17 days of using the log, the swimmers demonstrated improved in-pool adherence to a training prescription, compared to a baseline period. This beneficial effect of logs for swimmers’ adherence was not sustained across the entire intervention, with values during the final 9 days regressing so that they were no better than those during baseline . Filling out this daily log resulted in the athletes becoming more aware of tardiness and punctuality behaviors . Following the intervention, athletes reported greater confidence to manage their time and demands outside of swimming, and to be punctual at all practices .
Saw et al. (2017) recently investigated the use of ASRM with 335 athletes, ranging from recreational to international-level competitors . One month into using ASRM, there was a slight increase in confidence and motivation, with a slight decrease in satisfaction, across all athletes . After four months of ASRM use, expert athletes reported substantial increases in confidence and motivation, and non-expert athletes reported a small increase in motivation, whereas novice athletes reported a decrease in motivation and self-awareness .
Since confidence is a positive predictor of behavior change [30, 31], ASRM implementation may have potential to lead to improved training and performance-related behaviors . Additionally, Saw et al. (2018) contend that increasing self-awareness can promote beneficial practices among athletes. For example, if an athlete notices a dip in sleep duration or quality, he or she may be prompted to take a nap or get a better night’s sleep the next day. Along the same vein, if an athlete sees a USG reading that indicates dehydration, he or she may be prompted to drink more water .
Overall, there’s a paucity of research in the area of ASRM use and athlete psychology, but the current body of work is pertinent when discussing the potential for successful ASRM implementation with athletes.
Value of ASRM for Identifying the Unmeasured Stressors
Athlete self-report measures (ASRM) can bring tremendous value to both the athlete and coaching staff when being used to better understand how non-traditional stressors are impacting the body. We routinely analyze and interpret physiological data with a heart rate monitor, GPS, or accelerometry device, but many other forms of stress exist, oftentimes unmeasured. For example, research suggests that academic stress may affect injury risk in Division I football players , and that extensive travel demands may play a part in reducing power output in Division I ice hockey players . Using ASRM allow for a means to collect data on stressors that cannot be pragmatically measured with technology, such as academic and travel stresses. I’m in the process of writing a separate piece that covers this topic; check in later for more details.
ASRM to Assist Practitioners and Coaches
Implementing daily athlete self-report measures (ASRM) into an athlete monitoring program may assist practitioners in understanding the perceptual fatigue and preparedness of athletes, how they are coping with training and competition schedule, and provide insight into intensity of output expected from an athlete during training that day .
Coaches and athletes aren’t always on the same page regarding the perceived physiological stress to which the athlete is being exposed. Research validates that there can be significant differences between how coaches and athletes perceive the same training stimulus. Under ideal circumstances, the perceptions of training load should match between athlete and coach to support optimal adaptation to training, if the plan used by the coach is scientifically and optimally structured . Although high correlation between coach and athlete perceptions has been observed previously , most research suggests that these perceptions differ more than they are alike [32, 36-41].
Kraft et al. (2018) observed discrepancies between perceived recovery ratings between 56 NCAA Division II athletes and 4 NCAA Division II coaches of the athletes . Although the athletes and coaches had similar perceptions of the session training load, coaches overestimated the recovery of their athletes prior to the session; athletes felt less recovered compared with what the coaches perceived the athletes’ recovery levels to be .
Inconsistent correspondence between coach and athlete perceptions have been reported to be suboptimal in swimming , endurance [36, 37], basketball , soccer , and futsal athletes , as well. Although the effect appears to be quite small, supplying the coach with formative feedback may result in a significant improvement in agreement between observed and perceived exertion, especially following hard training sessions .
Relying on subjective interpretations of athlete recovery or training load solely from coach perceptions could result in unintended training demands, which may increase athlete injury risk and compromise team performance. ASRM provides a means to gauge athlete perceptions. When contextually interpreted, practitioners and coaches may be able to prevent overtraining and coinciding injury and/or performance reductions by better understanding the stress/load perceptions of their athletes.
ASRM Interpretation and Implementation General Guidelines
Athlete self-report measures (ASRM), such as answers to wellness questionnaires, are a relatively simple and inexpensive approach for monitoring athlete health and readiness to train . There is also growing support in the literature suggesting that ASRM may be more sensitive and reliable than traditional physiological, biochemical and performance measures used for athlete monitoring [1, 18-23].
Although valid and reliable, without consistent collection of data, the implementation of ASRM has substantial limitations. The design of the data collection system, and understanding the social ecological influences that come into play, are key components of successful ASRM implementation. It’s the complex interrelationship of these factors that will ultimately determine athlete compliance, data accuracy, and the potential ASRM use for improving athletic outcomes .
In addition to staff support for the athletes, there are specific ASRM qualities that must be taken into consideration. It’s imperative that ASRM are implemented in a way that allows for the collection of consistent, high quality, and meaningful data, with minimal effort from the athlete.
Questions should be simply stated in order to facilitate comprehension; the number of questions asked should be limited to only those necessary for efficient data collection, frequency of questionnaire completion should be “as needed”, technology familiar to the athletes should be utilized for data collection, and a myriad of other factors must be considered to achieve consistent athlete participation, or “buy in” .
ASRM Implementation Specifics
In previous research, using Likert scales for data collection has been a commonly applied method, likely due to the simplicity of comprehension and analysis . Various numerical rating ranges for Likert scales have been used with success, including 1-to-5 [1-5, 13, 17, 25, 27], 1-to-7 [6, 7, 10, 14, 15], 1-to-10 [12, 26], and 1-to-9 . Many of the 1-to-5 scales used are adapted from an original questionnaire developed by McLean et al. (2010), displayed below :
Many practitioners take it upon themselves to design their own ASRM forms. These forms generally consist of 5-12 items using 1-to-5 or 1-to-10 point Likert scales, or modification of existing questionnaires by placing greater emphasis on ratings of muscle soreness, physical fatigue and general wellness . Many modified questionnaires have not been validated, which can be a tremendous issue. Although it’s certainly possible to use invalid questions or questionnaires effectively with athletes, there may be more advisable approaches. Here are a few questionnaires that have been validated and have been frequently used with athletes :
- POMS. The Profile of Mood States 
- RESTQ-S. Recovery Stress Questionnaire for Athletes 
- DALDA. Daily Analyses of Life Demands of Athletes 
Commonly, questions asked pertain to fatigue [1, 3, 4, 6, 7, 10, 12-15, 17, 25-27], sleep quality/quantity [1, 3-7, 10, 13-15, 17, 25-17], muscle soreness [1, 3-7, 10, 12, 13-15, 17, 25-27], stress [1, 3, 4, 10, 14, 15, 17, 27], and mood [1, 3, 4, 10, 17, 25, 27]. Questions relating to energy [5, 13, 14], recovery , and appetite/diet [15, 26] have been asked, but to lesser extents. Questions relating to depression, confusion, emotional stress, social recovery, sleep quality, and self-efficacy may not be very useful indicators for monitoring athlete well-being  whereas general non-training stress, fatigue, physical recovery, general health/well-being, being in shape, conflicts/pressure, self-regulation, and lack of energy appear to be better indicators .
After evaluating all of these data in concert with my experiences in the field, I suggest applied sport professionals consider the following when developing and implementing ASRM with their athletes:
- Number of questions. Limit the number of questions (I suggest 3-7) to minimize athlete labor; this will force you to think about and identify the questions that will provide the most value.
- Type of questions. Ask questions that are not easily captured objectively and/or that add context to objective data being collected. I suggest questions relating to fatigue, sleep quality, soreness, and outside (i.e. “life”) stressors. When possible, use validated questions and/or questionnaires.
- Question clarity. Make questions easy to interpret for the athlete and simple to answer and analyze. This can be done by displaying the answers in text format (on most occasions), with numerical correspondence to answers on the back-end for analysis and calculations.
- Numerical scaling. If you decide to expose the athlete to front-facing text answers, using a Likert scale of 1-to-5 (behind the scenes) may work best because having an athlete choose between >5 answer choices may become confusing or cumbersome. If you’re using a sliding scale display where the athlete does not have to select from multiple set text options (such as a red-to-green color scale), 1-to-10 or 1-to-100 may be more appropriate to increase question sensitivity. An issue I’ve personally had with 1-to-5 Likert scales is a lack of sensitivity for detecting change in well-being status, and this downfall warrants serious consideration when determining if/how to implement ASRM.
- Question frequency. Although some researchers advise only asking questions once, or a few times, per week, I suggest asking questions daily at the same time relative to the day’s primary event (practice, game, etc.), to give athletes the opportunity to include well-being assessment into their daily routine.
- Technology. Implementing ASRM through the athlete’s personal mobile device provides an easy access point for the athlete, and promotes comfort and confidentiality when answering questions. Tablets may also be used in the team setting, but, depending on the team culture, this setup may compromise athlete comfort and confidentiality, which may increase risk for bias in answer selections.
- Staff Support. Send the questions to the athletes electronically each morning; staff will have time to review responses and identify the athletes who did not respond. If athletes come into the training environment with questions unanswered, staff will have an opportunity to remind these athletes to complete the ASRM, if appropriate. Staff will also have the opportunity to greet and/or discuss responses with the athletes who did respond to the ASRM, which may enhance staff-athlete camaraderie and communication. Research suggests that when athlete responses are not acknowledged and/or addressed, “buy in” can become compromised [46, 47].
- Education. Educate the athletes prior to implementation as to what will be collected, how it will help, and to expect a lack of feedback in the beginning of the collection process as their respective “norms” or “reference values” are developed.
ASRM Common Barriers and What to Do About Them
There can be an initial decrease in athlete satisfaction and intrinsic motivation during the first month of ASRM implementation but, when the process becomes familiar, increased confidence and extrinsic motivation result . Staff have an opportunity to help athletes overcome their initial satisfaction and motivation reductions by providing praise, positive reinforcement, and constructive feedback.
Athletes may feel the need to alter their responses to fall in line with perceived expectations. Staff should also facilitate the creation of a supportive environment in which athletes do not feel the need to shade their responses, so that this does not occur.
Wellness monitoring presumes honest, consistent reporting, and relies upon trust between all parties. Without an environment that fosters this trust, the applicability of wellness data is limited. Even with absolute trust, it’s essential to understand that athlete perceptions can be affected by their performance during, or at the outcome of, a game . Fessi et al. (2018) found that professional soccer players reported reduced sleep quality alongside higher levels of stress and fatigue the morning after losing a match, compared with drawing or winning the match . Additionally, player rating of perceived exertion was higher by a very large magnitude following a loss compared with a draw or a win .
Another barrier to consider is the potential for players to misrepresent their wellness data in attempt to manipulate subsequent workload requirements or coach perceptions . This is a tricky barrier to conquer, but it can be done. Ensuring that only the eyes of the support staff see the data, as opposed to inclusion of coaches and/or management, may help promote athlete honesty. Anonymizing the data or aggregating the data by player position are other strategies for protecting player privacy, while still providing value for coaches and management.
A Key Concept for ASRM Data Interpretation
It must be understood that wellness data is not standardized between individuals, and equivalent scores may not indicate equivalent levels of fatigue and/or wellness . The data must be considered within the individual context of each player and, thus, it’s necessary to use relative change within each player when interpreting longitudinal trends amongst groups.
Using the z-score deviation from a player’s mean (i.e. average) or standard difference score to determine worthwhile differences is a viable strategy and has been used previously [5, 10, 14, 26, 48]. It would be wise to first collect a decent amount of data per athlete in order to get an understanding of the smallest worthwhile change (SWC) and typical error of measurement . This strategy will allow the applied sport professional to be confident if/when making decisions surrounding the meaningfulness of any observed changes .If data is misinterpreted by the performance specialist, or even worse, the coaching staff/management, distrust between players and staff may result.
The main purpose of athlete self-report measures (ASRM) implementation is to identify athletes whose recovery–stress states do not correspond with the training schedule. We shouldn’t allow ourselves to be distracted by a sea of cool and fancy technology; ASRM can be a relatively simple and dirt-cheap means of determining an athlete’s training load and subsequent responses to that training. Through early intervention by support staff, individual training can be adapted in order to help the athlete deal with training stress, optimize recovery, and subsequently prevent overtraining and/or improve performance.
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