Overtraining syndrome (OTS) is reflected as a long-term decrement in performance caused by an imbalance between recovery and the accumulation of training and/or nontraining stress (22). It is estimated that between 30% and 60% of athletes will experience some form of performance decrement associated with OTS and this performance decrement can persist for weeks to months, and in some cases, even years (24). Furthermore, symptoms of OTS have been associated with rates of injury incidence (43) and overload and overuse musculoskeletal injuries (14,17). Although the etiology of OTS is considered to be multifactorial in nature (3,18), researchers have developed many different methodologies to diagnose OTS (18,40,41) and to monitor recovery (15,16).
One such methodology is the use of resting measures of heart rate variability (HRV) as a noninvasive method of monitoring the autonomic nervous system (ANS) function of an individual (1). Heart rate variability is defined as the variability between successive R-R intervals on an electrocardiogram (38) and has been used as a methodology to describe the balance between the parasympathetic nervous system (PSNS) and sympathetic nervous system (SNS) branches of the ANS (4). Therefore, higher measures of HRV are generally reflective of greater PSNS activity (or lesser SNS activity) and lower measures of HRV are reflective of lesser PSNS activity (or greater SNS activity).
It has been hypothesized that the implementation of a short-term, high-volume training load among trained individuals results in a shift in ANS function that favors a reduction in PSNS activity during rest (or an increase in SNS activity), reflecting a systemic physiological overload to the athlete (19). Recent research has provided support for this hypothesis as recent studies have suggested that a short-term, yet marked, increase in physical training results in a decrease in measures of HRV (23,28,30,39). Furthermore, the literature also suggests that these same measures of HRV return to baseline after a decrease in training load (i.e., a taper) (28,30). Collectively, these results suggest that measures of HRV are capable of describing the influence of this increased physiological load on the ANS in a noninvasive fashion and that measures of HRV can also monitor the athlete's subsequent recovery from this training overload (6). Accordingly, researchers and practitioners alike have begun to use measures of HRV to examine the influence of short-term training loads on ANS function and the diagnosis of athletes potentially suffering from OTS has begun to grow among researchers in recent years (5,6,10).
Moreover, it has recently been hypothesized that a link exists between ANS dysfunction, as characterized by measures of HRV, and overuse musculoskeletal injuries (12). Specifically, it has been hypothesized the accumulating microtrauma of the skeletal muscle tissue and other peripheral anatomical structures may alter ANS function by increasing systemic SNS activity. As such, it is possible that measures of HRV could be used longitudinally to monitor the cumulative physical workload being placed on an athlete, and therefore, attempt to prevent the physiological maladaptations associated with OTS (27,30) and overuse musculoskeletal injuries (12).
Because of the high-intensity nature of baseball pitching (9,36), and the high incidence rate of musculoskeletal injury among professional baseball starting pitchers (7,32) it is important to monitor both the acute and longitudinal stress and recovery to ensure optimal performance and the prevention of overuse injury among this population cohort. As such, the use of HRV measures to monitor workload and recovery, as well as prevent potential injury, may prove to be useful among professional baseball starting pitchers. However, because of their regimented rotation schedule, changes in resting measures of HRV between pitching rotation days must first be understood before measures of HRV can be used to prevent potential OTS and/or injury among baseball starting pitchers.
Therefore, the primary purpose of the current study was to observationally investigate the changes in resting measures of HRV across a 5-day pitching rotation schedule collected during an entire baseball season among professional baseball starting pitchers. A secondary purpose was to examine potential individual differences in resting measures of HRV between these starting pitchers. It was hypothesized that resting measures of HRV would decrease after completion of a normally scheduled start, suggesting that the ANS of these individuals was acutely altered at rest. Based on previous research among other athlete populations, these results would imply resting measures of HRV could theoretically be used to monitor the physiological workload and recovery among the population of professional baseball starting pitchers. In turn, these results would imply that it may be possible for practitioners to use resting measures of HRV to prevent potential OTS and/or musculoskeletal injury among this population cohort. Preliminary results from this investigation have only been previously published in abstract form (8).
Experimental Approach to the Problem
To date, resting measures of HRV among professional baseball starting pitchers have yet to be reported in the literature. Accordingly, the current study was an observational investigation designed to examine the changes in resting measures of HRV (dependent variable) across a 5-day pitching rotation schedule during the course of an entire baseball season among professional baseball starting pitchers. As such, resting HRV and the rotation day of each starting pitcher's respective rotation schedule (i.e., days 1, 2, 3, 4, and 5) served as the as the dependent and independent variables in this investigation, respectively.
Eight male Single-A level professional baseball starting pitchers volunteered to participate in the current study (mean ± SD at start of data collection; age = 21.9 ± 1.3 years, height = 185.4 ± 3.6 cm, weight = 85.2 ± 7.5 kg). For the duration of the entire study, all participants were at least 18 years of age (range = 19–23 years) and free from any musculoskeletal injury that resulted in a disabled list designation, as determined by the baseball club. Before any data were collected, the methods of this study were approved by the institutional review board at the University of Wisconsin-Milwaukee. In addition, all participants signed documents providing written informed consent to the study protocol.
Resting measures of HRV were collected daily throughout the entire season (April 3–August 31) by the baseball team's strength and conditioning and/or athletic training staff. Due to limitations in team travel arrangements, variability in game start times, and overall schedule commitments of the participants, the exact time of day in which these measures were collected varied across days. However, all resting measures were recorded on arrival to the team facility and before the completion of any physical training or team practice.
All resting measures of HRV were collected using Bioharness 3 wireless physiological status monitors (Zephyr Technology Corp., Annapolis, MD, USA) at a sampling rate of 250 Hz and stored on a laptop (ThinkPad T440; Lenovo Ltd., Morrisville, NC, USA) for analysis. The HRV data were collected with the participant quietly lying supine on a treatment table in the locker room for 10 minutes. Per the recommendations by the Task Force of the European Society of Cardiology and The North American Society of Pacing and Electrophysiology (38), the square root of the mean sum of the squared differences (RMSSD) between R-R intervals was calculated during the middle 5 minutes of each 10-minute R-R series data file using Kubios HRV 2.1 software (University of Eastern Finland, Kuopio, Finland). Ectopic beats in the HRV data sample were automatically removed and replaced with interpolated adjacent R-R interval values using a “low” filter as described by Tarvainen et al. (37). In addition, to reduce any potential nonuniformity or skewness in the HRV data, all RMSSD data were log transformed (ln) by taking the natural logarithm (lnRMSSD) before conducting any statistical analyses (27).
Based on the guidelines previously recommended in the literature in regards to collecting HRV data with athletes (6), the time domain HRV measure of RMSSD was chosen over other time domain or frequency domain measures of HRV as RMSSD is considered more accurate (20), stable (44), and reliable (2) during short-term HRV recordings collected in the supine position under a spontaneous breathing pattern. Specifically, moderate-to-good (31) test-retest reliability and concurrent validity (in comparison with a 12-lead ECG module) has been previously identified in the literature among the HRV measure of RMSSD during resting conditions of HRV data collection, with intraclass correlation coefficients ranging from 0.72 to 0.85 (21,35) and 0.97 (25), respectively. In addition, Al Haddad et al. (2) suggests that the typical error of measurement of resting lnRMSSD measures is 0.40 (90% confidence interval [CI] = 0.33–0.49), with a coefficient of variation of 12.3% (90% CI = 10.3–15.5), which is lower than other commonly used HRV measures. Furthermore, because participant breathing rate during HRV data collection was not controlled by the investigators, the time domain HRV measure of RMSSD was also chosen because previous research also suggests that RMSSD is not influenced by changes in breathing rate, unlike various frequency domain measures of HRV (26), and thus, is recommended for longitudinal monitoring of HRV among athletes (6,33).
Finally, per recommendations in the previous literature (6,29), measures of resting HRV were obtained from at least 3 days per week throughout the participant's inclusion in the study. However, if a participant suffered a musculoskeletal injury, he was removed as an active starting pitcher, or was traded or released from the baseball team, data collection was ceased with that respective participant for the remainder of the study. As such, although all participants started the data collection process of the current study together, the number of total HRV measures varied across participants (Table 1).
Last, the rotation day in each starting pitcher's respective rotation schedule (i.e., days 1, 2, 3, 4, and 5) was recorded daily for each participant. Specifically, day 1 represented the day of pitching (i.e., a schedule start) and day 5 represented the last day before the next start.
Because measures of HRV were collected repeatedly across participants, and that these measures were taken throughout the duration of the season, a split-plot repeated-measures analysis of variance was used to examine the influence of pitching rotation day on resting lnRMSSD, as well as differences in resting lnRMSSD among pitchers. In addition, effect sizes were evaluated using a partial eta squared (
< 0.06, 0.06 ≤
< 0.14, and 0.14 ≤
indicating a small, medium, and large effect, respectively (13). All statistical analyses were conducted using IBM SPSS 22 statistical software (IBM Corp., Armonk, NY, USA). An alpha level of p ≤ 0.05 determined statistical significance for all analyses.
The interaction effect between rotation day and pitcher was not statistically significant (F28,706 = 1.020, p = 0.438,
= 0.039). However, a statistically significant main effect of rotation day on resting lnRMSSD was identified (F4,706 = 3.139, p = 0.029,
= 0.304). Follow-up pairwise analyses indicated that resting lnRMSSD on day 2 was significantly lower than all other rotation days (p ≤ 0.05). When coupled with the large effect size, this implies that the resting lnRMSSD among these professional baseball starting pitchers was substantially lower 1 day after completing their normally scheduled start. However, the resting lnRMSSD among this population group returned to its original baseline value (i.e., day 1) by day 3 of the rotation schedule, and thus, before their next scheduled start (Table 2).
In addition, a statistically significant main effect of pitcher on resting lnRMSSD was also identified (F7,706 = 83.388, p < 0.001,
= 0.954). When coupled with the large effect size, this implies that the season average resting lnRMSSD of each participant varied drastically among this population of professional baseball starting pitchers (Figure 1).
The primary purpose of the current study was to identify potential changes in ANS function across a 5-day pitching rotation schedule by examining resting measures of HRV collected from the previously uninvestigated athlete population of professional baseball starting pitchers during an entire baseball season. The results of the current study suggest that professional baseball starting pitchers exhibit altered ANS function 1 day after completing a normally scheduled start, as the resting measures of HRV among this population cohort were significantly (p ≤ 0.05) lower on day 2 than any other rotation day (Table 2). Furthermore, the large effect size (
= 0.304) implies that a substantial shift in ANS function, toward predominately less vagal tone (i.e., PSNS activation) during rest, had occurred by day 2. These results are similar to the acute decreases in HRV that have been previously associated with large concurrent increases in physical training among other elite athlete populations (28). When coupled with the fact that starting pitching has been previously identified as a highly intense physiological task (9,36), it is possible that the acutely altered ANS function observed in the current study reflects the systemic physiological response to the intense workload that was recently placed on the pitcher in the previous 24 hours. As such, these results suggest that resting measures of HRV can be used to noninvasively monitor the systemic physiological workload being acutely placed on these athletes.
In addition, the results of the current study also suggest that this ANS dysfunction is no longer apparent 2 days after completing a normally scheduled start, as the resting measures of HRV among these professional baseball starting pitchers returned to their baseline values by day 3. Therefore, although a single bout of pitching appears to have a negative impact on a starting pitcher's ANS, this dysfunction seems to dissipate roughly 48 hours after the pitching bout, and thus, this systemic physiological impact is theoretically resolved before their next scheduled start. Collectively, these results suggest that resting measures of HRV can be used to noninvasively monitor the workload being placed on these athletes as well as track recovery between subsequent starts. Similar to other previously examined athlete populations (4,6,11,23,30,39), this methodology could potentially be used to monitor the longitudinal balance between workload and recovery in an attempt to prevent the physiological maladaptations associated with OTS and musculoskeletal injury among professional baseball starting pitchers. For example, if the resting measures of HRV for a given starting pitcher remain depressed beyond day 2, this could potentially be an indicator of an inappropriate level of previous workload and/or inadequate recovery between starts. Furthermore, if this decline in resting measures of HRV among a given starting pitcher becomes prolonged across the season (i.e., identified during more than 1 rotation cycle), this trend may theoretically indicate that a pitcher is at risk of developing OTS (16) and/or a musculoskeletal injury (12). However, to confirm the practical impact of these changes in ANS among professional baseball starting pitchers, further research investigating the relationship between changes in resting measures of HRV and subsequent declines in pitching performance and/or musculoskeletal injury remains warranted.
In addition, the results of the current study are also similar to previous findings among other athlete populations that suggest that HRV measures must be collected a minimum of 3 days per week (6,29). However, if researchers and/or practitioners are attempting to track workload and recovery across a normal pitching rotation schedule among this athlete population, the results of the current study also suggest that perhaps daily measures of HRV must be collected. For example, a decline in resting HRV on day 2 could simply be the result of the acute impact that a normally scheduled start has on the ANS of a professional baseball starting pitcher, and thus, may not accurately reflect ANS dysfunction that warrants intervention by a practitioner. Furthermore, when coupled with the fact that a standard pitching rotation schedule is shorter than a typical week (i.e., 5 vs. 7 days), the ability of a researcher and/or practitioner to identify this acute impact would be diminished if daily measures of HRV were not collected.
A secondary purpose of the current study was to examine potential individual differences in resting measures of HRV between these professional baseball starting pitchers. The statistically significant main effect of pitcher on resting lnRMSSD indicates that the season average resting lnRMSSD was uniquely different for each participant in this population cohort (Figure 1). Similar to previous research among other athlete populations (6,11,27,30,42), these results also suggest that a single-subject case analysis approach may be warranted when longitudinally using this methodology to monitor ANS function among the athlete population of professional baseball starting pitchers. As such, practitioners should examine and interpret the resting HRV data of each starting pitcher on an individual basis when attempting to track the workload and recovery of a given starting pitcher over the course of a baseball season.
There are, however, limitations to the current study that should be noted. Because of the previously described differences in the number of total HRV measures collected across participants (Table 1), the number of total HRV measures differed across rotation days as well (Table 2). Thus, the differences in total number of HRV measures across pitchers may have influenced the results of the current study. The population cohort used in the current study also consisted of a small sample size (N = 8) of professional baseball starting pitchers who were all members of the same Single-A minor league team. Although the results are the first to provide insight into daily HRV responses among professional baseball starting pitchers, caution must be used when generalizing beyond minor league starting pitchers. Accordingly, future research should use similar methodology to investigate if comparable results are observed among different competition levels (e.g., Single-A vs. Double-A vs. Triple-A) of professional baseball starting pitchers. In addition, there is a need to examine daily HRV responses between pitcher type (e.g., starter vs. reliever), as well as among position players.
It should also be noted that although each player followed a similar sport-specific training program, the daily activities and training load completed by the participants across the rotation schedule was not controlled by the investigators. As such, variations in activities and/or training load may have influenced the HRV measures when examined across rotation days. These variations in training load across participants were also not controlled during the months leading up to the beginning of the study (i.e., the off-season). Furthermore, although all HRV data were collected immediately on arrival to the clubhouse and before the completion of any physical training, the time of day in which these measures were collected varied because of limitations in team travel arrangements, variability in game start times, and overall schedule commitments of the participants. Because the conditions in which HRV measures were collected may influence the reliability of the respective HRV measures (1,34), this fact should be noted as a limitation of the current study. However, because athletes are commonly prescribed individualized training loads by a team's training staff, and the described logistical limitations are similar among all professional baseball starting pitchers, the results of the current study not only indicate an ecologically valid assessment of the impact of a pitching start on ANS function among this population cohort, but further substantiates the rationale for using a single-subject case analysis approach when longitudinally examining measures of HRV. Moreover, the HRV data were collected across the entire baseball season, and thus, reflects a longitudinal representation of the changes in HRV across a standard 5-day rotation schedule.
In conclusion, the results of the current study suggest that professional baseball starting pitchers exhibit altered ANS function during the course of a standard 5-day rotation schedule, as the resting measures of HRV among this population cohort were significantly lower 1 day after completing a normally scheduled start. However, this ANS dysfunction was no longer apparent roughly 2 days after completing this normally scheduled start. In addition, these resting measures of HRV differed significantly across participants. Collectively, these results imply that resting measures of HRV are not only capable of noninvasively monitoring the physiological workload that is placed on professional baseball starting pitchers, but can also be used to track recovery between subsequent starts. Therefore, it is possible that HRV measures could be used in an attempt to prevent OTS and overuse musculoskeletal injury among professional baseball starting pitchers. Accordingly, future research should use a single-subject case analysis approach in a longitudinal manner to monitor the balance between workload and recovery across an entire baseball season. Furthermore, future research should also use similar methodology to investigate if changes in ANS function can be identified among different competition levels of professional baseball starting pitchers, as well as among other position players and other previously uninvestigated athlete populations.
The results of the current study suggest that resting measures of HRV can be used by researchers and practitioners alike as a method of noninvasively monitoring the acute systemic physiological workload, and subsequent recovery, during a standard 5-day rotation schedule among professional baseball starting pitchers. Specifically, the results of the current study suggest that the ANS among this population cohort is altered 1 day after completing a normally scheduled start (i.e., day 2), but that this ANS dysfunction is restored to baseline values 1 day later (i.e., day 3). Based on these results, practitioners may be able to use in measures of HRV among professional baseball starting pitchers in an attempt to prevent OTS and potential musculoskeletal injury from occurring because of inappropriate cumulative physiological workloads and/or inadequate recovery between starts.
However, although previous research suggests that only 3 measures of HRV are required for an accurate weekly representation of HRV for an individual (29), it is possible that these measures of HRV may need to be collected daily among professional baseball starting pitchers. Specifically, the results of the current study suggest that the measures of HRV vary across rotation days, and thus, if practitioners wish to appropriately capture the changes in ANS function throughout the rotation schedule, daily measures of HRV may need to be collected when longitudinally monitoring workload across the entire baseball season.
In addition, the results of the current study also suggest that the resting measures of HRV differ drastically between individuals, and thus, when attempting to use resting HRV in a longitudinal manner, a single-subject case analysis approach may be warranted. Although future research is required, it is possible that resting measures of HRV could be used by practitioners to monitor cumulative workload and recovery patterns across an entire season in an attempt to prevent OTS and overuse musculoskeletal injury among individual professional baseball starting pitchers.
The authors acknowledge the support of the Milwaukee Brewers Baseball Club for providing access to equipment and funding for travel and research expenses related to this project. In particular, the authors thank Robert Flees for assistance with data entry and the players and staff of the Wisconsin Timber Rattlers team for their assistance with the data collection process.
1. Achten J, Jeukendrup AE. Heart rate monitoring: Applications and limitations. Sports Med 33(7): 517–538, 2003.
2. Al Haddad H, Laursen PB, Chollet D, Ahmaidi S, Buchheit M. Reliability of resting and postexercise heart rate measures. Int J Sports Med 32: 598–605, 2011.
3. Armstrong LE, VanHeest JL. The unknown mechanism of the overtraining Syndrome: Clues from depression and psychoneuroimmunology. Sports Med 32: 185–209, 2002.
4. Aubert AE, Seps B, Beckers F. Heart rate variability in athletes. Sports Med 33: 889–919, 2003.
5. Bosquet L, Merkari S, Arvisais D, Aubert AE. Is heart rate a convenient tool to monitor over-reaching? A systematic review of the literature. Br J Sports Med 42: 709–714, 2008.
6. Buchheit M. Monitoring training status with HR measures: Do all roads lead to Rome? Front Physiol 5: 1–19, 2014.
7. Conte S, Requa RK, Garrick JG. Disability days in major league baseball. Am J Sports Med 29: 431–436, 2001.
8. Cornell DJ, Ebersole KT, Caplinger RA, Seligman JR, Paxson JL, Davis NA. Influence of pitching rotation day on resting heart rate variability among professional baseball starting pitchers [abstract]. Med Sci Sports Exerc 47: 49, 2015.
9. Cornell DJ, Paxson JL, Caplinger RA, Seligman JR, Davis NA, Flees RJ, Ebersole KT. In-game heart rate responses among professional baseball starting pitchers. J Strength Cond Res. Epub ahead-of-print.
10. dos Santos Leite G, Sampaio LM, Serra AJ, de Jesus Miranda ML, Ferreira MR, Brandão RB. Analysis of knowledge production about overtraining associated with heart rate variability. J Exerc Physiol Online 15: 20–29, 2012.
11. Flatt AA, Esco MR. Evaluating individual training adaptation with smartphone-derived heart rate variability in a collegiate female soccer team. J Strength Cond Res 30: 378–385, 2016.
12. Gisselman AS, Baxter GD, Wright A, Hegedus E, Tumilty S. Musculoskeletal overuse injuries and heart rate variability: Is there a link? Med Hypotheses 87: 1–7, 2016.
13. Huck SW. Two-way analyses of variance. In: Reading Statistics and Research (6th ed.). Huck SW, ed. Boston, MA: Pearson, 2012. pp. 276–311.
14. Johnson MB, Thiese SM. A review of overtraining syndrome—Recognizing the signs and symptoms. J Athl Train 27: 352–354, 1992.
15. Kellmann M. Preventing overtraining in athletes in high-intensity sports and stress/recovery
monitoring. Scand J Med Sci Sports 20(Suppl 2): 95–102, 2010.
16. Kenttä G, Hassmén P. Overtraining and recovery
: A conceptual model. Sports Med 26: 1–16, 1998.
17. Kibler WB, Chandler TJ, Stracener ES. Musculoskeletal adaptations and injuries due to overtraining. Exerc Sport Sci Rev 20: 99–126, 1992.
18. Kreher JB, Schwartz JB. Overtraining syndrome: A practical guide. Sports Health 4: 128–138, 2012.
19. Kuipers H. Training and overtraining: An introduction. Med Sci Sports Exerc 30: 1137–1139, 1998.
20. Kuss O, Schumann B, Kluttig A, Greiser KH, Haerting J. Time domain parameters can be estimated with less statistical error than frequency domain parameters in the analysis of heart rate variability. J Electrocardiol 41: 287–291, 2008.
21. Leicht AS, Allen GD. Moderate-term reproducibility of heart rate variability during rest and light to moderate exercise in children. Braz J Med Biol Res 41: 627–633, 2008.
22. Meeusen R, Duclos M, Foster C, Fry A, Gleeson M, Nieman D, Raglin J, Rietjens G, Steinacker J, Urhausen A. Prevention, diagnosis, and treatment of the overtraining syndrome: Joint consensus statement of the European College of Sport Science and the American College of Sports Medicine. Med Sci Sports Exerc 45: 186–205, 2013.
23. Morales J, Álamo JM, García-Massó X, 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.
24. Nederhof E, Lemmink KAMP, Visscher C, Meeusen R, Mulder T. Psychomotor speed: Possibly a new marker for overtraining syndrome. Sports Med 36: 817–828, 2006.
25. Nunan D, Donovan G, Jakovljevic DG, Hodges LD, Sandercock GRH, Brodie DA. Validity and reliability of short-term heart-rate variability from the Polar S810. Med Sci Sports Exerc 41: 243–250, 2009.
26. Penttilä J, Helminen A, Jartti T, Kussela T, Huikuri HV, Tulppo MP, Coffeng R, Scheinin H. Time domain, geometrical and frequency domain analysis of cardiac vagal outflow: Effects of various respiratory patterns. Clin Physiol 21: 365–376, 2001.
27. 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.
28. Plews DJ, Laursen PB, Kilding AE, Buchheit M. Heart rate variability and training-intensity distribution in elite rowers. Int J Sports Physiol Perform 9: 1026–1032, 2014.
29. 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.
30. Plews DJ, Laursen PB, Stanley J, Kilding AE, Buchheit M. Training adaptation and heart rate variability in elite endurance athletes: Opening the door to effective monitoring. Sports Med 43: 773–781, 2013.
31. Portney LG, Watkins MP. Statistical measures of reliability. In: Foundations of Clinical Research: Applications to Practice (3rd ed.). Portney LG, Watkins MP, eds. Upper Saddle River, NJ: Pearson Education, 2009. pp. 585–618.
32. Posner M, Cameron KL, Wolf JL, Belmont PJ Jr, Owens BD. Epidemiology of major league baseball injures. Am J Sports Med 39: 1676–1680, 2010.
33. Saboul D, Pialoux V, Hautier C. The impact of breathing on HRV measurements: Implications for the longitudinal follow-up of athletes. Eur J Sport Sci 13: 534–542, 2013.
34. Sandercock GRH, Bromley PD, Brodie DA. The reliability of short-term measurements of heart rate variability. Int J Cardiol 103: 238–247, 2005.
35. Sinnreich R, Kark JD, Friedlander Y, Sapoznikov D, Luria MH. Five minute recordings of heart rate variability for population studies: Repeatability and age-sex characteristics. Heart 80: 156–162, 1998.
36. Szymanski DJ. Physiology of baseball pitching dictates specific exercise intensity for conditioning. Strength Cond J 31: 41–47, 2009.
37. Tarvainen MP, Niskanen JP, Lipponen JA, Ranta-Aho PO, Karjalainen PA. Kubios HRV–heart rate variability analysis software. Comput Methods Programs Biomed 113: 210–220, 2014.
38. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation 93: 1043–1065, 1996.
39. Tian Y, He ZH, Zhao JX, Tao DL, Xu KY, Earnest CP, Mc Naughton LR. Heart rate variability threshold values for early-warning nonfunctional overreaching in elite female wrestlers. J Strength Cond Res 27: 1511–1519, 2013.
40. Urhausen A, Kindermann W. Diagnosis of overtraining: What tools do we have? Sports Med 32: 95–102, 2002.
41. Uusitalo AL. Overtraining: Making a difficult diagnosis and implementing target treatment. Phys Sportsmed 29: 35–50, 2001.
42. Vesterinen V, Nummela A, Heikura I, Laine T, Hynynen E, Botella J, Häkkinen K. Individual endurance training prescription with heart rate variability. Med Sci Sports Exerc 48: 1347–1354, 2016.
43. Vetter RE, Symonds ML. Correlations between injury, training intensity, and physical and mental exhaustion among college athletes. J Strength Cond Res 24: 587–596, 2010.
44. Young FL, Leicht AS. Short-term stability of resting heart rate variability: Influence of position and gender. Appl Physiol Nutr Metab 36: 210–218, 2011.