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Daniel J. Plews, Paul B. Laursen, Yann Le Meur, Christophe Hausswirth, Andrew E. Kilding and Martin Buchheit

Purpose:

To establish the minimum number of days that heart-rate-variability (HRV, ie, the natural logarithm of square root of the mean sum of the squared differences between R-R intervals, Ln rMSSD) data should be averaged to achieve correspondingly equivalent results as data averaged over a 1-wk period.

Methods:

Standardized changes in Ln rMSSD between different phases of training (normal training, functional overreaching (FOR), overall training, and taper) and the correlation coefficients of percentage changes in performance vs changes in Ln rMSSD were compared when averaging Ln rMSSD from 1 to 7 d, randomly selected within the week.

Results:

Standardized Ln rMSSD changes (90% confidence limits, CL) from baseline to overload (FOR) were 0.20 ± 0.28, 0.33 ± 0.26, 0.49 ± 0.33, 0.48 ± 0.28, 0.47 ± 0.26, 0.45 ± 0.26, and 0.43 ± 0.29 on days 1 to 7, respectively. Correlations (90% CL) over the same time sequence and training phase were –.02 ± .23, –.07 ± .23, –.17 ± .22, –.25 ± .22, –.26 ± .22, –.28 ± .21, and –.25 ± .22 on days 1 to 7. There were almost perfect quadratic relationships between standardized changes/r values vs the number of days Ln rMSSD was averaged (r 2 = .92 and .97, respectively) in trained triathletes during FOR. This indicates a plateau in the increase in standardized changes/r values’ magnitude after 3 and 4 d, respectively, in trained triathletes.

Conclusion:

Practitioners using HRV to monitor training adaptation should use a minimum of 3 (randomly selected) valid data points per week.

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Paula B. Debien, Marcelly Mancini, Danilo R. Coimbra, Daniel G.S. de Freitas, Renato Miranda and Maurício G. Bara Filho

the monitored training sessions was specified as the inclusion criterion. The athletes were familiarized with all adopted procedures and tests, which were commonly used during their training program. The study was approved by the Ethics Committee in Research with Humans at the Federal University of

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Daniele Conte, Nicholas Kolb, Aaron T. Scanlan and Fabrizio Santolamazza

, Smiley K , Thomas C , Favero TG . Performance profile of NCAA Division I men’s basketball games and training sessions . Biol Sport . 2016 ; 33 ( 2 ): 189 – 194 . PubMed ID: 27274114 doi:10.5604/20831862.1200512 10.5604/20831862.1200512 2. Halson SL . Monitoring training load to understand

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Lilian Roos, Wolfgang Taube, Carolin Tuch, Klaus Michael Frei and Thomas Wyss

athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review . Br J Sports Med . 2016 ; 50 ( 5 ): 281 – 291 . PubMed ID: 26423706 doi:10.1136/bjsports-2015-094758 10.1136/bjsports-2015-094758 12. Halson SL . Monitoring training load to

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Carl Foster, Jose A. Rodriguez-Marroyo and Jos J. de Koning

Training monitoring is about keeping track of what athletes accomplish in training, for the purpose of improving the interaction between coach and athlete. Over history there have been several basic schemes of training monitoring. In the earliest days training monitoring was about observing the athlete during standard workouts. However, difficulty in standardizing the conditions of training made this process unreliable. With the advent of interval training, monitoring became more systematic. However, imprecision in the measurement of heart rate (HR) evolved interval training toward index workouts, where the main monitored parameter was average time required to complete index workouts. These measures of training load focused on the external training load, what the athlete could actually do. With the advent of interest from the scientific community, the development of the concept of metabolic thresholds and the possibility of trackside measurement of HR, lactate, VO2, and power output, there was greater interest in the internal training load, allowing better titration of training loads in athletes of differing ability. These methods show much promise but often require laboratory testing for calibration and tend to produce too much information, in too slow a time frame, to be optimally useful to coaches. The advent of the TRIMP concept by Banister suggested a strategy to combine intensity and duration elements of training into a single index concept, training load. Although the original TRIMP concept was mathematically complex, the development of the session RPE and similar low-tech methods has demonstrated a way to evaluate training load, along with derived variables, in a simple, responsive way. Recently, there has been interest in using wearable sensors to provide high-resolution data of the external training load. These methods are promising, but problems relative to information overload and turnaround time to coaches remain to be solved.

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Ville Vesterinen, Ari Nummela, Sami Äyrämö, Tanja Laine, Esa Hynynen, Jussi Mikkola and Keijo Häkkinen

Regular monitoring of adaptation to training is important for optimizing training load and recovery, which is the main factor in successful training.

Purpose:

To investigate the usefulness of a novel submaximal running test (SRT) in field conditions in predicting and tracking changes of endurance performance.

Methods:

Thirty-five endurance-trained men and women (age 20–55 y) completed the 18-wk endurance-training program. A maximal incremental running test was performed at weeks 0, 9, and 18 for determination of maximal oxygen consumption (VO2max) and running speed (RS) at exhaustion (RSpeak) and lactate thresholds (LTs). In addition, the subjects performed weekly a 3-stage SRT including a postexercise heart-rate-recovery (HRR) measurement. The subjects were retrospectively grouped into 4 clusters according to changes in SRT results.

Results:

Large correlations (r = .60–.89) were observed between RS during all stages of SRT and all endurance-performance variables (VO2max, RSpeak, RS at LT2, and RS at LT1). HRR correlated only with VO2max (r = .46). Large relationships were also found between changes in RS during 80% and 90% HRmax stages of SRT and a change of RSpeak (r = .57, r = .79). In addition, the cluster analysis revealed the different trends in RS during 80% and 90% stages during the training between the clusters, which showed different improvements in VO2max and RSpeak.

Conclusions:

The current SRT showed great potential as a practical tool for regular monitoring of individual adaptation to endurance training without time-consuming and expensive laboratory tests.

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Joshua Christen, Carl Foster, John P. Porcari and Richard P. Mikat

Purpose:

The session rating of perceived exertion (sRPE) has gained popularity as a “user friendly” method for evaluating internal training load. sRPE has historically been obtained 30 min after exercise. This study evaluated the effect of postexercise measurement time on sRPE after steady-state and interval cycle exercise.

Methods:

Well-trained subjects (N = 15) (maximal oxygen consumption = 51 ± 4 and 36 ± 4 mL/kg [cycle ergometer] for men and women, respectively) completed counterbalanced 30-minute steady-state and interval training bouts. The steady-state ride was at 90% of ventilatory threshold. The work-to-rest ratio of the interval rides was 1:1, and the interval segment durations were 1, 2, and 3 min. The high-intensity component of each interval bout was 75% peak power output, which was accepted as a surrogate of the respiratory compensation threshold, critical power, or maximal lactate steady state. Heart rate, blood lactate, and rating of perceived exertion (RPE) were measured. The sRPE (category ratio scale) was measured at 5, 10, 15, 20, 25, 30, and 60 min and 24 h after each ride using a visual analog scale (VAS) to prevent bias associated with specific RPE verbal anchors.

Results:

sRPE at 30 min postexercise followed a similar trend: steady state = 3.7, 1 min = 3.9, 2 min = 4.7, 3 min = 6.2. No significant differences (P > .05) in sRPE were found based on postexercise sampling times, from 5 min to 24 h postexercise.

Conclusions:

Postexercise time does not appear to have a significant effect on sRPE after either steady-state or interval exercise. Thus, sRPE appears to be temporally robust and is not necessarily limited to the 30-min-postexercise window historically used with this technique, although the presence or absence of a cooldown period after the exercise bout may be important.

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Dajo Sanders, Grant Abt, Matthijs K.C. Hesselink, Tony Myers and Ibrahim Akubat

Purpose:

To assess the dose-response relationships between different training-load methods and aerobic fitness and performance in competitive road cyclists.

Methods:

Training data from 15 well-trained competitive cyclists were collected during a 10-wk (December–March) preseason training period. Before and after the training period, participants underwent a laboratory incremental exercise test with gas-exchange and lactate measures and a performance assessment using an 8-min time trial (8MT). Internal training load was calculated using Banister TRIMP, Edwards TRIMP, individualized TRIMP (iTRIMP), Lucia TRIMP (luTRIMP), and session rating of perceived exertion (sRPE). External load was measured using Training Stress Score (TSS).

Results:

Large to very large relationships (r = .54–.81) between training load and changes in submaximal fitness variables (power at 2 and 4 mmol/L) were observed for all training-load calculation methods. The strongest relationships with changes in aerobic fitness variables were observed for iTRIMP (r = .81 [95% CI .51–.93, r = .77 [95% CI .43–.92]) and TSS (r = .75 [95% CI .31–.93], r = .79 [95% CI .40–.94]). The strongest dose-response relationships with changes in the 8MT test were observed for iTRIMP (r = .63 [95% CI .17–.86]) and luTRIMP (r = .70 [95% CI .29–.89).

Conclusions:

Training-load quantification methods that integrate individual physiological characteristics have the strongest dose-response relationships, suggesting this to be an essential factor in the quantification of training load in cycling.

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Monoem Haddad, Johnny Padulo and Karim Chamari

Despite various contributing factors, session rating of perceived exertion has the potential to affect a large proportion of the global sporting and clinical communities since it is an inexpensive and simple tool that is highly practical and accurately measures an athlete’s outcome of training or competition. Its simplicity can help optimize performance and reduce negative outcomes of hard training in elite athletes.

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Annie C. Jeffries, Lee Wallace and Aaron J. Coutts

Purpose:

To describe the training demands of contemporary dance and determine the validity of using the session rating of perceived exertion (sRPE) to monitor exercise intensity and training load in this activity. In addition, the authors examined the contribution of training (ie, accelerometry and heart rate) and non-training-related factors (ie, sleep and wellness) to perceived exertion during dance training.

Methods:

Training load and ActiGraphy for 16 elite amateur contemporary dancers were collected during a 49-d period, using heart-rate monitors, accelerometry, and sRPE. Within-individual correlation analysis was used to determine relationships between sRPE and several other measures of training intensity and load. Stepwise multiple regressions were used to determine a predictive equation to estimate sRPE during dance training.

Results:

Average weekly training load was 4283 ± 2442 arbitrary units (AU), monotony 2.13 ± 0.92 AU, strain 10677 ± 9438 AU, and average weekly vector magnitude load 1809,707 ± 1015,402 AU. There were large to very large within-individual correlations between training-load sRPE and various other internal and external measures of intensity and load. The stepwise multiple-regression analysis also revealed that 49.7% of the adjusted variance in training-load sRPE was explained by peak heart rate, metabolic equivalents, soreness, motivation, and sleep quality (y = –4.637 + 13.817%HRpeak + 0.316 METS + 0.100 soreness + 0.116 motivation – 0.204 sleep quality).

Conclusion:

The current findings demonstrate the validity of the sRPE method for quantifying training load in dance, that dancers undertake very high training loads, and a combination of training and nontraining factors contribute to perceived exertion in dance training.