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Submission type: Case Study Title: Cardiac parasympathetic activity and race performance: An elite triathlete case study Jamie Stanley1,2,3, Shaun D’Auria4, Martin Buchheit5 1 Centre of Excellence for Applied Sport Science Research, Queensland Academy of Sport, Brisbane, Australia 2 The University of Queensland, School of Human Movement Studies, Brisbane, Australia 3 Physiology Department, South Australian Sports Institute, Adelaide, Australia 4 Triathlon Program, Queensland Academy of Sport, Brisbane, Australia 5 Sport Science Unit, Myorobie Association, Montvalezan, France Address for correspondence: Jamie Stanley, School of Human Movement Studies, The University of Queensland, Brisbane, Queensland 4072, Australia; E-mail:
[email protected]; Tel: +61 7 3365 6482; Fax: +61 7 3365 6877. Running heading: Monitoring training adaptation with HRV Abstract word count: 249 Text only word count: 1598 Number of Table: 1 Number of Figures: 2 Disclosures: Nothing to disclose
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Abstract We examined whether changes in heart rate (HR) variability (HRV) could consistently track adaptation to training and race performance during a 32-week competitive season. An elite male long-course triathlete recorded resting HR (RHR) each morning and vagal-related indices of HRV (natural logarithm of square-root of mean squared differences of successive R−R intervals; ln rMSSD and the ratio of ln rMSSD to R−R interval length; ln rMSSD:RR) were assessed. Daily training load was quantified using a power meter and wrist-top GPS device. Trends in HRV indices and training load were examined by calculating standardised differences (ES). The following trends in week-to-week changes were consistently observed: 1) when the triathlete was coping to a training block, RHR decreased [ES, −0.38 (90% confidence limits, −0.05;−0.72)] and ln rMSSD increased [+0.36 (0.71;0.00)], 2) when the triathlete was not coping, RHR increased [+0.65 (1.29;0.00)] and ln rMSSD decreased [−0.60 (0.00;−1.20)], 3) optimal competition performance was associated with moderate decreases in ln rMSSD [−0.86 (−0.76;−0.95)] and ln rMSSD:RR [−0.90 (−0.60;−1.20)] in the week prior to competition, and 4) suboptimal competition performance was associated with small decreases in ln rMSSD [−0.25 (−0.76;−0.95)] and trivial changes in ln rMSSD:RR [−0.04 (0.50;−0.57)] in the week prior to competition. To conclude, in this triathlete, a decrease in RHR concurrent with increased ln rMSSD compared with the previous week consistently appears indicative of positive training adaptation during a training block. A simultaneous reduction in ln rMSSD and ln rMSSD:RR during the final week preceding competition appears consistently indicative of optimal performance. Keywords: monitoring; cardiac parasympathetic function; triathlon
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Introduction Knowing how an athlete is responding during key precompetition phases of training and pre-event taper is of particular importance to elite athletes and coaches. Longitudinal monitoring of heart rate (HR) variability (HRV) (e.g., natural logarithm of square-root of mean squared differences of successive R−R intervals; ln rMSSD), has potential for better prescription of training loads,1 predicting maladaptation2 or aerobic performance.3 However, equivocal findings may be due to methodological inaccuracies associated with the large day-to-day variation in HRV.2-4 Further, correct practical interpretation of changes in HRV requires understanding of sympathetic vs. vagal inputs, which can be achieved by normalizing HRV data for the prevailing HR (e.g., ln rMSSD to R−R interval ratio; ln rMSSD:RR).3,4 For example, a trend towards reduced ln rMSSD with concurrent reduction in ln rMSSD:RR suggests vagal saturation, while concurrent increase in ln rMSSD:RR suggests increased sympathetic activity.3 Such responses demonstrate how interpreting HRV alone may inaccurately diagnose adaptive responses to training.4 Despite the potential of these HRV indices for monitoring training adaptation,1,3,5 such observations are currently limited to isolated events in elite rowers.3,6 Whether such HRV patterns are reproducible and consistent throughout repeated events for athletes of different sports remains unknown. Therefore, the aim of this study was to monitor a long-course triathlete during a 32-week competitive season to determine whether trends in vagal-related indices of HRV with respect to training stimulus and competition performance were reproduced and consistent. Methods Subjects A professional male (28 years) triathlete was monitored throughout a 32-week competitive season that included 5 triathlons (1.9 km swim, 90.1 km cycle, 21.1 km run). At the start of the monitoring period his height was 1.79 m , body mass 71.8 kg, maximal aerobic capacity 72.2 mL.kg–1.min–1, and peak power output 438 W.7 Maximal values were obtained during a test to exhaustion (25 W increments every 60 s starting at 125 W). Training was periodised with the goal of peaking for each successive event. The study procedure was approved by the Human Research Ethics Committee at The University of Queensland. Experimental overview Training and racing was monitored using a power meter (Riken, Quarq Technology, USA) for cycling and a wrist-top GPS (Ambit, Suunto Oy, Finland) for running, and processed 3
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in dedicated software (WKO+ v3.0, PeaksWare, USA). Every swimming session was recorded and processed manually. HR was recorded upon waking in a seated position for 5 min using a HR monitor (Ambit, Suunto Oy, Finland). Data analysis Daily training load was expressed as a Training Stress Score (TSS), Acute Training Load, and Chronic Training Load as described by Allen and Coggan.8 Daily waking resting HR (RHR) and HRV data was processed7 and analysed as reported elsewhere.2,3 Responses to training were assessed via retrospective analysis of training logs. A ‘coping’ response to training was deemed only if all weekly programmed sessions were completed successfully. A race performance within 10% of the winner’s overall time was classified as optimal based on this triathlete’s initial world ranking (>300). The winner’s performance was assumed comparable between events because each was either an Olympian or ex-world champion. Statistical analysis Weekly data are expressed as means (Monday–Sunday) and 90% confidence intervals (CI) unless stated otherwise. Weekto-week differences in HR-based variables and training load are expressed as standardised mean difference (ES) and assessed using an approach based on magnitudes of change.9 The following threshold values for ES statistics were: ≤0.2 (trivial), >0.2 (small), >0.6 (moderate), >1.2 (large), >2.0 (very large).9 Results Average weekly training duration ± SD for the 32-week monitoring period was 14 ± 5 h. Training comprised of 24.6% swimming, 48.3% cycling and 27.1% running based on the weekly total TSS. The weekly average ± SD for RHR was 52.3 ± 2.5 bpm, ln rMSSD was 4.0 ± 0.2 ms, and ln rMSSD:RR(×103) was 3.5 ± 0.1. Figure 1 depicts longitudinal changes in race performance relative to the winner, ratings of perceived fatigue, HR-based measures, and training load. The triathlete improved his world ranking from >300 to 69th after the 5th competition. Table 1 displays weekly changes in perceived fatigue, HRbased measures and contextual interpretation.4 The following trends in week-to-week changes were consistently observed: 1) when the triathlete was ‘coping’, small decreases in RHR [ES, −0.38 (90% confidence limits, −0.05;−0.72)] and small increases in ln rMSSD [+0.36 (0.71;0.00)] were observed, 2) when the triathlete was not ‘coping’, RHR moderately increased [+0.65 (1.29;0.00)] and ln rMSSD moderately decreased [−0.60 (0.00;−1.20)], 3) optimal competition performance was associated with moderate decreases in ln 4
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rMSSD [−0.86 (−0.76;−0.95)] and ln rMSSD:RR [−0.90 (−0.60;−1.20)] in the week prior to competition, and 4) suboptimal competition performance was associated with small decreases in ln rMSSD [−0.25 (−0.76;−0.95)] and trivial changes in ln rMSSD:RR [−0.04 (0.50;−0.57)] in the week prior to competition. Discussion This study provides novel and practical findings regarding the use of HR-based indexes for monitoring training adaptation. First, variations in training load induce changes in HR-based indices, which may be dissociated from the triathlete’s training status. Second, consistent trends in HR-based indices were observed when the triathlete was coping vs. not coping during a training block and during the week prior to an optimal vs. suboptimal competition performance. In the present study, the triathlete cycled through four precompetition training blocks of progressively increased training load (e.g., days 1−21, 56−63, 119−140, 182−210; Figure 1).10 ‘Coping’ with prescribed training load during pre-competition training blocks is fundamental for inducing positive adaptations and making performance gains (i.e., overload or functional overreaching). Data from elite rowers,3 elite2 and well-trained5 triathletes suggest that a ~4–9% increase in vagal-related HRV during pre-competition training blocks may be required for optimal competition performance following a taper. Consistent with these data, we observed increases in ln rMSSD of up to 10% during pre-competition training blocks (Figure 1d). Further, we observed that when the triathlete was ‘coping’ with the prescribed training load, week-to-week changes in HRbased indices consistently demonstrated decreases in RHR with concurrent increases in ln rMSSD (Figure 1, Table 1). By contrast, when the triathlete was not ‘coping’, week-to-week changes in HR-based indices consistently demonstrated increases in RHR with concurrent decreases in ln rMSSD (Figure 1, Table 1). Based on these patterns, stagnation/decrease in ln rMSSD during a pre-competition training block may suggest insufficient training stimulus— confirmation requires data from the off-season or a sustained period of reduced training that was not captured in this study. While daily variations exist due to acute changes in training load and recovery kinetics,1 our data suggest that weekly changes in HR-based measures assessed in parallel with the training context can provide practically useful information regarding adaptation to training. For positive adaptations initiated in pre-competition training blocks to be translated into optimised competition performance, training load must be reduced for a variable period of time prior 5
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to competition.11 Accordingly, training load was reduced prior to each competition, (e.g., days 28−42, 70, 147−161, 217−224; Figure 1f, 2d). Preceding both optimal overall race performances (race 4 and 5; Figure 1a), moderate decreases in ln rMSSD and ln rMSSD:RR were observed (Figure 2b, c). Because these changes were associated with an increased RHR, possible HRV saturation can be excluded, suggesting a likely increase in sympathetic activity probably resulting from both the reduced training volume and pre-race anxiety.3,4,12 By contrast, small decreases in ln rMSSD and trivial change in ln rMSSD:RR was observed preceding sub-optimal competition performances (Figure 2b, c). The sub-optimal performance in race 1 was likely due to poor training adaptation (weeks 2 and 4), reduced training load (loss of fitness), elevated fatigue and sickness during the preceding training block. Consequently, race 2 was also a sub-optimal performance—irrespective of HR-based indices suggesting the triathlete had adapted well (i.e., increased ln rMSSD, days 49−63 Figure 1; weeks 8 and 9, Table 1) and was ready to race (week 10, Table 1). The suboptimal performance in race 3 was likely due to an inappropriate taper because the triathlete experienced a substantial increase in perceived fatigue despite a stable training load (days 133−140, Figure 1; weeks 20−21, Table 1) yet appeared to have coped well with the preceding training block (days 112−132, Figure 1). While our data largely confirm those reported in elite rowers3 during the lead up to a race, these examples of sub-optimal competition performance highlight the importance of interpreting HR-based indices with knowledge of the training context.4 Further, this data demonstrate the importance of adapting (coping) well during pre-competition training blocks, because if the work has not been done, an athlete’s fitness becomes the limitation despite HR-based indices suggesting a readiness for optimal performance. Practical Applications Endurance sports such as long-course triathlon require consistently high training loads therefore monitoring adaptation to training is fundamental for 1) achieving performance gains and minimizing overtraining or injury risk during precompetition training block, and 2) optimizing the precompetition taper. The present data suggest that provided the training context is known, HR-based indices provide consistent trends that can discriminate between coping vs. not coping during training blocks and, an optimal vs. sub-optimal precompetition taper. It must be noted that in practice, acute variations in training load may substantially influence shortterm HR responses.6 Therefore, while weekly trends may flag important (adverse) responses, practitioners should focus on interpretation the overall trends throughout a training block 6
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when assessing its success. How this information can be used to direct daily training prescription is an area of future research. Conclusion This case study demonstrates the utility of HR-based indices for monitoring adaptation to training. HR-based indices displayed consistent trends in response to changes in training that can discriminate between coping vs. not coping during a training block and, an optimal vs. sub-optimal pre-competition taper. Acknowledgements: This study was supported by the Centre of Excellence for Applied Sport Science Research and the Triathlon Program at the Queensland Academy of Sport. References 1. Stanley J, Peake J, Buchheit M. Cardiac parasympathetic reactivation following exercise: Implications for training prescription. Sports Med. 2013;43:1259-1277. 2. Plews D, Laursen P, Kilding A, Buchheit M. Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison. Eur. J. Appl. Physiol. 2012;112:3729-3741. 3. 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. 2013;43:773-781. 4. Buchheit M. Monitoring training status with hr measures: Do all roads lead to rome? Frontiers in Physiology. 2014;5. 5. Le Meur Y, Pichon A, Schaal K, et al. Evidence of parasympathetic hyperactivity in functionally overreached athletes. Med Sci Sports Exer. 2013;45:2061-2071. 6. Plews D, 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. 2013. 7. Stanley J, Peake J, Coombes J, Buchheit M. Central and peripheral adjustments during high-intensity exercise following cold water immersion. Eur. J. Appl. Physiol. 2014;114:147-163. 8. Allen H, Coggan AR. Training with a powermeter. Boulder: VeloPress; 2006. 9. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med. Sci. Sports Exerc. 2009;41:3-13.
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Figure Captions Figure 1. Changes in A Half-ironman competition performance (overall time relative to winner), B perceptions of general fatigue, C resting heart rate (RHR), D natural logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R−R intervals measured on waking (ln rMSSD), E ln rMSSD to mean R−R interval ratio (ln rMSSD:RR), and F distribution of acute training load8 and chronic training load8 for the entire 32-week competition season. The horizontal dashed line represents the threshold for a good and poor competition performance. The grey shaded area for general fatigue, RHR, ln rMSSD, and ln rMSSD:RR represents the smallest worthwhile change and was calculated from 0.5 the individual coefficient of variation over the entire 32-weeks competition season.9 Arrows indicate day of competition.
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Issurin V. New horizons for the methodology and physiology of training periodization. Sports Med. 2010;1:189-206. Mujika I, Padilla S, Pyne D, Busso T. Physiological changes associated with the pre-event taper in athletes. Sports Med. 2004;34:891-927. Plews DJ, Laursen PB, Kilding AE, Buchheit M. Heart rate variability and training intensity distribution in elite rowers. Int J Sports Physiol Perform. 2014.
Figure 2. Individual and mean (±90% CI) changes with respect to the preceding week for the entire 32-week competition season during the weeks preceding the five competitions. Upward triangles represent changes preceding an optimal competition performance; downwards triangles represent changes preceding sub-optimal competition performance. Changes in the weekly mean A resting heart rate (RHR), B resting vagal-related heart rate variability (ln rMSSD), C resting vagal-related heart rate variability to R−R interval ratio (ln rMSSD:RR), D acute training load.8 The hatched shaded area represents a trivial difference.9
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Table 1. Contextual based interpretation and mechanisms of changes in heart rate measures and perceived fatigue compared with the previous week Week 2 3
Load block [1030.3]
4
Load block [671.2]*
ATL
Fatigue
RHR
HRV
HRV :RR
↑↑
↑↑
↓
↑
↓
↓↓
↓↓
↔
↔
↔
↔
↑↑
↑↑
↔
↑↑
↓↓↓↓
↓↓
↑
↓↓
↓↓
Likely mechanism4 Increased PA
Increased SA/reverse saturation Increased PA/saturation
Taper [384.3]
6
Taper+Race [704.3]
↔
↔
↔
↔
↔
7
Recovery week [499.5] Load block [1004.0] Load block [1011.8]
↓↓
↓↓↓
↓↓
↑↑
↑
Increased SA
↑↑↑↑
↔
↓↓↓
↑↑↑
↑
↑
↑↑
↓
↓↓
↓↓↓
↔
↓↓↓
↑
↓↓
↓↓
↓↓↓↓
↑↑
↑↑
↓
↑
Increased SA Increased PA/saturation Increased SA Increased SA
↑↑ ↑↑↑↑ ↑↑↑↑
↓↓↓ ↑↑↑ ↑↑
↔ ↔ ↓
↑↑ ↓ ↔
↑↑↑ ↓ ↓
↓↓↓↓
↔
↑
↓
↔
↓↓
↔
↔
↓
↓↓
Increased PA Increased SA
16 17
Load block [1296.9]
↑↑↑↑
↓↓↓
↔
↑↑
↑
18
Load Block [850.4]* §
↔
↑↑
↑
↓↓
↓
19
Load block [870.9]
↓↓
↓↓↓
↓↓↓
↑↑
↓
20
Load block [1073.2]
↑↑↑
↑↑
↑↑↑
↓
↑
21
Load block+Taper § [588.2]* Taper+Race [683.1] Taper+Race [625.8] Recovery week [157.0] Load block [853.0] Load block [1170.1] Load block [1285.0]
↓↓↓↓
↑
↑↑↑
↓↓↓
↑↑
↓↓ ↓↓
↓↓ ↑↑
↔ ↑
↔ ↓↓
↑ ↓↓
↓↓↓
↑
↔
↔
↔
↑ ↑↑↑↑
↓ ↓↓
↓↓ ↓
↑↑ ↔
↔ ↓
↑↑↑↑
↔
↓↓↓
↑↑↑↑
↑↑↑
↓
↑↑
↑↑
↓↓
↔
Increased SA
↑↑
↑↑
↓↓
↑↑
↔
↓
↓
↑↑↑
↓↓
↑
Increased PA Increased SA
↓ ↓↓↓
↔ ↓
↓↓ ↑
↑↑ ↓↓
↓ ↓↓
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Recovery week [958.4]* Load block [1208.5] Load block+Taper [1100.7] Taper [928.6] Taper+Race [817.3]
Alternative Interpretation Adequate training reduction
Accumulated fatigue Coping well Unknown and highly individual
Taper+Race [724.5] Recovery week [138.4] Load block [941.7] Load block [983.9] Load block [1162.6] Recovery week [553.6] Load block [703.4]*
10 11
Practical Interpretation4 Coping well
Unknown and highly individual
5
8 9
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Training Context [TSS] Load block [642.9]* §
Increased SA Increased PA Increased PA Increased SA
Increased PA Increased PA/saturation Increased SA Increased SA
Accumulated fatigue Ready to perform Coping well Coping well Ready to perform Ready to perform Coping well Coping well Accumulated fatigue Unknown and highly individual Accumulated fatigue Coping well
Adequate training reduction
Coping well Accumulated fatigue Accumulated fatigue
Increased SA Increased SA
Ready to perform Coping well Detraining
Increased PA Increased PA Increased PA
Coping well Coping well Accumulated fatigue Accumulated fatigue Coping well Accumulated fatigue Coping well Coping well
Increased PA Increased SA
Ready to perform
Ready to perform
Functional overreaching
Ready to perform
Training context based on training logs, TSS; total Training Stress Score for the week,8 ATL; Acute Training Load,8 Fatigue; perception of general fatigue, RHR; resting heart rate (bpm), HRV; natural logarithm of the square root of the mean of the sum of the square differences between adjacent normal R-R intervals (ms), HRV:RR; HRV:R-R interval, PA; parasympathetic activity, SA; sympathetic activity. Standardized week-to-week differences: ↔ trivial, ↑ small increase, ↑↑ moderate increase, ↑↑↑ large increase, ↑↑↑↑ very large increase, ↓ small decrease, ↓↓ moderate decrease, ↓↓↓ large decrease, ↓↓↓↓ very large decrease. *missed training session(s) that week (i.e., not coping; see “methods”). §Upper respiratory tract infection.
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Figure 1. 10
366
Figure 2. 11
