Monitoring Training Stress in Endurance Sports Using Biological Parameters

Fry, Rod W.; Lawrence, Steven; Morton, Alan R.; Schreiner, Andrea B.; Polglaze, Ted; Keast, David

doi:10.1097/00042752-199301000-00003

Cited by 20 publications

(20 citation statements)

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“…The difficulty to distinguish FOR and specially NFOR from OT is well recognized [2], [6]. This is partially due to the fact that the multiple clinical signs of fatigue stem not only from training overload with inadequate rest but also from various inputs as psychological stress, nutritional imbalance, mild inflammatory disorders, etc… Several tools have been proposed to assess the fatigue level in daily routine.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Shifts and Scatters Heart Rate Variability in Elite Endurance Athletes

Schmitt¹,

et al. 2013

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PurposeThis longitudinal study aimed at comparing heart rate variability (HRV) in elite athletes identified either in ‘fatigue’ or in ‘no-fatigue’ state in ‘real life’ conditions.Methods57 elite Nordic-skiers were surveyed over 4 years. R-R intervals were recorded supine (SU) and standing (ST). A fatigue state was quoted with a validated questionnaire. A multilevel linear regression model was used to analyze relationships between heart rate (HR) and HRV descriptors [total spectral power (TP), power in low (LF) and high frequency (HF) ranges expressed in ms2 and normalized units (nu)] and the status without and with fatigue. The variables not distributed normally were transformed by taking their common logarithm (log10).Results172 trials were identified as in a ‘fatigue’ and 891 as in ‘no-fatigue’ state. All supine HR and HRV parameters (Beta±SE) were significantly different (P<0.0001) between ‘fatigue’ and ‘no-fatigue’: HRSU (+6.27±0.61 bpm), logTPSU (−0.36±0.04), logLFSU (−0.27±0.04), logHFSU (−0.46±0.05), logLF/HFSU (+0.19±0.03), HFSU(nu) (−9.55±1.33). Differences were also significant (P<0.0001) in standing: HRST (+8.83±0.89), logTPST (−0.28±0.03), logLFST (−0.29±0.03), logHFST (−0.32±0.04). Also, intra-individual variance of HRV parameters was larger (P<0.05) in the ‘fatigue’ state (logTPSU: 0.26 vs. 0.07, logLFSU: 0.28 vs. 0.11, logHFSU: 0.32 vs. 0.08, logTPST: 0.13 vs. 0.07, logLFST: 0.16 vs. 0.07, logHFST: 0.25 vs. 0.14).ConclusionHRV was significantly lower in 'fatigue' vs. 'no-fatigue' but accompanied with larger intra-individual variance of HRV parameters in 'fatigue'. The broader intra-individual variance of HRV parameters might encompass different changes from no-fatigue state, possibly reflecting different fatigue-induced alterations of HRV pattern.

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Section: Introductionmentioning

confidence: 99%

Fatigue Shifts and Scatters Heart Rate Variability in Elite Endurance Athletes

Schmitt¹,

et al. 2013

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“…It has become customary, during athletic training programmes, to use biochemical and hormonal markers to monitor adaptation to training [5,15,17]. Several biochemical and hormonal parameters have been associated with failing adaptation [17,23,33,36], while few have been concomitant with positive adaptation [21]. However, the most valuable biochemical and/or hormonal indicator, in the assessment of training performance, would be one which tracks both positive and negative training adaptation.…”

Section: Introductionmentioning

confidence: 99%

Monitoring 6 Weeks of Progressive Endurance Training with Plasma Glutamine

et al. 2007

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The distinction between positive and negative training adaptation is an important prerequisite in the identification of any marker for monitoring training in athletes. To investigate the glutamine responses to progressive endurance training, twenty healthy males were randomly assigned to a training group or a non-exercising control group. The training group performed a progressive (3 to 6 x 90 minute sessions per week at 70 % V.O (2max)) six-week endurance training programme on a cycle ergometer, while the control group did not participate in any exercise during this period. Performance assessments (V.O (2max) and time to exhaustion) and resting blood samples (for haemoglobin concentration, haematocrit, cortisol, ferritin, creatine kinase, glutamine, uric acid and urea analysis) were obtained prior to the commencement of training (Pre) and at the end of week 2, week 4 and week 6. The training group showed significant improvements in time to exhaustion (p < 0.01), and V.O (2max) (p < 0.05) at all time points (except week 2 for V.O (2max)), while the control group performance measures did not change. In the training group, haemoglobin concentration and haematocrit were significantly lower (p < 0.01) than pretraining values at week 2 and 4, as percentage changes in plasma volume indicated a significant (p < 0.01) haemodilution (+ 6 - 9 %) was present at week 2, 4 and 6. No changes were seen in the control group. In the training group, plasma glutamine (week 2, 4 and 6), creatine kinase (week 2 and 4), uric acid (week 2 and 4) and urea (week 2 and 4) all increased significantly from pretraining levels. No changes in cortisol or ferritin were found in the training group and no changes in any blood variables were present in the control group. Plasma glutamine was the only blood variable to remain significantly above pretraining (966 +/- 32 micromol . 1 (-1)) levels at week 6 (1176 +/- 24 micromol . 1 (-1); p < 0.05) The elevation seen here in glutamine levels, after 6 weeks of progressive endurance training, is in contrast to previous reports of decreased glutamine concentrations in overtrained athletes. In conclusion, 6 weeks of progressive endurance training steadily increased plasma glutamine levels, which may prove useful in the monitoring of training responses.

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“…To prevent overtraining and to ensure that the athletic training programme is resulting in performance improvements, or at least the maintenance of performance standards, it is necessary to include regular performance tests as a component of the training programme. There have also been a wide range of biochemical, haematological and physiological markers reported to be stress indicators in the athlete as training loads are progressively increased (Fry et al 1993). Among them, cortisol (F) and testosterone (T) have been recommended as good markers of training stress (Ban® et al 1993;Passelergue and Lac 1999).…”

Section: Introductionmentioning

confidence: 99%

Preliminary results on mood state, salivary testosterone:cortisol ratio and team performance in a professional soccer team

Filaire

Bernain

Sagnol

et al. 2001

European Journal of Applied Physiology

137

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Mood, as measured by the profile of mood states questionnaire (POMS), salivary cortisol (F) and testosterone (T) levels, and performance were examined in 17 male soccer players 4 times during a season. Soccer players provided three saliva samples when getting up (resting values, 8 a.m.), before breakfast (11.30 a.m.), and between 4.00 p.m. and 6.00 p.m. The initial measures were performed 1 day following the start of season training (T1). They were then performed before and after a high-intensity training programme (T2 and T3, respectively) and 16 weeks after T3 (T4). Iceberg profiles of POMS were observed during T1, T2 and T3, which coincided with successful performance. Subsequent decreased performance between T3 and T4 coincided with a decrease in vigor and an increase in tension and depression. Indeed, when the normal nycthemeral rhythm for F was observed (i.e. a decrease from morning until evening at all times; T1-T4), there was seemingly a non-statistical elevation of F on the morning of T3, which only became statistically significant at 11.30 a.m. on T3. In spite of a post-high-intensity training programme (T3) increase in catabolism, the soccer players presented iceberg profiles together with a high percentage of winning. Our results could suggest that in team soccer, a decreased T:F ratio does not automatically lead to a decrease in team performance or a state of team overtraining. It appears that combined psychological and physiological changes during high-intensity training are primarily of interest when monitoring training stress in relation to performance.

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Monitoring Training Stress in Endurance Sports Using Biological Parameters

Cited by 20 publications

References 0 publications

Fatigue Shifts and Scatters Heart Rate Variability in Elite Endurance Athletes

Fatigue Shifts and Scatters Heart Rate Variability in Elite Endurance Athletes

Monitoring 6 Weeks of Progressive Endurance Training with Plasma Glutamine

Preliminary results on mood state, salivary testosterone:cortisol ratio and team performance in a professional soccer team

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