Physiological, Perceptual, and Performance Responses to the 2-Week Block of High- versus Low-Intensity Endurance Training

It's about the long game, not epic workouts: unpacking HIIT for endurance athletes

High-intensity interval training (HIIT) prescriptions manipulate intensity, duration, and recovery variables in multiple combinations. Researchers often compare different HIIT variable combinations and treat HIIT prescription as a "maximization problem", seeking to identify the prescription(s) that induce the largest acute VO2/HR/RPE response. However, studies connecting the magnitude of specific acute HIIT response variables like work time >90% of VO2max and resulting cellular signalling and/or translation to protein upregulation and performance enhancement are lacking. This is also not how successful endurance athletes train. First, HIIT training cannot be seen in isolation. Successful endurance athletes perform most of their training volume below the first lactate turn point ( Collapse

Key Words

• endurance athletes
• exercise intensity
• interval training
• molecular signalling
• stress responses
• training monitoring

Collapse

MESH Headings

• Humans
• Physical Endurance/physiology
• High-Intensity Interval Training/methods
• Athletes
• Adaptation, Physiological
• Oxygen Consumption
• Athletic Performance/physiology
• Lactic Acid/blood

Collapse

Grants Collapse

Affiliation(s)

Stephen Seiler Department of Sport Science and Physical Education, University of Agder, Kristiansand, Norway

Collapse

Examining the acute cardiovagal consequences of supine recovery during high-intensity interval exercise

PURPOSE

Exercise training requires the careful application of training dose to maximize adaptation while minimizing the risk of illness and injury. High-intensity interval training (HIIT) is a potent method for improving health and fitness but generates substantial autonomic imbalance. Assuming a supine posture between intervals is a novel strategy that could enhance physiological readiness and training adaptations. This study aimed to establish the safety and feasibility of supine recovery within a HIIT session and explore its acute effects.

METHODS

Fifteen healthy, active males (18-34 years) underwent assessment of cardiopulmonary fitness. Participants completed two identical HIIT treadmill sessions (4 x [3 min at 95% VO2max, 3 min recovery]) employing passive recovery in standing (STANDard) or supine (SUPER) posture between intervals. Heart rate variability (HRV), HRV recovery (HRVrec; lnRMSSD) and heart rate recovery at 1 min (HRrec) were assessed using submaximal constant speed running tests (CST) completed prior to, immediately after and 24 h following HIIT.

RESULTS

No severe adverse events occurred with SUPER, and compliance was similar between conditions (100 ± 0%). The change in HRVrec from the CST pre-to-post-HIIT was not different between conditions (p = 0.38); however, HRrec was faster following SUPER (39 ± 7 bpm) vs. STANDard (36 ± 5 bpm). HRV 24 h post-SUPER was also greater (3.56 ± 0.57 ms) compared to STANDard (3.37 ± 0.42 ms). Despite no differences in perceived exertion (p = 0.23) and blood lactate levels (p = 0.35) between SUPER and STANDard, average running HRs were lower (p = 0.04) with SUPER (174 ± 7 bpm) vs. STANDard (176 ± 7 bpm).

CONCLUSIONS

Supine recovery within HIIT attenuates acute cardioautonomic perturbation and accelerates post-exercise vagal reactivation. SUPER enhances recovery of vagal modulation, potentially improving physiological preparedness 24 h post-HIIT. Further research exploring the chronic effects of SUPER are now warranted.

Monitoring fatigue state with heart rate-based and subjective methods during intensified training in recreational runners

The purpose of this study was firstly to examine the sensitivity of heart rate (HR)-based and subjective monitoring markers to intensified endurance training; and secondly, to investigate the validity of these markers to distinguish individuals in different fatigue states. A total of 24 recreational runners performed a 3-week baseline period, a 2-week overload period, and a 1-week recovery period. Performance was assessed before and after each period with a 3000m running test. Recovery was monitored with daily orthostatic tests, nocturnal HR recordings, questionnaires, and exercise data. The participants were divided into subgroups (overreached/OR, n = 8; responders/RESP, n = 12) based on the changes in performance and subjective recovery. The responses to the second week of the overload period were compared between the subgroups. RESP improved their baseline 3000 m time (p < 0.001) after the overload period (-2.5 ± 1.0%), and the change differed (p < 0.001) from OR (0.6 ± 1.2%). The changes in nocturnal HR (OR 3.2 ± 3.1%; RESP -2.8 ± 3.7%, p = 0.002) and HR variability (OR -0.7 ± 1.8%; RESP 2.1 ± 1.6%, p = 0.011) differed between the subgroups. In addition, the decrease in subjective readiness to train (p = 0.009) and increase in soreness of the legs (p = 0.04) were greater in OR compared to RESP. Nocturnal HR, readiness to train, and exercise-derived HR-running power index had ≥85% positive and negative predictive values in the discrimination between OR and RESP individuals. In conclusion, exercise tolerance can vary substantially in recreational runners. The results supported the usefulness of nocturnal HR and subjective recovery assessments in recognizing fatigue states.

The effects of high-intensity interval training versus moderate-intensity continuous training on athletes' aerobic endurance performance parameters

OBJECTIVE

To systematically evaluate and meta-analyze the effects of high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) on athletes of aerobic endurance performance parameters.

METHODS

PubMed, Web of Science, EBSCO, Embase, and Cochrane databases were searched. The assessment of quality was conducted employing The Cochrane Risk of Bias Assessment Tool, while heterogeneity examination and subgroup analysis were performed. Moreover, regression and sensitivity analyses were executed.

RESULTS

There was no significant difference between the effects of HIIT and MICT on the enhancement of athletes' running economy (RE) (P > 0.05); 1-3 weeks and 4-9 weeks of HIIT were more effective in improving athletes' maximum oxygen uptake (VO2max) (P < 0.05), and 10 weeks and above were not significant (P > 0.05); 1-3 weeks of HIIT was more effective in improving athletes' anaerobic threshold (AT) (P < 0.05), and 4-10 weeks was not significant (P > 0.05); 3 weeks of high-intensity interval training (HIIT) did not significantly enhance athletes' minute ventilation (VE) (P > 0.05), whereas a duration of 6-10 weeks yielded superior results (P < 0.05); 8 weeks of moderate-intensity continuous training (MICT) did not significantly enhance athletes' hemoglobin (Hb) level (P > 0.05), whereas a duration of 2-3 weeks yielded superior results (P < 0.05).

CONCLUSIONS

(1) HIIT and MICT have similar effects on enhancing athletes' RE. (2) 6-9 weeks' HIIT was more effective in improving athletes' VO2max and VE, and 3 weeks' HIIT was more effective in improving athletes' AT. (3) Within 3 weeks, MICT was more effective in improving the Hb level of athletes.

REGISTRATION NUMBER ON PROSPERO

CRD42024499039.

Practices and Applications of Heart Rate Variability Monitoring in Endurance Athletes

Heart rate variability reflects fluctuations in the changes in consecutive heartbeats, providing insight into cardiac autonomic function and overall physiological state. Endurance athletes typically demonstrate better cardiac autonomic function than non-athletes, with lower resting heart rates and greater variability. The availability and use of heart rate variability metrics has increased in the broader population and may be particularly useful to endurance athletes. The purpose of this review is to characterize current practices and applications of heart rate variability analysis in endurance athletes. Important considerations for heart rate variability analysis will be discussed, including analysis techniques, monitoring tools, the importance of stationarity of data, body position, timing and duration of the recording window, average heart rate, and sex and age differences. Key factors affecting resting heart rate variability will be discussed, including exercise intensity, duration, modality, overall training load, and lifestyle factors. Training applications will be explored, including heart rate variability-guided training and the identification and monitoring of maladaptive states such as overtraining. Lastly, we will examine some alternative uses of heart rate variability, including during exercise, post-exercise, and for physiological forecasting and predicting performance.

Individualized Endurance Training Based on Recovery and Training Status in Recreational Runners

PURPOSE

Long-term development of endurance performance requires a proper balance between strain and recovery. Because responses and adaptations to training are highly individual, this study examined whether individually adjusted endurance training based on recovery and training status would lead to greater adaptations compared with a predefined program.

METHODS

Recreational runners were divided into predefined (PD; n = 14) or individualized (IND; n = 16) training groups. In IND, the training load was decreased, maintained, or increased twice a week based on nocturnal heart rate variability, perceived recovery, and heart rate-running speed index. Both groups performed 3-wk preparatory, 6-wk volume, and 6-wk interval periods. Incremental treadmill tests and 10-km running tests were performed before the preparatory period ( T0 ) and after the preparatory ( T1 ), volume ( T2 ), and interval ( T3 ) periods. The magnitude of training adaptations was defined based on the coefficient of variation between T0 and T1 tests (high >2×, low <0.5×).

RESULTS

Both groups improved ( P < 0.01) their maximal treadmill speed and 10-km time from T1 to T3 . The change in the 10-km time was greater in IND compared with PD (-6.2% ± 2.8% vs -2.9% ± 2.4%, P = 0.002). In addition, IND had more high responders (50% vs 29%) and fewer low responders (0% vs 21%) compared with PD in the change of maximal treadmill speed and 10-km performance (81% vs 23% and 13% vs 23%), respectively.

CONCLUSIONS

PD and IND induced positive training adaptations, but the individualized training seemed more beneficial in endurance performance. Moreover, IND increased the likelihood of high response and decreased the occurrence of low response to endurance training.

Reliability and Sensitivity of Nocturnal Heart Rate and Heart-Rate Variability in Monitoring Individual Responses to Training Load

PURPOSE

To assess the reliability of nocturnal heart rate (HR) and HR variability (HRV) and to analyze the sensitivity of these markers to maximal endurance exercise.

METHODS

Recreational runners recorded nocturnal HR and HRV on nights after 2 identical low-intensity training sessions (n = 15) and on nights before and after a 3000-m running test (n = 23). Average HR, the natural logarithm of the root mean square of successive differences (LnRMSSD), and the natural logarithm of the high-frequency power (LnHF) were analyzed from a full night (FULL), a 4-hour (4H) segment starting 30 minutes after going to sleep, and morning value (MOR) based on the endpoint of the linear fit through all 5-minute averages during the night. Differences between the nights were analyzed with a general linear model, and intraclass correlation coefficient (ICC) was used for internight reliability assessments.

RESULTS

All indices were similar between the nights followed by low-intensity training sessions. A very high ICC (P < .001) was observed in all analysis segments with a range of .97 to .98 for HR, .92 to .97 for LnRMSSD, and .91 to .96 for LnHF. HR increased (P < .001), whereas LnRMSSD (P < .01) and LnHF (P < .05) decreased after the 3000-m test compared with previous night only in 4H and FULL. Increments in HR (P < .01) and decrements in LnRMSSD (P < .05) were greater in 4H compared with FULL and MOR.

CONCLUSIONS

Nocturnal HR and HRV indices are highly reliable. Demanding maximal exercise increases HR and decreases HRV most systematically in 4H and FULL segments.