Monitoring the Breath-Hold Training Load during an Ecological Session: A Pilot Study

• Abstract
• Introduction
• Materials and Methods
• Participants
• Design
• Statistical Analyses
• Results
• Discussion
• Conclusion
• References

Abstract

This study aimed to create a training load index to measure physiological stress during breath-hold (BH) training and examine its relationship with memory performance. Eighteen well-trained BH divers (Age: 35.8±6.6 years, BH training practice: 5.3±4.5 years) participated in this study. During a standard 1.5-hour BH training in the pool, perceived exertion, heart rate, distance, and duration were measured. The training load index was modelled on the basis of a TRIMP (TRaining IMPulse) with four different equations and was used to measure the stress induced by this BH training. A reference value, based on the ratio between the average heart rate during all BHs and the lowest heart rate during BH training, was used for comparing training load index. Memory assessment was conducted both before and after this training. Of the four equations proposed, equation no. 4, named aTRIMP for “apnoea,” showed the strongest correlation with our reference value (r=0.652, p<0.01). No difference was found between any of the memory tests before and after the BH training. The aTRIMP was a new representative index for monitoring habitual training of well-trained BH divers. Furthermore, this training had no negative impact on memory performance.

Introduction

Competitive breath-hold (BH) diving is a recent sport in which the aim is to achieve the longest possible BH without any movement (Static Breath-Hold discipline, S_BH) or the longest distance in a swimming pool (Dynamic Breath-Hold discipline, DYN_BH) with or without fins or as deep as possible in the sea (constant weight) also with or without fins. The regular new world records in all the BH disciplines bear witness to the improved training techniques and increased training load of these top athletes [1]. Breath-hold training enables athletes to be very economical with oxygen thanks to an effective diving response, characterized mainly by bradycardia with peripheral vasoconstriction [1] [2]. Precise control and manipulation of the training load are necessary to adjust the stress applied to the athlete at the individual level [3]. This training load can be described as being internal (all the psychophysiological responses) and external (the physical work prescribe in the training plan) [4]. External and internal loads are therefore linked by a causal relationship [5]. The Training Impulse (TRIMP) is one of the indices for integrating external and internal training loads [6]. It combines the heart rate (HR) reserve method (the internal load), the duration of training (the external load) and a weighting factor [6]. The Edward TRIMP model (eTRIMP) is an alternative of TRIMP for quantifying the physiological impact of training by using the accumulated duration in five arbitrary HR zones multiplied by a weighting factor [7]. Such methods based on eTRIMP cannot be used for Breath-Hold Divers (BHDs) because HR decreases during BH in a non-linear way with the exercise intensity [8]. Nevertheless, it is important to define the BH training load because such efforts results in significant desaturation (below 50%), which can lead to loss of consciousness (LOC) [9] or potential brain lesions [10] [11]. The impact on cognitive function is less certain. Billaut et al. [12] suggest an impairment of executive functions or attention deficit characterized by poorer Stroop test scores in elite BHDs compared to novice BHDs or control subjects, while Ridgway et al. [13] do not observe any deleterious effects. Apart from the fact that the vast majority of BHDs are not high-level, most of them train in the pool and some in the sea, with repetitive BHs during training that generally last between one and three hours. To date, the relationship between the impact of BH training load and its different training zones, on cognitive performance, especially memory, remains an open question that needs to be investigated in an ecological (real-life) situation.

Our first aim was to develop several specific indices to measure the BH training load. Secondly, we investigated the relationship between these load levels during ecological BH pool training and memory in well-trained BHDs. We hypothesized that i) our new index would provide a reliable training load estimation offering coaches a valuable tool for individualizing and optimizing BHD training programs and ii) the training load would be negatively correlated with memory test performances.

Materials and Methods

Participants

Eighteen participants took part in this study (14 men and 4 women) with a mean age of 35.8±6.6 years and BH diving practice for 5.3±4.5 years. Their personal bests in S_BH and DYN_BH with bifins were 294.2±65.1 seconds and 87.5±31.1 meters, respectively. Of the 18 BHDs tested, six experienced at least one LOC, with an average of two LOC (ranging from 1 to 6). All participants were non-smokers, not dependent on alcohol or drugs, and had no contraindication to BH training. The local ethics committee approved the entire protocol, and informed written consent was obtained from all participants.

Design

After answering standard questions about their BH diving history (personal bests, years of BH practice, number of LOC); they took three different memory tests 20 minutes before and 10 minutes after their usual BH training in a pool. The BHDs are equipped with a HR monitor (Polar© H10) during their BH training in the pool. The HRs are then collected after the training using the Polar Beat Software© and exported as an Excel file for further analysis. An experimenter recorded the beginnings and endings of BHs throughout the training while remaining out of the water. At the end of the training course, the BHDs rated their perceived exertion on a Borg scale (RPE) and their sensation of breathlessness on a dyspnea scale (RPD) from 0 to 10. The duration and configuration of BH training were the same for all tested BHDs and start for all at 19:00 hours ([Fig. 1]). The BH training took place as follows: (i) first, a warm-up period involving free swimming; (ii) followed by hypercapnic BH exercises (short, moderately intense efforts with short recovery) combined with technical exercises to improve hydrodynamics; (iii) and finally, hypoxic BH exercises (long efforts with almost complete recovery) to train for the lack of oxygen. All BHDs’ dive durations and distances were summed and averaged over the entire training. These parameters were used to calculate the average BH speed (m/s). Bradycardia was calculated as the difference in HR between the start of BH (HR_start) and the lowest HR value recorded during BH (HR_min) and expressed as a percentage (%HR). A reference value (Rv) was used to compare our training load indices and calculated as the average heart rate (HR_avg) normalized by HR_min during all effective BHs. This Rv is used as an indicator of intensity, a lower value indicating more intense bradycardia and possibly a more advanced state of hypoxia.

We also calculated different load indices, considering both the internal and/or external load during their BH training. We used the relationship between HR, speed and RPE with exertion intensity as representative of the level of hypoxia during BH [14] [15]. The first equation (eq. 1) represents the product of HR_avg expressed as percentage of the HR_start (%HR_avg), by the average BH duration (BHd_avg) and the total number of BHs (n).

The second equation (eq. 2), represents the product between the average speed (V_avg) by the total BH duration during training (BHdtot) and the total number of BHs (n).

The third equation (eq. 3), represents the product between the RPE and the total BH duration during training (BHd_tot). This equation quantifies the training load in a subjective way [16].

The fourth equation (eq. 4) represents the sum of the ratios of the starting HR (HR_start) and the minimum HR (HR_min) for each BH, and standardizes HR_min values according to the theoretical maximum HR (HR_maxth) [17] to obtain %HR_min (eq. 4a). Then, this ratio is multiplied by the BH duration (BHd) (eq. 4b).

We propose to call the eq. 4b, the “apnea” TRIMP (aTRIMP). The aTRIMP will enable us to quantify the impact of BH training, taking into account both bradycardia and intensity of effort during DYN_BH.

This equation (eq. 4b) is used for each BH to establish intensity zones from 1 to 6 and is calculated as follows:

Finally, the percentage of time spent in each zone is determined by dividing the total BH duration in a specific zone by the BH_dtot (eq. 4c).

The number of significant figures before the decimal point in the equations used to calculate the training load were adjusted to ensure consistent values and make comparisons easier. Before and within ten minutes after their BH training, the BHDs performed the same memory tests. Three tests classically used to measure the different components of memory were chosen: the phonemic fluency test [18], the categorical verbal fluency test and the Mnemonic Similarity Task (MST) test [19]. The phonemic verbal fluency test evaluates semantic memory and executive function. In this test, participants have one minute to say as many words as possible beginning with the letter “P”, avoiding proper nouns. The number of legal words generated in one minute was recorded and used as a test score. The categorical verbal fluency test or semantic fluency is used to evaluate the integrity of semantic memory. Participants had one minute to list as many animal names as possible. The score of this test corresponded to the number of words listed by the participant. Finally, the Mnemonic Similarity Task measures a behavioral proxy of pattern separation, which consists in the assignment of non-overlapping neural activation patterns to similar memory representations by the hippocampus. In other words, pattern separation allows to make sure that similar events remain distinguishable in memory. We used a version of the MST that consists of an initial incidental encoding phase in which participants have to perform a semantic judgment task on pictures, followed by a test phase evaluating different aspects of memory [19] [20]. Pattern separation (PS) index was defined as the percentage of correct responses identifying images similar to images shown in the encoding phase (“lure” images), minus the percentage of wrong responses when the participants incorrectly identified lure images as images identical to the ones of the first phase (“target” images). Moreover, the item memory index (IM), which measures recognition memory, was defined as the percentage of correctly identified “target” images minus the percentage of wrong responses when the participants incorrectly identified “target” images as new images (“foil” images) [19] [20].

Statistical Analyses

In order to be able to calculate a priori the number of subjects required for this study, we chose as the main criterion the variation in HR during dynamic BH in BHDs (delta HR %=0.33±0.4) according to the study by Andersson et al. [21]. For a type I error rate of 5%, the inclusion of 11 participants provides 80% power to detect the effect of BH on heart rate (GPower v3.1.9.2). Given the possibility that some participants may not complete the intervention period, it was planned to include 18 BHDs to ensure the above power level was achieved, guaranteeing the minimum sample size of 11 participants. Dependent (HR, RPE, swimming speed, training load equation results, memory test results) and independent (Total BH distance, training duration) variables were tested for normality of distribution with the Shapiro-Wilk’s test. The z-score of all memory tests were calculated using norms to assess whether the participants had any deficits in these cognitive functions beforehand [18]. The z-score will also be compared to 40 healthy control participants (20 women and 21 men, mean age 30.42±12.34 years, range=20.00–56.00) without a history of BH training, neurological or psychiatric issues. A Pearson correlation analysis was performed to investigate the association between our reference value and the training load indices, as well as training parameters and the training load indices. The optimal training load index will be determined as the one exhibiting the highest correlation with our reference value. These correlations were additionally employed to investigate potential relationships between memory tests and both training parameters and training load indices. Statistical analyses were performed using JASP Team (2023 Version 0.18.1). The level of statistical significance was defined at 95% and the probability values of p<0.05.

Results

BH training parameters and training load indices results are presented as means±SD and coefficient of variation (CV) in [Table 1]. BHDs spent an average of 80% (±11%) of the total BH duration during training in zone 3 (moderate), 19% (±10%) in zone 4 (vigorous) and 1% (±1%) in zone 5 (very hard) of the aTRIMP. Two significant positive correlations were observed between our four training load equations and the Rv: equation 1 (r=0.507, p<0.05) ([Fig. 2a]) and equation 4 (aTRIMP) (r=0.652, p<0.01) ([Fig. 2b]). Correlation analyses between the training load equations showed only a significant correlation between equation 1 and aTRIMP (r=0.671, p<0.01). The total BH duration showed a significant negative correlation with the HR_min during training (r=− 0.610, p<0.01). Of the four equations, only equation 3 showed a positive correlation with RPE (r=0.840, p<0.001) and RPD (r=0.806, p<0.001).

No differences were found for the phonemic fluency test score, the categorical verbal fluency test score and the PS index score between before and after training. The IM index was higher after training compared to before training (p<0.01). No difference was found between the 40 controls participants and the 18 BHDs for the PS index and IM index. All Z-score are within±2 SD point of the norm [18]. The results of categorical verbal fluency performed after training were negatively correlated with the total BH duration during training (r=− 0.486, p=0.041) and the total number of BHs (r=− 0541, p=0.020). There was a negative correlation between the years of BH practice and the result of the phonemic fluency test before (r=− 0.590, p=0.010) ([Fig. 3a]) and after training (r=− 0.505, p=0.033) without effect of age. The BH training loads assessed with eq. 1 were correlated negatively with PS index after training (r=− 0.608, p=0.007) ([Fig. 3b]).

Discussion

This pilot study is a first attempt to establish a training load index capable of estimating the stress induced by a BH training, while simultaneously studying its possible impact on the BHDs memory performances in ecological situation. The main results suggest that aTRIMP is the most representative measure of training load for our well-trained BHDs. Training conducted under ecological conditions does not seem to affect negatively the BHDs’ memory. However, the negative correlations observed between years of BH practice and the phonemic fluency test indicate that long-term exposure could have a negative impact.

Although many studies have focused on monitoring training load in activities such as endurance sport [22], limited research has specifically focused on BHD training. The aTRIMP takes into account the BHD internal and external loads. The top portion of the aTRIMP (HR_start) considers the HR at the beginning of the BH. The higher the HR at start, the greater the level of difficulty. Meanwhile, the denominator of the aTRIMP (%HR_min) assesses the extent of the BH [8] as shown by the correlation between HR_min during BH and total BH duration during training (r=− 0.610, p<0.01). For BHs that last a long time, the lowest HRs are often associated with the highest levels of hypoxia [23]. This suggests that internal training load, represented by HR_min, can serve as an index of both the intensity (state of hypoxia) and volume of stress experienced during BH training. In our study, the BHDs, on average, allocated around 80% (±11%) of the duration of their training to zone 3. This intensity zone, calculated by the aTRIMP, therefore indicates that their training was more of a “moderate” type and probably moderately hypoxic. Therefore, training at higher levels (aTRIMP>zone 3) would allow for a hypoxic dose high enough to generate physiological adaptations and, ultimately, improve the BHDs performance [24] [25]. It has been shown that the optimum intensity for maximizing the diving response and achieving longer BH durations appears to be at a speed corresponding to 30% of the maximum oxygen consumption [15]. Without speed instructions, it is probable that the BHDs unconsciously adjusts their speed to this intensity [26]. Our results also indicate that there was a positive impact on IM, which was higher after the BH training than before. This result is probably due to a learning effect, re-encoding during the second session (after training) may increase performance. We also identified a negative correlation between the BH_dtot and performance on the categorical verbal fluency test, specifically after the training suggesting that more experienced BHDs’ prolonged practice could lead in the long term to alterations in the neural networks involved in semantic memory processes. As with the effects of exercise, it is likely that a certain hypoxic dose (duration x frequency x oxygen fraction) must be reached for observable changes in memory to occur. This dose may vary according to individual factors such as training history and physiological adaptation to hypoxia [27]. It appears that the duration and intensity of exercise, the specific type of cognitive performance assessed, as well as the physical condition of the participants, play significant roles as moderators in determining the effects on cognitive function [28] [29]. A decrease in executive function in elite BHDs compared to non-BHDs has been demonstrated, indicating that the duration of BH training was related to this decrease [12]. Our study revealed no negative effect on memory performance in the BHDs compared to the control group prior to BH training or when comparing tests conducted before and after BH training. This suggests that a single BH training of this intensity (zone 3 aTRIMP, 20 minutes of cumulative BH, at a speed of 0.8 m/s for 33 BHs) for BHDs with five years of experience does not seem to influence the memory. This may seem reassuring since this BHD training was a normal and usual training. The utilization of aTRIMP zones within training protocols will require future validation through the integration of muscle oxygenation measurements in addition to cardiac and performance assessments. It is also important to recognize that the other equations provided acceptable estimates of BH training load, despite some limitations. Equation 1 did not take into account individual variability in HR response during the BHD training, as it only considered the HR_avg over the entire training. Equation 2 did not incorporate the internal load factor, neglecting the impact of BH’s response to external load demands. Equation 3 incorporates the individual's subjective perception using the RPE, but requires prior familiarization with the RPE scale in order to use it more accurately [30] and does not take into account the different parts of the BHD training, alternating repetitive BHs with more or less long recovery periods. Moreover, it was observed that both the RPE and the RPD did not exhibit any significant correlation with equations 1 and 2, as well as aTRIMP. These findings suggest that these particular indices may not be well suited for evaluating the physiological demands of BHDs’ training. In future, it will be necessary to validate the aTRIMP and also test all the other training load indices during a training follow-up lasting several weeks, while measuring oxygenation parameters. Despite these limitations, our pilot study and the aTRIMP, in particular, offers valuable information for the creation of a training load equation specific to BHD, enabling coaches to manage their athletes’ training load.

Conclusion

Our pilot study indicates that of the different equations tested, the aTRIMP appears to be the most representative for evaluating BH training. This type of training does not adversely affect short-term memory performance. These results contribute to our understanding of the evaluation of the BH training load and underline the importance of considering the BH duration and intensity from an ecological point of view. A longitudinal study with training follow-up will optimize the reliability and reproducibility of these load indices.

Conflict of Interest

The authors declare that they have no conflict of interest.

Acknowledgement

We would like to express our sincere gratitude to the Breath hold diving club and participating breath hold divers for their invaluable contribution to this study. Their willingness to get involved and their dedication to the research process were crucial to the success of this project.

• References

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Correspondence

Publication History

Received: 20 December 2023

Accepted: 26 April 2024

Article published online:
15 July 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

• References

• 1 Elia A, Gennser M, Harlow PS. et al. Physiology, pathophysiology and (mal)adaptations to chronic apnoeic training: a state-of-the-art review. Eur J Appl Physiol 2021; 121: 1543-1566
  CrossrefPubMedSearch in Google Scholar
• 2 Costalat G, Coquart J, Castres I. et al. The oxygen-conserving potential of the diving response: A kinetic-based analysis. Journal of Sports Sciences 2017; 35: 678-687
  CrossrefPubMedSearch in Google Scholar
• 3 Halson SL. Monitoring Training Load to Understand Fatigue in Athletes. Sports Med 2014; 44: 139-147
  CrossrefPubMedSearch in Google Scholar
• 4 Impellizzeri FM, Marcora SM, Coutts AJ. Internal and External Training Load: 15 Years On. International Journal of Sports Physiology and Performance 2019; 14: 270-273
  CrossrefPubMedSearch in Google Scholar
• 5 Impellizzeri FM, Shrier I, McLaren SJ. et al. Understanding Training Load as Exposure and Dose. Sports Med 2023; 53: 1667-1679
  CrossrefPubMedSearch in Google Scholar
• 6 Calvert TW, Banister EW, Savage MV. et al. A Systems Model of the Effects of Training on Physical Performance. IEEE Transactions on Systems, Man, and Cybernetics 1976; SMC-6: 94-102
  CrossrefPubMedSearch in Google Scholar
• 7 Edwards S. Heart Rate Monitor Book. First Edition. Port Washington, N.Y.: Sacramento, CA: Polar CIC Inc.,US; 1993
  Search in Google Scholar
• 8 Mulder E, Sieber A, Schagatay E. Using Underwater Pulse Oximetry in Freediving to Extreme Depths to Study Risk of Hypoxic Blackout and Diving Response Phases. Frontiers in Physiology 2021; 12: 651128
  CrossrefPubMedSearch in Google Scholar
• 9 Willie CK, Ainslie PN, Drvis I. et al. Regulation of brain blood flow and oxygen delivery in elite breath-hold divers. J Cereb Blood Flow Metab 2015; 35: 66-73
  CrossrefPubMedSearch in Google Scholar
• 10 Kjeld T, Jattu T, Nielsen HB. et al. Release of erythropoietin and neuron-specific enolase after breath holding in competing free divers. Scandinavian Journal of Medicine & Science in Sports 2015; 25: e253-e257
  CrossrefPubMedSearch in Google Scholar
• 11 Gren M, Shahim P, Lautner R. et al. Blood biomarkers indicate mild neuroaxonal injury and increased amyloid β production after transient hypoxia during breath-hold diving. Brain Injury 2016; 30: 1226-1230
  CrossrefPubMedSearch in Google Scholar
• 12 Billaut F, Gueit P, Faure S. et al. Do elite breath-hold divers suffer from mild short-term memory impairments?. Appl Physiol Nutr Metab 2018; 43: 247-251
  CrossrefPubMedSearch in Google Scholar
• 13 Ridgway L, McFarland K. Apnea Diving: Long-Term Neurocognitive Sequelae of Repeated Hypoxemia. The Clinical Neuropsychologist 2006; 20: 160-176
  CrossrefPubMedSearch in Google Scholar
• 14 Rodríguez-Zamora L, Iglesias X, Barrero A. et al. Perceived exertion, time of immersion and physiological correlates in synchronized swimming. Int J Sports Med 2014; 35: 403-411
  PubMedSearch in Google Scholar
• 15 Guimard A, Joulia F, Prieur F. et al. Exponential Relationship Between Maximal Apnea Duration and Exercise Intensity in Non-apnea Trained Individuals. Frontiers in Physiology 2022; 12: 815824
  CrossrefPubMedSearch in Google Scholar
• 16 Foster C, Florhaug JA, Franklin J. et al. A new approach to monitoring exercise training. J Strength Cond Res 2001; 15: 109-115
  PubMedSearch in Google Scholar
• 17 Gellish RL, Goslin BR, Olson RE. et al. Longitudinal modeling of the relationship between age and maximal heart rate. Med Sci Sports Exerc 2007; 39: 822-829
  CrossrefPubMedSearch in Google Scholar
• 18 St-Hilaire A, Hudon C, Vallet GT. et al. Normative data for phonemic and semantic verbal fluency test in the adult French-Quebec population and validation study in Alzheimer’s disease and depression. Clin Neuropsychol 2016; 30: 1126-1150
  CrossrefPubMedSearch in Google Scholar
• 19 Bouyeure A, Patil S, Mauconduit F. et al. Hippocampal subfield volumes and memory discrimination in the developing brain. Hippocampus 2021; 31: 1202-1214
  CrossrefPubMedSearch in Google Scholar
• 20 Stark SM, Kirwan CB, Stark CEL. Mnemonic Similarity Task: A Tool for Assessing Hippocampal Integrity. Trends Cogn Sci 2019; 23: 938-951
  CrossrefPubMedSearch in Google Scholar
• 21 Andersson JPA, Linér MH, Rünow E. et al. Diving response and arterial oxygen saturation during apnea and exercise in breath-hold divers. J Appl Physiol (1985) 2002; 93: 882-886
  CrossrefPubMedSearch in Google Scholar
• 22 Mujika I. Quantification of Training and Competition Loads in Endurance Sports: Methods and Applications. International Journal of Sports Physiology and Performance 2017; 12: S2-S17
  CrossrefPubMedSearch in Google Scholar
• 23 Lemaître F, Buchheit M, Joulia F. et al. Static Apnea Effect on Heart Rate and Its Variability in Elite Breath-Hold Divers. Aviation, Space, and Environmental Medicine 2008; 79: 99-104
  CrossrefPubMedSearch in Google Scholar
• 24 Bouten J, Debusschere J, Lootens L. et al. Six weeks of static apnea training does not affect Hbmass and exercise performance. Journal of Applied Physiology 2022; 132: 673-681
  CrossrefPubMedSearch in Google Scholar
• 25 Seiler S. What is Best Practice for Training Intensity and Duration Distribution in Endurance Athletes?. International Journal of Sports Physiology and Performance 2010; 5: 276-291
  CrossrefPubMedSearch in Google Scholar
• 26 AKIScedil T, Alkan N. Predictors of Competitive Dynamic Apnea Performance in Elite Free Diving Athletes. Cases 2023; 2: 1-10
  PubMedSearch in Google Scholar
• 27 Loprinzi PD, Matalgah A, Crawford L. et al. Effects of Acute Normobaric Hypoxia on Memory Interference. Brain Sciences 2019; 9: 323
  CrossrefPubMedSearch in Google Scholar
• 28 Chang YK, Labban JD, Gapin JI. et al. The effects of acute exercise on cognitive performance: A meta-analysis. Brain Research 2012; 1453: 87-101
  CrossrefPubMedSearch in Google Scholar
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