# Accumulated Workload, Neuromuscular Fatigue, Wellbeing and Hormonal Variations in Professional Offshore Sailors: A Pre-competition Study

Kilian Philippe, Paillard Thierry, Olivier Maurelli, Jeremy Moody, Prioux Jacques

<figure><img src="image_1.png" /></figure>

## To cite this version:

Kilian Philippe, Paillard Thierry, Olivier Maurelli, Jeremy Moody, Prioux Jacques. Accumulated Workload, Neuromuscular Fatigue, Wellbeing and Hormonal Variations in Professional Offshore Sailors: A Pre-competition Study. International Journal of Strength and Conditioning, 2025, 5 (1), 10.47206/ijsc.v5i1.386. hal-05141991

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Submitted on 3 Jul 2025

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# Accumulated Workload, Neuromuscular Fatigue, Wellbeing and Hormonal Variations in Professional Offshore Sailors: A Pre-competition Study

Kilian Philippe<fnref n="1" />,², Thierry Paillard<fnref n="1" />, Olivier Maurelli³, Jeremy Moody⁴,⁵ and Jacques Prioux⁶

## ABSTRACT

This study aimed to comprehensively analyse the distribution of workload (WL) and its multifaceted impact on neuromuscular performance, wellbeing, and stress hormone levels within the professional offshore sailing context. Over a 27-week pre-competition period, ten professional sailors (8 males and 2 females) participated in regular assessments to evaluate their workload and its effects. Weekly workload (WL<sub>wk</sub>), strain (WL<sub>str</sub>), and monotony (WL<sub>mono</sub>) were quantified using the session-rating of perceived exertion (sRPE), a validated tool for assessing training intensity. To measure subjective markers of recovery, the Hooper-index (HI) was collected weekly, providing insights into the athletes' perceived recovery status. Furthermore, salivary cortisol (C) levels, indicative of stress response, along with countermovement jump height (CMJ) and maximal strength on bench press (BP1RM), were assessed at the beginning of each training block to evaluate neuromuscular performance changes over time. Significant correlations were identified between the monitored workload indices and various variables. WL<sub>wk</sub>

showed associations with CMJ, BP1RM, HI, and C, indicating a relationship between workload and both physical performance and stress levels. WL<sub>mono</sub> was correlated with HI, highlighting the impact of workload monotony on perceived recovery status. Similarly, WL<sub>str</sub> was associated with CMJ, BP1RM, HI, and C, suggesting that workload strain affects neuromuscular performance, wellbeing, and stress hormone levels. In conclusion, the study suggests that integrating both objective (WL<sub>wk</sub>, WL<sub>str</sub>, WL<sub>mono</sub>) and subjective (HI) monitoring tools can provide practitioners with valuable insights into optimising performance and managing stress in the demanding environment of offshore sailing.

**Keywords:** Monitoring; fatigue; adaptations; preparedness; performance; sailing.

## INTRODUCTION

In the realm of sports science literature, the meticulous documentation of training and competition workload (WL) has garnered substantial attention. Over recent years, monitoring and

<footnote n="1">¹Movement, Balance, Performance and Health laboratory (MEPS, EA-4445), University of Pau and Pays de l'Adour, Tarbes, France, ²Orient Express Racing Team, America's Cup French Challenger, 56100 Lorient, France, ³Laboratory of Muscular Dynamism and Metabolism (UMR-866), University of Montpellier, France, ⁴Cardiff School of Sport and Health Science, Cardiff Metropolitan University, Cardiff, United-Kingdom, ⁵School of Physical Education and Sports, Nisantasi University, Istanbul, Turkey, ⁶Movement, Sport and Health laboratory (EA-1274), Faculty of sport science, Rennes, France.</footnote>
managing WL have evolved as the gold standard for measuring the stress induced by physical activity with substantial research focusing on how training volume, frequency, and intensity influence non-functional overreaching, injury, and illness (16). However, a growing body of literature is now exploring the intricate relationships between WL, strain (WL<sub>str</sub>), monotony (WL<sub>mono</sub>), and data smoothing methods, and their combined effects on neuromuscular adaptations, hormonal balance, and overall sports performance (27).

Sailing is a multi-disciplinary and environmental dependent sport requiring sustained cognitive involvement for boat handling, navigation, and tactical skills combined with physically strenuous activities to manoeuvre the boat (30). Sailing, a sport that demands a unique blend of cognitive acuity and physical capacity, presents its own set of challenges. Previous research has documented the profound effects of sailing on neuromuscular functions, energy demands, and stress hormones, noting significant increases in neuromuscular fatigue, muscle damage, cognitive impairment, and elevated cortisol levels post-competition (12, 14, 15, 26, 31). Despite these findings, the literature remains sparse when it comes to understanding the physiological demands during the preparatory periods leading up to major competitions. Given the importance of recovery and stress management in achieving peak performance, there is a clear need to monitor both objective (internal and external) and subjective markers to comprehensively assess a sailor's readiness (39).

Cortisol, a key physiological marker of stress present in saliva, serum, and urine, provides valuable insights into the body's response to training loads, particularly when measured in saliva (19). Alongside cortisol, subjective assessments like the Hooper-Index (HI), which evaluates sleep quality, stress levels, fatigue, and muscle soreness, have shown promise in monitoring fatigue and recovery in team sports, suggesting potential applicability in sailing as well (28). Complementary field tests, such as the countermovement jump (CMJ) for neuromuscular fatigue and maximal strength tests, offer additional layers of insight into an athlete's physical condition, thereby supporting more effective training load management (7).

Despite significant advancements in understanding WL and performance in popular sports, the application of these principles to strategy-intensive sports like sailing remains underexplored. Sailing,

particularly with the advent of modern hydro-
foiling technology (4), demands not only sustained
cognitive focus but also intense physical exertion,
making WL management a complex challenge.
As competitive sailing has evolved, so have the
physical demands and injury risks faced by sailors
(36). However, scientific literature on offshore sailing
is notably sparse, partly due to the sport's variability
and dependence on climatic and meteorological
conditions. Implementing effective WL strategies
in this ever-changing environment presents a
substantial challenge for performance practitioners
(13). However, performance in sailing is still linked
to fitness (30) and, despite this precept, the
relationship between variations in fitness levels and
accumulated WL is not yet clearly established.

This study aims to address these gaps by first describing the WL distribution of a professional offshore sailing team participating in The Ocean Race (TOR) over a 6-month pre-competition period. Secondly, it seeks to identify dose-response predictors from periodically monitored indices using commonly employed WL variables. We hypothesise that these monitored indices will be directly influenced by WL, providing new insights into the complex interplay between workload and performance in professional sailing.

MATERIALS AND METHODS

Experimental Approach to the Problem

This study used a longitudinal prospective
design to analyse the relationship between WL,
neuromuscular performance, wellbeing, and
salivary cortisol. All data was collected during the
preparation of TOR 2021. The study was completed
over a 27-week (W<sub>1-27</sub>) pre-competition period.
Figure 1 illustrates the training that was planned and
scheduled by the skipper. Planning was adjusted to
accommodate to weather conditions and Covid-19
crisis related restrictions. When sailing or training
facilities were not available, sailors were prescribed
training sessions by the strength and conditioning
coach. On-land training included resistance training
and metabolic conditioning (Figure 1B). All gym
sessions were conducted in the Club Naval de
Cascais (Portugal) or at home, when needed.

Together with WL variables, monitoring contained
several subjective and objective markers of
adaptation. Subjective markers included a wellness
questionnaire for monitoring fatigue and were
<table><caption>A)</caption><thead><tr><th colspan="15">Pre-Competition Preparation</th></tr><tr><th></th><th colspan="5">TB1</th><th colspan="5">TB2</th><th colspan="5">TB3</th><th colspan="5">TB4</th><th colspan="5">TB5</th><th colspan="5">TB6</th><th colspan="2">TB7</th><th>TOR</th></tr><tr><th></th><th>W<sub>1</sub></th><th>W<sub>2</sub></th><th>W<sub>3</sub></th><th>W<sub>4</sub></th><th>W<sub>5</sub></th><th>W<sub>6</sub></th><th>W<sub>7</sub></th><th>W<sub>8</sub></th><th>W<sub>9</sub></th><th>W<sub>10</sub></th><th>W<sub>11</sub></th><th>W<sub>12</sub></th><th>W<sub>13</sub></th><th>W<sub>14</sub></th><th>W<sub>15</sub></th><th>W<sub>16</sub></th><th>W<sub>17</sub></th><th>W<sub>18</sub></th><th>W<sub>19</sub></th><th>W<sub>20</sub></th><th>W<sub>21</sub></th><th>W<sub>22</sub></th><th>W<sub>23</sub></th><th>W<sub>24</sub></th><th>W<sub>25</sub></th><th>W<sub>26</sub></th><th>W<sub>27</sub></th><th>W<sub>28</sub></th></tr></thead><tbody><tr><th>BSL</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Test<sub>1</sub></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Test<sub>2</sub></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Test<sub>3</sub></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Test<sub>4</sub></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Sailing Camp</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Test<sub>5</sub></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Sailing Camp</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Test<sub>6</sub></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Test<sub>7</sub></th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<figure><img src="image_3.png" /></figure>

<table><thead><tr><th colspan="8">Sample Week (No Team Sailing)</th></tr><tr><th>Monday</th><th>Tuesday</th><th>Wednesday</th><th>Thursday</th><th>Friday</th><th>Saturday</th><th>Sunday</th></tr></thead><tbody><tr><td>Resistance Training</td><td>Metabolic Conditioning</td><td>Mobility and Individual Sailing Activities</td><td>Resistance Training</td><td>Metabolic Conditioning</td><td>Individual Sailing Activities</td><td>Off</td></tr></tbody></table>

<table><thead><tr><th colspan="8">Sample Week (Sailing Camp)</th></tr><tr><th>Monday</th><th>Tuesday</th><th>Wednesday</th><th>Thursday</th><th>Friday</th><th>Saturday</th><th>Sunday</th></tr></thead><tbody><tr><td>Metabolic Conditioning</td><td>Coastal Sailing</td><td>Mobility and Coastal Sailing</td><td>Resistance Training and Boat Preparation</td><td>Offshore Sailing Day 1</td><td>Offshore Sailing Day 2</td><td>Offshore Sailing Day 3</td></tr></tbody></table>

<table><caption>**Figure 1.** A) Schematic diagram showing the workload structure of the pre-competition blocks. The preparation was divided into seven distinct periods including two sailing camps. B) Organisation of sample weeks and training sessions. BSL: Baseline; TB<sub>1-7</sub>: Training Block; W<sub>1-28</sub>: Week; Test<sub>1-7</sub>: Testing Battery; R: Rest; RPM: Revolutions Per Minute.</caption><thead><tr><th colspan="4">Sample Training Sessions</th></tr><tr><th>Resistance Training</th><th>Metabolic Conditioning</th><th colspan="2">Sailing</th></tr></thead><tbody><tr><td>1) 3 x 6 heavy plyometry jumps R=2'<br>2) 5 x 10 supersets (Bench press/bent over rows) @ 70% 1RM, R=1'30<br>3) 4 x 5 clean from hips @ 65% 1RM, R=2'<br>4) 3 x 20 full squats @ 40% 1RM, R=1'30<br>5) 2 x core circuit R=45"<br>6) 2 x 10 anti-rotation exercise R=45"</td><td>Arm ergometer:<br>3 x 8' @ 85% of Functional Threshold Power<br>Fixed Resistance mode. Maintain 90-110 RPM<br>R=3' active recovery @ 150W</td><td colspan="2">Coastal<br>1 hour work on starts<br>2 hours on manoeuvres<br>45' testing sails<br>Offshore<br>48 hours sailing<br>Team Coordination and shift management</td></tr></tbody></table>

assessed weekly (Mondays). Objective markers included monitoring of strength and power capabilities as well as hormonal biomarkers. These measurements were assessed on seven occasions (Test<sub>1-7</sub>) which were held on the first day of each of the 7 training blocks (TB<sub>1-7</sub>) (Figure 1A). Test<sub>1-7</sub> were conducted in the team's training facilities, located at the Club Naval de Cascais (Portugal), to ensure minimal disruption to the established sailing program. Athletes were requested to abstain from caffeinated drinks and alcohol 24 hours prior to testing. Data collection was made possible through a customised smartphone application (Appsheet). Athletes were permanently supervised by a qualified team doctor and nutritionist.

## Subjects

The required sample size to conduct the study was estimated using statistical software (G*power; University 130 of Düsseldorf, Düsseldorf, Germany). The following variables were included in the a priori power analysis: study design, 1 group, 7

measurements, alpha error <0.05, nonsphericity correction = 1, correlation between repeated measures = 0.5, desired power (1-β error) = 0.80, effect size of 0.5 based on a previous study that monitored self-reported wellness and CMJ performance in professional ruby union players over a 12-week period (17).

The results of the a priori power analysis indicated that a minimum of 6 participants was necessary to achieve statistical significance. The sample size computation took into account within-group variances and their correlations (p), thereby enhancing the methodological robustness and contributing to overall research integrity. Ten professional sailors (8 males and 2 females) from the same sailing team participated in the routine monitoring process (mean ± SD; age = 32.2 ± 3.96 years; stature = 179.1 ± 7.30 cm; body mass = 84.4 ± 11.8 kg). All participants were engaged in full-time training in preparation for TOR and were familiar with the assessments. None reported injuries or illnesses that would have impaired their
physical performance in the six months preceding
the monitoring. Although the data were collected
as part of routine athlete monitoring procedures,
and formal ethics committee approval was not
required (40), the study adhered to the principles
of the Declaration of Helsinki. All participants were
informed about the purpose and procedures of
the monitoring, and written informed consent was
obtained. Data were anonymised before analysis to
ensure confidentiality, and the sailing club granted
permission for the data to be used for research and
publication.

Procedures

Baseline Measures

On the first week of the pre-competition period, sailors underwent a medical screening battery. The testing battery occurred on Monday of the first week of the preparation phase and established the baseline (BSL) measurements. The protocol consisted of neuromuscular performance, wellbeing, and salivary cortisol testing. Sailors had not participated in high intensity training in the week prior to testing.

Workload Monitoring

WL was evaluated using session-rating of perceived exertion (sRPE) methods. Several variables were recorded: Weekly WL (WL<sub>wk</sub>), strain (WL<sub>str</sub>) and monotony (WL<sub>mono</sub>). After each training session, sailors were requested to subjectively measure their physical exertion. RPE was recorded using the modified Borg 0 to 10 Scale (20). Athletes answered the question "How was your session?" within 30 min of finishing to maximise the accuracy of assessment of the full training session. sRPE was then measured by calculating RPE x the duration of the session (in minutes) (10). When offshore sailing data was collected for measurement, the time spent asleep, or resting was subtracted from the total sailing time. The following variables were then calculated: (i) WL<sub>week</sub> as the sum of all WL during the week (Monday-Sunday); (ii) WL<sub>mono</sub> as the mean of the WL during the seven days divided by the standard deviation of WL<sub>wk</sub> and; (iii) WL<sub>str</sub> as WL<sub>wk</sub> multiplied by WL<sub>mono</sub> (27).

Neuromuscular adaptations

Neuromuscular performance was evaluated using
countermovement jump (CMJ) and bench press
(BP) tests. Lower body power was tested on the

CMJ using a Chronojump contact mat (Chronojump-Boscosystem™, Software, Spain). Each subject completed three attempts with 1 min of rest between trials. A countdown of "3, 2, 1, jump" was indicated orally before initiating the jump. All attempts were completed with subjects placing arms akimbo to avoid any parasitic movement. Athletes were instructed to perform a rapid eccentric phase, immediately followed by an explosive concentric contraction with the intention to jump as high as possible. The height of the best CMJ performance was reported for analysis. Markovic et al. (22) observed that CMJ, when measured by means of contact mat and a digital timer, is a valid and reliable field test for estimating muscle power of the lower limbs.

Maximal upper body pushing strength was assessed using the 1 repetition maximal (1RM) estimation based on Brzycki's equations (5). The estimation was calculated during training sets with less than 6-repetitions to avoid any risk of injury with the 1RM test. Bench press 1RM (BP<sub>1RM</sub>) was not tested for Test<sub>7</sub> to avoid the delayed onset of muscle soreness before the start of TOR. Reliability of Brzycki's equation for measuring maximal strength on the BP1RM from a submaximal test has been validated by Amarante do Nascimento et al (2).

Wellbeing fluctuations

Wellbeing was assessed using the Hooper-
Index (HI) with the recommendations of Hooper
and Mackinnon (18). Athletes answered the
questionnaire every Monday morning upon waking.
Sleep quality, muscle soreness, stress and fatigue
were scored on a 7-point scale where "1" and "7"
represent "very, very good" and "very, very poor"
respectively. The Hooper-Index was computed by
summing its four subgroups (sleep quality, delayed
onset muscle soreness, stress and fatigue). The
reliability of using the HI for monitoring sports
induced fatigue has previously been demonstrated
(33).

Hormonal adaptations

Given the constraints of training schedules and
logistical considerations, hormonal adaptations were
evaluated by collecting salivary cortisol (C) samples
upon waking (7:00 - 8:00am) acknowledging that
this timing may introduce variability due to natural
individual diurnal fluctuations in C levels (37). To
ensure more rigorous testing and to limit cortisol
measurement errors, athletes were required
to refrain from eating, brushing their teeth and chewing gum prior to testing. Sailors placed the swab (Soma Bioscience, Wallingford, United Kingdom) on their tongue and closed their mouth. When the indicator on the swab's stem turned blue, the test was considered complete (swab collected 0.5 mL of oral fluid). The swab was then placed in the buffer bottle of assay (sodium phosphate, salts, detergents and preservatives) before gently mixing the sample for 2 min. Two drops of the sample were then placed in the window of the lateral flow device. The device was left still during the incubation period (10 min). The strip was then placed in the real-time reader and the results were ready within 20 s. C was then collected as nanograms per millilitre (ng/mL). Soma Bioscience oral fluid collector has been validated against enzyme linked immunosorbent assay (ELISA) and has been proven to be a reliable method to collect and analyse C (9).

## Statistical Analyses

Over the 27-week study period, data from 148.12 ± 17.53 training sessions was collected and analysed. Data was calculated through standard statistical methods and is presented as mean ± standard deviation (SD). All variables were considered normally distributed through analysis with the Shapiro-Wilk test, histograms, and skewness values prior to analysis. One-way ANOVA with repeated measures was used to determine the differences between the different parameters measured at Test₁⁻₇. The Pearson correlation coefficient (r) was used to assess the associations between WL variables and monitoring markers. Correlation coefficients were interpreted as: < 0.1 (trivial), from 0.1 to 0.3 (small), from 0.3 to 0.5 (moderate), from 0.5 to 0.7 (large), from 0.7 to 0.9 (very large), and ≥ 0.9 (nearly perfect) (6). The correlations were consistently presented with 95% confidence interval (CI). For all analyses, α was set at 0.05. Statistical analyses were performed using the SPSS package (15.0 version; SPSS, Inc., Chicago, IL, USA).

## RESULTS

Figure 2A illustrates the weekly distribution of WL during the 27 analysed weeks of TOR pre-competition preparation. WL<sub>wk</sub> variation displayed a high coefficient of variation (CV=1.18). Higher (*p*<0.001) WL<sub>wk</sub> were observed during weeks of sailing camp (19792.6 ± 9428 AU; W<sub>17-18</sub> & W<sub>23-26</sub>) compared to those not included in the camps (2813.2 ± 817.3 AU; W<sub>1-16</sub>, W<sub>19-22</sub> & W<sub>27</sub>). Higher WL

were observed in the training blocks towards the end of the preparation schedule (Figure 2B). TB6 (22313 ± 6679.8 AU) confirmed higher WL (*p*<0.001) than TB₁ (2537.9 ± 289.1 AU), TB₂ (2555.3 ± 375.5 AU), TB₃ (2717.8 ± 642.5 AU), TB₄ (3236.3 ± 1074.7 AU), TB₅ (7652.3 ± 718.6 AU) and TB₇ (3116.3 ± 712.7 AU). TB5 was also significantly different (*p*<0.001) to all the other TB. TB₇, which was considered a taper week, showed lower (*p*<0.001) WL<sub>wk</sub> when compared to TB₅ and TB₆ (Figure 2B). Repartition of WL during TB₁⁻₇ is presented in Figure 3.

Within-group changes concerning CMJ, BP<sub>1RM</sub>, HI and C, during the different phases of preparation, are provided in Table 1. CMJ and BP<sub>1RM</sub> were significantly different (*p*≤0.05<*p*< *p*≤0.01) from BSL during Test₁, Test₂, Test₃, Test₄, Test₅, Test₆ and Test₇ respectively. HI (*p*=0.05) and C (*p*=0.05) were both significantly different from BSL only at Test₆.

The correlational analysis found multiple outcomes between WL variables and monitored markers. Large and very large positive correlations were found between WL<sub>wk</sub> and BP<sub>1RM</sub> (*r*=0.69, 95%, CI [0.11;0.92], *p*=0.02), HI (*r*=0.85, 95% CI [0.47;0.96], *p*<0.001) and C (*r*=0.84, 95% CI [0.44;0.96], *p*=0.001). Moderate negative correlations were also found between WL<sub>wk</sub> and CMJ (*r*=-0.39, 95% CI [-0.81;0.31], *p*=0.05). WL<sub>mono</sub> showed large positive correlations to the HI (*r*=0.55, 95% CI [-0.12;0.87], *p*=0.05). WL<sub>str</sub> showed moderate negative correlations with CMJ (*r*=-0.48, 95% CI [-0.85;0.21], *p*=0.04) and large positive correlations with BP<sub>1RM</sub> (*r*=0.52, 95%, CI [-0.16;0.87], *p*=0.05). WL<sub>str</sub> also showed very large positive correlations with C (*r*=0.83, 95% CI [0.42;0.96], *p*=0.002) and very large positive correlations with the HI (*r*=0.88, 95% CI [0.56;0.97], *p*<0.001).

Within-group changes concerning CMJ, BP<sub>1RM</sub>, HI and C, during the different phases of preparation, are provided in Table 1. CMJ and BP<sub>1RM</sub> were significantly different (*p*≤0.05<*p*< *p*≤0.01) from BSL during Test₁, Test₂, Test₃, Test₄, Test₅, Test₆ and Test₇ respectively. HI (*p*=0.05) and C (*p*=0.05) were both significantly different from BSL only at Test₆.

The correlational analysis found multiple outcomes between WL variables and monitored markers. Large and very large positive correlations were found between WL<sub>wk</sub> and BP<sub>1RM</sub> (*r*=0.69, 95%, CI [0.11;0.92], *p*=0.02), HI (*r*=0.85, 95% CI [0.47;0.96], *p*<0.001) and C (*r*=0.84, 95% CI [0.44;0.96], *p*=0.001). Moderate negative correlations were also found between WL<sub>wk</sub> and CMJ (*r*=-0.39, 95%
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 2.** A) Weekly workload during the 27 weeks of preparation. Data are mean (± SD). B) Training block variations of weekly workload average. Data are mean (± SD). 1, 2, 3, 4, 5, 6 and 7: indicates to which TB it is significantly different (p≤0.05). AU: Arbitrary Units; TB<sub>1-7</sub>: Training Block.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 3.** Repartition of workload specificity during each training block.</figcaption></figure>
<table><caption>**Table 1.** Changes (mean ± SD) in monitoring markers throughout the preparation period.</caption><thead><tr><th></th><th>BSL</th><th>Test<sub>1</sub></th><th>Test<sub>2</sub></th><th>Test<sub>3</sub></th><th>Test<sub>4</sub></th><th>Test<sub>5</sub></th><th>Test<sub>6</sub></th><th>Test<sub>7</sub></th></tr></thead><tbody><tr><td>CMJ (cm)</td><td>34.1±8.5</td><td>37.2±9.9*†</td><td>40.1±9.6**†</td><td>39.8±8.1**</td><td>42.6±7.5**†</td><td>43.0±8.1*</td><td>37.4±6.6*†</td><td>42.6±6.7**††</td></tr><tr><td>BP1RM (kg)</td><td>78.5±25.5</td><td>81.3±26.3*†</td><td>83.6±28.4*</td><td>84.8±27.9*</td><td>84.9±28.9*</td><td>88.6±30.3*</td><td>88.1±29.3*</td><td></td></tr><tr><td>HI (AU)</td><td>13.2±3.3</td><td>13.7±2.2</td><td>14.8±2.1</td><td>12.2±2.3†</td><td>13.8±2.9</td><td>13.3±3.0</td><td>17.6±2.9**††</td><td>13.5±2.8††</td></tr><tr><td>C (ng/mL)</td><td>16.1±11.3</td><td>16.4±11.2</td><td>12.9±6.6*††</td><td>16.3±7.2</td><td>8.8±3.9†</td><td>14.7±10.1††</td><td>25.4±9.8**††</td><td>12±4.6††</td></tr></tbody></table>

BSL: Baseline; Test<sub>1,7</sub>; Testing Battery; CMJ: Counter Movement Jump; BP1RM: Bench Press 1RM; HI: Hooper Index; C: Salivary Cortisol; * Significant difference with values of BSL; p≤0.05. ** Significant difference with value of BSL; p≤0.01; † Significant difference with values of preceding Test; p≤0.05. †† Significant difference with values of preceding Test; p≤0.01.

CI [-0.81;0.31], p=0.05). WL<sub>mono</sub> showed large positive correlations to the HI (r=0.55, 95% CI [-0.12;0.87], p=0.05). WL<sub>str</sub> showed moderate negative correlations with CMJ (r=-0.48, 95% CI [-0.85;0.21], p=0.04) and large positive correlations with BP<sub>1RM</sub> (r=0.52, 95%, CI [-0.16;0.87], p=0.05). WL<sub>str</sub> also showed very large positive correlations with C (r=0.83, 95% CI [0.42;0.96], p=0.002) and very large positive correlations with the HI (r=0.88, 95% CI [0.56;0.97], p<0.001).

## DISCUSSION

The present study i) described the WL distribution during the pre-competition period of TOR and ii) aimed to identify the best dose-response predictors of periodically monitored indices with WL variables. The results indicate the significance of these assessments in monitoring neuromuscular performance, wellbeing, and physiological adaptations in professional offshore sailing. Indeed, the analysis showed correlations between WL parameters and the monitored indices. These findings offer practical insights for planning and programming interventions in professional crewed offshore sailing.

Despite extensive scientific interest in Olympic and America's Cup sailing (26, 38), the offshore sailing context remains relatively unexplored. To date, limited research concerning sailing WL and its' impact on neuromuscular performance, wellbeing and stress hormone levels has been documented (12, 18). This lack of literature may be explained, in part, by the complexity of conducting controlled research protocols in such a challenging environment. For instance, the utilisation of wearable technology, such as heart rate monitoring devices, on yawing and rolling vessels poses practical difficulties when sailing offshore for multiple days (12). Hence, as a substitute to objective exercise intensity, the researchers in this study used the RPE to quantify the psycho-physiological responses to training (12). Moreover, sRPE is a practical method

for measuring WL completed through various training modalities such as cross-training and resistance training (35). WL<sub>mono</sub> and WL<sub>str</sub> indexes can be calculated from sRPE data methods. WL<sub>mono</sub> is a measure of day-to-day training variability and has been found to be related to overtraining when monotonous training is combined with high WL<sub>wk</sub> (11). WL<sub>str</sub> has been found to be an advantageous tool as recovery only becomes fundamental to training when high WL<sub>wk</sub> are being undertaken (32).

The observed variations in WL during the 27-week pre-competition period were substantial, indicating a noticeable difference between sailing and non-sailing weeks. Indeed, a difference of 703 % in WL<sub>wk</sub> was found between weeks of training during sailing camp (19792.6 ± 9428 AU; W<sub>17-18</sub> & W<sub>23-26</sub>) and on land training weeks (2813.2 ± 817.3 AU; W<sub>1-16</sub>, W<sub>19-22</sub> & W<sub>27</sub>) (Figure 2A). Additionally, a reduction of 50.4 % was observed in WL<sub>wk</sub> during the taper week (3116.3 ± 712.7 AU; W<sub>27</sub>) compared to preceding WL (6731.5 ± 8497.3 AU; W<sub>1-26</sub>) (Figure 2A). The significant difference (p<0.001) in WL between sailing weeks and non-sailing weeks highlights the complex nature of planning within the training regimen. Indeed, variations in WL may be due to the numerous extraneous weather and environmental conditions as confirmed in previous research (1). Moreover, the considerable CV in WL<sub>wk</sub> distribution (CV=1.18) emphasises the challenge of managing WL in the sailing setting. Although similar values of WL<sub>wk</sub>, during non-sailing weeks, have been reported in other sports (8), the exceptionally high WL quantities observed during sailing weeks remain unparalleled.

To date, research focusing on the sailing environment has predominantly focused on aspects such as energy expenditure, sleep deprivation, nutritional challenge and epidemiology, yet WL management is conspicuous by its absence. Recently, high WL have been associated with risks of overtraining, injury, and decreased performance (8). Consequently, because of the high WL<sub>wk</sub> observed in this study, the necessity for strict WL monitoring
practices in offshore sailing environments seems
crucial for optimising human performance. Beyond
the observed WL patterns during TOR preparation,
the study also delved into analysing the impacts of
various WL parameters on neuromuscular function,
wellbeing, and stress hormone levels.

Throughout the season, fluctuations were noted
in the monitored indices. While past research
has concentrated on establishing predictive
associations between neuromuscular adaptations
and sports performance (3), investigations into
environment-based sports, such as sailing, are yet
to be fully investigated. The results found in this
study could, therefore, provide valuable insights for
WL management in such environments.

Current literature has observed the impact of offshore sailing on lower body strength and power exertion (31). Notably, maintaining postural control through constant muscular tension, due to hull movement, appears to increase the mechanical loading of hip extensors and hip flexors and, thus, impair CMJ performance during and post-competition (31). CMJ assessments are commonly used to assess neuromuscular fatigue in professional athletes (7). In this study, the negative correlations found between WL parameters (WL<sub>wk</sub> and WL<sub>str</sub>) and the CMJ seems to be in agreement with research analysing the effects of WL on muscle power (21). Additionally, high chronic WL has been associated with inflammatory response, catabolic state and muscle damage which results in neuromuscular disturbances (23). Hence, this could partially explain the adverse effects of WL<sub>wk</sub> on CMJ as TB<sub>6</sub> was a high sailing volume block and provoked a 15 % decrease, from Test<sub>5</sub>, in CMJ performance. These results coincide with findings highlighting neuromuscular disruption due to the constant proprioceptive muscle tension encountered by sailors during the yawing, rolling and pitching motions of the hull (38). Furthermore, a 55 % decrease in WL<sub>wk</sub> during TB<sub>7</sub> resulted in supercompensation from TB<sub>6</sub>, positively impacting CMJ performance (+14 %). This highlights the sensitivity of CMJ performance to both fatigue and overcompensation (3).

Contrary to expectations, our findings revealed a
notable increase in strength expression (BP<sub>1RM</sub>)
alongside the rise in WL<sub>wk</sub>. Interestingly, although
on land training WL (i.e., resistance training and
metabolic conditioning) decreased exponentially
after TB<sub>3</sub> (Figure 3), BP<sub>1RM</sub> evolved positively (Table
1). The vessel in this study was equipped with

upper body pedestals used to hoist and trim sails
by driving the winches attached to the sail lines.
The physical action of grinding, engaging these
upper body pedestals, has received considerable
amount of interest in sailing literature (29). The
demands placed on the upper body during grinding
have been well-documented, linking bench press
performance to both sailing performance (29) and
manoeuvre efficiency (26). Furthermore, the work-
to-rest ratio, while using the pedestals, appears
to facilitate adequate recovery between bouts of
intense activity (26) and thus potentially mitigating
the risks of overtraining. This may explain the
positive upper body neuromuscular adaptations in
response to a high sailing WL.

A novel aspect of the hereby study was the
monitoring of professional sailors' perceptual
wellbeing throughout an entire pre-competition
period. To the best of the authors' knowledge, past
research in offshore sailing has shown interest in
establishing dose-response relationships between
environmental and wellbeing factors such as anxiety
and perception of fatigue (15). Similarly, when
comparing to longer assessments (i.e., QEFA Likert
scale), the HI has been found to be (i) a promising
tool for monitoring training induced fatigue in team
sports (28) and (ii) has shown associations with WL
in professional athletes (25). In our study, the Test<sub>1-7</sub>
HI was found to be positively correlated with WL<sub>wk</sub>,
WL<sub>str</sub> and WL<sub>mono</sub>. These findings are of important
value when aiming to bridge the gap between
WL and fatigue markers. The HI can therefore be
considered as a cost-effective and practical means
of monitoring both physiological and psychological
stress in professional sailing environments.

From a physiological perspective, fluctuations in
C may significantly impact both neuromuscular
and cognitive performance. For instance, cortisol
could influence the sympathetic nervous system's
activity, alter muscle contractile properties and/
or behaviour and serves as a primary indicator of
physiological stress (19). There is a substantial
body of literature addressing cortisol concentration
increase in response to WL and competition. In
the current study, the interactions between WL
(WL<sub>wk</sub> and WL<sub>str</sub>) and C were predominantly clear.
Indeed, the hormonal response to training appears
to be impacted by short (7-days) preceding WL.
This observation is similar to findings in other team
sports (34) where C increased in response to WL.
In our study, the C concentrations at Test<sub>6</sub> were the
highest observed during the pre-competition period
and were significantly different from baseline levels.
These results suggest that high sailing WL may
have an impact on stress hormone balance. This
is in-line with reported studies that have analysed
biomarkers in sailing activities (14). Furthermore,
the increase in C could reflect the athletes' natural
response to the previous training phase (28).

While our findings provide important and novel information for performance practitioners in offshore sailing, several limitations warrant consideration. First, we acknowledge that the data was collected on a small sample of sailors (i.e., one team). The use of larger sample sizes (e.g., more teams) would allow further and more precise correlational analysis. Second, caution is warranted when interpreting the reported hormonal concentrations as the Soma Bioscience device may potentially underestimate C levels compared to gold-standard ELISA (24). Third, due to sailing's dependency on climatic variables, the training structure may represent a different organisation when compared to other sports. Future research, based on sailing, could therefore explore the effects of sailing WL on adaptation markers via daily measurements. Moreover, exploring external load (e.g., GPS tracking, manoeuvre count) may prove to be useful for establishing valid methods able to quantify sailing WL. Furthermore, the absence of research concerning WL management in sports characterised by prolonged training sessions raises caution regarding data analysis. Indeed, the validity and reliability of the sRPE method in such activity has yet to be explored. Hence, exploring these aspects becomes necessary for a more comprehensive understanding of WL dynamics in extended-duration sports.

This is the first longitudinal study to provide
information on WL parameters within a professional
mixed-sex crew offshore sailing team during the
preparation period of TOR. This investigation
observed (i) significant differences between
BSL levels and periodical evaluations and (ii)
significant correlations between WL parameters
and neuromuscular performance, wellbeing and
stress hormone levels. Furthermore, objective
markers such as BP<sub>1RM</sub>, CMJ and C may be used as
indicators of preparedness for an offshore regatta.
HI may provide valuable information concerning
physiological and psychological stress with less
time-consuming/costly procedures. The results
presented in this study confirm the importance of
managing WL in professional offshore sailing in the
quest to optimising training related adaptations and
recovery.

FUTURE RECOMMENDATIONS

This study offers foundational insights into
the intricate relationship between WL and key
parameters such as neuromuscular performance,
wellbeing, and stress hormone levels within the
unique context of offshore sailing. Given the
substantial and variable nature of WL within this
setting, daily monitoring during pre-competition
phases becomes imperative. This continuous
assessment not only enables a comprehensive
understanding of the fluctuations in WL but also
equips performance practitioners with invaluable
guidance. Through this data analysis, practitioners
can adeptly (i) adapt training plans to suit dynamic
environmental conditions, (ii) furnish skippers with
crucial insights into potential injury or overtraining
risks, and (iii) strategically devise and ensure
adequate recovery interventions to enhance
performance. In essence, this study underscores
the pivotal role of systematic WL monitoring in the
domain of offshore sailing. Such proactive and data-
driven approaches not only fortify injury prevention
strategies but also serve as a cornerstone for
fostering peak performance, ultimately contributing
to the overall success and well-being of professional
sailing crews.

ACKNOWLEDGMENTS

The authors thank the Club Naval de Cascais,
skipper and sailors for their participation in this
study.

DISCLOSURE OF INTEREST

The authors report there are no competing interests
to declare.

FUNDING DETAILS

No funding was received.

ETHICAL APPROVAL

Ethics for this study were approved in line with the
University of Pau and Pays de l'Adour's Institutional
Review Board
DATES OF REFERENCE

Submission - 07/06/2024

Acceptance - 07/10/2024

Publication - 21/02/2024

CORRESPONDING AUTHOR

Kilian Philippe - kaj.philippe@gmail.com

REFERENCES

1. Allen PH, Wadsworth EJ, Smith A. Seafarers' fatigue: a review of the recent literature. Int Marit Health. 2008;59:81-92.

2. Amarante do Nascimento M, Cyrino ES, Nakamura FY, Romanzini M, Pianca HU, Queiroga MR. Validation of the Brzycki equation for the estimation of 1-RM in the bench press. Rev Bras Med Esporte. 2007;13:40e-42e.

3. Borresen J, Lambert MI. The quantification of training load, the training response and the effect on performance. Sports Med. 2009;39(9):779-795. doi:10.2165/11317780-000000000-00000.

4. Bot P, Voisin D, Soulier A, Braud C, Astolfi J. A new sensor to characterize flow separation on a hydrofoil. In: Proceedings of the 9th Conference on Computational Methods in Marine Engineering (Marine 2021). 2022:1. doi:10.2218/marine2021.6779.

5. Brzycki M. Strength testing—Predicting a one-rep max from reps-to-fatigue. J Phys Educ Recreat Dance. 1993;64:88-90.

6. Cohen J. The concepts of power analysis. 2nd ed. Hillsdale, NJ: Erlbaum; 1988.

7. Cormack SJ, Mooney MG, Morgan W, McGuigan MR. Influence of neuromuscular fatigue on accelerometer load in elite Australian football players. Int J Sports Physiol Perform. 2013;8(4):373-378.

8. Dubois R, Paillard T, McGrath D, Chamari K, Maurelli O, Polly S, Prioux J, Lyons M. Changes in training load, running performance, lower body power and biochemical characteristics of back players throughout a professional Rugby Union season. J Hum Sport Exerc. 2017;12:1-16. doi:10.14198/jhse.2017.121.01.

9. Fisher R, McLellan CP, Sinclair W. The validity and reliability for a salivary cortisol point of care test. J Athl Enhanc. 2015;4. doi:10.4172/2324-9080.1000204.

10. Foster C, Florhaug JA, Franklin J, et al. A new approach to monitoring exercise training. J Strength Cond Res. 2001;15(1):109-115.

11. Foster C. Monitoring training in athletes with reference to overtraining syndrome. Med Sci Sports Exerc. 1998;30:1164-1168. doi:10.1097/00005768-199807000-00023.

12. Galvani C, Ardigò LP, Alberti M, Daniele F, Capelli

C. Physical activity, sleep pattern and energy expenditure in double-handed offshore sailing. J Sports Med Phys Fitness. 2015;55(12):1480-1488.

13. Gastin PB, Fahrner B, Meyer D, Robinson D, Cook JL. Influence of physical fitness, age, experience, and weekly training load on match performance in elite Australian football. J Strength Cond Res. 2013;27(5):1272-1279.

14. Giacon T, Bosco G, Vezzoli A, Dellanoce C, Cialoni D, Paganini M, Mrakic-Sposta S. Oxidative stress and motion sickness during offshore sailing. Preprint. 2021. doi:10.21203/rs.3.rs-654012/v1.

15. Hagin V, Gonzales BR, Candau RB, Groslambert A. Influence of a conservative sleep management strategy during a solo Pacific Ocean crossing on anxiety and perceived fatigue: a case study. J Sports Sci. 2012;30(4):395-402. doi:10.1080/02640414.2011.644248.

16. Halson SL. Monitoring training load to understand fatigue in athletes. Sports Med. 2014;44(Suppl 2)-S147. doi:10.1007/s40279-014-0253-z.

17. Hills SP, Rogerson DJ. Associations between self-reported well-being and neuromuscular performance during a professional Rugby Union season. J Strength Cond Res. 2018;32(9):2498-2509. doi:10.1519/JSC.0000000000002531.

18. Hooper SL, Mackinnon LT. Monitoring overtraining in athletes. Recommendations. Sports Med. 1995;20(5):321-327. doi:10.2165/00007256-199520050-00003.

19. Lindsay A, Lewis J, Scarrott C, Draper N, Gieseg SP. Changes in acute biochemical markers of inflammatory and structural stress in rugby union. J Sports Sci. 2015;33(9):882-891. doi:10.1080/02640414.2015.14.971047.

20. Lovell TW, Sirotic AC, Impellizzeri FM, Coutts AJ. Factors affecting perception of effort (session rating of perceived exertion) during rugby league training. Int J Sports Physiol Perform. 2013;8(1):62-69. doi:10.1123/ijspp.8.1.62.

21. Malone S, Owen A, Newton M, Mendes B, Collins KD, Gabbett TJ. The acute workload ratio in relation to injury risk in professional soccer. J Sci Med Sport. 2017;20(6):561-565. doi:10.1016/j.jsams.2016.10.014.

22. Markovic G, Dizdar D, Jukic I, Cardinale M. Reliability and factorial validity of squat and countermovement jump tests. J Strength Cond Res. 2004;18(3):551-555. doi:10.1519/1533-4287(2004)18<551>2.0.CO;2.

23. McLellan CP, Lovell DI, Gass GC. Creatine kinase and endocrine responses of elite players pre, during, and post rugby league match play. J Strength Cond Res. 2010;24(11):2908-2919. doi:10.1519/JSC.0b013e3181c1fcb1.

24. Mitsuishi H, Okamura H, Moriguchi Y, Aoki Y. The validity of the salivary cortisol analysis method using the cube reader in Japanese university students. Jpn Psychol Res. 2023;65(4):369-378. doi:10.1111/jpr.12402

25. Moalla W, Fessi MS, Farhat F, Nouira S, Wong DP,
Dupont G. Relationship between daily training load and psychometric status of professional soccer players. Res Sports Med. 2016;24(4):387-394. doi:10.1080/15438627.2016.1239579.

26. Neville V, Calefato J, Pérez-Encinas C, Rodilla-Sala E, Rada-Ruiz S, Dorochenko P, Folland JP. America's Cup yacht racing: race analysis and physical characteristics of the athletes. J Sports Sci. 2009;27(9):915-923. doi:10.1080/02640410902946485.

27. Nobari H, Barjaste A, Haghighi H, Clemente FM, Carlos-Vivas J, Pérez-Gómez J. Quantification of training and match load in elite youth soccer players: a full-season study. J Sports Med Phys Fitness. 2022;62(4):448-456. doi:10.23736/S0022-4707.21.12236-4.

28. Oliveira R, Brito JP, Martins A, Mendes B, Marinho DA, Ferraz R, Marques MC. In-season internal and external training load quantification of an elite European soccer team. PLoS One. 2019;14(4). doi:10.1371/journal.pone.0209393.

29. Pearson SN, Hume PA, Cronin JB, Slyfield D. Strength and power determinants of grinding performance in America's Cup sailors. J Strength Cond Res. 2009;23(6):1883-1889. doi:10.1519/JSC.0b013e3181b2ba55.

30. Philippe K, Paillard T, Dubois R, Maurelli O, Prioux J. Key performance indicators in Tour de France sailing. J Sports Sci. 2021;39(8):944-954. doi:10.1080/02640414.2020.1851925.

31. Philippe K, Paillard T, Maurelli O, Moody J, Prioux J. Effects of an offshore sailing competition on anthropometry, muscular performance, subjective wellness, and salivary cortisol in professional sailors. Int J Sports Physiol Perform. 2022;1-8. doi:10.1123/ijspp.2021-0575.

32. Putlur P, Foster C, Miskowski JA, Kane MK, Burton SE, Scheett TP, et al. Alteration of immune function in women collegiate soccer players and college students. J Sports Sci Med. 2004;3:234-243.

33. Rabbani A, Clemente FM, Kargarfard M, Chamari K. Match fatigue time-course assessment over four days: usefulness of the Hooper Index and heart rate variability in professional soccer players. Front Physiol. 2017;8:45. doi:10.3389/fphys.2017.00045.

34. Rowell AE, Aughey RJ, Hopkins WG, Esmaeili A, Lazarus BH, Cormack SJ. Effects of training and competition load on neuromuscular recovery, testosterone, cortisol, and match performance during a season of professional football. Front Physiol. 2018;9:668. doi:10.3389/fphys.2018.00668.

35. Ryan S, Impellizzeri FM, Kempton T, Coutts AJ. Training monitoring in professional Australian football: theoretical basis and recommendations for coaches and scientists. J Sci Med Sport. 2020;4(1):52-58.

36. Sjögaard G, Inglés E, Narici M. Science in sailing: interdisciplinary perspectives in optimizing sailing performance. Eur J Sport Sci. 2015;15(3):191-194. doi:10.1080/17461391.2015.1008583.

37. Stawski RS, Cichy KE, Piazza JM, Almeida DM.

Associations among daily stressors and salivary cortisol: Findings from the National Study of Daily Experiences. Psychoneuroendocrinology. 2013;38(11):2857-2865. doi:10.1016/j.psyneuen.2013.06.023.

38. Stoffregen TA, Smart LJ Jr. Postural instability precedes motion sickness. Brain Res Bull. 1998;47(5):437-448. doi:10.1016/s0361-9230(98)00102-6.

39. Thorpe RT, Atkinson G, Drust B, Gregson W. Monitoring Fatigue Status in Elite Team-Sport Athletes: Implications for Practice. Int J Sports Physiol Perform. 2017;12(Suppl 2):S227-S234. doi:10.1123/ijspp.2016-0434

40. Winter EM, Maughan RJ. Requirements for ethics approvals. J Sports Sci. 2009;27(10):985. doi:10.1080/02640410903178344.