<figure><img src="image_63.png" /><figcaption>**FIGURE 2** | Muscle soreness reported by men and women for the quadriceps (Q), hamstring (H), and triceps surae (TS) muscle groups as a function of time. Outliers are indicated by dots. Significant time effect as compared to PRE for Q ($) and TS (#). ***Sex-dependent difference for H (p < 0.001).</figcaption></figure>

MVC<sub>BLT</sub> at POST (p = 0.123). At 2D and 4D, only men showed delayed moderate (ES = 0.7) and small (ES = 0.5) decreases, respectively. For the unilateral tests, regardless of sex, the MVCs were reduced up to 2D. Men showed large acute drops, at POST (DL: ES = 2.3; NDL: ES = 2.1) and 2H (DL: ES = 1.8; NDL: ES = 1.4), but small ones at 2D (DL and NDL: ES = 0.5). Women showed mostly moderate acute drops, at POST (DL: ES = 0.8; NDL: ES = 1.3) and 2H (DL: ES = 0.8; NDL: ES = 1.1), and small ones at 2D (DL and NDL: ES = 0.5).

The pure concentric SJ and HF-V tests were not associated with any significant change in performance (vto) along the 4D recovery period. However, a main time effect was found in SJ for the entire group in $\bar{F}_{po}$ (p < 0.05) due to a small increase at 4D

<table><caption>**TABLE 2** | Relative changes (as compared to PRE) in the bilateral (BLT), dominant leg (DL), and non-dominant leg (NDL) conditions of the maximal voluntary isometric test of knee extension.</caption><thead><tr><th></th><th>POST</th><th>2H</th><th>2D</th><th>4D</th></tr></thead><tbody><tr><td><strong>BLT (%)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Men</td><td>-47 ± 8***</td><td>-35 ± 18***</td><td>-15 ± 11**</td><td>-11 ± 14*</td></tr><tr><td>Women</td><td>-26 ± 19**</td><td>-28 ± 9**</td><td>-14 ± 18</td><td>-13 ± 14</td></tr><tr><td>All</td><td>-37 ± 14***</td><td>-32 ± 14***</td><td>-15 ± 20**</td><td>-12 ± 14*</td></tr><tr><td><strong>DL (%)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Men</td><td>-40 ± 10***</td><td>-25 ± 17***</td><td>-10 ± 8*</td><td>-6 ± 8</td></tr><tr><td>Women</td><td>-37 ± 20***</td><td>-31 ± 14**</td><td>-18 ± 26*</td><td>-10 ± 12</td></tr><tr><td>All</td><td>-39 ± 15***</td><td>-28 ± 16***</td><td>-14 ± 17**</td><td>-8 ± 10*</td></tr><tr><td><strong>NDL (%)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Men</td><td>-39 ± 11***</td><td>-28 ± 13***</td><td>-10 ± 6*</td><td>-6 ± 5</td></tr><tr><td>Women</td><td>-22 ± 10***</td><td>-19 ± 11***</td><td>-15 ± 23*</td><td>-9 ± 11</td></tr><tr><td>All</td><td>-31 ± 10***</td><td>-24 ± 12***</td><td>-13 ± 15**</td><td>-8 ± 8*</td></tr></tbody></table>

(p < 0.05, ES = 0.2). In the HF-V test, independently of the load, a significant interaction effect (sex × time) was found for $P_{max}$ (p < 0.05) and $\bar{F}_{po}$ (p < 0.05), due to moderate increases of $P_{max}$ (+9.9%, p < 0.001, ES = 1) and $\bar{F}_{po}$ (+3.6%, p < 0.01, ES = 0.7) for women at 4D.

Analysis of the DJ test revealed a main time effect for the entire group that reflected large to moderate acute changes in most variables, followed by moderate to small ones at 2D and 4D (Figure 4 and Table 3). A sex × time interaction effect was found for $P_{max}$ (p < 0.05), as men presented large decreases at POST (ES = 1.5) and moderate ones from 2H to 4D (ES = 1) while

<figure><img src="image_64.png" /><figcaption>**FIGURE 3** | Maximal voluntary isometric contraction of knee extensors as a function of time for men (blue curve) and women (pink curve) in the bilateral (MVC<sub>BLT</sub>), the dominant (MVC<sub>DL</sub>), and non-dominant (MVC<sub>NDL</sub>) unilateral tests. Performances are expressed in absolute values (A) and in individually and group-mean normalized values (B), with significant time differences (compared to PRE) showed for men (#) and women ($) (p < 0.05).</figcaption></figure>

<footnote>*P < 0.05, **P < 0.01, and ***P < 0.001.</footnote>
<figure><img src="image_65.png" /><figcaption>**FIGURE 4** | Pluriarticular dynamic test performances (vto: velocity at take-off) as a function of time for men (blue curve) and women (pink curve) in the squat jump (SJ), drop jump (DJ), and horizontal force-velocity (HF-V) tests. Performances are expressed in absolute values (A) and normalized values, with the significant time changes compared to PRE indicated for the entire group (*) ($p$ < 0.05). Due to the absence of significant interaction, the normalized values (B) are shown for each sex group with no statistics.</figcaption></figure>

women showed only acute moderate decreases (POST: ES = 0.6; 2H: ES = 0.5). No interaction effect was found for the $P_{max}$ data once normalized. The recovery of $\bar{F}_{po}$ was found to be leg-dependent, the decrease being observed at POST only for NDL ($p$ < 0.001, ES = 1.1) while at both POST ($p$ < 0.001, ES = 1.5) and 2H ($p$ < 0.01, ES = 0.9) for DL.

## The MFA Analysis

The two principal dimensions of the MFA on the individual changes are shown on **Figure 5**. The first dimension, which explained 24.03% of the variance, was mainly correlated to the acute and delayed functional decrements in DJ (such as decreases in force, velocity and power as well as increases in braking and push-off times), and the acute drops in MVC tests, notably for $MVC_{BLT}$ and $MVC_{DL}$ ($r$ = 0.72, $p$ < 0.001), and also for $MVC_{NDL}$ ($r$ = 0.56, $p$ < 0.05). A significant correlation was found between sex and the first dimension ($r$ = -0.54, $p$ < 0.05). Most women displayed moderate or higher values on this dimension, reflecting their lower functional reductions in DJ than men. The second dimension, explaining 16.20% of the variance, was mainly represented by the functional SJ and HF-V variables, which generally varied in the opposite way to the DJ ones. The 16 best explanatory variables correlated with each dimension are shown in **Supplementary Table S1**.

The MFA analysis of the inter-session relationships revealed the highest correlations between sex and the functional changes at POST (RV coefficient = 0.41) and 4D (RV coefficient = 0.31) (**Supplementary Table S2**). The first dimensions of the POST

and 4D sessions were highly correlated to the first dimension of the MFA ($r$ = 0.91 and $r$ = 0.77, respectively), and thus mainly explained by the DJ and MVC changes. Women presented higher values compared to men on the first dimension at POST (men = -1.34, women = 1.63) and 4D (men = -1.26, women = 1.01). This reflected their lower functional decrements in the DJ and MVC tests at POST, and their remaining functional decrements in the DJ test at 4D while showing an increased HF-V performance.

**Analysis of the ascending hierarchical clustering with five dimensions** (representing 66.6% of the variance) identified six clusters. The dendrogram revealed globally two parts: one including the cluster 6 and a second one with the five other clusters (**Figure 6**). These were chosen from the inertia gain between the number of clusters. When considering the best explanatory variables of each cluster (**Table 4**), cluster 1 included only one runner, who presented large acute fatigue effects in SJ and delayed ones in DJ. Cluster 2 included the only runner who showed large delayed decrements in the HF-V and SJ tests. Cluster 3 included three runners showing large acute decrements in DJ, but improvements in SJ. Cluster 4 included four runners with decrements in HF-V and DOMS at 4D and low racing effort perception. Cluster 5 included the four runners showing slight drops in SJ, but slight improvements in the HF-V test (in particular for the ISOM condition) at 2H and 2D. Cluster 6 included the five runners who showed either no delayed decrease or even a slight improvement in DJ.
<table><caption>**TABLE 3** | Relative changes (Δ% as compared to PRE) for men and women for the braking and push-off phase parameters in the drop jump test: impact peak force (IPF), braking phase duration (t<sub>brake</sub>), push-off duration (t<sub>po</sub>) and height (H<sub>po</sub>), mean force (F̄), velocity (V̄), and power (P̄) of the braking (brake) or push-off (po) phase.</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="4">Men</th><th colspan="4">Women</th><th colspan="2">Statistics</th></tr><tr><th>POST</th><th>2H</th><th>2D</th><th>4D</th><th>POST</th><th>2H</th><th>2D</th><th>4D</th><th>Time</th><th>Sex x Time</th></tr></thead><tbody><tr><td rowspan="3">Braking phase</td><td>IPF</td><td>-20 ± 21</td><td>-13 ± 21</td><td>-7 ± 13</td><td>-9 ± 22</td><td>-18 ± 21</td><td>-11 ± 24</td><td>+6 ± 24</td><td>+11 ± 29</td><td>***</td><td>NS</td></tr><tr><td>t<sub>brake</sub></td><td>+36 ± 19</td><td>+26 ± 32</td><td>+13 ± 23</td><td>+18 ± 32</td><td>+41 ± 42</td><td>+30 ± 59</td><td>-1 ± 22</td><td>-2 ± 31</td><td>***</td><td>NS</td></tr><tr><td>F̄<sub>brake</sub></td><td>-18 ± 9</td><td>-12 ± 16</td><td>-6 ± 13</td><td>-9 ± 14</td><td>-15 ± 12</td><td>-9 ± 18</td><td>+2 ± 14</td><td>+5 ± 20</td><td>***</td><td>NS</td></tr><tr><td rowspan="6">Push-off phase</td><td>P<sub>max</sub></td><td>-25 ± 12</td><td>-15 ± 15</td><td><b>-15 ± 13</b></td><td><b>-15 ± 15</b></td><td>-12 ± 12</td><td>-12 ± 20</td><td>-6 ± 16</td><td>-1 ± 22</td><td>***</td><td>*</td></tr><tr><td>vto</td><td>-12 ± 9</td><td>-6 ± 6</td><td>-8 ± 5</td><td>-6 ± 5</td><td>-5 ± 5</td><td>-8 ± 13</td><td>-5 ± 8</td><td>-5 ± 11</td><td>***</td><td>NS</td></tr><tr><td>F̄<sub>po</sub></td><td>-16 ± 7</td><td>-9 ± 14</td><td>-8 ± 10</td><td>-8 ± 11</td><td>-11 ± 8</td><td>-6 ± 13</td><td>-1 ± 11</td><td>+3 ± 15</td><td>***</td><td>NS</td></tr><tr><td>V̄<sub>po</sub></td><td>-15 ± 7</td><td>-8 ± 5</td><td>-8 ± 4</td><td>-7 ± 6</td><td>-11 ± 7</td><td>-10 ± 14</td><td>-5 ± 9</td><td>-3 ± 13</td><td>***</td><td>NS</td></tr><tr><td>P̄<sub>po</sub></td><td>-25 ± 12</td><td>-14 ± 13</td><td>-15 ± 10</td><td>-13 ± 14</td><td>-17 ± 11</td><td>-13 ± 20</td><td>-6 ± 16</td><td>-2 ± 22</td><td>***</td><td>NS</td></tr><tr><td>Hpo</td><td>0 ± 14</td><td>+6 ± 36</td><td>-2 ± 20</td><td>+1 ± 9</td><td>+8 ± 13</td><td>-5 ± 17</td><td>-8 ± 14</td><td>-12 ± 19</td><td>NS</td><td>NS</td></tr><tr><td></td><td>t<sub>po</sub></td><td>+17 ± 13</td><td>+15 ± 37</td><td>+7 ± 21</td><td>+10 ± 17</td><td>+22 ± 19</td><td>+6 ± 20</td><td>-3 ± 16</td><td>-8 ± 22</td><td>***</td><td>NS</td></tr></tbody></table>

NS, non-significant, *P* < 0.05 and **P* < 0.001, significantly different from the PRE values. ES = effect size amplitude: NS (white), small (clear gray), moderate (medium), and large (dark gray). The sex × time interaction at 2D and 4D is indicated using bold police.

## DISCUSSION

This study aimed to assess the sex influence on the functional recovery pattern after a 20 km graded road endurance race. The racing time (relative and absolute), subjective rate of perceived exertion and pacing strategy being similar between women and men, the functional results have been compared independently of these race parameters.

<figure><img src="image_66.png" /></figure>

## Testing Task- and Sex-Dependent Effects on the Functional Recovery

Supporting our hypothesis that the functional recovery pattern would be testing-task dependent, the MVC and DJ tests revealed large acute performance decreases followed by moderate ones up to 4D whereas the concentric jump (i.e., SJ and HF-V) performances did not change significantly. Also confirming our expectations, significant sex × time interaction effects were found in all tests that provided for women indices of lesser functional decrements at POST and earlier recovery completeness than men. To allow comparison with the scientific literature that mostly used a unilateral MVC test to evaluate running-induced fatigue in men, the results of the MVC tests are discussed prior to the analysis of the other test results and/or sex × time interactions.

Following the 20 km graded road race, the MVC tests revealed mean decreases of 42 and 28% for men and women, respectively at POST, which are close to those observed after a 110-km walking/running ultra-trail with a ~6000 m gradient (38 and 29% for experienced men and women runners) (Temesi et al., 2015). The current MVC decreases observed in men are larger than the 25–30% reported after shorter leveled 10 km (Nicol et al., 2003) or marathon runs (Nicol et al., 1991b; Avela et al., 1999). Both men and women MVC decreases are also larger than the 17.9 and 1.8% reported after a 2 h treadmill run (Glace et al., 1998) and the 10% of both men and women after a 20 km running race without gradient (Boccia et al., 2018). At 2H, men's decrements in MVCs were still large, but close to the ones reported after marathon (Avela et al., 1999) or downhill (Chen et al., 2007) runs. Taken together, these observations emphasize the large acute fatigue induced by the present race profile for recreational men and women runners. Within this framework, our race included a continuous uphill (+382 m) from the 5–10th km that resulted in a 2 km.h⁻¹ drop of the mean running velocity for both men and women. This type of effort requires an overall increased activity of the lower limb extensor muscles (Cai et al., 2010) and in turn, leads to increased metabolic fatigue (Giandolini et al., 2016). The subsequent downhill sections (~6 km with
<figure><img src="image_67.png" /></figure>

−294 m gradient including a maximum slope of 8.4 and 3.5% on average) may be considered as a moderate running effort (Cai et al., 2010). However, given the lack of specific downhill training for most of our recreational runners, the succession of prolonged uphill and downhill sections may have increased the occurrence of muscle damage due to the metabolic fatigue state of the lower limb muscles while working eccentrically. As suggested by Fridén and Lieber (1992), metabolic fatigue is likely to result in inhomogeneous sarcomere resistance to stretch leading to muscle damage. In this regard, our previous fatigue study (Nicol et al., 2003) revealed higher serum creatine kinase activity and greater functional decreases in the delayed recovery phase in participants with previously high acidosis (reflected by elevated serum lactate concentration) at the time of eccentric muscle actions. In the current study, this agrees with the large acute and 4D delayed recovery of the men runners in MVC and DJ.

Regarding the other tests, the DJ revealed for both men and women at POST current temporal and kinetic changes reflecting fatigue-induced loss of resistance to ground impact and deteriorated stretch-shortening cycle type performance (Nicol and Komi, 2010). In the delayed recovery phase, a deteriorated DJ performance was still reflected by reduced velocity and power during the push-off phase while no change was found in the mean force during the braking and push-off phases. As previously reported, polyarticular dynamic tests such as the DJ have the advantage to allow other muscle groups to compensate for the quadriceps femoris fatigue (Nicol et al., 2006; Nicol and Komi, 2010). In the current DJ test, instruction was given to perform it with a short contact time and thus to avoid touching the floor with the heel. This so-called forefoot

technique in DJ is reported to rely on a greater use of the gastrocnemii muscles during the braking phase than in the flat foot technique (Cappa and Behm, 2013). Consequently, the major involvement of the triceps surae in the DJ test seems to have compensated for the contractile failure of the quadriceps muscle group, at least during the braking phase. The influence of intermuscular compensation is further supported by the lack of significant decline in the maximal, but pluri-articular, isometric and concentric performances of the HF-V testing protocol. Thus, confirming our expectations, these overall findings warn about the varied conclusions that can be drawn when using a single testing task. As originally emphasized by Bigland-Ritchie and Woods (1984), fatigue may still exist regardless of whether or not a given force or power output can be sustained. The present combination of maximal tests demonstrates that the MVC and DJ tests seem appropriate to detect delayed functional defects, the latter test being likely to require neural compensatory adjustments (i.e., intermuscular compensation). The HF-V and SJ tests may rather reveal unchanged performances while revealing varied muscle activation and/or synergies.

Sex × time interactions were observed on absolute values in the MVC, DJ, and HF-V tests. In the acute recovery phase for the uni- and bilateral MVC tests as well as for the DJ test, the large effect size observed in men, in contrast to the moderate effect size in women, indicates a lesser acute fatigue in women. At POST, the largest drops in performance for male runners resulted in similar absolute levels of isometric force for men and women in the bilateral MVC test. Thus, the well-known statement that men are stronger than women (clearly observed in all PRE-tests) was not systematically observed at the end of the race. This is interesting in terms of the attenuation of the sex difference reported in the performance of longer running races. In the delayed recovery phase, only men showed functional decreases at 2D and 4D in the bilateral MVC test, while no sex difference was found in the unilateral ones. This discrepancy should be explored further in future studies. Regarding the DJ test, the delayed recovery of men was based solely on the decrease of their $P_{max}$ at 4D. As reported by methodological studies (Samozino et al., 2008; Morin et al., 2019), this parameter is nevertheless considered the most accurate measure of lower limb explosivity. Take-off velocity (vto), commonly used to evaluate the jump performance, does not take into account changes in jump technics (such as push-off distance and time) or body mass, which may individually change among sessions. Based on these overall observations, the current recreational female runners can be considered as less affected and recovering earlier than their male counterparts. On the other hand, since no sex × time interaction effect was found on the normalized MVC and DJ values, the lesser fatigue effects observed in women may be partially attributed to their lower initial levels of maximal force and power output. Among the underlying mechanisms, women skeletal muscles are reported to have a greater proportional area of slow twitch muscle fibers, which may contribute to their lower power, higher aerobic endurance and lower sensitivity to muscle damage than men (Fridén et al., 1983; Lundsgaard and Kiens, 2014). The lesser acute fatigability of women has also been attributed to the influence of estrogen on muscle perfusion and metabolism (Hunter, 2014, 2016a,b), as
<table><caption>TABLE 4 | Clusters of the hierarchical clustering analysis.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Cluster 1 [Men = 1 (100%)]</th><th colspan="3">Cluster 2 [Men = 1 (100%)]</th><th colspan="3">Cluster 3 [Men = 1 (33.3%); Women = 2 (66.7%)]</th></tr><tr><th>Category (%)</th><th>Overall mean (%)</th><th>P-value</th><th>Category (%)</th><th>Overall mean (%)</th><th>P-value</th><th>Category (%)</th><th>Overall mean (%)</th><th>P-value</th></tr></thead><tbody><tr><td colspan="10"><strong>POST/PRE</strong></td></tr><tr><td>Πpo_SJ</td><td>-32.2</td><td>0.4</td><td>&lt;0.01</td><td>tpo_HF-V_20%</td><td>22.5</td><td>2</td><td>&lt;0.01</td><td>Πbrake_DJ</td><td>-22.5</td><td>-13.1</td><td>&lt;0.05</td></tr><tr><td>Vpo_SJ</td><td>-29.2</td><td>-1.4</td><td>&lt;0.05</td><td>tpo_HF-V_0%</td><td>16.7</td><td>4.1</td><td>&lt;0.05</td><td>tbrake_DJ</td><td>90.8</td><td>38.6</td><td>&lt;0.01</td></tr><tr><td>Πpo_SJ</td><td>-13.1</td><td>0.4</td><td>&lt;0.05</td><td>Vpo_HF-V_20%</td><td>-30.2</td><td>-1.7</td><td>&lt;0.001</td><td>tpo_DJ</td><td>44</td><td>19.6</td><td>&lt;0.01</td></tr><tr><td colspan="10"><strong>2H/PRE</strong></td></tr><tr><td>MVC<sub>DL</sub></td><td>-42.1</td><td>-23</td><td>&lt;0.01</td><td>Πpo_HF-V_20%</td><td>-41.6</td><td>-2.2</td><td>&lt;0.001</td><td colspan="3"><strong>2H/PRE</strong></td></tr><tr><td>hpo_DJ</td><td>86.4</td><td>-0.3</td><td>&lt;0.01</td><td>Vpo_HF-V_60%</td><td>-23</td><td>-2.7</td><td>&lt;0.01</td><td>Vpo_DJ</td><td>-25.8</td><td>-8.9</td><td>&lt;0.01</td></tr><tr><td>tpo_DJ</td><td>93.7</td><td>10.2</td><td>&lt;0.05</td><td>Πpo_HF-V_60%</td><td>-32.6</td><td>-2.8</td><td>&lt;0.01</td><td>vto_DJ</td><td>-20.8</td><td>-7.2</td><td>&lt;0.05</td></tr><tr><td colspan="10"><strong>2D/PRE</strong></td></tr><tr><td>hpo_DJ</td><td>38.1</td><td>-5</td><td>&lt;0.05</td><td>Πpo_HF-V_60%</td><td>31.9</td><td>2.9</td><td>&lt;0.01</td><td>Πbrake_DJ</td><td>-13.7</td><td>-39.5</td><td>&lt;0.05</td></tr><tr><td>Πpo_DJ</td><td>-25.9</td><td>-4.2</td><td>&lt;0.05</td><td>Πpo_HF-V_60%</td><td>-17.8</td><td>0.2</td><td>&lt;0.01</td><td>IPF_DJ</td><td>-42.5</td><td>-12.2</td><td>&lt;0.05</td></tr><tr><td colspan="10"><strong>4D/PRE</strong></td></tr><tr><td>Πpo_HF-V_60%</td><td>-25.8</td><td>-2.3</td><td>&lt;0.001</td><td>tpo_HF-V_20%</td><td>-22.1</td><td>-1.1</td><td>&lt;0.001</td><td>Πpo_DJ</td><td>-7.4</td><td>-24</td><td>&lt;0.05</td></tr><tr><td>IPF_DJ</td><td>-50.6</td><td>1.7</td><td>&lt;0.01</td><td>Πpo_HF-V_20%</td><td>-28</td><td>0.1</td><td>&lt;0.01</td><td>tbrake_DJ</td><td>107</td><td>27.8</td><td>&lt;0.01</td></tr><tr><td>Pmax_DJ</td><td>-44.3</td><td>-7.1</td><td>&lt;0.01</td><td>Πpo_HF-V_40%</td><td>19.3</td><td>1.9</td><td>&lt;0.05</td><td>hpo_SJ</td><td>30.8</td><td>3.5</td><td>&lt;0.01</td></tr><tr><td>Πbrake_DJ</td><td>-36.5</td><td>-1</td><td>&lt;0.01</td><td>Pmax_HF-V_40%</td><td>11.8</td><td>2</td><td>&lt;0.05</td><td colspan="3"><strong>2D/PRE</strong></td></tr><tr><td>Pmax_SJ</td><td>-20.5</td><td>5.8</td><td>&lt;0.05</td><td>Πpo_HF-V_60%</td><td>-32.2</td><td>-1.2</td><td>&lt;0.001</td><td>hpo_SJ</td><td>33.7</td><td>-5.4</td><td>&lt;0.01</td></tr><tr><td>Πpo_DJ</td><td>-37.8</td><td>-6.5</td><td>&lt;0.05</td><td>tpo_SJ</td><td>36.4</td><td>-3.4</td><td>&lt;0.05</td><td>tpo_SJ</td><td>36.4</td><td>-3.4</td><td>&lt;0.05</td></tr><tr><td>Πpo_DJ</td><td>-29</td><td>-2.2</td><td>&lt;0.05</td><td>vto_SJ</td><td>14.1</td><td>-0.3</td><td>&lt;0.05</td><td>vto_SJ</td><td>14.1</td><td>-0.3</td><td>&lt;0.05</td></tr><tr><td colspan="10"><strong>4D/PRE</strong></td></tr><tr><td>hpo_SJ</td><td>35.3</td><td>-2.8</td><td>&lt;0.05</td><td>Πbrake_DJ</td><td>-10.3</td><td>-3.8</td><td>&lt;0.05</td><td>tpo_SJ</td><td>22.7</td><td>-7.1</td><td>&lt;0.05</td></tr><tr><td>tpo_SJ</td><td>22.7</td><td>-7.1</td><td>&lt;0.05</td><td>MVC<sub>DL</sub></td><td>2.5</td><td>-7.7</td><td>&lt;0.05</td><td></td><td></td><td></td><td></td></tr></tbody></table>

<table><thead><tr><th colspan="4">Cluster 4 [Men = 3 (75%); Women = 1 (25%)]</th><th colspan="4">Cluster 5 [Men = 1 (25%); Women = 3 (75%)]</th><th colspan="4">Cluster 6 [Men = 1 (20%); Women = 4 (80%)]</th></tr><tr><th></th><th>Category (%)</th><th>Overall mean (%)</th><th>P-value</th><th></th><th>Category (%)</th><th>Overall mean (%)</th><th>P-value</th><th></th><th>Category (%)</th><th>Overall mean (%)</th><th>P-value</th></tr></thead><tbody><tr><td colspan="10"><strong>2D/PRE</strong></td></tr><tr><td>Πpo_HF-V_40%</td><td>-9</td><td>1.1</td><td>&lt;0.05</td><td>RFD_HF-V</td><td>174.2</td><td>26.9</td><td>&lt;0.01</td><td colspan="3"><strong>POST/PRE</strong></td></tr><tr><td>Pmax_HF-V_20%</td><td>-4.8</td><td>1.1</td><td>&lt;0.05</td><td>Pmax_HF-V_0%</td><td>19.2</td><td>4.3</td><td>&lt;0.01</td><td>Πbrake_DJ</td><td>-5.9</td><td>-16.5</td><td>&lt;0.01</td></tr><tr><td>Vpo_HF-V_40%</td><td>0.7</td><td>-5.1</td><td>&lt;0.05</td><td>vto_HF-V_0%</td><td>11.2</td><td>2.1</td><td>&lt;0.01</td><td colspan="3"><strong>2H/PRE</strong></td></tr><tr><td>Pmax_HF-V_0%</td><td>-5.4</td><td>4.3</td><td>&lt;0.05</td><td>Fmax_HF-V_ISOM</td><td>17</td><td>0.1</td><td>&lt;0.05</td><td>tpo_DJ</td><td>-8.9</td><td>10.2</td><td>&lt;0.05</td></tr><tr><td>vto_DJ</td><td>-11.4</td><td>-6.4</td><td>&lt;0.05</td><td colspan="3"><strong>4D/PRE</strong></td><td></td><td>tbrake_DJ</td><td>-11.6</td><td>27.8</td><td>&lt;0.05</td></tr><tr><td colspan="10"><strong>4D/PRE</strong></td></tr><tr><td>Vpo_HF-V_40%</td><td>-5.1</td><td>1.5</td><td>&lt;0.01</td><td>vto_HF-V_0%</td><td>12.3</td><td>2.8</td><td>&lt;0.01</td><td colspan="3"><strong>2D/PRE</strong></td></tr><tr><td>Πpo_HF-V_40%</td><td>-7.4</td><td>2.9</td><td>&lt;0.01</td><td>RFD_HF-V</td><td>124.1</td><td>19.3</td><td>&lt;0.01</td><td>tbrake_DJ</td><td>-21.5</td><td>5.4</td><td>&lt;0.001</td></tr><tr><td>RFD_HF-V</td><td>-33.1</td><td>19.3</td><td>&lt;0.05</td><td>Pmax_HF-V_0%</td><td>28.1</td><td>9.9</td><td>&lt;0.05</td><td>Πpo_DJ</td><td>8.6</td><td>-9.6</td><td>&lt;0.01</td></tr><tr><td>Effort perception (a.u.)</td><td>13.8</td><td>15.9</td><td>&lt;0.05</td><td>tpo_HF-V_60%</td><td>6.3</td><td>-0.5</td><td>&lt;0.05</td><td>tpo_DJ</td><td>-15.2</td><td>1.8</td><td>&lt;0.05</td></tr><tr><td>DOMS_4D (a.u.)</td><td>4.85</td><td>1.98</td><td>&lt;0.05</td><td>Πpo_HF-V_0%</td><td>18.7</td><td>2.8</td><td>&lt;0.05</td><td>Πbrake_DJ</td><td>15.7</td><td>-1.7</td><td>&lt;0.01</td></tr><tr><td></td><td></td><td></td><td></td><td>tpo_HF-V_20%</td><td>6.6</td><td>-0.1</td><td>&lt;0.05</td><td>Vpo_DJ</td><td>2</td><td>-6.3</td><td>&lt;0.01</td></tr><tr><td colspan="10"><strong>4D/PRE</strong></td></tr><tr><td>Πpo_DJ</td><td>9.3</td><td>-4.2</td><td>&lt;0.01</td><td colspan="3"><strong>4D/PRE</strong></td><td></td><td>Πpo_DJ</td><td>17.1</td><td>-2.2</td><td>&lt;0.001</td></tr><tr><td>Πbrake_DJ</td><td>24.9</td><td>-1</td><td>&lt;0.001</td><td colspan="3"><strong>4D/PRE</strong></td><td></td><td>Πbrake_DJ</td><td>24.9</td><td>-1</td><td>&lt;0.001</td></tr><tr><td>hpo_DJ</td><td>-19</td><td>-5.9</td><td>&lt;0.05</td><td colspan="3"><strong>4D/PRE</strong></td><td></td><td>hpo_DJ</td><td>-19</td><td>-5.9</td><td>&lt;0.05</td></tr><tr><td>Πpo_DJ</td><td>19.6</td><td>-6.5</td><td>&lt;0.01</td><td colspan="3"><strong>4D/PRE</strong></td><td></td><td>Πpo_DJ</td><td>19.6</td><td>-6.5</td><td>&lt;0.01</td></tr><tr><td>tbrake_DJ</td><td>-31.2</td><td>7.2</td><td>&lt;0.001</td><td colspan="3"><strong>4D/PRE</strong></td><td></td><td>tbrake_DJ</td><td>-31.2</td><td>7.2</td><td>&lt;0.001</td></tr><tr><td>tpo_DJ</td><td>-24.5</td><td>0.2</td><td>&lt;0.01</td><td colspan="3"><strong>4D/PRE</strong></td><td></td><td>tpo_DJ</td><td>-24.5</td><td>0.2</td><td>&lt;0.01</td></tr><tr><td>Vpo_DJ</td><td>7.1</td><td>-4.9</td><td>&lt;0.01</td><td colspan="3"><strong>4D/PRE</strong></td><td></td><td>Vpo_DJ</td><td>7.1</td><td>-4.9</td><td>&lt;0.01</td></tr><tr><td>Pmax_DJ</td><td>19.6</td><td>-6.5</td><td>&lt;0.01</td><td colspan="3"><strong>4D/PRE</strong></td><td></td><td>Pmax_DJ</td><td>19.6</td><td>-6.5</td><td>&lt;0.01</td></tr><tr><td>Masse (kg)</td><td>55.5</td><td>65.5</td><td>&lt;0.05</td><td colspan="3"><strong>4D/PRE</strong></td><td></td><td>Masse (kg)</td><td>55.5</td><td>65.5</td><td>&lt;0.05</td></tr><tr><td>Taille (m)</td><td>1.62</td><td>1.70</td><td>&lt;0.05</td><td colspan="3"><strong>4D/PRE</strong></td><td></td><td>Taille (m)</td><td>1.62</td><td>1.70</td><td>&lt;0.05</td></tr></tbody></table>

The overall mean corresponds to the averaged relative difference for the whole group as compared to the PRE session. Impact peak force (IPF); vertical push-off distance (Hpo), duration (tpo), and velocity at take-off (vto) in squat jump (SJ); maximal power (Pmax); mean force (F), velocity (V), and power (P) of the braking (brake) or push-off (po) phase in drop jump (DJ) or in horizontal force-velocity (HF-V) test at given resistive loads set from 0 to 60% bodyweight; Delayed onset muscle soreness (DOMS).
well as its antioxidant and membrane-stabilizing effects (Enns and Tiidus, 2010). In the current study, however, the influence of estrogen is attenuated by the lack of homogeneity in the phase of the menstrual cycle of the runners at the time of the experimental protocol.

## Subjective DOMS Sensation

In agreement with our second hypothesis, DOMS were found as being both sex- and muscle-dependent, women reporting higher muscle soreness than men, but only for the hamstring muscle group at 2D. This variability could explain the sex-independent DOMS sensations reported by Oosthuysse and Bosch (2017) after a downhill running, in which the authors did not investigate separately each muscle soreness. On the other hand, our expectation regarding the DOMS disappearance prior to a complete functional recovery was confirmed for the entire population whose triceps surae and quadriceps DOMS disappeared at 2H and 2D, respectively, whereas MVC and DJ were still decreased up to 4D. Confirming the lack of direct relationship between DOMS and functional decrements (Nicol and Komi, 2010), the present DOMS were smaller than the ones previously reported for the quadriceps muscle group after marathon runs leading to comparable delayed functional decrements (Nicol et al., 2003; Kyröläinen et al., 2005).

The major observation was that, despite similar running time and running strategy along the race, only women reported higher DOMS values in the hamstring muscle group. During the second half of the race that consisted mostly of downhills, larger ground impact peaks are likely to have occurred as most runners presented a similar speed than during the first 5 km (level running part) of the race. At that time, their active resistance to ground impact is likely to have been reduced as indicated by their impaired braking phase in the DJ test at POST. Although information is scarce in the literature regarding the hamstring involvement and fatigue in downhill running, Aldret et al. (2017) reported DOMS and a decreased MVC of hamstrings 12 h after a downhill treadmill run. This study included a control group of runners (equipped with an elastic hamstring assistance device) who presented less indirect markers of hamstring damage. Based on the current DOMS sensations, a greater mechanical stress of the hamstring muscles may have occurred in women during the downhill section. In this regard, women are known to have a wider patellar tendon-tibial shaft at all angles of knee flexion (Nunley et al., 2003), which should lead to an increase in the shearing force applied to the hamstring muscle group. The absence of DOMS reported by the male runners for the hamstrings is partly attributed to their greater peak torque and velocity hamstrings/quadriceps ratio (Hewett et al., 2008; Martín-San Agustín et al., 2019).

## Recovery Pattern Specificities

The present work highlights the importance of the multidimensional analyses to reveal interactions between several quantitative and qualitative variables. First, the MFA analysis revealed for the first dimension (explaining 24.03% of

the variance) that the DJ and the MVCs were the most effective tests to evaluate the individuals' recovery profile, while revealing significantly larger decreases for men than for women, especially at POST and 4D. These results reflected the lower functional decrements in DJ and MVCs at POST for women and the remaining deficits for men in DJ at 4D. Importantly, none of the qualitative variables (e.g., DOMS, effort perception) correlated with the MFA dimensions, supporting the absence of relationship with the functional decrements.

The clustering analysis revealed a greater number of recovery profiles than the sole fast and slow recovery profiles classically reported (Nicol et al., 2003; Paulsen et al., 2012). This revealed a panel of recovery profiles along the 4 days (Figure 6). In particular, cluster 6 was mostly composed of women who showed only acute decrements in DJ, emphasizing their rapid recovery. Cluster 2 included the only runner showing large acute and delayed reductions in the pure concentric and pluri-articular HF-V test. Thus, this runner presented an overall decrease in all tests indicating that this participant deserved more time to recover than the others. Cluster 3 included the runners who presented the largest acute decreases in DJ, limited delayed ones, and even some improvements in SJ at 4D showing that their recovery was almost completed. Considering the large inter-individual variability observed in the functional recovery patterns, this analysis presents the major advantage to consider all tests, variables and their variations in the acute and delayed recovery periods, thus characterizing more specifically the individual profiles. The interest of inter-individual analysis has recently been demonstrated by Welch et al. (2020) for the analysis of injured individuals based on their physical performance analysis. This overall analysis is thus considered of importance to define the optimal time for returning to the running practice depending on the recovery phase and on the tests where the decrements were observed. It is interesting to note that when tested a posteriori, this analysis brings additional insight on the potential influence of descriptive variables such as relative race score, effort perception and muscle soreness on the observed clusters. For example, the cluster 4, which reported low values in effort perception after the race, showed delayed decrements in the HF-V test and DOMS up to 4D. This highlights the potential mismatch between the acute subjective perception of exhaustion and the delayed functional decrements.

Some methodological limitations need to be addressed. First, even if this is a methodological limitation commonly encountered in running race studies, the time delay for measuring the fatigue effects at POST (35 ± 15 min on average) may have led to an underestimation of the actual fatigue effects. Further, the limited number of participants (8 men and 10 women) lowered our statistical power. While it was controlled, the involvement of women in different phases of their menstrual cycle may represent a limitation. It should also be noted that, by choosing an ecological race condition, the maximal metabolic involvement may not have been reached by each of the runners. Nevertheless, differing from the review of Deaner et al. (2015) showing that women may take lesser risks than men in marathon races, we did not find any sex difference neither in pacing strategy nor
in effort perception. Moreover, the absence of the usual sex-difference in the racing performance may suggest a relatively higher training level for the women's group. However, no sex-difference was still found on the relative performances once normalized by the winner's running time for each sex. Finally, based on the improvements observed in the SJ and HF-V tests at 4D, a longer familiarization phase should be used for unusual tests in endurance runners.

## CONCLUSION

According to our knowledge, this is the first graded running study using an experimental ecologic setting to describe up to 4 days the functional recovery pattern of women vs. men. Supporting our first hypothesis, the functional recovery pattern was found as testing task and sex dependent. Women showed an earlier functional recovery than men in the knee $MVC_{BLT}$ and DJ tests. Supporting our second hypothesis, only women reported DOMS for the hamstring muscle group while, for both men and women, DOMS disappeared prior to the complete functional recovery. As expected, the multidimensional analysis was found as a pertinent way to explore the complexity and sex-dependency of the functional recovery pattern. The use of the clustering analyses may thus lead to the identification of runners who may face greater risks of injury when starting to run again. Finally, the analyses based on the normalized functional values emphasize the major influence of the initially higher values of men compared to women.

## DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.

## ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the CERSTAPS (Comité d'Ethique pour la

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## AUTHOR CONTRIBUTIONS

RM, FV, AH, GA, GE, PC, EG, RS, and CN: study conception and design. RM and CN: analysis of data. RM and FV: statistical analysis. RM, FV, and CN: data interpretation and drafting of manuscript. RM, FV, EG, RS, YK, and CN: critical revision. All authors contributed to the acquisition of the data, contributed to the article, and approved the submitted version.

## FUNDING

RM was supported by a grant of the GDR Sport Santé. This study benefited of an ISM funding for the manuscript submission.

## ACKNOWLEDGMENTS

We are particularly grateful for the loan of three identical knee extension ergometers by S2P Ltd., Company (Ljubljana, Slovenia), G. Millet (St-Etienne University), and V. Martin (University Clermont Auvergne). We sincerely thank G. Lagrange and D. Marziale, the Provence & Corsica delegation of the CNRS and the SCO Ste Marguerite (race organizers), especially C. Cayol, for the racing time data and their helpful support. We would like to thank the engineer A. Jacques for the design of the drop jump height platforms and the development of the labview software for the data acquisition of the horizontal force-velocity test. We also thank A. Bar-Hen for reviewing of the manuscript especially the statistical part.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys. 2021.649396/full#supplementary-material

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**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Macchi, Vercruyssen, Hays. Aubert, Exubis, Chavet, Goubert. Souron, Kunimasa and Nicol. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Nous avons précédemment détaillé les deux phases de la récupération biphasique (aiguë et retardée) et les événements physiologiques qui les sous-tendent potentiellement. La phase aiguë est décrite comme étant plutôt liée à une fatigue d'origine métabolique tandis que la phase retardée serait associée à des mécanismes inflammatoires. On peut alors se poser la question s'il existe ou non un lien entre ces deux phases. Plus précisément, les individus présentant les plus fortes altérations fonctionnelles en phase aiguë sont-ils ceux qui présenteront les altérations les plus importantes en phase retardée ? D'un point de vue appliqué, si une phase peut prédire l'autre, est-il utile de multiplier les mesures temporelles ? Cette question est d'autant plus importante que la majorité des études se sont focalisées sur la phase de récupération aiguë. De plus, comme nous l'avons précédemment souligné, l'évaluation de la fatigue dépend fortement de l'interaction entre le sexe et le type de test utilisé. En ce sens, l'objectif de la présentation affichée en (Poster 3) qui suit visait à quantifier les corrélations entre ces deux phases, tout tenant compte du sexe et du type de test effectué. Chaque phase et chaque test comportant un ensemble de variables, des modèles multifactoriels ont été utilisés afin d'obtenir les coefficients RV (pour rhô-vectoriel) permettant de quantifier la similarité entre plusieurs jeux de données.
# Multivariate modelling of the recovery pattern and its sex dependence after a graded run

Macchi, R. ¹, Chavet, P. ¹, Hays, A. ¹, Kunimasa, Y. ¹, Vercruyssen, F. ², Nicol, C. ¹

¹ ISM, CNRS & Aix Marseille Univ, Marseille, France ; ² IAPS, University of Toulon, Toulon, France

## INTRODUCTION:

Endurance running exercises are known to induce in men a biphasic recovery, with acute and delayed functional decrements attributed to metabolic fatigue and inflammatory process respectively (1). However, most studies have focused on the acute recovery phase and only a few on its potential influence on the delayed phase (1,2). Our recent study of a 20 km (~400 m) graded run revealed sex- and test-dependent acute and delayed recovery patterns (3). Using a statistical approach called RV coefficient (4), the present study aims to investigate the potential relationships between recovery phases, tests and their sex sensitivity.

## METHODS:

Eighteen healthy runners (32±7 yrs), 10 women (W) and 8 men (M), completed the race. The protocol included 5 testing sessions: a week before (PRE), 35±15 min after (POST) as well as at 2h, 2 and 4 days (2D & 4D). Each session included maximal isometric voluntary contractions of the knee extensors (KE MVCs), a squat jump (SJ) and a drop jump (DJ) tests. Muscle soreness was assessed using a visual analogic scale. In case of significant decrease in performance, the functional changes as compared to PRE were centred, reduced and added to multivariate models. RV coefficients (4), from 0 to 1, were used to test the similarity between the sets of variables defined for the same individuals from each test and session.

## RESULTS:

Regardless of sex, the SJ did not reveal any significant decrease in performance. Inter-session relationships were found for both KE MVC and DJ between the fatigue-induced changes at POST and 2h (RV=0.67 and 0.71, p<0.001) as well as at 2D and 4D (RV=0.54, p<0.05; RV=0.62, p<0.001). The DJ changes at POST and 2h were also related to those at 2D (RV=0.70 and 0.64, p<0.001), and for men only between POST and 4D (RV=0.64, p<0.05). Inter-test relationships were found only for men between the KE MVC and DJ changes at POST (RV=0.67, p<0.05). Muscle pain was not significantly correlated with functional changes.

## DISCUSSION:

Multivariate analyses confirmed, for all tests and regardless of sex, relationships between functional changes occurring within the acute (POST and 2h) or delayed (2D and 4D) recovery phase. As expected, no relationship was found with muscle soreness (1,3). It is noteworthy that only the DJ test revealed similarities between its acute and 2D-delayed changes with fatigue, and only for men up to 4D. Acute indices of reduced resistance to stretch may thus predict a delayed recovery in men as well as in women. Finally, the limited number of inter-test relationships highlights the importance of using a battery of tests rather than specific tests.

## REFERENCES

1. Nicol & Komi, in: Neuromuscular Aspects of Sports Performance, 2010

2. Nicol et al., Eur J Appl Physiol, 2003

3. Macchi et al., Front Physiol, 2021

4. Josse et al., Comput Stat Data Anal, 2008
Ainsi, les relations entre les phases de la récupération sont faibles ; l'une ne peut donc pas se substituer à l'autre. Comme l'ont souligné quelques études (i.e., Strojnik *et al.*, 2001), la phase retardée pourrait être propice aux blessures. Nos travaux confirment en effet, chez les hommes comme chez les femmes, la disparition des DOMS avant que leur récupération fonctionnelle ne soit complète. Cela constitue un risque car nombre de coureurs se fient à la disparition des DOMS pour retourner s'entraîner. Les études suivantes se centreront donc sur la phase retardée de la récupération.

De plus, cette étude nous a permis de constater une influence à la fois du sexe et du type de test sur la cinétique de récupération. En ce sens, les tests de sauts n'impliquant pas d'impact, tels que le SJ ou le CMJ, ne montrent généralement pas de déficits (Nicol *et al.*, 1991c ; Vuorimaa *et al.*, 2006). Confirmant ces observations, nos analyses des tests dynamiques purement concentriques n'a révélé aucun changement significatif, et ce malgré les charges additionnelles propre au test horizontal Force-Vitesse. L'absence de déficit fonctionnel ne signifiant pas l'absence de fatigue (Bigland-Ritchie et Woods, 1984), il convient dans ce type de test pluriarticulaire d'approfondir les ajustements neuromusculaires susceptibles d'avoir compensé l'affaiblissement contractile.

L'étude 3 se focalisera donc sur l'influence du sexe sur les synergies musculaires lors d'un test force-vitesse pluriarticulaire réalisé en phase retardée de la récupération. L'extrapolation des paramètres force-vitesse révèlera potentiellement des altérations fonctionnelles qui ne seraient pas décelées en considérant chaque poussée séparémment. Il convient toutefois de souligner les limites de cette analyse puisque les paramètres obtenus ($F_0$, $V_0$ et $P_{max}$) sont purement théoriques. L'analyse des synergies musculaires précédemment détaillée dans le cadre théorique devrait nous permettre d'appréhender la richesse autant que la complexité des coordinations inter-musculaires.
Résumé étude 3 (Macchi et al. 2021) *Sex influence on muscle synergies in a ballistic force-velocity test during the delayed recovery phase after a graded endurance run*

**Objectif** : Les phases aigue et retardée de la récupération après une course d'endurance ont été principalement étudiées chez les hommes. Cependant, la littérature suggère que les femmes sont moins fatigables et/ou récupèrent de manière plus rapide que les hommes, du moins dans des tâches isométriques.

**Méthode** : Après une course de 20-km avec dénivelé, l'influence du sexe sur la phase retardée de la récupération (2 à 4 jours) a été étudiée par un test force-vitesse balistique réalisé en position de décubitus dorsal. Dans ce test, les poussées étaient produites par les membres inférieurs, à 4 niveaux de charge différents. 9 coureurs de sexe féminin et 8 de sexe masculin, tous récréatifs, ont terminé la course et participé aux 3 sessions de test : une semaine avant la course (PRE), 2 et 4 jours après (D2 et D4) celle-ci. Les forces de réaction et l'activité électromyographique de 8 des principaux muscles du membre inférieur ont été enregistrées. Pour chaque session, le profil force-vitesse-puissance (i.e. la force (F₀), la vitesse (V₀) et la puissance (Pmax) maximales théoriques) était calculé, les activités musculaires moyennées pour chaque muscle et les synergies musculaires (3 pour chaque sexe) extraites.

**Résultats** : Indépendemment de la session de test, les femmes différaient des hommes au regard de la sollicitation des muscles bi-articulaires de la cuisse (ischo-jambiers de la partie médiale ainsi que le rectus femoris). À mi-poussée, les femmes ont utilisé des activités musculaires des membres inférieurs mieux réparties au sein du membre inférieur. L'analyse des forces moyennes n'a révélé aucun déficit significatif. Cependant, le profil force-vitesse a évolué différemment au cours de la récupération selon le sexe : seuls les hommes ont montré à la fois une diminution de V₀ (p < 0.05) et de Pmax (p < 0.01) à D2 comparé à PRE. L'activité du *vastus medialis* était réduite pour les deux sexes jusqu'à D4, mais seules les synergies musculaires des hommes étaient impactées à D2 : le centre d'activité de la première synergie et celui de la seconde synergie arrivaient plus tardivement au cours de la poussée.

**Conclusion** : Cette étude suggère que les femmes pourraient récupérer plus tôt dans une tâche dynamique pluri-articulaire et que l'organisation des synergies musculaires propre à chaque sexe pourrait avoir contribué aux différences de récupération fonctionnelle observées.
Research article

Sex influence on muscle synergies in a ballistic force-velocity test during the
delayed recovery phase after a graded endurance run

<figure><img src="image_68.png" /></figure>

Check for
updates

Robin Macchi<sup>a, *</sup>, Alessandro Santuz<sup>b, c</sup>, Arnaud Hays<sup>a</sup>, Fabrice Vercruyssen<sup>d</sup>,
Adamantios Arampatzis<sup>b, c</sup>, Avner Bar-Hen<sup>e</sup>, Caroline Nicol<sup>a</sup>

HIGHLIGHTS

* The use of bi-articular thigh muscles differed between men and women.

* Muscle synergies were sex dependent.

* Only men showed decreased theoretical maximal velocity and power 2 days after running.

* Centers of activity of muscle synergies were shifted later for men 2 days after the race.

* Sex-specific organization of muscle synergies may explain the different recovery times.

ARTICLE INFO

Keywords:

Fatigue

Women

Endurance running

EMG

Explosive movement

Jump

Non-negative matrix factorization

A B S T R A C T

The acute and delayed phases of the functional recovery pattern after running exercise have been studied mainly in men. However, it seems that women are less fatigable and/or recover faster than men, at least when tested in isometric condition. After a 20 km graded running race, the influence of sex on the delayed phase of recovery at 2-4 days was studied using a horizontal ballistic force-velocity test. Nine female and height male recreational runners performed maximal concentric push-offs at four load levels a week before the race (PRE), 2 and 4 days (D2 and D4) later. Ground reaction forces and surface electromyographic (EMG) activity from 8 major lower limb muscles were recorded. For each session, the mechanical force-velocity-power profile (i.e. theoretical maximal values of force (F 0), velocity (V 0), and power (P max)) was computed. Mean EMG activity of each recorded muscle and muscle synergies (three for both men and women) were extracted. Independently of the testing sessions, men and women differed regarding the solicitation of the bi-articular thigh muscles (medial hamstring muscles and rectus femoris). At mid-push-off, female made use of more evenly distributed lower limb muscle activities than men. No fatigue effect was found for both sexes when looking at the mean ground reaction forces. However, the force-velocity profile varied by sex throughout the recovery: only men showed a decrease of both V 0 (p < 0.05) and P max (p < 0.01) at D2 compared to PRE. Vastus medialis activity was reduced for both men and women up to D4, but only male synergies were impacted at D2: the center of activity of the first and second synergies was reached later. This study suggests that women could recover earlier in a dynamic multi-joint task and that sex-specific organization of muscle synergies may have contributed to their different recovery times after such a race.

<footnote><sup>a</sup> ISM, CNRS & Aix Marseille University, Marseille, France</footnote>

<footnote>b Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, 10115 Berlin, Germany</footnote>

<footnote>c Berlin School of Movement Science, Humboldt-Universität zu Berlin, 10115 Berlin, Germany</footnote>

<footnote>d IAPS, University of Toulon, Toulon, France</footnote>

<footnote>e CEDRIC, Conservatoire National des Arts et Métiers, Paris, France</footnote>

<footnote>* Corresponding author.
E-mail address: robin.macchi@gmail.com (R. Macchi).</footnote>
# 1. Introduction

With the growing involvement and success of women in endurance running races, studies have been conducted during the last three decades on the biomechanical and physiological characteristics of women compared to men during exercise [1, 2]. Across a range of speeds [1, 3, 4, 5] and surface inclines [3], female recreational runners are characterized by specific kinematic movements and neuromuscular mechanisms [6] compared to male runners. For instance, in terms of neuromuscular control, female runners are reported to have a higher gluteus maximus activity during the stride cycle, regardless of speed (1.8–3.6 m/s) and gradient (0–15% grade) [3, 7]. Looking at the specific phases of running, women present a higher preactivation of the peroneal muscle group [8] than men but, conversely, a lower activation of this muscle group during the braking and push-off phases. Interestingly, sex differences are influenced by the gradient as it has been reported for female runners, a higher preactivation of the medial hamstring muscles during a downhill treadmill run (−15° at 7.5 km/h) [9] and a greater increase in activation of the vastus lateralis with increasing inclination [3]. Based on the differences in muscle activation reported between male and female runners, it can be expected that a strenuous running task would result in sex-selective muscle fatigue.

After exhaustive stretch-shortening cycle (SSC) type exercises such as endurance running, the functional recovery pattern has been studied mainly in men and reported to be testing-time dependent and biphasic in nature [10]. Acute functional decreases, mainly attributed to metabolic fatigue, partially recover within 2 h, while secondary central and reflex inhibition and functional decreases, related to the inflammatory/r-emodeling process after ultrastructural muscle damage, usually peak one to two days later and last for 8–15 days. The secondary recovery phase may be associated with delayed onset muscle soreness (DOMS) resulting from sensitization of the nociceptive part of muscle afferents III and IV around the inflammatory peak. DOMS thus appear late but also disappear early despite the subsistence of protective and/or compensatory neural adjustments that could represent a potential source of injury when people consider that they have recovered. Compared to men, women are generally characterized by smaller muscle mass, greater muscle perfusion and a relatively greater surface area of type I muscle fibers, resulting in less metabolite production, sensitization of muscle afferents, and muscle inhibition that would potentially result in less fatigue than in men [11, 12, 13]. Given the early disappearance of DOMS and the usual tendency of runners to train as soon as the pain disappears, it seems necessary to characterize their exact delayed recovery pattern in order to define the optimal time to resume running and thus to prevent injury.

Surprisingly, only a few studies have investigated the endurance running-induced fatigue effects in women [14]. Compared to male runners in the acute recovery phase, females showed attenuated peripheral plantar flexor fatigue and a smaller decrease in maximal voluntary contraction (MVC) of knee extensors after ultra-endurance races [15, 16]. A smaller acute decrease in knee extensor MVC was also reported after a 20 km level run [17, 18], while Boccia et al. (2018) [19] found no sex difference. Recently, our research group investigated the functional recovery pattern up to 4 days after a graded 20 km race [18] and found an earlier recovery in female runners but this was dependent on the testing task. Indeed, at 4 days, only men showed decreases in bilateral knee extension MVC and maximal power in drop jump, while similar recovery time was found in unilateral knee extension MVC tests and velocity at take-off in drop jump. In the pure concentric tests, no fatigue effects were observed in either men or women. Thus, women might be less prone to damage after endurance running race, potentially due to the protective effects of sex hormones, particularly estrogens which are antioxidant, membrane stabilizer and satellite cell proliferators [20]. On the other hand, while DOMS was reported for the quadriceps independently of sex 2 days after the race, only female runners reported then DOMS for the hamstring muscle group. The observed dependence of sex differences on task and muscle underlines the need to further evaluate the associated neural adjustments.

Neuromuscular adjustments to SSC-type fatigue have been studied mainly in men and shown to be testing-task dependent [e.g. 21], with multi-joint maximal dynamic tests being considered more mechanically meaningful than conventional MVC tests [10, 22, 23]. Unlike maximal SSC testing tasks with ground impacts (e.g. drop-jump), maximal dynamic tests involving slight or no impact (counter-movement or squat jumps) are usually showing no significant decreases in performance, or even improvements attributed to a change in neural strategies [24]. In this context, García-Ramos et al. (2018) [25] recently demonstrated the interest of the force-velocity relationship (FV) to assess the effects of fatigue on the distinct abilities of muscles to produce force, velocity, and power output while performing a multi-joint maximal ballistic task. The linear force-velocity and polynomial power-velocity relationships depend on individual muscle-tendon structural and mechanical properties as well as on the neural activation [26]. These relationships can be summarized by three typical parameters: the theoretical values of the maximal force at zero velocity (F0) and maximal velocity at zero force (V0), and the maximal power output (Pmax). García-Ramos et al. (2018) [25] found, for ballistic upper limb movements, that the model remained linear after various fatigue protocols while revealing potentially specific decreases in force or velocity. Yet, to the best of our knowledge, the force-velocity mechanical profile in ballistic movement has never been assessed for the lower limbs after an endurance running task. In addition, the running task has been reported to be asymmetric ranging from 3 to 54% when looking at kinetic variables [27]. Therefore, it is of particular interest to distinguish the dominant from the non-dominant limb for the force-velocity test in a bilateral push-off. Although to our knowledge there is no much scientific knowledge on the effect of unilateral fatigue on bilateral tasks, Marchetti et al. (2011) [28] showed an increase in the asymmetry index in a bilateral jump after a unilateral fatigue task.

Considering the protective vs. compensatory neural adjustments and the different neural strategies reported to take place depending on the testing time and task after an exhaustive endurance run [10], it seems of particular interest to perform electromyographic (EMG) recordings of the main lower limb muscles during a FV test. Complementary EMG analyses may then be used to explore the richness of the neuromuscular adjustments to fatigue in relation with sex-differences. The root mean square (RMS) is considered as the main method in the time domain [29]. Considering that movements are not the result of independent muscle activations, but rather of linear combined patterns of activations (called muscle synergies) [30], complex mathematical models, including linear machine learning such as non-negative matrix factorization (NMF), seem well-suited to assess such a dynamic task. This method has been used to quantify muscle synergies in a wide variety of tasks [31, 32, 33, 34] and seems particularly suitable for revealing potential sex-differences in muscle fatigue and intermuscular compensations during the recovery period.

To this end, this study focused on the sex influence on the horizontal force-velocity (FV) profile and associated muscle activation pattern of major lower limb muscles before as well as 2 and 4 days after a 20 km graded running race. The hypotheses were that (i) the FV profile would change less in women than in men during the delayed recovery period, and (ii) the muscle activity pattern would be sex-dependent before fatigue and evolve differently during the 4 days of recovery. This study aims to improve the knowledge of delayed neuromuscular fatigue in women through the analysis of movement coordination in order to determine their optimal time to resume running and thus prevent injuries.

# 2. Method

## 2.1. Participants

All participants were recreational runners who had registered 6 months earlier to take part to the international Marseille-Cassis race of 20 km including positive and negative gradients (+382 m and -294 m).
Prior to the experiment, the sample size was calculated using G Power (Version 3.1.9.7). According to the experimental design and for a medium effect size (i.e. η² = 0.06), 14 participants in each sex group were required to obtain a statistical power of 80%. Due to the loss of three participants (two because of back pain and torn muscle during the race and one for noisy EMG recordings), the final group included only 9 female (age: 35 ± 7 years (26–45), body mass: 59.8 ± 8.7 kg, height: 1.66 ± 0.08 m) and 8 male (age: 29 ± 7 years (21–38), body mass: 70.9 ± 6.2 kg, height: 1.76 ± 0.06 m) runners. Two women were in the follicular phase, 4 in the luteal phase and 3 were amenorrhoeic. The current study was part of a larger study on the effects of graded running race on fatigue [18] which was approved by the local ethics committee (2019-15-09-35) and, in accordance with the Helsinki Convention, written informed consent was obtained from all runners.

## 2.2. Experimental design

The experimental design included a familiarization and three experimental sessions: a week before the running race (PRE), 2 and 4 days later (D2 and D4, respectively). As far as possible, the 3 test sessions were individually scheduled at the same time of day. Each experimental session started by checking the runner's body mass on a Tanita scale (MC980MA Tanita) before performing a sex-standardized 10 min warm-up. Uni- and bilateral maximal isometric voluntary contractions of the knee extensors (MVC), a squat jump (SJ), and a drop jump (DJ) tests as well as delayed muscle soreness (DOMS) for the quadriceps, hamstring and triceps surae muscle groups were evaluated and reported previously [18]. The participants were then equipped with surface EMG electrodes before performing a FV test in the supine position. This ergometer (Figure 1) allows the assessment of participants' peak performance while minimizing the risk of injury, and providing a good standardized position as it is known to impact on the results [35].

## 2.3. Measurement protocol

The horizontal FV (HFV) test consisted of two trials of ballistic squat jumps performed in a supine position on a frictionless sled (INPI deposit n°: FR2011204) at 4 load levels: 0, 20, 40 and 60% bodyweight (BW) (Figure 1). The dynamic conditions were performed in a random order among the participants. For a given runner, the order of the testing conditions remained the same in each session. The starting position was

held still for 2–3 s with hip, knee and ankle joints at 90°. They were asked to apply their force as rapidly as possible on the force plates (one under each foot) in order to reach the highest velocity at take-off. All trials were performed on two Kistler force plates (Kistler 9260AA3 (0.5 × 0.3 m)) which recorded the three-dimensional (3D) ground reaction force (GRF) components produced by the dominant and non-dominant lower limbs. Horizontal displacement over time was recorded with an accuracy of 0.1 cm using a linear encoder attached to the sled.

Surface EMG activity was recorded from 8 muscles of the dominant lower limb using DTS EMG sensor Model 542 (Noraxon Inc., USA): *vastus medialis*, *vastus lateralis*, *rectus femoris*, *medial hamstrings*, *tibialis anterior*, *gastrocnemius medialis*, *gastrocnemius lateralis* and *soleus*. To keep skin impedance low, the site for electrode placement was prepared by shaving, gently abrading the skin using sandpaper and cleaning with 70% isopropyl alcohol. Bipolar electrodes were placed in accordance with Seniam recommendations [36]. To secure an identical EMG electrode placement among sessions, their location was precisely marked on the skin using an indelible marker. All signals were recorded continuously at 1.5 kHz and synchronized by the means of an external trigger. To define the dominant lower-limb, the runners had to climb up and down a 50 cm high wooden box with their self-selected lower limb designed as the dominant limb.

## 2.4. Data processing

Data processing was performed using custom Matlab (Matworks Inc, Novi, USA) and R (R v3.6.3, R Core Team, 2020, R Foundation for Statistical Computing, Vienna, Austria) routines. The push-off start was defined as the inflexion point of the force curve and the end of push-off was set when the normal component of the ground reaction force fell to zero (Figure 2). Since the first phase of the push is motionless (Figure 2) due to the high inertia of the device, for the force-velocity variables, the onset of the average force was set as the first measurable displacement in order to calculate the average force and velocity at a similar time. As the push-off distance may influence the results, it was computed and checked that there was no significant change between the loads and the sessions.

## 2.5. Force-velocity processing

Force-velocity relationships were determined by least-squares linear regressions using the average normal force component and velocity at

<figure><img src="image_69.png" /><figcaption>Figure 1. Experimental set-up on the horizontal force-velocity ergometer.</figcaption></figure>
<figure><img src="image_70.png" /><figcaption>Figure 2. Horizontal force of the dominant limb (in black) and displacement (in blue) curves as a function of time for a typical participant during the 40% BW resistive condition. The two vertical dotted black lines represent the beginning and the end of the push-off. The dotted blue line represents the beginning of the first measurable displacement named “start of motion”.</figcaption></figure>

each load. Individual force-velocity slopes were extrapolated to obtain the intercepts corresponding to the theoretical maximal values of “F 0” and “V 0” [26]. Then, the theoretical peak value of the velocity-power polynomial (second-degree) relationships was obtained and noted as “P max” [26]. The force-velocity-power relationships were fitted for both legs (FVbilateral) as well as for the dominant and non-dominant legs separately. A selection among the 8-dynamic push-offs was performed for each participant to obtain the most accurate linear FV relationship due to a potential hip lift, slight countermovement or non-maximal trial which could distort linearity. To perform this, an automatic selection was set from a Matlab routine. This routine deleted the points with residuals superior to 100 N, beginning by those below the linear regression. The same routine was applied on the velocity-power polynomial relationship. In addition, the routine checked that computed value of “P max” was close to the actual power achieved. Finally, the automatic selection was compared to the manual selection and the mismatch was computed. In the literature, manual selection is the most used and consists in visually selecting the right trials based on the experimenter's expertise. Figure 3 presents an example of the automatic selection for one participant.

Each polynomial and linear relationships (without the extrapolated F0 and V0 parameters) were assessed by the coefficient of determination (R²) representing the variance explained by the model. The

slope of the linear FV relationship (noted S<sub>FV</sub>) was computed as follows [26]:

$$S_{FV} = - \frac{F0}{V0}$$

To analyze the imbalance between the dominant and non-dominant leg FV relationships, the asymmetry index (SI) was computed between the S<sub>FV</sub> of the dominant and non-dominant legs as follow [37]:

$$SI_{FV} = \left( \frac{|S_{FV}(L_{dom}) - S_{FV}(L_{non\_{dom}})|}{\text{avg}(S_{FV}(L_{dom}), S_{FV}(L_{non\_{dom}}))} \right)$$

Where Ldom and Lnon<sub>dom</sub> correspond to the dominant and non-dominant lower limbs, respectively.

The mean 3D GRF were computed from the actual onset of push-off (i.e. the breakpoint between the stabilized force and the rate of force development) to account for the force produced to overcome the inertia of the system.

To quantify the push-off effectiveness and potential compensations, the effective force ratio was computed as the mean normal force component (horizontal performance axis) divided by the mean of the resultant force in 3D [38]. The rate of force development was computed using a linear least-squares linear regression fitting all points from the

<figure><img src="image_71.png" /><figcaption>Figure 3. Illustration for a given female runner of the automatic selection of the trials kept for the force-velocity (a) and power-velocity (b) relationships in the PRE-session. The black points represent the selected trials whereas the red point represents the deleted trial (1/8). The straight lines and curves represent the force-velocity and power-velocity relationships. The black ones correspond to the selected trials whereas the red cut lines correspond to all trials.</figcaption></figure>
first 75 ms from the onset of the push-off and then the best trial was selected for each load [39].

## 2.6. Mean muscle activity

As previously described [40], the raw EMG signals were band-pass filtered with cut-off frequencies between 10 and 500 Hz. Then, the signals were high-pass filtered (cut-off frequency 50 Hz), full-wave rectified and lastly low-pass filtered (cut-off frequency 20 Hz) to obtain a linear envelope using a 4th order IIR Butterworth zero-phase filter. After subtracting the minimum, the amplitude of the EMG recordings was normalized by the maximal EMG activity recorded among the 8 trials of the PRE-session. Each push-off was time-normalized to 100 points to allow inter-participant comparisons.

The mean activity of each recorded muscle was computed from a root mean squared (RMS) analysis for the global push-off and for its first (0–25%), middle (25–75%) and last (75%–100%) parts.

## 2.7. Muscle synergies

Muscles synergies data were extracted from the recorded EMG activities (normalized by the maximal EMG activity of the session) of all trials through a custom script [41] using the classical Gaussian non-negative matrix factorization (NMF) algorithm [34, 40]. NMF is an iterative optimization method that minimizes the normative error-matrix computed as:

$$E = ||M - V||$$

Where M is the $m \times n$ (m rows and n columns) matrix of the initial 8 time-dependent muscle activity vectors. V represents the new matrix reconstructed multiplying the two matrices W and H, approximates the initial matrix M. W is an $m \times s$ matrix containing the relative activities of each muscle (i.e. motor modules), where s is the number of synergies selected for extraction and H is an $s \times n$ matrix containing the time-varying activity of each synergy (i.e. motor primitives) [42]. The update rules for W and H are presented in Eqs. (1) and (2) (Eq1 and Eq2 respectively).

$$ \left\{ \begin{array}{l} H_{i+1} = H_i \frac{W_i^T V}{W_i^T W_i H_i} \\ W_{i+1} = W_i \frac{V(H_{i+1})^T}{W_i H_{i+1} (H_{i+1})^T} \end{array} \right. \qquad (1) $$

Where “T” represents the transposed of the matrix.

The quality of the reconstruction was assessed by the coefficient of determination ($R^2$) between the matrix M and V. For each synergy, the iterations were stopped when the change in the $R^2$ was smaller than the 0.01% in the last 20 iterations [43] meaning that, with this amount of synergies, the signal could not be reconstructed any better. This operation was first completed by setting the number of synergies to one. It was then repeated by increasing the number of synergies, up to a maximum of 6 synergies, which was chosen to be equal to 75% of the number of muscles. For each synergy, the factorization (NMF) was repeated 10 times, each time creating new randomized initial matrices W and P, in order to avoid local minima [44]. The solution with the highest $R^2$ was then selected for each of the 6 synergies. To choose the minimum number of synergies required to represent the original signals, the curve of $R^2$ values vs. synergies was fitted using a simple linear regression model using all 6 synergies. The mean squared error [45] between the curve and the linear interpolation was then calculated. Afterwards, the first point in the $R^2$-vs.-synergies curve was removed and the error between this new curve and its new linear interpolation was calculated. The operation was repeated until only two points were left on the curve or until the mean squared error fell below $10^{-4}$. This was done to search for the most linear part of the $R^2$-vs.-synergies curve, assuming that in this section the

reconstruction quality could not increase considerably when adding more synergies to the model.

The recognition of fundamental synergies (which can be defined as an activation pattern a single main peak of activity in the motor primitive) was performed by clustering similar motor primitives and motor modules by NMF, using the same algorithm employed for synergy extraction with the number of synergies set to the maximum factorization rank plus one. For each synergy, the center of activity (CoA) was defined as the angle of the vector (in polar coordinates) that points to the center of mass of that circular distribution [46] using the following equations:

$$ \left\{ \begin{array}{l} A = \sum_{t=1}^{100} (\cos \theta \times P_t) \\ B = \sum_{t=1}^{100} (\cos \theta \times P_t) \end{array} \right. $$

$$ \mathrm{CoA} = \arctan \left( \frac{B}{A} \right) $$

The full width at half maximum (FWHM) representing the duration of activation patterns [47] was also computed as the number of points exceeding half maximum of the synergy. For each condition, the number of clusters was defined as the maximum of synergies found by the NMF. If two or more fundamental synergies were blended into one, the synergy was classified as “combined”. In our dataset, combined synergies represented between 13% to 36% of the total synergies extracted. Furthermore, fundamental synergies can be compared given their similar function (i.e. motor primitives and motor modules are comparable since they serve a specific task), combined synergies often differ from one another making their classification impossible. Thus, combined synergies were excluded from the analysis although they could be interesting for an individual analysis.

## 2.8. Statistics

For each variable, a linear mixed model was performed from the *lmerTest* package [48] and *lme4* package [49]. Restricted maximum Likelihood (REML) estimation was used in the model. Sex, session, load, trial and muscle (for EMG variables) were selected as fixed effects. The intercepts for the participants were chosen as random effect as well as the random slope by-participants for the session effect. For all models, the significance of the random factors was tested. The best model (i.e number of fixed effects and interactions) was chosen by likelihood ratio tests of model comparisons using a backward selection method. Then an ANOVA (degrees of freedom estimated with Satterthwaite formula) was performed on the selected model. This was followed by pairwise comparisons with Tukey's (for main effect) or Holm's (for interaction) adjustment. For the synergy variables, “synergy” was added as a fixed factor and the outliers were removed from a Grubbs's test performed on the residuals.

Despite the high robustness of the mixed model [50], before each mixed model, normality was verified from a Shapiro-Wilk test on the residuals. In addition, the independence between random variables and residuals were checked from the deviance profile. If an assumption was violated, ANOVA or mixed-model by permutation with 10 000 approximate permutation distribution by randomly exchanging pairs of Y elements (lmPerm package for R) was performed. All results are presented as estimated mean ± standard error. The degrees of freedom and F-scores were reported and varied between permutation and classical tests.

# 3. Results

## 3.1. Ground reaction force analyses

As illustrated on Figure 1 (B & C), each lower limb produced a major normal force component in the direction of the sled displacement (performance axis) as well as a downward reaction force along the antero-
posterior axis and lateral forces in opposite directions, leading to a resultant lateral force close to zero along the medio-lateral axis (Figure 1A). Regarding each ground reaction force variable, significant effects were found for sex, load and for recovery session. The results are shown in Table 1.

ANOVA by permutation of the bilateral mean force production revealed load- (F(1,345) = 304.8) and sex- (F(1,15) = 35.4) dependent effects (p < 0.001), and interaction (p < 0.05) on the normal force component: All resistive loads, except 40% from 20% BW (p = 0.069), differed from each other (106 ± 13 N; p < 0.001), and women produced lower mean force values than men (−310 ± 49 N; p < 0.001), but this difference decreased along recovery (−303 ± 48 N at PRE; -290 ± 45 N at D2 and -273 ± 47 N at D4). Analysis of the antero-posterior force component showed a load effect (F(3,357) = 176.8, p < 0.001), with greater values at 60% BW (49 ± 8 N; p < 0.001), and a session effect with lower values at D4 compared to PRE (−39 ± 13 N; p < 0.01). Analysis of the medio-lateral force component showed a sex × session interaction (F(2,17) = 7.6, p < 0.01), with higher force values at D4 compared to D2 for men only (10 ± 13 N; p < 0.01). Analysis of the dominant vs. non-dominant lower limb revealed similar results except for the normal force component, which showed no sex × session interaction for the non-dominant limb. For the medio-lateral force component, only a load effect was found for each lower limb (F(3,345) = 25.2 and F(3,357) = 53.9 for dominant and non-dominant limb respectively, p < 0.001): The medio-lateral force at 0% BW was lower than at 40% and 60% BW for the two lower limbs (p < 0.01) and lower than at 20% BW for the sole dominant-limb (p < 0.05).

In all testing conditions, women showed a lower effective force ratio than men for the dominant limb (−5.5 ± 1.8%; p < 0.01) and bilaterally (−4.5 ± 1.6%; p < 0.01). Independently of sex, the bilateral ratio was slightly increased at D4 compared with PRE (2.1 ± 0.8%; p < 0.05). Only the dominant limb showed a sex × session interaction (F(2,345) = 3.4, p < 0.05) because of a smaller difference between women and men at D4 (−4.6 ± 1.6%) than at PRE (−5.6 ± 1.8%) and D2 (−5.7 ± 1.9). Load factor also influenced the effective force ratio (F(3,345) = 4, p < 0.01), showing a lower value at 60% than at 20% BW (−1.3 ± 0.5%; p < 0.05) except for the non-dominant limb. A main effect of sex was found for the rate of force development (F(1,15) = 6.8, p < 0.05), which was lower in women than in men (−3915 ± 1406 N/s; p < 0.01) independently of the load and session.

## 3.2. Force-velocity relationship

The FV slope based on automatically selected trials was 82.4% similar to the slope based on manual selection. The number of trials selected

averaged 7.2 ± 1.2 (maximum was 8 points) regardless of sex and session, with a similar R² (0.80 ± 0.13). Significant effects of sex, load, and recovery session were found for most variables (Table 2).

As shown in Figure 4, women showed lower values of F̄ 0 (F(1,15) = 12.6, p < 0.01), V̄ 0 (F(1,17) = 39.1, p < 0.001), and P̄ max (F(1,17) = 122.3, p < 0.001) than men. V̄ 0 and P̄ max showed a sex × session interaction (F(2,34) = 4.7 and F(2,34) = 4.8 respectively, p < 0.05): Only men showed lower V̄ 0 and P̄ max at D2 compared to PRE, for both lower limbs (−0.68 ± 0.23 m/s, p < 0.05 and -205 ± 66 W, p < 0.01, respectively) (Figure 4C) and for the dominant limb (−0.85 ± 0.28 m/s, p < 0.05 and -130 ± 36 W, p < 0.01, respectively) (Figure 4A). No significant effects were found for the slope and asymmetry index parameters (SFV and SI<sub>SFV</sub>).

## 3.3. Mean muscle activity

The statistical analysis of muscle EMG-activity revealed significant effects of sex, load, and recovery session on the RMS values. The relative muscle activity changes (expressed in percentage of the maximal values at PRE) were dependent on the push-off phase considered (i.e. global, first, mid-, or last part of the push-off) (Table 2 and Figure 5).

For the global push-off (Figure 5A): Vasti muscles showed the greatest average relative activity during the push-off (p < 0.01). Considering the entire group of participants, vastus medialis activity decreased at both D2 and D4 compared with PRE (−5 ± 1.6%, p < 0.05 and -8.2 ± 1.6%, p < 0.001, respectively). Women showed lower relative medial hamstring activity than men (5.6 ± 1.7%, p < 0.01) independently of the session, and reduced vastus medialis activity (−10 ± 2.3%, p < 0.001) at D4.

For the first part of the push-off (Figure 5B): Vastus medialis muscle showed the highest relative activity (21 ± 0.9%) compared to other muscles and greater relative rectus femoris activity was found at 60% BW than at other loads (−5.5 ± 1.1%, p < 0.01). Vastus medialis activity was reduced at D2 and D4 for the whole group (D2: -5.8 ± 1.1% and D4: -5.9 ± 1%, p < 0.001) and for men (D2: -8.9 ± 1.6%, p < 0.001; D4: -5.9 ± 1.5%, p < 0.01) but at D4 only for women (−6 ± 1.4%, p < 0.01). Women also showed lower relative medial hamstring activity than men at PRE (−9.6 ± 2.2%, p < 0.001) and D2 (−10.4 ± 2.5%, p < 0.001), but not at D4 (p = 0.067).

For the mid-part (Figure 5C): Vasti muscles had the highest relative activity (39.7 ± 1.2%) compared to other muscles. Independently of session, women had higher relative activity of rectus femoris (7.6 ± 2.3%, p < 0.01) and tibialis anterior (6.3 ± 2.3%, p < 0.05), but lower relative medial hamstring activity (6.3 ± 2.3%, p < 0.05) than men. Vastus medialis activity was reduced at both D2 and D4 for the whole group (D2: 7.7 ± 1.7% and D4: 9.8 ± 1.8%, respectively, p < 0.001) and

<table><caption>Table 1. Summary of statistical effects for the ground reaction force variables.</caption><thead><tr><th></th><th></th><th>Sex</th><th>Load</th><th>Session</th><th>Sex × Load</th><th>Sex × Session</th></tr></thead><tbody><tr><td rowspan="5">Bilateral</td><td>Normal force</td><td>***</td><td>***</td><td>NS</td><td>*</td><td>*</td></tr><tr><td>Antero-posterior force</td><td>NS</td><td>***</td><td>*</td><td>NS</td><td>NS</td></tr><tr><td>Medio-lateral force</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>**</td></tr><tr><td>Effective force ratio</td><td>**</td><td>**</td><td>*</td><td>NS</td><td>NS</td></tr><tr><td>Rate of force development</td><td>*</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td rowspan="5">Dominant lower limb</td><td>Normal force</td><td>***</td><td>***</td><td>NS</td><td>*</td><td>*</td></tr><tr><td>Antero-posterior force</td><td>NS</td><td>***</td><td>*</td><td>NS</td><td>NS</td></tr><tr><td>Medio-lateral force</td><td>NS</td><td>***</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Effective force ratio</td><td>**</td><td>**</td><td>NS</td><td>NS</td><td>*</td></tr><tr><td>Rate of force development</td><td>*</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td rowspan="5">Non-dominant lower limb</td><td>Normal force</td><td>***</td><td>***</td><td>NS</td><td>*</td><td>NS</td></tr><tr><td>Antero-posterior force</td><td>NS</td><td>***</td><td>*</td><td>NS</td><td>NS</td></tr><tr><td>Medio-lateral force</td><td>NS</td><td>***</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Effective force ratio</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Rate of force development</td><td>**</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr></tbody></table>

Statistical difference: *p < 0.05, **p < 0.01, ***p < 0.001; NS: non-significant.
<table><caption>Table 2. Summary of statistical effects for the FV and EMG variables.</caption><thead><tr><th></th><th></th><th></th><th>Sex</th><th>Load</th><th>Session</th><th>Sex × Load</th><th>Sex × Session</th></tr></thead><tbody><tr><td rowspan="6">Force Velocity parameters</td><td rowspan="3">Bilateral</td><td>F̄ 0</td><td>**</td><td></td><td>NS</td><td></td><td>NS</td></tr><tr><td>V̄ 0</td><td>***</td><td></td><td>NS</td><td></td><td>*</td></tr><tr><td>P̄ max</td><td>***</td><td></td><td>NS</td><td></td><td>*</td></tr><tr><td rowspan="3">Dominant lower limb</td><td>S<sub>FV</sub></td><td>NS</td><td></td><td>NS</td><td></td><td>NS</td></tr><tr><td>SI<sub>SFV</sub></td><td>NS</td><td></td><td>NS</td><td></td><td>NS</td></tr><tr><td>F̄ 0</td><td>**</td><td></td><td>NS</td><td></td><td>NS</td></tr><tr><td rowspan="6">Non-dominant lower limb</td><td>F̄ 0</td><td>***</td><td></td><td>NS</td><td></td><td>*</td></tr><tr><td>P̄ max</td><td>***</td><td></td><td>NS</td><td></td><td>*</td></tr><tr><td>S<sub>FV</sub></td><td>NS</td><td></td><td>NS</td><td></td><td>NS</td></tr><tr><td>SI<sub>SFV</sub></td><td>NS</td><td></td><td>NS</td><td></td><td>NS</td></tr><tr><td>F̄ 0</td><td>**</td><td></td><td>NS</td><td></td><td>NS</td></tr><tr><td>P̄ max</td><td>***</td><td></td><td>NS</td><td></td><td>*</td></tr><tr><td rowspan="3">Mean muscle activity</td><td>RMS global push-off</td><td></td><td>***</td><td>NS</td><td>***</td><td>NS</td><td>***</td></tr><tr><td>RMS first part of the push-off</td><td></td><td>***</td><td>***</td><td>***</td><td>NS</td><td>*</td></tr><tr><td>RMS middle part of the push-off</td><td></td><td>***</td><td>NS</td><td>***</td><td>NS</td><td>***</td></tr><tr><td rowspan="6">Muscle synergies</td><td rowspan="5">Temporal synergies</td><td>RMS last part of the push-off</td><td>***</td><td>NS</td><td>**</td><td>NS</td><td>**</td></tr><tr><td>CoA Motor primitive 1</td><td>***</td><td>NS</td><td>*</td><td>*</td><td>***</td></tr><tr><td>FWHM Motor primitive 1</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>CoA Motor primitive 2</td><td>NS</td><td>NS</td><td>NS</td><td>*</td><td>***</td></tr><tr><td>FWHM Motor primitive 2</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td rowspan="6">Spatial synergies</td><td>CoA Motor primitive 3</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>FWHM Motor primitive 3</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Motor modules synergy 1</td><td>***</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Motor modules synergy 2</td><td>*</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Motor modules synergy 3</td><td>*</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr></tbody></table>

Statistical difference: *p < 0.05, **p < 0.01, ***p < 0.001; NS: non-significant. Grey boxes are used when the effect is inappropriate. For clarification reasons, muscle and trial factors are not shown. For the synergy analysis, only post-hoc of the interactions are shown. NS: non-significant (p > 0.05).

<figure><img src="image_72.png" /><figcaption>Figure 4. Group mean Force-velocity and velocity-power relationships for dominant (a), non-dominant (b) and both (c) lower limbs between men (blue curves) and women (pink curves) as function of session: PRE, D2 and D4 in bold, light and very light respectively. The red arrows indicate the session order. *: p < 0.05 and **: p < 0.01 compared to PRE for men. Sex-differences were statistically significant (p < 0.001) in F̄ 0, V̄ 0 and P̄ max.</figcaption></figure>
<figure><img src="image_73.png" /><figcaption>Middle part of the push-off</figcaption></figure>

<figure><img src="image_74.png" /><figcaption>First part of the push-off</figcaption></figure>

<figure><img src="image_75.png" /><figcaption>Last part of the push-off</figcaption></figure>

<figure><img src="image_76.png" /></figure>

**Figure 5.** RMS of normalized muscle activation values averaged for women (pink curves) and men (blue curves) for each session: PRE, D2 and D4 in bold, light and very light respectively; and each part (global, middle, first and last part) of the push-off. Muscle abbreviations: RF, rectus femoris; VM, vastus medialis; VL, vastus lateralis; MH, medial hamstrings; TA, tibialis anterior; GM, gastrocnemius medialis; GL, gastrocnemius lateralis; SO, soleus. "$\$$" and "*" represent significant differences at D2 and D4 as compared to PRE, respectively. "$\approx$" indicates a significant sex difference. The number of symbols indicates the statistical level: one for $P < 0.05$, two for $P < 0.01$ and three for $P < 0.001$ in pink for women and in blue for men.

for women (D2: -8.2 ± 2.3%, $P < 0.05$ and D4: -12.5 ± 2.5%, $P < 0.001$). Men showed at D2 a decreased vastus medialis activity (-7.8 ± 2.4%, $P < 0.05$) and increased medial hamstring activities (7.9 ± 2.4%, $P < 0.05$).

For the last part (Figure 5D): Both gastrocnemii muscles were highly activated (57.9 ± 1.2%). Women showed lower relative gastrocnemius medialis (-8.2 ± 2.5%, $P < 0.01$) and vastus medialis (-6.7 ± 2.5%, $P < 0.05$) activities than men. At D2, men showed increased gastrocnemius medialis activity (12.5 ± 3.5%, $P < 0.05$) that resulted in an increased sex difference (17 ± 4.3%, $P < 0.01$).

### 3.4. Muscle synergies

The EMG activity recorded from 8 muscles of the lower limb could be factorized into three task-related muscle synergies in all test conditions (Figure 6). A good reconstruction quality was obtained by the NMF for all conditions (for men: $R^2 = 0.910 \pm 0.002$ and for women: $R^2 = 0.908 \pm 0.002$), with negligible effect of load ($P < 0.001$): The $R^2$ was slightly higher at 0% and 20% BW than at 60% BW ($0.025 \pm 0.009$; $0.026 \pm 0.009$ respectively, $P < 0.05$).

The three fundamental synergies occurred at specific phases of the push-off: the first synergy at the push initiation with a major influence of knee extensors ($P < 0.001$). The second synergy at the mid push-off, considered as the transfer between the other 2 synergies, showed an overall muscle contribution especially in women. The third synergy at the end of the push-off phase, showed a primary contribution from the ankle extensors. The mid push-off was not found at the 20% BW condition in the PRE-session where both sexes exhibited only the movement initiation

and the end push-off synergies (Figure 6). Thus, the 20% BW condition was not included in the linear mixed model.

For each muscle synergy variable (i.e., CoA, FWHM and motor modules; Table 2), the main significant results are summarized in the following section, first independently of the recovery session and then as a function of it:

At the push initiation (first synergy), women had higher rectus femoris (0.07 ± 0.02, $P < 0.01$) and vastus lateralis (0.11 ± 0.02, $P < 0.001$) contributions, but lower medial hamstring contributions (-0.05 ± 0.02, $P < 0.05$) than men. For both sexes, vastus medialis was the most activated muscle (0.50 ± 0.02 for men and 0.52 ± 0.02 for women, $P < 0.001$). The center of activity (CoA) of the motor primitive was located at 41.6 ± 0.6% of the push-off time, but was reached later by women at 40% BW (4.2 ± 1.5%, $P < 0.05$) and 60% BW (9.7 ± 1.5%, $P < 0.001$). The duration of the main activity of motor primitive (quantified by the FWHM parameter) did not differ depending on either sex or load condition although it tended to be longer in women than in men ($P = 0.07$).

Regarding the mid-push off (second synergy) in men, medial hamstring and vastus lateralis were the most activated muscles (0.45 ± 0.02; 0.37 ± 0.02, respectively, $P < 0.05$). Compared to men, women presented relatively smaller contributions from the medial hamstring ($P < 0.001$) and vastus lateralis ($P < 0.01$), but greater contributions from the triceps surae (gastrocnemii and soleus) and rectus femoris ($P < 0.05$). The CoA was located at 63.2 ± 0.5% of the push-off time and was reached later by women at 60% BW (5.9 ± 1.5%, $P < 0.001$) compared to men. Neither resistance conditions nor sex differed in the duration of the main activity of motor primitive.
<figure><img src="image_77.png" /><figcaption>**Figure 6.** Motor modules and motor primitives (averaged for each participant and load excepted 20% BW condition) of the three fundamental synergies for each recovery session (PRE, D2 and D4) in women and men. The motor modules are presented on a normalized y-axis base: each muscle contribution within one synergy can range from 0 to 1. For the mean motor primitives, the x-axis full scale represents the normalized push-off and the y-axis the normalized amplitude. Muscle abbreviations: RF, rectus femoris; VM, vastus medialis; VL, vastus lateralis; MH: Medial Hamstrings; TA, tibialis anterior; GM, gastrocnemius medialis; GL, gastrocnemius lateralis; SO, soleus. “*” indicates a significant sex difference. The number of symbols indicates the statistical level: one for *P* < 0.05, two for *p* < 0.01 and three for *p* < 0.001. Difference of center of activity of the motor primitive between men and women is indicated by blue and pink curvy symbols, and inter-session differences by curvy symbols of similar colors (blue or pink).</figcaption></figure>

At the end of the push-off phase (third synergy), and in contrast to the sex differences in the other synergies, women had greater contributions of medial hamstring (0.05 ± 0.02, *p* < 0.05) and lower vastus lateralis (−0.06 ± 0.02, *p* < 0.01) muscles than men. The CoA was located at 80.7 ± 0.3% of the push-off time. Independently of sex, the main activity of this motor primitive was shorter in duration (FWHM) than that of the first (−13.9 ± 2.4, *p* < 0.001) and second (−10.7 ± 2.4, *p* < 0.001) motor primitives.

The recovery session did not influence motor modules and FWHM, but impacted CoA as follows: A synergy × sex × recovery session interaction was found (*p* < 0.05). For the first synergy, men showed a CoA reached later at D2 than at PRE (4.1 ± 1.4%, *p* < 0.01). At D4, both men and women showed a CoA reached earlier than at PRE (−3.7 ± 1.4%, *p* < 0.05 and −4.8 ± 1.4%, *p* < 0.01, respectively) and D2 (−7.9 ± 1.5%, *p* < 0.001 and −6.0 ± 1.4%, *p* < 0.001, respectively). Thus, women showed a delayed CoA compared with men at PRE and D4, but not at D2. For the second synergy, men showed: Delayed CoA at D2 (6.5 ± 1.5%, *p* < 0.001) and D4 (3.1 ± 1.5%, *p* < 0.05) compared with PRE, and earlier CoA at D4 than at D2 (−3.4 ± 1.5%, *p* < 0.05). The 20% BW was characterized for both sexes by only 2 synergies at PRE vs. 3 synergies at D2 and D4. The PRE-session lacked the second synergy corresponding to the mid push-off, while showing a longer first synergy and an unchanged third synergy (Figure 6).

## 4. Discussion

This study was designed to explore the influence of sex on the FV profile and neuromuscular pattern when performing a ballistic multi-joint HFV test before a graded running race and at 2 and 4 days of its delayed recovery phase. Independently of the load and session, women produced lower mean force values, showed a lower effective force ratio and a lower rate of force development than men, that resulted in lower F̄ 0, V̄ 0 and P̄ max values. Supporting partially our first hypothesis of a lesser change in the FV profile in women than in men during the delayed recovery phase, only men showed significant decrements in V̄ 0 and P̄ max but not in F̄ 0. On the other hand, confirming our second hypothesis, the muscle activity pattern was found as sex-dependent before fatigue, with sex-specific changes along the 4 days of recovery.

Confirming the interest of using the FV profile for assessing sex influence on the functional fatigue effects, the FV relationship presented a good linearity (measured by R²) in all recovery sessions and revealed significant sex-dependent fatigue effects that were not detected by the classical analyses using the global mean force value [18]. The discrepancy between the two analyses could be attributed to the fact that the FV relationship included most of the performed push-off trials whereas the classical analysis was limited to the best trial per resistive load. As the inter-trial variability may be expected to change with fatigue [51], the FV
profile is considered as pertinent as it includes most of the trials. Extrapolation of the extreme force (F̄ 0), velocity (V̄ 0) and power (P̄ max) values may also be considered relevant to reveal small changes in this purely concentric multi-joint task known to favor inter-muscular compensations leading to no fatigue-induced drop in performance [10]. Vuorimaa et al. (2006) [24] even found a performance improvement in a loaded half-squat test after running until exhaustion.

In the present experiment, only men showed decreases of V̄ 0 and P̄ max at D2 when averaged for both limbs and also for the dominant limb alone. As expected, these functional decrements were limited compared to those found at D2 and D4 for these male runners in more stressful drop jump (jump with ground impact) and bilateral MVC (monoarticular knee extension) tests [18]. The lack of delayed functional deficits found for the present female endurance runners is consistent with their reported lower sensitivity to muscle damage [52, 53] and shorter inflammatory process [54, 55]. Among the potential underlying mechanisms, women are considered to have a proportionally larger area of slow twitch muscle fibers (known as more resistant to metabolic fatigue and mechanical stress) and to benefit from the direct and indirect effects of estrogen (on muscle membranes, perfusion and metabolism) [11, 12, 13, 20].

On the other hand, the unexpected lack of change in F̄ 0 during the delayed recovery period suggests that the 20-km graded running race impacted more the velocity than the force capacity. These findings differ from the greater decrease in F̄ 0 than V̄ 0 reported in male runners at the end of longer races (60 and 100 km UTMB races) with larger gradient [56] than the present one. Any comparison with the present study is unfortunately limited by the obvious differences in running distance, gradient, testing time (acute vs. delayed) and testing task (cycling vs. ballistic jump). The decrease in V̄ 0 means a decrease in the ability of runners to produce vertical force at high speeds, which could increase the risk of injury involving a negative influence on the quality of specific sport skills [57]. The decrease of V̄ 0 and P̄ max in men may be favored on this particular ergometer since the push-off starts at low pre-activation level and higher extrapolated values of V̄ 0 (4.7 m/s in PRE for men) are obtained compared to the 2–4 m/s generally reported when using the classical vertical squat jump [58]. Finally, the unilateral fatigue effects found at D2 may be attributed to the asymmetry reported for the knee extensor strength, running kinetics and kinematics of endurance runners [27]. For the present runners, confounding factors may be considered such as their recreational running level, the positive and negative gradients of the 20-km race and their limited participation to this graded race. Furthermore, the unilateral fatigue effects revealed by the bilateral HFV test confirms the lack of “cross-over effects” previously reported in a maximal bilateral jump test performed D2 after exhaustive unilateral rebounds [59]. Maximal bilateral jump test may thus be used to examine fatigue effects in each lower-limb separately.

Supporting our second hypothesis, women showed a different EMG pattern than men in the HFV test, and different neural adjustments at D2 and D4. The respective analyses of mean muscle activity and muscle synergies agreed with each other and revealed complementary findings. Independently of the recovery sessions, men and women clearly differed regarding the solicitation of the bi-articular thigh muscles (medial hamstring and rectus femoris). Compared to men, women showed lower activity and relative contribution of the medial hamstring muscles during the global push-off, especially at the push initiation and during the subsequent mid-phase. Considering the lower peak torque and velocity of the hamstring/quadriceps ratio reported for women [60, 61], the contribution of the medial hamstring muscles to the horizontal push-off may be expected to be lower in women than in men. Conversely, women presented a higher relative contribution of rectus femoris to the initiation and mid-push phases. Although men and women initiated the push from the same hip and knee joint angles (set at 90°), the greater reliance of women on the rectus femoris rather than on the hamstring muscle group as men could be considered a disadvantage due to the non-optimized muscle length of the rectus femoris [62]. At the push-initiation, women

thus relied more on the knee than on the hip extensors whereas men relied much on both. Analysis of the synergies revealed sex-specific characteristics in both spatial and temporal structure of the muscle synergies, notably for the second synergy, which showed that men activated selectively their vasti and medial hamstring muscle groups whereas women involved also the rectus femoris and the triceps surae (soleus and gastrocnemii). This global involvement of the lower limb muscles in women seems to have resulted in some loss of energy given their lower effective strength ratio. Furthermore, the first and second motor primitives exhibited a later center of muscle activity in women than in men, which may have contributed to the lower rate of force development in women.

Regarding the neural adjustments to fatigue, the RMS analysis of the dominant lower limb revealed for both men and women a reduced vastus medialis activity at D2 and D4 (especially in the mid-phase of the push). This result is in agreement with the delayed decreases found for these runners in the knee extension MVC tests [18]. In the present HFV test, although men showed at D2 increased activities of the medial hamstrings at mid-push and of the gastrocnemius medialis in the final push-off phase, these compensations were not sufficient to compensate for the loss of vastus medialis activity as shown by the reduced V̄ 0 and P̄ max. The short duration of the end push-off synergy (where the contribution of the triceps surae was the most important) may also have limited the potential compensatory role of this muscle group. Interestingly also, as women relied on a more evenly distributed involvement of all lower limb muscles than men at mid-push-off, the fatigue-induced decrease in vastus medialis activity is likely to have had a lesser functional impact on their performance. On the other hand, although women reported specific DOMS from the hamstrings [18], the fact that they activated less than men this muscle group in the HFV test is likely to have limited its negative influence.

While no change was found with respect to the motor modules of each synergy, men exhibited a delayed center of activity (CoA) at D2 in the first and second motor primitives, so that their earlier CoA than women at PRE no longer differed at D2. At D4, the medial hamstring activity did not differ anymore between men and women at the push initiation and both sex groups showed earlier CoA than at PRE for the first motor primitive, and for the second motor primitive also for men only. Interestingly, these temporal shifts of CoA paralleled the functional changes: Men showed delayed CoA and decreased V̄ 0 and P̄ max at D2, followed by earlier CoA and a return to their PRE-functional values at D4. Women showed earlier CoA, improved effective force ratio, and lesser functional sex difference at D4 suggesting a potential learning effect of the ballistic task. Finally, the 20% BW condition differed from the others by showing for both sexes only two synergies at PRE, but three synergies at both D2 and D4 that could reflect a fatigue effect for both sexes. However, as about 30% of combined synergies were found for the 20% BW at PRE for both men and women compared to about 15% for the other loads, this result may still be attributed to the variability of the computation method [30].

Some methodological limitations need to be addressed. First, the small sample size in this study is a major limitation as we did not have the number of participants required for 80% statistical power. Thus, we calculated a second time (a posteriori) the statistical power based on the theoretical maximal power (P̄ max) indicating that the statistical power was of about 45% regarding the interaction effect. It is important to note that sex-fatigue differences in terms of functional parameters were only found for pure theoretical values of the FV profile. Differing from the vertical squat jump test, the present ballistic ergometer was particular since the squat jumps were performed while lying in a supine position with little pre-activation compared to a vertical push. Using this ergometer, higher extrapolated values of V̄ 0 were obtained compared to those using the classical vertical squat jump [58]. In this context, Lindberg et al. (2021) [63] reported high repeatability for all FV parameters on a leg press (close to our ergometer) that was attributed to the highly standardized position [63]. Although the present FV was calculated for
each lower limb separately, which is interesting as they were impacted
differently by fatigue, it should be mentioned that the same mean ve-
locity was applied for both lower limbs. Based on the asymmetrical fa-
tigue effects, it could be interesting to perform also unilateral tests.
Concerning the EMG analysis, the model of muscle synergies must be
considered as perfectible since only 8 muscles of one lower-limb were
included so that the gluteus maximus, expected to play a major role at the
initiation of the push-off, was not recorded.

5. Conclusion

This study provides new insights into the impact of graded endurance running on functional and neuromuscular recovery between men and women. First, women were found to recover earlier in a dynamic multijoint push-off task as only men showed decreases in V̄ 0 and P̄ max at D2. This ballistic force-velocity test revealed clear sex-differences in the lower limb activities and muscle synergies both before fatigue and during the recovery phase suggesting that the coordination analysis may be of interest to reveal neuromuscular fatigue and different neural strategies. Interestingly, although both men and women showed reduced activity of the vastus medialis muscle up to D4, only male runners showed a delayed timing of the center of activity of the muscle synergies at D2 before returning to baseline values at D4. The lesser influence of this fatigued quadriceps muscle on the women synergies is attributed to their use of a more evenly distributed activation of lower limb muscles than men. Despite the earlier functional recovery of women, additional results are needed to define their optimal time to resume running. In future studies, it would be of interest to repeat such analyses in running to better appreciate the richness of the intermuscular compensations of men and women with fatigue.

Declarations

Author contribution statement

Author name as it appears on the manuscript: Conceived and
designed the experiments; Performed the experiments; Analyzed and
interpreted the data; Contributed reagents, materials, analysis tools or
data; Wrote the paper.

Robin Macchi & Caroline Nicol: Conceived and designed the experi-
ments; Performed the experiments; Analyzed and interpreted the data;
Wrote the paper.

Arnaud Hays: Conceived and designed the experiments; Performed
the experiments; Analyzed and interpreted the data.

Fabrice Vercruyssen: Conceived and designed the experiments; Per-
formed the experiments.

Alessandro Santuz, Avner Bar-Hen & Adamantios Arampatzis:
Analyzed and interpreted the data.

Funding statement

Robin Macchi was supported by the GDR Sport & Activité Physique.

Data availability statement

Data will be made available on request.

Declaration of interests statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

Acknowledgements

We sincerely thank G. Lagrange and D. Marziale, the Provence & Corsica delegation of the CNRS and the SCO Ste Marguerite (race organizers). We would like to thank the engineer A. Jacques for the design of the horizontal force-velocity device as well as for the development of the Labview software for its data acquisition. The authors thank Pr Denis Bertin for his contribution to the patent registration of the force-velocity machine. We are grateful to Gaetan Exubis and Gaetan Aubert for their help during the experiments.

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# CHAPITRE IV

## Influence du sexe sur la cinétique des récupérations structurale et fonctionnelle : liens entre ces deux types de récupération

Ce chapitre s'appuie sur l'étude 4 prochainement soumise pour publication et une présentation affichée à la journée de l'école doctorale :

1. **Macchi, R.**, Kunimasa, Y., Chavet, P., Corcelle, B., Pomportes, L., Fazzari, C., Hays, A., Vercruyssen, F., Rossi, F., Soustelle, L., Bendahan, D., Nicol, C. Relationship between indirect markers of muscle damage and functional performance after endurance running: focus on sex specificities with a Bayesian approach. Sera soumis prochainement pour publication à *Acta physiologica*

2. **Macchi, R.**, Kunimasa, Y., Corcelle, B., Pomportes, L., Fazzari, C., Chavet, P., Vercruyssen, F., Bendahan, D., Nicol, C. (2022). Do women recover better than men after a 20-km graded running race? *Journée de l'Ecole Doctorale Sciences du Mouvement Humain, Nice (France)*

Nous présenterons également les résultats des études 5 et 6 (en cours de rédaction) et les discuterons succinctement.
Les travaux présentés dans le Chapitre III étaient centrés sur la récupération fonctionnelle et ses distinctions liées au sexe en fonction des différents tests utilisés. Le challenge majeur des études présentées dans le chapitre IV réside dans le suivi en parallèle et la mise en relation des cinétiques de récupération structurale et fonctionnelle. Ces études portent sur un ensemble de 27 coureurs récréatifs (14 hommes et 13 femmes) ayant participé à l'édition 2021 de la Course Marseille-Cassis. En raison du faible risque de blessures associé à la phase aiguë de la récupération, nos travaux se sont focalisés sur la phase retardée de la récupération.

Concernant les aspects méthodologiques, l'étude de la récupération structurale s'appuiera sur l'utilisation de méthodes d'analyses reproductibles et valides d'imagerie médicale. Comme nous l'avons détaillé dans le cadre théorique (p.40-55), l'évaluation indirecte des dommages par les marqueurs plasmatiques est couramment utilisée mais reste sujet à caution. La technique d'imagerie magnétique (IRM) constitue un « gold standard » malheureusement coûteux. La technique d'échographie, certes moins précise que l'IRM mais moins chère, n'a été que peu utilisée dans ce but. Nos travaux de thèse ont donc utilisé ces deux techiques. En échographie, les tests ont été réalisés sur 10 muscles du membre inférieur dominant (Figure 56).

<figure><img src="image_78.png" /><figcaption>**Figure 56.** Illustration de la technique d'enregistrement échographique. Selon les caractéristiques morphologiques du muscle testé, la sonde (A) ou (B) a été utilisée. L'emplacement exact des sections de muscle enregistrées était choisi de façon précise par rapport à la longueur du segment, marqué sur la peau avec un marqueur de type blanco ainsi que sur un feuillet de plastique pouvant être précisément repositionné entre les sessions de test (plus de précisions seront fournies dans le manuscrit de l'étude 4). (C) Exemple de l'un des trois clichés de section musculaire réalisé pour chacun des 10 muscles examinés.</figcaption></figure>
La batterie de tests utilisée ne comprend que de tests pluriarticulaires de type cycle étirement-détente (squat jump et drop jump maximaux et série de rebonds sous-maximaux) effectués sur un ergomètre de type « sledge » (Figure 57) développé par le Neuromuscular Research Centre, de l'Universite de Jyväskylä en Finlande (Kaneko, 1984). Ce type d'ergomètre est composé d'un siège coulissant le long de deux rails qui peuvent être inclinés de façon variable par rapport à l'horizontale. Une plate-forme de force mono-axiale (composante normale des forces de réaction du sol) est fixée perpendiculairement à l'extrémité inférieure des deux rails. Un potentiomètre rotatif sans fin, dont l'axe est équipé d'une poulie à friction, est fixé sur le côté droit du siège et en appui sur le rail correspondant. Il code ainsi les déplacements du siège. Deux dispositifs permettent d'assurer la sécurité des participants : une ceinture de sécurité les maintient au siège et une sangle, fixée entre l'arrière du siège et la partie haute des rails, stoppe le siège 30 cm au-dessus de la plate-forme.

Cet ergomètre présente plusieurs avantages : il standardise les tests en limitant notamment l'implication de la hanche, sécurise les participants peu habitués à ce type de tâche, accroît la reproductibilité des patrons d'activation et du moyennage de ces derniers sur plusieurs rebonds (cf. détection de la réponse réflexe dans la littérature). En drop jump, l'objectif était de favoriser l'implication du triceps sural par la contrainte d'un temps de freinage court (flexion maximale de 100° du genou) similairement au protocole de drop jump vertical de l'étude 2.

<figure><img src="image_79.png" /><figcaption>**Figure 57.** Illustration du test de drop jump max sur l'ergomètre de type sledge. (A) Le test débutait à partir d'une hauteur de départ (flèche rouge), correspondant à 80 % de la hauteur de rebond atteinte lors du test précédent de squat jump maximal (H80%SJ). (B) Les participants devaient respecter une hauteur minimale (flèche jaune pour l'expérimentateur) à la fin de la phase de freinage. Ils disposaient pour cela d'un repère visuel et d'un retour auditif par l'expérimentateur. Cette hauteur, individuellement mesurée au préalable, correspondait à l'atteinte d'un angle de genou de 100° (C) Ils avaient pour consigne de rebondir aussi vite que possible afin d'atteindre une hauteur maximale.</figcaption></figure>

A la différence des études du Chapitre III, qui ne comportaient que des tests d'intensité maximale, nous avons choisi d'ajouter une tâche de rebonds sous-maximaux sur le sledge. Cette
dernière devrait favoriser l'émergence de compensations inter-musculaires des déficiences contractiles par diverses adaptations de la commande nerveuse centrale et/ou réflexe. Comme nous l'avons abordé précédemment (p. 59 de la section 2.5.2), Regueme et al. (2005) ont souligné la différence autant que la richesse des ajustements neuro-musculaires (différent selon le muscle et la phase du CED) dans ce type de tâche comparé à ceux observés en drop jump maximal (Avela et al., 1999). En phase de récupération retardée, les rebonds sous-maximaux présentaient une pré-activation accrue du soleus, suivie d'une activation moyenne inchangée en freinage, mais suffisament accrue en poussée pour contribuer au maintien de la hauteur de rebond (Regueme et al., 2005). En test de saut maximal, une augmentation de la pré-activation est rapportée pour engendrer une augmentation du pic d'impact (Gollhofer et al., 1987a ; Komi et Gollhofer, 1987), ce qui conduit avec la fatigue à une diminution de la résistance à celui-ci (Avela et al., 1999 ; Nicol et al., 1991c). Ces résultats indiquent, qu'en situation de fatigue, l'augmentation de la pré-activation pourrait n'être bénéfique que dans des tâches sous-maximales, ce que confirme la moindre performance qui lui est associée en drop jump max. (Horita et al., 1999). Ces observations soulignent la nécessité d'une régulation optimale de la raideur à l'impact. L'examen de ces différents types de tâches devrait ainsi nous renseigner sur les capacités d'adaptation du système neuromusculaire en cas de perturbation sur plusieurs jours de l'effecteur musculo-tendineux. Enfin, les tests sous-maximaux présentent l'avantage de se rapprocher des ajustements potentiels en course à pied. Il convient d'ailleurs de souligner que la majorité des exercices de fatigue utilisés pour étudier la « fatigue de type CED » étaient des séries de sauts maximaux ou de rebonds effectués sur le sledge (Nicol et Komi, 2010). Ces exercices différaient donc nettement des courses d'endurance faisant l'objet de ce travail de thèse. Les baisses fonctionnelles raportées dans la littérature pourraient dès lors différer de celles observées après la présente épreuve de course à pied.

L'étude 4 présentée dans ce chapitre présentera les cinétiques structurale et fonctionnelle au cours de la phase retardée de récupération. L'analyse structurale comprendra les données obtenues par échographie. Cette étude utilisera une méthode d'analyse bayésienne pour investiguer les effets de la récupération en fonction du sexe.

Les résultats issus des analyses IRM et leurs liens aux données électromyographiques et fonctionnelles seront alors présentés.
Relationship between indirect markers of muscle damage and functional performance
after endurance running: focus on sex specificities with a Bayesian approach

Robin Macchi<fnref n="1" />,<fnref n="2" />, Yoko Kunimasa<fnref n="3" />, Pascale Chavet<fnref n="1" />, Baptiste Corcelle<fnref n="4" />, Laura Pomportes<fnref n="1" />,
Camille Fazzari<fnref n="1" />, Arnaud Hays,<fnref n="1" /> Fabrice Vercruyssen<fnref n="5" />, Francesca Rossi<fnref n="6" />,<fnref n="7" />, Lucas Soustelle<fnref n="7" />,
David Bendahan<fnref n="7" />, Caroline Nicol<fnref n="1" />

<footnote n="1">¹ Aix-Marseille Univ, CNRS, ISM, Marseille, France</footnote>

<footnote n="2">² French Institute of Sport (INSEP), SEP-EA 7370, Paris, France</footnote>

<footnote n="3">³ Niigata University, Niigata, Japan</footnote>

<footnote n="4">⁴ Université Côte d'Azur, LAMHESS, Nice, France</footnote>

<footnote n="5">⁵ University of Toulon, IAPS, Toulon, France</footnote>

<footnote n="6">⁶ Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy</footnote>

<footnote n="7">⁷ CRMBM Laboratory UMR 7339, CNRS, Marseille, France</footnote>
NOTATION

Measurement sessions

<table><tr><td>PRE</td><td>Before the race</td></tr><tr><td>D1-2</td><td>1 or 2 days after the race (at the expected peak of the inflammatory process)</td></tr><tr><td>D3-4</td><td>3 or 4 days after the race (2 days after the D1-2 test session)</td></tr></table>

Technical abbreviations

<table><tr><td>BW</td><td>Body weight</td></tr><tr><td>DOMS</td><td>Delayed onset muscle soreness</td></tr><tr><td>DJ</td><td>Drop jump</td></tr><tr><td>EMG</td><td>Electromyographic</td></tr><tr><td>GRF</td><td>Ground reaction force</td></tr><tr><td>SJ</td><td>Squat jump</td></tr><tr><td>SSC</td><td>Stretch-shortening cycle</td></tr></table>

Muscle names

<table><tr><td>BFl</td><td>Biceps femoris long head</td></tr><tr><td>BFs</td><td>Biceps femoris short head</td></tr><tr><td>GM</td><td>Gastrocnemius medialis</td></tr><tr><td>GL</td><td>Gastrocnemius lateralis</td></tr><tr><td>RF</td><td>Rectus femoris</td></tr><tr><td>SM</td><td>Semimenbranosus</td></tr><tr><td>SOL</td><td>Soleus</td></tr><tr><td>ST</td><td>Semitendinosus</td></tr><tr><td>TA</td><td>Tibialis anterior</td></tr><tr><td>VL</td><td>Vastus lateralis</td></tr><tr><td>VM</td><td>Vastus medialis</td></tr></table>

Variables

<table><tr><td>CSA</td><td>Cross sectional area</td></tr><tr><td>ES</td><td>Effect size</td></tr><tr><td>F&#773;<sub>brake</sub></td><td>Mean braking force</td></tr><tr><td>F&#773;<sub>push-off</sub></td><td>Mean push-off force</td></tr><tr><td>h<sub>brake</sub></td><td>Braking distance</td></tr><tr><td>hpo</td><td>Push-off distance</td></tr><tr><td>index<sub>ipush</sub></td><td>Ratio of the integral of the push-off force to the integral of the total force</td></tr><tr><td>L<sub>stiff</sub></td><td>Lower-limb stiffness</td></tr><tr><td>Rope</td><td>Region of practical equivalence</td></tr><tr><td>RSI</td><td>Reactive strength index</td></tr><tr><td>pd</td><td>Probability of direction</td></tr><tr><td>P&#773;<sub>brake</sub></td><td>Mean braking power</td></tr><tr><td>P<sub>max</sub></td><td>Maximal power output</td></tr><tr><td>P&#773;<sub>push-off</sub></td><td>Mean push-off power</td></tr><tr><td>t<sub>brake</sub></td><td>Braking time</td></tr><tr><td>t<sub>push-off</sub></td><td>Push-off time</td></tr><tr><td>V&#773;<sub>brake</sub></td><td>Mean braking velocity</td></tr><tr><td>V&#773;<sub>push-off</sub></td><td>Mean push-off velocity</td></tr><tr><td>vto</td><td>Velocity at take-off</td></tr></table>
# INTRODUCTION

In recent years, the number of participants in endurance running races has increased dramatically, especially for the half-marathon distance (Anthony *et al.*, 2014). Running is commonly considered to have significant beneficial health effects (Ooms *et al.*, 2013). Yet, endurance running is characterized by thousands of ground impacts and eccentric muscle actions that are likely to induce focal muscle damage eventually triggering structural and functional recovery processes that last a few days (Nicol & Komi, 2010). However, as recently highlighted (Besson *et al.*, 2022), although oestrogen (Enns and Tiidus 2010) may make women less prone to fatigue (Hunter, 2016), comparative analyses between women and men regarding recovery process after endurance running are still lacking.

Endurance running is defined as a stretch-shortening cycle (SSC) type fatiguing exercise, which is likely to result in a biphasic functional recovery pattern (Nicol *et al.*, 2006; Nicol & Komi, 2010). In men, exhaustive SSC exercises are indeed reported to induce an immediate decline in performance that may partially recover within two hours, before a secondary decline that usually lasts a few days. Considering the partial recovery at 2 h, the acute recovery phase has been attributed to metabolic fatigue rather than to exercise-induced muscle damage (EIMD). The delayed recovery phase is mainly attributed to the structural and functional consequences of the inflammatory/remodelling processes that follow EIMD. This phase is typically associated with delayed onset muscle soreness (DOMS), but it is noteworthy that neither the timing nor the amplitude of DOMS are correlated with the EIMD (Newham *et al.*, 1983). In particular, DOMS usually disappear before the full functional recovery, leading to an overestimation of actual functional abilities, which may increase the risk of injury upon return to practice (Strojnik *et al.*, 2001).

So far, only a few studies have analysed the sex-specific recovery pattern, and such comparative analyses have been performed for the acute recovery phase only. After a 110 km ultra-trail race, women showed a smaller decrease in maximal knee extension force and less peripheral fatigue of plantar flexor muscles compared to men (Temesi *et al.*, 2015; Besson *et al.*, 2021). After 20 km runs, a smaller decrease in maximal knee extension force was reported in women (Glace *et al.*, 1998; Macchi *et al.*, 2021), whereas Boccia et al. (2018) found no sex effect. After an ultra-marathon, Fouré et al. (2022) reported no sex influence on the mechanical properties of the muscle-tendon when looking at the shear wave velocity of the lower-limb muscles.

Part of these discrepancies could be attributed to differences in the type of running exercise (e.g. distance, gradient) as well as in the test protocol (acute or delayed timing of measurements, testing tasks). In this line, it has been reported that 4 days after a 20 km graded run, female runners showed an earlier recovery which was clearly dependent on the testing task (Macchi *et al.*, 2021). In particular, the knee extensor isometric test and the drop jump test (with minimal knee flexion) were found as meaningful in revealing sex differences: smaller decrease and earlier recovery for women. In addition, the sex-specific organization of muscle synergies to perform varied tests, which we recently reported, may also account for differences in recovery times (Macchi *et al.*, 2022). Finally, although a causal relationship between muscle damage and force production has been reported after eccentric exercise (Raastad *et al.*, 2010; Paulsen *et al.*, 2012), none of these running studies have compared functional to structural recovery after endurance running in either men or women. Interestingly, two days after the 20 km graded run, only women reported DOMS for the hamstrings (Macchi *et al.*, 2021), a muscle
group known to be relatively weaker in women than in men (Hewett *et al.*, 2008; Martín-San Agustín *et al.*, 2019).

The lack of information on the recovery pattern of women is all the more surprizing as the potential protective effects of oestrogen, as antioxidants, membrane stabilizers and satellite cell proliferators (Enns & Tiidus, 2010), could significantly modulate their sensitivity to damage after endurance running. Although these oestrogen effects are well accepted in animal models, there is no consensus in humans (Hubal & Clarkson, 2009). To the best of our knowledge, only three studies have directly quantified by muscle biopsies the effects of sex on ultrastructural muscle damage and/or inflammation after eccentric lower limb exercise. Stupka et al. (2000, 2001) reported no sex difference in muscle damage up to 6-7 days, but an attenuated inflammatory response in women. Conversely, Kerksick et al. (2008) found greater cell death at 6 h and 24 h in men than in women after repeated eccentric knee extensions. Biopsies monitoring of ultrastructural recovery have a strong limitation related to the fact that the corresponding surgical procedure can generate inflammation and bias the analyses (Staron *et al.*, 1992; Malm *et al.*, 2000; Dubowitz *et al.*, 2013). The use of indirect blood markers of muscle inflammation (e.g. creatine kinase) also failed to reach a consensus on the influence of sex on the extent and duration of structural recovery (Rogers *et al.*, 1985; Eston *et al.*, 2000; Sorichter *et al.*, 2001; Kaikkonen *et al.*, 2002; Temesi *et al.*, 2015; Oosthuysse & Bosch, 2017; Tiller *et al.*, 2022). Irrespective of sex, due to bias and high inter-individual variability (Hoffman *et al.*, 2012), none of these indirect blood markers are considered to accurately reflect ultrastructural damage and recovery. After a 21 km endurance run, Goodman *et al.* (1997) also pointed out that these markers could result from the effects of free radicals on the cell membrane, leading to damage and increased permeability, rather than from eccentrically induced ultrastructural damage.

In this context, imaging techniques such as ultrasonography, MRI and shear-wave elastography have been used to indirectly quantify muscle damage and/or the inflammatory process (Shellock *et al.*, 1991; Ishikawa *et al.*, 2006; Yu *et al.*, 2015). While ultrasound is the most widely used non-invasive technique for measuring fascicular and muscle characteristics, only a few studies have used it to monitor the evolution of the muscle inflammatory process over time (Ishikawa *et al.*, 2006). In particular, muscle cross-sectional area (CSA) is considered a reliable marker of the oedema-induced muscle swelling (Nosaka *et al.*, 2002; Damas *et al.*, 2016). Unfortunately, information on the structural influence of sex after endurance running is still limited.

The present study aimed to analyse the influence of sex on the time course of delayed structural and functional recovery after a 20 km graded run. Specifically, we investigated the location and time-evolution of an indirect marker of muscle damage (increase in CSA quantified by ultrasonography), as well as the neural and biomechanical changes using a battery of maximal and submaximal jump tests. We hypothesized that, compared to men, women would have: (i) smaller increases in CSA in most muscles, but higher in hamstrings, and (ii) smaller functional deficits, especially in the maximal drop jump test. We also expected (iii) relationships between structural, neural and functional changes induced by running which should be dependent on sex.
# METHODS

## Participants

A total of 39 participants volunteered for this study. The experimental group included 14 male and 14 female recreational endurance runners who had registered 6 months earlier to participate in the international Marseille-Cassis race of 20 km with positive and negative gradients (+382 m and -294 m, respectively). After the loss of one participant due to knee pain, the experimental group comprised 13 women and 14 men (Table 1). The control group (24 ± 4 years, 170 ± 7 cm, 64 ± 8 kg) consisted of 4 female and 7 male runners who did not participate in the race. Each runner completed the race as well as all ultrasound measurements and functional tests. Each individual of the control group performed the same test protocol, but not the run. Inclusion criteria included age between 20 and 45 years, no previous injury in the last 6 months and, for the runners, 4 hours or less of running-type training per week. Approval for the project was obtained from the French Ethical Research committee (CPP IDF VII, ethics committee agreement # 2021-A00355-36) and, in accordance with the Helsinki Convention, written informed consent was obtained from all runners.

<table><caption>**Table 1.** Group-mean (± standard deviation) characteristics of the experimental group in terms of racing time, anthropometrics and jumping performances in the PRE-session. SJ, squat jump; DJ, drop jump tests; vto, velocity at take-off. * indicated a significant sex effect. The number of symbols indicates the statistical level: one for p < 0.05, two for p < 0.01, and three for p < 0.001.</caption><thead><tr><th></th><th>Men (n=14)</th><th>Women (n=13)</th><th></th></tr></thead><tbody><tr><td>Racing time (hh:mm:ss)</td><td>01:44:00 ± 0:09</td><td>01:58:00 ± 0:15</td><td>**</td></tr><tr><td>% of winner's time per sex</td><td>167.2 ± 14</td><td>160.5 ± 20.7</td><td>NS</td></tr><tr><td>Age (yr)</td><td>36 ± 9</td><td>28 ± 6</td><td>**</td></tr><tr><td>Height (m)</td><td>1.81 ± 0.06</td><td>1.67 ± 0.07</td><td>***</td></tr><tr><td>Body mass (kg)</td><td>77 ± 7</td><td>60 ± 8</td><td>***</td></tr><tr><td>vto SJ (m/s)</td><td>2.32 ± 0.24</td><td>1.85 ± 0.20</td><td>***</td></tr><tr><td>vto DJ (m/s)</td><td>2.50 ± 0.16</td><td>2.00 ± 0.16</td><td>***</td></tr><tr><td>vto rebound (m/s)</td><td>2.14 ± 0.15</td><td>1.72 ± 0.12</td><td>***</td></tr></tbody></table>

## Experimental design

The overall design is presented schematically in Figure 1. The test protocol of the exercise participants consisted of a familiarisation session and three actual test sessions: one during the last two weeks before the race (PRE) and two after the race, either on days 1 and 3 (group G1) or on days 2 and 4 (group G2). Thus, each group was tested once at the expected peak of DOMS and functional defects (on day 1 or 2, noted D1-2) and once at a more advanced, though potentially incomplete, phase of the recovery process (on day 3 or 4, noted D3-4). The control group performed 2 test sessions 2 days apart (D0 and D2) with no fatigue exercise in between.

The familiarisation session was used to individually define: the dominant lower-limb, its segment lengths and the precise locations for the ultrasound recordings. Each participant was then familiarised with the functional tests while recording the individual positioning for each jump test. This information is detailed in the measurement protocol. More precisely, for the ultrasound recordings, a tape was used to measure the distance between the tip of the greater trochanter and the lateral epicondyle of the femur, and between the lateral epicondyle of the
fibula and the apex of lateral malleolus. Then, starting from the proximal end, the thigh was divided into 3 sections (30%, 50%, and 70% of the femur length) and the shank into 2 sections (20% and 30% of the shank length); 0% corresponding to the proximal tip. Based on the approximate location of the middle belly of each muscle (Franchi et al. 2020; Balshaw et al. 2021), CSA of each muscle was measured as follows: *rectus femoris* (RF) at 30% of the femur length; *vastus lateralis* (VL), *semitendinosus* (ST), *semimembranosus* (SM), *biceps femoris* short head (BFs) and *biceps femoris long head* (BFl) at 50%; *vastus medialis* (VM) at 70%. Similarly, *gastrocnemius lateralis* (GL) at 20%, and *gastrocnemius medialis* (GM) and *tibialis anterior* (TA) at 30% of the shank length.

For all participants, each experimental session lasted about 1.5 h at approximately the same time of day. First, the runner's body mass was checked on a Tanita scale (MC980MA Tanita) and the soreness of the major muscle groups of each lower limb using a visual analogic scale (0-10). This was followed by ultrasound measurements at rest (Aixplorer Mach 30 and Linear SL18-5, Supersonic Imagine, Aix-en-Provence, France). Functional tests were preceded by a standardized 9-minute warm up (treadmill run at the preferred speed). The test battery included two maximal jump tests, namely a squat jump (SJ) and a drop jump (DJ), and a submaximal rebound (REB) test. All jumps were performed in a seated position on a sledge ergometer, which consists of a sled sliding with minimal friction along two rails inclined at 21° from the horizontal and ending in a perpendicular force platform (graph on **Figure 1**).

<figure><img src="image_80.png" /><figcaption>**Figure 1.** Experimental protocol. SJ, squat jump test; DJ, drop jump test.</figcaption></figure>

## Measurement protocol

To assess muscle soreness, runners had to step on and off a 50 cm high wooden box with the same lower limb, and then with the other limb. Visual analogic scale (0-10) was used to evaluate muscle soreness from the quadriceps, hamstrings and triceps surae muscle groups after each up- and downward movement.

The ultrasound measurement (B-mode US) was performed on the dominant lower-limb in the supine or prone resting position, with the knee in 175-180° extension and the ankle at 15-20° plantar flexion (0° = neutral position with the foot perpendicular to the tibia) to ensure that
no passive tension was occurring in the lower leg muscles. To measure CSAs at the same location from session to session, their locations were precisely marked on the skin using a permanent pen in the PRE session. CSAs of all muscles were taken using the panoramic scan mode (Ahtiainen et al. 2010; Franchi et al. 2020), except for muscles small enough to be captured in a single image. The probe was moved across the thigh and tibia along the marked lines and a single continuous image was taken. A custom-made device was used to assist in keeping the probe perpendicular to the thigh and tibia. After each session, the quality of each panoramic image was carefully checked, and each muscle was individually identified to ensure that all muscles examined were depicted in the recorded images. A minimum of three ultrasound scans was obtained for each of the 10 selected muscles for each session. By using an analysis software (Image J, NIH, Bethesda, Maryland, USA), each CSA was quantified three times.

The functional evaluation included three maximal trials for the SJ and DJ tests and one series of 15 submaximal rebounds on the sledge. The tests and trials were separated by 2 min and 30 s of passive recovery, respectively. The repeatability of the SJ test was assured individually as follows: the hip angle was fixed at 100° by the sledge seat. A white line indicated the height of the forefeet location naturally adopted when rebounding in PRE, and to be respected thereafter. The height ($H_0$) reached by the sledge seat (sled) by simply extending the lower limbs was measured in PRE and taken into consideration to calculate the net jump height. For the SJ test, the sled height at the isometric starting position ($H_{min}$: on the forefeet, with the knee flexed at 100°) was recorded and then pre-set at each session. Participants had to reach the highest jumping height ($H_{max}$). For the DJ test, the dropping height was individually set at 80% of the maximal net jump height reached in SJ in the PRE-session ($H_{SJ80\%} = 0.8(H_{SJ} - H_0)$). To ensure the involvement of the entire triceps surae muscle group, participants were instructed to rebound with maximal effort immediately after ground contact (Komi & Bosco, 1978), with minimal knee flexion while avoiding touching the force plate with the heel (Cappa & Behm, 2013). The submaximal rebound task consisted of 15 successive bounces, between the $H_{SJ80\%}$ and $H_{min}$. If this range was not respected (± 3 cm / marks), additional rebounds were performed.

For all jump tests, the normal component of the reaction forces was recorded by the force plate located perpendicular to the end of the rails, and the sled displacement by an infinite potentiometer wheel attached to the sled seat. For each test, surface electromyography (EMG) activity was recorded from 10 muscles of the dominant lower limb using Cometa system (Wave Plus wireless EMG with PicoEMG sensors, Cometa srl, Bareggio, Italy): for the *quadriceps* (VM, VL and RF), the *hamstrings* (BFl and ST-SM), the *triceps surae* (GM, GL and SOL, *soleus*) as well as for two other shank muscles (TA and PL, *peroneus longus*). Skin preparation and position of the bipolar EMG electrodes followed SENIAM recommendations (Hermens *et al.*, 2000). To ensure proper and identical placements of the EMG electrodes from session to session, their location was checked by US during the PRE-session, marked accurately on the skin with a white pen and on transparent plastic sheets that could be repositioned around the thigh or the shank (as a double check). The sampling frequency of all devices was set at 2 kHz. Due to the sitting position during the jump tests, some EMG signals from the BF and STSM muscles were noised by the friction on the seat and therefore lost, especially at the end of the push-off phase.
## Data processing

The pacing strategy was obtained from the running times provided by the organizers for every 5 km of the race. Individual performance in terms of race time was also expressed in relation to the best runner's time for each sex group. Processing of all experimental data was performed using custom Matlab (Matworks Inc, Novi, USA) and R (R v3.6.3, R Core Team, 2020, R Foundation for Statistical Computing, Vienna, Austria) routines.

The normal force and sled displacement signals were low-pass filtered (cut-off frequency 50 Hz and 15 Hz, respectively) using a 4th order IIR Butterworth zero-phase filter. The instantaneous velocity was obtained by deriving the displacement signal of the sled over time. The output power was computed from the product of the instantaneous velocity and force. For both maximal jump tests, the performance was set as the velocity at take-off (vto) measured from the instantaneous sled velocity. For the SJ, the onset of the push-off was defined as the inflexion point of the force curve, and the end of push-off was set when the normal component of force fell to zero. For the SJ and DJ tests, only the best of the three trials (selected from vto) was retained for statistical analysis. For the DJ and rebound series, the contact time was divided into its braking and push-off phases based on the minimum sled displacement. The reactive strength index (RSI) was calculated as the net rebound height (above H₀) divided by the contact time. The braking phase analysis included the braking distance (hbrake), braking duration (tbrake) and the associated mean values of force, velocity and power (F̄brake, V̄brake and P̄brake, respectively). The lower limb stiffness (Lstiff) was calculated by dividing the reaction force at the end of the braking phase by the corresponding sled seat displacement (Kuitunen et al., 2007). The respective push-off analysis included for all tests: the push-off distance (hpo), duration (tpush-off), and the associated mean values of force, velocity and power (F̄push-off, V̄push-off and P̄push-off, respectively) as well as the maximum power output (Pmax). Force and power were individually normalized by body mass. Finally, the ratio of the integral of the push-off force to the integral of the total force (including braking and push-off phases) was calculated and denoted indexipush.

The raw EMG signals were band-pass filtered with cut-off frequencies between 10 and 500 Hz, full-wave rectified and low-pass filtered using a 75 Hz critically damped filter (Morio et al., 2012). After subtracting the minimum, the mean activity of each recorded muscle was computed from a root mean squared (RMS) analysis performed separately for the different phases of each jump (e.g. preactivation, braking and push-off phases in DJ). The preactivation phase in the DJ and rebound tests was defined as the 100 ms prior to ground contact (Komi et al., 1987).

## Statistics

Linear mixed model from the lmerTest package (Kuznetsova et al., 2017) was used to determine sex differences in the racing time, DOMS and pacing strategy. Shapiro-Wilk test was used to verify data normality based on the residuals.

**To investigate the effects of the recovery session as a function of sex** on the functional and structural variables, we used a Bayesian multilevel modelling approach implemented in the *R package brms* (Carpenter et al., 2017; Bürkner, 2017). As previously described, the Bayesian framework was chosen so as to avoid potential misinterpretations of the frequentist approach (such as p-value) (Nalborczyk et al., 2019). More specifically, Bayesian statistics are well appropriate for multiple comparisons, limited data with large uncertainties. Thus, for each dependent variable of interest, a linear mixed model was performed.
Sex and recovery session were selected as fixed effects. Random effects included the intercept for the participants as well as the random slope by-participants for the recovery session effect. The slope of the recovery session effect as function of the group of experimental day (G1, G2) was also added as a random effect. *The complete model can be summarized as follow:*

$$y \sim \text{recovery session} + \text{sex: recovery session} + (1 + \text{recovery session} | \text{participant}) + (1 + \text{recovery session} | \text{experimental group}), y \text{ being the dependent variable.}$$

Other models were tested by deleting fixed or random effects. *The most predictive model was retained from the Watanabe Akaike Information Criterion (Watanabe, 2010), a generalisation of the Akaike Information Criterion (Akaike, 1974). Default priors from the “get prior” function (brms package) were used for the intercept and population variances without distinction of the group or recovery session. Each model was run with four independent Markov chains of 2000 iterations, with the first 1000 warm-up iterations used only for calibration before being discarded, thus resulting in 4000 post-warm-up samples. Convergence of the algorithm was assessed by the potential scale reduction factor $\hat{\mathcal{R}}$ which had to be less than 1.1 (Gelman & Rubin, 1992). *Posterior distribution* was summarized by the slope estimated ($\beta$) ± estimation error and the estimated 95% credible intervals (CrI).

*The probability of direction (pd) of the slope of the recovery session* was reported for each variable. Mathematically, the probability of direction is described by its posterior distribution strictly positive or negative (whichever is the most probable) (Myers *et al.*, 2021). As recommended by the “bayestestR” package (Makowski *et al.*, 2019a, 2019b), pd was interpreted using the following thresholds: 95% to 97%, 97% to 99%, 99% to 99.9%, and >99.9% for possibly, likely, probably and certainly existing effect, respectively.

*To assess the sex difference in case of significant probability of direction for both men and women*, the region of practical equivalence (ROPE) was computed based on the relative changes as compared to PRE. ROPE was defined as the proportion of the 95% CI of men within the ROPE boundaries (Kruschke, 2018; Makowski *et al.*, 2019a) that corresponded to the range of the estimated slope of women ± 0.1 multiplied by the estimated standard deviation of the entire group. The ROPE represents the probability that the credible effect values are not negligible.

*To take into account the potential familiarisation effect of the functional tests performed on the sledge (Morio *et al.*, 2012), the direction probability was not relative to the null value but relative to slope of the control group. The other parameters reported (such as $\beta$, CI...) were however relative to the null value. For the ultrasound analysis, because no familiarization effect can be expected, the reproducibility of the measurement was quantified by the standard error of measurement (SEM) between the two sessions of the control group (Hopkins *et al.*, 2009). The change in structural variables for a typical muscle was then only reported if the pd was greater than 95% and greater than the SEM. The control group was used to consider the reproducibility of the US operator.*

*To quantify the variance predicted by the model, we used the Bayesian version of the $R^2$ ($R^2_{\text{Bayes}}$). To report the effect size of the comparisons, we computed the Bayesian $\delta t$ as the estimate of the slope divided by the square root of the sum of all variance components (Nalborczyk *et al.*, 2019). $\delta t$ and $R^2_{\text{Bayes}}$ should be interpreted in the same way as Cohen's d and the classical $R^2$, respectively. Thus, *the effect size magnitude (ES)* was assessed using the following thresholds: <0.2, 0.2 to <0.6, 0.6 to <1.2 and 1.2 to <2.0 for trivial, small, moderate and large effects, respectively (Hopkins *et al.*, 2009). For ease of reading, trivial effects have
not been reported for the functional variables. Due to the low R²<sub>Bayes</sub> found for the EMG
variables which could lead to misinterpretation, ANOVA per permutation was performed on
the relative changes from the PRE-session to compare men and women against the control
group.

To analyse the relationships between the different variables, including structural and
functional changes, a Spearman rank correlation matrix was performed by considering the sex
effect. In order to detect specific features of the relationships between structural, functional and
neural changes, self-organizing maps (SOMs) (Kohonen, 1997) were made with the “Kohonen”
package of R software (Kruisselbrink, 2022). The data were first centred and reduced, and then
the change matrix was reduced to a 3 × 3 map. Then, each output node of the Kohonen analysis
was described by reporting variables that presented higher change than the reproducibility
(measured by SEM on the control group). This is particularly important for EMG variables
which show some variability from day to day due to electrode placement.

RESULTS

Weather conditions did not vary along the race, reaching a maximal temperature of 18°C with 40 km/h of wind and 84% of humidity. Men (1:44 ± 0:09 h) ran the 20 km race faster than women (1:58 ± 0:15 h, p<0.01) but, once expressed relative to the fastest runner for each sex, no-intergroup difference was found. The pacing strategy was affected by sex for the uphill portion of the race (from 5 to 10 km): women spent more relative time than men (29.2 ± 0.5% and 28.6 ± 0.5% compared to the first 5 km respectively).

Because the random effects of the multilevel models showed no influence of the
experimental day, between days 1 and 2 (around the expected inflammatory peak) and between
days 3 and 4 (after the inflammatory peak), the functional and structural results will be
presented for D1-2 and D3-4.

Structural recovery pattern

DOMS did not differ between dominant and non-dominant lower-limbs, nor between the ascending and descending movements tested. Thus, all subsequent analyses of this parameter were performed on the mean individual values of the lower limbs in both movements (Figure 2). Independently of sex, DOMS increased at D1-2, being highest in the quadriceps and lowest in the triceps surae.

<figure><img src="image_81.png" /><figcaption>**Figure 2.** Muscle soreness (median, first and third quartiles) reported by men and women using a visual analogic scale (0-10) for the quadriceps (Q), hamstrings (H), and triceps surae (TS) muscle groups over time. Outliers are</figcaption></figure>
indicated by black spots. Significant time effect compared to the PRE-session values are represented by the red symbol *. The number of symbols indicates the statistical level: one for P < 0.05, and three for p < 0.001.

**The ultrasound analysis (Figure 3)** indicated high $R^2_{Bayes}$ values (0.99 ± 0.004), but only trivial effects were found. This was partly due to the high inter-individual variability of the CSA. As detailed in **Table 2**, the experimental group showed increased CSA of TA and GL at D1-2 and of TA, GL and VL at D3-4. Only the SM muscle showed potential sex-specific changes in CSA: both women and men showed an increased CSA of SM at D1-2, but only women showed a trend towards a larger increase at D3-4 (8.1% in ROPE).

Some Spearman rank correlations were found between the CSA changes. At D1-2, positive correlations were found between hamstring muscles: SM with BFl (p < 0.05, rho = 0.36) and BFs (p < 0.05, rho = 0.35). Conversely, negative correlations were found between the CSA changes of the hamstrings and quadriceps muscles: SM with RF (p < 0.01, rho = -0.58), and BFs with VM (p < 0.05, rho = -0.27). Regarding the triceps surae muscle group, GM was positively correlated with BFl (p < 0.05, rho = 0.42), and GL with TA (p < 0.05, rho = 0.4). At D3-4, similarly to D1-2, SM was positively correlated with ST (p < 0.05, rho = 0.41), but negatively with RF (p < 0.05, rho = -0.43) and VL (p < 0.05, rho = -0.41). A negative correlation was also found between BFs and TA (p < 0.05, rho = -0.43).

<figure><img src="image_82.png" /><figcaption>**Figure 3.** Relative changes in cross-sectional area (CSA) over time (% PRE) in men and women for the *semimembranosus* (SM), *gastrocnemius lateralis* (GL), and *tibialis anterior* (TA) muscles. Time effects relative to PRE are reported according to the probability of direction (pd) as: 97% < pd < 99% (**); 99% < pd < 99.9% (***); and pd > 99.9% (****). In case of sex specific changes, they are indicated for each group (in pink for women and blue for men).</figcaption></figure>
<table><caption>**Table 2.** Relative changes (compared to the PRE-session) in muscle cross-sectional area (CSA) changes (estimated slope (β) ± estimation error and 95% credible intervals (CrI)) for the experimental group (A) and as a function of sex (B). The pd represents the probability of direction compared to the zero value. The magnitude of the effect size is reported according to the following thresholds: <0.2, 0.2 to <0.6, 0.6 to <1.2 and 1.2 to <2.0 for trivial, small, moderate and large effects, respectively (Hopkins et al. 2009) and based on the δt values. The standard error of measurement (SEM) is reported for each muscle and represents the reproducibility based on the control group analysis.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Testing time</th><th colspan="4">Experimental group (n=27)</th><th>Control group (n=11)</th></tr><tr><th>pd (%)</th><th>Estimated slope β (Δ%)</th><th>CrI (Δ%)</th><th>Effect size</th><th>SEM (%)</th></tr></thead><tbody><tr><td rowspan="2">GL CSA</td><td>D1-2</td><td>100</td><td>2.6 ± 0.8</td><td>[1.2, 4.2]</td><td>trivial</td><td rowspan="2">1.4</td></tr><tr><td>D3-4</td><td>100</td><td>2.9 ± 0.8</td><td>[1.3, 4.5]</td><td>trivial</td></tr><tr><td rowspan="2">TA CSA</td><td>D1-2</td><td>100</td><td>4.5 ± 0.9</td><td>[2.8, 6.2]</td><td>small</td><td rowspan="2">1.4</td></tr><tr><td>D3-4</td><td>100</td><td>5.5 ± 0.9</td><td>[3.6, 7.4]</td><td>small</td></tr><tr><td>VL CSA</td><td>D3-4</td><td>99.6</td><td>2.7 ± 1.0</td><td>[0.9, 4.6]</td><td>trivial</td><td>2.2</td></tr><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="4">Women runners (n=13)</th><th rowspan="2"></th></tr><tr><th>pd (%)</th><th>Estimated slope β (Δ%)</th><th>CrI (Δ%)</th><th>Effect size</th></tr><tr><td rowspan="2">SM CSA</td><td>D1-2</td><td>97.2</td><td>6.3 ± 3.5</td><td>[-0.1, 13.7]</td><td>trivial</td><td rowspan="2">1.8</td></tr><tr><td>D3-4</td><td>99.7</td><td>10.5 ± 3.9</td><td>[3.3, 18.6]</td><td>small</td></tr><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="4">Men runners (n=14)</th><th rowspan="2"></th></tr><tr><th>pd (%)</th><th>Estimated slope β (Δ%)</th><th>CrI (Δ%)</th><th>Effect size</th></tr><tr><td rowspan="2">SM CSA</td><td>D1-2</td><td>99.6</td><td>5.4 ± 2.1</td><td>[1.4, 9.9]</td><td>trivial</td><td rowspan="2">1.8</td></tr><tr><td>D3-4</td><td>98.3</td><td>4.8 ± 2.3</td><td>[0.4, 9.4]</td><td>trivial</td></tr></tbody></table>

CSA, cross sectional area; SEM, standard error measurement; GL, *gastrocnemius lateralis*; TA, *tibialis anterior*; VL, *vastus lateralis*; SM, *semimembranosus*.

## Functional recovery pattern

In the SJ test, relative functional changes were found at both D1-2 and D3-4, but independently of sex only (Figure 4 and table 3). For the experimental group, performance (vto), tpush-off and hpo were certainly decreased by the race at both D1-2 and D3-4. At D1-2, relative changes in vto were positively correlated with those in hpo (p < 0.01, rho = 0.56) and at D3-4, with those in hpo and tpo (p < 0.05, rho = 0.51 and p < 0.05, rho = 0.44, respectively).
<figure><img src="image_83.png" /><figcaption>**Figure 4.** Relative functional changes in the squat jump test over time for the experimental group (D1-2 and D3-4 vs. PRE) and for the control group (D2 vs. D0). R²<sub>Bayes</sub> are reported for all variables. δt (representing the effect size) are reported for the changes with a probability of direction superior to 95%. Trivial effects are not reported. vto, velocity at take-off; hpo, push-off distance; t<sub>push-off</sub>, push-off time; V&#773;<sub>push-off</sub>, mean push-off velocity; F&#773;<sub>push-off</sub>, mean push-off force; P&#773;<sub>push-off</sub>, mean push-off power; P<sub>max</sub>, maximal power output.</figcaption></figure>
<table><caption>**Table 3.** Relative functional changes (estimated slope (β) ± estimation error and 95% credible intervals (CrI)) for the experimental group as a function of the PRE-session. pd represents the probability of direction compared to the slope of the control group. The magnitude of the effect size is reported according to the following thresholds: <0.2, 0.2 to <0.6, 0.6 to <1.2 and 1.2 to <2.0 for trivial, small, moderate and large effects, respectively (Hopkins et al. 2009) and based on the δt values.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Testing time</th><th rowspan="2">pd (%)</th><th colspan="2">Experimental group (n=27)</th><th colspan="2">Control group (n=11)</th></tr><tr><th>Estimated slope β (D %)</th><th>CrI (D%)</th><th>Effect size</th><th>Estimated slope β (D %)</th></tr></thead><tbody><tr><td rowspan="4">SQUAT JUMP</td><td>D1-2</td><td>100</td><td>-8.2 ± 1.5</td><td>[-11.1, -5.2]</td><td>small</td><td rowspan="2">0.3 ± 2.3</td></tr><tr><td>vto</td><td>D3-4</td><td>100</td><td>-5.5 ± 1.5</td><td>[-8.5, -2.7]</td><td>small</td></tr><tr><td>D1-2</td><td>100</td><td>-8.2 ± 1.6</td><td>[-11.4, -5]</td><td>moderate</td><td rowspan="2">6.6 ± 3.9</td></tr><tr><td>hpo</td><td>D3-4</td><td>100</td><td>-6.0 ± 1.5</td><td>[-9, -3]</td><td>moderate</td></tr><tr><td rowspan="2">tpush-off</td><td>D1-2</td><td>100</td><td>-3.5 ± 2.3</td><td>[-7.9, 1.2]</td><td>moderate</td><td rowspan="2">6.7 ± 3.7</td></tr><tr><td>D3-4</td><td>100</td><td>-4.6 ± 2.1</td><td>[-8.7, -0.4]</td><td>moderate</td></tr><tr><td rowspan="4">DROP JUMP</td><td>D1-2</td><td>100</td><td>-3.0 ± 2.7</td><td>[-8.3, 2.5]</td><td>small</td><td rowspan="2">6.8 ± 4.7</td></tr><tr><td>hbrake</td><td>D3-4</td><td>100</td><td>-3.7 ± 2.6</td><td>[-8.8, 1.7]</td><td>moderate</td></tr><tr><td>D1-2</td><td>97.5</td><td>-0.8 ± 2.9</td><td>[-6.5, 4.9]</td><td>small</td><td rowspan="2">5.2 ± 3.8</td></tr><tr><td>tbrake</td><td>D3-4</td><td>99.4</td><td>-2.0 ± 2.7</td><td>[-7.4, 3.4]</td><td>small</td></tr><tr><td rowspan="2">Fbrake</td><td>D1-2</td><td>99.8</td><td>2.9 ± 2.5</td><td>[-1.9, 7.9]</td><td>small</td><td rowspan="2">-4.7 ± 4.0</td></tr><tr><td>D3-4</td><td>99.5</td><td>2.4 ± 2.8</td><td>[-3, 7.9]</td><td>small</td></tr><tr><td rowspan="2">Pbrake</td><td>D1-2</td><td>99.4</td><td>4.0 ± 4.0</td><td>[-3.6, 1.2]</td><td>small</td><td rowspan="2">-5.6 ± 5.9</td></tr><tr><td>D3-4</td><td>97.4</td><td>3.2 ± 4.6</td><td>[-5.6, 1.3]</td><td>small</td></tr><tr><td rowspan="2">Legstiffness</td><td>D1-2</td><td>97.7</td><td>6.9 ± 4.6</td><td>[-2, 16]</td><td>small</td><td rowspan="2">-1.7 ± 5.3</td></tr><tr><td>D1-2</td><td>100</td><td>-5.4 ± 2.0</td><td>[-9.3, -1.3]</td><td>moderate</td></tr><tr><td rowspan="2">tpush-off</td><td>D1-2</td><td>100</td><td>-6.0 ± 1.9</td><td>[-9.7, -2.3]</td><td>moderate</td><td rowspan="2">4.0 ± 2.3</td></tr><tr><td>D3-4</td><td>99.8</td><td>-2.4 ± 1.1</td><td>[-4.6, -0.3]</td><td>moderate</td></tr><tr><td rowspan="2">indexipush-off</td><td>D1-2</td><td>100</td><td>-2.0 ± 0.8</td><td>[-3.6, -0.4]</td><td>moderate</td><td rowspan="2">1.1 ± 1.9</td></tr><tr><td>D3-4</td><td>100</td><td>-2.0 ± 0.8</td><td>[-3.6, -0.4]</td><td>moderate</td></tr><tr><td rowspan="6">REBOUNDS</td><td>D1-2</td><td>99.1</td><td>-5.9 ± 1.3</td><td>[-8.4, -3.3]</td><td>small</td><td>-2.6 ± 3.1</td></tr><tr><td>D1-2</td><td>98.9</td><td>-4.2 ± 1.5</td><td>[-7.2, 1.2]</td><td>small</td><td>-7.7 ± 6.4</td></tr><tr><td>D1-2</td><td>100</td><td>-2.0 ± 0.7</td><td>[-3.4, -0.5]</td><td>small</td><td>2.8 ± 2.4</td></tr><tr><td>D1-2</td><td>99.9</td><td>1.2 ± 2.4</td><td>[-3.4, 6.2]</td><td>small</td><td>10.2 ± 7.9</td></tr><tr><td>D1-2</td><td>99.5</td><td>-6.8 ± 1.2</td><td>[-9.3, -4.5]</td><td>small</td><td>-3.8 ± 3.8</td></tr><tr><td>D1-2</td><td>99.5</td><td>-5.5 ± 1.3</td><td>[-8.0, -3.0]</td><td>small</td><td>-9.0 ± 6.4</td></tr><tr><td>D1-2</td><td>99.3</td><td>-1.3 ± 1.2</td><td>[-3.7, 1.1]</td><td>small</td><td>2.8 ± 1.8</td></tr><tr><td>D1-2</td><td>96.1</td><td>3.2 ± 2.9</td><td>[-2.5, 9.1]</td><td>small</td><td>8.1 ± 5.2</td></tr></tbody></table>

vto, velocity at take-off; hbrake, braking distance; tpush-off, push-off time; tbrake, braking time; Fbrake, mean braking force; Pbrake, mean braking power; Lstiff, lower limb stiffness; indexipush, integral of the push-off force/total force; RSI, reactive strength index; Vpush-off, mean push-off velocity; Ppush-off, mean push-off power; D1-2, on day 1 or 2; D3-4, on days 3 or 4.
**The DJ test** analysis revealed functional changes at D1-2 and D3-4 for the experimental group, and sex-specific fatigue effects (Figure 5). The decreases in SJ and DJ performances were not correlated with each other.

As detailed in **Table 3**, compared to the control group, the experimental group showed during the braking phase larger decreases in $t_{\text{brake}}$ and $h_{\text{brake}}$, and larger increases in $\bar{F}_{\text{brake}}$, $\bar{P}_{\text{brake}}$ and $L_{\text{stiff}}$ (at D1-2 only for the latter parameter). During the push-off phase, $t_{\text{push-off}}$ certainly decreased at D1-2 and D3-4. Consequently, the index$_{ipush}$ decreased at both D1-2 and D3-4.

As detailed in **Table 4**, DJ performance (vto) certainly decreased for men at both D1-2 and D3-4, and probably decreased for women but only at D1-2. Based on the ROPE parameter, the sex difference in vto can be considered significant and non-negligible at both D1-2 (2.1%) and D3-4 (0.9%). hpo certainly decreased at D1-2 and D3-4 for both sexes, but to a larger extent in men at D1-2 (0.4% in ROPE). Sex-dependent changes were also found at both D1-2 and D3-4 during the push-off phase, as only men decreased in mean velocity and increased in mean force.

In men, at D1-2 the change in vto was positively correlated with those of hpo (rho = 0.72, p < 0.05). At D3-4, it was positively correlated with those of hpo (rho = 0.75, p < 0.001), RSI (rho = 0.38, p < 0.05), $\bar{V}_{\text{push-off}}$ (rho = 0.76, p < 0.05) and $\bar{P}_{\text{push-off}}$ (rho = 0.72, p < 0.05). In women, the change in vto at D1-2 was positively correlated with those of $\bar{V}_{\text{brake}}$ (rho = 0.55, p < 0.05), $\bar{V}_{\text{push-off}}$ (rho = 0.72, p < 0.01), hpo (rho = 0.72, p < 0.01) and RSI (rho = 0.7, p < 0.01).

<figure><img src="image_84.png" /><figcaption>**Figure 5.** Relative functional changes in the maximal drop jump test over time for the experimental group (D1-2 and D3-4 vs. PRE) and for the control group (D2 vs. D0). $R^2_{\text{Bayes}}$ are reported for all variables. $\delta t$ (representing the effect size) is reported for the changes with a probability of direction superior to 95%. Trivial effects are not reported. vto, velocity at take-off; hpo, push-off distance; $\bar{F}_{\text{push-off}}$, mean push-off force; $\bar{V}_{\text{push-off}}$, mean push-off velocity; $\bar{P}_{\text{push-off}}$, mean push-off power; $t_{\text{push-off}}$, push-off time; $h_{\text{brake}}$, braking distance; $\bar{F}_{\text{brake}}$, mean braking force; $\bar{V}_{\text{brake}}$, mean braking velocity; $t_{\text{brake}}$, braking time; $\bar{P}_{\text{brake}}$, mean braking power; $L_{\text{stiff}}$, lower limb stiffness; $\bar{P}_{\text{push-off}}$, mean push-off power; index$_{ipush}$, integral of the push-off force/total force; RSI, reactive strength index.</figcaption></figure>
<table><caption>**Table 4.** Sex specific functional changes (estimated slope (β) ± estimation error and 95% credible intervals (CrI)) as a function of the PRE-session. pd represents the probability of direction compared to the slope of the control group. The magnitude of the effect size (ES) is reported according to the following thresholds: <0.2, 0.2 to <0.6, 0.6 to <1.2 and 1.2 to <2.0 for trivial, small, moderate and large effects, respectively (Hopkins et al. 2009) and based on the δt values. The region of practical equivalence (ROPE) was calculated on the relative changes as compared to PRE. It represents the probability that the credible effect values are not negligible.</caption><thead><tr><th colspan="8">Experimental group (n=27)</th><th rowspan="3">Ctrl group</th></tr><tr><th colspan="4">Women runners (n=13)</th><th colspan="4">Men runners (n=14)</th></tr><tr><th>DROP JUMP</th><th>Testing time</th><th>pd (%)</th><th>Estimated slope β (Δ %)</th><th>Effect size</th><th>pd (%)</th><th>Estimated slope β (Δ %)</th><th>Effect size</th><th>ROPE (%)</th><th>β (Δ %)</th></tr></thead><tbody><tr><td rowspan="2">vto</td><td>D1-2</td><td>99.4</td><td>-4.6 ± 1.1</td><td>-6.7, -2.5</td><td>small</td><td>100</td><td>-6.1 ± 1.1</td><td>-8.2, -4</td><td>moderate</td><td>2.1</td><td rowspan="2">1.8 ± 1.8</td></tr><tr><td>D3-4</td><td>&lt; 95</td><td></td><td></td><td></td><td>100</td><td>-6.2 ± 1.4</td><td>-9, -3.4</td><td>moderate</td><td>0.9</td></tr><tr><td rowspan="2">hpo</td><td>D1-2</td><td>99.6</td><td>-3.5 ± 3.4</td><td>-10, -3.4</td><td>moderate</td><td>100</td><td>-10.4 ± 2.7</td><td>-15.7, -5.1</td><td>large</td><td>0.4</td><td rowspan="2">5.8 ± 2.9</td></tr><tr><td>D3-4</td><td>100</td><td>-5.5 ± 3.3</td><td>-11.6, 0.9</td><td>moderate</td><td>100</td><td>-10.2 ± 2.6</td><td>-15.2, -4.8</td><td>large</td><td>10.5</td></tr><tr><td rowspan="2">F<sub>push-off</sub></td><td>D1-2</td><td>&lt; 95</td><td></td><td></td><td></td><td>99.4</td><td>2.2 ± 2.0</td><td>-1.5, 6.3</td><td>small</td><td></td><td rowspan="2">-2.7 ± 2.1</td></tr><tr><td>D3-4</td><td>&lt; 95</td><td></td><td></td><td></td><td>97.9</td><td>1.7 ± 2.1</td><td>-2.4, 5.8</td><td>small</td><td></td></tr><tr><td rowspan="2">V<sub>push-off</sub></td><td>D1-2</td><td>&lt; 95</td><td></td><td></td><td></td><td>100</td><td>-4.0 ± 1.5</td><td>-6.9, -1.2</td><td>small</td><td></td><td rowspan="2">1.7 ± 1.5</td></tr><tr><td>D3-4</td><td>&lt; 95</td><td></td><td></td><td></td><td>99.9</td><td>-3.7 ± 1.6</td><td>-6.8, -0.5</td><td>small</td><td></td></tr></tbody></table>

vto, velocity at take-off; hpo, push-off distance; $\bar{F}_{\text{push-off}}$, mean push-off force; $\bar{V}_{\text{push-off}}$, mean push-off velocity.

**The submaximal rebound test** showed no significant change in vto, meaning that the pre-set rebound height could be reached despite of fatigue. The other parameters showed only small changes at D1-2 and no sex-specific fatigue effects (Figure 6). As detailed in Table 3, compared to the control group, the experimental group showed a larger decrease in $h_{\text{brake}}$ and $\bar{V}_{\text{brake}}$ and a smaller decrease in $t_{\text{brake}}$. RSI was found as certainly increased at D1-2, but this was due to the decrease observed for the control group while the experimental group did not vary compared to PRE. During the push-off phase, the experimental group showed larger decreases in hpo as well as a smaller decrease in $t_{\text{push-off}}$ and a smaller increase in $\bar{F}_{\text{push-off}}$ than the control group. Both sexes presented a larger decrease in $\bar{V}_{\text{push-off}}$ at D1-2 than the control group, and only for men at D3-4.
<figure><img src="image_85.png" /><figcaption>**Figure 6.** Relative functional changes in the submaximal rebound test over time for the entire experimental group (D1-2 and D3-4 vs. PRE) and for the control group (D2 vs. D0). δt (representing the effect size) is reported for the changes with a probability of direction superior to 95%. Trivial effects are not reported. R²<sub>Bayes</sub> are reported for all variables. vto, velocity at take-off; hpo, push-off distance; F̄<sub>push-off</sub>, mean push-off force; V̄<sub>push-off</sub>, mean push-off velocity; P̄<sub>push-off</sub>, mean push-off power; t<sub>push-off</sub>, push-off time; h<sub>brake</sub>, braking distance; F̄<sub>brake</sub>, mean braking force; V̄<sub>brake</sub>, mean braking velocity; t<sub>brake</sub>, braking time; P̄<sub>brake</sub>, mean braking power; L<sub>stiff</sub>, lower limb stiffness; P̄<sub>push-off</sub>, mean push-off power; index<sub>ipush</sub>, integral of the push-off force/total force; RSI, reactive strength index.</figcaption></figure>

**The analysis of the associated neuromuscular changes,** using permutation ANOVA, revealed no group mean changes in muscle activities in the SJ and rebound tests but a high inter-individual variability was observed. Similarly, there was no changes in DJ, with the exception of GM activity, which increased in men during the braking phase at D3-4 (p < 0.05, 46.7 ± 81.9%) compared to the control group.

**Relationships between functional, structural and muscle activity changes**

The analysis using the matrix of Spearman rank correlations revealed significant correlations between functional and structural changes. The Kohonen's map analysis revealed relationships between the structuro-functional changes and the associated neural adjustments, leading to subgroups of participants (nodes) with similarities (cluster plots) and dissimilarities (distance). Most clusters included both men and women. To illustrate the richness of the neural adjustments revealed by the Kohonen's map analysis, two subgroups (nodes) showing opposite trends of changes will be shown for each jump test.

**At D1-2,** sex specific correlations were only found in the DJ test (graphs framed in green in **Figure 7**): In women, changes in GM and GL CSAs were positively correlated with those of F̄<sub>brake</sub>. In men, the changes in GM CSA were positively and negatively correlated with those of t<sub>brake</sub> and L<sub>stiff</sub>, respectively.

Independently of sex, significant correlations were found between some structural and functional changes in the SJ test (graphs framed in blue in **Figure 7**), but only one was found in the DJ test. The change in VM CSA was negatively correlated with changes in Pmax in SJ
and $\bar{F}_{\text{push-off}}$ in DJ. In contrast, the changes in CSA of the GL and ST muscles were positively correlated with the change in SJ performance (vto). The change in BFs CSA was also positively correlated with those of $\bar{V}_{\text{push-off}}$, $\bar{F}_{\text{push-off}}$ and $\bar{P}_{\text{push-off}}$, and negatively with that of $t_{\text{push-off}}$.

<figure><img src="image_86.png" /><figcaption>**Figure 7.** Significant correlations between relative changes at D1-2 in structural (CSA) and functional variables in squat jump (SJ, blue edges) and drop jump (DJ, green edges) tests. Sex specific correlations are represented in pink and blue for women and men, respectively.</figcaption></figure>

In the SJ test, based on the Kohonen's analysis (Figure 8A), Node 7 included 4 women and 2 men who showed increased CSA of hamstrings and gastrocnemii muscles, but an overall increase in lower limb muscle activities allowing the performance (vto) to be preserved. In contrast, node 8 included 2 women and 2 men who showed increased hamstring CSA, but large reductions in the hamstrings and triceps surae activity as well as within the quadriceps resulting in a decreased performance.

In the DJ test, the neural adjustments were found to be muscle and SSC phase dependent. As illustrated in Figure 8B, Node 8 (2 women and 4 men) and Node 3 (2 women and 1 man) showed similar decreases in performance (vto), but different CSA and EMG changes. Node 8 showed increased CSA of hamstrings, gastrocnemii and TA muscles, but an increase in EMG activities of some inflamed muscle and of VL during all DJ phases. In contrast, Node 3 showed increases in VM, hamstrings and TA CSA, but an overall decrease in lower limb muscle activities except for GM.
<figure><img src="image_87.png" /></figure>

<figure><img src="image_88.png" /><figcaption>**Figure 8.** Schematic Kohonen's map analysis for the relative structural, neural and functional changes at D1-2 in the squat-jump (A) and drop jump (B) tests. The mapping plot shows, for each of the 9 nodes, the number and sex of the participants (women in pink, men in blue). The distance plot indicates the distance (dissimilarity) between nodes and the cluster plot the subgroups with similarities. For each jump test, two nodes with opposite trends are detailed and schematically represented by graphs showing (from left to right): The muscles with an increased CSA (in red), the associated muscle activity changes (in green for an increase; in orange for a decrease), and the functional and performance changes (in black above the graphs of the sledge).</figcaption></figure>

**At D3-4 (Figure 9),** sex specific correlations were observed at D3-4. In SJ (graphs framed in blue), women showed negative correlations between the changes of ST CSA and $\bar{P}_{\text{push-off}}$, and between those of GL CSA and $\bar{F}_{\text{push-off}}$. In the DJ test (graphs framed in green), women presented a negative correlation between the changes in VM CSA and those of $\bar{V}_{\text{brake}}$.
Men showed a negative correlation between the changes of TA CSA and in vto, but a positive correlation between the changes of BFs CSA and $\bar{V}_{push-off}$.

Independently of sex, in the SJ test, the change in GL CSA was still positively correlated with the change in performance (vto). Other relationships differed by muscle: Changes in $t_{push-off}$ were negatively with those in SM CSA, but positively correlated with those in GL CSA. The changes in $\bar{V}_{push-off}$ were negatively with those in GL CSA, but positively correlated with those in BFs CSA. Only the change in SM CSA was positively correlated with the change in $\bar{F}_{push-off}$. In the DJ test (graphs framed in green), the changes in RSI, $\bar{P}_{push-off}$, and $\bar{V}_{push-off}$ were negatively correlated with those in VM CSA.

<figure><img src="image_89.png" /><figcaption>**Figure 9.** Significant correlations between relative changes at D1-2 in structural (CSA) and functional variables in squat jump (SJ, blue edges) and drop jump (DJ, green edges) tests. Sex specific correlations are represented in pink and blue for women and men, respectively.</figcaption></figure>

The results of the Kohonen's map analysis, showing the dissimilarities (distance) and similarities (cluster plots) between nodes of participants, are presented in **Figure 10**. Please, note that these clusters do not include the same participants than the clusters at D1-2.

For the SJ test, **Figure 10A** shows two subgroups (nodes 7 and 1) with similar structural increases in hamstrings and TA CSA, but different patterns of neural and functional changes. Nodes 7 (4 women and 3 men) showed an overall decrease in hamstring activities (STSM), but an increase in VM and TA muscles. $\bar{F}_{push-off}$ and $\bar{V}_{push-off}$ increased while vto and $t_{push-off}$ were slightly reduced leading to a limited decrease in performance. Node 1 (1 woman and 1 man) showed a larger decrease in performance despite increased EMG activities in many but not all muscles.

In the drop-jump test, the neural adjustments were found to be muscle and SSC phase dependent (**Figure 10B**). Node 2 (2 men and 1 woman) showed increases in CSA of the SM, GM and TA muscles, but a limited decrease in performance. Among others, the observed increases in the gastrocnemii and VL activities during the preactivation and braking phases resulted in an increased $L_{stiff}$ and decreased $h_{brake}$ and $t_{brake}$. During the push-off phase, $\bar{P}_{push-off}$, $\bar{V}_{push-off}$ decreased despite the increased triceps surae activity, so that hpo and $t_{push-off}$ decreased also. Conversely, node 9 (2 men and 2 women) showed an increase in performance despite slight CSA increases in several muscles. The EMG analysis revealed an overall increase in preactivation. The braking phase showed varied EMG changes including an increased activity of the inflamed RF muscle. RF activity also increased also in push-off phase with the PL muscle.
<figure><img src="image_90.png" /><figcaption>B.</figcaption></figure>

<figure><img src="image_91.png" /></figure>

**Figure 10.** Schematic Kohonen's map analysis for the relative structural, neural and functional changes at D3-4 in the squat-jump (A) and drop jump (B) tests. The mapping plot shows, for each of the 9 nodes, the number and sex of the participants (women in pink, men in blue). The distance plot indicates the distance (dissimilarity) between nodes and the cluster plot the subgroups with similarities. For each jump test, two nodes with opposite trends are detailed and schematically represented by graphs showing (from left to right): The muscles with an increased CSA (in red), the associated muscle activity changes (in green for an increase; in orange for a decrease), and the functional and performance changes (in black above the graphs of the sledge).

DISCUSSION

The present study was designed to explore the relationships between muscle
inflammation and functional decrements after a 20 km graded endurance run, while focusing
on sex specificities. Based on the expected protective effects of oestrogen (Hunter, 2016), we
first hypothesised that muscle inflammation would be lower in women, with the exception of