Durant les deux dernières décénnies, les études se sont notamment intéressées aux parties du système nerveux central impliquées dans les synergies musculaires. Celles-ci pourraient être structurées au niveau du bulbe rachidien et de la moelle épinière (Bizzi et al., 2008 ; Bizzi et Cheung, 2013 ; Hart et Giszter, 2004 ; Roh et al., 2011a ; Tresch et al., 2002). En ce sens, Roh et al. (2011) ont montré que des comportements naturels, tels que marcher, sauter, nager ou encore donner des coups de pied, persistaient chez l'animal après des transsections réalisées aux niveaux du mésencéphale ou de la médulla rostrale ventromédiale. En revanche, tous les comportements ne pouvaient plus être produits correctement lorsque la transsection était plus caudale (au niveau de la jonction médullaire du tronc cérébral). Chez des patients atteints de lésions corticales suite à un accident vasculaire cérébral unilatéral, aucune différence n'a été observée entre les bras affectés et non affectés au regard des contributions musculaires des synergies lors de mouvements volontaires du membre supérieur, alors que la performance (quantifiée par l'amplitude et la vitesse du bras) était bien affectée (Cheung et al., 2009). Ainsi, il a été suggéré que des commandes nerveuses descendantes modulent l'activation des synergies musculaires pour l'exécution correcte de différents comportements.

Dans le cadre de la locomotion, Gizzi et al. (2011) ont mis en avant une importante similarité de la temporalité des synergies entre des participants sains comparés à des patients ayant eu un accident vasculaire cérébral alors que les contributions musculaires associées à chaque synergie temporelle étaient remaniées. Ces résultats vont dans le même sens que la réduction de la dimensionalité (c'est-à-dire des contributions musculaires fusionnées) rapportée pour certains patients ayant eu un accident vasculaire cérébral (Clark et al. 2010). La partie temporelle de la synergie est suggérée comme étant générée par un processus central vers les motoneurones, via un réseau prémoteur distribuant les contributions aux motoneurones en fonction de la tâche, des feedbacks issus des afférences et de l'information de la commande descendante (Rossignol et al., 2006). Cette hypothèse est accréditée par des études ayant montré que l'instant d'activation des synergies, représenté par les primitives motrices, était bien synchronisé avec les évènements du cycle locomoteur chez des populations saines (e.g., préactivation). En revanche, la synchronisation était altérée chez des populations présentant des voies cortico-spinales immatures (Dominici et al., 2011) ou lésées (Janshen et al., 2021). Ces commandes descendantes pourraient provenir majoritairement du cortex moteur primaire et plus précisément du tractus pyramidal. Bien que la locomotion résulte de l'action de plusieurs structures centrales, il est connu qu'une lésion du tractus pyramidal conduit à l'incapacité de marcher sur des surfaces instables (Liddell et Phillips, 1944). En outre, plusieurs études ont montré une augmentation de la fréquence de décharge et de la phase d'activité du tractus
pyramidal en condition de marche avec obstacles (Amos et al., 1990 ; Beloozerova et Sirota, 1993 ; Drew, 1988, 1993 ; Drew et al., 1996, 2002 ; Marple-Horvat et al., 1993 ; Widajewicz et al., 1994). Il a notamment été rapporté que les neurones du tractus pyramidal sont activés séquentiellement lors de la locomotion ou de mouvements d'atteinte, tout comme les groupes de muscles synergistes (Drew et al., 2008 ; Morrow et al., 2009 ; Yakovenko et al., 2011). Ainsi, il est suggéré qu'une sous-population de neurones du tractus pyramidal exerce une action de régulation du timing, de l'intensité et de la durée de petits groupes de synergies musculaires qui agissent à des instants temporels différents pour une tâche donnée (Drew et al., 2008a). En revanche, dû à l'instant d'activation du tractus pyramidal (juste avant ou pendant une modification de la locomotion, il a été suggéré que ceux-ci agissent davantage au cours de l'exécution du mouvement plutôt que dans la planification de celui-ci, un rôle qui serait plutôt décernée au cortex pariétal postérieur (Drew et al., 2008a, 2008b). Drew et al. (2008b) ont donc suggéré le modèle suivant (Figure 43).

<figure><img src="image_45.png" /><figcaption>**Figure 43.** Schématisation de la conceptualisation des synergies musculaires. Une sous-population de neurones du tractus pyramidal agit via un réseau d'interneurones. Ces interneurones sont organisés sous une forme de modules (de la synergie 1 (S1) à « n » synergies) qui activent les muscles agissant au niveau de diverses articulations (Adapté de Drew et al., 2008b).</figcaption></figure>

Enfin, bien que des avancées majeures aient été faites concernant la façon dont le cortex, le tronc cérébral et la moelle épinière coopèrent pour exécuter un mouvement en combinant des synergies musculaires, certains auteurs soulignent la nécessité d'apporter des clarifications additionnelles (Roh et al., 2011 ; Rana et al., 2015).
## 4.2. Méthodes d'extraction des synergies

Au cours des 20 dernières années, plusieurs modèles de synergies musculaires ont été proposés. On distingue *le modèle de modularité espace-temps* qui présente une combinaison linéaire de synergies spatiales et temporelles distinctes mais simultanées, combinées par des coefficients scalaires (Delis et al., 2018), *le modèle qui varie au cours du temps* où l'on obtient une contribution musculaire à chaque instant pour chaque synergie (d'Avella et al., 2003), et *le modèle spatial invariant au cours du temps* (Cheung et al., 2020 ; Clark et al., 2010). Ce dernier, qui est le plus utilisé aujourd'hui. Il permet d'extraire des activations temporelles dénommées « primitives motrices » qui sont associées à des contributions musculaires (dénommées « modules moteurs »).

**Plusieurs algorithmes ont été proposés pour extraire les synergies musculaires.** Le point commun de ces modèles est qu'ils peuvent tous s'écrire sous la forme :

$$ \bar{x} = \sum_{i=1}^{N} c_i \bar{w}_i + \vec{\epsilon} \quad (1) $$

Où $\bar{x}$ est un vecteur qui contient les activités temporelles des muscles enregistrés, $N$ est le nombre de synergies, $c_i$ représente les primitives motrices (les coefficients temporels), $\bar{w}_i$ représente les modules moteurs (vecteurs indépendants du temps) représentant les contributions musculaires de chaque muscle les uns par rapport aux autres. Enfin, $\vec{\epsilon}$ représente les résidus du modèle (c'est-à-dire l'erreur).

Parmi les algorithmes les plus utilisés, on retrouve l'analyse en composantes principales (PCA) (Ranganathan et Krishnan, 2012), l'analyse factorielle (FA) (Kieliba et al., 2018), l'analyse en composantes indépendantes (ICA) (Rasool et al., 2016) et enfin la factorisation matricielle non-négative (NMF ou NNMF) (Lee et Seung, 2000). La méthode NMF est la plus utilisée aujourd'hui, notamment en raison de son caractère intuitif puisqu'elle n'inclut que des valeurs positives tout comme l'activation musculaire qui est indéniablement non-négative (Rabbi et al., 2020). Bien que ces algorithmes soient basés sur des hypothèses différentes (Attias, 1999 ; Basilevsky, 2009 ; Dayan et Abbott, 2005 ; Hyvärinen et Oja, 2000 ; Roweis et Ghahramani, 1999), les synergies extraites ne sont pas dépendantes de l'algorithme utilisé mais bien représentatives des aspects fondamentaux de l'organisation des patrons neuromusculaires qui sous-tendent les comportements (Tresch et al., 2006). D'après une récente étude, la NMF pourrait même surpasser les performances des autres algorithmes (Zhao et al., 2022). De plus,
la NMF pourrait davantage révéler les différentes parties d'un objet tandis que les autres algorithmes, tels que la PCA, aurait un résultat final plus holistique (Lee et Seung, 1999). Illustrant ce propos, lorsque les algorithmes sont utilisés sur une image représentant un visage d'humain, la NMF extrait les différentes parties du visage, telles que le nez, les yeux ou encore les cils..., tandis que les composantes de la PCA représentent davantage l'ensemble du visage, lequel se modifie de plus en plus au fur et à mesure que l'on avance dans les dimensions (Lee et Seung, 1999).

L'algorithme de la NMF peut se résumer sous la forme suivante :

$$V(t) = V_R(t) = WH(t) \qquad (2)$$

Où $V_R$ représente la matrice reconstruite, qui approxime la matrice V contenant les données d'EMG de chaque muscle. W est une matrice de dimensions $m \times N$ représentant les modules moteurs tandis que H est de dimensions $N \times n$ représentant les primitives motrices, où m est le nombre de muscles enregistrés, n le nombre de points temporels et N le rang de la factorisation (i.e. le nombre de synergies extraites).

En général le nombre de synergies est choisi selon la règle : $(n + m)N < nm$ (Lee et Seung, 1999). Plusieurs règles ont été proposées pour la factorisation matricielle à partir de la méthode NMF. L'approche classique, proposée par Lee and Seung, (1999), est basée sur la maximisation de la vraisemblance gaussienne de la reconstruction du signal EMG original. On distingue ainsi les équations suivantes :

$$H_{i+1} = H_i \frac{W_i^T V}{W_i^T W_i H_i} \qquad (3)$$

$$W_{i+1} = W_i \frac{V(H_{i+1})^T}{W_i H_{i+1} (H_{i+1})^T} \qquad (4)$$

Où T représente la transposée de la matrice. Les deux équations fonctionnent par itérations et l'itération départ de H et W est réalisée avec des valeurs complètement aléatoires. Le numéro de l'itération est matérialisé par la lettre i. Une factorisation typique d'activités EMG est représentée (Figure 44). V est approximé par la combinaison linéaire de W (les modules moteurs) et H (les primitives motrices). Lorsque H et W sont multipliés, il est possible de reconstruire le jeu de données original. Le niveau de reconstruction est généralement évalué par le coefficient de détermination noté R².
<figure><img src="image_46.png" /><figcaption>**Figure 44.** Représentation graphique des EMG factorisées en utilisant l’algorithme de la factorisation matricielle non-négative (FNM). Dans le cas présent, les sept activités musculaires initiales ($m_1$ à $m_7$) sont factorisées en 4 synergies. Les modules moteurs (indiqués par la lettre W) sont les coefficients indépendants du temps. Les primitives motrices (indiquées par la lettre H) sont les coefficients dépendants du temps. La multiplication de W et H permet la reconstruction (approximée) de la matrice initiale V (Adapté de Santuz, 2018).</figcaption></figure>

En complément de l'approche linéaire de la NMF, des métriques non-linéaires ont été utilisées ces dernières années afin d'extraire des caractéristiques que les méthodes conventionnelles ne permettraient pas de révéler (Santuz et Akay, 2020). Parmi ces méthodes, on retrouve le concept des fractales qui part du principe que la nature présente des modèles similaires à différentes échelles. **L'analyse fractales a permis, dans le domaine du contrôle moteur, de mieux appréhender la complexité du mouvement.**

## 4.3. Les fractales

En 1977, alors que jusque-là les mathématiques se limitaient à des formes lisses, le mathématicien polono-franco-américain Benoît Mandelbrot publie un livre suggérant que la plupart des formes dans la nature peuvent être décrites mathématiquement comme des fractales, un mot qu'il a lui-même inventé pour désigner des formes au contour irrégulier et fragmenté
(Mandelbrot, 1977). A ce moment-là, il affirme qu'on peut générer des fractales à partir de formes au contour lisse qu'on fractionne successivement en petits morceaux. Cette découverte a ouvert la porte à une nouvelle forme de création graphique. Le film « Star Trek 2 : la colère de Khan » en est un bon exemple où une planète entière est générée à partir de la géométrie fractale ; c'est la première image de synthèse dans un long métrage. La caractéristique principale des fractales est l'auto-similarité, c'est-à-dire que le tout ressemble à la partie qui ressemble elle-même à une partie encore plus petite, le motif se répète ainsi par un processus itératif infini (Mandelbrot, 1977). Cette auto-similarité se retrouve en fait partout : arbres, montagnes, vaisseaux sanguins, surface de la lune... Le flocon de Koch en est un bel exemple (Figure 45) (Koch, 1904).

<figure><img src="image_47.png" /></figure>

**Figure 45.** Le flocon de Koch représentant l'une des premières courbes fractales à avoir été décrites. On part d'un triangle équilatéral (figure classique de la géométrie euclidienne), sur chaque côté on remplace un tiers du segment par deux sous-segments de même longueur, et sur chacun de nouveaux côtés obtenus on fait de même. Plus on fractionne la ligne, plus sa longueur augmente. Si on répète le processus une infinité de fois alors on obtient une figure dont la longueur est infinie.

**La courbe de Koch** s'est révélée cruciale pour mesurer la longueur d'une côte terrestre. Le mathématicien britannique Lewis Fry Richardson avait mis en avant que les résultats obtenus pour mesurer la longueur d'une côte étaient très variables. Cela dépendait entre autres de la taille des segments (donc de l'unité de référence) qu'on s'autorisait à utiliser pour avoir une mesure précise de celle-ci. Cependant, en 1967, Mandelbrot comprend que les côtes terrestres sont des fractales et que bien qu'il ne puisse pas en mesurer la longueur exacte, il peut en mesurer la rugosité. Il publie donc un article dans la revue « Science » s'intitulé : « How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension » (Mandelbrot, 1967). Il a ainsi repensé le concept de dimensions qui sous-tendait les mathématiques
classiques. Jusqu'alors, une dimension représentait une ligne droite, deux dimensions une surface plane et trois dimensions un cube. Mandelbrot a introduit la dimension fractale qui se situe entre ces dimensions. Plus les fractales sont fragmentées et plus leur dimension fractale est élevée. Les fractales ont ensuite été appliquées à différents contextes, que ce soit pour les effets spéciaux au cinéma, pour la couture, l'économie, la médecine ou encore dans les télécommunications (les fractales étant le seul moyen de réduire la taille des antennes ou encore de capter une large gamme de fréquences). Les fractales se sont également développées en traitement du signal, notamment dans le domaine de la physiologie où elles sont utilisées pour étudier le rythme cardiaque (Perkiömäki et al., 2005). Le rythme cardiaque est en effet inconstant mais, lorsqu'on regarde les intervalles entre les battements, on s'aperçoit que plus on grossit les observations, plus il y a d'oscillations. De fait, le rythme cardiaque sain a une architecture fractale. De manière intéressante, les primitives motrices suivent également une architecture fractale. Bien que celles-ci ne suivent pas strictement un schéma dit auto-similaire, on dit qu'elles sont statistiquement auto-similaires ou auto-affines (Mandelbrot, 1985; Santuz et Akay, 2020).

### 4.3.1. La dimension fractale de Higuchi

S'il existe aujourd'hui plusieurs estimateurs de la dimension fractale d'une série temporelle, la dimension fractale de Higuchi (notée HFD) (Higuchi, 1988) est l'un des plus performants, notamment lorsqu'il est couplé avec des approches linéaires (Kesić et Spasić, 2016). Ainsi par son approche non-linéaire, HFD permet entre autres de quantifier la complexité d'un signal neurophysiologique, tel que les primitives motrices.

Les calculs pour quantifier la dimension fractale d'Higuchi (Figure 46) sont bien décrites dans la littérature (Higuchi, 1988; Kesić et Spasić, 2016; Santuz et Akay, 2020). Pour chaque primitive motrice, $P(t)[P(1), P(2), \dots, P(n)]$, $k$ représente le taux de sous échantillonnages à construire pour chaque primitive où $k$ est un intervalle de temps et $2 < k < k_{max}$. Pour une série temporelle notée $X$ et échantillonnée à temps régulier, on a :

$$X_k^m: X(m), X(m + k), H(m + 2k), \dots, X(m + \left\lfloor \frac{N - m}{k} \right\rfloor \cdot k) \quad (4)$$

Où [] représente l'approximation de Gauss, $m$ est un entier qui indique le temps initial et $k$ est un entier indiquant le temps d'intervalle. Pour un temps d'intervalle $k$, on a $k$ ensembles de nouvelles séries temporelles. Dans le cas où $k = 3$ et $N = 100$ on a 3 nouvelles séries temporelles comme suit :
$$X_3^1 = [X(1) \ X(4) \ X(7) \ \dots \ X(100) \tag{5}$$

$$X_3^2 = [X(2) \ X(5) \ X(8) \ \dots \ X(98) \tag{6}$$

$$X_3^3 = [X(3) \ X(6) \ X(9) \ \dots \ X(99) \tag{7}$$

La longueur non-euclidienne de chaque courbe est définie comme suit :

$$L_m^k = \frac{1}{k} \left[ \frac{N-1}{\left[ \frac{N-m}{k} \right] k} \left( \sum_{i=1}^{\left[ \frac{N-m}{k} \right]} |X(m+ik) - X(m+(i-1)k)| \right) \right] \tag{8}$$

La longueur est normalisée par la fenêtre *k* choisie. Le terme $N - 1/[N - m]/k$ représente le facteur de normalisation pour la longueur de la courbe des sous-séries temporelles. Le terme de droite représente simplement la somme des amplitudes des différents sous-segments de taille *k*. La longueur de la courbe pour l'intervalle de temps *k* est définie comme $\langle L(k) \rangle$, c'est-à-dire la valeur moyenne des *k* sous-séries de $L_m(k)$ :

$$\langle L(k) \rangle = \frac{1}{k} \sum_{t_0=1}^{k} L_{t_0}(k) \tag{9}$$

Si $\langle L(k) \rangle \propto k^{-\text{HFD}}$, alors la courbe est fractale avec la dimension HFD et devrait conduire le tracé log(L(k)) en fonction de log(1/k) à une ligne droite avec une pente correspondant à -HFD. Il est recommandé de tracer la droite sur la partie la plus linéaire du graphique log-log (généralement les 10 premiers points) (Santuz et Akay, 2020). La valeur de HFD va de 1 (droite) à 2 (rectangle) et est indépendante de l'amplitude du signal. Plus la valeur de HFD est élevée et plus le signal est complexe ; 1,5 représentant un bruit gaussien aléatoire.
<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /></figure>

**Figure 46.** (A) La dimension fractale d'Higuchi est calculée par la distance non-euclidienne d'Higuchi (courbe grise) de longueur L (Higuchi, 1988) pour différents échelles de *k* et pour différents points de départ (non matérialisé sur la figure) sur un signal original présentant des cycles (un cycle étant de longueur 200), d'une primitive motrice (courbe jaune). (B) Un exemple de log-log d'Higuchi est représenté pour une tâche de locomotion dite normale, les droites de régression et les valeurs de HFD sont présentées pour les 10, 150 et 300 premiers points de couleur jaune, grise et orange respectivement (Adapté de Santuz and Akay, 2020).

### 4.3.2. L'exposant de Hurst

Une seconde métrique des fractales complémentaire du HFD est l'exposant de Hurst, noté H (Hurst, 1951). Celui-ci indique les tendances à l'augmentation ou à la diminution d'un signal au cours du temps. Alors que HFD représente une mesure de la complexité locale du signal (au niveau d'un cycle de course par exemple), l'exposant de Hurst représente davantage une métrique de la complexité globale du signal (c'est-à-dire l'irrégularité d'un cycle à l'autre) (Figure 47). L'exposant de Hurst provient de l'hydrologue Hurst (Hurst, 1951) lors de son travail sur la modélisation de la série temporelle de la hauteur des crues du Nil, de l'antiquité à nos jours. Selon cette méthode, l'exposant de Hurst (H) représente la probabilité qu'un événement soit suivi par un événement similaire. On parle donc d'analyse de la persistance (Delignières, 2001).

L'exposant de Hurst *H*, qui est compris entre 0 et 1, permet de classifier les séries temporelles en trois catégories :

- **Persistantes** ($0,5 < H < 1$) : les accroissements successifs sont positivement corrélés (i.e. une augmentation de la variable tend à être suivie d'une autre augmentation)

- **Anti-persistantes** ($0 < H < 0,5$) : les accroissements successifs sont négativement corrélés

- **Aléatoires** ($H = 0,5$) : la série temporelle ne présente pas de corrélations à long terme
Les algorithmes pour calculer cet exposant peuvent légèrement différer mais nous détaillerons ici la méthode originale de l'auteur, nommée analyse des étendues normalisées (R/S) (Mandelbrot et Wallis, 1969) (Figure 48) :

<figure><img src="image_50.png" /><figcaption>**Figure 47.** Schématisation de l'exposant de Hurst. (A) Exemple : le cours de clôture quotidienne du marché pour les actions Twitter (NASDAQ Composite 2020) pendant la crise de la Covid 19. La courbe temporelle n'est pas strictement auto-similaire ; elle est dite auto-affine, comme montré dans le grossissement matérialisé par un cercle. L'exposant de Hurst est calculé pour plusieurs intervalles qui représentent ici des jours. La plus petite fenêtre a été fixée à la moitié de la longueur de l'intervalle considéré. 0,5 < H < 1 indique *la persistance* (tendance négative ou positive, par exemple celles des prix croissants entre les jours 615 et 672), tandis que 0 < H < 0,5 indique *l'anti-persistance* (absence de tendance ou oscillation autour d'une valeur moyenne, par exemple lorsque l'on considère la série temporelle du jour 1 au jour 712). (B) Un exemple de log-log d'Hurst est représenté pour une tâche de locomotion dite normale. Les droites de régression et les valeurs de HFD sont représentées pour les 7 premiers points (droite jaune), les 6 derniers (droite grise) et l'ensemble de points (droite orange). Le graphique log-log présente un point d'inflexion aux alentours de n=100, qui témoigne de la susceptibilité de H par rapport à des courbes quasi périodiques (les cycles des primitives motrices). Pour quantifier H, il est conseillé de prendre la plus petite fenêtre plus grande que la période, c'est-à-dire la longueur d'un cycle dans le cadre des synergies musculaires (Adapté de Santuz and Akay, 2020).</figcaption></figure>

Pour une série temporelle de taille N, notée x(t). La méthode consiste à prendre une série de subdivisions indépendantes d'effectif τ. On subdivise jusqu'à l'effectif le plus élevé qui permet de distinguer seulement deux subdivisions. La première étape consiste à calculer la moyenne pour chaque τ données :

$$ \langle x \rangle_{\tau} = \frac{1}{\tau} \sum_{t=1}^{\tau} x(t) \qquad (10) $$

Pour chaque subdivision, on soustrait la moyenne locale afin de centrer les données. On établit ensuite la série de valeurs cumulées (c'est-à-dire qu'on ajoute à chaque donnée la somme des valeurs centrées qui la précède) :
$$X(t, \tau) = \sum_{u=1}^{t} \{x(u) - \langle x \rangle_{\tau}\} \quad (11)$$

Ainsi, pour chaque valeur de $t$ ($1 \le t \le \tau$), on a une valeur de $X(t, \tau)$. L'étendue $R$ est la différence entre le maximum et le minimum de $X(t, \tau)$ :

$$R = \max_{1 \le t \le \tau} X(t, \tau) - \min_{1 \le t \le \tau} X(t, \tau) \quad (12)$$

L'étendue $R$ est ensuite normalisée par l'écart-type local ($S(t,\tau)$). On quantifie alors la moyenne par niveau d'effectif des étendues normalisées $R/S$. Hurst a montré que l'étendue normalisée notée $R/S$ est liée à la grandeur de l'intervalle considérée par la relation suivante :

$$\frac{R}{S} = (a\tau)^H \quad (13)$$

Où $a$ est une constante et $H$ l'exposant de Hurst. $H$ peut être estimé par la méthode des moindres carrés ou par la pente de la régression log-log de $R/S$ sur $\tau$ puisque $R/S$ doit être calculée sur un ensemble de valeurs de $\tau$.

<figure><img src="image_51.png" /><figcaption>**Figure 48.** HFD est une mesure de la complexité locale qui augmente avec la rugosité des primitives motrices au niveau d'un seul cycle (d'où le terme local). L'exposant de Hurst est une mesure de la complexité globale, qui augmente lorsque l'irrégularité d'un cycle à l'autre augmente (Adapté de Santuz et al., 2022).</figcaption></figure>

Dans le cadre des synergies musculaires, ces métriques ont principalement été utilisées en locomotion et notamment lorsque celle-ci est perturbée. Ainsi, la locomotion sur tapis roulant à reliefs (Santuz et al., 2020a) et le passage de la marche à la course (Santuz et al., 2020b, 2020a) sont caractérisés par une diminution de la complexité locale (i.e. de la « grossièreté ») des primitives motrices. Comparé à la locomotion en situation écologique, celle sur tapis roulant est par contre associée à une diminution de la complexité globale (i.e. de leur irrégularité) (Mileti et al., 2020a). Ceci a été attribué à la nécessaire simplification du contrôle moteur lorsque la locomotion est challengée. Les deux métriques sont donc complémentaires, pas forcément corrélées et peuvent afficher des valeurs qui ne varient pas dans le même sens (Gneiting et Schlather, 2004)(Gneiting et Schlather 2004 ; Mandelbrot 1997).
4.4. Synergies musculaires-fractales et fatigue

Alors que les synergies musculaires et l'analyse fractale de leurs composantes constituent un
concept particulièrement intéressant pour l'étude de la fatigue, ces métriques ont été très peu
utilisées pour la quantifier. Bueno et al. (Bueno et al. 2015) ont analysé l'évolution des
synergies musculaires lors d'un test dynamique après des tâches de fatigue sélective du biceps,
du triceps et du deltoïdus. Ces auteurs ont rapporté des modules moteurs similaires alors que
les primitives motrices semblaient être affectées. De manière intéressante, ils ont également
rapporté une augmentation des paramètres fractales (mesurée par l'analyse des fluctuations
retardées) en situation de fatigue, ce qui témoignait d'une plus grande variabilité. Le patron
devenait de plus en plus persistant, témoignant d'un schéma moins stable. Ces observations
vont dans le même sens que l'analyse du rythme cardiaque par l'utilisation des fractales en
situation de fatigue lors d'un marathon (Billat et al., 2009). Également, après une tâche de
fatigue des membres supérieurs, Ortega-Auriol et al. (2018) ont trouvé que la structure des
modules moteurs restait similaire alors que les primitives motrices étaient fortement affectées
lors d'un test isométrique. Après un exercice fatiguant de squats jusqu'à épuisement chez 9
jeunes sportifs, l'analyse par synergie a révélé des changements subtils lors d'un squat réalisé
avec les deux membres inférieurs (Smale et al., 2016). Cependant, lorsque le squat était réalisé
de façon unilatérale, les changements étaient plus prononcés et entraînaient un changement de
stratégie : une co-activation des muscles était observée en pré-fatigue alors qu'une dominance
des extenseurs du genou était observée en post-fatigue (Smale et al., 2016). Il convient de
préciser que ce changement concernait la 4ème synergie qui avait probablement une influence
faible comparée aux 3 premières. Lors d'un mouvement volontaire de balancement antéro-
postérieur en situation de fatigue où l'objectif est de minimiser le balancement dans la direction
médio-latérale, la performance (déplacement du centre des pressions) était préservée mais cela
s'accompagnait d'une augmentation de la variance de l'activation des muscles non-fatigués et
de la co-variation entre les activations musculaires (Singh et Latash, 2011). Enfin, lors d'une
course à vitesse de confort après un protocole de course fatiguant, les pics d'activité des
primitives motrices étaient (selon la synergie) avancés ou retardés décalés dans le temps. Une
légère restructuration des modules moteurs était observée bien que l'architecture globale était
conservée (Hajiloo et al., 2020). Les résultats de cette étude sont néanmoins à prendre avec
précaution. En effet, ils comportaient certaines limites dont l'utilisation de l'algorithme de la
NMF insuffisamment détaillée. De plus, la détermination des phases de contact et de vol dans
le cycle de course n'étaient pas clairement définies, et 5 synergies étaient observées, ce qui
différait des 4 synergies habituellement décrites à la course.

Bien que l'évolution des synergies musculaires en situation de fatigue soit encore peu
définie, celle-ci semble pertinente pour révéler d'éventuelles compensations inter-musculaires
en situation de fatigue. De plus, les synergies n'ont jamais été explorées durant la phase retardée
de la récupération (i.e. c'est-à-dire en présence de dommages musculaires et d'inflammation).
Enfin, il y a un manque important de comparaison des synergies entre les hommes et les
femmes, même en l'absence de fatigue.

Synthèse :

Les protocoles d'étude de la fatigue comportant des tests dynamiques ont souvent quantifié
cette dernière sur la base de l'activité quadratique moyenne de chaque muscle pris isolément
ou moyennée par groupe musculaire. Dans ce domaine, l'analyse linéaire des synergies
musculaires apparaît comme susceptible d'enrichir notre compréhension des patrons
d'activation musculaire, des coordinations et compensations inter-musculaires, que ce soit avec
ou sans fatigue. Elle pourrait notamment révéler des coordinations spécifiques au sexe. De plus,
des méthodes non-linéaires, telles que l'approche par fractales, pourraient être complémentaires
et nous permettre de mieux comprendre comment évolue la complexité d'une tâche en situation
de fatigue.

Malgré le peu d'études ayant analysé les synergies en situation de fatigue, ces dernières
ont rapporté que les primitives motrices seraient affectées alors que l'architecture des modules
moteurs le serait peu. La littérature manque néanmoins d'informations sur le sujet, notamment
concernant la phase de retardée de la récupération. Ce constat s'applique d'autant plus à
l'influence du sexe sur les synergies, y compris en l'absence de fatigue. Bien que les synergies
musculaires ne puissent pas se substituer aux méthodes classiques dans l'étude de la fatigue,
elles permettraient d'avoir une vision complémentaire en abordant davantage les coordinations
inter-musculaires adoptées pour réaliser un mouvement en considérant chaque muscle par
rapport aux autres.
## Objectifs et hypothèses :

Ce travail de recherche vise à caractériser l'influence du sexe sur le patron de récupération structurale et fonctionnelle de coureurs récréatifs après une épreuve de course à pied de 20 km avec dénivelé, susceptible d'engendrer des microlésions musculaires chez ce type de coureurs.

Considérant que la femme n'est pas une version « plus petite » de l'homme (Boisseau *et al.*, 2009), les spécificités physiologiques et biomécaniques liées au sexe en course à pied seront détaillées dans le **chapitre II**, dans notre **revue de la littérature (Etude 1)**. Nous préciserons ainsi certains facteurs susceptibles d'expliquer les meilleures performances des hommes avant de discuter si les femmes peuvent ou non les battre sur de longues distances, comme le suggèrent les récentes performances féminines. Ce travail de synthèse nous permettra de préciser les potentielles altérations structurales et fonctionnelles rapportées à ce jour pour les hommes et pour les femmes à la fin d'une épreuve de course à pied.

Au regard du nombre limité d'études ayant examiné le patron de récupération chez des coureurs femmes, nos premières études expérimentales (*Etudes 2 et 3*) viseront, dans le **chapitre III**, à **définir le patron de récupération fonctionnelle des femmes comparé à celui des hommes** après une épreuve donnée de 20 km avec dénivelé (Course Marseille-Cassis – édition 2019). Ces études comprendront une batterie de tests fonctionnels d'intensité maximale, comportant des actions isométriques, concentriques et de type cycle-étirement-détente (CED). Nous émettons comme première hypothèse que *les femmes présenteront de moindres déficits fonctionnels, notamment dans les tests de type CED*. Notre seconde hypothèse est que *les patrons de récupération fonctionnelle différeront tant en fonction du test que du sexe. L'étude 2* visera plus spécifiquement à caractériser les différents profils de récupération. Nous considérons que la batterie de tests complétée par la *clusterisation des différents profils de récupération* nous permettra de révéler la richesse des patrons de récupération, voire de détecter les coureurs à risque en cas de reprise trop précoce de la course. Nous supposons que ce groupe sera davantage composé d'hommes. *L'étude 3* examinera dans un test force-vitesse balistique s'il existe une réponse adaptative du système neuromusculaire (notamment au regard des synergies musculaires) qui soit spécifique au sexe. La majorité des études ayant examiné les effets de la fatigue en phase aiguë de récupération, nous examinerons les liens potentiels avec ceux observés en phase retardée et l'influence du sexe à cet égard (*article de congrès ECSS*).
Le **chapitre IV** comprendra l'étude 4 (en cours de rédaction en vue d'une prochaine soumission) ainsi que les méthodes et résultats actuels des études 5 et 6. Ces travaux, portant sur l'édition 2021 de la course, visent à **quantifier de concert l'influence du sexe sur les récupérations structurale et fonctionnelle**. Nous nous focaliserons sur la phase retardée de la récupération. La première originalité de ce travail résidera dans l'évaluation de la récupération structurale par l'utilisation de méthodes d'imagerie médicale ; la seconde dans la mise en relation de l'évolution des paramètres structuraux avec celles de l'activité musculaire et des changements fonctionnels en conditions de tests pluri-articulaires. Afin d'enrichir notre compréhension de l'influence du sexe sur les compensations inter-musculaires, l'activité musculaire sera enregistrée dans chacun des tests d'une batterie qui comprendra des sauts maximaux et une série de rebonds d'intensité sous-maximale. **L'étude 4** examinera les liens entre la récupération structurale, la récupération fonctionnelle et les ajustements neuromusculaires associés. L'examen de la récupération structurale s'appuiera sur les données obtenues par échographie. Cette étude se caractérisera également par son *approche bayésienne* des spécifités liées au sexe. Notre première hypothèse est que *l'inflammation musculaire sera moins importante chez les femmes, à l'exception des ischios-jambiers connus pour être relativement plus faibles chez ces dernières*. Notre seconde hypothèse est que *les femmes présenteront moins de déficits fonctionnels, notamment dans les tests de type CED*. Notre troisième hypothèse est que *les patrons neuromusculaires différeront entre les sexes dans les tests pré-course et dans leur évolution en phase de récupération*. Enfin, nous nous attendons à ce que des liens existent entre les récupérations structurale et fonctionnelle, et que les ajustements neuromusculaires adoptés par les femmes diffèrent de ceux des hommes. **L'étude 5** visera à compléter l'examen de la récupération structurale par les données actuelles obtenues en IRM. Nous en présenterons les données actuelles, qui seront prochainement complétées lors du suivi de l'édition 2023 de la course Marseille-Cassis. **L'étude 6** examinera les synergies musculaires propres aux différents tests fonctionnels utilisés, afin de comprendre leur impact sur la performance et les changements fonctionnels en période de récupération. Compte tenu de l'intérêt que représente cette méthode et bien que le manuscrit soit en cours de rédaction, nous avons choisi d'en présenter et discuter succinctement les résultats.

Ces études expérimentales déboucheront sur une **discussion générale** (chapitre V), qui visera à effectuer une synthèse critique de l'ensemble de nos travaux, de leurs apports et limites, afin d'ouvrir de nouvelles perspectives tant d'un point de vue fondamental qu'appliqué.
# CHAPITRE II

## Influence du sexe sur la course à pied

Ce chapitre s'appuie sur la revue (étude 1) publiée suivante :

1. Besson, T., **Macchi, R.**, Rossi, J., Morio, C. Y. M., Kunimasa, Y., Nicol, C., Vercruyssen, F., Millet, G. Y. (2022). Sex Differences in Endurance Running. *Sports Medicine* (Auckland, N.Z.). https://doi.org/10.1007/s40279-022-01651-w
Au regard de l'évolution de la participation et des performances des femmes dans les épreuves de course à pied, l'influence du sexe revêt un réel intérêt. Le cadre théorique s'étant focalisé sur l'étude de la course à pied indépendamment du sexe, le premier chapitre de ce travail de thèse visera à clarifier, à travers une revue de la littérature (étude 1), les spécificités biomécaniques et physiologiques rapportées pour les femmes et les hommes en course à pied. Cette revue précisera leurs différences, tant du point de vue structural que fonctionnel. Elle apportera de ce fait des précisions sur les facteurs pouvant contribuer aux différences de performance observées entre les hommes et les femmes en course à pied. Ces informations nous permettront de considérer les mécanismes sous-jacents à l'influence du sexe sur les profils de récupération étudiés dans le cadre de ce travail de thèse.
**Objectif** : Ces deux dernières décennies, la participation des femmes à des compétitions de course à pied sur route et en trail a considérablement augmenté. La majorité des études s'étant focalisées sur les réponses physiologiques et les caractéristiques biomécaniques des hommes à la course, il est nécessaire de s'attacher à approfondir celles des femmes. Parmi les différences connues, VO₂max est souvent rapportée comme étant supérieure chez les hommes mais il conviendrait de prendre en compte l'ensemble des autres paramètres susceptibles d'influencer les écarts de performance et la fatigue engendrée. Cette étude narrative vise donc à effectuer un bilan de litérature concernant les différences rapportées en termes de biomécanique de la course à pied, d'économie (tant en l'absence qu'en présence de fatigue), d'utilisation des substrats, de caractéristiques musculaires (incluant les micro-lésions musculaires), d'altérations neuromusculaires, de thermorégulation et enfin de stratégie de course.

**Résultats** : Les femmes et les hommes ne diffèrent pas en termes d'économie et d'endurance de course (i.e. pourcentage de VO₂max maintenu). Toutefois, il existe des spécificités liées au sexe en ce qui concerne la biomécanique de course. Les différences biomécaniques observées sont en partie expliquées par la morphologie propre à chaque sexe. Les femmes présentent notamment un pelvis plus large, un angle entre le fémur et le tibia plus important ainsi que des membres inférieurs relativement plus courts. Comparé aux hommes, les femmes montrent aussi une plus grande proportion de fibre de type I, oxydent davantage les acides gras, préservent mieux leurs réserves de glucides dans des exercices prolongés et diffèrent même au niveau de la stratégie de course (en relatif, les femmes terminent mieux une épreuve de marathon). Les observations tendent à suggérer une moindre fatigue chez ces dernières après des courses à pied d'endurance.

**Conclusion** : Ces différences physiologiques pourraient conférer un avantage aux femmes dans la performance dans les épreuves d'ultra-endurance. Cependant, d'autres facteurs, tels qu'une moindre capacité de transport de l'oxygène ou un pourcentage de masse grasse plus élevé pourraient contrebalancer ces potentiels avantages, suggérant que la 'super performance' des femmes par rapport aux hommes ne soit qu'une rare exception. Cette revue souligne néanmoins le manque d'études ayant investigué l'effet du sexe en situation de fatigue, que ce soit à l'exercice ou pendant le processus de récupération.
# Sex Differences in Endurance Running

Thibault Besson¹ · Robin Macchi² · Jeremy Rossi¹ · Cédric Y. M. Morio³ · Yoko Kunimasa² · Caroline Nicol² · Fabrice Vercruyssen⁴ · Guillaume Y. Millet¹,⁵ ID

Accepted: 12 January 2022

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022

## Abstract

In recent years, there has been a significant expansion in female participation in endurance (road and trail) running. The often reported sex differences in maximal oxygen uptake ($VO_{2max}$) are not the only differences between sexes during prolonged running. The aim of this narrative review was thus to discuss sex differences in running biomechanics, economy (both in fatigue and non-fatigue conditions), substrate utilization, muscle tissue characteristics (including ultrastructural muscle damage), neuromuscular fatigue, thermoregulation and pacing strategies. Although males and females do not differ in terms of running economy or endurance (i.e. percentage $VO_{2max}$ sustained), sex-specificities exist in running biomechanics (e.g. females have greater non-sagittal hip and knee joint motion compared to males) that can be partly explained by anatomical (e.g. wider pelvis, larger femur-tibia angle, shorter lower limb length relative to total height in females) differences. Compared to males, females also show greater proportional area of type I fibres, are more able to use fatty acids and preserve carbohydrates during prolonged exercise, demonstrate a more even pacing strategy and less fatigue following endurance running exercise. These differences confer an advantage to females in ultra-endurance performance, but other factors (e.g. lower $O_2$ carrying capacity, greater body fat percentage) counterbalance these potential advantages, making females outperforming males a rare exception. The present literature review also highlights the lack of sex comparison in studies investigating running biomechanics in fatigue conditions and during the recovery process.

## Key Points

Female participation in endurance running has increased considerably over the last 20 years. Some physiological factors are known to explain sex differences in 'classic' endurance running performance (e.g. $VO_{2max}$); this narrative review discusses via a multidisciplinary approach—physiological, biomechanical and neuromuscular factors that differentiate sexes in prolonged running exercise.

Although males and females demonstrate differences in running biomechanics, they do not differ in terms of running economy. A greater proportion of type I fibres and greater ability to use fatty acids in females could explain the lower neuromuscular fatigue of the quadriceps and triceps surae reported after prolonged running races compared to males, and could confer an advantage to females over ultra-endurance competitions.

More direct comparisons between males versus females in response to fatiguing running exercise are needed.

<figure><img src="image_52.png" /></figure>

Guillaume Y. Millet
guillaume.millet@univ-st-etienne.fr

¹ Univ Lyon, UJM-Saint-Étienne, Inter-University Laboratory of Human Movement Biology, EA 7424, 42023 Saint-Étienne, France

² Institute of Movement Sciences (ISM), National Center of Scientific Research (CNRS) and Aix Marseille Univ, Marseille, France

³ Decathlon SportsLab, Movement Sciences Department, Lille, France

⁴ Impact of Physical Activity on Health (IAPS), University of Toulon, Toulon, France

⁵ Institut Universitaire de France (IUF), Paris, France
# 1 Introduction

Running, one of the most popular recreational sports, is associated with many health benefits [1–3]. Supported by major sports companies, running as a leisure activity has grown rapidly since the 1970s in North America and in Europe since the beginning of the 1980s [3]. During the last decade, female participation in running events has increased considerably. According to a recent survey (https://run repeat.com/state-of-running), covering more than 70,000 road races from 5 km to marathons all over the world, female par- ticipation had reached 50% in 2018. The International Trail Running Association recently reported an increase in female participation in trail running from 18% in 2013 to 26% in 2019 (https://itra.run/). In ultra-trail races, the participation rate of females is much lower, yet a sixfold increase has also been reported since the 1980s [4]. Despite the increasing female participation, the vast majority of scientific studies are still conducted on males. Indeed, based on data collected from three major sport and exercise medicine journals, the average ratio of male to female participants per article is ~ 65:35 [5]. Part of this difference has been attributed to sex differences in willingness to participate in certain types of research. In other words, a volunteer bias in female partici- pation in running research may exist [6].

With the growth of the number of females engaged in running events, their performance rapidly improved through the 1980s [7] and, from then, researchers started to compare male and female running performance from sprint to mara- thon distances [8, 9]. In the 1990s, it was believed that there would be a reduction [10–12] or even disappearance [8] of sex differences in running performance. For instance, using a regression analysis between running velocities over several distances and historical time in males and females, Whipp and Ward [8] reported in 1992 that females could outrun males in marathon distance in 1998. However, the difference in performance between sexes remains around 10–12% for running events from sprint to marathon [13–15]. The issue of sex differences in performance as a function of distance is complex. When using the world best times from 100 m to 200 km, Coast et al. [14] reported that the difference in performance increases with distance, particularly in events longer than the marathon. It has also been reported that the difference between sexes when comparing the best male and the best female runners was ~17% for the 100-km Lauf Biel in Switzerland [16] and ~20% for the 161 km of various north American ultra-marathons [7]. Additionally, da Fon- seca-Engelhardt et al. [17] observed the same tendency on two of the hardest ultramarathons worldwide, i.e. the ‘Bad- water’ in the USA and the ‘Spartathlon’ in Greece, with performances being ~ 20% slower for females than for males (based on the top five finishers). On the contrary, Peter et al.

[18] reported a ~ 10–13% gap when comparing the fastest male and female runners ever of the 24-h ultra-marathon. How do we explain the variability in sex differences in run- ning performance between studies regarding events longer than the marathon? First, reported sex differences depend on how the question is framed, for example, comparing the top female and male runners or considering all participants. In the latter case, sex differences seem to even decrease with increasing race distance and no differences were reported in races longer than 315 km (e.g. https://runrepeat.com/state- of-ultra-running), whereas, based on unpublished data, the sex difference in performance appears constant with increas- ing distance when considering the first males and the first females. Second, the percentage of female participation in these events is much lower than reported for males, being as low as 10% in some events, which may confound the per- formance differences in the very long-distance races [19]. Third, many of the races above the marathon distance are trail running events. These races are not as standardized as road races, and consist of regular changes in terrain, techni- cal difficulty, elevation gain and exact distance, and it is not known if this has a greater impact on males than females, or vice versa. Despite the substantially lower participation rate of females, ultra-distance in running is one of the rare dis- ciplines where females are able to outperform men. To cite only a few, Corinne Favre, Pamela Reed, Hiroko Okiyama and Jasmin Paris finished at first place overall at the ‘CCC’ in 2006 (86 km), ‘Badwater’ 2002 and 2003 (217 km), the ‘Deutschland-lauf’ in 2007 (1,204 km), and the 268-mile Montane Spine race, respectively. Jasmin Paris not only won the race, but also shattered the previous male record by 12 h. It is, however, likely that these exceptional performances are not only due to the great mental and physical capacities of these runners but also to the fact that the best men were not running in these particular events. In summary, although the question of the evolution of performance across race distance according to sex is a complex one, largely due to a question of parity in trail and ultra-trail running participa- tion, the sex gap in performance appears relatively constant with increasing distance when focusing on top level runners.

Long-distance running performance (up to the marathon) depends on the interaction between $V̇O_{2max}$, the fraction of $V̇O_{2max}$ sustained ($F$) on the covered distance that is closely linked to lactate threshold (LT), and running economy (RE) (e.g. [20, 21]). Endurance factors such as F or LT do not seem to be affected by sex (e.g. [22–25]). For instance, Davies and Thompson [22] reported that male and female athletes were able to sustain similar F over the marathon dis- tance (82 and 79%, respectively). The most obvious physi- ological variable to explain running performance difference is the maximal oxygen uptake ($V̇O_{2max}$). When expressed in $mlO_2·min^{-1}·kg^{-1}$ (of body mass), $V̇O_{2max}$ is considered as the main factors explaining sex differences in endurance
running performance [20, 26], mainly due to greater per-
centage of body fat and lower haematocrit levels in females
[26, 27]. When V̇O₂max in males and females is adjusted to
fat-free mass, some controversy exists in the literature, with
some studies showing no sex differences [28–30] whereas
other report a higher V̇O₂max in males [31, 32]. Lower
O₂-carrying capacity is definitively one factor explaining
the lower V̇O₂max in females [33]. As sex differences in V̇
O₂max have largely been reviewed elsewhere [26, 31, 32, 34],
the present review focuses on the other factors that differ-
entiate males and females in endurance running. Thus, the
aim of the present narrative review was to examine whether
males and females differ in terms of running biomechanics,
economy and physiological responses during short (< 1 h)
and prolonged (i.e. several hours of running) running exer-
cises. This narrative review offers a novel comprehensive
and multidisciplinary (i.e. psychological, physiological, neu-
romuscular and biomechanical aspects) overview of the sex
differences in endurance running. Article databases of the
US National Library of Medicine (PubMed), ScienceDirect
and SPORTDiscuss were searched using the search general
(Gender or Sex differences) and specific terms (correspond-
ing to the studied scientific area of each part). For instance,
article databases focusing on the “Running economy” part
were searched as follows: Gender OR Sex differences AND
Running economy OR oxygen demand OR Energy cost of
running OR Oxygen cost of running OR oxygen uptake OR
metabolic efficiency. Any additional relevant literature was
obtained from the reference lists of the published papers.
This searching process has been applied for each part of the
current review.

## 2 Biomechanics of Running

Many studies have been conducted to compare the run-
ning biomechanics of males and females (Table 1). Several
parameters such as the studied population, footwear, run-
ning surface and speed may contribute to the disparity in the
results observed between studies. In the absence of fatigue,
differences in spatiotemporal, kinematic and kinetics param-
eters of the running pattern have been extensively studied,
but primarily in experimental conditions at given absolute
running speeds and for a short period of time rather than in
endurance condition. The major sex differences are indicated
in blue on Fig. 1.

## 2.1 Spatiotemporal Parameters

Sex differences in spatiotemporal parameters have been
investigated over a large range of running velocities (from
7.5 to 24 km h⁻¹) [35–38]. For given absolute running

speeds above 16 km h⁻¹, female runners present shorter
absolute contact time, longer absolute flight time, shorter
absolute stride length and higher stride frequency than males
[36]. Because of shorter contact time and longer flight time,
females present a lower duty factor, i.e. percentage of stride
time spent on the ground, indicating that females are not
a “scaled-down version of the male model” [36]. Interest-
ingly, these sex differences seem to disappear at slower
speeds (10–14 km h⁻¹), with the exception of contact time
as females still present shorter contact time than males
when the speed decreases [35]. At high absolute speeds,
females probably have to adjust their biomechanics, in par-
ticular through adopting a high stride frequency as they are
closer than males to their maximal speed. Indeed, once nor-
malized to maximal velocity (from 60 to 90% of maximal
velocity), sex differences in contact time and in duty fac-
tor tend to disappear and step frequency is actually lower
in females [36]. Sex differences in body proportions might
also explain part of the sex differences in spatiotemporal
parameters. For instance, several studies reported shorter
step length in females at various running speeds (from
7.5 to 24 km h⁻¹), but these differences vanished or even
reversed once adjusted to stature or leg length [35, 36, 38,
39]. Furthermore, a significant positive association between
contact time and body height, leg length and body mass has
been found [37], but the extent to which morphological dif-
ferences contribute to the sex influence on spatiotemporal
parameters is not yet clear.

## 2.2 Running Kinematics and Muscle Electromyographic Activity

The multitude of variables studied and the diversity of the
methods of running kinematics analysis make comparisons
between studies quite challenging. However, a shared obser-
vation across studies is that sex differences mostly existed at
the hip and knee level [40–51].

In recreational runners, females exhibited significantly
greater hip adduction and internal rotation during the
stance phase of running compared to males across a range
of speeds [40, 42, 43, 49] and surface inclinations [42].
Sex differences in running kinematics at the hip level have
been attributed to the fact that females have a wider pelvis
[52] and possibly weaker hip abductor and external rotator
muscle strength compared to males [49, 53]. Despite mod-
erate correlations between hip, pelvis and trunk kinemat-
ics and hip abductors or external rotator strength in both
males or females, hip strength parameters remain a sub-
stantial portion of the explained variance in running kin-
ematics for both sexes [45]. Females have also been shown
to present greater knee abduction during the stance phase
of running compared to males [43, 44, 47–50]. Greater
knee abduction has been associated with smaller rearfoot
<table><caption>**Table 1** Overview of sex differences in running biomechanics</caption><thead><tr><th>References</th><th>n</th><th>Footstrike</th><th>Footwear</th><th>Surface</th><th>Speed (km h<sup>-1</sup>) and slope</th><th>Spatiotemporal parameters</th><th>Kinematics</th><th>Kinetics</th></tr></thead><tbody><tr><td>Almonroeder and Benson [40]</td><td>14 M<br>18 F</td><td>Rearfoot</td><td>Std</td><td>Δ</td><td>14.4</td><td></td><td>Peak angles:<br>Knee flex: =<br>Knee add: =<br>Knee IR: =<br>Hip add: F+</td><td><strong>MOMENT</strong><br>Peak moment:<br>Knee ext moment: F-<br>PFJ stress: F-<br>PFJ reaction Force: F-</td></tr><tr><td>Bazuelo-Ruiz et al. [41]</td><td>28 M<br>27 F</td><td>Rearfoot</td><td>Std</td><td>Δ</td><td>11.9</td><td></td><td>Peak angles:<br>Knee flex: F+<br>Ankle dorsiflexion: F+</td><td><strong>GRF</strong><br>Impact peak: =<br>Active peak: F-<br>Loading rate: F+<br>Peak braking force: =<br>Peak propulsive force: F+</td></tr><tr><td>Chumanov et al. [42]</td><td>17 M<br>17 F</td><td>NS</td><td>NS</td><td>Δ.<br>6.5, 9.7, 13<br>With 0, 10 and 15% uphill<br>incline</td><td></td><td></td><td>For all speed and incline:<br>Peak angles:<br>Hip add: F+<br>Hip IR: F+<br>Lateral pelvic tilt: =<br>Amplitudes:<br>Hip add: F+<br>Hip rotation and lateral<br>pelvic tilt: =</td><td></td></tr><tr><td>Esculier et al. [193]</td><td>51 M<br>36 F</td><td>NS</td><td>Std</td><td>Δ.<br>Preferred running speed*<br>M: 11.2<br>F: 10.1</td><td>Stride length: F-<br>Step width: F-</td><td></td><td>Peak angles:<br>Knee flex: =<br>Knee abd: =</td><td><strong>MOMENT</strong><br>Peak moment:<br>Medial TFJ forces: F-<br>External knee flex: F-<br>External knee add: =</td></tr><tr><td>Ferber et al. [43]</td><td>20 M<br>20 F</td><td>Rearfoot</td><td>NS</td><td>Δ</td><td>13.1</td><td>Contact time: =</td><td>Peak angles:<br>Hip flex: =<br>Hip add: F+<br>Hip IR: F+<br>Knee flex: =<br>Knee abd: F+<br>Knee IR: =</td><td><strong>MOMENT</strong><br>Peak moment:<br>Hip ext, add and IR: =<br>Knee ext, abd and IR: =<br><strong>WORK</strong><br>Negative work:<br>Hip sagittal plane: =<br>Hip frontal and trans planes:<br>F+</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>Knee sagittal, frontal and<br>trans planes: =</td></tr></tbody></table>
<table><caption>Table 1 (continued)</caption><thead><tr><th>References</th><th>n</th><th>Footstrike</th><th>Footwear</th><th>Surface</th><th>Speed (km h<sup>-1</sup>) and slope</th><th>Spatiotemporal parameters</th><th>Kinematics</th><th>Kinetics</th></tr></thead><tbody><tr><td>Garcia-Pinillos et al. [35]</td><td>51 M<br>46 F</td><td>NS</td><td>NS</td><td>NS</td><td>10, 11, 12, 13, 14, 15, 16</td><td>Contact time: F- at 10 to 16<br>km h<sup>-1</sup><br>Flight time: = at 10 to 16<br>km h<sup>-1</sup><br>Stride length: = at 10 to 13<br>km h<sup>-1</sup><br>F- at 14 to 16 km h<sup>-1</sup><br>Stride length (norm.): = at<br>10 to 14 km h<sup>-1</sup><br>F- at 15 and 16 km h<sup>-1</sup><br>Step frequency: = at 10 to<br>13 km h<sup>-1</sup><br>F+ at 14 to 16 km h<sup>-1</sup></td><td></td><td></td></tr><tr><td>Gehring et al. [44]</td><td>16 M<br>16 F</td><td>Rearfoot</td><td>Std</td><td>Std</td><td>10.8, 14.4, 18</td><td>Contact time: F-</td><td>Peak angles:<br>Hip add: F+<br>Knee add: F-</td><td>MOMENT<br>Peak moment:<br>Hip add at Impact and Mid-<br>stance: F+<br>Knee add at Impact and Mid-<br>stance: =<br>WORK<br>Hip joint impulse: F+<br>Knee joint impulse: F+</td></tr><tr><td>Hannigan et al. [45]</td><td>37 M<br>23 F</td><td>NS</td><td>Std</td><td>Std</td><td>Self-selected "easy" run<br>pace*<br>M: 12.6<br>F: 11.9</td><td></td><td>Amplitudes:<br>Hip ext: F+<br>Hip add: =<br>Hip IR: F+<br>Anterior pelvic tilt, Con-<br>tralateral pelvic drop and<br>Pelvic IR: =</td><td>GRF<br>Impact peak: F-<br>Act. peak: =</td></tr><tr><td>Hennig [194]</td><td>17 M<br>15 F</td><td>NS</td><td>Std</td><td>Std</td><td>11.9</td><td>Contact time: =</td><td></td><td></td></tr></tbody></table>
<table><caption>Table 1 (continued)</caption><thead><tr><th>References</th><th>n</th><th>Footstrike</th><th>Footwear</th><th>Surface</th><th>Speed (km h<sup>-1</sup>) and slope</th><th>Spatiotemporal parameters</th><th>Kinematics</th><th>Kinetics</th></tr></thead><tbody><tr><td>Isherwood et al. [60]</td><td>20 M<br>20 F</td><td>Rearfoot</td><td>Std</td><td>Δ</td><td>11.9</td><td>Contact time: F-</td><td>Peak angles:<br>Hip flex and add: =<br>Hip IR: F+<br>Knee flex. add: =<br>Knee IR: F-</td><td>GRF<br>Impact peak and active peak:<br>=<br>Loading rate: F+<br>Peak braking force and pro-<br>pulsive force: =<br>MOMENT<br>Peak moment:<br>In the sagittal plane, for Hip,<br>Knee and Ankle: F-</td></tr><tr><td>Keller et al. [63]</td><td>13 M<br>10 F</td><td>NS</td><td>Std</td><td>Δ</td><td>From 12.6 to maximal speed</td><td></td><td></td><td>GRF<br>Active peak: F- at 18 km h<sup>-1</sup><br>Loading rate: =</td></tr><tr><td>Malinzak et al. [46]</td><td>11 M<br>9 F</td><td>NS</td><td>Perso</td><td>Δ</td><td>M: 18.8<br>F: 18.4</td><td></td><td>Peak angles:<br>Knee flex : F-</td><td></td></tr><tr><td>Nelson et al. [36]</td><td>24 M<br>21 F</td><td>NS</td><td>NS</td><td>Δ</td><td>16.5 to 24.1</td><td>Absolute speed:<br>Stride length, contact time<br>and duty factor: F-<br>Step frequency and flight<br>time: F+<br>Stride length (normalized):<br>F+<br>Relative speed:<br>Stride length, step frequency<br>and duty factor: F-<br>Contact time: =<br>Flight time: F+<br>Stride length (normalized):<br>F-</td><td></td><td></td></tr><tr><td>Nigg et al. [47]</td><td>47 M<br>46 F</td><td>Rearfoot</td><td>Std</td><td>Δ</td><td>12</td><td></td><td></td><td></td></tr><tr><td>Phinyomark et al. [48]</td><td>220 M<br>263 F</td><td>NS</td><td>Std</td><td>Δ.</td><td>Self-selected speed<br>8 to 12.1</td><td></td><td></td><td>Amplitudes:<br>Pelvic tilt: F+<br>Hip add: F+<br>Knee abd: F+<br>Peak angles:<br>Hip add: F+<br>Knee abd and ER: F+<br>Knee flex: F-<br>Ankle eversion and dorsi-<br>flexion: F-</td></tr><tr><td>Roche-Seruendo et al. [37]</td><td>52 M<br>45 F</td><td>NS</td><td>Perso</td><td>Δ.</td><td>12</td><td>Contact time: =<br>Flight time: =<br>Step length: =<br>Step frequency: =</td><td></td><td></td></tr></tbody></table>
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>References</th><th>n</th><th>Footstrike</th><th>Footwear</th><th>Surface</th><th>Speed (km h<sup>-1</sup>) and slope</th><th>Spatiotemporal parameters</th><th>Kinematics</th><th>Kinetics</th></tr></thead><tbody><tr><td>Sakaguchi et al. [49]</td><td>11 M<br>11 F</td><td>NS</td><td>Std</td><td>Δ</td><td>12.6</td><td>Contact time: =</td><td>Peak angles:<br>Hip add and IR: F+<br>Knee IR: =<br>Knee abd: F+<br>Tibial IR: =<br>Rearfoot eversion: F-</td><td></td></tr><tr><td>Schache et al. [39]</td><td>22 M<br>22 F</td><td>NS</td><td>Perso</td><td>Δ</td><td>14.4</td><td>Stride time: F-<br>Stride frequency: F+<br>Stride length: F-<br>Stride length (normalized):<br>=<br>Contact time: F-<br>Flight time: F-<br>Contact time: =</td><td>Amplitudes:<br>Pelvis (antero post tilt, obliquity and axial rotation): F+<br>Hip flex-ext and IR-ER: =<br>Hip Add-abd: F+</td><td></td></tr><tr><td>Sinclair et al. [50]</td><td>12 M<br>12 F</td><td>NS</td><td>Std</td><td>Δ</td><td>14.4</td><td></td><td>Only significant differences are reported</td><td>Δ</td></tr><tr><td>Sinclair and Selfe [67]</td><td>15 M<br>15 F</td><td>Rearfoot</td><td>Std</td><td>Δ</td><td>14.4</td><td></td><td>Peak angles:<br>Hip flex: F-<br>Knee abd and IR: F+<br>Ankle eversion: F+</td><td>Δ</td></tr><tr><td>Sinclair and Taylor [55]</td><td>20 M<br>20 F</td><td>NS</td><td>Std</td><td>Δ</td><td>14.4</td><td></td><td>Peak angles:<br>Ankle eversion: F+<br>Tibial IR: F+<br>Amplitudes:<br>Ankle eversion and Tibial IR: =</td><td>Δ</td></tr></tbody></table>
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>References</th><th>n</th><th>Footstrike</th><th>Footwear</th><th>Surface</th><th>Speed (km h<sup>-1</sup>) and slope</th><th>Spatiotemporal parameters</th><th>Kinematics</th><th>Kinetics</th></tr></thead><tbody><tr><td>Takabayashi et al. [38]</td><td>12 M<br>12 F</td><td>Rearfoot</td><td>Barefoot</td><td>Δ<sub>Δ</sub></td><td>Individual transition speed<br>from walking to running<br>on a treadmill*<br>M: 7.8<br>F: 7.3</td><td>Relative speed:<br>Step frequency: =<br>Step length: F−<br>Step length (normalized): =</td><td>Only significant differences<br>are reported<br>Peak angles:<br>Rearfoot plantarflexion: F+<br>Midfoot dorsiflexion and<br>Abd: F+</td><td></td></tr><tr><td>Willson et al. [51]</td><td>19 M<br>19 F</td><td>NS</td><td>Std</td><td>Δ<sub>Δ</sub></td><td>Between 12.7 and 14</td><td>Amplitudes:<br>Hip add and ER: =<br>Knee IR: F−<br>Knee add: =<br>Angle at mid stance:<br>Hip add: F+<br>Hip ER: =<br>Knee IR and add: =</td><td>Rearfoot sagittal plane: F+<br>Midfoot sagittal plane: F+</td></tr></tbody></table>

For kinematic variables, only peak and amplitude joint angles were reported. The results reported are based on the *p* value according to the significant level set in each studies.

*M* males, *F* females, Δ overground, Δ treadmill, GRF ground reaction forces, Std standardized footwear (i.e. all participants wore the same pair of shoes), *Perso* personal shoes, *Flex* flexion, *Ext* extension, *Abd* abduction, *Add* adduction, *IR* internal rotation, *ER* external rotation, *TFJ* tibio-femoral joint, *PFJ* patello-femoral joint, *PFJ patello-femoral joint*, *NS* not specified, *F+* superior in females, *F−* inferior in females, *F−* no sex difference
<figure><img src="image_53.png" /><figcaption>**Fig. 1** This schematic representation of a female runner is an overview of the main psychological, physiological, neuromuscular and biomechanical sex differences in endurance running. Parameters that could give an advantage to males and to females in endurance running</figcaption></figure>

performance are boxed in green and orange, respectively. >: superior in females, <: inferior in females, =: no sex difference, AT Achilles tendon, $O_2$ oxygen, $VO_{2max}$ maximal oxygen uptake

eversion to compensate for larger hip adduction, as typically seen in females [49], i.e. sex differences in kinematics at the hip level can explain differences at the knee level. In other words, greater knee abduction in females could also be a plausible consequence of morphological differences and especially the greater Q-angle (i.e. the acute angle formed by the vector for the combined pull of the quadriceps femoris muscle and the patellar tendon [54]) reported in females [43, 50]. Smaller knee flexion angles have been reported in females both at high running velocities (around $18 \text{ km h}^{-1}$ [46]) and at preferred running speed among a very large sample of 483 (263 females and 220 males) recreational and competitive runners [48]. However, previous investigations showed no sex difference in knee sagittal plane motion between females and males [43, 50]. These discrepancies could be explained by the imposed speed ($13$ and $14.4 \text{ km h}^{-1}$) in previous studies [43, 50], where running speeds varied from $8$ to $12 \text{ km h}^{-1}$ in Phinyomark et al. [48]. At the ankle level, one study found smaller rearfoot eversion in female runners [49], whereas two other studies found greater rearfoot eversion in females [50, 55]. In these studies, as males and females ran at the same absolute speed, Takabayashi et al. [38] suggested that differences could be attributed

to differences in body size and spatiotemporal parameters [38], as described in Sect. 2.1.

Given the identified sex differences in joint kinematics, research has been conducted to identify sex specificities in lower limb muscle activities. Unfortunately, sex comparisons have mostly been studied at given absolute running speeds and for very short periods of time (about 10 s). The neuromuscular strategies adopted may thus differ from those used in endurance running and racing conditions. In these experimental running conditions, females had a higher activation of the gluteus maximus [42, 51] than males at all speed (from $9.7$ to $14 \text{ km h}^{-1}$) and incline ($0$, $10$ and $15\%$) conditions and a larger increase in activity of the vastus lateralis than males as incline increased [42]. Females may thus use different neuromuscular strategies than males in case of increased task demand, i.e. changes in speed and incline [56]. At similar running speeds of around $18 \text{ km h}^{-1}$, Malinzak et al. [46] demonstrated a higher activation of the quadriceps but a lower activation of the hamstrings in recreational female athletes compared to males, which potentially induced a higher anterior knee shear force (i.e. perpendicular force applied by the quadriceps muscles on the tibia) in female runners. In contrast, a higher medial hamstring pre-activation has been reported
in downhill running at a slow running speed (7.5 km h⁻¹ on a 15° declined treadmill) in female runners, which may contribute to better stabilization of the knee joint in this situation [57]. Similarly, an increased pre-activation of the peroneal muscle group was found in female runners during level running, and this was considered as a strategy to stabilize and protect the ankle joint [58]. As these studies only considered a small number of muscles, further analysis of the inter-muscular coordination in running was limited. However, in a recent study conducted in 60 females and 60 males [59], the concept of muscle synergies could be used based on the electromyographic recording of 13 lower limb muscles at running speeds ranging from 7.2 to 13.7 km h⁻¹. This study found sparse sex-specific modulations of the relative muscle contribution in running. During the stance phase, no sex differences were found in the contribution of the hip extensors, tensor fasciae latae and plantar flexors, yet females used the vastus medialis less during the weight acceptance. In this particular phase, the timing of the main peak of activity was similar but the synergy was narrower in females, implying a shorter time-duration of activation in females than in males. The authors suggested that the neuromotor strategy showed by females could reduce their fatigability during prolonged locomotion efforts. During the early swing, females used the rectus femoris less but the gluteus medius more whereas during the late swing, females used the tibialis anterior more compared to males.

Overall, despite sex-specificities of the running kinematics, the reported differences in muscle activities, such as the female-specific protective joint strategies, are still controversial and unlikely to reduce the sex-related gap in performance. Further studies should focus on the sex differences in muscle synergies under endurance and competitive running conditions.

Most of these studies investigated running mechanics at the same absolute speed, putting female runners at a greater relative demand on their musculoskeletal system. Indeed, most of the previous literature on kinematics differences between male and female runners failed to establish a relationship with sex, often because of the lack of control for confounding factors such as running speed and participants' height and body mass. It is therefore necessary to conduct more studies with larger sample sizes allowing consideration of confounding factors in order to obtain a clearer picture of the real influence of sex on running biomechanics.

A recent study assessed sex differences in running biomechanics in a population of Chinese males and females [60]. The authors report similarities with sex differences in Western populations (e.g. increased hip and knee range of motion in females), but also differences in biomechanics (e.g. knee and ankle joint kinematics, vertical ground

reaction force and plantar pressure) between Western and Chinese runners. Thus, the authors emphasized the fact that ethnicity should be considered when comparing running biomechanics in males and females, in particular by running shoe manufacturers. Another under-studied topic is the influence of females' breasts on their running biomechanics. A recent review [61] pointed out that, although there is some information on breast motion during walking or running, the previous breast-focused literature had “poor research design and inadequate biomechanical methods used to quantify complex three-dimensional breast motion”. Brown and Scurr [62] found a slightly negative influence of breast mass on the marathon finish-time; however, no studies have been conducted so far to determine the influence of the biomechanics of females' breasts and trunk on their running performance. This specificity of females' morphology should also be considered in future studies examining sex difference in running biomechanics.

## 2.3 Running Kinetics and Muscle-Tendon Stiffness Regulation

At given running speeds up to 21.6 km h⁻¹, Keller et al. [63] did not report any sex difference in the changes in maximal vertical ground reaction force and loading rate (normalized by body weight) with speed. However, sex differences in the normalized force data have been reported in a larger group of recreational runners when running at the same absolute running speed of 11.9 km h⁻¹ [41]. Females exhibited a significantly higher loading rate but a lower active peak force than males in both pre-fatigue and fatigue conditions. They also showed higher peak propulsive force (antero-posterior), but only before fatigue. Again, as this study was conducted at the same absolute running speed, greater peak propulsion force (measured on the antero-posterior axis) observed in females may have been due to the greater relative effort compared to males.

Joint moments and mechanical stress applied to lower limb joints have been compared to better understand potential sex differences in running-related injuries, rather than running performance [64]. Specifically, females appeared to be at greater prevalence [65] and incidence [66] to sustain patellofemoral joint pain compared to males. Conflicting results exist on sex differences in patellofemoral joint mechanical stress (PFJS) [40, 67, 68], probably due to different methods used to calculate it. In addition, contradictory results exist in sex differences when considering peak knee extension moment (normalized by body mass) in running since no difference [43], greater [67] or lower [40] moment in females have been reported. In the frontal plane, females have been shown to display greater normalized hip and knee adduction moment compared to males [44]. Similar to sex
differences in running kinematics, sex differences in kinetics have mainly been attributed to – yet not measured—morphological sex differences.

Running is a stretch-shortening cycle type activity in which higher Achilles tendon (AT) stiffness is expected to affect the storage of elastic energy during the braking phase, followed by its recoil during the subsequent push-off phase. Therefore AT stiffness is expected to affect RE [69–73]. Tendon structural quality and resistance to deformation, tendon collagen synthesis rate and hypertrophic effect of regular running exercise on the patellar and AT have been reported to be lower in females than in males [74, 75]. In particular, no significant hypertrophic effect on the patellar and AT was found in young and regularly trained female runners (at least 40 km of running mileage per week for the previous 5 years) as compared to untrained ones, whereas a significant gain was found in similarly trained males [75]. Both non-athletic [71] and trained female runners [73] have been reported with lower stiffness and hysteresis of AT than their male counterparts. Despite this AT difference, trained female and male runners had a similar RE (see Sect. 2.2), but this remains the only study on such a sex comparison. Other confounding factors should be considered, such as AT length and moment arm, but these factors were studied primarily in elite male runners [76]. Thus, further work is needed to determine in females the exact influence of a less stiff AT on their RE and running performance.

In addition to their effects on tendons, oestrogens are reported to reduce ligament stiffness. Although suggested as a potential risk factor for anterior cruciate ligament (ACL) injuries for female participants during sports activity [77], a causal link is refuted by other studies reporting no associated decrease in anterior knee stiffness [78]. ACL laxity also appears to be affected by the menstrual cycle, as evidenced by its increase reported in the late follicular phase, i.e. when the serum level of estradiol peaks and progesterone is low (e.g. [78, 79]). Once again, no associated change was found in anterior knee stiffness [78]. Within this framework, muscle stiffness being an important component of joint stability, it is noteworthy that Khowailed et al. [80] revealed different muscle activation strategies in running during different phases of the menstrual cycle. During the follicular phase, when oestrogen levels are low, females placed greater reliance on the quadriceps than on the hamstrings. During ovulation, when oestrogen level and knee laxity are high, increased hamstring pre-activation and co-activation during the braking phase have been observed. Thus, as oestrogen levels seem to influence the stretch-shortening cycle and potentially RE, running performance could possibly vary across the menstrual cycle.

# 3 Substrate Utilization and Running Economy

## 3.1 Substrate Utilization

Numerous original studies and review studies have analyzed sex differences in substrate utilization during exercise lasting from 60 to 120 min (e.g. [81–86]) at different intensities (from 35 to 75% VO₂max). Comparing sexes in substrate use is methodologically challenging since this parameter is influenced by several factors such as training status, menstrual cycle phase, diet, age, etc. Controlling for age, diet and physical activity level (i.e. using VO₂max expressed relative to fat-free mass) would allow avoiding heterogeneity in the results when comparing exercise-induced metabolic responses between males and females [82]. Menstrual cycle phase has been reported to have a small impact on sex difference in substrate utilization [86]. Oral contraceptives have a slightly greater impact on substrate oxidation [86]. Together this implies that when the variance of these two factors is not controlled, sex differences in substrate utilization may be biased.

When considering only the studies that presented as well controlled with regard to these factors, the overall conclusion is that females oxidize more lipid and less carbohydrate (CHO) and protein than males during prolonged exercise (at least until 120 min of exercise duration). Females have also greater metabolic flexibility since substrate oxidation is readily adjusted according to the availability of nutrients. In this regard, the respiratory exchange ratio (RER) has been reported to be lower in females during low-intensity, prolonged exercises, supporting a lower reliance on whole-body CHO oxidation (e.g. [86]). For instance, lower RER values were found for females (0.87) than for males (0.94) during a 15.5 km treadmill run at 65% VO₂max [84]. During an incremental treadmill run test to exhaustion performed in a large cohort of 157 males and 143 females, Venables et al. [87] showed that females displayed higher maximal rates of fat oxidation. In addition, females exercised at a higher %VO₂max, for which a CHO as dominant fuel is observed. Although less used than CHO and fat, protein provides approximately 5–8% of total energy in both sexes [29] during prolonged exercise. However, it should be noted that the protein catabolism seems to be lower in females, potentially due to the higher fatty acid oxidation [29, 30, 84, 88, 89]. Theoretically, this may constitute an advantage for females during ultra-endurance exercises (Fig. 1).

The higher metabolic flexibility observed in females constitutes a sex-specific difference that depends on several factors (and their interaction), including higher skeletal muscle insulin sensitivity as well as high levels of circulating and intramuscular fatty acids required for the oxidation
process (for a review, see [83]). Some authors have reported
the crucial role of female sex steroid hormones (e.g. oestro-
gens) in the regulation of whole-body insulin sensitivity and
substrate metabolism (e.g. [83, 86, 90]) at rest and during
endurance exercise. Within the endogenous forms of oes-
trogens, 17-β-estradiol and associated oestrogen receptors
α are important in the regulation of substrate metabolism in
skeletal muscle. Higher levels of 17-β-estradiol are reported
to spare glycogen stores and decrease liver glucose output
and muscle uptake by shifting metabolism toward free fatty
acids at certain (low to moderate) exercise intensities [28,
86, 91–93]. This metabolic hormonal action might thus
contribute to enhanced performance capabilities of females
compared to males for ultra-endurance exercise. Although
lower RER values have been reported at submaximal run-
ning intensities (30–60% VO₂max) in the luteal phase of the
menstrual cycle [94, 95], other studies reported no signifi-
cant differences throughout the menstrual cycle (e.g. [96]).
Part of this discrepancy has been attributed to the complex
balance between oestrogen and progesterone concentra-
tions and their fluctuations during the menstrual cycle [82].
According to D'Eon et al. [92], the ratio of oestrogen/pro-
gesterone would be sufficiently elevated to lead to metabolic
changes between the menstrual phases.

Although females and males do not differ in their glyco-
gen stores and capacity for storage of glucose into glycogen
[83, 84], females have a higher plasma fatty acid availability
due to a greater number of intramyocellular lipid (IMCL)
droplets (and not larger droplets) [97]. Moreover, female
skeletal muscle is reported to be better able to store lipids
and oxidize fatty acids during exercise, which would con-
tribute to maintaining a high level of IMCL turnover [83].
During exercise, this dependence on lipid substrate to cover
energy expenditure in females might be explained by a
greater relative area of type I muscle fibers (see next section)
characterized by higher oxidative capacity, capillary density
and higher insulin sensitivity [98]. Indeed, Essén et al. [99]
previously reported that IMCL content was 2.8-fold higher
in type I fibers compared to type II fibers. Despite inconsist-
encies in the literature [82], higher IMCL use during sub-
maximal exercise has been reported in females as compared
to males, irrespective of training status [100–102].

The majority of studies focusing on sex differences in
substrate oxidation have been conducted on a cycle ergom-
eter rather than in running (see Table 1 in Tarnopolsky [86]).
The differences in substrate oxidation observed between
sexes highlights the importance of quantifying the cycle
menstrual phase, oral contraception, training level and diet
in females.

## 3.2 Running Economy

RE is known to be the most predictive variable of running
performance within homogeneous groups of trained athletes
(i.e. similar VO₂max values) (e.g. [21]). RE depends on sev-
eral physiological, biomechanical, anatomical and neuro-
muscular (NM) attributes, supporting the importance of a
holistic approach in RE determination (for reviews, see [103,
104]). Oxygen cost in mlO₂ kg⁻¹ km⁻¹ is derived from the
mass-specific VO₂, dividing the steady-state VO₂ the resting
value by the running speed. Given that substrate metabolized
to provide energy may vary with exercise constraints such
as intensity [105], duration [106], training and sex (e.g. [97,
107]), it has been proposed to express RE as gross energy
cost [105] in J·kg⁻¹·m⁻¹ or kcal·kg⁻¹·km⁻¹. An enhanced/
improved RE is associated with a decrease in this value.

During submaximal running tests (<10 min), no sci-
entific consensus exists on the differences in RE between
trained or recreational male and female runners, with con-
flicting results strongly related to differences in methodo-
logical setting [73, 108–114]. VO₂max values, training back-
ground, running performance, body fat and quantification of
RE have probably contributed to the lack of consensus on
this issue. In this regard, sex-specific differences have often
been observed when authors expressed RE from oxygen cost
or submaximal VO₂ (e.g. [23, 108, 112, 115]). Moreover,
the majority of studies investigating sex differences in RE
have used small sample sizes (e.g. *n* < 30) and/or compari-
sons across absolute speeds (rarely with relative intensities).
In a study comparing RE between trained female and male
runners, Fletcher et al. [73] did not show any sex differ-
ence at similar relative intensities (75, 85 and 95% of the
speed at LT) when expressed in either energy cost or oxygen
cost, or when using of an allometric scaling factor in order
to take into account sex differences in body mass. These
findings have been confirmed by the same research group
with the inclusion of elite and trained male and female run-
ners [113]. In addition, using a larger sample size (*n* = 95),
Black et al. [109] recently reported no sex differences in
RE in kcal·kg⁻¹·km⁻¹ at similar relative and absolute run-
ning speeds among females and males of equivalent stand-
ard. Considering these findings, no clear difference in RE
responses exists between male and female runners even
when applying different RE units.

In most of the articles focusing on RE responses in
females, a qualitative analysis of menstrual cycle is lacking,
and this might constitute an important methodological bias
in RE evaluation. During endurance exercises, females gen-
erally use less CHO and have lower RER values [107, 116,
117]. These metabolic changes may depend on the phases of
the menstrual cycle as well as short-term oral contraceptives
and, in turn, affect the RE response in females. RE may also
be naturally influenced by the specificity of each phase (e.g.
early, late follicular and mid-luteal phases), and, therefore,
circulating hormone levels (i.e. oestrogen and progesterone).
For instance, investigating three different times during the
menstrual cycle in females taking oral contraceptives (i.e.
constant doses of oestrogen and progestogen for 21 days),
Giacomoni and Falgairette [118] reported an improved RE
at low intensities (7–9 km h⁻¹) only during the late period
of oral contraceptive use (i.e. days 19–21). In this regard,
Dokumacı and Hazır [119] recently reported an improved
RE (i.e. in terms of energy or oxygen cost) in the luteal
phase compared to the follicular phase in female competitive
athletes with no oral contraceptives. Conversely, two stud-
ies have indicated that the mid-luteal phase (high levels of
oestrogen and progesterone) was associated with worse RE
responses compared to early and late follicular phases [120,
121]. More work is needed to better understand the effects
of the menstrual phase in RE, but these findings suggest that
those effects must be considered.

4 Fatigue Resistance

NM function, RE and biomechanics are often measured in
a non-fatigued state. However, our group and others have
consistently shown in the last two decades using labora-
tory or field experiments (e.g. marathon, trail/ultra-trails),
that fatiguing endurance running exercise drastically affects
maximal strength, RE and running patterns. Some sex differ-
ences exist at these levels and are discussed below.

4.1 Neuromuscular Fatigue

NM fatigue has traditionally been defined as an exercise-
induced reduction of maximal strength or power [122]. The
causes can be peripheral (i.e., within the muscle) and/or
central (i.e., proximal to the neuromuscular junction), and
NM fatigue etiology in running depends on many factors
such as exercise duration, intensity, altitude, and positive
and negative slope [123]. Central fatigue reflects an inability
to voluntarily activate muscles, and has been described as
a protective mechanism acting to limit peripheral perturba-
tions [124, 125]. In contrast to central fatigue, which occurs
predominantly after very long and low intensity exercise,
peripheral fatigue is greater at higher intensities, shorter
duration exercise [126] including downhill running [127],
and could impair (1) action potential transmission along the
sarcolemma, (2) excitation–contraction coupling, and (3)
actin–myosin interaction [123].

Sex differences in fatigability have been mostly inves-
tigated in laboratory conditions (for a review, see [128]).
While females are less strong and less powerful than males,
they were found to be less fatigable than males for sustained
or intermittent isometric contractions performed at a similar

relative intensity [129]. However, sex differences in fatiga-
bility are less clear following dynamic tasks as females have
been reported to be either less than or as fatigable as males
(see Fig. 1 in Hunter [128]). Among these studies, a lower
fatigue index (i.e. smaller decline in each isokinetic variable)
was found in females compared to males after a series of 30
maximal dynamic knee extension [130]. This finding could
be of importance given that in a recent paper, Ehrstrom
et al. [131] showed local endurance of the knee extensors
was a good predictor of performance on a short trail run-
ning exercise (27 km with 1400 m of positive elevation) in
trained male runners. On a fatiguing cycling task, Ansdell
et al. [132] compared males and females after normalizing
exercise intensity to critical intensity, and found that females
displayed a greater relative critical intensity compared to
males, yet contrary to their hypothesis, females had a longer
time to task failure than males above critical power [132].
Females also exhibited lower strength loss and peripheral
fatigue at exhaustion. However, sex differences in fatigabil-
ity following prolonged running exercise is not well docu-
mented. After 2 h of laboratory treadmill running at a speed
corresponding to their first ventilatory threshold, Glace et al.
[133] reported that knee extensor/flexor strength measured
in isokinetic conditions (60° s⁻¹) decreased in males only,
while decreased hip strength was independent of sex.

Under real endurance racing conditions on short distances
(<40 km), Boccia et al. [134] reported no sex difference in
the decrease in knee extension force after a half-marathon
performed by moderately trained runners under real rac-
ing conditions. These authors also found that the origin of
fatigue was independent of sex. After a graded 20-km race
(±350 m) in recreational males and female runners, sex
differences were found in both the acute and delayed (up
to 4 days) recovery period [135]. Females showed smaller
functional decrements within the first 2 h and earlier full
recovery than males. Indeed, only males showed decreases
in maximal isometric knee extension force and maximal
power in drop jump up to 4 days after the race. However,
regarding the relative decrements (normalized by pre-val-
ues), no sex difference was found, so that the lower fatigue
effects observed in females may be partly attributed to their
lower initial strength levels.

Over a much longer distance, Temesi et al. [136] assessed
NM fatigue in experienced male and female ultra-endurance
trail runners matched by relative level of performance (i.e.
same average percentage of performance as the winner of
each sex) after a 110-km ultra-trail-running. In the ultra-trail
study, female runners had (1) less peripheral fatigue in the
plantar flexors and (2) a lower decrease in maximal force in
the knee extensors when compared to their male counter-
parts. The authors suggested that females were more fatigue
resistant following ultra-endurance running effort, which
could partly explain why they manage to outperform males
in races of this format. A recent study from our group investigated sex differences in NM fatigue in trail running races of various distances (from 40 to 170 km) [137]. Male and female participants were matched by a relative level of performance using the same criteria as in Temesi et al. [136]. Greater knee extensor strength loss was found in males compared to females and, contrary to our hypothesis, this sex difference was independent of the race distance. Interestingly, we observed greater peripheral fatigue on plantar flexor muscles in males compared to females in races under 60 km but no sex difference in races above 100 km. This result could be partly due to sex differences in the “competition intentions” of the participants: with races < 60 km females reported that they performed the race in a more “pleasure mode” whereas males were more competitively oriented. This possibility should be considered when concluding on fatigability. In other words, less fatigue post-exercise does not necessarily mean a better resistance to fatigue, at least in non-elite runners.

## 4.2 Muscle Tissue Characteristics

Among the underlying mechanisms, males and females have distinct muscle properties. An obvious difference that mainly explains the difference in maximal strength is muscle mass, due to larger diameter muscle fibers in males rather than the number of fibers [138], this larger size being due to sex-related differences in human skeletal muscle gene (e.g. GRB10 and ACVR2B) expression [139]. This factor has been presented as partly explaining the sex differences in fatigability in most of the studies using single-joint exercise since stronger muscles tend to be less perfused because of higher blood flow occlusion resulting from higher intramuscular pressure. In order to overcome the occlusion effect, Ansdell et al. [140] compared NM fatigue in males and females across submaximal and short duration intermittent isometric contractions (i.e. they used a 3-s contraction duration with a duty cycle allowing to negate the influence of occlusion). After normalizing the intermittent exercise intensity by maximal force, lower force reduction and longer time to task failure was still found in females compared to males. However, this is not the only factor explaining the fatigability. Despite no sex differences in peak force, in power and shortening velocity of single fibers when normalized to muscle cell size (e.g. [141]), there is considerable evidence that females have a greater proportional area of type I fibers in several key muscles for locomotion and daily function [128]. Females also have greater capillarization [100] and lower glycolytic enzyme activity [142] when compared to males. Related to these important features, a better vasodilatory response to exercise has been reported for females. For example, vasodilatory responses of the femoral artery during dynamic knee extensor exercise were found to be greater in

females than in males [143]. Overall, these results explain why females show attenuated impairments in contractile function compared to males [144], and are able to recover at a faster rate from repeated fatiguing exercise [145].

In addition to directly explaining the sex-related difference in fatigability at the muscle level, the attenuated metabolic disturbance related to the greater proportional area of type I may also lead to attenuated afferent feedback (groups III and IV). This may induce less inhibitory inputs to the motoneuronal pool, and thereby less central fatigue. Indeed, greater levels of fatigue observed in males have been associated with greater central deficits, but these studies were using maximal efforts and it is not known if this is also true for submaximal contractions [128].

In conclusion, as generally reported following single-joint fatiguing tasks, females appear less fatigued following trail and ultra-trail races. A greater proportion of type I muscle fibres and higher muscle capillarization (i.e. less muscle de-oxygenation) in females compared to males are possible explanations for lower fatigability in females, and could be of advantage for females over ultra-endurance running performance (Fig. 1). Although the results obtained with the scale on “the competitive intention” of the runners must be treated with caution (i.e. the scale has not undergone a validation process), the fact that females (non-elite) have been showed to be less competition oriented than males could also at least partly explain sex differences in fatigability following such trail running races.

## 4.3 Muscle Fiber Damage, Inflammation and Delayed-Onset Muscle Soreness

Endurance running races induce metabolic stress and include thousands of ground impacts and subsequent eccentric muscle actions that are likely to induce very focal and limited ultrastructural muscle damage. In the most severe case, less than 10% of the sarcomeres appeared damaged [146], which clearly differ from those induced by pure eccentric muscle actions [147]. The subsequent ultrastructural recovery process involves oedema, remodelling/inflammation, and delayed-onset muscle soreness (DOMS) [148, 149]. The ultrastructural damage is expected to be less or even absent in trained runners [150, 151]. Importantly, (1) neither the degree nor the timing of exercise-induced muscle damage (EIMD) per se correlates well with the DOMS sensation [152, 153], and (2) the timing of DOMS disappearance usually occurs prior to complete structural and functional recoveries [149]. The only study that has directly addressed in humans (via muscle biopsies) the effect of sex on ultrastructural muscle damage and/or inflammation up to 6 days after an eccentric exercise reported no sex difference in muscle damage, yet an attenuated inflammatory response in females as compared to males [154]. In our own study
of the sex differences after a graded 20-km running race [135], DOMS were recorded for the major lower limb muscles. Independently of sex, DOMS was reported at day 2 for the quadriceps muscle group, but only in female runners for the hamstrings, suggesting that possible sex differences are muscle-group dependent. At day 4, despite the disappearance of DOMS, males still had functional deficits in MVC and drop jump test. Female runners also had functional deficits in DJ, but no longer reported DOMS and had already recovered in MVC.

Sorichter et al. [155] have summarized the literature on the identification, usefulness and limits of indirect markers of ultrastructural muscle damage widely used in males and females. Aside from DOMS, the most reported indirect indicators include myoglobin (Mb) and lactate dehydrogenase (LDH) plasma levels as well as serum creatine kinase (CK) activity [156]. According to Goodman et al. [157], the increased serum levels of CK and Mb after an endurance run might result from free radical-induced cell membrane damage and/or transiently increased permeability rather than from mechanical ultrastructural damage. Part of these perturbations are likely to decrease with training (e.g. Margaritis et al. [158]). However, owing to high individual variability none of these indirect markers accurately reflect the magnitude of muscle damage. This appears clearly in the extremely large range of CK concentration values (1500–264,300 IU L⁻¹) reported among finishers of ultra-trail running events, but no differences between sexes have been reported [136, 159]. These methodological limits apply to the serum CK [160], which is still widely used as a marker of the sex difference in the exercise-induced muscle damage [161].

As reviewed by Enns and Tiidus [162], sex and oestrogen are reported as potentially attenuating indices of exercise-induced skeletal muscle damage by influencing the inflammation and repair processes. Although the underlying mechanisms are not yet fully elucidated in humans, oestrogens are thought to exert their protective effects by: (1) acting as an antioxidant, thus limiting oxidative damage; (2) acting as a membrane stabilizer by intercalating within membrane phospholipids; and (3) binding to oestrogen receptors, thus governing downstream muscle repair processes, including the activation and proliferation of muscle satellite cells. Supporting the oestrogen protective effect in runners, lower resting serum CK values are reported in female compared to male athletes [161] and in eumenorrheic compared to amenorrhoeic female athletes [163], whereas no sex effect was found in the peak values after a marathon [164] in moderately trained runners. Illustrating the limitations of using indirect markers of muscle damage and inflammation, the downhill running study of Oosthuysé and Bosch [165] found that DOMS peaked after 24 h in males and females, but the time to disappearance of CK and DOMS then differed by

sex. Compared with males, females showed earlier restoration of the pre-run CK activity level (at 48 vs. 72 h) but longer-lasting DOMS (up to 72 vs. 48 h).

In conclusion, despite the lack of direct assessment of sex differences in the running-induced ultrastructural muscle damage, the reported time-evolution of indirect markers of muscle inflammation (such as plasmatic CK activity and DOMS) during the recovery period suggest less damage or at least earlier structural recovery in female runners.

## 4.4 Change in Running Biomechanics and Economy with Fatigue

An altered RE has been repeatedly reported after prolonged road and trail exercises in male runners (e.g. [166–170]), but only a few studies have been conducted in trained or recreational female runners [133]. After a 2-h treadmill run session at a given relative intensity, Glace et al. [133] showed an increase in oxygen cost in male but not in female distance runners. In male athletes, the exacerbated increase in oxygen cost was related to their greater muscle fatigue characterized by the larger strength decrement of the knee extensors (see Sect. 4.1). To the best of our knowledge, only two other studies have examined sex differences with regard to RE change with fatigue, albeit with shorter running bouts. Similar changes in RE were found for males and females following (1) a 1-h run at marathon pace [171] and (2) a 5-km run at 80–85% of VO₂max [172]. These three experiments were conducted on a treadmill in laboratory conditions. A recent study from our group reported an increase in RE following trail running races from 40 to 170 km; however, no significant sex difference were observed in the changes in RE measured on both flat (+3.1 vs. +8.7% in females vs. males, respectively) and uphill (+1.4 vs. +6.9% in females vs. males, respectively) conditions [137]. Further investigations are warranted to further examine sex differences in RE in the ecological context using models of prolonged road and trail run exercises.

While many studies have investigated sex differences in running biomechanics under acute conditions (see Sect. 2.2), the literature is very limited on sex differences in the alteration of running biomechanics following a fatiguing task. Bazuelo-Ruiz et al. [41] reported a reduced ankle dorsiflexion angle at touchdown in females only after a succession of three consecutive tasks at high intensities (shuttle-run, running up and downstairs, and jumps), this parameter being associated with a decrease in loading rate and vertical impact peak force. These authors suggested a potential adaptative strategy to optimize shock attenuation and thus prevent running injuries. However, these results should be interpreted with caution since the fatiguing protocol was quite unusual. After a 110-km trail running race, Giandolini et al. [173] reported that males adopted a flatter
pattern (i.e. a decreased foot/ground angle) and increased step frequency contrary to females, who landed with a more dorsiflexed ankle accentuating their rear-foot strike pattern. Changes in males were considered as a compensatory adjustment to possibly greater decreased muscle capacity compared to females [173]. Although female adaptations to fatigue observed in the studies of Bazuelo-Ruiz et al. [41] and Giandolini et al. [173] seem to go in opposite directions (concerning the alteration of ankle angle with fatigue), the fatiguing tasks differed considerably between the two studies and could explain the discrepancies in the results. As also suggested by the sex-dependent DOMS sensations per muscle after a graded running race [135] (see Sect. 4.3), it appears that males and females adjust their running pattern differently, which suggests sex-relative biomechanical adaptations. Under laboratory conditions, no sex differences were reported in running biomechanical parameters previously associated with the aetiology or exacerbation of patellofemoral joint pain after a run on a treadmill at 3.5 m s⁻¹ until reaching a RPE of 17 out of 20 [68]. These studies compared males and females following different modalities of exercise and under different methodological approaches. Further studies are needed to better investigate potential sex differences in biomechanical alterations following prolonged running exercise.

# 5 Other Factors Influencing Running Performance

## 5.1 Thermoregulation

Whether sex differences exist in thermoregulation was uncertain until recently [174], i.e. it was not known if thermoregulatory differences between males and females were due to sex per se or to other differences such as body size or aerobic fitness. Indeed, a correlation between $\dot{V}O_{2max}$ (lower in females) and the ability to maintain lower core temperatures during exercise in the heat has been found, potentially related to skin blood flow. Females may generate less heat in high ambient temperatures because of lower muscle mass and smaller body size but may be disadvantaged as their lower $\dot{V}O_{2max}$ can result in faster increases in body temperature. Notley et al. [175] concluded that, contrary to popular belief, sex does not significantly impact exercise thermoregulation [175]. A review by Yanovich et al. [176] confirmed the lack of sex differences and concluded that females were not at greater risk of heat illness, provided the usual risk-management techniques are in place (pacing, clothing, hydration, acclimation). It has been reported that female athletes used more heat illness prevention strategies than male athletes [177]. It is noteworthy that the American College of Sports

Medicine does not provide specific guidelines or strategies for the female athlete. However, it has been argued that a sex difference in thermoregulation may depend on the percentage of humidity and exercise intensity [178]. Indeed, Shapiro et al. [179] found that core temperature increased less in females than in males for hot/humid environments (due to larger surface area to body mass ratio and more efficient sweating suppression), whereas the opposite was true in hot/dry climates (due to lower overall sweating capacity in females despite a higher density of sweat glands). Although thermoregulatory responses vary over the cycle, Notley et al. [175] suggested that there was no additional thermoregulatory concern for female athletes if an oral contraceptive was used or at any phase of the menstrual cycle phase. It is not known if potential sex differences exist in the time-course of acclimation.

It has also been suggested that performance was less affected by warm weather in females than in males during a 161-km ultra-marathon [180], although no sex difference was found in marathons. The opposite may actually be true since performance is more negatively affected for slower populations of runners on that distance [181]. Importantly, because of their smaller body size, lower heat generation, and less sweat loss (i.e. better efficiency in sweating that may decrease the total amount of so-called “wasted sweating” in a humid environment), the water intake required to maintain hydration and thermal balance is attenuated in females. Since hydration guidelines have been developed using data mainly collected in studies with males, they should be interpreted with caution when applied to females [176]. This is particularly important in ultra-endurance exercises since over-hydration may induce gastro-intestinal distress.

## 5.2 Pacing

Several studies, including a large study with data from 14 US marathons encompassing ~92,000 performances [182], have shown that females tend to have a more even pacing strategy in marathon races [183]. A similar finding has been found in half-marathons [184], 10-km races (at least in fast runners [185]) or even shorter races such as 5-km cross-country race when males and females have similar relative performance [186]. It has been suggested that sex difference in pacing partly reflects a sex difference in decision making [185] rather than greater susceptibility to glycogen depletion [186]. Interestingly, the sex differences in pacing were also found to be true for top runners. A study collected finishing and split times in 673 males and 549 females across nine Olympic and World Championship marathons and found that females slowed less and were more likely to run a negative split than males [187]. In addition, in a study from Trubee et al. [188], the more even pacing strategy in females was
found to be an even greater advantage during warm weather conditions.

The number of studies on sex differences in pacing strategy related to ultramarathon is low and there is no clear consistency among the results. Indeed, females showed lower relative starting speeds and higher finishing speeds than males on a 100-km race [189], yet no sex differences in pacing strategy [190] or even better preservation of mean speed throughout the race have been found in males [191] on 24-h races. It has been advised that, given the possibility of a “herd principle”, comparison of competition dynamics between sexes must be further explored in ultra-marathon.

The level of performance and age may be confounding factors in pacing strategies although, again, the results are not that clear. As stated in the present section, sex differences in pacing on 10-k races were mostly found in good runners [185], whereas they occurred across all finishing time groups in the marathon [182]. Deaneer et al. [182] also showed that sex differences in pacing occurred across age groups. On the contrary, it has been found that females showed no differences in pace variability across ages, whereas younger (<30 years) and older (>60 years) males showed a greater variability in pacing than other age groups [192].

## 6 Conclusion

Through this narrative review, we have identified a number of sex differences in running related to biomechanical, physiological and NM aspects, some of which potentially play a role in endurance running performance (Fig. 1). In particular, the current literature reports that females show (1) greater hip and knee joint motion in non-sagittal planes and (2) greater lower limb muscle activation, compared to their male counterparts at a given absolute speed. However, most of these sex differences disappear when normalized either by maximal aerobic velocity or anthropometry. Despite those sex differences in running biomechanics, males and females do not differ in terms of RE, suggesting that both sexes optimize their running pattern to adjust to their characteristics. During prolonged exercise, females use more fatty acids than males, which allows them to save carbohydrates. In addition, attenuated NM fatigue, particularly less peripheral impairments on plantar flexor muscles, is observed in females compared to males following endurance and ultra-endurance running bouts. The fact that oestrogen seems to have some protective effects may indicate that females could be less prone to damage induced by repetitive eccentric muscle actions. Yet these differences do not allow females to outrun males in endurance and ultra-endurance running exercises. It is also still not clear whether prolonged running affects males and females differently in terms of running

biomechanics and economy. Sex differences in a fatigued condition are a clearly under-investigated research area and further direct physiological and biomechanical comparisons between males and females following prolonged running are warranted. Considering that females used different pacing (i.e. more even) strategies than males, other psychological and/or sociological parameters that could affect performance or help to understand sex differences in running participation also need to be explored.

**Acknowledgements** The authors sincerely thank Callum Brownstein for English editing.

## Declarations

**Author contributions** TB, RM, CN, FV and GYM drafted the manuscript; JR, CM and YK provided additional comments and contributions; all authors approved the final version.

**Funding** No sources of funding were used to assist in the preparation of this article.

**Conflict of interest** Thibault Besson, Robin Macchi, Jérémy Rossi, Cédric Morio, Yoko Kunimasa, Caroline Nicol, Fabrice Vercruyssen and Guillaume Millet declare that they have no conflicts of interest relevant to the content of this review.

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# CHAPITRE III

## Influence du sexe sur la cinétique de récupération fonctionnelle et neuromusculaire après une course d'endurance

Ce chapitre s'appuie sur l'étude 2 et l'étude 3 publiées et les 3 présentations affichées suivantes :

2. **Macchi, R.**, Vercruyssen, F., Hays, A., Aubert, G., Exubis, G., Chavet, P., Goubert, E., Souron, R., Yoko, K., Nicol, C. (2021). Sex influence on the functional recovery pattern after a graded running race: original analysis to identify the recovery profiles. *Frontiers in Physiology*, 12. https://doi.org/10.3389/fphys.2021.649396

3. **Macchi, R.**, Santuz, A., Hays, A., Vercruyssen, F., Arampatzis, A., Bar-Hen, A., Nicol, C. (2022). Sex influence on muscle synergies in a ballistic force-velocity test during the delayed recovery phase after a graded endurance run. *Heliyon*, 8(6), e09573. https://doi.org/10.1016/j.heliyon.2022.e09573

1. **Macchi, R.**, Hays, A., Vercruyssen, F., Chavet, P., Goubert, E., Souron, R., Nicol, C. (2020). Sex influence on the functional recovery pattern after a 20 km graded running race. *European College of Sport Sciences, Séville (Espagne)*

2. **Macchi, R.**, Santuz, A., Hays, A., Vercruyssen, F., Arampatzis, A. et Nicol, C. (2021). Sex influence on the neuromuscular fatigue examined by a force-velocity concentric test. *International Society of Biomechanics, Stockholm (Suède)*

3. **Macchi, R.**, Chavet, P., Hays, A., Kunimasa, Y., Vercruyssen, F., Nicol, C. (2021). Multivariate modelling of the recovery pattern and its sex dependence after a graded run. *European College of Sport Sciences, Séville (Espagne)*

Nous présenterons successivement l'étude 2, le poster 3 et l'étude 3.
Au regard de l'augmentation de la participation des femmes à des épreuves de course à pied et des différences liées au sexe détaillées dans le Chapitre II, il convient de mieux caractériser l'influence du sexe sur les conséquences neuromusculaires de ce type d'épreuves. La majorité des études de la litérature s'étant focalisées sur la récupération en phase aiguë (< 2 h), rares sont les données sur l'ensemble de la cinétique de récupération, et ce pour les deux sexes. De plus, ces études s'étant focalisées sur l'analyse de l'éthiologie de la fatigue par des méthodes d'électrostimulation lors de tâches isométriques, il y a un manque de connaissances concernant le suivi des ajustements neuromusculaires en condition de tests dynamiques, tant en phase aiguë qu'en phase retardée de la récupération. En ce sens, les déficits neuromusculaires étant rapportés comme dépendant de la tâche d'évaluation, il paraît pertinent de quantifier ceux-ci par des tests pluriarticulaires impliquant différents mécanismes neuromusculaires.

Concernant l'influence de la population, l'entraînement est connu pour être le facteur clé permettant de limiter les dommages musculaires potentiellement engendrés par des actions musculaires excentriques ou de type CED. Nous avons précédemment évoqué cet effet, qui est connu sous le nom de « repeated bout effect » en anglais (Ebbeling et Clarkson, 1990). De surcroît, l'entraînement permet de récupérer plus rapidement (Chalchat et al., 2022 ; Paulsen et al., 2012). En ce sens, les coureurs experts présentent généralement pas ou peu de déficits fonctionnels et de courbatures musculaires (Ehrström et al., 2018b). Par conséquent, les coureurs récréatifs semblent plus concernés par le risque de microlésions musculaires engendrés par une course d'endurance et sur leurs conséquences structuro-fonctionnelles dans les jours qui suivent. Les coureurs et coureuses ayant l'habitude de reprendre leur pratique dès que les sensations de courbatures s'estompent alors que la récupération reste incomplète, cela représente un risque potentiel de blessure qu'il convient d'évaluer (Strojnik et al. 2001). Les travaux de cette thèse se focaliseront donc sur l'analyse de coureurs/coureuses de niveau amateur, dont le kilométrage hebdomadaire est inférieur à 6 h.

Les rares études ayant comparé les femmes et les hommes après une course d'endurance inférieure au marathon ont été réalisées soit sur tapis roulant (Glace et al., 1998) soit en situation écologique de course mais avec un profil sans dénivelé (Boccia et al., 2018). La distance et le profil de la Course Marseille-Cassis choisie (Figure 49) sont considérés comme étant susceptibles de favoriser l'occurrence de micro-lésions musculaires. Ce profil est d'autant plus intéressant que les coureurs féminins et masculins sont rapportés pour ne pas adopter les mêmes changements d'activité musculaire lorsque le dénivelé ou la vitesse de course sont modifiés (Chumanov et al., 2008 ; von Tscharner et Goepfert, 2003 ; Willson et al., 2012).
<figure><img src="image_54.png" /><figcaption>**Figure 49.** Profil de la course Marseille-Cassis</figcaption></figure>

De plus, cette course comprend environ 20.000 coureurs chaque année (à l'exception des 12.000 coureurs de l'édition 2021) et se caractérise par une proportion élevée de femmes (31%) (Figure 50A). La majorité de celles-ci ont entre 20 et 33 ans (Figure 50B).

<figure><img src="image_55.png" /><figcaption>**Figure 50.** Répartition relative (%) des coureurs ayant participé à l'édition 2021 de la Course Marseille-Cassis en fonction du sexe (A) et selon leur tranche d'âges (B) et en fonction du sexe (B). F, femmes ; H, hommes.</figcaption></figure>

Quelle que soit la tranche d'âge, la performance sur cette course est plus élevée pour les hommes que pour les femmes (Figure 51). Cet écart semble relativement constant (11.5 ± 1.5 %) et proche de la littérature (Cheuvront *et al.*, 2005). Par ailleurs, la performance moyenne ne semble pas différer entre 18 et 44 ans, ce qui est également en accord avec la littérature. Les travaux de cette thèse se concentreront donc sur des coureurs et coureuses âgé(e)s de 18 à 45 ans afin d'éviter les effets liés à la ménopause et au vieillissement.
<figure><img src="image_56.png" /><figcaption>**Figure 51.** Moyenne (± écart-type) des temps de course, par catégorie d'âge et par sexe, pour l'édition 2021 de la course Marseille-Cassis.</figcaption></figure>

Le site web de la course (https://www.marseille-cassis.com/fr/resultats-course) fournit les temps enregistrés tous les 5 km de la course (exprimés en pourcentage du temps total). Ces données suggèrent l'existence de stratégies différentes en fonction du sexe (Figure 52). Comparé aux hommes, les femmes âgées de 18 à 33 ans ont passé relativement plus de temps que les hommes dans la portion de course comportant la plus grande pente positive (5-10 km) mais moins que ces derniers dans la dernière portion de course (10-20 km) qui comportait la plus forte pente négative (Figure 49). Des stratégies différentes selon le sexe ont déjà été rapportées en marathon. Les femmes étaient, entre autres, plus régulières que les hommes (Deaner et al., 2015), notamment lorsque le temps de course total augmentait. Ces divergences de stratégie de course pourraient être liées à des facteurs tant psychologiques que physiologiques mais la littérature manque à nouveau de données sur ce point. Les hommes ayant une masse musculaire supérieure à celle des femmes, la force concentrique que requiert une pente positive pourrait les favoriser, tout en entraînant une dépense métabolique potentiellement accrue du fait de la moindre baisse relative de leur vitesse de course. En descente, les femmes pourraient présenter une plus grande capacité d'absorption des chocs en raison de leur moindre hystérésis et de la souplesse accrue de leur tendon d'achille (Fletcher et al., 2013). L'accroissement des pics d'impact à une vitesse donnée en descente pourrait par contre engendrer plus de lésions musculaires chez les femmes, notamment en raison de leur moindre masse musculaire, de la faiblesse relative de leurs ischios-jambiers comparés au quadriceps et de leur laxité ligamentaire accrue (Constantini et al., 2005).
<figure><img src="image_57.png" /><figcaption>**Figure 52.** Temps moyen (± écart-type de chaque portion normalisé par le temps total de course) associé à chaque portion (tous les 5 km) de la Course Marseille-Cassis, et rapporté au temps total de course pour chaque sexe. L'analyse a porté sur tous les coureurs âgés de 23 et 33 ans (2510 hommes et 1289 femmes) ayant participé à l'édition 2021 de la course Marseille-Cassis. Les données ont été fournies par les organisateurs de la course Marseille-Cassis.</figcaption></figure>

La **Figure 53** présente les temps relatifs individuels et leur moyenne par sexe sur chacune des 4 portions de 5 km de la course. Pour les coureurs/coureuses dont le temps total de course était inférieur à ≈ 2 h, ces graphes soulignent l'absence d'influence du sexe sur la stratégie de course à l'exception de la dernière portion (15-20 km). En effet, sur cette portion qui intègre la plus grande pente négative, les femmes ont présenté un temps relativement inférieur à celui des hommes, et ce quelque soit leur niveau. Pour des temps de course supérieurs à 2 h, les femmes ont passé relativement plus de temps que les hommes sur la première portion de course (0-5 km) et sur la seconde portion intégrant la pente positive la plus importante (5-10 km). A l'inverse, elles ont passé relativement moins de temps sur les deux dernières portions de course comportant des pentes négatives, et cette observation est d'autant plus vraie pour les 5 derniers km. La figure souligne également que les coureurs ayant une performance supérieure à 2 h semblent passer relativement moins de temps sur la première portion (0-5 km) de course, témoignant d'une mauvaise gestion de l'effort puisqu'ils passent plus de temps dans la dernière portion de descente (10-20 km). Ainsi, les stratégies de course semblent être influencées par une interaction entre le niveau de course et le sexe. **Les femmes pourraient adopter une stratégie plus protectrice en début de course leur permettant de mieux résister en fin de course.**
<figure><img src="image_58.png" /><figcaption>**Figure 53.** Temps relatif associé à chaque portion de 5 km de la Course Marseille-Cassis, individuellement exprimé en pourcentage du temps total de course. L'analyse comprenait tous les coureurs et coureuses âgés(e)s de 18 ans à + de 65 ans (8560 hommes et 3846 femmes) ayant participé à l'édition 2021. La courbe de régression calculée de manière non-paramétrique est affichée pour chaque sexe (en rose les femmes et en bleu les hommes). L'aire grisée représente l'écart type de la courbe de régression ajustée pour chaque sexe.</figcaption></figure>

L'analyse de la viabilité du temps relatif attribué à chaque portion de course (Figure 54) ne révèle pas néanmoins d'influence du sexe ou de la performance.

<figure><img src="image_59.png" /><figcaption>**Figure 54.** Variabilité individuelle du temps relatif (% temps total) attribué à chacune des 4 portions de course en fonction de la performance finale. L'analyse comprenait tous les coureurs âgés de 18 ans à + de 65 ans (8560 hommes et 3846 femmes) de l'édition 2021. La courbe de régression calculée de manière non-paramétrique est</figcaption></figure>
affichée pour chaque sexe (en rose les femmes et en bleu les hommes). L'aire grisée représente l'écart type de la courbe de régression ajustée pour chaque sexe.

La **Figure 55** représente le temps attribué à chaque portion de course par chaque coureur, normalisé par les temps du meilleur coureur (ou de la meilleure coureuse) sur chaque portion. L'analyse suggère que la portion (10-20 km), où la pente est négative, correspond à la partie où le meilleur coureur (Felix BOUR lors de l'édition 2021 en 1 h 1 min et 48 s) a creusé le plus son écart avec les autres coureurs. Cela conforte l'observation précédemment mentionnée d'un temps relativement accru sur cette dernière portion chez les coureurs les moins rapides sur la course (> 2 h). Chez les coureurs récréatifs, cela pourrait résulter de moindres qualités musculaires et techniques pour résister aux impacts, les conduisant à adopter une stratégie plus protectrice en ralentissant leur vitesse course.

<figure><img src="image_60.png" /><figcaption>**Figure 55.** Temps individuels sur chacune des 4 portions de 5 km de la course, normalisé par la meilleure performance homme ou femme finale. L'analyse comprenait tous les coureurs âgés de 18 ans à + de 65 ans (12 407 coureurs) de l'édition 2021 de la course Marseille-Cassis. La courbe de régression est affichée pour chaque portion de course et calculée de manière non-paramétrique. L'aire grisée représente l'écart type de la courbe de régression ajustée pour chaque portion de course.</figcaption></figure>
La première étude expérimentale de ce travail de thèse (Etude 2) aura donc pour objectif de comparer le patron de récupération jusqu'à 4 jours post-effort de coureurs récréatifs, hommes et femmes, après cette course d'endurance de 20 km incluant un dénivelé positif puis négatif. Le patron de récupération sera quantifié par une batterie de tests fonctionnels maximaux, incluant certes un test classique monoarticulaire de CMV mais également des tests dynamiques pluriarticulaires. Nous nous centrerons sur les différences liées au sexe tout en essayant d'établir les différents profils de récupération, voire d'identifier les coureurs potentiellement à risque s'ils reprenaient la course à pied trop précocément. La proportion de femmes et d'hommes dans chacun de ces profils sera examinée.
**Résumé étude 2** (Macchi et al. 2021) *Sex influence on the functional recovery pattern after a graded running race: original analysis to identify the recovery profiles*

**Objectif** : Cette étude a examiné l'influence du sexe sur les effets de la fatigue aiguë et retardée d'une course à pied de 20 km avec dénivelé.

**Méthode** : 18 coureurs récréatifs (10 femmes et 8 hommes) ont terminé la course et participé à l'ensemble du protocole de test. Celui-ci incluait 5 sessions : une semaine avant la course (PRE), 35 ± 15 min après (POST), 2 h, 2 et 4 jours (2D and 4D) plus tard. Chaque session de test comprenait des contractions volontaires isométriques maximales (uni- et bilatérales) des extenseurs du genou (MVC), un squat jump (SJ) et un drop-jump (DJ). Les douleurs musculaires aiguës et retardées (DOMS) ont été évaluées pour les groupes musculaires du quadriceps, des ischio-jambiers et du triceps sural. Les sessions 2D et 4D incluaient aussi un test ballistique de type force-vitesse réalisé à l'horizontal avec les membres inférieurs (HF-V), à 5 niveaux de charge relative différentes. Pour chaque test, un ensemble de variables clés a été calculé pour caractériser la récupération fonctionnelle des membres inférieurs.

**Résultats** : Les ANOVA mixtes (intégrant un facteur apparié et un facteur indépendant) ont révélé des interactions entre le temps de la mesure et le sexe. En phase aiguë, des baisses plus importantes étaient observées pour les hommes lors des tests de CMV alors que les femmes présentaient une récupération plus précoce dans le test bilatéral CMV (p < 0.001) et de la puissance maximale en phase de poussée au DJ (p < 0.05). Seules les femmes ont rapporté des DOMS pour les ischio-jambiers à 2D (p < 0.001), tout en présentant à 4D de légères améliorations dans les tests purement concentriques de type SJ (p < 0.05) et HF-V (p < 0.01). Comme espéré, les DOMS ont disparu avant la récupération fonctionnelle complète.

**Conclusion** : Ces résultats ont confirmé l'influence combinée du type de test et du sexe sur la cinétique de récupération fonctionnelle tout en suggérant une moindre fatigue et une récupération plus rapide chez les femmes. L'originalité de cette étude réside dans l'utilisation d'une analyse à facteurs multiples (AFM) pour identifier les tests et variables les plus discriminants pour caractériser la récupération fonctionnelle. L'analyse par clusters a révélé une large variabilité de récupération fonctionnelle et des groupes présentant un risque potentiel de blessures à la reprise de la course. Cependant, l'absence d'interaction sexe × temps pour les valeurs normalisées souligne l'influence majeure des valeurs fonctionnelles initialement plus élevées chez les hommes que chez les femmes.
<figure><img src="image_61.png" /></figure>

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# Sex Influence on the Functional Recovery Pattern After a Graded Running Race: Original Analysis to Identify the Recovery Profiles

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**Edited by:**

Alessandro Moura Zagatto,
São Paulo State University, Brazil

**Reviewed by:**

Nicolas Babault,
Université Bourgogne
Franche-Comté, France
Hamdi Chtourou,
University of Sfax, Tunisia

*Correspondence:

Robin Macchi
robin.macchi@gmail.com
Fabrice Vercruyssen
fabrice.vercruyssen@univ-tln.fr
Arnaud Hays
arnaud.hays@univ-amu.fr
Gaetan Aubert
gae0aubert@gmail.com
Gaetan Exubis
gaetan.exubis@univ-amu.fr
Pascale Chavet
pascale.chavet@univ-amu.fr
Emmanuelle Goubert
emanuelle.goubert@univ-amu.fr
Robin Souron
robin.souron@gmail.com
Yoko Kunimasa
yoko.kunimasa@gmail.com
Caroline Nicol
caroline.nicol@univ-amu.fr

**Specialty section:**

This article was submitted to
*Exercise Physiology,*
a section of the journal
*Frontiers in Physiology*

**Received:** 04 January 2021

**Accepted:** 23 February 2021

**Published:** 18 March 2021

**Citation:**

Macchi R, Vercruyssen F, Hays A,
Aubert G, Exubis G, Chavet P,
Goubert E, Souron R, Kunimasa Y
and Nicol C (2021) *Sex Influence on
the Functional Recovery Pattern After
a Graded Running Race: Original
Analysis to Identify the Recovery
Profiles*. *Front. Physiol.* 12:649396.
doi: 10.3389/fphys.2021.649396

Robin Macchi<fnref n="1" />*, Fabrice Vercruyssen<fnref n="2" />*, Arnaud Hays<fnref n="1" />*, Gaetan Aubert³*, Gaetan Exubis³*, Pascale Chavet<fnref n="1" />*, Emmanuelle Goubert<fnref n="1" />*, Robin Souron<fnref n="2" />*, Yoko Kunimasa⁴* and Caroline Nicol<fnref n="1" />*

This study investigated the sex influence on the acute and delayed fatigue effects of a 20 km graded running race. Eighteen recreational runners, 10 women and 8 men, completed the race. The testing protocol included five sessions: a week before the race (PRE), 35 ± 15 min after (POST), 2 h, 2 and 4 days (2D and 4D) later. Each session included uni- and bilateral maximal isometric voluntary contractions of the knee extensors (MVC), a squat jump (SJ), and a drop jump (DJ). Acute and delayed muscle soreness (DOMS) were evaluated for the quadriceps, hamstring and triceps surae muscle groups. The 2D and 4D sessions included also a horizontal force-velocity test (HF-V) performed under five resistive conditions. For each test, a set of key variables was computed to characterize the lower limb functional recovery. Mixed ANOVA analyses revealed significant (sex × time) interactions, with larger acute drops for men in MVCs and earlier recovery for women in the bilateral MVC (p < 0.001) and DJ (p < 0.05) tests. Only women reported DOMS for the hamstrings at 2D (p < 0.001) and showed small improvements in pure concentric SJ (p < 0.05) and HF-V (p < 0.01) tests at 4D. As expected, DOMS disappeared prior to the complete functional recovery. These results confirmed the combined influence of testing task and sex on the functional recovery pattern while supporting a lesser and faster recovery in women. The originality of this study lies in the complexity and sex-dependence of the functional recovery pattern revealed by a multiple factorial analysis which was used to identify the most discriminating tests and variables in the recovery pattern. The obtained clusters highlighted some recovery profiles associated with greater risks of injury when starting to run again. However, the lack of sex × time interaction for normalized values emphasizes the major influence of men's initially higher functional values compared to women.

**Keywords:** sex influence, endurance running, functional recovery, fatigue, recovery profiles, multivariate analysis

**Abbreviations:** MVC, maximal voluntary contraction; SJ, squat jump; HF-V, horizontal force-velocity; DJ, drop jump; IPF, impact peak force (in N); hpo, distance of push-off (in m); GRF, ground reaction force (in N); tpo, push-off time (in s); tbrake, braking time duration (in s); SI, asymmetry index (in %); Fbrake, mean braking force (in N); ISOM, isometric; Fp0, mean force (in N); DOMS, delayed muscle soreness (a.u.); Vp0, mean velocity (in m/s); RFD, rate of force development (N/s); Pp0, mean power (in W); DL, dominant leg; vto, velocity at take-off (in m/s); NDL, non-dominant leg; Pmax, maximal power output (in W); BLT, bilateral leg; Fmax, peak force (in N); g, gravity (9.81 m/s<fnref n="2" />).

<footnote n="1">¹Institute of Movement Sciences (ISM), National Center of Scientific Research (CNRS) & Aix Marseille Univ, Marseille, France,</footnote>

<footnote n="2">²Impact of Physical Activity on Health (IAPS), University of Toulon, Toulon, France, ³Institut Carnot STAR, Aix Marseille Univ, Marseille, France, ⁴Osaka University of Health and Sport Sciences, Osaka, Japan</footnote>
# INTRODUCTION

Women participation and/or performances among endurance running races (from 5 to 1,000 km) have increased over the last 25 years (Bam et al., 1997; Zingg et al., 2014; Joyner, 2017; Scheer, 2019). For instance, in 2019, Jasmin Paris won the 430 km Spine Race in 83 h. Breaking the previous male record (by 12 h), this female athlete demonstrated impressive qualities of both muscular endurance and recovery between the consecutive running phases of the race.

In endurance running exercises, the impact loads are repeated over time, stressing metabolic, mechanical, and neural components (Nicol and Komi, 2010). The repeated ground impact loads may vary from 2 to 3 times bodyweight, leading to subsequent eccentric muscle actions that are likely to induce reversible ultrastructural muscle damage with subsequent edema, remodeling/inflammation processes and delayed onset muscle soreness (DOMS) (Nicol et al., 2006; Nicol and Komi, 2010). Lower-limb muscles become weak, sore, stiff and occasionally swollen for a few days. DOMS is a subjective sensation of dull pain and discomfort that increases in intensity during the first 2 days, remaining symptomatic for 1–2 additional days before disappearing on days 5–7. It should be mentioned that neither the degree nor the timing of exercise-induced muscle damage per se correlates well with the changes in DOMS sensation (Nurenberg et al., 1992; Howell et al., 1993; Paulsen et al., 2012). The timing of DOMS disappearance is particularly important for injury prevention, as it usually occurs prior to complete structural and functional recoveries (Nicol and Komi, 2010), resulting in an overestimation of the actual functional capacities that could increase the risk of injuries by starting to run again too soon.

After endurance running exercises, it remains unclear whether sex differences exist in the functional recovery pattern. Mostly examined in men, the classical recovery pattern is usually considered as biphasic (Nicol et al., 2006; Nicol and Komi, 2010). First, this pattern is characterized by large acute drops in maximal voluntary contraction (MVC), maximal activation and stretch-reflex response leading to reduced maximal isometric as well as dynamic performances. This is followed by a short-term “acute” recovery within the next 2 h, which precedes “delayed” functional reductions 1–2 days later. Depending on the training status and on the exercise severity, the functional recovery may still require 1–2 weeks to be completed. Importantly, the functional recovery pattern appears to be testing task dependent. For instance, our earlier marathon studies (Nicol et al., 1991a,b) revealed larger acute functional decrements in maximal isometric knee extension test than in maximal sprint and drop-jump performances. In contrast, pluri-articular jump tests with no ground impact showed no significant decrement in performance. Furthermore, scientific evidence suggests that sex-based differences are specific to the involved muscle(s) and to the fatiguing task (contraction mode, intensity and velocity) (Hunter, 2016a) as well as to the testing task (Hill et al., 2018). This emphasizes the need for using a battery of varied tests to examine the sex-influence on the recovery pattern.

It is noteworthy that the recovery pattern after running for women has only been investigated by a few studies that are difficult to compare. Among expert runners, it has been reported that a 110 km ultra-trail race induced acute lower drops in maximal force of knee extensors and peripheral fatigue of plantar flexors in women compared to men (Temesi et al., 2015). In regularly trained recreational runners (>220 min or 50 km per week), it has been found that a half-marathon results either in a lower decline in maximal knee extension force in women than in men (Glace et al., 1998), or in similar functional deficits between them (Boccia et al., 2018) after the race. In the delayed recovery phase, young sportsmen and women are reported to present similar time course of structural recovery (Sorichter et al., 2001) and neuromuscular changes (Martin et al., 2004) after a 20–30 min downhill treadmill run. However, in young sedentary people, a given downhill exercise led to a shorter inflammatory process in women than in men (48 vs. 72 h) whereas DOMS remained elevated up to 72 h in women (Oosthuysse and Bosch, 2017). In these studies, the first 2-h post-exercise enabling the differentiation of the acute vs. delayed period has not been examined, thus the possibility to identify a biphasic recovery pattern for these populations remains to be clarified.

As recently reviewed by Hunter (2016a), both past and even recent studies on the underlying fatigue mechanisms generally involved men, or often lacked the sex-distinction in their set-up and discussion. Although women are usually characterized by lower muscle mass, force and power than men, women are often less fatigable than men at similar relative intensity of fatiguing isometric contractions (Hunter, 2014). Aside of a relatively greater proportional area of slow twitch muscle fibers, the lesser fatigability of women might be attributed to the combined direct and indirect influences of sexual hormones (estrogens in particular) on muscle perfusion and metabolism, leading to lesser production of metabolites, sensitization of muscle afferents and reduction in voluntary activation (Hunter, 2014, 2016a,b). As reviewed by Enns and Tiidus (2010), sex and estrogen are reported as potentially attenuating indirect indices of exercise-induced skeletal muscle damage (such as DOMS and CK) by influencing the inflammation and repair processes.

This study was thus designed to characterize the women vs. men recovery pattern including measures within the first 2-h post-exercise and up to 4 days consecutively to a graded 20 km road race expected to induce muscle damage. It was first hypothesized that the functional recovery pattern would be both testing task- and sex-dependent, while occurring earlier in women. Our second hypothesis was that DOMS would be sex and muscle dependent while disappearing prior to the complete functional recovery. In addition, a multidimensional analysis that extracts the main information by reducing the dimensions while considering the different times of the recovery was deemed appropriate to further examine specific and/or descriptive aspects of the recovery pattern. In addition, a hierarchical clustering analysis was considered as the key analysis to identify the different recovery profiles after the exhaustive running race.
# MATERIALS AND METHODS

## 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 (two between groups and five repeated measures) and for a medium effect size (i.e., η² = 0.06), 10 participants in each group were required to obtain a statistical power of 80%. Due to the complexity in recruiting runners who could participate to the five testing sessions and the loss of two participants due to back pain and torn muscle during the race, the final sample size was slightly smaller. Thus, the final group of participants (Table 1) included 18 volunteers: 10 healthy females (age: 35 ± 7 years, body mass: 61.1 ± 11.4 kg, height: 1.66 ± 0.08 m) and 8 healthy males (age: 29 ± 7 years, body mass: 70.9 ± 6.2 kg, height: 1.76 ± 0.06 m). Three women were in the follicular phase, four in the luteal phase, and three were amenorrhoeic. All participants were recreational endurance runners, as inclusion criteria included running training for 4 h or less each week and not having participated more than once in the current running race. It is worth mentioning that they all trained mainly level running. The current study was approved by the local ethics committee and, in accordance with the Helsinki Convention, written informed consent was obtained from all runners.

## Experimental Design

The testing protocol included five experimental sessions: a week before the running race (PRE), as quickly as possible (i.e., 35 ± 15 min) after the race (POST), 2 h (2H), 2 and 4 days later (2D and 4D, respectively) (Figure 1). The PRE session, which included a familiarization phase for each test, followed by a complete rest period dedicated to the medical interview before providing the control values for the following testing sessions. The PRE, 2D and 4D sessions lasted approximately 1.5 h including electromyographic (EMG) electrode placement, warm-up and the five tests. The POST and 2H sessions, which did not include the HF-V test and therefore did not require any EMG measurements, lasted only 15 min each, while including a warm-up at 2H but not at POST. Between these two measurement times, food and drinks were offered to all runners who could rest near the test room. All sessions began with a check of the runner's body mass on a Tanita scale (MC980MA Tanita).

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

Functional tests were preceded by a sex-standardized 10 min warm-up, except for POST session. The warm-up started by cycling for 6 min at 100 W for men and 75 W for women at a cadence ranging from 60 to 80 rpm (e.g., Hintzy et al., 2001). This

<table><caption>TABLE 1 | Group-mean (± standard deviation) of the participants' characteristics, racing time, and testing performance values at the pre-race session (PRE).</caption><thead><tr><th></th><th>Men</th><th>Women</th><th></th></tr></thead><tbody><tr><td>Finishing time (hh:mm)</td><td>02:05:00 ± 0:15</td><td>02:08:00 ± 0:23</td><td>NS</td></tr><tr><td>% of winner's time per sex</td><td>205 ± 24</td><td>188 ± 33</td><td>NS</td></tr><tr><td>Age (yr)</td><td>29 ± 7</td><td>35 ± 7</td><td>NS</td></tr><tr><td>Height (m)</td><td>1.76 ± 0.06</td><td>1.66 ± 0.08</td><td>*</td></tr><tr><td>Mass (kg)</td><td>70.9 ± 6.2</td><td>61.1 ± 11.4</td><td>NS</td></tr><tr><td>PRE MVC<sub>BLT</sub> (N)</td><td>1,784 ± 469</td><td>906 ± 268</td><td>***</td></tr><tr><td>PRE MVC<sub>DL</sub> (N)</td><td>1,022 ± 261</td><td>570 ± 143</td><td>***</td></tr><tr><td>PRE MVC<sub>NDL</sub> (N)</td><td>959 ± 204</td><td>564 ± 115</td><td>***</td></tr><tr><td>PRE vto SJ (m/s)</td><td>2.71 ± 0.23</td><td>2.12 ± 0.20</td><td>***</td></tr><tr><td>PRE vto DJ (m/s)</td><td>2.28 ± 0.19</td><td>1.81 ± 0.23</td><td>***</td></tr><tr><td>PRE vto HF-V 0% BW (m/s)</td><td>2.7 ± 0.19</td><td>2.05 ± 0.17</td><td>***</td></tr><tr><td>PRE vto HF-V 20% BW (m/s)</td><td>2.42 ± 0.14</td><td>1.86 ± 0.17</td><td>***</td></tr><tr><td>PRE vto HF-V 40% BW (m/s)</td><td>2.22 ± 0.14</td><td>1.68 ± 0.14</td><td>***</td></tr><tr><td>PRE vto HF-V 60% BW (m/s)</td><td>1.87 ± 0.15</td><td>1.42 ± 0.13</td><td>***</td></tr><tr><td>PRE HF-V ISOM (N)</td><td>1,611 ± 201</td><td>1,040 ± 158</td><td>***</td></tr></tbody></table>

Maximal voluntary isometric tests of knee extension: bilateral (MVC<sub>BLT</sub>), and unilateral with the dominant leg (MVC<sub>DL</sub>) or the non-dominant leg (MVC<sub>NDL</sub>). Velocity at take-off (vto) in the horizontal force-velocity (HF-V) test at 4 different loads expressed in percentage of body weight (BW). Statistical sex difference: *p < 0.05, **p < 0.001; NS: non-significant.

was followed by two sequences that were repeated twice: 1 min at 50 W for men (40 W for women), and 30 s at 200 W for men (150 W for women) at a self-selected cycling cadence. Warm-up ended with an active recovery phase of 1 min at 40 W. Four maximal tests were performed in a given order: uni- and bilateral MVCs, three squat jumps (SJ) and three drop jumps (DJ) with 5–10 min of intermediate passive recovery. The PRE, 2D and 4D sessions included also a horizontal force-velocity (HF-V) test.

## Measurement Protocol

The knee MVC tests included two series of three MVCs performed on a knee extension ergometer (S2P Ltd, Ljubljana, Slovenia, EN 60204-1) with the arms crossed on the chest. Each series started with the unilateral MVC test with the dominant lower limb (MVC<sub>DL</sub>), followed by the non-dominant (MVC<sub>NDL</sub>) and the bilateral one (MVC<sub>BLT</sub>). The series and the trials were separated by 2 min and 30 s of passive recovery, respectively. To secure the repeatability of the testing position, the hip and knee flexion angles were set at 100° and the leg lever arm was individually adjusted at the same position between sessions. Participants were verbally encouraged and instructed to develop their force as fast as possible and to keep on producing maximal effort until the scheduled stop 3 s later. The produced torque was recorded at 1 kHz and the corresponding force was computed using a calibration matrix determined for the left and right lever arms of the ergometers.

The SJ test consisted of three maximal trials (with a 30 s intermediate passive recovery) performed from a static position at 90° knee angle and with the arms held akimbo. To record the potential fatigue effect on the runner's proprioception, their self-adopted starting position was recorded, but each trial was then performed from the position at PRE. For this purpose, a
<figure><img src="image_62.png" /><figcaption>FIGURE 1 | Experimental protocol. MVC, maximal voluntary isometric test of knee extension; HF-V, horizontal force-velocity test; SJ, squat jump; DJ, drop jump tests.</figcaption></figure>

laser (CL2i STANLEY) was used to quantify the vertical height of a passive reflective marker set on the left greater trochanter. Countermovement was prohibited and additional trials were performed when the criteria were not met.

The DJ test consisted of three maximal trials (with 1 min intermediate passive rest) from their optimal dropping height (set at either 30, 40, or 50 cm) previously checked during the familiarization phase. They were instructed to keep the arms akimbo, look straight ahead at a visual cue on the wall before dropping down and rebounding with maximal effort just after ground contact (Komi and Bosco, 1978), with minimal knee flexion while avoiding touching the floor with the heel (Cappa and Behm, 2013). When all requirements were not met, the trial was repeated.

The HF-V test consisted of two trials of ballistic squat jumps performed in a supine position on a frictionless sled (INPI deposit n°: FR2011204) under five resistive conditions: 0, 20, 40, and 60% bodyweight (BW) as well as in an isometric (ISOM) condition similarly to the tests used by Meylan et al. (2015). In all testing conditions, the starting position was held still for 2–3 s with hip, knee and ankle joints at 90°. The isometric test was always performed at first while the dynamic conditions were performed in a randomized order among the subjects. For a given runner, the order of the testing conditions remained the same in each session.

They were asked to apply their force as rapidly as possible on the force plates to reach the highest velocity at the end of the push-off. Countermovement was verbally forbidden and carefully checked; the trial being repeated if any. Recoveries of 30 s and 180 s were provided between trials and between loads, respectively.

For all jump tests, two Kistler force plates [SJ and DJ: Kistler 9287CA (0.9 × 0.6 m); HF-V: Kistler 9260AA3 (0.5 × 0.3 m)] were used to record the normal component of the ground reaction forces produced under each foot. The sampling frequency was set at 1.5 kHz.

## Data Analyses

The pacing strategy was obtained from the running time records provided by the organizers every 5 km of the race. The subjective rate of perceived exertion was evaluated after the race at the POST session using a 6–20 Borg scale (Borg, 1970).

For the knee MVC tests, maximal torque was the highest peak torque of the two trials for uni- and bilateral conditions. The obtained torque values were expressed as force values using the calibration matrix and the peak force ($F_{max}$) calculated. The absolute asymmetry index (SI) was computed as follows:

$$SI = 100 \left( \frac{|L_{dom} - L_{non_{dom}}|}{\text{avg} (L_{dom}, L_{non_{dom}})} \right) \quad (\text{Zifchock et al., 2008}),$$

where $L_{dom}$ and $L_{non_{dom}}$ correspond to the dominant and non-dominant lower limbs, respectively. The non-absolute asymmetry index was also computed.

For the SJ and DJ tests, only the best of the three trials (selected from the velocity at takeoff) was considered for post-processing. All calculations were subsequently based on the vertical component of the ground reaction forces considered as the gold standard computation method (Attia et al., 2017). The instantaneous vertical velocity was obtained during the push-off phase by integrating the vertical acceleration of the center of mass (a) over time (Samozino et al., 2008):

$$a(t) = \frac{\text{GRF}(t)}{m} - g$$

where “GRF” refers to the vertical component of the ground reaction force, “m” to the runner’s body mass and “g” to the gravity acceleration.

For the DJ test, the impact peak force (IPF) was also measured. The contact time was divided into its braking and push-off phases based on the velocity signal obtained from the GRF (Prieske et al., 2013). This led to the calculations of the braking phase duration ($t_{brake}$) and mean force ($\bar{F}_{brake}$). The respective push-off phase analysis included for both SJ and DJ tests: the mean force ($\bar{F}_{po}$), velocity ($\bar{V}_{po}$) and power ($\bar{P}_{po}$) calculations. Both vertical velocity at take-off (vto) and maximal power output ($P_{max}$) were calculated (Morin et al., 2019). To analyze the potential discrepancy between these last two parameters due to a modified technique, vertical push-off time (tpo) and push-off distance ($hpo = tpo \times \bar{V}_{po}$) were calculated. The SI between the two legs was calculated from the vertical mean force values. For both jumps, force and power were normalized by body mass.
For the HF-V test, the horizontal displacement over time was recorded with an accuracy of 0.1 cm, using a linear encoder (Micro-Epsilon WDS-3000-P115-SR-U) attached to the sled. This signal was used to calculate the horizontal velocity. For each resistive load, the trial with the highest vto value was kept for post-processing. In the ISOM condition, the rate of force development (RFD) was calculated between 20 and 80% of the peak force. The subsequent push-off phase analysis was performed similarly than the one used for the SJ test.

## Statistical Analyses

Statistical analyses were performed with JASP (Version 0.10.2) and (R v3.6.3, R Core Team, 2020, R Foundation for Statistical Computing, Vienna, Austria). All data are presented as mean ± standard deviation (SD). One-tailed independent *t*-tests were performed to determine sex differences in the racing time, subjective rate of perceived exertion, and weight loss after the race (at POST). Shapiro-Wilk test was used to verify data normality based on the residuals. If not met, Mann-Whitney U test was used.

To test the sex-time fatigue effect, mixed two-way repeated-measures ANOVAs for time (PRE-2D-4D for the HF-V test; PRE-POST-2h-2D-4D for the other tests), with sex as a between-subject factor, were performed for each functional variable and each muscle soreness. For the pacing strategy, the time factor included the four successive sections of 5 km of the race. For the HF-V test, a mixed three-way repeated-measures ANOVAs was performed as the load factor included four load levels. Due to large inter-group differences at PRE in force, velocity and power, additional ANOVAs were performed after normalizing the data within each sex group by the group-mean PRE values. If a main time effect was found, Holm's *post-hoc* test was used to assess the changes as compared to PRE. If a sex × time interaction effect was found, planned contrasts were used to test the time effect (as compared to PRE) for each sex group and the sex effect at each given time. This analysis avoided examining all possible comparisons, which increases the risk of error (Bartlett, 2017). Mauchly tests preceded each ANOVA analysis to verify sphericity. If the assumption of sphericity was violated, an epsilon Greenhouse-Geisser correction was applied. Each ANOVA was performed after checking for distribution normality with Shapiro-Wilk test. If not met, a simulation of 10.000 data sets representing the asymmetric distribution observed with groups having similar means was performed. If the obtained type 1 error exceeded 6%, a non-linear transformation of logarithm was applied to restore normality (Sainani, 2012). The alpha level was set at 0.05. Effect size was computed using Cohen's *d* coefficient (Cohen, 1998) computed as the *t*-value standardized to the root mean square of the number of participants (Rosenthal, 1991). The effect size magnitude (ES) was then 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).

To globally analyze the dataset and to reveal the interactions between the absolute changes of all variables as compared to PRE, a multiple factor analysis (MFA) with FactoMineR and Factoshiny R-packages (Lê et al., 2008) was used, in which the

time sessions represented the different groups. Such a subdivision could not have been performed by using a principal component analysis (PCA) (Pagès, 2014). The correlations between the different sessions were assessed by the RV coefficient (bounded between 0 and 1) which represents the link between two groups of variables based on inertia and normalized by the dimensionality of each group. All data were centered and reduced (corresponding to Z score) due to the non-equality of most variables. Sex and other descriptive variables (such as age, body mass, height, muscle soreness and racing time) were set in a supplementary group, which was only projected on the MFA dimensions. An ascendant hierarchical clustering on principal components was then performed to construct recovery profiles corresponding to functional responses to the race-induced fatigue.

## RESULTS

Weather conditions varied slightly along the race, reaching a maximal temperature of 21°C with no wind. No significant difference in racing time was found between men and women (2:05 ± 0:15 and 2:08 ± 0:23, respectively). Although there was no significant difference in relative performance (i.e., relative to the fastest runner for each sex), women tended to be slightly faster than men (Table 1). Similarly, no significant differences were found between men and women in the pacing strategy that was mostly influenced by the graded profile of the race, the subjective perception of effort after the race (16.3 ± 2.2 and 15.7 ± 2.6, respectively), as well as the variation of body mass at POST (2.9 ± 1.2% and 2.1 ± 1.4%, respectively) or between sessions.

Since muscle soreness did not differ between dominant and non-dominant lower limbs, all subsequent analyses were performed on the mean values of the lower limbs for this parameter (Figure 2). A significant interaction effect was found (*p* < 0.001), reporting higher DOMS in women for the hamstring muscle group at 2D (*p* < 0.001, ES = 1.1). Independently of sex, a significant time effect was found for the quadriceps (*p* < 0.001) and triceps surae (*p* < 0.001) muscle soreness. Holm's *post-hoc* showed increased muscle soreness at POST (*p* < 0.001, ES = 1.4) and 2D (*p* < 0.05, ES = 0.7) for the quadriceps while at POST (*p* < 0.01, ES = 1.0) and 2H (*p* < 0.05, ES = 0.7) for the triceps surae muscle group.

## Mixed Anova Analyses on the Functional Variables

When focusing on the MVC tests (Figure 3 and Table 2), a main time effect was found and reflected large acute changes (i.e., at POST and 2H), followed by moderate delayed ones in each test. A moderate increase in the absolute SI was found at POST in the $MVC_{BLT}$ (+24.1%, *p* < 0.05, ES = 1.1). A sex × time interaction effect was also found for each testing condition (BLT, *p* < 0.001; DL, *p* < 0.001; NDL, *p* < 0.05) on the absolute, but not on the normalized MVC values. For the $MVC_{BLT}$, planned contrasts revealed large acute decreases for men (POST: ES = 2.3, 2H: ES = 1.8) and moderate ones for women (POST: ES = 0.7, 2H: ES = 0.8), so that the sex groups did not differ in absolute