Mechanical Running Power and Energy Expenditure in Uphill and Downhill Running

rail running involves constant changes in terrain and slope, complicating the accurate assessment of energy expenditure during performance. This study aimed to examine the relationship between running power output (RPO), oxygen consumption (VO2), carbon dioxide production (VCO2), and energy expenditure per minute (EEmin) across positive and negative slopes in trained trail runners under standardized laboratory conditions. Fifteen male trail runners performed ve randomized 5 min treadmill runs at 70% of VO2maximal speed on 7%, 5%, 0%, +5%, and +7% slopes. VO2, VCO2, EEmin, respiratory exchange ratio (RQ), heart rate (HR), and RPO were recorded. Statistical analysis included Shapiro– Wilk tests for normality, repeated-measures ANOVA to compare variables across slopes, and Spearman or Pearson correlations between RPO and physiological variables. Moderate to strong positive correlations were found between RPO and VO2(Rho = 0.80–0.84,p< 0.001) and between RPO and EEmin(Rho= 0.74–0.87,p< 0.01) across all conditions. These ndings suggest that RPO measured via a wearable device may re

analysis included Shapiro– Wilk tests for normality, repeated-measures ANOVA to compare variables across slopes, and Spearman or Pearson correlations between RPO and physiological variables. Moderate to strong positive correlations were found between RPO and VO2(Rho = 0.80–0.84,p< 0.001) and between RPO and EEmin(Rho= 0.74–0.87,p< 0.01) across all conditions. These ndings suggest that RPO measured via a wearable device may re ect changes in energy expenditure and supports the integration of wearable power metrics into training and nutritional strategies for trail running. However, further studies in female athletes, outdoor settings, extreme slopes, and altitude conditions are needed to con rm the generalizability of these results. Keywords: trail running; energy expenditure; metabolism; athletic performance; nutrition; races 1. Introduction Trail running races have signi cantly grown in popularity among runners driven by its unique environmental challenges, including altitude changes, uneven terrain, constant elevation shifts, and steep slopes, covering a wide range of distances, from shorter uphill vertical kilometer to long-distance ultra races [1]. These factors have generated interest within the scienti c community, as these races push human physiology to its limits [2]. The variations in terrain and distance characteristic of trail running races demand a compre- hensive understanding of the physiological responses for optimizing both performance and health [3,4]. Sports2025,13, 294 https://doi.org/10.3390/sports13090294

Sports2025,13, 294 2 of 17 In mountain running, for better performance runners often alternate strategically between walking and running based on gradient and terrain type [5]. This challenge makes it dif cult to accurately monitor physical demands using metrics like average pace (time per kilometer) or speed variables commonly employed in road races [6] that fail to account for the complexities of terrain in mountain running. As a result, coaches, nutritionists, athletes, and researchers often lack reliable data for quantifying performance and energetic demand, limiting their ability to optimize training, recovery, and race preparation strategies [7]. This mechanical effort to accelerate, maintain speed, or brake is supported by the body's energy expenditure (EE) obtained through the different metabolic pathways. EE is a key factor in endurance sports, as maintaining an appropriate energy balance is crucial for performance optimization and athlete health [8]. Negative energy balance, where energy output exceeds intake, can lead to adverse outcomes such as muscle mass loss, increased fatigue, and a decline in overall performance [9]. Currently, scienti c evidence suggests that EE can be monitored in real time through various methods. The most popular monitoring method for estimating EE is cardiac activity, due to its strong correlation with oxygen consumption [10]. In endurance sports, heart rate (HR) is often used as an indicator due to its linear relationship with oxygen uptake (VO2) [11]. However, this relationship breaks down at extreme intensities, during intermit- tent efforts, or under the in uence of factors like emotions, posture, and environmental conditions [12]. Prolonged exercise, particularly in warm environments, often induces cardiovascular drift, an upward shift in HR accompanied by a reduction in stroke volume, exacerbated by dehydration and thermal strain. These responses are associated with re- duced plasma volume, impaired venous return, and increased sympathetic activation and can occur independently of any change in oxygen uptake [13]. Sleep restriction or depri- vation can also elevate submaximal HR through increased sympathetic drive and altered thermoregulation, even when oxygen uptake remains unchanged [14]. Both conditions are commonly encountered in trail running, either due to early race start times, prolonged ultradistance events,

impaired venous return, and increased sympathetic activation and can occur independently of any change in oxygen uptake [13]. Sleep restriction or depri- vation can also elevate submaximal HR through increased sympathetic drive and altered thermoregulation, even when oxygen uptake remains unchanged [14]. Both conditions are commonly encountered in trail running, either due to early race start times, prolonged ultradistance events, or sustained efforts in warm environments and can lead to a decou- pling of HR from VO2, reducing the reliability of HR as indicator of energetic demand. Alternatively, indirect calorimetry is considered the gold standard for accurately measuring EE and physiological responses [15], yet its use is limited in the laboratory setting. Conse- quently, other systems have been highlighted in the scienti c literature as possible ways for solving the issue and try to estimate this data. One of them is the possibility of using Global Navigation Satellite Systems watches, very frequently used by mountain runners. This method provides speed and distance, two variables proposed for estimating EE; however, their accuracy in assessing EE is often questioned [16]. Thus, accelerometers have been proposed as a potentially reliable alternative [17]. Although early models, limited to single- axis measurements, presented notable challenges in reliability [18], recent advancements including three-dimensional axis capabilities. The triaxial accelerometer, with its metrics of mechanical power, stride length, and ground contact time, can detect changes in running technique [19], attributable to terrain characteristics. Also, this sensor enhances its ability to capture kinematic data if they are combined with other sensors such as gyroscopes and magnetometers [20]. This new and accurate approach brings to practitioners a novel method for monitoring training load in the trail running population, especially if we take into account the complexities of this discipline characterized by varying terrain, steep gradients, and alternating movement strategies [21]. In this context, the ability to obtain real-time data through wearable technology represents a signi cant advancement in train- ing load monitoring [22]. Despite the well-established understanding of the theoretical models of metabolic cost and mechanical work during level and uphill running [23,24], the relationship between mechanical running power and

by varying terrain, steep gradients, and alternating movement strategies [21]. In this context, the ability to obtain real-time data through wearable technology represents a signi cant advancement in train- ing load monitoring [22]. Despite the well-established understanding of the theoretical models of metabolic cost and mechanical work during level and uphill running [23,24], the relationship between mechanical running power and energy expenditure across different

Sports2025,13, 294 3 of 17 inclines remains unexplored. While previous studies have demonstrated a relationship between power mechanical and metabolic parameters [25], none have directly analyzed whether instantaneous mechanical power can reliably predict energy expenditure across a full range of inclinations, including negative slopes. Understanding this relationship is essential for optimizing training load management and nutritional strategies in endurance athletes, particularly in mountain and trail running disciplines. The aim of this study was to explore the relationship between oxygen uptake, carbon dioxide, and mechanical running power at different slopes. Additionally, this study sought to determine the relationship between EE per minute and instantaneous running power output (RPO) readings at varying slopes, both positive and negative. 2. Materials and Methods 2.1. Participants A convenience [26] sample of 15 high-endurance-trained [27] mountain runners, males (n= 15), participated in this trial (age 37.27 6.55 years; body weight 70.89 7.05 kg; height 176.06 5.96 cm; body mass index 22.85 1.63 kg m 2 ) (Table). The sample size was consistent with that of previous studies involving trail runners [28]. The inclusion criteria were to train at least 10 h per week, all participants had at least three years of mountain run- ning experience and were injury-free for at least the preceding three months. Competitive level was assessed using the International Trail Running Association (ITRA) Performance Index, a globally recognized scoring system based on recent race performances that allows for standardized comparison between trail runners [29]. Intentions, procedures, as well as potential risks and bene ts were communicated to the participants and were con rmed by signing an informed consent form according to the Declaration of Helsinki. This study was approved by the Committee for Clinical Investigations of the Sports Administration of Catalonia (020-CEICGC-2022). Table 1.Anthropometrics and physiological characteristics of the participants. Characteristic Mean SD Age (years)(years) 37.27 6.55 ITRA (points) 676 101 Weight (kg) 70.89 7.05 Height (m) 176.06 5.96 BMI (kg m 2 ) 22.85 1.63 vVO2max (m s 1 ) 4.85 0.44 70% vVO2max (m s 1 ) 3.33 0.31 HRmax (bpm) 171.47 10.31 VO2max (mL min 1 ) 4307.38 487.71 VO2max

1.Anthropometrics and physiological characteristics of the participants. Characteristic Mean SD Age (years)(years) 37.27 6.55 ITRA (points) 676 101 Weight (kg) 70.89 7.05 Height (m) 176.06 5.96 BMI (kg m 2 ) 22.85 1.63 vVO2max (m s 1 ) 4.85 0.44 70% vVO2max (m s 1 ) 3.33 0.31 HRmax (bpm) 171.47 10.31 VO2max (mL min 1 ) 4307.38 487.71 VO2max (mL kg 1 min 1 ) 61.04 6.91 RPOmax (W) 368.07 38.04 RPOmax (W kg 1 ) 5.20 0.32 Age (years); ITRA (points): International trail running association ranking; Weight (kg); Height (m); BMI (kg m 2 ): body mass index; vVO2max (m s 1 ): maximal speed at VO2max; 70% vV;O2max (m s 1 ): running speed set at 70% of the velocity associated with VO2max; VO2max (mL kg 1 1 min 1 ): maximal oxygen uptake relative to weight; VO2max (mL min 1 ): maximal oxygen uptake; RPOmax: maximal running power output; RPOmax (W kg 1 ): maximal running power output relative to weight. Participants' height (Holtain ® stadiometer, Holtain Limited © , Crosswell, UK) and body mass were measured (Seca 220 ® scale, Seca Corp © , Hamburg, Germany) before starting running protocols. The rst running test was aimed to obtain maximal peak values of physiological variables of interest by performing an incremental maximal protocol on

Sports2025,13, 294 4 of 17 a motorized treadmill (Cosmos HP, Nussdorf-Traunstein, Germany). Among these, the maximal oxygen consumption (VO2max) (Cosmed K5 ® , Cosmed SRL, Rome, Italy), maximal speed at VO2max (vVO2max), and maximal heart rate (HRmax) (Polar H10 ® , Polar Electro Oy, Kempele, Finland; rmware v3.2.0, software v7.15) were determined. Also, participants were equipped with an inertial movement unit, Stryd Footpod (Stryd, Boulder, CO, USA; rmware v2.1.15, software v7.8), which incorporates a triaxial accelerometer, a gyroscope, and a barometer into a compact shoe-mounted chip, allowing the researchers to obtain the mechanical power, expressed as running power output RPO applied on the treadmill during the test [30]. Speci cally (Figure), after a 5 min warm-up at 8.0 km/h, the incremental test began at 10.0 km/h and increased by 1 km/h every minute until volitional exhaustion [31]. The average test duration (excluding the warm-up) was 9 min and 37 s, with an SD of 1 min and 10 s. Figure 1. Schematic representation of the maximal oxygen uptake (VO 2max) in the incremental treadmill test used in the present study. The protocol consisted of a 5 min warm-up phase at a constant speed of 8 km h 1 , followed by an incremental phase starting at 10 km h 1 with speed increases of +1 km h 1 every minute until volitional exhaustion. The participants average test duration (excluding the warm-up) was 9 0 37 00 , SD 1 0 10 00 . A week later, all participants were called to the laboratory again to do the experimental protocol (Figure): a running test with different slopes [ 32,33]. This protocol consisted of running ve series of ve minutes at 70% of the speed associated with each participant's maximal oxygen uptake which was selected as the standardized speed for all subsequent trials. This standardized speed and slopes were selected to elicit measurable physiological responses across slope conditions while ensuring submaximal intensity below the second ventilatory threshold [34], thus preserving the metabolic validity of the protocol. Varying slope conditions ( 7%, 5%, 0%, +5%, and +7%) were administered in ve separate trials

which was selected as the standardized speed for all subsequent trials. This standardized speed and slopes were selected to elicit measurable physiological responses across slope conditions while ensuring submaximal intensity below the second ventilatory threshold [34], thus preserving the metabolic validity of the protocol. Varying slope conditions ( 7%, 5%, 0%, +5%, and +7%) were administered in ve separate trials of 5 min each, with a 5 min passive seated recovery between trials. The order of the slopes was randomized for each participant. During each set of the test, VO2, carbon dioxide output (VCO2), and respiratory exchange ratio (RQ) were monitored using the Cosmed K5 ® portable system for recording breath-by-breath gas exchange measurements (Cosmed

Sports2025,13, 294 5 of 17 SRL, Rome, Italy). From gas exchange data, energy expenditure per minute (EEmin) was calculated using Weir's equation [35]. HR was assessed by the Polar H10 ® (Polar Electro Oy, Kempele, Finland). Rating of perceived exertion (RPE) was assessed at the end of each set using the Borg CR10 scale following standardized procedures [36]. Figure 2. Illustrative example from one participant (P13). VO 2(mL min 1 ), EE (kcal min 1 ), and RPO (W) during treadmill running at ve slopes (0%, 5%, and 7%) at constant speed (m s 1 ). Each trial lasted 5 min with a 5 min passive recovery. Trials were performed consecutively in a single session; slope order was randomized. Running power output was assessed using the Stryd Footpod (Stryd, Boulder, CO, USA) which has been previously validated for running power estimation under controlled conditions [25] and managed via the Stryd mobile application ( rmware v2.1.15, soft- ware v7.8), with manual input of slope prior to each effort. Data were uploaded to the Stryd Power Center platform [37], exported in FIT format, and converted to .csv les using Golden Cheetah (version 3.4), a free-license software, for further analysis in Microsoft Excel ® (2016; Microsoft Corp., Redmond, WA, USA). While all these data were monitored during the total duration of each set, mean values from minutes two to four of each set were analyzed to better re ect steady-state physio- logical responses. All running tests were performed under the same ambient laboratory's conditions (temperature 21.7 1.2 C; humidity 78.0 7.4%). 2.2. Statistical Analysis Descriptive statistics (mean standard deviation) were computed for all variables considering two levels of analysis: (i) the entire sample, and (ii) subgroups clustered according to slope conditions. The distribution of the data was rst veri ed using the Shapiro–Wilk test. From this moment, either parametric or nonparametric procedures were subsequently applied. Associations between VO2and VCO2with PO and EEminacross

Sports2025,13, 294 6 of 17 multiple slope conditions (+7%, +5%, 0%, 5%, and 7%) were examined by correlation analysis. Depending on the distribution, Pearson's r or Spearman's rank correlation coef - cient was used. Correlation strength was interpreted following Cohen's [38] thresholds: small (0–0.30), moderate (0.31–0.49), large (0.50–0.69), very large (0.70–0.89), and nearly perfect ( 0.91). Additionally, differences analyses were conducted to compare differences across the ve slope conditions (+7%, +5%, 0%, 5%, and 7%) and one-way analysis of variance was employed for normally distributed variables, while the Kruskal–Wallis test was applied when the assumption of normality was violated [39]. For variables that followed a normal distribution, homogeneity of variances was assessed with Levene's test. When the assumption of equal variances was con rmed, Fisher's was applied, whereas Welch's was used when variances were unequal [39]. Post hoc comparisons were then performed to identify speci c group differences, using Tukey's for equal variances and the Games–Howell procedure for unequal variances. For variables showing signi cant group differences in the Kruskal–Wallis test, pairwise comparisons were conducted using the Dwass–Steel–Critchlow–Fligner procedure [39]. Statistical signi cance was set atp< 0.05. Data organization was performed using Microsoft Excel, and all statistical analyses were conducted using Jamovi (version 2.3; The Jamovi Project, 2023) [39]. Most metabolic variables (VO2, RPO, and EEmin) exhibited a non-normal distribution (p< 0.05) for all slope conditions, necessitating the use of Spearman's rank correlation (Rho). However, RQ showed normality in some conditions (0% and +5%), allowing Pearson's correlation (r) to be used when applicable. Post hoc power for correlation tests was computed (two-tailed; = 0.05), using Spearman's (Rho) for power estimation, with n = 15 per slope. The Kruskal–Wallis test was conducted to compare differences across the ve slope conditions (+7%, +5%, 0%, 5%, and 7%). The results of the Kruskal–Wallis test indicated that there were signi cant differences in the variables across the different slopes (p< 0.001), con rming the need for nonparametric analysis. Following this, Games–Howell post hoc tests were applied to examine speci c differences between the slope conditions. These tests revealed signi cant differences between several pairwise

+5%, 0%, 5%, and 7%). The results of the Kruskal–Wallis test indicated that there were signi cant differences in the variables across the different slopes (p< 0.001), con rming the need for nonparametric analysis. Following this, Games–Howell post hoc tests were applied to examine speci c differences between the slope conditions. These tests revealed signi cant differences between several pairwise comparisons (e.g., between +7% and 7%, between -% and 0%, and between 0% and +7%, all withp< 0.05), highlighting the impact of different slopes on metabolic variables. 3. Results The descriptive analyses of each variable according to the different slopes are summa- rized in Table, while the differences are shown in Figure. Table 2.Descriptive (mean and deviation) results of the 3 min mean values by slope. Slope 7% 5% 0% 5% 7% VO 2(mL min 1 ) 2521.4 397.3 2682.8 425.4 3233.7 525.4 4111.6 662.4 4390.2 694.2 VO 2(mL kg min 1 ) 35.66 4.89 37.91 5.15 45.75 6.85 58.12 8.15 62.09 8.87 VCO 2(mL min 1 ) 1919.1 290.9 2031.9 283.6 2478.0 372.9 3360.6 470.8 3772.2 569.9 RQ 0.77 0.08 0.76 0.06 0.77 0.08 0.82 0.09 0.87 0.11 HR (bpm) 122.5 11.0 124.3 9.7 134.3 11.7 151.6 8.8 158.5 9.4 EE (kcal min 1 ) 11.9 1.8 12.7 1.9 15.3 2.4 19.7 3.0 21.3 3.1 RPO (W) 175.9 18.9 192.0 21.3 242.3 26.9 305.7 32.5 335.1 35.7 RPE 3.03 1.78 2.60 1.39 3.07 1.10 4.80 1.57 6.33 1.79 a.u.: arbitrary unit; VO2 (mL min 1 ): oxygen uptake; VO2(mL kg min 1 ): relative oxygen uptake; VCO2 (mL min 1 ): carbon dioxide; RQ: respiratory quotient; HR (bpm): heart rate; EE (kcal min 1 ): energy expenditure minute; RPO (W): running power output; RPE: rate of perceived exertion.

This study examines the relationship between running power output and energy expenditure in trained trail runners.