Abstract
Background: The aim of this study was to investigate the effects of gradient and speed on running variability (RV) and local dynamic stability (LDS) during uphill running.
Hypotheses: (1) Both gradient and speed increase metabolic effort, in terms of heart rate (HR) and perceived exertion (CR10), in line with the contemporary literature, and (2) gradient increases RV and impairs LDS.
Study design: “Crossover” observational design.
Level of Evidence: Level 3.
Methods: A total of 25 runners completed 10-minute running trials in 3 different conditions and in a randomized order: gradient at 0% (0CON), 2% (2CON), and 2% at isoefficiency speed (2IES). 0CON and 2CON speeds were calculated as the “best 10-km race performance” minus 1 km·h−1, whereas 2IES speed was adjusted to induce the same metabolic expenditure as 0CON. HR and perceived exertion as well as running kinematic variables were collected across all trials and conditions. Running variability was calculated as the standard deviation of the mean stride-to-stride intervals over 100 strides, while LDS was expressed by the Lyapunov exponent (LyE) determined on running cycle time over different running conditions.
Results: Increases in HR and CR10 were observed between 0CON and 2CON ( P < 0.001) and between 2IES and 2CON ( P < 0.01). Higher RV was found in 2CON compared with 0CON and 2IES (both P < 0.001). Finally, the largest LyE was observed in 2IES compared with 0CON and 2CON ( P = 0.02 and P = 0.01, respectively).
Conclusion: Whereas RV seems to be dependent more on metabolic effort, LDS is affected by gradient to a greater extent.
Clinical Relevance: Running variability could be used to monitor external training load in marathon runners.
Hypotheses: (1) Both gradient and speed increase metabolic effort, in terms of heart rate (HR) and perceived exertion (CR10), in line with the contemporary literature, and (2) gradient increases RV and impairs LDS.
Study design: “Crossover” observational design.
Level of Evidence: Level 3.
Methods: A total of 25 runners completed 10-minute running trials in 3 different conditions and in a randomized order: gradient at 0% (0CON), 2% (2CON), and 2% at isoefficiency speed (2IES). 0CON and 2CON speeds were calculated as the “best 10-km race performance” minus 1 km·h−1, whereas 2IES speed was adjusted to induce the same metabolic expenditure as 0CON. HR and perceived exertion as well as running kinematic variables were collected across all trials and conditions. Running variability was calculated as the standard deviation of the mean stride-to-stride intervals over 100 strides, while LDS was expressed by the Lyapunov exponent (LyE) determined on running cycle time over different running conditions.
Results: Increases in HR and CR10 were observed between 0CON and 2CON ( P < 0.001) and between 2IES and 2CON ( P < 0.01). Higher RV was found in 2CON compared with 0CON and 2IES (both P < 0.001). Finally, the largest LyE was observed in 2IES compared with 0CON and 2CON ( P = 0.02 and P = 0.01, respectively).
Conclusion: Whereas RV seems to be dependent more on metabolic effort, LDS is affected by gradient to a greater extent.
Clinical Relevance: Running variability could be used to monitor external training load in marathon runners.
| Original language | English |
|---|---|
| Pages (from-to) | 67-73 |
| Number of pages | 7 |
| Journal | Sports Health: A Multidisciplinary Approach |
| Volume | 15 |
| Issue number | 1 |
| Early online date | 27 Mar 2022 |
| DOIs | |
| Publication status | Published - 28 Feb 2023 |
Keywords
- stride-to-stride
- gait
- metabolic demand
- perceived exertion
- endurance runners