The effect of fatigue on running mechanics in older and younger runners

Prediction of instantaneous perceived effort during outdoor running using accelerometry and machine learning

The rate of perceived effort (RPE) is a subjective scale widely used for defining training loads. However, the subjective nature of the metric might lead to an inaccurate representation of the imposed metabolic/mechanical exercise demands. Therefore, this study aimed to predict the rate of perceived exertions during running using biomechanical parameters extracted from a commercially available running smartwatch. Forty-three recreational runners performed a simulated 5-km race on a track, providing their RPE from a Borg scale (6-20) every 400 m. Running distance, heart rate, foot contact time, cadence, stride length, and vertical oscillation were extracted from a running smartwatch (Garmin 735XT). Machine learning regression models were trained to predict the RPE at every 5 s of the 5-km race using subject-independent (leave-one-out), as well as a subject-dependent regression method. The subject-dependent method was tested using 5%, 10%, or 20% of the runner's data in the training set while using the remaining data for testing. The average root-mean-square error (RMSE) in predicting the RPE using the subject-independent method was 1.8 ± 0.8 RPE points (range 0.6-4.1; relative RMSE ~ 12 ± 6%) across the entire 5-km race. However, the error from subject-dependent models was reduced to 1.00 ± 0.31, 0.66 ± 0.20 and 0.45 ± 0.13 RPE points when using 5%, 10%, and 20% of data for training, respectively (average relative RMSE < 7%). All types of predictions underestimated the maximal RPE in ~ 1 RPE point. These results suggest that the data accessible from commercial smartwatches can be used to predict perceived exertion, opening new venues to improve training workload monitoring.

Spatiotemporal and kinematic adjustments in master runners may be associated with the relative physiological effort during running

Master runners maintain a similar running economy to young runners, despite displaying biomechanical characteristics that are associated with a worse running economy. This apparent paradox may be explained by a greater physiological effort-i.e., percentage of maximal oxygen uptake (VO2-max)-that master runners perform at a given speed. Moreover, age-related responses to non-exhaustive sustained running are yet underexplored. The aims of this study were, therefore, to examine if biomechanical adjustments in master runners are physiological-effort dependent, and to explore the age-related biomechanical changes during a non-exhaustive sustained run. Young (23.9 ± 6; n = 12) and master (47.3 ± 6.9; n = 12) runners performed a sustained 30-minute treadmill run matched for relative physiological effort (70% VO2-max), while spatiotemporal and lower-limb kinematic characteristics were collected during the 1st and 30th minute. Group differences were observed in step/stride length, knee touch-down angle, and knee stiffness. However, both groups of runners had a similar step frequency, vertical center of mass oscillation, and knee range of motion. Age-related adjustment in these latter characteristics may thus not be an inevitable result of the aging process but rather a strategy to maintain running economy. The relative physiological effort of runners should, therefore, be considered when examining age-related adjustments in running biomechanics.

The effect of exercise modality on age-related changes observed during running

INTRODUCTION

With the increase in participation by older adults in endurance events, research is needed to evaluate how exercising throughout the lifespan can affect the aging process regarding gait and mobility. The purpose of this study was to determine how the type of exercise modality one participates in will affect age-related declines observed during running.

METHODS

Fifty-six individuals between the ages of 18-65 who considered running, resistance training or cycling/swimming as their primary form of activity participated in this study. Kinematics were captured using a 10-camera motion capture system while participants ran at a controlled pace of 3.5 m/s (± 5%) over a 10-m runway with force platforms collecting kinetic data. Eight successful trials were chosen for analysis. A one-way ANOVA assessed differences in mean kinematic and kinetic variables of interest between physical activity groups (α = 0.05).

RESULTS

Older resistance trainers exhibited greater maximal knee power compared to older runners. No other group differences were observed.

CONCLUSION

Despite type of exercise modality, regularly participating in exercise has positive effects. This is evident through the preservation of the function of the lower extremity with age, specifically function of the ankle, and its contribution to healthy movement patterns.