The Relationship Between Running Biomechanics and Running Economy: A Systematic Review and Meta-Analysis of Observational Studies.

BACKGROUND: Running biomechanics is considered an important determinant of running economy (RE). However, studies examining associations between running biomechanics and RE report inconsistent findings. OBJECTIVE: The aim of this systematic review was to determine associations between running biomechanics and RE and explore potential causes of inconsistency. METHODS: Three databases were searched and monitored up to April 2023. Observational studies were included if they (i) examined associations between running biomechanics and RE, or (ii) compared running biomechanics between groups differing in RE, or (iii) compared RE between groups differing in running biomechanics during level, constant-speed, and submaximal running in healthy humans (18-65 years). Risk of bias was assessed using a modified tool for observational studies and considered in the results interpretation using GRADE. Meta-analyses were performed when two or more studies reported on the same outcome. Meta-regressions were used to explore heterogeneity with speed, coefficient of variation of height, mass, and age as continuous outcomes, and standardization of running shoes, oxygen versus energetic cost, and correction for resting oxygen or energy cost as categorical outcomes. RESULTS: Fifty-one studies (n = 1115 participants) were included. Most spatiotemporal outcomes showed trivial and non-significant associations with RE: contact time r = - 0.02 (95% confidence interval [CI] - 0.15 to 0.12); flight time r = 0.11 (- 0.09 to 0.32); stride time r = 0.01 (- 0.8 to 0.50); duty factor r = - 0.06 (- 0.18 to 0.06); stride length r = 0.12 (- 0.15 to 0.38), and swing time r = 0.12 (- 0.13 to 0.36). A higher cadence showed a small significant association with a lower oxygen/energy cost (r = - 0.20 [- 0.35 to - 0.05]). A smaller vertical displacement and higher vertical and leg stiffness showed significant moderate associations with lower oxygen/energy cost (r = 0.35, - 0.31, - 0.28, respectively). Ankle, knee, and hip angles at initial contact, midstance or toe-off as well as their range of motion, peak vertical ground reaction force, mechanical work variables, and electromyographic activation were not significantly associated with RE, although potentially relevant trends were observed for some outcomes. CONCLUSIONS: Running biomechanics can explain 4-12% of the between-individual variation in RE when considered in isolation, with this magnitude potentially increasing when combining different variables. Implications for athletes, coaches, wearable technology, and researchers are discussed in the review. PROTOCOL REGISTRATION: https://doi.org/10.17605/OSF.IO/293 ND (OpenScience Framework).

Differences in running technique between runners with better and poorer running economy and lower and higher milage: An artificial neural network approach.

BACKGROUND: Prior studies investigated selected discrete sagittal-plane outcomes (e.g., peak knee flexion) in relation to running economy, hereby discarding the potential relevance of running technique parameters during noninvestigated phases of the gait cycle and in other movement planes. PURPOSE: Investigate which components of running technique distinguish groups of runners with better and poorer economy and higher and lower weekly running distance using an artificial neural network (ANN) approach with layer-wise relevance propagation. METHODS: Forty-one participants (22 males and 19 females) ran at 2.78 m∙s-1 while three-dimensional kinematics and gas exchange data were collected. Two groups were created that differed in running economy or weekly training distance. The three-dimensional kinematic data were used as input to an ANN to predict group allocations. Layer-wise relevance propagation was used to determine the relevance of three-dimensional kinematics for group classification. RESULTS: The ANN classified runners in the correct economy or distance group with accuracies of up to 62% and 71%, respectively. Knee, hip, and ankle flexion were most relevant to both classifications. Runners with poorer running economy showed higher knee flexion during swing, more hip flexion during early stance, and more ankle extension after toe-off. Runners with higher running distance showed less trunk rotation during swing. CONCLUSION: The ANN accuracy was moderate when predicting whether runners had better, or poorer running economy, or had a higher or lower weekly training distance based on their running technique. The kinematic components that contributed the most to the classification may nevertheless inform future research and training.

Changes in running economy and running technique following 6 months of running with and without wearable-based real-time feedback.

BACKGROUND: An increasing number of commercially available wearables provide real-time feedback on running biomechanics with the aim to reduce injury risk or improve performance. OBJECTIVE: Investigate whether real-time feedback by wearable insoles (ARION) alters running biomechanics and improves running economy more as compared to unsupervised running training. We also explored the correlation between changes in running biomechanics and running economy. METHODS: Forty recreational runners were randomized to an intervention and control group and performed ~6 months of in-field training with or without wearable-based real-time feedback on running technique and speed. Running economy and running biomechanics were measured in lab conditions without feedback pre and post intervention at four speeds. RESULTS: Twenty-two individuals (13 control, 9 intervention) completed both tests. Both groups significantly reduced their energetic cost by an average of -6.1% and -7.7% for the control and intervention groups, respectively. The reduction in energy cost did not significantly differ between groups overall (-0.07 ± 0.14 J∙kg∙m-1 , -1.5%, p = 0.63). There were significant changes in spatiotemporal metrics, but their magnitude was minor and did not differ between the groups. There were no significant changes in running kinematics within or between groups. However, alterations in running biomechanics beyond typical session-to-session variation were observed during some in-field sessions for individuals that received real-time feedback. CONCLUSION: Alterations in running biomechanics as observed during some in-field sessions for individuals receiving wearable-based real-time feedback did not result in significant differences in running economy or running biomechanics when measured in controlled lab conditions without feedback.

Determination of energy expenditure in professional cyclists using power data: Validation against doubly labeled water.

INTRODUCTION: Accurate determination of total daily energy expenditure (TDEE) in athletes is important for optimal performance and injury prevention, but current approaches are insufficiently accurate. We therefore developed an approach to determine TDEE in professional cyclists based on power data, basal metabolic rate (BMR), and a non-exercise physical activity level (PAL) value, and compared energy expenditure (EE) between multi-day and single-day races. METHODS: Twenty-one male professional cyclists participated. We measured: (1) BMR, (2) the relationship between power output and EE during an incremental cycling test, which was used to determine EE during exercise (EEE ), and (3) TDEE using doubly labeled water (DLW). A non-exercise PAL-value was obtained by subtracting EEE from TDEE and dividing this by BMR. RESULTS: Measured BMR was 7.9 ± 0.8 MJ/day, which was significantly higher than predicted by the Oxford equations. A new BMR equation for elite endurance athletes was therefore developed. Mean TDEE was 31.7 ± 2.8 and 27.3 ± 2.8 MJ/day during the Vuelta a España and Ardennes classics, while EEE was 17.4 ± 1.8 and 10.1 ± 1.4 MJ/day, respectively. Non-exercise PAL-values were 1.8 and 2.0 for the Vuelta and Ardennes classics, respectively, which is substantially higher than currently used generic PAL-values. CONCLUSION: We show that the proposed approach leads to a more accurate estimation of non-exercise EE than the use of a generic PAL-value in combination with BMR predictive equations developed for non-elite athletes, with the latter underestimating non-exercise EE by ~28%. The proposed approach may therefore improve nutritional strategies in professional cyclists.

Assessment of Peak Oxygen Uptake with a Smartwatch and its Usefulness for Training of Runners.

Peak oxygen uptake (˙VO2peak) is an important factor contributing to running performance. Wearable technology may allow the assessment of ˙VO2peak more frequently and on a larger scale. We aim to i) validate the ˙VO2peak assessed by a smartwatch (Garmin Forerunner 245), and ii) discuss how this parameter may assist to evaluate and guide training procedures. A total of 23 runners (12 female, 11 male; ˙VO2peak: 48.6±6.8 ml∙min-1∙kg-1) visited the laboratory twice to determine their ˙VO2peak during a treadmill ramp test. Between laboratory visits, participants wore a smartwatch and performed three outdoor runs to obtain ˙VO2peak values provided by the smartwatch. The ˙VO2peak obtained by the criterion measure ranged from 38 to 61 ml∙min-1∙kg-1. The mean absolute percentage error (MAPE) between the smartwatch and the criterion ˙VO2peak was 5.7%. The criterion measure revealed a coefficient of variation of 4.0% over the VO2peak range from 38-61 ml∙min-1∙kg-1. MAPE between the smartwatch and criterion measure was 7.1, 4.1 and -6.2% when analyzing ˙VO2peak ranging from 39-45 ml∙min-1∙kg-1, 45-55 ml∙min-1∙kg-1 or 55-61 ml∙min-1∙kg-1, respectively.