Accuracy of respiratory gas variables, substrate, and energy use from 15 CPET systems during simulated and human exercise.

PURPOSE: Various systems are available for cardiopulmonary exercise testing (CPET), but their accuracy remains largely unexplored. We evaluate the accuracy of 15 popular CPET systems to assess respiratory variables, substrate use, and energy expenditure during simulated exercise. Cross-comparisons were also performed during human cycling experiments (i.e., verification of simulation findings), and between-session reliability was assessed for a subset of systems. METHODS: A metabolic simulator was used to simulate breath-by-breath gas exchange, and the values measured by each system (minute ventilation [V̇E], breathing frequency [BF], oxygen uptake [V̇O2 ], carbon dioxide production [V̇CO2 ], respiratory exchange ratio [RER], energy from carbs and fats, and total energy expenditure) were compared to the simulated values to assess the accuracy. The following manufacturers (system) were assessed: COSMED (Quark CPET, K5), Cortex (MetaLyzer 3B, MetaMax 3B), Vyaire (Vyntus CPX, Oxycon Pro), Maastricht Instruments (Omnical), MGC Diagnostics (Ergocard Clinical, Ergocard Pro, Ultima), Ganshorn/Schiller (PowerCube Ergo), Geratherm (Ergostik), VO2master (VO2masterPro), PNOE (PNOE), and Calibre Biometrics (Calibre). RESULTS: Absolute percentage errors during the simulations ranged from 1.15%-44.3% for V̇E, 1.05-3.79% for BF, 1.10%-13.3% for V̇O2 , 1.07%-18.3% for V̇CO2 , 0.62%-14.8% for RER, 5.52%-99.0% for Kcal from carbs, 5.13%-133% for Kcal from fats, and 0.59%-12.1% for total energy expenditure. Between-session variation ranged from 0.86%-21.0% for V̇O2 and 1.14%-20.2% for V̇CO2 , respectively. CONCLUSION: The error of respiratory gas variables, substrate, and energy use differed substantially between systems, with only a few systems demonstrating a consistent acceptable error. We extensively discuss the implications of our findings for clinicians, researchers and other CPET users.

Per-step and cumulative load at three common running injury locations: The effect of speed, surface gradient, and cadence.

Understanding how loading and damage on common running injury locations changes across speeds, surface gradients, and step frequencies may inform training programs and help guide progression/rehabilitation after injuries. However, research investigating tissue loading and damage in running is limited and fragmented across different studies, thereby impairing comparison between conditions and injury locations. This study examined per-step peak load and impulse, cumulative impulse, and cumulative weighted impulse (hereafter referred to as cumulative damage) on three common injury locations (patellofemoral joint, tibia, and Achilles tendon) across different speeds, surface gradients, and cadences. We also explored how cumulative damage in the different tissues changed across conditions relative to each other. Nineteen runners ran at five speeds (2.78, 3.0, 3.33, 4.0, 5.0 m s-1 ), and four gradients (-6, -3, +3, +6°), and three cadences (preferred, ±10 steps min-1 ) each at one speed. Patellofemoral, tibial, and Achilles tendon loading and damage were estimated from kinematic and kinetic data and compared between conditions using a linear mixed model. Increases in running speed increased patellofemoral cumulative damage, with nonsignificant increases for the tibia and Achilles tendon. Increases in cadence reduced damage to all tissues. Uphill running increased tibial and Achilles tendon, but decreased patellofemoral damage, while downhill running showed the reverse pattern. Per-step and cumulative loading, and cumulative loading and cumulative damage indices diverged across conditions. Moreover, changes in running speed, surface gradient, and step frequency lead to disproportional changes in relative cumulative damage on different structures. Methodological and practical implications for researchers and practitioners are discussed.

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.

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.

The role of pennation angle and architectural gearing to rate of force development in dynamic and isometric muscle contractions.

BACKGROUND: Associations between muscle architecture and rate of force development (RFD) have been largely studied during fixed-end (isometric) contractions. Fixed-end contractions may, however, limit muscle shape changes and thus alter the relationship between muscle architecture an RFD. AIM: We compared the correlation between muscle architecture and architectural gearing and knee extensor RFD when assessed during dynamic versus fixed-end contractions. METHODS: Twenty-two recreationally active male runners performed dynamic knee extensions at constant acceleration (2000°s-2) and isometric contractions at a fixed knee joint angle (fixed-end contractions). Torque, RFD, vastus lateralis muscle thickness, and fascicle dynamics were compared during 0-75 and 75-150 ms after contraction onset. RESULTS: Resting fascicle angle was moderately and positively correlated with RFD during fixed-end contractions (r = 0.42 and 0.46 from 0-75 and 75-150 ms, respectively; p < 0.05), while more strongly (p < 0.05) correlated with RFD during dynamic contractions (r = 0.69 and 0.73 at 0-75 and 75-150 ms, respectively; p < 0.05). Resting fascicle angle was (very) strongly correlated with architectural gearing (r = 0.51 and 0.73 at 0-75 ms and 0.50 and 0.70 at 75-150 ms; p < 0.05), with gearing in turn also being moderately to strongly correlated with RFD in both contraction conditions (r = 0.38-0.68). CONCLUSION: Resting fascicle angle was positively correlated with RFD, with a stronger relationship observed in dynamic than isometric contraction conditions. The stronger relationships observed during dynamic muscle actions likely result from different restrictions on the acute changes in muscle shape and architectural gearing imposed by isometric versus dynamic muscle contractions.

ChatGPT Generated Training Plans for Runners are not Rated Optimal by Coaching Experts, but Increase in Quality with Additional Input Information.

ChatGPT may be used by runners to generate training plans to enhance performance or health aspects. However, the quality of ChatGPT generated training plans based on different input information is unknown. The objective of the study was to evaluate ChatGPT-generated six-week training plans for runners based on different input information granularity. Three training plans were generated by ChatGPT using different input information granularity. 22 quality criteria for training plans were drawn from the literature and used to evaluate training plans by coaching experts on a 1-5 Likert Scale. A Friedmann test assessed significant differences in quality between training plans. For training plans 1, 2 and 3, a median rating of <3 was given 19, 11, and 1 times, a median rating of 3 was given 3, 5, and 8 times and a median rating of >3 was given 0, 6, 13 times, respectively. Training plan 1 received significantly lower ratings compared to training plan 2 for 3 criteria, and 15 times significantly lower ratings compared to training plan 3 (p < 0.05). Training plan 2 received significantly lower ratings (p < 0.05) compared to plan 3 for 9 criteria. ChatGPT generated plans are ranked sub-optimally by coaching experts, although the quality increases when more input information are provided. An understanding of aspects relevant to programming distance running training is important, and we advise avoiding the use of ChatGPT generated training plans without an expert coach's 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.

The accuracy of markerless motion capture combined with computer vision techniques for measuring running kinematics.

BACKGROUND: Markerless motion capture based on low-cost 2-D video analysis in combination with computer vision techniques has the potential to provide accurate analysis of running technique in both a research and clinical setting. However, the accuracy of markerless motion capture for assessing running kinematics compared to a gold-standard approach remains largely unexplored. OBJECTIVE: Here, we investigate the accuracy of custom-trained (DeepLabCut) and existing (OpenPose) computer vision techniques for assessing sagittal-plane hip, knee, and ankle running kinematics at speeds of 2.78 and 3.33 m s-1 as compared to gold-standard marker-based motion capture. METHODS: Differences between the markerless and marker-based approaches were assessed using statistical parameter mapping and expressed as root mean squared errors (RMSEs). RESULTS: After temporal alignment and offset removal, both DeepLabCut and OpenPose showed no significant differences with the marker-based approach at 2.78 m s-1 , but some significant differences remained at 3.33 m s-1 . At 2.78 m s-1 , RMSEs were 5.07, 7.91, and 5.60, and 5.92, 7.81, and 5.66 degrees for the hip, knee, and ankle for DeepLabCut and OpenPose, respectively. At 3.33 m s-1 , RMSEs were 7.40, 10.9, 8.01, and 4.95, 7.45, and 5.76 for the hip, knee, and ankle for DeepLabCut and OpenPose, respectively. CONCLUSION: The differences between OpenPose and the marker-based method were in line with or smaller than reported between other kinematic analysis methods and marker-based methods, while these differences were larger for DeepLabCut. Since the accuracy differed between individuals, OpenPose may be most useful to facilitate large-scale in-field data collection and investigation of group effects rather than individual-level analyses.

The accuracy of commercially available instrumented insoles (ARION) for measuring spatiotemporal running metrics.

Spatiotemporal metrics such as step frequency have been associated with running injuries in some studies. Wearables can measure these metrics and provide real-time feedback in-field, but are often not validated. This study assessed the validity of commercially available wireless instrumented insoles (ARION) for quantifying spatiotemporal metrics during level running at different speeds (2.78-5.0 m s-1 ,) and slopes (3° and 6° up/downhill) to an instrumented treadmill. Mean raw, percentage and absolute percentage error, and limits of agreement (LoA) were calculated. Agreement was statistically quantified using four thresholds: excellent, <5%; good, <10%; acceptable, <15%; and poor, >15% error. Excellent agreement (<5% error) was achieved for stride time across all conditions, and for step frequency across all but one condition with good agreement. Contact time and swing time generally showed at least good agreement. The mean difference across all conditions was -0.95% for contact time, 0.11% for stride time, 0.6% for swing time, -0.11% for step frequency, and -0.09% when averaged across all outcomes and conditions. The accuracy at an individual level was generally good to excellent, being <10% for all but two conditions, with these conditions being <15%. Additional experiments among four runners showed that step length could also be measured with an accuracy of 1.76% across different speeds with an updated version of the insoles. These findings suggests that the ARION wearable may not only be useful for large-scale in-field studies investigating group differences, but also to quantify spatiotemporal metrics with generally good to excellent accuracy for individual runners.

The relationship between elastography-based muscle properties and vertical jump performance, countermovement utilization ratio, and rate of force development.

This study explored the relationships between passive muscle stiffness (shear modulus) and vertical jumping performance, countermovement utilization ratio (CUR) and rate of force development (RFD) in an attempt to unravel the mechanism that may explain the association between shear modulus and performance. 32 recreationally active participants (16 males, 16 females; age: 22.4 ± 5.1 years) participated. Shear modulus was assessed for the lateral and medial gastrocnemius (GL and GM), and vastus medialis (VM) and lateralis (VL) muscles using shear wave elastography. Squat jump (SJ) and countermovement (CMJ) jump were determined, with CUR being expressed as the ratio between the two. RFD in ankle and knee extension tasks was measured using isometric dynamometers. Our results suggest that within a heterogeneous group of recreational athletes, passive muscle stiffness is not related to RFD and jump performance, but positively related to CUR. In males, shear modulus of the GL was positively related to SJ height (r = 0.55). We also found inverse moderate correlations between VL and VM shear modulus and RFD in females only (r = -0.50 to -0.51), but this relationship was possibly affected by age and body fat content. Different mechanisms may underpin the association between shear modulus and performance depending on the muscle, task and population investigated.

Correlation properties of heart rate variability to assess the first ventilatory threshold and fatigue in runners.

PURPOSE: The short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA-a1) of heart rate variability (HRV) has shown potential to delineate the first ventilatory threshold (VT1). The aims of this study were to investigate the accuracy of this method for VT1 determination in runners using a consumer grade chest belt and to explore the effects of acute fatigue. METHODS: We compared oxygen uptake (V̇O2) and heart rate (HR) at gas exchange VT1 to V̇O2 and HR at a DFA-a1 value of 0.75. Gas exchange and HRV data were obtained from 14 individuals during a treadmill run involving two incremental ramps. Agreement was assessed using Bland-Altman analysis and linear regression. RESULTS: Bland-Altman analysis between gas exchange and HRV V̇O2 and HR at VT1 during the first ramp showed a mean (95% limits of agreement) bias of -0.5 (-6.8 to 5.8) ml∙kg-1∙min-1, and -0.9 (-12.2 to 10.5) beats∙min-1, with R2 of 0.83 and 0.56, respectively. During the second ramp, the differences were -7.3 (-18.1 to 3.5) ml∙kg-1∙min-1 and -12.3 (-30.4 to 5.9) beats∙min-1, with R2 of 0.62 and 0.43, respectively. CONCLUSION: A chest-belt derived DFA-a1 of 0.75 is closely related to gas exchange VT1, with the variability in accuracy at an individual level being similar to gas exchange methods. This suggests this to be a useful method for exercise intensity demarcation. The altered relationship during the second ramp indicates that DFA-a1 is only able to accurately demarcate exercise intensity thresholds in a non-fatigued state, but also opens opportunities for fatigue-based training prescription.

The first ventilatory threshold determined with a nonlinear method (DFA-a1) to analyse heart rate variability derived from a chest-belt shows close agreement to the gas exchange first ventilatory threshold, with the variability in accuracy at an individual level being similar to gas exchange methods. This suggests this to be a useful method for exercise intensity demarcation.The altered relationship during fatigue indicates that DFA-a1 is only able to accurately demarcate exercise intensity thresholds in a non-fatigued state, but this also opens opportunities for fatigue-based training prescription.

The Between-Day Reliability of Correlation Properties of Heart Rate Variability During Running.

The short-term scaling exponent of detrended fluctuation analysis (DFA-a1) of heart rate variability may be a helpful tool to assess autonomic balance as a prelude to daily, individualized training. For this concept to be useful, between-session reliability should be acceptable. The aim of this study was to explore the reliability of DFA-a1 during a low-intensity exercise session in both a non-fatigued and a fatigued condition in healthy males and females. Ten participants completed two sessions with each containing an exhaustive treadmill ramp protocol. Before and after the fatiguing ramp, a standardized submaximal low-intensity exercise bout was performed during which DFA-a1, heart rate, and oxygen consumption (VO2) were measured. We compared between-session reliability of all metrics prior to the ramps (i.e., non-fatigued status) and after the first ramp (i.e., fatigued status). Intraclass correlation coefficients (ICC) with 95% confidence intervals (CI), the standard error of measurement, and the smallest worthwhile change (SWC) were determined. The ICC and SWC pre fatiguing ramp were 0.85 (95% CI 0.39-0.96) and 5.5% for DFA-a1, 0.85 (0.38-0.96) and 2.2% for heart rate, and 0.84 (0.31-0.96) and 3.1% for VO2. Post fatiguing ramp, the ICC and SWC were 0.55 (0.00-0.89) and 7.9% for DFA-a1, 0.91 (0.62-0.98) and 1.6% for heart rate, and 0.80 (0.17-0.95) and 3.0% for VO2. DFA-a1 shows generally acceptable to good between-session reliability with a SWC of 0.06 and 0.07 (5.5-7.9%) during non-fatigued and fatigued conditions. This suggests that this metric may be useful to inform on training readiness.

Muscle forces and fascicle behavior during three hamstring exercises.

Knowledge about muscular forces and fascicle behavior during hamstring exercises can optimize exercise prescription, but information on these outcomes across different exercises is lacking. We aimed to characterize and compare lower-limb muscle forces and biceps femoris long head muscle fascicle behavior between three hamstring exercises: the Nordic hamstring curl (NHC), single-leg Roman chair (RCH), and single-leg deadlift (DL). Ten male participants performed the exercises while full-body kinematics, ground reaction forces, surface muscle activation, and biceps femoris long head fascicle behavior were measured. Mean fascicle length was highest in the DL, followed by the RCH and NHC. Fascicle lengthening was higher in the NHC compared with the RCH and DL, with no difference between the RCH and DL. Biceps femoris short and long head, semitendinosus, and semimembranosus peak forces were generally higher in the NHC compared with the RCH and DL, while mean forces during the eccentric phase were generally not different between the NHC and RCH. Peak forces in the NHC coincided with low biceps femoris long head and semimembranosus muscle activation. The NHC generally has the highest peak hamstring muscle forces and results in more fascicle lengthening when compared to the DL and RCH. The NHC may therefore be most effective to promote increases in fascicle length. While the NHC may be effective to promote biceps femoris short head and semitendinosus strength adaptations, the RCH and DL may be more effective to promote strength increases in the biceps femoris long head and semimembranosus.

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.

The effect of countermovement on force production capacity depends on extension velocity: A study of alpine skiers and sprinters.

In jumping, countermovement increases net propulsive force and improves performance. We aimed to test whether this countermovement effect is velocity specific and examine the degree to which this varies between athletes, sports or performance levels. Force-velocity profiles were compiled in high-level skiers (N= 23) and sprinters (N= 30), with their performance represented in their overall world ranking and season-best 100 m time, respectively. Different ratios between force-velocity variables were computed from squat and countermovement jumps (smaller = less effect): jump height (CRh), maximum power (CRP), force (CRF), and velocity (CRv). Countermovement effect differed per velocity (inverse relationship between CRF and CRv, rs = -0.74, p< .001), and variation force-velocity profiles with countermovement. Skiers exhibited smaller CRF (rrb = -0.675, p< .001), sprinters smaller CRv (rrb = 0.426, p= .008), and "moderate" velocity conditions did not differentiate groups (CRP or CRh, p> .05). 33% of the variance in skiers' performance level was explained by greater maximum force and a lower CRF (i.e., high explosiveness at low-velocities without countermovement), without an association for sprinters. Countermovement effect appears specific to movement velocity, sport and athlete level. Consequently, we advise sports-specific assessment, and potentially training to reduce the countermovement effect per the relevant velocity.

Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements.

Injuries and lack of motivation are common reasons for discontinuation of running. Real-time feedback from wearables can reduce discontinuation by reducing injury risk and improving performance and motivation. There are however several limitations and challenges with current real-time feedback approaches. We discuss these limitations and challenges and provide a framework to optimise real-time feedback for reducing injury risk and improving performance and motivation. We first discuss the reasons why individuals run and propose that feedback targeted to these reasons can improve motivation and compliance. Secondly, we review the association of running technique and running workload with injuries and performance and we elaborate how real-time feedback on running technique and workload can be applied to reduce injury risk and improve performance and motivation. We also review different feedback modalities and motor learning feedback strategies and their application to real-time feedback. Briefly, the most effective feedback modality and frequency differ between variables and individuals, but a combination of modalities and mixture of real-time and delayed feedback is most effective. Moreover, feedback promoting perceived competence, autonomy and an external focus can improve motivation, learning and performance. Although the focus is on wearables, the challenges and practical applications are also relevant for laboratory-based gait retraining.

Mechanical Properties of Treadmill Surfaces Compared to Other Overground Sport Surfaces.

The mechanical properties of the surfaces used for exercising can affect sports performance and injury risk. However, the mechanical properties of treadmill surfaces remain largely unknown. The aim of this study was, therefore, to assess the shock absorption (SA), vertical deformation (VD) and energy restitution (ER) of different treadmill models and to compare them with those of other sport surfaces. A total of 77 treadmills, 30 artificial turf pitches and 30 athletics tracks were assessed using an advanced artificial athlete device. Differences in the mechanical properties between the surfaces and treadmill models were evaluated using a repeated-measures ANOVA. The treadmills were found to exhibit the highest SA of all the surfaces (64.2 ± 2; p < 0.01; effect size (ES) = 0.96), while their VD (7.6 ± 1.3; p < 0.01; ES = 0.87) and ER (45 ± 11; p < 0.01; ES = 0.51) were between the VDs of the artificial turf and track. The SA (p < 0.01; ES = 0.69), VD (p < 0.01; ES = 0.90) and ER (p < 0.01; ES = 0.89) were also shown to differ between treadmill models. The differences between the treadmills commonly used in fitness centers were much lower than differences between the treadmills and track surfaces, but they were sometimes larger than the differences with artificial turf. The treadmills used in clinical practice and research were shown to exhibit widely varying mechanical properties. The results of this study demonstrate that the mechanical properties (SA, VD and ER) of treadmill surfaces differ significantly from those of overground sport surfaces such as artificial turf and athletics track surfaces but also asphalt or concrete. These different mechanical properties of treadmills may affect treadmill running performance, injury risk and the generalizability of research performed on treadmills to overground locomotion.

A Proposed Method to Assess the Mechanical Properties of Treadmill Surfaces.

The aim of this study was to define a reliable and sensitive test method for assessing Shock Absorption (SA), Vertical Deformation (VD), and Energy Restitution (ER) in treadmill surfaces. A total of 42 treadmills belonging to four different models were included in the study: (a) Technogym Jog700 Excite (n = 10), (b) Technogym Artis Run (n = 12), (c) LifeFitness Integrity Series 97T (n = 11), and (d) LifeFitness Integrity Series DX (n = 9). An advanced artificial athlete (AAA) device was used to assess SA, VD, and ER at three different locations along the longitudinal axis of each treadmill and in the support area of the athletes' feet. For each location, our results show that the error assumed when performing one impact with the AAA instead of three (SA ≤ |0.1|%, VD ≤ |0.0| mm, and ER ≤ |0.2|%) is lower than the smallest changes that can be detected by the measuring device (SA = 0.4%, VD = 0.2 mm, and ER = 0.9%). Also, our results show the ability of the test method to detect meaningful differences between locations once the one-impact criterium is adopted, since absolute minimum differences between zones (SA: |0.6|%, VD: |0.3| mm, and ER: |1.2|%) were above the uncertainty of the measuring device. Therefore, performing a single impact with the AAA in each of the three locations described in this study can be considered a representative and reliable method for assessing SA, VD, and ER in treadmill surfaces.

Single-Leg Roman Chair Hold Is More Effective Than the Nordic Hamstring Curl in Improving Hamstring Strength-Endurance in Gaelic Footballers With Previous Hamstring Injury.

Macdonald, B, O'Neill, J, Pollock, N, and Van Hooren, B. Single-leg Roman chair hold is more effective than the Nordic hamstring curl in improving hamstring strength-endurance in Gaelic footballers with previous hamstring injury. J Strength Cond Res 33(12): 3302-3308, 2019-Poor hamstring strength-endurance is a risk factor for hamstring injuries. This study investigated the effectiveness of the single-leg Roman hold and Nordic hamstring curl in improving hamstring strength-endurance. Twelve Gaelic footballers (mean ± SD age, height, and mass were 25.17 ± 3.46 years, 179.25 ± 5.88 cm, 85.75 ± 4.75 kg, respectively) with a history of hamstring injury were randomized into 2 groups that performed 6 weeks of either Nordic hamstring curl or single-leg Roman chair hold training. The single-leg hamstring bridge (SLHB) was measured before and after intervention. The Roman chair group showed a very likely moderate magnitude improvement on SLHB performance for both legs (23.7% for the previously injured leg [90% confidence interval 9.6-39.6%] and 16.9% for the noninjured leg [6.2-28.8%]). The Nordic curl group showed a likely trivial change in SLHB performance for the noninjured leg (-2.1% [-6.7 to 2.6%]) and an unclear, but possibly trivial change for the previously injured leg (0.3% [-5.6 to 6.6%]). The Roman chair group improved very likely more with a moderate magnitude in both the noninjured (19.5% [8.0-32.2%]) and the previously injured leg (23.3% [8.5-40.0%]) compared with the Nordic curl group. This study demonstrated that 6-week single-leg Roman chair training substantially improved SLHB performance, suggesting that it may be an efficacious strategy to mitigate hamstring (re-) injury risk. Conversely, 6-week Nordic curl training did not substantially improve SLHB performance, suggesting this may not be the intervention of choice for modifying this risk factor.