Predicting Musculoskeletal Loading at Common Running Injury Locations using Machine Learning and Instrumented Insoles.

INTRODUCTION: Wearables have the potential to provide accurate estimates of tissue loads at common running injury locations. Here we investigate the accuracy by which commercially available instrumented insoles (ARION) can predict musculoskeletal loading at common running injury locations. METHODS: 19 runners (10 males) ran at five different speeds, four slopes, with different step frequencies, and forward trunk lean on an instrumented treadmill, while wearing instrumented insoles. The insole data was used as input to an artificial neural network that was trained to predict the Achilles tendon strain, and tibia and patellofemoral stress impulses and weighted impulses (damage proxy) determined with musculoskeletal modelling. Accuracy was investigated using leave-one-out cross-validation and correlations. The effect of different input metrics was also assessed. RESULTS: The neural network predicted tissue loading with overall relative percentage errors of 1.95 ± 8.40, -7.37 ± 6.41, and -12.8 ± 9.44% for the patellofemoral joint, tibia and Achilles tendon impulse, respectively. The accuracy significantly changed with altered running speed, slope, or step frequency. Mean (95% confidence interval) within-individual correlations between modelled and predicted impulses across conditions were generally nearly perfect, being 0.92 (0.89 to 0.94); 0.95 (0.93 to 0.96); and 0.95 (0.94 to 0.96) for the patellofemoral, tibial, and Achilles tendon stress/strain impulses, respectively. CONCLUSIONS: This study shows that commercially available instrumented insoles can predict loading at common running injury locations with variable absolute, but (very) high relative accuracy. The absolute error was lower than methods that measure step-count only, or assume a constant load per speed or slope. This developed model may allow for quantification of in-field tissue loading and real-time tissue loading-based feedback to reduce injury risk.

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.

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).

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.

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.

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.

The Effect of Wearable-Based Real-Time Feedback on Running Injuries and Running Performance: A Randomized Controlled Trial.

BACKGROUND: Running technique and running speed are considered important risk factors for running injuries. Real-time feedback on running technique and running speed by wearables may help reduce injury risk. PURPOSE: To investigate whether real-time feedback on spatiotemporal metrics and relative speed by commercially available pressure-sensitive insoles would reduce running injuries and improve running performance compared with no real-time feedback. STUDY DESIGN: Randomized controlled trial; Level of evidence, 1. METHODS: A total of 220 recreational runners were randomly assigned into the intervention and control groups. Both groups received pressure-sensitive insoles, but only the intervention group received real-time feedback on spatiotemporal metrics and relative speed. The feedback aimed to reduce loading on the joint/segment estimated to exhibit the highest load. Injury rates were compared between the groups using Cox regressions. Secondary outcomes compared included injury severity, the proportion of runners with multiple injuries, changes in self-reported personal best times and motivation (Behavioral Regulation in Exercise Questionnaire-2), and interest in continuing wearable use after study completion. RESULTS: A total of 160 participants (73%) were included in analyses of the primary outcome. Intention-to-treat analysis showed no significant difference in injury rate between the groups (Hazard ratio [HR], 1.11; P = .70). This was expected, as 53 of 160 (33%) participants ended up in the unassigned group because they used incorrect wearable settings, nullifying any interventional effects. As-treated analysis showed a significantly lower injury rate among participants receiving real-time feedback (HR, 0.53; P = .03). Similarly, the first-time injury severity was significantly lower (-0.43; P = .042). Per-protocol analysis showed no significant differences in injury rates, but the direction favored the intervention group (HR, 0.67; P = .30). There were no significant differences in the proportion of patients with multiple injuries (HR, 0.82; P = .40) or changes in running performance (3.07%; P = .26) and motivation. Also, ~60% of the participants who completed the study showed interest in continuing wearable use. CONCLUSION: Real-time feedback on spatiotemporal metrics and relative speed provided by commercially available instrumented insoles may reduce the rate and severity of injuries in recreational runners. Feedback did not influence running performance and exercise motivation. REGISTRATION: NL8472 (Dutch Trial Register).

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.

Physiological characteristics of a 92-yr-old four-time world champion indoor rower.

This study assessed the physiological, performance, nutritional intake, and training characteristics of a 92-yr-old four-time master world champion indoor male rower. Body composition was assessed via bioelectrical impedance. Oxygen uptake, carbon dioxide production, ventilation, and heart rate were measured at rest and during a 2,000-m time trial on a rowing ergometer. Maximal power was assessed to compute anaerobic power reserve. Training included ≈ 30 km/wk on the rowing ergometer. Herein, 70% of distances were covered at light intensities (RPE, 10-12), 20% at hard (RPE, 13-17), and 10% at near maximal or maximal (RPE, 17-20). Resistance training was performed during ≈ 2 sessions/wk, and involved three sets of dumbbell lunges, rows, and curls, respectively, taken close (or to) failure. Dietary intake was high in protein [2.3 ± 0.1 g·kg-1 lean body mass (LBM)], conferring a caloric intake of 33.4 ± 1.7 kcal·kg-1 LBM. The participant demonstrated muscle mass of 47.7 kg, fat mass of 9.1 kg (15.4% body fat), forced vital capacity of 3.36 L, time constant (τ) to steady state of 30.2 s, peak relative oxygen pulse of 0.18 ([mL·O2/beats/min]/kg), peak heart rate of 153 beats/min, and maximum power of 220 W (140 W anaerobic power reserve). This 92-yr-old athlete demonstrated remarkably fast oxygen uptake kinetics, akin to values for a healthy young adult, indicating well-developed and/or maintained cardiopulmonary function. The high values for cardiopulmonary function, muscle mass, metabolic efficiency, and maximum power output may infer the pliability of these systems to maintain high functionality at an advanced age.NEW & NOTEWORTHY To our knowledge, this study is the first to characterize the physiological attributes of a competitive rower (4-time master world champion) at an advanced age (≥ 85 yr). The participant demonstrated a high muscle mass (47.7 kg; 80.6% body mass), maximal power (220 W), and exceptional oxygen uptake kinetics (τ of 30.2 s), similar to values reported for healthy young adults.

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.

A comparison of five methods to normalize joint moments during running.

BACKGROUND: Net joint moments (NJM) are typically normalized for a (combination of) physical body characteristics such as mass, height, and limb length using ratio scaling to account for differences in body characteristics between individuals. Four assumptions must be met when normalizing NJM data this way to ensure valid conclusions. First, the relationship between the non-normalized NJM and participant characteristic should be linear. Second, the regression line between NJM and the characteristic(s) used should pass through the origin. Third, scaling should not significantly perturb the statistical distribution of the data. Fourth, normalizing a NJM should eliminate its correlation with the characteristic(s) normalized for. RESEARCH QUESTION: This study assessed these assumptions using data collected among 59 individuals running at 10 km h-1. METHODS: Standard inverse dynamics analyses were conducted, and ratios were computed between the sagittal-plane hip, knee and ankle NJM's and the participant's mass, height, leg length, mass × height, and mass × leg length. RESULTS: The most important finding of this study was that none of the scaling variables fulfilled all assumptions across all joints. However, scaling by mass, mass*height and mass*leg length satisfied the assumptions for the knee joint moment and log-transformed hip joint moment, suggesting these methods generally performed best. SIGNIFICANCE: Our findings suggests that scaling by mass, mass*height and mass*leg length may be considered to normalize joint moments during running. Nevertheless, we urge researchers to check the statistical assumptions to ensure valid conclusions. We provide supplementary code to check the statistical assumptions, and discuss consequences of inappropriate scaling.

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.

Myoelectric activity during electromagnetic resistance alone and in combination with variable resistance or eccentric overload.

The purpose of this study was to compare the effects of electromagnetic resistance alone, as well as in combination with variable resistance or accentuated eccentric methods, with traditional dynamic constant external resistance exercise on myoelectric activity during elbow flexion. The study employed a within-participant randomized, cross-over design whereby 16 young, resistance-trained male and female volunteers performed elbow flexion exercise under each of the following conditions: using a dumbbell (DB); using a commercial electromagnetic resistance device (ELECTRO); variable resistance (VR) using a setting on the device that attempts to match the level of resistance to the human strength curve, and; eccentric overload (EO) using a setting on the device that increases the load by 50% on the eccentric portion of each repetition. Surface electromyography (sEMG) was obtained for the biceps brachii, brachioradialis and anterior deltoid on each of the conditions. Participants performed the conditions at their predetermined 10 repetition maximum. " The order of performance for the conditions was counterbalanced, with trials separated by a 10-min recovery period. The sEMG was synced to a motion capture system to assess sEMG amplitude at elbow joint angles of 30°, 50°, 70°, 90°, 110°, with amplitude normalized to the maximal activation. The anterior deltoid showed the largest differences in amplitude between conditions, where median estimates indicated greater concentric sEMG amplitude (~ 7-10%) with EO, ELECTRO and VR compared with DB. Concentric biceps brachii sEMG amplitude was similar between conditions. In contrast, results indicated a greater eccentric amplitude with DB compared to ELECTRO and VR, but unlikely to exceed a 5% difference. Data indicated a greater concentric and eccentric brachioradialis sEMG amplitude with DB compared to all other conditions, but differences were unlikely to exceed 5%. The electromagnetic device tended to produce greater amplitudes in the anterior deltoid, while DB tended to produce greater amplitudes in the brachioradialis; amplitude for the biceps brachii was relatively similar between conditions. Overall, any observed differences were relatively modest, equating to magnitudes of ~ 5% and not likely greater than 10%. These differences would seem to be of minimal practical significance.

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.

A physiological comparison of the new-over 70 years of age-marathon record holder and his predecessor: A case report.

Purpose: This study assessed the body composition, cardiorespiratory fitness, fiber type and mitochondrial function, and training characteristics of a 71-year-old runner who broke the world record marathon of the men's 70-74 age category and held several other world records. The values were compared to those of the previous world-record holder. Methods: Body fat percentage was assessed using air-displacement plethysmography. V ˙ O 2 max , running economy, and maximum heart rate were measured during treadmill running. Muscle fiber typology and mitochondrial function were evaluated using a muscle biopsy. Results: Body fat percentage was 13.5%, V ˙ O 2 max was 46.6 ml kg-1 min-1, and maximum heartrate was 160 beats∙min-1. At the marathon pace (14.5 km h-1), his running economy was 170.5 ml kg-1 km-1. The gas exchange threshold and respiratory compensation point occurred at 75.7% and 93.9% of the V ˙ O 2 max , i.e., 13 km h-1 and 15 km h-1, respectively. The oxygen uptake at the marathon pace corresponded to 88.5% of V ˙ O 2 max . Vastus lateralis fiber content was 90.3% type I and 9.7% type II. Average distance was 139 km∙w-1 in the year prior to the record. Conclusion: The 71-year-old world-record holder marathon showed a relatively similar V ˙ O 2 max , lower percentage of V ˙ O 2 max at marathon pace, but a substantially better running economy than his predecessor. The better running economy may result from an almost double weekly training volume compared to the predecessor and a high type I fiber content. He trained every day in the last ∼1.5 years and achieved international performance in his age group category with a small (<5% per decade) age-related decline in marathon performance.

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.

Using beat frequency in music to adjust running cadence in recreational runners: A randomized multiple baseline design.

Running with music has been shown to acutely change cadence. However, it is unclear if the increased cadence remains long-term when running without music in an in-field situation. The aim of this 12-week study was to investigate the effect of a 4-week music running program on cadence, speed and heartrate during and after the music running program. Seven recreational runners with a cadence of <170 steps per minute were randomly assigned to a baseline and post-intervention period of different durations. During the intervention phase, the participants ran with a musical beat that was 7.5-10% higher than their mean cadence at the start of the study. Cadence, heartrate and running speed were measured twice a week during a 5-kilometer run with a watch, and were analyzed using randomization tests and visual data inspection. Two participants dropped-out due to shortage of time (n = 1) and an acute calf injury (n = 1). Cadence significantly increased during the intervention period (+8.5%), and remained elevated during the post-intervention period (+7.9% (p = .001)) in comparison with the baseline period. Heartrate and running speed did not significantly change during any period. This study among five participants shows that four weeks of running with a musical beat that is 7.5-10% higher than the preferred cadence may be an effective and feasible intervention to increase running cadence. Importantly, the increased cadence occurred without simultaneous increases in running speed and heartrate, hereby potentially reducing mechanical loading without increasing metabolic load.HighlightsRunning with a musical rhythm that is higher than the preferred cadence leads to an increased running cadence, without increasing heartrate and running speed.This cadence remains elevated for at least three to five weeks after the music intervention period.All individuals showed a practically relevant increase in cadence during and after the intervention.