Response to Mr. Di Pietro van Hooren, Bas; Souren, Tjeu; Bongers, Bart
Scandinavian journal of medicine & science in sports,
April 2024, 2024-Apr, 2024-04-00, 20240401, Letnik:
34, Številka:
4
Journal Article
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.
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), PNOĒ (PNOĒ), 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.
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.
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.
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.
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.
Forty-one participants (22 males and 19 females) ran at 2.78 m∙s
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.
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.
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.
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.
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.
It is widely believed that an active cool-down is more effective for promoting post-exercise recovery than a passive cool-down involving no activity. However, research on this topic has never been ...synthesized and it therefore remains largely unknown whether this belief is correct. This review compares the effects of various types of active cool-downs with passive cool-downs on sports performance, injuries, long-term adaptive responses, and psychophysiological markers of post-exercise recovery. An active cool-down is largely ineffective with respect to enhancing same-day and next-day(s) sports performance, but some beneficial effects on next-day(s) performance have been reported. Active cool-downs do not appear to prevent injuries, and preliminary evidence suggests that performing an active cool-down on a regular basis does not attenuate the long-term adaptive response. Active cool-downs accelerate recovery of lactate in blood, but not necessarily in muscle tissue. Performing active cool-downs may partially prevent immune system depression and promote faster recovery of the cardiovascular and respiratory systems. However, it is unknown whether this reduces the likelihood of post-exercise illnesses, syncope, and cardiovascular complications. Most evidence indicates that active cool-downs do not significantly reduce muscle soreness, or improve the recovery of indirect markers of muscle damage, neuromuscular contractile properties, musculotendinous stiffness, range of motion, systemic hormonal concentrations, or measures of psychological recovery. It can also interfere with muscle glycogen resynthesis. In summary, based on the empirical evidence currently available, active cool-downs are largely ineffective for improving most psychophysiological markers of post-exercise recovery, but may nevertheless offer some benefits compared with a passive cool-down.
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).
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