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How Does Power During Running Change when Measured at Different Time Intervals?
Abstract
This study aimed to examine how the power output changes while running at a continuous comfortable velocity on a motorized treadmill by comparing running power averaged during different time intervals. Forty-nine endurance runners performed a running protocol on a treadmill at self-selected comfortable velocity. Power output (W) was estimated with the Stryd™ power meter, and it was examined over six recording intervals within the 3-min recording period: 0–10 s, 0–20 s, 0–30 s, 0–60 s, 0–120 s and 0–180 s. The ANOVAs showed no significant differences in the magnitude of the power output between the recording intervals (p=0.276, F=1.614, partial Eta 2 =0.155). An almost perfect association was also observed in the magnitude of the power output between the recording intervals (ICC≥0.999). Bland-Altman plots revealed no heteroscedasticity of error for the power output in any of the between-intervals comparisons (r 2<0.1), although longer recording intervals yield smaller systematic bias, random errors, and narrower limits of agreement for power output. The results show that power data during running, as measured through the Stryd™ system, is a stable metric with negligible differences, in practical terms, between shorter (i. e., 10, 20, 30, 60 or 120 s) and longer recording intervals (i. e., 180 s).
... While several studies have already analysed power output in running [18,19] and others have investigated the relation between VO2max and power production [16,20], to the best of the authors' knowledge, there are no studies assessing the difference in power output between shod and barefoot running. In order to bridge this gap, this study aims to identify the effect of footwear on power output in endurance runners. ...
... For each trial, participants completed two successive 3 min running bouts (i.e., shod for the first and barefoot for the latter), separated by a 2 min period to change from shod to barefoot condition. Since power output [19] and spatiotemporal parameters [22] reach a steady state in less than 2 min, data were recorded during both running trials and 6-8 strides were analysed [23]. ...
... Given that power can be defined as the product of force and velocity [29], and that in the present study both running bouts (i.e., shod and barefoot) were executed at the same comfortable velocity for every participant, it seems reasonable that MPO and MPOnorm remained stable under both footwear conditions. The power output values reported in the present study during shod running (210.05 ± 44.16 W) are supported by those found by previous works using the Stryd™ power meter at the same running velocity [15,19]. ...
Several studies have already analysed power output in running or the relation between VO2max and power production as factors related to running economy; however, there are no studies assessing the difference in power output between shod and barefoot running. This study aims to identify the effect of footwear on the power output endurance runner. Forty-one endurance runners (16 female) were evaluated at shod and barefoot running over a one-session running protocol at their preferred comfortable velocity (11.71 ± 1.07 km·h−1). The mean power output (MPO) and normalized MPO (MPOnorm), form power, vertical oscillation, leg stiffness, running effectiveness and spatiotemporal parameters were obtained using the Stryd™ foot pod system. Additionally, footstrike patterns were measured using high-speed video at 240 Hz. No differences were noted in MPO (p = 0.582) and MPOnorm (p = 0.568), whereas significant differences were found in form power, in both absolute (p = 0.001) and relative values (p < 0.001), running effectiveness (p = 0.006), stiffness (p = 0.002) and vertical oscillation (p < 0.001). By running barefoot, lower values for contact time (p < 0.001) and step length (p = 0.003) were obtained with greater step frequency (p < 0.001), compared to shod running. The prevalence of footstrike pattern significantly differs between conditions, with 19.5% of runners showing a rearfoot strike, whereas no runners showed a rearfoot strike during barefoot running. Running barefoot showed greater running effectiveness in comparison with shod running, and was consistent with lower values in form power and lower vertical oscillation. From a practical perspective, the long-term effect of barefoot running drills might lead to increased running efficiency and leg stiffness in endurance runners, affecting running economy.
... The main characteristics of the studies included in this review (n = 19) are presented in the Tables 1 and 2. Table 1 shows a summary of 12 studies using wearable sensors with the capacity of measuring power during different running exercises. Whereas three of those studies [11,27,28] examine the PW kinetics during different running protocols, the other four studies [15,25,26,29] investigate the relationship between PW and physiological parameters such as oxygen consumption (VO2) at different intensities. Additionally, two further works [30,31] analyse the application of mathematical models, based on power laws, to predict running performance, whereas a recent study [32] ...
... The main characteristics of the studies included in this review (n = 19) are presented in the Tables 1 and 2. Table 1 shows a summary of 12 studies using wearable sensors with the capacity of measuring power during different running exercises. Whereas three of those studies [11,27,28] examine the PW kinetics during different running protocols, the other four studies [15,25,26,29] investigate the relationship between PW and physiological parameters such as oxygen consumption (VO 2 ) at different intensities. Additionally, two further works [30,31] analyse the application of mathematical models, based on power laws, to predict running performance, whereas a recent study [32] assesses the agreement level between two mathematical models and five power meter devices through different running conditions. ...
... Furthermore, the Stryd reliability for PW during treadmill running at a self-selected constant speed with a slope gradient at 0% was proved to be a stable data between short and long intervals (i.e., 10-120 s and 180 s, respectively) [28]. No significant differences were found in the amount of power production between the different spans of times acquired (p = 0.276, partial ETA 2 = 0.155) and an almost perfect association in the previously mentioned amount of power production recorded over the intervals (ICC ≥ 0.999). ...
Mechanical power may act as a key indicator for physiological and mechanical changes during running. In this scoping review, we examine the current evidences about the use of power output (PW) during endurance running and the different commercially available wearable sensors to assess PW. The Boolean phrases endurance OR submaximal NOT sprint AND running OR runner AND power OR power meter, were searched in PubMed, MEDLINE, and SCOPUS. Nineteen studies were finally selected for analysis. The current evidence about critical power and both power-time and power-duration relationships in running allow to provide coaches and practitioners a new promising setting for PW quantification with the use of wearable sensors. Some studies have assessed the validity and reliability of different available wearables for both kinematics parameters and PW when running but running power meters need further research before a definitive conclusion regarding its validity and reliability.
... That way, power output was only crucial for speed and power athletes (e.g., sprinters, jumpers, football, or rugby players, [4]. Moreover, these assessments were almost exclusively performed in laboratories [2], [5], thus limiting the results' practical application. ...
... In other endurance sports, such as running, monitoring power output is a relatively novel practice that allows endurance runners to quantify the work they generate during running [5]. Power in Watts, generated by a running human, corresponds to an output of the work during some time. ...
... Furthermore, the Stryd power metric was able to distinguish between exercise intensities near the MLSS. In agreement with our results, previous investigations also reported that Stryd running power was stable during constant-speed running [33], repeatable [11], and sensitive between conditions [34]; however, our investigation is the first to evaluate these running power parameters near the MLSS, an important threshold for training programs and fitness assessment [35,36]. In support of the Stryd running power metric results, besides a significantly lower RPE measurement during the second compared to the first MLSS trial (i.e., 0.8 units on the Borg 6-20 scale), which may indicate increased comfort during testing, the · VO 2 (Table 2) and other physiological and perceptual responses to running near the MLSS were also stable, sensitive, and reliable (Table S1). ...
We sought to determine the utility of Stryd, a commercially available inertial measurement unit, to quantify running intensity and aerobic fitness. Fifteen (eight male, seven female) runners (age = 30.2 [4.3] years; V·O2max = 54.5 [6.5] ml·kg−1·min−1) performed moderate- and heavy-intensity step transitions, an incremental exercise test, and constant-speed running trials to establish the maximal lactate steady state (MLSS). Stryd running power stability, sensitivity, and reliability were evaluated near the MLSS. Stryd running power was also compared to running speed, V·O2, and metabolic power measures to estimate running mechanical efficiency (EFF) and to determine the efficacy of using Stryd to delineate exercise intensities, quantify aerobic fitness, and estimate running economy (RE). Stryd running power was strongly associated with V·O2 (R2 = 0.84; p < 0.001) and running speed at the MLSS (R2 = 0.91; p < 0.001). Stryd running power measures were strongly correlated with RE at the MLSS when combined with metabolic data (R2 = 0.79; p < 0.001) but not in isolation from the metabolic data (R2 = 0.08; p = 0.313). Measures of running EFF near the MLSS were not different across intensities (~21%; p > 0.05). In conclusion, although Stryd could not quantify RE in isolation, it provided a stable, sensitive, and reliable metric that can estimate aerobic fitness, delineate exercise intensities, and approximate the metabolic requirements of running near the MLSS.
... Additionally, data training metrics have been analyzed with this device [42][43][44]. The CP concept has been investigated from a physiological perspective, including its relationship to ventilatory thresholds and maximum oxygen uptake [20] as well as its association with physiological variables such as oxygen consumption [45] and changes in power as functions of different intervals [46]. One of the main advantages of the Stryd running-power meter is its practical applications for data collection in both training and competition settings, in contrast to laboratory-based devices [47]. ...
The differences in power meters and gestures between cycling and running can have an impact on determining Critical Power (CP) intensity in each sport. CP is a concept that has been extensively researched in cycling, but with the advent of power measurement in running, it can now be examined in that discipline as well. The purpose of the present study was to determine whether power output at CP intensity is interchangeable between cycling and running segments measured with their respective discipline-specific power meters. A group of 18 trained triathletes (age 33.0 ± 11.1 years, height 1.75 ± 0.06 m, body mass 71.2 ± 7.1 kg) performed a CP test in cycling (3-min All-Out Test) and running (9/3-min Stryd CP Test). The main results of the present study showed significant differences (p < 0.001) between CP in cycling and running. The running CP (301.8 W ± 41.5 W) was 20.2% overestimated compared with the cycling CP (251.1 W ± 37.0 W). Cycling power only explained 26.7% of the running power (R 2 = 0.267; p = 0.284). Therefore, power would not be interchangeable between the cycling and running disciplines at CP intensity. In conclusion, it would be necessary to carry out a specific test for each discipline to be able to make a correct determination of CP.
... Monitoring power output in running is rather a novel routine that allows endurance runners to quantify the output of the work they are generating (García-Pinillos et al., 2019). With the recent development of light and accurate accelerometers (in a form of foot pods), power output in running became simple to monitor (Austin et al., 2018). ...
... Knowledge of the reliability and validity of these IMU devices is of paramount importance to collect and interpret data accurately. Some researchers have analyzed the Stryd's reliability and validity during running [2,5,[8][9][10][11]. However, the reliability and validity have been less investigated during walking [12,13], and never during walking on positive slopes using different backpack loads. ...
Background: The Styrd Power Meter is gaining special interest for on-field gait analyses due to its low-cost and general availability. However, the reliability and validity of the Stryd during walking on positive slopes using different backpack loads have never been investigated.
Research Question: Is the Stryd Power Meter reliable and valid to quantify gait mechanics during walking on positive inclines and during level walking incorporating load carriage?
Methods: Seventeen participants from a police force rescue team performed 8 submaximal walking trials for 5-min at 3.6 km•h-1 during different positive slope (1, 10 and 20%) and backpack load (0, 10, 20, 30 and 40% of body mass) conditions. Two Stryd devices were utilized for reliability analyses. Validity of cadence and ground contact time (GCT) were analyzed against a gold standard device (Optojump).
Results: The Stryd demonstrated acceptable reliability [mean bias: <2.5%; effect size (ES): <0.25; standard error of the mean: <1.7%; r: >0.76] for power, cadence, and GCT. Validity measures (mean bias: <0.8%; ES: <0.07; r: >0.96; Lin’s Concordance Coefficient: 0.96; Mean Absolute Percent Error: <1%) for cadence were also found to be acceptable. The Stryd overestimated (P < 0.001; ES: >5.1) GCT in all the walking conditions. A significant systematic positive bias (P < 0.022; r = 0.56 to 0.76) was found in 7 conditions.
Significance: The Stryd Power Meter appears to produce reliable measurements for power output, cadence and GCT. The Stryd produced valid measurements for cadence during walking on positive slopes and during level walking with a loaded backpack. However, the Stryd is not valid for measuring GCT during these walking conditions. This study adds novel data regarding the reliability and validity of this device and might be of particular interest for scientists, practitioners, and first responders seeking reliable devices to quantify gait mechanics during walking.
... Monitoring power output in running is rather a novel routine that allows endurance runners to quantify the output of the work they are generating (García-Pinillos et al., 2019). With the recent development of light and accurate accelerometers (in a form of foot pods), power output in running became simple to monitor (Austin et al., 2018). ...
Introduction
The primary goals of the research are to test the anaerobic ability and explosive power of the legs of
female basketball players age 14 to 16. The obtained values will be compared in relation to the players
positions (defender / wing / center) of the basketball players in the team. The research hypothesis refers
to the fact that, due to the sensitivity of growth and development and consequently the sensitivity of
developing motor skills in this period, there is no difference in anaerobic ability and explosive power in
female basketball players in relation to the team positions.
Method
The study involved 48 female basketball players with an average age of 15.9 ± 0.86 years. The girls were
divided into three groups of 16 players according to the position they played in the team (defender/wing
/center). Anaerobic endurance was measured by a 300-yard shuttle test on a basketball court. The
explosive power of the vertical movement from a standing position with the hands on the hips (counter
movement jump - CMJ) was estimated using a jumping platform (Globus Ergo Tester Platform). The
platform is connected to a digital timer that records the time and altitude of the jump and the power is
estimated based on the reaction force of the ground and the duration of the flight phase.
Results and discussion
Statistical data processing (ANOVA) did not show statistical significance in terms of differences in
anaerobic endurance in girls in relation to the position in the team (p = 0.714). In the vertical jump, no
statistically significant difference was also observed (p = 0.245). One of the reasons for the obtained
results may be that there is no distinct selection in women's basketball at that age due to the insufficient
number of girls who train basketball in the Belgrade region. The results are also of practical importance,
especially for the processes of selection, training control and for modeling the condition of female
basketball players and their development path.
Conclusion
Girls are placed in certain players positions in relation to their basketball skills, and very rarely in relation
to their anthropological and motor skills. Another reason why there is no statistically significant
difference in the results is that in the younger categories there is an early specialization and some players
have not reached the maximum in their development.
... To compare 4 power meter devices in terms of repeatability and concurrent validity between P data and oxygen consumption (VO 2 [12] Stryd (foot pod) ...
Wearable technology has allowed for the real-time assessment of mechanical work employed in several sporting activities. Through novel power metrics, Functional Threshold Power have shown a reliable indicator of training intensities. This study aims to determine the relationship between mean power output (MPO) values obtained during three submaximal running time trials (i.e., 10 min, 20 min, and 30 min) and the functional threshold power (FTP). Twenty-two recreationally trained male endurance runners completed four submaximal running time trials of 10, 20, 30, and 60 min, trying to cover the longest possible distance on a motorized treadmill. Absolute MPO (W), normalized MPO (W/kg) and standard deviation (SD) were calculated for each time trial with a power meter device attached to the shoelaces. All simplified FTP trials analyzed (i.e., FTP10, FTP20, and FTP30) showed a significant association with the calculated FTP (p < 0.001) for both MPO and normalized MPO, whereas stronger correlations were found with longer time trials. Individual correction factors (ICF% = FTP60/FTPn) of ~90% for FTP10, ~94% for FTP20, and ~96% for FTP30 were obtained. The present study procures important practical applications for coaches and athletes as it provides a more accurate estimation of FTP in endurance running through less fatiguing, reproducible tests.
Stryd is a foot pod that reliably estimates running power. Our objectives were to examine the efficacy of the website-generated Stryd critical power (CPSTRYD) as a meaningful parameter for runners. 20 runners performed their regular training while wearing Stryd for a minimum of 6 weeks to generate CPSTRYD. Runners completed laboratory graded exercise testing, and outdoor 1500 m and 5000 m time trails. CPSTRYD was most similar to the second ventilatory threshold (VT2) or the onset of blood lactate accumulation (OBLA) and is highly predictive of running performance. Stryd ground contact time (GCT) was a predictor of performance when comparing runners at the same submaximal treadmill speed. CPSTRYD generated from outdoor running is equivalent to that calculated using an established CP model. However, variance between different methods of CP estimation must be a consideration for runners and coaches. Stryd offers meaningful data for runners including a realistic estimate of CP.
Problem Statement: Power meters have helped performance cyclists to revolutionisetheir training and competitions. However, running power is not obtained by a power meter, as in cycling, but is estimated through accelerometers, gyroscopes or inertial measurements units. Therefore, this relatively new concept must be correctly contextualised. Approach:The most widely used deviceis the summitmodel of the Stryd Running Power Meter, butthe validity, reliability and repeatability of this device must be studied extensively, both regarding the estimation of the running power and the biomechanical parameters. Purpose:The main purpose was to examine all articles where the Stryd device was used to analyse both running power and biomechanical parameters. Methods: Electronic databases were searched using key related terminology such as:Stryd, running power and biomechanical parameters. Results: The production of portable and low-cost equipmenthas led to the capacity toanalyse power and biomechanical parameters in running using different devices. Nevertheless, to avoid erroneous conclusions, it is necessary to take into account considerations in the different studies such as the device used, its placement and the level of the participantsunder study.Conclusions:The Stryd device could be considered as the most recommended device to measure running power compared to other available devices. Although the Stryd system could be a valid tool for measuring temporal parameters, RunScribe seems to be a more accurate device to measure temporal parameters and step length. From a practical point of view, future studies should alsoassess running power in comparison to cycling power in elite triathletes, a population with a high level in both disciplines and who could provide useful data for practical applications in training and competition.
Background:
The force- and power-velocity (F-V and P-V, respectively) relationships have been extensively studied in recent years. However, its use and application in endurance running events is limited.
Research question:
This study aimed to determine if the P-V relationship in endurance runners fits a linear model when running at submaximal velocities, as well as to examine the feasibility of the "two-point method" for estimating power values at different running velocities.
Methods:
Eighteen endurance runners performed, on a motorized treadmill, an incremental running protocol to exhaustion. Power output was obtained at each stage with the Stryd™ power meter. The P-V relationship was determined from a multiple-point method (10, 12, 14, and 17 km·h-1) as well as from three two-point methods based on proximal (10 and 12 km·h-1), intermediate (10 and 14 km·h-1) and distal (10 and 17 km·h-1) velocities.
Results:
The P-V relationship was highly linear ( r = 0.999). The ANOVAs revealed significant, although generally trivial (effect size < 0.20), differences between measured and estimated power values at all the velocities tested. Very high correlations ( r = 0.92) were observed between measured and estimated power values from the 4 methods, while only the multiple-point method ( r2 = 0.091) and two-point method distal ( r2 = 0.092) did not show heteroscedasticity of the error.
Significance:
The two-point method based on distant velocities (i.e., 10 and 17 km·h-1) is able to provide power output with the same accuracy than the multiple-point method. Therefore, since the two-point method is quicker and less prone to fatigue, we recommend the assessment of power output under only two distant velocities to obtain an accurate estimation of power under a wide range of submaximal running velocities.
A novel running wearable called the Stryd Summit footpod fastens to a runner’s shoe and estimates running power. The footpod separates power output into two components, Stryd power and form power. The purpose of this study was to measure the correlations between running economy and power and form power at lactate threshold pace. Seventeen well-trained distance runners, 9 male and 8 female, completed a running protocol. Participants ran two four-minute trials: one with a self-selected cadence, and one with a target cadence lowered by 10%. The mean running economy expressed in terms of oxygen cost at self-selected cadence was 201.6 ± 12.8 mL·kg−1·km−1, and at lowered cadence was 204.5 ± 11.5 mL·kg−1·km−1. Ventilation rate and rating of perceived exertion (RPE) were not significantly different between cadence conditions with one-tailed paired t-test analysis (ventilation, p = 0.77, RPE, p = 0.07). Respiratory exchange ratio and caloric unit cost were significantly greater with lower cadence condition (respiratory exchange ratio, p = 0.03, caloric unit cost, p = 0.03). Mean power at self-selected cadence was 4.4 ± 0.5 W·kg−1, and at lowered cadence was 4.4 ± 0.5 W·kg−1. Mean form power at self-selected cadence was 1.1 ± 0.1 W·kg−1, and at lowered cadence was 1.1 ± 0.1 W·kg−1. There were positive, linear correlations between running economy and power (self-selected cadence and lower cadence, r = 0.6; the 90% confidence interval was 0.2 to 0.8); running economy and form power (self-selected cadence and lower cadence r = 0.5; the 90% confidence interval was 0.1 to 0.8). The findings suggest running economy is positively correlated with Stryd’s power and form power measures yet the footpod may not be sufficiently accurate to estimate differences in the running economy of competitive runners.
Aubry, RL, Power, GA, and Burr, JF. An assessment of running power as a training metric for elite and recreational runners. J Strength Cond Res XX(X): 000-000, 2018-Power, as a testing and training metric to quantify effort, is well accepted in cycling, but is not commonly used in running to quantify effort or performance. This study sought to investigate a novel training tool, the Stryd Running Power Meter, and the applicability of running power (and its individually calculated run mechanics) to be a useful surrogate of metabolic demand (V[Combining Dot Above]O2), across different running surfaces, within different caliber runners. Recreational (n = 13) and elite (n = 11) runners completed a test assessing V[Combining Dot Above]O2 at 3 different paces, while wearing a Stryd Power Meter on both an indoor treadmill and an outdoor track, to investigate relationships between estimated running power and metabolic demand. A weak but significant relationship was found between running power and V[Combining Dot Above]O2 considering all participants as a homogenous group (r = 0.29); however, when assessing each population individually, no significant relationship was found. Examination of the individual mechanical components of power revealed that a correlative decrease in V[Combining Dot Above]O2 representing improved efficiency was associated with decreased ground contact time (r = 0.56), vertical oscillation (r = 0.46), and cadence (r = 0.37) on the treadmill in the recreational group only. Although metabolic demand differed significantly between surfaces at most speeds, run power did not accurately reflect differences in metabolic cost between the 2 surfaces. Running power, calculated via the Stryd Power Meter, is not sufficiently accurate as a surrogate of metabolic demand, particularly in the elite population. However, in a recreational population, this training tool could be useful for feedback on several running dynamics known to influence running economy.
García-Pinillos, F, Roche-Seruendo, LE, Marcen-Cinca, N, Marco-Contreras, LA, and Latorre-Román, PA. Absolute reliability and concurrent validity of the Stryd system for the assessment of running stride kinematics at different velocities. J Strength Cond Res XX(X): 000-000, 2018-This study aimed to determine the absolute reliability and to evaluate the concurrent validity of the Stryd system for measuring spatiotemporal variables during running at different velocities (8-20 km·h) by comparing data with another widely used device (the OptoGait system). Eighteen trained male endurance runners performed an incremental running test (8-20 km·h with 3-minute stages) on a treadmill. Spatiotemporal parameters (contact time [CT], flight time [FT], step length [SL], and step frequency [SF]) were measured using 2 different devices (Stryd and OptoGait systems). The Stryd system showed a coefficient of variation (CV) <3%, except for FT (3.7-11.6%). The OptoGait achieved CV <4%, except for FT (6.0-30.6%). Pearson correlation analysis showed large correlations for CT and FT, and almost perfect for SL and SF over the entire protocol. The intraclass correlation coefficients partially support those results. Paired t-tests showed that CT was underestimated (p < 0.05, effect size [ES] > 0.7; ∼4-8%), FT overestimated (p < 0.05, ES > 0.7; ∼7-65%), whereas SL and SF were very similar between systems (ES < 0.1, with differences <1%). The Stryd is a practical portable device that is reliable for measuring CT, FT, SL, and SF during running. It provides accurate SL and SF measures but underestimates CT (0.5-8%) and overestimates FT (3-67%) compared with a photocell-based system.
The current study examined variability and fluctuation in the running gait cycle, focusing on differences between trained distance runners and non-runners. The two groups of participants performed treadmill running at 80%, 100%, and 120% of their preferred speed for 10 min. Stride-interval time-series were recorded during running using footswitches. The average preferred speed was significantly higher for the trained runners than for the non-runners. The trained runners showed significantly smaller variability of stride interval than did the non-runners, and at the same time the scaling exponent alpha evaluated by detrended fluctuation analysis tended to be smaller for the trained runners. These results suggest that expert runners can reduce variability in the trained movement without loosing dynamical degrees of freedom for spatiotemporal organization of the gait pattern.
Minimal measurement error (reliability) during the collection of interval- and ratio-type data is critically important to sports medicine research. The main components of measurement error are systematic bias (e.g. general learning or fatigue effects on the tests) and random error due to biological or mechanical variation. Both error components should be meaningfully quantified for the sports physician to relate the described error to judgements regarding 'analytical goals' (the requirements of the measurement tool for effective practical use) rather than the statistical significance of any reliability indicators. Methods based on correlation coefficients and regression provide an indication of 'relative reliability'. Since these methods are highly influenced by the range of measured values, researchers should be cautious in: (i) concluding acceptable relative reliability even if a correlation is above 0.9; (ii) extrapolating the results of a test-retest correlation to a new sample of individuals involved in an experiment; and (iii) comparing test-retest correlations between different reliability studies. Methods used to describe 'absolute reliability' include the standard error of measurements (SEM), coefficient of variation (CV) and limits of agreement (LOA). These statistics are more appropriate for comparing reliability between different measurement tools in different studies. They can be used in multiple retest studies from ANOVA procedures, help predict the magnitude of a 'real' change in individual athletes and be employed to estimate statistical power for a repeated-measures experiment. These methods vary considerably in the way they are calculated and their use also assumes the presence (CV) or absence (SEM) of heteroscedasticity. Most methods of calculating SEM and CV represent approximately 68% of the error that is actually present in the repeated measurements for the 'average' individual in the sample. LOA represent the test-retest differences for 95% of a population. The associated Bland-Altman plot shows the measurement error schematically and helps to identify the presence of heteroscedasticity. If there is evidence of heteroscedasticity or non-normality, one should logarithmically transform the data and quote the bias and random error as ratios. This allows simple comparisons of reliability across different measurement tools. It is recommended that sports clinicians and researchers should cite and interpret a number of statistical methods for assessing reliability. We encourage the inclusion of the LOA method, especially the exploration of heteroscedasticity that is inherent in this analysis. We also stress the importance of relating the results of any reliability statistic to 'analytical goals' in sports medicine.
The purpose of this study was to compare the kinematic and kinetic parameters of treadmill running to those of overground running.
Twenty healthy young subjects ran overground at their self-selected moderate running speed. Motion capture and ground reaction force (GRF) data for three strides of each limb were recorded and the subjects' average running speed was evaluated. The subjects then ran on an instrumented treadmill set to their average overground running speed while motion capture and GRF data were recorded. The kinematics (body segment orientations and joint angles) and kinetics (net joint moments and joint powers) for treadmill (15 consecutive gait cycles) and overground running (three cycles each limb) were calculated and compared.
The features of the kinematic and kinetic trajectories of treadmill gait were similar to those of overground gait. Statistically significant differences in knee kinematics,the peak values of GRF, joint moment, and joint power trajectories were identified.
Parameters measured with an adequate instrumented treadmill are comparable to but not directly equivalent to those measured for overground running. With such an instrument, it is possible to study the mechanics of running under well-controlled and reproducible conditions.
Treadmill-based analysis of running mechanics can be generalized to overground running mechanics, provided the treadmill surface is sufficiently stiff and belt speed is adequately regulated.
Background
Treadmills are often used to assess running biomechanics, however the validity of applying results from treadmill graded running to overground graded running is currently unknown.
Research question
The purpose of this study was to investigate whether treadmill and overground graded running have comparable kinematics and ground reaction force parameters.
Methods
Eleven healthy male adults ran overground and on an instrumented treadmill as motion capture and force platform data were collected for the following conditions: downhill running at a slope of −8° at 10, 13 and 16 km⋅h⁻¹; level running at 10 and 13 km⋅h⁻¹; uphill running at a slope of +8° at 8, 10 and 13 km⋅h⁻¹. Sagittal joint angles at heel strike, mid-stance, and toe-off were computed for the ankle, knee and hip. Ground reaction force parameters including peak average and instantaneous normal loading rate, peak impact and active normal force, peak tangential (braking and propulsive) forces, and normal and tangential impulses were also calculated.
Results
Joint kinematics and ground reaction forces for level running were generally similar between overground and treadmill conditions. The following variables were significantly higher during overground uphill running (mean difference ± SD): average normal loading rate (14.4 ± 7.1 BW⋅s⁻¹), normal impulse (0.04 ± 0.02 BW⋅s), propulsive impulse (0.04 ± 0.02 BW⋅s), and vertical center of mass excursion (0.092 ± 0.031 m). The following variables were significantly higher during overground downhill running (mean difference ± SD): ankle plantarflexion at toe-off (−5.39 ± 6.19°) and vertical center of mass excursion (0.046 ± 0.039 m).
Significance
These findings suggest that subtle differences in kinematics and ground reaction forces exist between overground and treadmill graded running. These differences aside, we believe that overground kinematics and ground reaction forces in graded running are reasonably replicated on a treadmill.
The duration that exercise can be maintained decreases as the power requirements increase. In this review, we describe the power–duration (PD) relationship across the full range of attainable power outputs in humans. We show that a remarkably small range of power outputs is sustainable (power outputs below the critical power, CP). We also show that the origin of neuromuscular fatigue differs considerably depending on the exercise intensity domain in which exercise is performed. In the moderate domain (below the lactate threshold, LT), fatigue develops slowly and is predominantly of central origin (residing in the central nervous system). In the heavy domain (above LT but below CP), both central and peripheral (muscle) fatigue are observed. In this domain, fatigue is frequently correlated with the depletion of muscle glycogen. Severe-intensity exercise (above the CP) is associated with progressive derangements of muscle metabolic homeostasis and consequent peripheral fatigue. To counter these effects, muscle activity increases progressively, as does pulmonary oxygen uptake (
), with task failure being associated with the attainment of
max. Although the loss of homeostasis and thus fatigue develop more rapidly the higher the power output is above CP, the metabolic disturbance and the degree of peripheral fatigue reach similar values at task failure. We provide evidence that the failure to continue severe-intensity exercise is a physiological phenomenon involving multiple interacting mechanisms which indicate a mismatch between neuromuscular power demand and instantaneous power supply. Valid integrative models of fatigue must account for the PD relationship and its physiological basis.
Mobile power meters provide a valid means of measuring cyclists’ power output in the field. These field measurements can be performed with very good accuracy and reliability making the power meter a useful tool for monitoring and evaluating training and race demands. This review presents power meter data from a Grand Tour cyclist’s training and racing and explores the inherent complications created by its stochastic nature. Simple summary methods cannot reflect a session’s variable distribution of power output or indicate its likely metabolic stress. Binning power output data, into training zones for example, provides information on the detail but not the length of efforts within a session. An alternative approach is to track changes in cyclists’ modelled training and racing performances. Both critical power and record power profiles have been used for monitoring training-induced changes in this manner. Due to the inadequacy of current methods, the review highlights the need for new methods to be established which quantify the effects of training loads and models their implications for performance.
Objective:
Intraclass correlation coefficient (ICC) is a widely used reliability index in test-retest, intrarater, and interrater reliability analyses. This article introduces the basic concept of ICC in the content of reliability analysis.
Discussion for researchers:
There are 10 forms of ICCs. Because each form involves distinct assumptions in their calculation and will lead to different interpretations, researchers should explicitly specify the ICC form they used in their calculation. A thorough review of the research design is needed in selecting the appropriate form of ICC to evaluate reliability. The best practice of reporting ICC should include software information, "model," "type," and "definition" selections.
Discussion for readers:
When coming across an article that includes ICC, readers should first check whether information about the ICC form has been reported and if an appropriate ICC form was used. Based on the 95% confident interval of the ICC estimate, values less than 0.5, between 0.5 and 0.75, between 0.75 and 0.9, and greater than 0.90 are indicative of poor, moderate, good, and excellent reliability, respectively.
Conclusion:
This article provides a practical guideline for clinical researchers to choose the correct form of ICC and suggests the best practice of reporting ICC parameters in scientific publications. This article also gives readers an appreciation for what to look for when coming across ICC while reading an article.
Conventional gait labs are limited in their ability to study running gait due to their size. There is no consensus in the literature regarding the ability to extrapolate results for adult treadmill running to overground. This comparison has not been studied in children. Twenty-four healthy children (mean age 11.7) ran overground at a slow running speed while motion capture, ground reaction force, and surface electromyography (EMG) data were obtained. The same data were then collected while participants ran for six minutes on an instrumented treadmill at a speed similar to their overground speed. The kinematic, kinetic, and EMG data for overground and treadmill running were compared. Sagittal plane kinematics demonstrated similar hip and knee waveforms with the exception of more knee extension just before toe off. Ankle kinematic waveforms were similar during stance phase but treadmill running demonstrated decreased dorsiflexion during swing. Kinetic data was significantly different between the two conditions with treadmill running having a more anterior ground reaction force compared to overground. Due to the numerous differences between overground and treadmill gait demonstrated in this study, it is felt that the use of an instrumented treadmill is not a surrogate to the study of overground running in a pediatric population. This data set will function as a normative data set against which future treadmill studies can be compared.
The purpose of this study was to determine the effects of treadmill training on the kinematic accommodation and habituation process of novice treadmill runners. Six experienced male college distance runners, but novice treadmill runners, trained on a treadmill operating at 4.0 m[mdot]s−1, 15 min daily for 10 days. Subjects were filmed three times each day in the frontal and sagittal planes, at Minutes 1, 8, and 14 of the run. Stride length, temporal data, and vertical and lateral horizontal displacements of the center of gravity were determined with a computer digitizer system. Analysis of variance revealed that significant alterations occurred in treadmill running kinematics between Days 1 and 2 of the 10-day treadmill training period. Further, for Days 1 through 3, significant within-day stride changes occurred between Minutes 1 and 8, but not between Minutes 8 and 14. These results suggest that minimal amounts of treadmill training are necessary for a subject to fully accommodate to the treadmill.
Running economy is a key determinant of endurance performance, and understanding the biomechanical factors that affect it is of great theoretical and applied interest. This study aimed to analyse how the ground-contact time and strike pattern used by competitive runners concurrently affect running economy.
Cross-sectional.
Fourteen sub-elite male competitive distance runners completed a 6-min submaximal running trial at 14kmh(-1) on an outdoor track using their habitual strike pattern (n=7 rearfoot strikers: average age, 25.3 years old (SD=2.4); average weight, 64.7kg (SD=5.6); average height, 175.3cm (SD=5.2); n=7 midfoot strikers: average age, 25.0 years old (SD=2.8); average weight, 69.6kg (SD=4.0); average height, 180.1cm (SD=5.1). During the run, the oxygen uptake and ground-contact time were measured.
Midfoot strikers showed a significantly shorter (p=0.015) mean contact time (0.228s (SD=0.009)) compared with rearfoot strikers (0.242s (SD=0.010)). Conversely, there was no significant difference (p>0.05) between the groups with respect to mean oxygen uptake (midfoot strikers: 48.4mlmin(-1)kg(-1) (SD=5.3); rearfoot strikers: 49.8mlmin(-1)kg(-1) (SD=6.4)). Linear modelling analysis showed that the effect of contact time on running economy was very similar in the two groups, with a 1ms longer contact time involving an approximately 0.51mlmin(-1)kg(-1) lower oxygen uptake. In contrast, when controlling for contact time, midfoot striking involved an approximately 8.7mlmin(-1)kg(-1) lower oxygen uptake compared with rearfoot striking.
When adjusting the foot-ground contact biomechanics of a runner with the aim of maximising running economy, a trade-off between a midfoot strike and a long contact time must be pursued.
This paper presents a general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies. The procedure essentially involves the construction of functions of the observed proportions which are directed at the extent to which the observers agree among themselves and the construction of test statistics for hypotheses involving these functions. Tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interobserver agreement are developed as generalized kappa-type statistics. These procedures are illustrated with a clinical diagnosis example from the epidemiological literature.
When comparing a new method of measurement with a standard method, one of the things we want to know is whether the difference between the measurements by the two methods is related to the magnitude of the measurement. A plot of the difference against the standard measurement is sometimes suggested, but this will always appear to show a relation between difference and magnitude when there is none. A plot of the difference against the average of the standard and new measurements is unlikely to mislead in this way. We show this theoretically and by a practical example.
This study investigated the time needed for familiarization to treadmill running. Seventeen young healthy adults, who were inexperienced on a treadmill, ran for 11 min on a treadmill at their self-selected speed. Discrete sagittal-plane angular kinematic parameters of the pelvis, hip, knee and ankle, and cadence and stride time data were captured with a three-dimensional motion analysis system at 0, 2, 4, 6, 8 and 10 min. Participants were considered familiarized to treadmill running by 6 min, as there were no significant changes in any dependent variables from this time. Furthermore, mean absolute difference scores between consecutive times were minimal (1.3 degrees ) and the average intraclass correlation coefficient [ICC(2,1)=.95] was maximal and highly reliable by this time. Future studies comparing treadmill and overground running need to provide an adequate treadmill familiarization time of at least 6 min prior to data capture of sagittal-plane kinematic events.