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Journal ArticleDOI: 10.1080/17461391.2020.1748117

Are we ready to measure running power? Repeatability and concurrent validity of five commercial technologies

04 Mar 2021-European Journal of Sport Science (Informa UK Limited)-Vol. 21, Iss: 3, pp 341-350
Abstract: Training prescription in running activities have benefited from power output (PW) data obtained by new technologies. Nevertheless, to date, the suitability of PW data provided by these tools is sti...

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Topics: Concurrent validity (53%)
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Open accessJournal IssueDOI: 10.7752/JPES.2020.S3276
31 Jul 2020-
Abstract: 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.

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Topics: Reliability (statistics) (68%)

6 Citations


Open accessJournal ArticleDOI: 10.3390/S20226482
13 Nov 2020-Sensors
Abstract: 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.

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5 Citations


Journal ArticleDOI: 10.1016/J.PHYSBEH.2020.112972
Abstract: Training prescription and load monitoring in running activities have benefited from power output (PW) data offered by new technologies. Nevertheless, to date, the sensitivity of PW data provided by these tools is still not completely clear. The aim of this study was to analyze the level of agreement between the PW estimated by five commercial technologies and the two main internationally theoretical models based on laws of physics, in different environments and running conditions. Ten endurance-trained male athletes performed three submaximal running protocols on a treadmill (indoor) and an athletic track (outdoor), with changes in speed, body weight, and slope. PW was simultaneously registered by the commercial technologies Stryd (StrydApp and StrydWatch), RunScribe, GarminRP and PolarV, whereas theoretical power output (TPW) was calculated by the two mathematical models (TPW1 and TPW2). Statistics included, among others, the Pearson's correlation coefficient (r) and standard error of measurement (SEM). The PolarV, and above all Stryd, showed the closest agreement with the TPW1 (Stryd: r ≥ 0.947, SEM ≤ 11 W; PolarV: r ≥ 0.931, SEM ≤ 64 W) and TPW2 (Stryd: r ≥ 0.933, SEM ≤ 60 W; PolarV: r ≥ 0.932, SEM ≤ 24 W), both indoors and outdoors. On the other hand, the devices GarminRP (r ≤ 0.765, SEM ≥ 59 W) and RunScribe. (r ≤ 0.508, SEM ≥ 125 W) showed the lowest agreement with the TPW1 and TPW2 models for all conditions and environments analyzed. The closest agreement of the Stryd and PolarV technologies with the TPW1 and TPW2 models suggest these tools as the most sensitive, among those analyzed, for PW measurement when changing environments and running conditions.

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4 Citations


Open accessJournal ArticleDOI: 10.3390/S20236737
25 Nov 2020-Sensors
Abstract: The foot strike pattern performed during running is an important variable for runners, performance practitioners, and industry specialists. Versatile, wearable sensors may provide foot strike information while encouraging the collection of diverse information during ecological running. The purpose of the current study was to predict foot strike angle and classify foot strike pattern from LoadsolTM wearable pressure insoles using three machine learning techniques (multiple linear regression-MR, conditional inference tree-TREE, and random forest-FRST). Model performance was assessed using three-dimensional kinematics as a ground-truth measure. The prediction-model accuracy was similar for the regression, inference tree, and random forest models (RMSE: MR = 5.16°, TREE = 4.85°, FRST = 3.65°; MAPE: MR = 0.32°, TREE = 0.45°, FRST = 0.33°), though the regression and random forest models boasted lower maximum precision (13.75° and 14.3°, respectively) than the inference tree (19.02°). The classification performance was above 90% for all models (MR = 90.4%, TREE = 93.9%, and FRST = 94.1%). There was an increased tendency to misclassify mid foot strike patterns in all models, which may be improved with the inclusion of more mid foot steps during model training. Ultimately, wearable pressure insoles in combination with simple machine learning techniques can be used to predict and classify a runner's foot strike with sufficient accuracy.

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1 Citations


Open accessJournal ArticleDOI: 10.3390/APP11052093
26 Feb 2021-Applied Sciences
Abstract: The recent popularity of trail running and the use of portable sensors capable of measuring many performance results have led to the growth of new fields in sports science experimentation. Trail running is a challenging sport; it usually involves running uphill, which is physically demanding and therefore requires adaptation to the running style. The main objectives of this study were initially to use three “low-cost” sensors. These low-cost sensors can be acquired by most sports practitioners or trainers. In the second step, measurements were taken in ecological conditions orderly to expose the runners to a real trail course. Furthermore, to combine the collected data to analyze the most efficient running techniques according to the typology of the terrain were taken, as well on the whole trail circuit of less than 10 km. The three sensors used were (i) a Stryd sensor (Stryd Inc., Boulder, CO, USA) based on an inertial measurement unit (IMU), 6 axes (3-axis gyroscope, 3-axis accelerometer) fixed on the top of the runner’s shoe, (ii) a Global Positioning System (GPS) watch and (iii) a heart belt. Twenty-eight trail runners (25 men, 3 women: average age 36 ± 8 years; height: 175.4 ± 7.2 cm; weight: 68.7 ± 8.7 kg) of different levels completed in a single race over a 8.5 km course with 490 m of positive elevation gain. This was performed with different types of terrain uphill (UH), downhill (DH), and road sections (R) at their competitive race pace. On these sections of the course, cadence (SF), step length (SL), ground contact time (GCT), flight time (FT), vertical oscillation (VO), leg stiffness (Kleg), and power (P) were measured with the Stryd. Heart rate, speed, ascent, and descent speed were measured by the heart rate belt and the GPS watch. This study showed that on a ≤10 km trail course the criteria for obtaining a better time on the loop, determined in the test, was consistency in the effort. In a high percentage of climbs (>30%), two running techniques stand out: (i) maintaining a high SF and a short SL and (ii) decreasing the SF but increasing the SL. In addition, it has been shown that in steep (>28%) and technical descents, the average SF of the runners was higher. This happened when their SL was shorter in lower steep and technically challenging descents.

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1 Citations


References
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31 results found


Journal ArticleDOI: 10.1016/S0140-6736(86)90837-8
08 Feb 1986-The Lancet
Abstract: In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. Such investigations are often analysed inappropriately, notably by using correlation coefficients. The use of correlation is misleading. An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.

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41,576 Citations


Open accessJournal ArticleDOI: 10.1016/J.JCM.2016.02.012
Terry K. Koo1, Mae Y. Li1Institutions (1)
Abstract: 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.

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6,924 Citations


Open accessJournal ArticleDOI: 10.2165/00007256-200030010-00001
Will G. Hopkins1Institutions (1)
01 Jul 2000-Sports Medicine
Abstract: Reliability refers to the reproducibility of values of a test, assay or other measurement in repeated trials on the same individuals. Better reliability implies better precision of single measurements and better tracking of changes in measurements in research or practical settings. The main measures of reliability are within-subject random variation, systematic change in the mean, and retest correlation. A simple, adaptable form of within-subject variation is the typical (standard) error of measurement: the standard deviation of an individual’s repeated measurements. For many measurements in sports medicine and science, the typical error is best expressed as a coefficient of variation (percentage of the mean). A biased, more limited form of within-subject variation is the limits of agreement: the 95% likely range of change of an individual’s measurements between 2 trials. Systematic changes in the mean of a measure between consecutive trials represent such effects as learning, motivation or fatigue; these changes need to be eliminated from estimates of within-subject variation. Retest correlation is difficult to interpret, mainly because its value is sensitive to the heterogeneity of the sample of participants. Uses of reliability include decision-making when monitoring individuals, comparison of tests or equipment, estimation of sample size in experiments and estimation of the magnitude of individual differences in the response to a treatment. Reasonable precision for estimates of reliability requires approximately 50 study participants and at least 3 trials. Studies aimed at assessing variation in reliability between tests or equipment require complex designs and analyses that researchers seldom perform correctly. A wider understanding of reliability and adoption of the typical error as the standard measure of reliability would improve the assessment of tests and equipment in our disciplines. CURRENT OPINION

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3,732 Citations


Journal ArticleDOI: 10.1080/02640419608727717
Andrew M. Jones1, Jonathan H. Doust1Institutions (1)
Abstract: When running indoors on a treadmill, the lack of air resistance results in a lower energy cost compared with running outdoors at the same velocity. A slight incline of the treadmill gradient can be used to increase the energy cost in compensation. The aim of this study was to determine the treadmill gradient that most accurately reflects the energy cost of outdoor running. Nine trained male runners, thoroughly habituated to treadmill running, ran for 6 min at six different velocities (2.92, 3.33, 3.75, 4.17, 4.58 and 5.0 m s‐1) with 6 min recovery between runs. This routine was repeated six times, five times on a treadmill set at different grades (0%, 0%, 1%, 2%, 3%) and once outdoors along a level road. Duplicate collections of expired air were taken during the final 2 min of each run to determine oxygen consumption. The repeatability of the methodology was confirmed by high correlations (r = 0.99) and non‐significant differences between the duplicate expired air collections and between the repeated runs...

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Topics: Running economy (59%), Treadmill (55%)

753 Citations


Open accessJournal ArticleDOI: 10.1002/UOG.5256
Jonathan W. Bartlett1, Chris Frost1Institutions (1)
Abstract: Clinical practice involves measuring quantities for a variety of purposes, such as aiding diagnosis, predicting future patient outcomes, and serving as endpoints in studies or randomized trials. Measurements are almost always prone to various sorts of errors, which cause the measured value to differ from the true value; accordingly, studies investigating measurement error frequently appear in this and other journals. The importance of measurement error depends upon the context in which the measurements in question are to be used. For example, a certain degree of measurement error may be acceptable if measurements are to be used as an outcome in a comparative study such as a clinical trial, but the same measurement errors may be unacceptably large to make measurements usable in individual patient management, such as screening or risk prediction. In the past 20 years many papers have been published advocating how studies of measurement error should be analyzed, with a paper by Bland and Altman1 being one of the most cited and well known examples. There has been much controversy concerning the choice of parameter to be estimated and reported, and consequently confusion surrounding the meaning and interpretation of results from studies investigating measurement error. In this paper we first distinguish between the general concepts of agreement and reliability to aid researchers in considering which are relevant for their particular application. We then review the statistical methods that can be used to investigate and quantify agreement and reliability, dealing separately with the different types of measurement error study, while emphasizing the largely common techniques that should be used for data analysis. We reiterate that the judgment of whether agreement or reliability are acceptable must be related to the clinical application, and cannot be proven by a statistical test. We highlight the fact that reliability depends on the population in which measurements are made, and not just on the measurement errors of the measurement method. We discuss the advantages of method comparison studies making at least two measurements with each measurement method on each subject. A key advantage is that the cause of a correlation between paired differences and means in the so-called Bland–Altman plot can be determined, in contrast to when only a single measurement is made with each method. Throughout the paper, we try to emphasize that calculated values of agreement and reliability from measurement error studies are estimates of parameters, and as such we should report such estimates with CIs to indicate the uncertainty with which they have been estimated. We restrict our attention to measurements of a continuous quantity; alternative methods are required for categorical data2.

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630 Citations


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