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Running power meters and theoretical models based on laws of physics: Effects of environments and running conditions
Abstract
Highlights
• Theoretical power models, based on laws of physics, would represent interesting proposals to examine the sensitivity of running power devices.
• Running power output estimated by commercial technologies are particularly influenced by environment (indoor vs. outdoor) and running conditions (body weight, slope and running speed).
• The PolarV, and above all the Stryd device, are the most sensitive technologies for running power measurement in different environments and running conditions
Key words: endurance, accelerometer, variability, physiology, biomechanics
... Additionally, the use of power meters in runners has grown in popularity. Recent research has investigated the use of a Stryd power meter regarding fluctuating conditions and found close agreement between measured and theoretical values, as well as sensitivity to the changing conditions (Cerezuela-Espejo et al., 2020). While this study incorporated an environmental component, it only assessed changes from an indoor to an outdoor track. ...
... Twelve recreationally endurance-trained male (n=7) and female (n=5) were recruited for this quasi-experimental study. Sample size was determined a priori using G*Power 3.1.9.6 software and following previous research showing a strong correlation using the Stryd device when comparing environmental conditions (Cerezuela-Espejo et al., 2020). Assuming an effect size of 0.3 with an alpha level of 0.5 using differences in and correlations between power output and internal/ external load as our primary outcomes, 12 participants were required with 80% power. ...
... Despite this, there was a significant increase in power output with the addition of the wind, suggesting a greater physical demand on the body. This also agrees with a previous study that suggests the use of a power meter in fluctuating environmental conditions to quantify and determine differences in work and power (Cerezuela-Espejo et al., 2020). While the literature is scarce on the Stryd power meter, the current study demonstrates the sensitivity of Stryd and its beneficial use in runners when tracking their power output. ...
Background: Wearable technology has increased in popularity due to its live feedback and ability to adjust within training sessions. In addition to heart rate (HR) monitoring, measuring power and internal load may provide useful insight and a more comprehensive view of training differences. Objectives: Assess the efficacy of wearable technology in endurance runners to determine changes in performance variables with varying wind resistance. Methods: A quasi-experimental study was designed and recruited twelve endurance-trained runners currently running ≥120 min/week for the past 3 months. Participants completed two sessions: V̇O2peak testing, and a 20-min run at 70% V̇O2peak. The run was evenly divided into no wind resistance (W0) and 16.1 km/h wind resistance (W16). Power was assessed via a power meter and internal/external load measured via surface EMG sensor-embedded compression shorts. A HR sensor was used and V̇O2 and RER were monitored using a metabolic cart. Paired t-tests were used to compare differences and Pearson correlations were conducted for each segment. Significance was set a priori at p0.05. Results: There were significant differences in power (W16 W0; p=0.002), as well as a strong positive correlation between power and internal load for W0 (r=0.692; p=0.013) and W16 (r=0.657; p=0.02). Conclusions: The lack of significance changes in HR, V̇O2, and RER demonstrates a sustained similar physiological response. The significant increase observed in power suggests the power meter can be useful in differentiating wind resistance, and the positive correlations suggest a combination of these devices may be beneficial in distinguishing performance changes during fluctuating conditions.
... 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] ...
... 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. Other studies examined some parameters provided by the RunScribe power meter to describe the effects of the fatigue induced over a marathon [33,34] and the influence of different types of ankle treatments on running biomechanics [35]. ...
... The Stryd system showed the higher concurrent validity to the VO 2 (r ≥ 0.911) between the five wearables, and it was also found as the more repeatable and sensitive in all the conditions studied. Furthermore, the level of agreement between these 5 wearable systems was also analysed against two physics theoretical models for PW estimation [10,52] in different running conditions [32], showing that the Stryd and Polar Vantage systems are the most sensitive tools for PW estimation in running given their close agreement with both theoretical models (r > 0.93). The Stryd power meter estimates power production while running separating this metric into two parts: power and form power. Apparently, power reflects the PW associated with changes in the athlete's horizontal movement, while form power represents the power production originated by the combination of the oscillatory up and down movements of the centre of mass and lateral power as the athlete moves forward. ...
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.
... Velocity and both the body height and weight of a runner, as well as external conditions such as slope and wind, may influence power output in running [13,14]. Although the level of agreement between power meter systems in running and two theoretical models for power output analysis has been assessed [15], the lack of scientific evidence for the use and interpretation of such metrics in endurance runners may prevent sport practitioners from adopting them as a means to monitor and assess running performance. A recent wearable system (i.e., Stryd™) calculates power production while running, separating this metric into two parts: power and form power. Apparently, power reflects the power output associated with changes in the athlete's horizontal movement. ...
... 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.
... 21 Since its market launch, knowing which type of power Stryd reports has been of great interest among the running community. Cerezuela-Espejo et al 7,22 determined the relationship between the power output reported from 5 commercial power meters and 2 theoretical power models varying in speed, weight, and slope. The Stryd power meter showed the greatest sensitivity to these factors among the other meters (r ≥ .947), ...
... showing that this device reported external work. 22 Thus, the power output (in watts per kilogram) reported by Stryd has shown a great relationship with VO 2 (in milliliters per kilogram per minute) and running velocity (in kilometers per hour) when measured during a GXT varying in speed as well as in the present study (r 2 = .97 and .99, ...
Purpose:
The critical power (CP) concept has been extended from cycling to the running field with the development of wearable monitoring tools. Particularly, the Stryd running power meter and its 9/3-minute CP test is very popular in the running community. Locating this mechanical threshold according to the physiological landmarks would help to define each boundary and intensity domain in the running field. Thus, this study aimed to determine the CP location concerning anaerobic threshold, respiratory compensation point (RCP), and maximum oxygen uptake (VO2max).
Method:
A group of 15 high-caliber athletes performed the 9/3-minute Stryd CP test and a graded exercise test in 2 different testing sessions.
Results:
Anaerobic threshold, RCP, and CP were located at 73% (5.41%), 86.82% (3.85%), and 88.71% (5.84%) of VO2max, respectively, with a VO2max of 66.3 (7.20) mL/kg/min. No significant differences were obtained between CP and RCP in any of its units (ie, in watts per kilogram and milliliters per kilogram per minute; P ≥ .184).
Conclusions:
CP and RCP represent the same boundary in high-caliber athletes. These results suggest that coaches and athletes can determine the metabolic perturbance threshold that CP and RCP represent in an easy and accessible way.
... Notbaly, all of the aforementioned studies were completed in laboratories, as the comparison to the gold standard method (treadmill and motion capture) was the main goal. Recent studies shown that RunScribe outcomes are dependent on running speed (Napier et al., 2021), surface (Hollis et al., 2021) and running environment (Cerezuela-Espejo et al., 2020). Therefore, such factors need to be considered in further research and practice. ...
... Another problem with the wearable sensors mounted on the shoe is that the actual shock variables experienced by the leg appear to be overestimated (Cheung et al., 2019). Power output is another outcome that has been a subject of studies using RunScribe, both for treadmill and outdoor running (Cerezuela-Espejo et al., 2020, however, the reported validity was poor. The reliability of power was unacceptable during the half-marathon pace within the session (TE = 14.3 %), as well as in all conditions between the sessions (TE = 13.1-13.8 ...
The aim of this study was to investigate the reliability of running biomechanics assessment with a wearable commercial sensor (RunScribeTM). Participants performed multiple 200-m runs over sand, grass and asphalt ground at the estimated 5-km tempo, with an additional trial with 21-km tempo at the asphalt. Intra-session reliability was excellent for all variables at 5-km pace (intra-class coefficient correlation (ICC) asphalt: 0.90–0.99; macadam: 0.94–1.00; grass: 0.92–1.00), except for shock (good; ICC = 0.83), and contact time and total power output (moderate; ICC = 0.68–0.71). Coefficient of variation (CV) were mostly acceptable in all conditions, except for horizontal ground reaction force (GRF) rate in asphalt 5-km pace trial (CV = 24.5 %), power (CV = 14.3 %) and foot strike type (CV = 30.9 %) in 21-km pace trial, and horizontal GRF rate grass trial (CV = 15.7 %). Inter-session reliability was high or excellent for the majority of the outcomes (ICC≥0.85). Total power output (ICC = 0.56–0.65) and shock (ICC = 0.67–0.75) showed only moderate reliability across all conditions. Power (CV = 12.5–13.8 %), foot strike type (CV = 14.9–29.4 %) and horizontal ground reaction force rate (CV = 12.4–36.4 %) showed unacceptable CV.
... 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.
... In different environments and running conditions, one study assessed the level of agreement between the power output data estimated by five commercial technologies and the two main international theoretical models based on laws of physics. The results showed that the Stryd and PolarV technologies were the most sensitive to the running conditions and environments [16]. ...
This study aims to determine the validity of the critical power (CP) and the work capacity over CP (W′) obtained from different two-time trial combinations with respect a five-point model. In a 3-week training period, 15 athletes (age: 23 ± 5 years; height: 166 ± 6 cm; body mass: 58 ± 8 kg; 5 km season-best: 15:29 ± 00:53 mm:ss) performed five time-trials (i.e. 3, 4, 5, 10, 20 min) on a 400 m track, from which the mean power outputs were obtained through the Stryd Power Meter. An acceptable level of agreement was considered if the following criteria were met: low bias and standard error of the estimate (SEE) (<14 W [values corresponding to the ±5% of the mean CP]; W′: <2.0 kJ [values corresponding to the ±10% of the mean W′]), R ² > 0.90, and ICC > 0.81. The CP presented an acceptable SEE for CP work (1.3 ± 0.5%) and CP 1/time (2.7 ± 1.1%) when using the five time-trials. For both CP models, the 3–10 min was the shortest valid combination, whereas the 3–20, 4–20, and 5–20 min showed the greatest level of agreement. The W′ presented a high SEE for CP work (14.1 ± 5.2%) and CP 1/time (13.8 ± 6.2%) when using the five time-trials, therefore, none of the two time-trials combinations were considered. The CP parameter can be accurately estimated from different two time-trial combinations, whereas none reached an acceptable level of accuracy for the determination of W′.
Background Running gait assessment has traditionally been performed using subjective observation or expensive laboratory-based objective technologies, such as 3D motion capture or force plates. However, recent developments in wearable devices allow for continuous monitoring and analysis of running mechanics in any environment. Objective measurement of running gait is an important (clinical) tool for injury assessment and provides measures that can be used to enhance performance.
Objectives To systematically review available literature investigating how wearable technology is being used for running gait analysis in adults.
Methods A systematic search of literature was conducted in the following scientific databases: PubMed, Scopus, WebofScience, and SportDiscus. Information was extracted from each included article regarding the type of study, participants, protocol, wearable device(s), main outcomes/measures, analysis, and key findings.
Results A total of 131 articles were reviewed: 56 investigated the validity of wearable technology, 22 examined the reliability and 77 focused on applied use. Most studies used inertial measurement units (IMU) (n=62) (i.e., a combination of accelerometers, gyroscopes, and magnetometers in a single unit) or solely accelerometers (n=40), with one using gyroscopes alone and 31 using pressure sensors. On average, studies used one wearable device to examine running gait. Wearable locations were distributed among the shank, shoe and waist. The mean number of participants was 26 (± 27), with an average age of 28.3 (± 7.0) years. Most studies took place indoors (n =93), using a treadmill (n =62), with the main aims seeking to identify running gait outcomes or investigate the effects of injury, fatigue, intrinsic factors (e.g., age, sex, morphology) or footwear on running gait outcomes. Generally, wearables were found to be valid and reliable tools for assessing running gait compared to reference standards.
Conclusions This comprehensive review highlighted that most studies that have examined running gait using wearable sensors have done so with young adult recreational runners, using one IMU sensor, with participants running on a treadmill and reporting outcomes of ground contact time, stride length, stride frequency and tibial acceleration. Future studies are required to obtain consensus regarding terminology, protocols for testing validity and reliability of devices and suitability of gait outcomes.
Inertial measurement units (IMUs) can be used to monitor running biomechanics in real-world settings, but IMUs are often used within a laboratory. The purpose of this scoping review was to describe how IMUs are used to record running biomechanics in both laboratory and real-world conditions. We included peer-reviewed journal articles that used IMUs to assess gait quality during running. We extracted data on running conditions (indoor/outdoor, surface, speed, and distance), device type and location, metrics, participants, and purpose and study design. A total of 231 studies were included. Most (72%) studies were conducted indoors; and in 67% of all studies, the analyzed distance was only one step or stride or <200 m. The most common device type and location combination was a triaxial accelerometer on the shank (18% of device and location combinations). The most common analyzed metric was vertical/axial magnitude, which was reported in 64% of all studies. Most studies (56%) included recreational runners. For the past 20 years, studies using IMUs to record running biomechanics have mainly been conducted indoors, on a treadmill, at prescribed speeds, and over small distances. We suggest that future studies should move out of the lab to less controlled and more real-world environments.
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.
Nowadays, common electrical household appliances are mostly being powered by means of alternate current (AC), although there are cases where direct current (DC) is used instead. In all cases, internal devices are supplied with DC, and this fact involves there are losses due to the need for AC/DC converters. At the same time, most electrical home consumption takes place during peak hours when electricity is more expensive in many electricity markets. The addition of a battery in these installations permits storing electrical energy during certain periods of the day with the aim of supplying it during other ones—when this operation is more efficient or convenient—simultaneously reducing costs and greenhouse gas emissions. In this paper, a comparison is proposed between three possible home consumption scenarios, i.e., one consisting of a current AC system, one consisting of an AC system with a battery, and a third consisting of a hybrid AC/DC system with a battery.
Training prescription and load monitoring 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 still uncertain. In order to clarify this aspect, the present study aimed to: i) analyze the repeatability of five commercially available technologies for running PW estimation, and ii) examine the concurrent validity through the relationship between each technology PW and oxygen uptake (VO2). On two occasions (test-retest), twelve endurance-trained male athletes performed on a treadmill (indoor) and an athletic track (outdoor) three submaximal running protocols with manipulations in speed, body weight and slope. PW was simultaneously registered by the commercial technologies StrydApp, StrydWatch, RunScribe, GarminRP and PolarV, while VO2 was monitored by a metabolic cart. Test-retest data from the environments (indoor and outdoor) and conditions (speed, body weight and slope) were used for repeatability analysis, which included the standard error of measurement (SEM), coefficient of variation (CV) and intraclass correlation coefficient (ICC). A linear regression analysis and the standard error of estimate (SEE) were used to examine the relationship between PW and VO2. Stryd device was found as the most repeatable technology for all environments and conditions (SEM≤12.5W, CV≤4.3%, ICC≥0.980), besides the best concurrent validity to the VO2 (r≥0.911, SEE≤7.3%). On the contrary, although the PolarV, GarminRP and RunScribe technologies maintain a certain relationship with VO2, their low repeatability questions their suitability. The Stryd can be considered as the most recommended tool, among the analyzed, for PW measurement.
This study aimed to analyze the agreement between five bar velocity monitoring devices, currently used in resistance training, to determine the most reliable device based on reproducibility (between-device agreement for a given trial) and repeatability (between-trial variation for each device). Seventeen resistance-trained men performed duplicate trials against seven increasing loads (20-30-40-50-60-70-80 kg) while obtaining mean, mean propulsive and peak velocity outcomes in the bench press, full squat and prone bench pull exercises. Measurements were simultaneously registered by two linear velocity transducers (LVT), two linear position transducers (LPT), two optoelectronic camera-based systems (OEC), two smartphone video-based systems (VBS) and one accelerometer (ACC). A comprehensive set of statistics for assessing reliability was used. Magnitude of errors was reported both in absolute (m s⁻¹) and relative terms (%1RM), and included the smallest detectable change (SDC) and maximum errors (MaxError). LVT was the most reliable and sensitive device (SDC 0.02–0.06 m s⁻¹, MaxError 3.4–7.1% 1RM) and the preferred reference to compare with other technologies. OEC and LPT were the second-best alternatives (SDC 0.06–0.11 m s⁻¹), always considering the particular margins of error for each exercise and velocity outcome. ACC and VBS are not recommended given their substantial errors and uncertainty of the measurements (SDC > 0.13 m s⁻¹).
This study was designed to validate a new short track test (Track(1:1)) to estimate running performance parameters maximal oxygen uptake (VO2max) and maximal aerobic speed (MAS), based on a laboratory treadmill protocol and gas exchange data analysis (Lab(1:1)). In addition, we compared the results with the University of Montreal Track Test (UMTT). Twenty-two well-trained male athletes (VO2max 60.3 ± 5.9 ml·kg−1·min−1; MAS ranged from 17.0 to 20.3 km·h−1) performed 4 testing protocols: 2 in laboratory (Lab(1:1)-pre and Lab(1:1)) and 2 in the field (UMTT and Track(1:1)). The Lab(1:1)-pre was designed to determine individuals' Vpeak and set initial speeds for the subsequent Lab(1:1) short ramp graded exercise testing protocol, starting at 13 km·h−1 less than each athlete's Vpeak, with 1 km·h−1 increments per minute until exhaustion. The Track(1:1) was a reproduction of the Lab(1:1) protocol in the field. A novel equation was yielded to estimate the VO2max from the Vpeak achieved in the Track(1:1). Results revealed that the UMTT significantly underestimated the Vpeak (−4.2%; bias = −0.8 km·h−1; p < 0.05), which notably altered the estimations (MAS: −2.6%, bias = −0.5 km·h−1; VO2max: 4.7%, bias = 2.9 ml·kg−1·min−1). In turn, data from Track(1:1) were very similar to the laboratory test and gas exchange methods (Vpeak: −0.6%, bias = <0.1 km·h−1; MAS: 0.3%, bias = <0.1 km·h−1; VO2max: 0.4%, bias = 0.2 ml·kg−1·min−1, p > 0.05). Thus, the current Track(1:1) test emerges as a better alternative than the UMTT to estimate maximal running performance parameters in well-trained and highly trained athletes on the field.
Background
Treadmills are routinely used to assess running performance and training parameters related to physiological or perceived effort. These measurements are presumed to replicate overground running but there has been no systematic review comparing performance, physiology and perceived effort between treadmill and overground running.
Objective
The objective of this systematic review was to compare physiological, perceptual and performance measures between treadmill and overground running in healthy adults.
Methods
AMED (Allied and Contemporary Medicine), CINAHL (Cumulative Index to Nursing and Allied Health), EMBASE, MEDLINE, SCOPUS, SPORTDiscus and Web of Science databases were searched from inception until May 2018. Included studies used a crossover study design to compare physiological (oxygen uptake [\(\dot{V}\)O2], heart rate [HR], blood lactate concentration [La]), perceptual (rating of perceived exertion [RPE] and preferred speed) or running endurance and sprint performance (i.e. time trial duration or sprint speed) outcomes between treadmill (motorised or non-motorised) and overground running. Physiological outcomes were considered across submaximal, near-maximal and maximal running intensity subgroups. Meta-analyses were used to determine mean difference (MD) or standardised MD (SMD) ± 95% confidence intervals.
Results
Thirty-four studies were included. Twelve studies used a 1% grade for the treadmill condition and three used grades > 1%. Similar \(\dot{V}\)O2 but lower La occurred during submaximal motorised treadmill running at 0% (\(\dot{V}\)O2 MD: – 0.55 ± 0.93 mL/kg/min; La MD: − 1.26 ± 0.71 mmol/L) and 1% (\(\dot{V}\)O2 MD: 0.37 ± 1.12 mL/kg/min; La MD: − 0.52 ± 0.50 mmol/L) grade than during overground running. HR and RPE during motorised treadmill running were higher at faster submaximal speeds and lower at slower submaximal speeds than during overground running. \(\dot{V}\)O2 (MD: − 1.25 ± 2.09 mL/kg/min) and La (MD: − 0.54 ± 0.63 mmol/L) tended to be lower, but HR (MD: 0 ± 1 bpm), and RPE (MD: – 0.4 ± 2.0 units [6–20 scale]) were similar during near-maximal motorised treadmill running to during overground running. Maximal motorised treadmill running caused similar \(\dot{V}\)O2 (MD: 0.78 ± 1.55 mL/kg/min) and HR (MD: − 1 ± 2 bpm) to overground running. Endurance performance was poorer (SMD: − 0.50 ± 0.36) on a motorised treadmill than overground but sprint performance varied considerably and was not significantly different (MD: − 1.4 ± 5.8 km/h).
Conclusions
Some, but not all, variables differ between treadmill and overground running, and may be dependent on the running speed at which they are assessed.
Protocol registration
CRD42017074640 (PROSPERO International Prospective Register of Systematic Reviews).
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.
Models for human running performances of various complexities and underlying principles have been proposed, often combining data from world record performances and bio-energetic facts of human physiology. The purpose of this work is to develop a novel, minimal and universal model for human running performance that employs a relative metabolic power scale. The main component is a self-consistency relation for the time dependent maximal power output. The analytic approach presented here is the first to derive the observed logarithmic scaling between world (and other) record running speeds and times from basic principles of metabolic power supply. Our hypothesis is that various female and male record performances (world, national) and also personal best performances of individual runners for distances from 800m to the marathon are excellently described by this model. Indeed, we confirm this hypothesis with mean errors of (often much) less than 1%. The model defines endurance in a way that demonstrates symmetry between long and short racing events that are separated by a characteristic time scale comparable to the time over which a runner can sustain maximal oxygen uptake. As an application of our model, we derive personalized characteristic race speeds for different durations and distances.
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.
Many empirical and descriptive models have been proposed since the beginning of the 2 0th century. In the present study, the power-law (Kennelly) and logarithmic (Péronnet-Thibault) models were compared with asymptotic models such as 2-parameter hyperbolic models (Hill and Scherrer), 3-parameter hyperbolic model (Morton), and exponential model (Hopkins). These empirical models were compared from the performance of 6 elite endurance runners (P. Nurmi, E. Zatopek, J. Väätäinen, L. Virén, S. Aouita, and H. Gebrselassie) who were world-record holders and/or Olympic winners and/or world or European champions. These elite runners were chosen because they participated several times in international competitions over a large range of distances (1500, 3000, 5000, and 10000 m) and three also participated in a marathon. The parameters of these models were compared and correlated. The less accurate models were the asymptotic 2-parameter hyperbolic models but the most accurate model was the asymptotic 3-parameter hyperbolic model proposed by Morton. The predictions of long-distance performances (maximal running speeds for 30 and 60 min and marathon) by extrapolation of the logarithmic and power-law models were more accurate than the predictions by extrapolation in all the asymptotic models. The overestimations of these long-distance performances by Morton’s model were less important than the overestimations by the other asymptotic models.
Background:
Running biomechanics have traditionally been analyzed in laboratory settings, but this may not reflect natural running gait. Wearable sensors may offer an alternative.
Methods:
A concurrent validation study to determine agreement between the RunScribe<sup>TM</sup> wearable sensor (triaxial accelerometer and gyroscope) and the 3D motion capture system was conducted. Twelve injury-free participants (6 males, 6 females; age = 23.1 ± 5.5 years, weekly mileage = 16.1 ± 9.3) ran 1.5 miles on a treadmill. Ten consecutive strides from each limb were collected, and the mean values were analyzed. Pronation excursion, maximum pronation velocity, contact time, and cycle time were compared between measurement platforms using intraclass correlation coefficients (ICC) and Bland-Altman analyses.
Results:
Excellent ICC estimates were found for maximum pronation velocity, contact time, and cycle time. Pronation excursion demonstrated fair ICC estimates. The mean differences between platforms were small with limits of agreement clustered around zero, except for contact time measures which were consistently higher with the RunScribe compared to the camera-based system.
Conclusion:
Our study revealed that the RunScribe wearable device showed good to excellent concurrent validity for maximum pronation velocity, contact time, and cycle time; however, direct comparisons or results between the two platforms should not be used.
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.
To validate the new PowerTap P1® pedals power meter (PP1), thirty-three cyclists performed 12 randomized and counterbalanced graded exercise tests (100–500 W), at 70, 85 and 100 rev·min-1 cadences, in seated and standing positions. A scientific SRM system and a pair of PP1 pedals continuously recorded cadence and power output data. Significantly lower power output values were detected for the PP1 compared to the SRM for all workloads, cadences, and pedalling conditions (2–10 W, p < 0.05), except for the workloads ranged between 150 W to 350 W at 70 rev·min-1 in seated position (p > 0.05). Strong Spearman’s correlation coefficients were found between the power output values recorded by both power meters in a seated position, independently from the cadence condition (rho ≥ 0.987), although slightly lower concordance was found for the standing position (rho = 0.927). The mean error for power output values were 1.2%, 2.7%, 3.5% for 70, 85 and 100 rev·min-1, respectively. Bland-Altman analysis revealed that PP1 pedals underestimate the power output data obtained by the SRM device in a directly proportional manner to the cyclist’s cadence (from -2.4 W to -7.3 W, rho = 0.999). High absolute reliability values were detected in the PP1 pedals (150–500 W; CV = 2.3%; SEM < 1.0 W). This new portable power meter is a valid and reliable device to measure power output in cyclists and triathletes for the assessment, training and competition using their own bicycle, although caution should be exercised in the interpretation of the results due to the slight power output underestimation of the PP1 pedals when compared to the SRM system and its dependence on both pedalling cadence and cyclist’s position (standing vs. seated).
Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best method- ologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the eld has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on “Monitoring Athlete Training Loads—The Hows and the Whys” was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary prac- tices in this rapidly growing eld and also to investigate where it may branch to in the future. This consensus statement brings together the key ndings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.
Purpose:
This study aimed to determine the validity, sensitivity, reproducibility and robustness of the Powertap (PWT), Stages (STG) and Garmin Vector (VCT) power meters in comparison with the SRM device.
Methods:
A national-level male competitive cyclist was required to complete three laboratory cycling tests that included a sub-maximal incremental test, a sub-maximal 30-min continuous test and a sprint test. Two additional tests were performed: the first on vibration exposures in the laboratory and the second in the field.
Results:
The VCT provided a significantly lower 5 s power output (PO) during the sprint test with a low gear ratio compared with the POSRM (-36.9%). The POSTG was significantly lower than the POSRM within the heavy exercise intensity zone (zone 2, -5.1%) and the low part of the severe intensity zone (zone 3, -4.9%). The POVCT was significantly lower than the POSRM only within zone 2 (-4.5%). The POSTG was significantly lower in standing position than in the seated position (-4.4%). The reproducibility of the PWT, STG and VCT was similar to that of the SRM system. The POSTG and POVCT were significantly decreased from a vibration frequency of 48 Hz and 52 Hz, respectively.
Conclusions:
The PWT, STG and the VCT systems appear to be reproducible, but the validity, sensitivity and robustness of the STG and VCT systems should be treated with some caution according to the conditions of measurement.
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 at 0% grade. The relationship between oxygen uptake (VO2) and velocity for each grade was highly linear (r > 0.99). At the two lowest velocities, VO2 during road running was not significantly different from treadmill running at 0% or 1% grade, but was significantly less than 2% and 3% grade. For 3.75 m s-1, the VO2 during road running was significantly different from treadmill running at 0%, 2% and 3% grades but not from 1% grade. For 4.17 and 4.58 m s-1, the VO2 during road running was not significantly different from that at 1% or 2% grade but was significantly greater than 0% grade and significantly less than 3% grade. At 5.0 m s-1, the VO2 for road running fell between the VO2 value for 1% and 2% grade treadmill running but was not significantly different from any of the treadmill grade conditions. This study demonstrates equality of the energetic cost of treadmill and outdoor running with the use of a 1% treadmill grade over a duration of approximately 5 min and at velocities between 2.92 and 5.0 m s-1.
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.
Performance testing is one of the most common and important measures used in sports science and physiology. Performance tests allow for a controlled simulation of sports and exercise performance for research or applied science purposes. There are three factors that contribute to a good performance test: (i) validity; (ii) reliability; and (iii) sensitivity. A valid protocol is one that resembles the performance that is being simulated as closely as possible. When investigating race-type events, the two most common protocols are time to exhaustion and time trials. Time trials have greater validity than time to exhaustion because they provide a good physiological simulation of actual performance and correlate with actual performance. Sports such as soccer are more difficult to simulate. While shuttle-running protocols such as the Loughborough Intermittent Shuttle Test may simulate physiology of soccer using time to exhaustion or distance covered, it is not a valid measure of soccer performance. There is a need to include measures of skill in such protocols. Reliability is the variation of a protocol. Research has shown that time-to-exhaustion protocols have a coefficient of variation (CV) of >10%, whereas time trials are more reliable as they have been shown to have a CV of <5%. A sensitive protocol is one that is able to detect small, but important, changes in performance. The difference between finishing first and second in a sporting event is <1%. Therefore, it is important to be able to detect small changes with performance protocols. A quantitative value of sensitivity may be accomplished through the signal : noise ratio, where the signal is the percentage improvement in performance and the noise is the CV.
Training quantification is basic to evaluate an endurance athlete's responses to the training loads, ensure adequate stress/recovery balance and determine the relationship between training and performance. Quantifying both external and internal workload is important, because the external workload does not measure the biological stress imposed by the exercise sessions. Generally used quantification methods include retrospective questionnaires, diaries, direct observation and physiological monitoring, often based on the measurement of oxygen uptake, heart rate and blood lactate concentration. Other methods in use in endurance sports include speed measurement and the measurement of power output, made possible by recent technological advances, such as power meters in cycling and triathlon. Among subjective methods of quantification the RPE stands out because of its wide use. Concurrent assessments of the various quantification methods allow researchers and practitioners to evaluate stress/recovery balance, adjust individual training programmes and determine the relationships between external load, internal load and athletes' performance. This brief review summarizes the most relevant external and internal workload quantification methods in endurance sports, and provides practical examples of their implementation to adjust the training programmes of elite athletes in accordance to their individualized stress/recovery balance.
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.
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.
The aim of this study is to show the relationship between test-retest reproducibility and responsiveness and to introduce the smallest real difference (SRD) approach, using the sickness impact profile (SIP) in chronic stroke patients as an example. Forty chronic stroke patients were interviewed twice by the same examiner, with a 1-week interval. All patients were interviewed during the qualification period preceding a randomized clinical trial. Test-retest reproducibility has been quantified by the intraclass correlation coefficient (ICC). the standard error of measurement (SEM) and the related smallest real difference (SRD). Responsiveness was defined as the ratio of the clinically relevant change to the SD of the within-stable-subject test-retest differences. The ICC for the total SIP was 0.92, whereas the ICCs for the specified SIP categories varied from 0.63 for the category 'recreation and pastime' to 0.88 for the category 'work'. However, both the SEM and the SRD far more capture the essence of the reproducibility of a measurement instrument. For instance, a total SIP score of an individual patient of 28.3% (which is taken as an example, being the mean score in the study population) should decrease by at least 9.26% or approximately 13 items, before any improvement beyond reproducibility noise can be detected. The responsiveness to change of a health status measurement instrument is closely related to its test-retest reproducibility. This relationship becomes more evident when the SEM and the SRD are used to quantify reproducibility, than when ICC or other correlation coefficients are used.
Stages, and Garmin Vector Power Meters in Comparison With the SRM Device
- A Bouillod
- J Pinot
- G Soto-Romero
- W Bertucci
- F Grappe
- Validity
- Reproducibility Sensitivity
- Robustness Of The Powertap
A. Bouillod, J. Pinot, G. Soto-Romero, W. Bertucci, F. Grappe, Validity, Sensitivity,
Reproducibility, and Robustness of the PowerTap, Stages, and Garmin Vector Power
Meters in Comparison With the SRM Device, Int. J. Sports Physiol. Perform. 12
(2017) 1023-1030. https://doi.org/10.1123/ijspp.2016-0436.
Table 2 Agreement between running power (P W ) estimated by each technology and the theoretical model of Dijk and Megen (TP W1 ), in different environments (indoor and outdoor) and running conditions (increasing speed, body weight, and slope)
- Megen Dijk
Dijk, Megen, The secret of running : maximum performance gains through effective
power metering and training analysis, (2017) 477. https://thesecretofrunning.com/
Table 2
Agreement between running power (P W ) estimated by each technology and the theoretical model of Dijk and Megen (TP W1 ), in different environments (indoor and
outdoor) and running conditions (increasing speed, body weight, and slope).
(accessed May 29, 2019).