An Assessment of Running Power as a Training Metric for Elite and Recreational Runners

Effects of pre-exercise glycerol supplementation on dehydration, metabolic, kinematic, and thermographic variables in international race walkers

BACKGROUND

Due to the increase in global temperature, it is necessary to investigate solutions so that athletes competing in hot conditions can perform in optimal conditions avoiding loss of performance and health problems. Therefore, this study aims to evaluate the effect of pre-exercise glycerol supplementation during a rectangular test at ambient temperature mid (28.2ºC) on dehydration variables in international race walkers.

METHODS

Eight international male race walkers (age: 28.0 years (4.4); weight: 65.6 kg (6.6); height: 180.0 cm (5.0); fat mass: 6.72% (0.66); muscle mass: 33.3 kg (3.3); VO2MAX: 66.5 ml · kg-1·min-1 (1.9)) completed this randomized crossover design clinical trial. Subjects underwent two interventions: they consumed placebo (n = 8) and glycerol (n = 8) acutely, before a rectangular test where dehydration, RPE, metabolic, kinematic, and thermographic variables were analyzed before, during and after the test.

RESULTS

After the intervention, significant differences were found between groups in body mass in favor of the placebo (Placebo: -2.23 kg vs Glycerol: -2.48 kg; p = 0.033). For other variables, no significant differences were found.

CONCLUSION

Therefore, pre-exercise glycerol supplementation was not able to improve any dehydration, metabolic, kinematic, or thermographic variables during a rectangular test at temperature mid in international race walkers. Possibly, a higher environmental temperature could have generated a higher metabolic and thermoregulatory stress, generating differences between groups like other previous scientific evidence.

Assessing Trail Running Biomechanics: A Comparative Analysis of the Reliability of StrydTM and GARMINRP Wearable Devices

This study investigated biomechanical assessments in trail running, comparing two wearable devices-Stryd Power Meter and GARMINRP. With the growing popularity of trail running and the complexities of varied terrains, there is a heightened interest in understanding metabolic pathways, biomechanics, and performance factors. The research aimed to assess the inter- and intra-device agreement for biomechanics under ecological conditions, focusing on power, speed, cadence, vertical oscillation, and contact time. The participants engaged in trail running sessions while wearing two Stryd and two Garmin devices. The intra-device reliability demonstrated high consistency for both GARMINRP and StrydTM, with strong correlations and minimal variability. However, distinctions emerged in inter-device agreement, particularly in power and contact time uphill, and vertical oscillation downhill, suggesting potential variations between GARMINRP and StrydTM measurements for specific running metrics. The study underscores that caution should be taken in interpreting device data, highlighting the importance of measuring with the same device, considering contextual and individual factors, and acknowledging the limited research under real-world trail conditions. While the small sample size and participant variations were limitations, the strength of this study lies in conducting this investigation under ecological conditions, significantly contributing to the field of biomechanical measurements in trail running.

Effect of cross-slope angle on running economy and gait characteristics at moderate running velocity

PURPOSE

Outdoor running surfaces are designed with a cross-slope, which can alter kinetic and kinematic gait parameters. The purpose of this study was to evaluate running economy, gait characteristics, and muscle activation while running on a surface with cross-slopes similar to those encountered on roads and trails.

METHODS

Eleven recreational runners (females n = 6) completed 5-min running trials on a treadmill at 10 km h-1 with cross-slopes of 0, 1.15, 2.29, and 6 deg in a randomized order.

RESULTS

There were no significant differences in VO2, HR, RER, or VE across cross-slope conditions. Compared to 0 deg of cross-slope, ground contact time and duty factor increased at 2.29 and 6 deg, with significant decreases in absolute and relative flight times. Rear foot angles increased in the upslope leg at 2.29 and 6 deg cross-slopes and decreased in the downslope leg at 6 deg compared to 0 deg of cross-slope, with differences between legs for the 2.29 and 6 deg conditions. Knee flexion at foot strike increased in the upslope leg at a 6 deg cross-slope. Vastus lateralis, biceps femoris, gastrocnemius, and tibialis anterior activation were not affected by the cross-slope conditions. While cross-slopes up to 6 deg result in changes to some gait kinematics, these effects do not impact running economy at moderate running velocity.

Power Is More Relevant Than Ascensional Speed to Determine Metabolic Demand at Different Gradient Slopes During Running

ABSTRACT

Hingrand, C, Olivier, N, Combes, A, Bensaid, S, and Daussin, FN. Power is more relevant than ascensional speed to determine metabolic demand at different gradient slopes during running. J Strength Cond Res 37(11): 2298-2301, 2023-Trail running is characterized by successive uphill and downhill running sessions. To prescribe training intensity, an assessment of maximal running capacity is required. This study compared 2 uphill incremental tests using the same ascensional speed increment to identify the influence of the slope gradient on performance. Ten subjects (8 men and 2 women) performed 3 incremental exercises on various slope (1%: IT01, 10%: IT10, and 25%: IT25), and the ascensional speed increment was similar between IT10 and IT25 (100 m·h-1 every minute). Gas exchanges, heart rate, and power were monitored continuously during the tests. Similar V̇o2max levels were observed in the 3 conditions: 68.7 ± 6.2 for IT01, 70.1 ± 7.3 for IT10, and 67.6 ± 7.0 for IT25. A greater maximal ascensional speed was reached in the IT25 (1760 ± 190 vs. 1,330 ± 106 for IT25 and IT10, respectively, p < 0.01). A significant relationship was observed between relative V̇o2 levels and relative power without any effect of slope. Power should be the parameter used for prescribing training intensity compared with ascensional speed in trail.

Estimation of horizontal running power using foot-worn inertial measurement units

Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed three machine-learning models to estimate peak horizontal power for level, uphill, and downhill running using gait spatiotemporal parameters, accelerometer, and gyroscope signals extracted from foot-worn IMUs. The prediction was compared to reference horizontal power obtained during running on a treadmill with an embedded force plate. For each model, we trained an elastic net and a neural network and validated it with a dataset of 34 active adults across a range of speeds and slopes. For the uphill and level running, the concentric phase of the gait cycle was considered, and the neural network model led to the lowest error (median ± interquartile range) of 1.7% ± 12.5% and 3.2% ± 13.4%, respectively. The eccentric phase was considered relevant for downhill running, wherein the elastic net model provided the lowest error of 1.8% ± 14.1%. Results showed a similar performance across a range of different speed/slope running conditions. The findings highlighted the potential of using interpretable biomechanical features in machine learning models for the estimating horizontal power. The simplicity of the models makes them suitable for implementation on embedded systems with limited processing and energy storage capacity. The proposed method meets the requirements for applications needing accurate near real-time feedback and complements existing gait analysis algorithms based on foot-worn IMUs.

A "Step-Ramp-Step" Protocol to Identify Running Speed and Power Associated with the Maximal Metabolic Steady State

PURPOSE

A previously established Step-Ramp-Step (SRS) exercise protocol was able to accurately predict the work rate associated with the maximal metabolic steady state (MMSS) in cyclists. The purpose of this study was to determine whether a modified SRS protocol could predict the running speed and power associated with the MMSS.

METHODS

Fifteen (8 male; 7 female) runners (V̇O 2max 54.5 [6.5] mL·kg -1 ·min -1 ) were recruited for this investigation composed of four to five visits. In the first visit, runners performed a moderate intensity step (MOD), an incremental exercise test, and a heavy intensity step (HVY), on a motorized treadmill. This SRS protocol was used to predict the running speed and power associated with the MMSS (i.e., the SRS-MMSS), where running power was assessed by a wearable device (Stryd) attached to each runner's shoe. Subsequent visits were used to confirm the maximal lactate steady state (MLSS) as a proxy measure of the MMSS (i.e., the MLSS-MMSS) and to validate the SRS-MMSS speed and power estimates.

RESULTS

The estimated SRS-MMSS running speed (7.2 [0.6] mph) was significantly lower than confirmed running speed at MLSS-MMSS (7.5 [0.8] mph; bias = 3.6%, P = 0.005); however, the estimated SRS-MMSS running power (241 [35] W) was not different than the MLSS-MMSS confirmed running power (240 [37] W; bias = -0.6%; P = 0.435). V̇O 2 at SRS-MMSS (3.22 [0.49] L·min -1 ) was not different than respiratory compensation point (3.26 [0.58] L·min -1 ; P = 0.430). Similarly, V̇O 2 at MLSS-MMSS (3.30 [0.54] L·min -1 ) was not different than respiratory compensation point ( P = 0.438).

CONCLUSIONS

The SRS protocol allows MMSS, as measured by MLSS, to be accurately determined using running power (Stryd), but not speed, in a single laboratory visit.

Wearables for Running Gait Analysis: A Systematic Review

BACKGROUND

Running gait assessment has traditionally been performed using subjective observation or expensive laboratory-based objective technologies, such as three-dimensional 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

We aimed to systematically review the available literature investigating how wearable technology is being used for running gait analysis in adults.

METHODS

A systematic search of the literature was conducted in the following scientific databases: PubMed, Scopus, Web of Science 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 (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 inertial measurement unit 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 the reliability of devices and suitability of gait outcomes.

CLINICAL TRIAL REGISTRATION

CRD42021235527.

Artificial Intelligence in Elite Sports—A Narrative Review of Success Stories and Challenges

This paper explores the role of artificial intelligence (AI) in elite sports. We approach the topic from two perspectives. Firstly, we provide a literature based overview of AI success stories in areas other than sports. We identified multiple approaches in the area of Machine Perception, Machine Learning and Modeling, Planning and Optimization as well as Interaction and Intervention, holding a potential for improving training and competition. Secondly, we discover the present status of AI use in elite sports. Therefore, in addition to another literature review, we interviewed leading sports scientist, which are closely connected to the main national service institute for elite sports in their countries. The analysis of this literature review and the interviews show that the most activity is carried out in the methodical categories of signal and image processing. However, projects in the field of modeling & planning have become increasingly popular within the last years. Based on these two perspectives, we extract deficits, issues and opportunities and summarize them in six key challenges faced by the sports analytics community. These challenges include data collection, controllability of an AI by the practitioners and explainability of AI results.

9/3-Minute Running Critical Power Test: Mechanical Threshold Location With Respect to Ventilatory Thresholds and Maximum Oxygen Uptake

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.

Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis

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.

Reliability and Validity of the SHFT Running Power Meter

The SHFT device is a novel running wearable consisting of two pods connected to your smartphone issuing several running metrics based on accelerometer and gyroscope technology. The purpose of this study was to investigate the reliability and validity of the power output (PO) metric produced by the SHFT device. To assess reliability, 12 men ran on an outdoor track at 10.5 km·h^-1 and 12 km·h^-1 on two consecutive days. To assess validity, oxygen uptake (VO₂) and SHFT data from eight men and seven women were collected during incremental submaximal running tests on an indoor treadmill on one to four separate days (34 tests in total). SHFT reliability on the outdoor track was strong with coefficients of variance (CV) of 1.8% and 2.4% for 10.5 and 12 km·h^-1, respectively. We observed a very strong linear relationship between PO and VO₂ (r² = 0.54) within subjects, and a very strong linear relationship within each subject within each treadmill test (r² = 0.80). We conclude that SHFT provides a reliable running power estimate and that a very strong relationship between SHFT-Power and metabolic rate exists, which places SHFT as one of the leading commercially available running power meters.

Running power: lab based vs. portable devices measurements and its relationship with aerobic power

In recent years, different companies have developed devices that estimate "running power". The main objective of this paper is to evaluate the effect of running speed on aerobic and running powers measured using force plates and by different devices. The second objective is to evaluate the relationship between aerobic power and running powers measured using force plates and by different devices. We enrolled 11 subjects in the study, they performed 5-min running trials at 2.22, 2.78, 3.33, 3.89 and 4.44 m/s respectively on a force-measuring treadmill while we collected metabolic data. We calculated running power as the dot product of ground reaction force and velocity of the centre of mass and compared it to the running power estimates of three devices: Skillrun (Technogym), Stryd Summit Powermeter (Stryd) and Garmin HRM-Run (Garmin). We found statistically significant linear correlations with running powers measured by all devices and running speed. Although absolute running power measurements were different among devices, an increase of 1 m/s in running speed translated to an increase of 0.944 W/kg in running power (p < 0.001). We found statistically significant linear correlations with running powers measured by all devices and aerobic power, in particular: as aerobic power increases by 1 W/kg, running power increases by 0.218 W/kg for all devices (p < 0.001). For level treadmill running, across speeds, running power measured by commercially available devices reflects force-based measurements and it can be a valuable metric, providing quasi real-time feedback during training sessions and competitions. HighlightsWe evaluated the effect of running speed on aerobic and running powers measured using force plates and by different devices.We also compared the relationship between aerobic power and running powers measured using force plates and by different devices.We found statistically significant linear correlations with running powers measured by all devices and aerobic power, in particular: as aerobic power increases by 1 W/kg, running power increases by 0.218 W/kg for all devices.For level treadmill running, across speeds, running power measured by commercially available devices reflects force-based measurements and it can be a valuable metric, providing quasi real-time feedback during training sessions and competitions.

Influence of Biomechanical Parameters on Performance in Elite Triathletes along 29 Weeks of Training

The purpose of the study was to assess how the modification of biomechanical parameters influences the performance of elite triathletes. Four elite international triathletes participated in this study. The anthropometric method ISAK was used to estimate the triathlete’s body composition. For the physiological and biomechanical parameters, a running test (RT) was performed on an outdoor track, with the participants wearing the Stryd Summit Footpod (Stryd, Boulder, CO, USA). The pre-test took place in the last week of an adaptation mesocycle; then, after 29 weeks of training, the triathletes performed the post-test. A within-subject repeated measures design was used to assess changes in the anthropometric, physiological and biomechanical parameters. Pearson correlations (r) were applied to determine the relationship between the performance at different intensities (VT1, VT2 and MAS) and the biomechanical parameters. Concerning the anthropometric characteristics, significant differences were found in the summation (Σ) of skinfold (8.1 cm); as a consequence, the % fat mass was reduced (1.2%). Significant differences were found in the physiological values (VO2 and % VO2max), speed and biomechanical parameters, such as step length normalized, to the specific physiological intensity of the short-distance triathlon, the VT2. Therefore, performance improvement in the running segment could not only be explained by physiological changes, but also by biomechanical parameters changes.

Estimating Functional Threshold Power in Endurance Running from Shorter Time Trials Using a 6-Axis Inertial Measurement Sensor

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.

The use of a running power-meter for performance analysis in five-a-side football

BACKGROUND

Power output considers all movement aspects of the game of football and could have meaningful impact for teams.

PURPOSE & METHODS

To assess inter-reliability of ten power meters designed for running; and as a descriptor of individual and team performance during a five-a-side football match. The work aimed to assess inter-device reliability of running power-meters combined with data analysis from intermittent running, along with descriptives of player work rate, gait and team performance during a small-sided game of football.

METHODS

10 different running power meters inter-reliability were on a treadmill at 8, 10, 12, and 16 km h^-1 for 60 s in a random order. Football players (N = 10) performed the Yo-Yo ET1 with the running power meters to determine participants' endurance capability, while assessing the ability to record metrics of gait and power output during intermittent running. Following a period of 7-days participants took part in a 20 min small-sided game of football wearing the running power meters to provide descriptors of work and gait.

RESULTS

Good inter-device reliability for the power meters (CV 1.67, range 1.51-1.94 %) during continuous treadmill running were found. Overall mean ± SD results for Yo-Yo ET1 power output 263 ± 36W, power:weight 3.59 ± 0.34W∙kg^-1 significantly (p < 0.05) increased with successive stages, while ground-contact time 234 ± 17 ms, and vertical oscillation 90.7 ± 27 mm did not change (p > 0.05). Descriptive analysis of the small-sided game presented mean ± SD absolute and relative power outputs of 148 ± 44W and 1.98 ± 0.53W∙kg^-1, equating to 54 ± 21 %W_max and 74 ± 5%HR_max. Characteristics of gait included cadence 125 ± 22 rpm, ground contact time 266 ± 19 ms, and vertical oscillation 76.7 ± 7 mm. The winning team worked relatively harder than the losing team (53.3 ± 0.7 %W_max vs 46.7 ± 0.4 %W_max, p < 0.0001) with more time (398 s vs 141 s) spent above 70 %W_max.

SIGNIFICANCE

As such, the use of a running power-meter is a useful tool for comparing work rate and aspects of gait between team members while more research is required to investigate relative work rate (%W_max) within the field.

Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques

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 Loadsol^TM 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.

Effects of Wearable Devices with Biofeedback on Biomechanical Performance of Running-A Systematic Review

This present review includes a systematic search for peer-reviewed articles published between March 2009 and March 2020 that evaluated the effects of wearable devices with biofeedback on the biomechanics of running. The included articles did not focus on physiological and metabolic metrics. Articles with patients, animals, orthoses, exoskeletons and virtual reality were not included. Following the PRISMA guidelines, 417 articles were first identified, and nineteen were selected following the removal of duplicates and articles which did not meet the inclusion criteria. Most reviewed articles reported a significant reduction in positive peak acceleration, which was found to be related to tibial stress fractures in running. Some previous studies provided biofeedback aiming to increase stride frequencies. They produced some positive effects on running, as they reduced vertical load in knee and ankle joints and vertical displacement of the body and increased knee flexion. Some other parameters, including contact ground time and speed, were fed back by wearable devices for running. Such devices reduced running time and increased swing phase time. This article reviews challenges in this area and suggests future studies can evaluate the long-term effects in running biomechanics produced by wearable devices with biofeedback.

Mechanical Power in Endurance Running: A Scoping Review on Sensors for Power Output Estimation during Running

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.

Validity of the Stryd Power Meter in Measuring Running Parameters at Submaximal Speeds

This study assessed the Stryd running power meter validity at sub-maximal speeds (8 to 19 km/h). Six recreational runners performed an incremental indoor running test. Power output (PO), ground contact time (GCT) and leg spring stiffness (LSS) were compared to reference measures recorded by portable metabolic analyser, force platforms and motion capture system. A Bayesian framework was conducted for systems validity and comparisons. We observed strong and positive linear relationships between Stryd PO and oxygen consumption ( R 2 = 0.82 , B F 10 > 100 ), and between Stryd PO and external mechanical power ( R 2 = 0.88 , B F 10 > 100 ). Stryd power meter underestimated PO ( B F 10 > 100 ) whereas GCT and LSS values did not show any significant differences with the reference measures ( B F 10 = 0.008 , B F 10 = 0.007 , respectively). We conclude that the Stryd power meter provides valid measures of GCT and LSS but underestimates the absolute values of PO.

Running power meters and theoretical models based on laws of physics: Effects of environments and running conditions

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 P_W 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. P_W was simultaneously registered by the commercial technologies Stryd (Stryd_App and Stryd_Watch), RunScribe, Garmin_RP and Polar_V, whereas theoretical power output (TP_W) was calculated by the two mathematical models (TP_W1 and TP_W2). Statistics included, among others, the Pearson's correlation coefficient (r) and standard error of measurement (SEM). The Polar_V, and above all Stryd, showed the closest agreement with the TP_W1 (Stryd: r ≥ 0.947, SEM ≤ 11 W; PolarV: r ≥ 0.931, SEM ≤ 64 W) and TP_W2 (Stryd: r ≥ 0.933, SEM ≤ 60 W; Polar_V: r ≥ 0.932, SEM ≤ 24 W), both indoors and outdoors. On the other hand, the devices Garmin_RP (r ≤ 0.765, SEM ≥ 59 W) and RunScribe_. (r ≤ 0.508, SEM ≥ 125 W) showed the lowest agreement with the TP_W1 and TP_W2 models for all conditions and environments analyzed. The closest agreement of the Stryd and Polar_V technologies with the TP_W1 and TP_W2 models suggest these tools as the most sensitive, among those analyzed, for P_W measurement when changing environments and running conditions.

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

Training prescription in running activities have benefited from power output (P_W) data obtained by new technologies. Nevertheless, to date, the suitability of P_W data provided by these tools is still uncertain. The present study aimed to: (i) analyze the repeatability of five commercially available technologies for running P_W estimation, and (ii) examine the concurrent validity through the relationship between each technology P_W 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. P_W was simultaneously registered by the commercial technologies Stryd_App, Stryd_Watch, RunScribe, Garmin_RP and Polar_V, 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 P_W and VO₂. Stryd device was found as the most repeatable technology for all environments and conditions (SEM ≤ 12.5 W, 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 Polar_V, Garmin_RP 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 P_W measurement.

Ischemic Preconditioning: Improved Cycling Performance Despite Nocebo Expectation

PURPOSE

Ischemic preconditioning (IPC) through purposeful circulatory occlusion may enhance exercise performance. The value of IPC for improving performance is controversial owing to challenges with employing effective placebo controls. This study examines the efficacy of IPC versus a deceptive sham protocol for improving performance to determine whether benefits of IPC are attributable to true physiological effects. It was hypothesized that IPC would favorably alter performance more than a sham treatment and that physiological responses to exercise would be affected only after IPC treatment.

METHODS

In a randomized order, 16 participants performed incremental exercise to exhaustion on a cycle ergometer in control conditions and after sham and IPC treatments. Participants rated their belief as to the efficacy of each treatment compared with control.

RESULTS

Time to exhaustion was greatest after IPC (control = 1331 [270] s, IPC = 1429 [300] s, sham = 1343 [255] s, P = .02), despite negative performance expectations after IPC and positive expectation after sham. Maximal aerobic power remained unchanged after both SHAM and IPC (control = 42.0 [5.2], IPC = 41.7 [5.5], sham = 41.6 [5.5] mL·kg-1·min-1, P = .7), as did submaximal lactate concentration (control = 8.9 [2.6], sham = 8.0 [1.9], IPC = 7.7 [2.1] mmol, P = .1) and oxygen uptake (control = 37.8 [4.8], sham = 37.5 [5.3], IPC = 37.5 [5.5] mL·kg-1·min-1, P = .6).

CONCLUSIONS

IPC before cycling exercise provides an ergogenic benefit that is not attributable to a placebo effect from positive expectation and that was not explained by traditionally suggested mechanisms.

Comparison of energy expenditure and substrate metabolism during overground and motorized treadmill running in Chinese middle-aged women

The purpose of this study was to compare differences of energy expenditure and substrate metabolism between motorized-treadmill and overground running in three different velocities in Chinese middle-aged women. In total, 74 healthy middle-aged women (age, 48 ± 4 years; height, 159.4 ± 4.9 cm; weight, 58.6 ± 6.7 kg; and body-mass index (BMI), 23.1 ± 2.7 kg/m²) volunteered to participate in this study. Bioelectrical-impedance analysis was used to measure body composition. Energy expenditure, carbohydrates (CHO), and fat oxidation were calculated with indirect calorimetry during motorized-treadmill and overground running. Running speed from slow to fast was 7.0, 8.0, and 9.0 km/h. The duration of each velocity was 6 min, separated by 5–15 min rest. There was no significant difference in energy expenditure between overground and treadmill running at the speed of 7 km/h (8.10 ± 1.25 vs. 7.75 ± 1.13 kcal/min, p > 0.05). Energy expenditure of overground running at 8 and 9 km/h was higher than that of treadmill running (9.36 ± 1.40 vs. 8.54 ± 1.21 kcal/min; 10.33 ± 1.55 vs. 9.54 ± 1.36 kcal/min; both p < 0.01). Fat contribution to energy consumption was significantly higher during treadmill running than during overground running (both p < 0.01) at speeds of 8 and 9 km/h. Overground running at high intensity incurred greater energy consumption than treadmill running did. However, results showed greater fat utilization during treadmill running than during overground running at high intensity. It is critical that these differences are taken into account when we prescribe training modes and intensities for middle-aged women.

Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements

Injuries and lack of motivation are common reasons for discontinuation of running. Real-time feedback from wearables can reduce discontinuation by reducing injury risk and improving performance and motivation. There are however several limitations and challenges with current real-time feedback approaches. We discuss these limitations and challenges and provide a framework to optimise real-time feedback for reducing injury risk and improving performance and motivation. We first discuss the reasons why individuals run and propose that feedback targeted to these reasons can improve motivation and compliance. Secondly, we review the association of running technique and running workload with injuries and performance and we elaborate how real-time feedback on running technique and workload can be applied to reduce injury risk and improve performance and motivation. We also review different feedback modalities and motor learning feedback strategies and their application to real-time feedback. Briefly, the most effective feedback modality and frequency differ between variables and individuals, but a combination of modalities and mixture of real-time and delayed feedback is most effective. Moreover, feedback promoting perceived competence, autonomy and an external focus can improve motivation, learning and performance. Although the focus is on wearables, the challenges and practical applications are also relevant for laboratory-based gait retraining.

A New Short Track Test to Estimate the V[Combining Dot Above]O2max and Maximal Aerobic Speed in Well-Trained Runners

Pallarés, JG, Cerezuela-Espejo, V, Morán-Navarro, R, Martínez-Cava, A, Conesa, E, and Courel-Ibáñez, J. A new short track test to estimate the V[Combining Dot Above]O2max and maximal aerobic speed in well-trained runners. J Strength Cond Res 33(5): 1216-1221, 2019-This study was designed to validate a new short track test (Track(1:1)) to estimate running performance parameters maximal oxygen uptake (V[Combining Dot Above]O2max) 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 (V[Combining Dot Above]O2max 60.3 ± 5.9 ml·kg·min; MAS ranged from 17.0 to 20.3 km·h) 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 less than each athlete's Vpeak, with 1 km·h 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 V[Combining Dot Above]O2max 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; p < 0.05), which notably altered the estimations (MAS: -2.6%, bias = -0.5 km·h; V[Combining Dot Above]O2max: 4.7%, bias = 2.9 ml·kg·min). 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; MAS: 0.3%, bias = <0.1 km·h; V[Combining Dot Above]O2max: 0.4%, bias = 0.2 ml·kg·min, 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.

Comparison of Critical Speed and D' Derived From 2 or 3 Maximal Tests

Purpose: The hyperbolic distance-time relationship can be used to profile running performance and establish critical speed (CS) and D' (the curvature constant of the speed-time relationship). Typically, to establish these parameters, multiple (3+) performance trials are required, which can be highly fatiguing and limit the usability of such protocols in a single training session. This study aimed to compare CS and D' calculated from a 2-trial (2-point model) and a 3-trial (3-point model) method. Methods: A total of 14 male distance runners completed 3 fixed-distance (3600, 2400, and 1200 m) time trials on a 400-m outdoor running track, separated by 30-min recoveries. Participants completed the protocol 9 times across a 12-mo period, with approximately 42 d between tests. CS and D' were calculated using all 3 distances (3-point model) and also using the 3600- and 1200-m distances only (2-point model). Results: Mean (SD) CS for both 3-point and 2-point models was 4.94 (0.32) m·s^-1, whereas the values for D' were 123.3 (57.70) and 127.4 (57.34) m for the 3-point and 2-point models, respectively. Overall bias for both CS and D' between 3-point and 2-point model was classed as trivial. Conclusion: A 2-point time-trial model can be used to calculate CS and D' as proficiently as a 3-point model, making it a less fatiguing, inexpensive, and applicable method for coaches, practitioners, and athletes for monitoring running performance in 1 training session.

The Relationship between Running Power and Running Economy in Well-Trained Distance Runners

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.