Microtechnology Devices Research Articles

DEVELOPMENT OF A NOVEL ALGORITHM FOR THE AUTOMATIC DETECTION OF MULTIDIRECTIONAL LOCOMOTION WITHIN TEAM SPORTS

INTRODUCTION & AIMS Field-based sports are characterised by their intermittent nature requiring both, multidirectional locomotion and, sports-specific movements at a range of intensities. Traditionally, athlete-monitoring has focused on quantifying workload based on movement intensity with minimal regard to the direction of locomotion [1]. The aim of this study was to develop and evaluate an algorithm to detect and classify multidirectional movement using signal characteristics from a microtechnology device. METHODS Rugby league referees (n=13) undertook a match-play simulation protocol (i.e., changes in movement speed and locomotion direction) [2], with microtechnology and video data collected across five-trials. Video data was reviewed to identify movement anomalies outside of the simulation protocol for exclusion. From the 100Hz microtechnology data, acceleration measures were used to classify the start and end point of each movement (i.e., backwards, forwards, sideways or other) or marked for exclusion from the algorithm development. The classified sensor data was processed in Python (v3.11), where data were split into training and testing datasets. A Recurrent Neural Network (Long Short-Term Memory) [3] was implemented to develop and validate an algorithm. Model performance was assessed via accuracy, sensitivity, precision and Area Under the Receiver Operating Characteristic Curve (AUC), using the testing dataset. RESULTS The accuracy of the model was 0.973 ± 0.010. Sensitivity and precision of the model varied between movement direction, but was >0.928 and >0.922, respectively. The AUC of the model was 0.988 ± 0.007. CONCLUSION The current study highlights the effectiveness of a microtechnology based algorithm for automatically classifying multidirectional locomotion of various velocities. Practically, such algorithm can be used to inform evidence-based training in relation to multidirectional locomotion. Whilst model performance was very-high, further research should examine the feasibility of applying the algorithm to match-play datasets to enhance athlete-monitoring processes.

Changing gears: data-driven velocity zones to support monitoring and research in men’s rugby league

ABSTRACT Objectives The study aimed to (1) apply a data-mining approach to league-wide microtechnology data to identify absolute velocity zone thresholds and (2) apply the respective velocity zones to microtechnology data to examine the locomotor demands of elite match-play. Methods League-wide microtechnology data were collected from elite male rugby league players representing all National Rugby League (NRL) teams (n = 16 teams, one excluded due to a different microtechnology device; n = 4836 files) over one season. To identify four velocity zones, spectral clustering with a beta smoothing cut-off of 0.1 was applied to each players’ instantaneous match-play velocity data. Velocity zones for each player were calculated as the median while the overarching velocity zones were determined through an incremental search to minimise root mean square error. Results The velocity zones identified through spectral clustering were 0–13.99 km · h−1 (i.e., low velocity), 14.00–20.99 km · h−1 (i.e., moderate velocity), 21.00–24.49 km · h−1 (i.e., high velocity) and >24.50 km · h−1 (i.e., very-high velocity). Conclusions The application of spectral clustering (i.e., a data-mining method) to league-wide rugby league microtechnology data yielded insights into the distribution of velocity data, thereby informing the cut-off values which best place similar data points into the same velocity zones. As the identified zones are representative of the intensities of locomotion achieved by elite male rugby league players, it is suggested that when absolute zones are used, the consistent application of the identified zones would facilitate standardisation, longitudinal athlete monitoring as well as comparisons between teams, leagues and published literature.

The Validity of Automated Tackle Detection in Women's Rugby League.

Cummins, C, Charlton, G, Naughton, M, Jones, B, Minahan, C, and Murphy, A. The validity of automated tackle detection in women's rugby league. J Strength Cond Res 36(7): 1951-1955, 2022-This study assessed the validity of microtechnology devices to automatically detect and differentiate tackles in elite women's rugby league match-play. Elite female players (n = 17) wore a microtechnology device (OptimEye S5 device; Catapult Group International) during a representative match, which involved a total of 512 tackles of which 365 were defensive and 147 were attacking. Tackles automatically detected by Catapult's tackle detection algorithm and video-coded tackles were time synchronized. True positive, false negative and false positive events were utilized to calculate sensitivity (i.e., when a tackle occurred, did the algorithm correctly detect this event) and precision (i.e., when the algorithm reported a tackle, was this a true event based on video-coding). Of the 512 video-derived attacking and defensive tackle events, the algorithm was able to detect 389 tackles. The algorithm also produced 81 false positives and 123 false negatives. As such when a tackle occurred, the algorithm correctly identified 76.0% of these events. When the algorithm reported that a tackle occurred, this was an actual event in 82.8% of circumstances. Across all players, the algorithm was more sensitive to the detection of an attacking event (sensitivity: 78.2%) as opposed to a defensive event (sensitivity: 75.1%). The sensitivity and precision of the algorithm was higher for forwards (sensitivity: 81.8%; precision: 92.1%) when compared with backs (sensitivity: 64.5%; precision: 66.1%). Given that understanding the tackle demands of rugby league is imperative from both an injury-prevention and physical-conditioning perspective there is an opportunity to develop a specific algorithm for the detection of tackles within women's rugby league.

Evolution of Physical Fitness in Formative Female Basketball Players: A Case Study.

Over the last few years, team sports increased the amount of physical demand and its importance. Therefore, work related to physical fitness and its assessment is essential to achieving success. However, there are few studies on this subject at the formative stage. The purpose of this study was then to analyze the physical fitness of an under-18 (U18) women’s team divided by game positions. In addition, physical fitness at different times of the season was characterized to identify differences and determine its evolution. To assess physical fitness, tests of aerobic and anaerobic capacities, lower body strength, centripetal force, agility and speed were carried out as designed in the SBAFIT battery. Each player was equipped with an inertial microtechnology device for the collection of data. This research is classified as empirical, with quasi-experimental methodology. The results showed significant differences in variables of the test of aerobic and anaerobic capacities, speed, agility (generic and specific), and centripetal force (right) based on game position and the moment of the season. The results also showed the importance of the specific physical aspect in relation to an optimal improvement in physical fitness, since training sessions and competition do not allow all players to improve equally or efficiently.

Validity of Real-Time Ultra-wideband Global Navigation Satellite System Data Generated by a Wearable Microtechnology Unit.

Johnston, RD, Hewitt, A, and Duthie, G. Validity of real-time ultra-wideband global navigation satellite system data generated by a wearable microtechnology unit. J Strength Cond Res 34(7): 2071-2075, 2020-This study aimed to determine the validity of real-time ultra-wideband data generated by a wearable microtechnology unit during rugby league training sessions using a repeated-measures crossover study. Twenty-four semiprofessional rugby league players wore a commercially available microtechnology device (StatSports Apex, Newry, Northern Ireland) during 10 training sessions. Total distance; moderate-speed running (3.6-4.9 m·s); high-speed running (5.0-6.9 m·s); very high-speed running (≥7 m·s); maximum velocity (m·s); the number of high-intensity accelerations (≥2.78 m·s) and decelerations (≥-2.78 m·s); dynamic stress load (AU); and high metabolic load distance (m) were recorded in real time through an Apex beacon over a secured wireless network before being exported to a csv file at the end of the session. The data were then downloaded to a computer after event. To determine the validity of the real-time data, they were compared with the postevent downloaded data using coefficient of variation and Pearson's correlation coefficient. There was almost perfect agreement between real-time and postevent downloaded data for all variables reported. The overall bias effect size scores were all trivial, ranging from 0.00 for total distance and high-speed running up to -0.12 for maximal velocity; Pearson's correlations were either perfect or nearly perfect (r = 0.98-1.00). Irrespective of the movement speed, the data collected by these devices in real time show excellent levels of agreement with postevent downloaded data.

There Is Little Difference in the Peak Movement Demands of Professional and Semi-Professional Rugby League Competition

Previous research has quantified the peak movement demands of elite rugby league match-play, but the peak accelerometer load or the semi-professional peak demands remain unknown. The aim of this research was to determine the peak movement demands of professional and semi-professional rugby league competition. Wearable microtechnology devices tracked the physical activity profiles of players during 26 professional (n = 351 files) and 22 semi-professional (n = 267 files) matches. Following each match, data were exported in raw form to extract the peak 1- to 10-min periods for speed, average acceleration, and accelerometer load of each player, using a rolling average method. To determine the difference between playing levels (professional vs. semi-professional) and position (forwards vs. backs), linear mixed models were used. The intercept and the slope were calculated based on the power law relationship to provide the peak, and rate of decay, of each dependent variable. Cohen’s effect size (ES) statistic was used to determine the magnitude of differences between positions and playing level. There was little difference between playing standards, with only small differences in running speed, with a greater intercept and slope for the professional forwards compared with semi-professional forwards (intercept ES: 0.37; 90%CL: 0.19 to 0.55; slope ES: 0.35; 0.15 to 0.55). For positional comparisons (forwards vs. backs), there was no difference in running speeds at the professional level, but there was substantially greater running speed for backs compared to forwards in semi-professional competition, with small to moderate differences (ES range: 0.60–0.39). Both professional and semi-professional forwards showed small to moderately higher accelerometer load compared to backs, which increased with period duration (ES range: 0.22–0.79). Similarly, acceleration demands were greater for forwards compared to backs across both playing standards, with moderate to large differences (ES range: 0.52–0.96). Overall, the results of this study show that there is a small difference in the peak running speed for forwards in professional competition, but otherwise there are no meaningful differences in movement demands of professional and semi-professional rugby league match-play. Forwards display greater acceleration and accelerometer load across a number of rolling average durations compared to backs.

Peak movement and collision demands of professional rugby league competition

ABSTRACTTo quantify the peak movement and contact demands of National Rugby League (NRL) and European Super League (ESL) competition players were tracked during 10 NRL (166 files) and 10 ESL (143 files) matches using microtechnology devices. The peak 1- to 5-min periods were then calculated for average match speed (m·min−1), and acceleration (m·s−2) when 0, 1, 2, and ≥3 collisions per min occurred. Linear mixed effect models and Cohen’s effect size statistic (± 90%CI) were used to determine the differences in movement profiles when collisions occurred. Compared to no collision periods, as frequency of collisions per minute increased, there were progressive reductions in running speed for most positional groups. The addition of 1 or more collisions per min resulted in average effect size reductions in match speed of −0.14 for NRL forwards, −0.89 for NRL backs, −0.48 for ESL forwards, and −2.41 for ESL backs. ESL forwards had the highest frequency of peak periods involving 3 or more collisions per min, 22% of all periods, followed by NRL forwards (14%), NRL backs (10%) and ESL backs (8%). This study highlights the peak movement and collision demands of professional rugby league competition and allows practitioners to develop training drills that reflect worst case scenarios.

Situational factors affecting rugby sevens match performance

ABSTRACTPurpose: To examine the independent influence of a range of situational factors on physical and technical match performance during international rugby sevens matches.Methods: Data was collected from 20 professional rugby sevens players from one team across one competitive season. Activity profiles were measured using wearable microtechnology devices, and technical performance measures were collected using match video analysis. A principal components analysis was conducted to reduce the dimensionality of the physical and technical match performance variables into single index values incorporating each variable’s distinct information. Linear mixed models were then constructed to examine the collective influence of external contextual factors on physical and technical index scores.Results: Increased points conceded, a winning match outcome, and more favourable weather all had positive impacts on the Physical Performance Factor, with all other situational factors examined not contributing to a significantly better model fit. Technical Performance Factor values were shown to increase with longer match involvement durations and decrease when playing against higher ranked opponents.Conclusions: These findings show that winning the match, conceding more points, and more favourable weather conditions are associated with increased physical performance; whilst technical performance improved with longer playing durations and playing lower ranked opponents.

Individual Factors Affecting Rugby Sevens Match Performance.

Purpose: To examine the collective independent influence of a range of individual characteristics on physical and technical match performance during international rugby sevens matches. Methods: Data were collected from 20 international rugby sevens players from 1 team across 1 season. Activity profiles were measured using wearable microtechnology devices, and technical performance measures were collected from match video analysis. Subjective well-being measures were collected using a well-being questionnaire completed on the morning of main training days, and groin-squeeze assessments at 0° and 60° knee flexion were also conducted using a sphygmomanometer. Assessments of aerobic fitness were completed periodically across the season, including time to complete a 2-km run and final velocity during the 30:15 Intermittent Fitness Test (VIFT). A principal-components analysis was conducted to reduce the dimensionality of the physical and technical variables into single-factor values. Linear mixed models were then constructed to examine the collective influence of a range of individual contextual variables on physical and technical performance factors. Results: Increased muscle soreness, stress, and VIFT were associated with trivial to small increases in physical and technical performance values, whereas trivial to small decreases were associated with higher perceived recovery, body weight, and groin squeeze (0° knee flexion). Conclusions: A range of well-being metrics are required to account for a significant portion of the variance in physical and technical performance. These factors may be manipulated by coaches or practitioners to achieve favorable physiological readiness that may lead to improved match performance.

Automatic detection of one-on-one tackles and ruck events using microtechnology in rugby union

ObjectivesTo automate the detection of ruck and tackle events in rugby union using a specifically-designed algorithm based on microsensor data. DesignCross-sectional study. MethodsElite rugby union players wore microtechnology devices (Catapult, S5) during match-play. Ruck (n=125) and tackle (n=125) event data was synchronised with video footage compiled from international rugby union match-play ruck and tackle events. A specifically-designed algorithm to detect ruck and tackle events was developed using a random forest classification model. This algorithm was then validated using 8 additional international match-play datasets and video footage, with each ruck and tackle manually coded and verified if the event was correctly identified by the algorithm. ResultsThe classification algorithm’s results indicated that all rucks and tackles were correctly identified during match-play when 79.4±9.2% and 81.0±9.3% of the random forest decision trees agreed with the video-based determination of these events. Sub-group analyses of backs and forwards yielded similar optimal confidence percentages of 79.7% and 79.1% respectively for rucks. Sub-analysis revealed backs (85.3±7.2%) produced a higher algorithm cut-off for tackles than forwards (77.7±12.2%). ConclusionsThe specifically-designed algorithm was able to detect rucks and tackles for all positions involved. For optimal results, it is recommended that practitioners use the recommended cut-off (80%) to limit false positives for match-play and training. Although this algorithm provides an improved insight into the number and type of collisions in which rugby players engage, this algorithm does not provide impact forces of these events.

Validity of a Microsensor-Based Algorithm for Detecting Scrum Events in Rugby Union.

Commercially available microtechnology devices containing accelerometers, gyroscopes, magnetometers, and global positioning technology have been widely used to quantify the demands of rugby union. This study investigated whether data derived from wearable microsensors can be used to develop an algorithm that automatically detects scrum events in rugby union training and match play. Data were collected from 30 elite rugby players wearing a Catapult OptimEye S5 (Catapult Sports, Melbourne, Australia) microtechnology device during a series of competitive matches (n = 46) and training sessions (n = 51). A total of 97 files were required to "train" an algorithm to automatically detect scrum events using random forest machine learning. A further 310 files from training (n = 167) and match-play (n = 143) sessions were used to validate the algorithm's performance. Across all positions (front row, second row, and back row), the algorithm demonstrated good sensitivity (91%) and specificity (91%) for training and match-play events when the confidence level of the random forest was set to 50%. Generally, the algorithm had better accuracy for match-play events (93.6%) than for training events (87.6%). The scrum algorithm was able to accurately detect scrum events for front-row, second-row, and back-row positions. However, for optimal results, practitioners are advised to use the recommended confidence level for each position to limit false positives. Scrum algorithm detection was better with scrums involving ≥5 players and is therefore unlikely to be suitable for scrums involving 3 players (eg, rugby sevens). Additional contact- and collision-detection algorithms are required to fully quantify rugby union demands.

The Physical Characteristics of Specific Phases of Play During Rugby Union Match Play.

To quantify the frequencies and timings of rugby union match-play phases (ie,attacking, defending, ball in play [BIP], and ball out of play [BOP]) and then compare the physical characteristics of attacking, defending, and BOP between forwards and backs. Data were analyzed from 59 male rugby union academy players (259 observations). Each player wore a microtechnology device (OptimEye S5; Catapult, Melbourne, Australia) with video footage analyzed for phase timings and frequencies. Dependent variables were analyzed using a linear mixed-effects model and assessed with magnitude-based inferences and Cohen d effect sizes (ES). Attack, defense, BIP, and BOP times were 12.7 (3.1), 14.7 (2.5), 27.4 (2.9), and 47.4 (4.1)min, respectively. Mean attack (26 [17]s), defense (26 [18]s), and BIP (33 [24]s) phases were shorter than BOP phases (59 [33]s). The relative distance in attacking phases was similar (112.2 [48.4] vs 114.6 [52.3]m·min-1, ES = 0.00 ± 0.23) between forwards and backs but greater in forwards (114.5 [52.7] vs 109.0 [54.8]m·min-1, ES = 0.32 ± 0.23) during defense and greater in backs during BOP (ES = -0.66 ± 0.23). Total time in attack, defense, and therefore BIP was less than BOP. Relative distance was greater in forwards during defense, whereas it was greater in backs during BOP and similar between positions during attack. Players should be exposed to training intensities from in-play phases (ie,attack and defense) rather than whole-match data and practice technical skills during these intensities.

The Same Story or a Unique Novel? Within-Participant Principal-Component Analysis of Measures of Training Load in Professional Rugby Union Skills Training.

To identify which combination metrics of external and internal training load (TL) capture similar or unique information for individual professional players during skills training in rugby union using principal-component (PC) analysis. TL data were collected from 21 male professional rugby union players across a competitive season. This included PlayerLoad™, total distance, and individualized high-speed distance (>61% maximal velocity; all external TL) obtained from a microtechnology device (OptimEye X4; Catapult Innovations, Melbourne, Australia) that was worn by each player and the session rating of perceived exertion (RPE) (internal TL). PC analysis was conducted on each individual to extract the underlying combinations of the 4 TL measures that best describe the total information (variance) provided by the measures. TL measures with PC loadings (PCL) above 0.7 were deemed to possess well-defined relationships with the extracted PC. The findings show that from the 4 TL measures, the majority of an individual's TL information (first PC: 55-70%) during skills training can be explained by session RPE (PCL: 0.72-0.95), total distance (PCL: 0.86-0.98), or PlayerLoad (PCL: 0.71-0.98). High-speed distance was the only variable to relate to the second PC (PCL: 0.72-1.00), which captured additional TL information (+19-28%). Findings suggest that practitioners could quantify the TL of rugby union skills training with one of PlayerLoad, total distance, or session RPE plus high-speed distance while limiting omitted information of the TL imposed during professional rugby union skills training.

Maximum running intensities during English academy rugby union match-play

ABSTRACTTo quantify and compare the maximum running intensities during rugby union match-play.Running intensity was quantified using micro-technology devices (S5 Optimeye, Catapult) from 202 players during 24 matches (472 observations). Instantaneous speed was used to calculate relative distance (m·min−1) using a 0.1 s rolling mean for different time durations (15 and 30 s and 1, 2, 2.5, 3, 4, 5, and 10 min). Data were analysed using a linear mixed-model and assessed with magnitude-based inferences and effect sizes (ES).Running intensity for consecutive durations (e.g., 15 s vs. 30 s, 30 s vs. 1 min, etc.) decreased as time increased (ES = 0.48–2.80). Running intensity was lower in forwards than backs during all durations (−0.74 ±0.21 to −1.19 ±0.21). Running intensity for the second row and back row positions was greater than the front row players at all durations (−0.58 ±0.38 to −1.18 ±0.29). Running intensity for scrum halves was greater (0.46 ±0.43 to 0.86 ±0.39) than inside and outside backs for all durations besides 15 and 30 s.Front rowers and scrum halves were markedly different from other sub-positional groups and should be conditioned appropriately. Coaches working in academy rugby can use this information to appropriately overload the intensity of running, specific to time durations and positions.

The Use of Microtechnology to Monitor Collision Performance in Professional Rugby Union.

To determine if microtechnology-derived collision loads discriminate between collision performance and compare the physical and analytical components of collision performance between positional groups. Thirty-seven professional male rugby union players participated in this study. Collision events from 11 competitive matches were coded using specific tackle and carry classifications based on the ball-carrier's collision outcome. Collisions were automatically detected using 10Hz microtechnology units. Collision events were identified, coded (as tackle or carry), and timestamped at the collision contact point using game analysis software. Attacking and defensive performances of 1609 collision events were analyzed. Collision loads were significantly greater during dominant compared with neutral and passive collisions (P < .001; effect size [ES] = 0.53 and 0.80, respectively), tackles (P < .0001; ES = 0.60 and 0.56, respectively), and carries (P < .001; ES = 0.48 and 0.79, respectively). Overall, forwards reported a greater number and frequency of collisions but lower loads per collision and velocities at collision point than did backs. Microtechnology devices can also accurately, sensitively, and specifically identify collision events (93.3%, 93.8%, and 92.8%, respectively). Microtechnology is a valid means of discriminating between tackle and carry performance. Thus, microtechnology-derived collision load data can be utilized to track and monitor collision events in training and games.

The Fit Matters: Influence of Accelerometer Fitting and Training Drill Demands on Load Measures in Rugby League Players

To determine the relationship between drill type and accelerometer-derived loads during various team-sport activities and examine the influence of unit fitting on these loads. Sixteen rugby league players were fitted with microtechnology devices in either manufacturer vests or playing jerseys before completing standardized running, agility, and tackling drills. Two-dimensional (2D) and 3-dimensional (3D) accelerometer loads (BodyLoad™) per kilometer were compared across drills and fittings (ie,vest and jersey). When fitted in a vest, 2D BodyLoad was higher during tackling (21.5 [14.8]AU/km) than during running (9.5 [2.5]AU/km) and agility (10.3 [2.7]AU/km). Jersey fitting resulted in more than 2-fold higher BodyLoad during running (2D = 9.5 [2.7] vs 29.3 [14.8]AU/km, 3D = 48.5 [14.8] vs 111.5 [45.4]AU/km) and agility (2D = 10.3 [2.7] vs 21.0 [8.1]AU/km, 3D = 40.4 [13.6] vs 77.7 [26.8]AU/km) compared with a vest fitting. Jersey fitting also produced higher BodyLoad during tackling drills (2D = 21.5 [14.8] vs 27.8 [18.6]AU/km, 3D = 42.0 [21.4] vs 63.2 [33.1]AU/km). This study provides evidence supporting the construct validity of 2D BodyLoad for assessing collision/tackling load in rugby league training drills. Conversely, the large values obtained from 3D BodyLoad (which includes the vertical load vector) appear to mask small increases in load during tackling drills, rendering 3D BodyLoad insensitive to changes in contact load. Unit fitting has a large influence on accumulated accelerometer loads during all drills, which is likely related to greater incidental unit movement when units are fitted in jerseys. Therefore, it is recommended that athletes wear microtechnology units in manufacturer-provided vests to provide valid and reliable information.

A novel method to establish inter-athlete measurement uncertainty

Background: Microtechnology devices (Global Positioning Systems (GPS) and integrated triaxial accelerometers) have enabled descriptive analysis of basic athlete movement patterns as well as comparisons of positional activity profiles, levels of competition and match-outcome. Such analysis however, provides minimal insight into the technical (individual athlete skills) and tactical (inter-athlete interactions) constructs of performance. Before microtechnology devices can be utilized to quantify inter-player cohesion and responsiveness, complex movement patterns and team tactics their ability to examine inter-athlete separation distances must be examined. This study examined the accuracy of microtechnology devices in quantifying inter-athlete separation distances.

Validity of Real-Time Data Generated by a Wearable Microtechnology Device.

The purpose of this study was to investigate the validity of global positioning system (GPS) and micro-electrical-mechanical-system (MEMS) data generated in real time through a dedicated receiver. Postsession data acted as the criterion as it is used to plan the volume and intensity of future training and is downloaded directly from the device. Twenty-five professional rugby league players completed 2 training sessions wearing an MEMS device (Catapult S5, firmware version: 5.27). During sessions, real-time data were collected through the manufacturer receiver and dedicated software (Openfield v1.14), which was positioned outdoors at the same location for every session. The GPS variables included total-, low- (0-3 m·s), moderate- (3.1-5 m·s), high- (5.1-7 m·s), and very high-speed (>7.1 m·s) distances. Micro-electrical-mechanical-system data included total session PlayerLoad. When compared to postsession data, mean bias for total-, low-, moderate-, high-, and very high-speed distances were all trivial, with the typical error of the estimate (TEE) small, small, trivial, trivial and small, respectively. Pearson correlation coefficients for total-, low-, moderate-, high- and very-high-speed distances were nearly perfect, nearly perfect, perfect, perfect, and nearly perfect, respectively. For PlayerLoad, mean bias was trivial, whereas TEE was moderate and correlation nearly perfect. Practitioners should be confident that when interpreting real-time speed-derived metrics, the data generated in real-time are comparable with those downloaded directly from the device postsession. However, practitioners should refrain from interpreting accelerometer-derived data (i.e., PlayerLoad) or acknowledge the moderate error associated with this real-time measure.

The Relationship Between Variables in Wearable Microtechnology Devices and Cricket Fast-Bowling Intensity.

To date, the monitoring of fast-bowling workloads across training and competition environments has been limited to counting total balls bowled. However, bowling at faster velocities is likely to require greater effort while also placing greater load on the bowler. This study investigated the relationship between prescribed effort and microtechnology outputs in fast bowlers to ascertain whether the technology could provide a more refined measure of workload. Twelve high-performing fast bowlers (mean ± SD age 20.3 ± 2.2y) participated in the study. Each bowler bowled 6 balls at prescribed bowling intensities of 60%, 70%, 85%, and 100%. The relationships between microtechnology outputs, prescribed intensity, and ball velocity were determined using polynomial regression. Very large relationships were observed between prescribed effort and ball velocity for peak PlayerLoad™ (R = .83 ± .19 and .82 ± .20). The PlayerLoad across lower ranges of prescribed effort exhibited a higher coefficient of variation (CV) (60% = 19.0% [17.0-23.0%]), while the CV at higher ranges of prescribed effort was lower (100% = 7.3% [6.4-8.5%]). Routinely used wearable microtechnology devices offer opportunities to examine workload and intensity in cricket fast bowlers outside the normal metrics reported. They offer a useful tool for prescribing and monitoring bowling intensity and workload in elite fast bowlers.

Energy expenditure, metabolic power and high speed activity during linear and multi-directional running.

The purpose of the study was to compare measures of energy expenditure derived from indirect calorimetry and micro-technology, as well as high power and high speed activity during linear and multi-directional running. Repeated measures. Twelve university standard team sport players completed a linear and multi-directional running condition. Estimated energy expenditure, as well as time at high speed (>14.4kmh-1) and high power (>20Wkg-1) were quantified using a 10Hz micro-technology device and compared with energy expenditure derived from indirect calorimetry. Measured energy expenditure was higher during the multi-directional condition (9.0±2.0 cf. 5.9±1.4kcalmin-1), whereas estimated energy expenditure was higher during the linear condition (8.7±2.1 cf. 6.5±1.5kcalmin-1). Whilst measures of energy expenditure were strongly related (r>0.89, p<0.001), metabolic power underestimated energy expenditure by 52% (95% LoA: 20-93%) and 34% (95% LoA: 12-59%) during the multi-directional and linear condition, respectively. Time at high power was 41% (95% LoA: 4-92%) greater than time at high speed during the multi-directional condition, whereas time at high power was 5% (95% LoA: -17-9%) lower than time at high speed during the linear condition. Estimated energy expenditure and time at high metabolic power can reflect changes in internal load. However, micro-technology cannot be used to determine the energy cost of intermittent running.