Purpose: To assess the validity of measuring locomotor activities and PlayerLoad using real-time (RT) data collection during soccer training. Methods: Twenty-nine English soccer players participated. Each player wore the same MEMS device (Micromechanical Electrical Systems; S5, Optimeye; CatapultSports, Melbourne, Australia) during 21 training sessions (N = 331 data sets) in the 2015–16 and 2016–17 seasons. An RT receiver (TRX; Catapultsports, Melbourne, Australia) was used to collect the locomotor activities and PlayerLoad data in RT and compared with the postevent downloaded (PED) data. PlayerLoad and locomotor activities (total distance covered; total high-speed running distance covered, >5.5#x00A0;m/s; total sprinting distance covered, >7 m/s; maximum velocity) were analyzed. Results: Correlations were near perfect for all variables analyzed (r = .98–1.00), with a varied level of noise between RT and PED also (0.3–9.7% coefficient of variation). Conclusions: Locomotor activities and PlayerLoad can use both RT and PED concurrently to quantify a player’s physical output during a training session. Caution should be taken with higher-velocity-based locomotor activities during RT compared to PED.
Ibrahim Akubat, Steve Barrett, and Grant Abt
This study aimed to assess the relationships of fitness in soccer players with a novel integration of internal and external training load (TL).
Ten amateur soccer players performed a lactate threshold (LT) test followed by a soccer simulation (Ball-Sport Endurance and Sprint Test [BEAST90mod]).
The results from the LT test were used to determine velocity at lactate threshold (vLT), velocity at onset of blood lactate accumulation (vOBLA), maximal oxygen uptake (VO2max), and the heart rate–blood lactate profile for calculation of internal TL (individualized training impulse, or iTRIMP). The total distance (TD) and high intensity distance (HID) covered during the BEAST90mod were measured using GPS technology that allowed measurement of performance and external TL. The internal TL was divided by the external TL to form TD:iTRIMP and HID:iTRIMP ratios. Correlation analyses assessed the relationships between fitness measures and the ratios to performance in the BEAST90mod.
vLT, vOBLA, and VO2max showed no significant relationship to TD or HID. HID:iTRIMP significantly correlated with vOBLA (r = .65, P = .04; large), and TD:iTRIMP showed a significant correlation with vLT (r = .69, P = .03; large).
The results suggest that the integrated use of ratios may help in the assessment of fitness, as performance alone showed no significant relationships with fitness.
Steve Barrett, Adrian Midgley, and Ric Lovell
The study aimed to establish the test–retest reliability and convergent validity of PlayerLoad™ (triaxial-accelerometer data) during a standardized bout of treadmill running.
Forty-four team-sport players performed 2 standardized incremental treadmill running tests (7–16 km/h) 7 d apart. Players’ oxygen uptake (VO2; n = 20), heart rate (n = 44), and triaxialaccelerometer data (PlayerLoad; n = 44) measured at both the scapulae and at the center of mass (COM), were recorded. Accelerometer data from the individual component planes of PlayerLoad (anteroposterior [PLAP], mediolateral [PLML], and vertical [PLV]) were also examined.
Moderate to high test–retest reliability was observed for PlayerLoad and its individual planes (ICC .80–.97, CV 4.2–14.8%) at both unit locations. PlayerLoad was significantly higher at COM vs scapulae (223.4 ± 42.6 vs 185.5 ± 26.3 arbitrary units; P = .001). The percentage contributions of individual planes to PlayerLoad were higher for PLML at the COM (scapulae 20.4% ± 3.8%, COM 26.5% ± 4.9%; P = .001) but lower for PLV (scapulae 55.7% ± 5.3%, COM 49.5% ± 6.9%; P = .001). Between-subjects correlations between PlayerLoad and VO2, and between PlayerLoad and heart rate were trivial to moderate (r = –.43 to .33), whereas within-subject correlations were nearly perfect (r = .92–.98).
PlayerLoad had a moderate to high degree of test–retest reliability and demonstrated convergent validity with measures of exercise intensity on an individual basis. However, caution should be applied in making between-athletes contrasts in loading and when using recordings from the scapulae to identify lower-limb movement patterns.
Justin W.L. Keogh, Steve Morrison, and Rod Barrett
The current study investigated the effect of 2 different types of unilateral resistance training on the postural tremor output of 19 neurologically healthy men age 70–80 yr. The strength- (n = 7) and coordination-training (n = 7) groups trained twice a week for 6 wk, performing dumbbell biceps curls, wrist flexions, and wrist extensions, while the control group (n = 5) maintained their normal activities. Changes in index-finger tremor (RMS amplitude, peak, and proportional power) and upper limb muscle coactivation were assessed during 4 postural conditions that were performed separately with the trained and untrained limbs. The 2 training groups experienced significantly greater reductions in mean RMS tremor amplitude, peak, and proportional tremor power 8–12 Hz and upper limb muscle coactivation, as well as greater increases in strength, than the control group. These results further demonstrate the benefits of resistance training for improving function in older adults.
Steve Barrett, Adrian W. Midgley, Christopher Towlson, Andrew Garrett, Matt Portas, and Ric Lovell
To assess the acute alterations in triaxial accelerometry (PlayerLoad [PLVM]) and its individual axial planes (anteroposterior PlayerLoad [PLAP], mediolateral PlayerLoad [PLML], and vertical PlayerLoad [PLV]) during a standardized 90-min soccer match-play simulation (SAFT90). Secondary aims of the study were to assess the test–retest reliability and anatomical location of the devices.
Semiprofessional (n = 5) and university (n = 15) soccer players completed 3 trials (1 familiarization, 2 experimental) of SAFT90. PlayerLoad and its individual planes were measured continuously using micromechanical-electrical systems (MEMS) positioned at the scapulae (SCAP) and near the center of mass (COM).
There were no between-halves differences in PLVM; however, within-half increases were recorded at the COM, but only during the 1st half at the SCAP. Greater contributions to PLVM were provided by PLV and PLML when derived from the SCAP and COM, respectively. PLVM (COM 1451 ± 168, SCAP 1029 ± 113), PLAP (COM 503 ± 99, SCAP 345 ± 61), PLML (COM 712 ± 124, SCAP 348 ± 61), and PLV (COM 797 ± 184, SCAP 688 ± 124) were significantly greater at the COM than at the SCAP. Moderate and high test–retest reliability was observed for PlayerLoad and its individual planes at both locations (ICC .80–.99).
PlayerLoad and its individual planes are reliable measures during SAFT90 and detected within-match changes in movement strategy when the unit was placed at the COM, which may have implications for fatigue management. Inferring alterations in lower-limb movement strategies from MEMS units positioned at the SCAP should be undertaken with caution.
Tzlil Shushan, Dean Norris, Shaun J. McLaren, Martin Buchheit, Tannath J. Scott, Steve Barrett, Antonio Dello Iacono, and Ric Lovell
Purpose: To survey team-sport practitioners on current practices and perceptions of submaximal fitness tests (SMFTs). Methods: A convenience sample of team-sport practitioners completed an online survey (September to November 2021). Descriptive statistics were used to obtain information of frequencies. A mixed-model quantile (median) regression was employed to assess the differences between the perceived influence of extraneous factors. Results: A total of 66 practitioners (74 discrete protocols) from 24 countries completed the survey. Time-efficient and nonexhaustive nature were considered the most important features of implementation. Practitioners prescribed a range of SMFTs, administered mostly on a monthly or weekly basis, but scheduling strategies appeared to differ across SMFT categories. Cardiorespiratory/metabolic outcome measures were collected in most protocols (n = 61; 82%), with the majority monitoring heart-rate-derived indices. Subjective outcome measures (n = 33; 45%) were monitored exclusively using ratings of perceived exertion. Mechanical outcome measures (n = 19; 26%) included either a combination of locomotor outputs (eg, distance covered) or variables derived from microelectrical mechanical systems. The perceived influence of extraneous factors on measurement accuracy varied according to outcome measure, and there was a lack of consensus among practitioners. Conclusions: Our survey showcases the methodological frameworks, practices, and challenges of SMFTs in team sports. The most important features for implementation perhaps support the use of SMFTs as a feasible and sustainable tool for monitoring in team sports. The wide variety of protocols, scheduling strategies, and outcome measures, along with their associated collection and analytical techniques, may reflect the absence of robust evidence regarding the application of SMFTs in team sports.