The purpose of this study was to validate peak acceleration data from an accelerometer contained within a wearable tracking device while walking, jogging and running. Thirty‐nine participants walked, jogged and ran on a treadmill while 10 peak accelerations per movement were obtained (n = 390). A single triaxial accelerometer measured resultant acceleration during all movements. To provide a criterion measure of acceleration, a 12‐camera motion analysis (MA) system tracked the position of a retro‐reflective marker affixed to the wearable tracking device. Peak raw acceleration recorded by the accelerometer significantly overestimated peak MA acceleration (P < 0.01). Filtering accelerometer data improved the relationship with the MA system (P < 0.01). However, only the 10 Hz and 8 Hz cut‐off frequencies significantly reduced the errors found. The walk movement demonstrated the highest accuracy, agreement and precision and the lowest relative errors. Linear increases in error were observed for jog compared with walk and for run compared to both other movements. As the magnitude of acceleration increased, the strength of the relationship between the accelerometer and the criterion measure decreased. These results indicate that filtered accelerometer data provide an acceptable means of assessing peak accelerations, in particular for walking and jogging.

This study assessed the validity of a tri-axial accelerometer worn on the upper body to estimate peak forces during running and change-of-direction tasks. Seventeen participants completed four different running and change-of-direction tasks (0°, 45°, 90°, and 180°; five trials per condition). Peak crania-caudal and resultant acceleration was converted to force and compared against peak force plate ground reaction force (GRF) in two formats (raw and smoothed). The resultant smoothed (10 Hz) and crania-caudal raw (except 180°) accelerometer values were not significantly different to resultant and vertical GRF for all running and change-of-direction tasks, respectively. Resultant accelerometer measures showed no to strong significant correlations (r = 0.00–0.76) and moderate to large measurement errors (coefficient of variation [CV] = 11.7–23.9%). Crania-caudal accelerometer measures showed small to moderate correlations (r = − 0.26 to 0.39) and moderate to large measurement errors (CV = 15.0–20.6%). Accelerometers, within integrated micro-technology tracking devices and worn on the upper body, can provide a relative measure of peak impact force experienced during running and two change-of-direction tasks (45° and 90°) provided that resultant smoothed values are used.

Despite the International System of Units (SI), as well as several publications guiding researchers on correct use of terminology, there continues to be widespread misuse of mechanical terms such as 'work' in sport and exercise science. A growing concern is the misuse of the term 'load'. Terms such as 'training load' and 'PlayerLoad' are popular in sport and exercise science vernacular. However, a 'load' is a mechanical variable which, when used appropriately, describes a force and therefore should be accompanied with the SI-derived unit of the newton (N). It is tempting to accept popular terms and nomenclature as scientific. However, scientists are obliged to abide by the SI and must pay close attention to scientific constructs. This communication presents a critical reflection on the use of the term 'load' in sport and exercise science. We present ways in which the use of this term breaches principles of science and provide practical solutions for ongoing use in research and practice.

This study assessed the validity of an accelerometer to measure impacts in team sports. 76 participants completed a team sport circuit. Accelerations were collected concurrently at 100 Hz using an accelerometer and a 36-camera motion analysis system. The largest peak accelerations per movement were compared in 2 ways: i) pooled together and filtered at 13 different cut-off frequencies (range 6–25 Hz) to identify the optimal filtering frequency, and ii) the optimal cut-off frequency split into the 7 movements performed (n=532). Raw and 25–16 Hz filtering frequencies significantly overestimated and 6 Hz underestimated motion analysis peak accelerations (P <0.007). The 12 Hz filtered accelerometer data revealed the strongest relationship with motion analysis data (accuracy − 0.01±0.27 g, effect size − 0.01, agreement − 0.55 to 0.53 g, precision 0.27 g, and relative error 5.5%; P=1.00). The accelerometer underestimated peak accelerations during tackling and jumping, and overestimated during walking, jogging, sprinting and change of direction. Lower agreement and reduced precision were associated with sprinting, jumping and tackling. The accelerometer demonstrated an acceptable level of concurrent validity compared to a motion analysis system when filtered at a cut-off frequency of 12 Hz. The results advocate the use of accelerometers to measure movements in team sport.

This study assessed the construct validity of accelerometry-derived net force to quantify the external demands of basketball movements. Twenty-eight basketballers completed the Yo-Yo intermittent recovery test (Yo-Yo-IR1) and basketball exercise simulation test (BEST). Intensity was quantified using accelerometry-derived average net force (AvF_Net) and PlayerLoad^TM per minute (PL/min). Within-player correlations were determined between intensity and running speed during Yo-Yo-IR1. Measured AvF_Net was determined for movements during the BEST and predicted AvF_Net was calculated using movement speed and correlations from Yo-Yo-IR1. Relationships between AvF_Net and running speed during Yo-Yo-IR1 were nearly perfect (r²=0.95, 95% CI: 0.94–0.96; p<0.001) and stronger than correlations between running speed and PL/min (r²=0.80, 95% CI: 0.73–0.87; p<0.001). Differences between measured and predicted AvF_Net were small during jogging and running (<1%), but large for basketball movements including jumping, change-of-direction and shuffling (15%–41%). As hypothesised, AvF_Net differed by playing position (11%–16%; p<0.001) and reflected the additional demand upon players with larger body mass and lower movement efficiency. Both sprint speed and AvF_Net reduced during the course of the BEST (p≤0.013). These findings confirm the construct validity of AvF_Net to quantify the external demand of basketball movements. Accelerometry-derived net force has the potential to quantify the external demands of basketballers during training and competition.