Monitoring external training load (eTL) has become popular for team sport for managing fatigue, optimizing performance, and guiding return-to-play protocols. During indoor sports, eTL can be measured via inertial measurement units (IMU) or indoor positioning systems (IPS). Though each device provides unique information, the relationships between devices has not been examined. Therefore, the purpose of this study was to assess the association of eTL between an IMU and IPS used to monitor eTL in team sport. Retrospective analyses were performed on 13 elite male National Collegiate Athletic Association (NCAA) Division I basketball players (age: 20.2 ± 1.2 years, height: 201.1 ± 7.6 cm, mass: 96.8 ± 8.8 kg) from three practices during the off-season training phase. A one-way analysis of variance was used to test differences in eTL across practices. Pearson’s correlation examined the association between the Distance traveled during practice captured by IPS compared to PlayerLoad (PL), PlayerLoad per Minute (PL/Min), 2-Dimensional PlayerLoad (PL^2D), 1-Dimensional PlayerLoad Forward (PL^1D-FWD), Side (PL^1D-SIDE), and Up (PL^1D-UP) captured from the IMU. Regression analyses were performed to predict PL from Distance traveled. The eTL characteristics during Practice 1: PL = 420.4 ± 102.9, PL/min = 5.8 ± 1.4, Distance = 1645.9 ± 377.0 m; Practice 2: PL = 472.8 ± 109.5, PL/min = 5.1 ± 1.2, Distance = 1940.0 ± 436.3 m; Practice 3: PL = 295.1 ± 57.8, PL/min = 5.3 ± 1.0, Distance = 1198.2 ± 219.2 m. Significant (p ≤ 0.05) differences were observed in PL, PL^2D, PL^1D-FWD, PL^1D-SIDE, PL^1D-UP, and Distance across practices. Significant correlations (p ≤ 0.001) existed between Distance and PL parameters (Practice 1: r = 0.799–0.891; Practice 2: r = 0.819–0.972; and Practice 3: 0.761–0.891). Predictive models using Distance traveled accounted for 73.5–89.7% of the variance in PL. Significant relationships and predictive capacities exists between systems. Nonetheless, each system also appears to capture unique information that may still be useful to performance practitioners regarding the understanding of eTL.

The purpose of this study was to examine age-related differences in muscle performance in women divided into young (YW, 20–39 years, n = 29) middle-aged (MAW, 40–59 years, n = 33), and older (OW, ≥60 years, n = 40) age groups. Methods: Hand grip strength, vertical jump performance, and knee extensor (KE) strength (0 deg/s, 60 deg/s, and 240 deg/s), speed of movement (SoM; at 1 Nm, 20%, 40%, and 60% isometric strength), and endurance (30-repetition test at 60 degs/s and 240 deg/s) were assessed. Computed tomography-acquired muscle cross-sectional area (mCSA) was measured and included to determine specific strength (KE strength/mCSA). Results: Hand grip strength was similar across groups, while jump performance declined with age (YW and MAW > OW, p < 0.001). KE strength declined significantly with age (all conditions p < 0.01), while specific strength was similar across groups. SoM was significantly higher for YW and MAW compared to OW (both p < 0.01). An age × velocity interaction revealed YW KE endurance was similar between conditions, whereas MAW and OW displayed significantly better endurance during the 60 deg/s condition. OW displayed impaired KE endurance at 240 deg/s (vs. YW and MAW, p < 0.01) but improved at 60 deg/s (vs. YW, p < 0.01). Dynamic torque decline increased with age (YW < OW, p = 0.03) and was associated with intramuscular adipose tissue (r = 0.21, p = 0.04). Conclusions: Performance declines were most evident among OW, but few performance deficits had emerged in MAW. Interestingly, strength declines disappeared after normalizing to mCSA and endurance appears to be velocity-dependent.