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In sports situations, players may be required to throw at different speeds. The question of how skilled players throw the ball accurately to the desired location under different speed conditions is of interest to biomechanics researchers. Previous research suggested that throwers use different types of joint coordination. However, joint coordination with a change in throwing speed has not been studied. Here, we show the effects of changes in throwing speed on joint coordination during accurate overhead throwing. Participants were seated on a low chair with their trunk fixed and threw a baseball aimed at a target under 2 different speed conditions (slow and fast). In the slow condition, the elbow flexion/extension angle coordinated with other joint angles and angular velocities to reduce the variability of the vertical hand velocity. In the fast condition, the shoulder internal/external rotation angle and the shoulder horizontal flexion/extension angular velocity coordinated with other joint angles and angular velocities to reduce the variability of the vertical hand velocity. These results showed that joint coordination differed with changes in throwing speed, indicating that joint coordination is not always fixed, but may differ depending on the task constraints, such as throwing speed.

Many head acceleration events (HAEs) observed in youth football emanate from a practice environment. This study aimed to evaluate HAEs in youth football practice drills using a mouthpiece-based sensor, differentiating between inertial and direct HAEs. Head acceleration data were collected from athletes participating on 2 youth football teams (ages 11–13 y) using an instrumented mouthpiece-based sensor during all practice sessions in a single season. Video was recorded and analyzed to verify and assign HAEs to specific practice drill characteristics, including drill intensity, drill classification, and drill type. HAEs were quantified in terms of HAEs per athlete per minute and peak linear and rotational acceleration and rotational velocity. Mixed-effects models were used to evaluate the differences in kinematics, and generalized linear models were used to assess differences in HAE frequency between drill categories. A total of 3237 HAEs were verified and evaluated from 29 football athletes enrolled in this study. Head kinematics varied significantly between drill categorizations. HAEs collected at higher intensities resulted in significantly greater kinematics than lower-intensity drills. The results of this study add to the growing body of evidence informing evidence-based strategies to reduce head impact exposure and concussion risk in youth football practices.

Inertial measurement units (IMUs) attached to the distal tibia are a validated method of measuring lower-extremity impact accelerations, called tibial accelerations (TAs), in runners. However, no studies have investigated the effects of small errors in IMU placement, which would be expected in real-world, autonomous use of IMUs. The purpose of this study was to evaluate the effect of a small proximal shift in IMU location on mean TAs and relationships between TAs and ground reaction force loading rates. IMUs were strapped to 18 injury-free runners at a specified standard location (∼1 cm proximal to medial malleolus) and 2 cm proximal to the standard location. TAs and ground reaction forces were measured while participants ran at self-selected and 10% slower/faster speeds. Mean TA was lower at the standard versus proximal IMU location in the faster running condition (P = .026), but similar in the slower (P = .643) and self-selected conditions (P = .654). Mean TAs measured at the standard IMU explained more variation in ground reaction force loading rates (r ² = .79−.90; P < .001) compared with those measured at the proximal IMU (r ² = .65−.72; P < .001). These results suggest that careful attention should be given to IMU placement when measuring TAs during running.

Electromechanical delay (EMD) of muscle is influenced in part by its in-series arrangement with connective tissue. Therefore, studying EMD might provide a better understanding of the muscle–connective tissue interaction. Here, EMD of the thoracic and lumbar erector spinae muscles were investigated under conditions that could influence muscle–connective tissue interaction. A total of 19 participants performed isometric back extension contractions in 3 different postures that influence lumbar spine angle: sitting, standing, and kneeling. They then performed a 15-minute dynamic stretching routine and repeated the standing contractions. Mean lumbar flexion angles of 0.5°, 9.9°, and 19.8° were adopted for standing, kneeling, and sitting, respectively. No statistically significant differences in the thoracic erector spinae EMD were found between the different postures. Lumbar erector spinae EMD was significantly longer in the sitting (94.1 ms) compared to the standing (69.9 ms) condition, with no differences compared to kneeling (79.7 ms). There were no statistically significant differences of the thoracic or lumbar erector spinae EMDs before and after dynamic stretching. These results suggest that dynamic stretching does not affect the mechanical behavior of the muscle-tendon–aponeurosis units in a way that alters force generation and transmission, but a sitting posture can alter how force is transmitted through the musculotendinous complex of the lumbar erector spinae.

In-lab, marker-based gait analyses may not represent real-world gait. Real-world gait analyses may be feasible using inertial measurement units (IMUs) in combination with open-source data processing pipelines (OpenSense). Before using OpenSense to study real-world gait, we must determine whether these methods estimate joint kinematics similarly to traditional marker-based motion capture (MoCap) and differentiate groups with clinically different gait mechanics. Healthy young and older adults and older adults with knee osteoarthritis completed this study. We captured MoCap and IMU data during overground walking at 2 speeds. MoCap and IMU kinematics were computed with OpenSim workflows. We tested whether sagittal kinematics differed between MoCap and IMU, whether tools detected between-group differences similarly, and whether kinematics differed between tools by speed. MoCap showed more anterior pelvic tilt (0%–100% stride) and joint flexion than IMU (hip: 0%–38% and 61%–100% stride; knee: 0%–38%, 58%–89%, and 95%–99% stride; and ankle: 6%–99% stride). There were no significant tool-by-group interactions. We found significant tool-by-speed interactions for all angles. While MoCap- and IMU-derived kinematics differed, the lack of tool-by-group interactions suggests consistent tracking across clinical cohorts. Results of the current study suggest that IMU-derived kinematics with OpenSense may enable reliable evaluation of gait in real-world settings.

Field-based tibial acceleration measurements are increasingly common but sampling frequencies vary between accelerometers. The purpose of this study was to determine the minimum sampling frequency needed for reliable and accurate measurement of peak axial and resultant tibial acceleration during running in the field. Tibial acceleration was measured at 7161 Hz in 19 healthy runners on concrete and grass. Acceleration data were down sampled to approximate previously used sampling frequencies. Peak axial and resultant tibial acceleration were calculated for each sampling frequency. The within-session reliability and accuracy of peak axial and resultant tibial accelerations were evaluated using intraclass correlation coefficients, mean differences, and 95% limits of agreements. Intraclass correlation coefficients greater than .9 indicated excellent within-session reliability for both peak axial and resultant tibial acceleration measured while running on concrete and grass. Peak axial and resultant tibial accelerations were 0.5 to 1.4 g lower and minimal detectable differences were up to 0.6 g higher at 102 Hz compared with higher sampling frequencies. We recommend a minimum sampling frequency of 199 Hz for accurate and reliable measurements of peak axial and resultant tibial acceleration in the field.

Spatiotemporal gait kinematics and their variability are commonly assessed in clinical and laboratory settings to quantify fall risk. Although the Hawthorne effect, or modifications in participant behavior due to knowledge of being observed, has the potential to impact such assessments, it has received minimal attention in the study of gait—particularly gait variability. The purpose of this study was to quantify the Hawthorne effect on variability and central tendency measures of fall-related spatiotemporal gait parameters. Seventeen healthy young adults walked on a treadmill at a self-selected velocity for 2 minutes under covert evaluation (ie, without awareness of being evaluated) and 2 minutes under overt evaluation. The movement was recorded using motion capture technology, from which we calculated mean value and step-to-step variability (using standard deviation and mean absolute deviation) of step length, step width, percent double support, percent stance phase, and stride time. Although central tendencies were unaffected by evaluation type, four-of-five measures of variability were significantly lower during overt evaluation for at least one-of-two metrics. Our results suggest a Hawthorne effect on locomotor control. Researchers should be aware of this phenomenon when designing research studies and interpreting gait assessments.

Computational approaches for movement onset detection can standardize and automate analyses to improve repeatability, accessibility, and time efficiency. With the increasing interest in assessing time-varying biomechanical signals such as force–time recordings, there remains a need to investigate the recently adopted 5 times the standard deviation (5 × SD) threshold method. In addition, other employed methods and their variations such as the reverse scanning and first derivative methods have been scarcely evaluated. The aim of this study was to compare the 5 × SD threshold method, 3 variations of the reverse scanning method, and 5 variations of the first derivative method against manually selected onsets, in the countermovement jump and squat. Limits of agreement with respect to onsets, manually selected from unfiltered data, were best for the first derivative method using a 10-Hz low-pass filter (limits of agreement: −0.02 to 0.05 s and −0.07 to 0.11 s for the countermovement jump and squat, respectively). Thus, even when the onset of unfiltered data is of primary interest, filtering before calculating the first derivative is necessary as it reduces the amplification of high frequencies. The first derivative approach is also less susceptible to inherent variation during the quiet phase prior to the onset compared to the other approaches investigated.