Journal of Applied Biomechanics最新文献

To increase understanding in development of running injuries, the biomechanical load over time should be studied. Ground reaction force (GRF) is an important parameter for biomechanical analyses and is typically measured in a controlled lab environment. GRF can be estimated outdoors, however, the repeatability of this estimation is unknown. Repeatability is a crucial aspect if a measurement is repeated over prolonged periods of time. This study investigates the repeatability of a GRF estimation algorithm using inertial measurement units during outdoor running. Twelve well-trained participants completed 3 running sessions on different days, on an athletics track, instrumented with inertial measurement units on the lower legs and pelvis. Vertical accelerations were used to estimate the GRF. The goal was to assess the algorithm's repeatability across 3 sessions in a real-world setting, aiming to bridge the gap between laboratory and outdoor measurements. Results showed a good level of repeatability, with an intraclass correlation coefficient (2, k) of .86 for peak GRF, root mean square error of .08 times body weight (3.5%) and Pearson correlation coefficients exceeding .99 between the days. This is the first study looking into the day-to-day repeatability of the estimation of GRF, showing the potential to use this algorithm daily.

The calf raise test (CRT) is commonly used to assess triceps surae muscle-tendon unit function. Often, a metronome set to 60 beats/min (30 repetitions/min) is used to set the cadence of calf raise repetitions, but studies report using cadences ranging from 30 to 120 beats/min. We investigated the effect of cadence on CRT outcomes, accounting for the potential confounders of sex, age, body mass index, and physical activity. Thirty-six healthy individuals (50% female) performed single-leg calf raise repetitions to volitional exhaustion in 3 randomized cadence conditions, 7 days apart: 30, 60, and 120 beats/min. Repetitions, total vertical displacement, total work, peak height, and peak power were recorded using the validated Calf Raise application. Cadence significantly affected all CRT outcomes (P ≤ .008), except repetitions (P = .200). Post hoc analysis revealed 60 beats/min resulted in significantly greater total vertical displacement and work than 30 and 120 beats/min. Peak height was greater at 60 and 120 than 30 beats/min, and peak power was greater at 120 beats/min. Males generated greater work and peak power (P ≤ .001), whereas individuals with greater body mass index completed less repetitions (P = .008), achieved lower total vertical displacements (P = .003), and generated greater peak power (P = .005). CRT cadence is important to consider when interpreting CRT outcomes and comparing data between studies.

In many sports, practitioners must reach their maximal jump height (hmax) under time constraints. This requires a reduction of the countermovement depth and so of the push-off distance (hPO). The purpose of this study was to investigate how hPO influences force-velocity (F-v) profiles (F¯0, v¯0, P¯max, and SFv) and performance. Eleven participants (age: 26 [5] y, height: 175.6 [11.2] cm, mass: 76 [15] kg; squat 1RM: 129 [34] kg) performed maximal countermovement jumps. Kinetic and kinematic measurements were used to assess individual F-v profiles for 3 different hPO conditions (hPO-SMALL, hPO-MEDIUM, hPO-LARGE) from countermovement jumps performed under different load conditions (bodyweight [BW], BW + 8 kg, BW + 17 kg, BW + 40%1RM, BW + 70%1RM). Results indicated that F¯0 and P¯max changed across hPO conditions, while v¯0 remained constant. A lower hPO led to a significantly higher F¯0 and P¯max. These changes resulted in a steeper SFv leading to a more force-oriented profile, a lower optimal SFv and a greater F-v imbalance. Reducing hPO and modifying F-v profile led, to some extent, to a reduction in hmax. Performance is a compromise between hPO, P¯max, and F-v imbalance, all influenced by countermovement depth. This explains why reducing countermovement depth to meet time constraint may lower performance.

Repetitive manual labor tasks involving twisting, bending, and lifting commonly lead to lower back and knee injuries in the workplace. To identify tasks with high injury risk, we recruited N = 9 participants to perform industry-relevant, 2-handed lifts with a 11-kg weight. These included symmetrical/asymmetrical, ascending/descending lifts that varied in start-to-end heights (knee-to-waist and waist-to-shoulder). We used a data-driven musculoskeletal model that combined force and motion data with a muscle activation-informed solver (OpenSim, CEINMS) to estimate 3-dimensional internal joint contact forces (JCFs) in the lower back (L5/S1) and knee. Symmetrical lifting resulted in larger peak JCFs than asymmetrical lifting in both the L5/S1 (+20.2% normal [P < .01], +20.3% shear [P = .001], +20.6% total [P < .01]) and the knee (+39.2% shear [P = .001]), and there were no differences in peak JCFs between ascending versus descending motions. Below-the-waist lifting generated significantly greater JCFs in the L5/S1 and knee than above-the-waist lifts (P < .01). We found a positive correlation between knee and L5/S1 peak total JCFs (R2 = .60, P < .01) across the task space, suggesting motor coordination that favors sharing of load distribution across the trunk and legs during lifting.

The metabolic cost of walking for individuals with transtibial amputation is generally greater compared with able-bodied individuals. One aim of powered prostheses is to reduce metabolic deficits by replicating biological ankle function. Individuals with transtibial amputation can activate their residual limb muscles to volitionally control bionic ankle prostheses for walking; however, it is unknown how myoelectric control performs outside the laboratory. We recruited 6 individuals with transtibial amputation to walk an outdoor course with the Open Source Leg prosthesis under continuous proportional myoelectric control and compared it with their passive device. There were no significant differences (P = .142) in cost of transport between prostheses. Participants significantly increased residual limb vastus lateralis (P = .042) and rectus femoris (P = .029) muscle activity during early and midstance phase of walking with the powered prosthesis compared with their passive device. All but one participant preferred walking with myoelectric control compared with their passive prosthesis. The additional mass of the powered ankle prosthesis coupled with increased residual quadriceps activity could explain why the energy cost of walking was not lower compared with a passive prosthesis. This study demonstrates participants can volitionally control a bionic ankle prosthesis to navigate real-world environments.

Middle-age and older runners demonstrate differences in running biomechanics compared with younger runners. Female runners demonstrate differences in running biomechanics compared with males, and females experience hormonal changes during menopause that may also affect age-related changes in running biomechanics. The purpose of this study was to determine the relationship between age and running biomechanics in healthy female recreational runners. Fifty-two participants (ages 27-65 y) ran on an instrumented treadmill at 2 different self-selected speeds: easy pace and 5 km race pace. Lower-extremity kinematic and kinetic variables were calculated from 14 consecutive strides. Linear regression was used to determine the relationship between age and lower-extremity running biomechanics, controlling for self-selected running speed. There was a negative relationship between age and easy pace (R = -.49, P < .001) and age and 5 km race pace (R = -.43, P = .001). After controlling for self-selected running speed, there were no significant relationships between age and running biomechanics for either running speed. Several biomechanical variables were moderately to strongly correlated with running speed. Running speed should be considered when investigating age-related differences in running biomechanics.

Gait abnormalities affect an individual's ability to navigate the world independently and occur in 10% of older adults. Examining age-related gait symmetry in nonlaboratory environments is necessary for understanding mobility limitations in older adults. This study examined gait symmetry differences between older and younger adults using in-shoe force sensors. Walking trials were performed at a preferred speed. This is a secondary analysis of data from different studies in which young adults completed 7 trials and older adults completed 3 trials to decrease the impact of fatigue on outcomes in the clinical trial. Peak weight acceptance, mid stance trough, peak push-off, stance time, and impulse were collected during each step within a trial. Symmetry was determined using the absolute symmetry index. A linear mixed effects model showed a significant difference in peak weight acceptance force (P = .039), mid stance trough (P < .001), and peak push-off (P = .007) symmetry between groups. These results indicate that older adults have lower symmetry in peak weight acceptance, mid stance trough, and peak push-off during gait compared with young adults. Understanding how natural loading patterns change throughout life could improve our understanding of how load and load symmetry relate to mobility impairments in older adults.

This study compares joint kinematics and kinetics of young stroke survivors who walk <0.79 m/s (slow) or >0.80 m/s (fast) with reference to a healthy able-bodied group and provides clinical recommendations for guiding the gait rehabilitation of stroke survivors. Twenty-two young stroke survivors (18-55 y) were recruited from 6 hospital sites in the United Kingdom. Stroke participants were classified by walking speed as slow (<0.79) or fast (>0.80 m/s), and joint kinematics and kinetics at the pelvis, hip, knee, and ankle were measured during walking on level ground at self-selected speed. Ten walking biomechanical parameters correlated to walking speed (ρ ≥ .550). Stroke survivors in the slow group walked with significantly greater range of sagittal plane pelvic motion (P < .009), reduced range of hip adduction and abduction (P < .011), and smaller peak hip extension angle (P < .011) and hip flexion moment (P < .029) for the paretic limb. For the nonparetic limb, a significantly reduced hip flexion moment (P < .040) was observed compared with the fast group and control. We are the first to report how biomechanical function during walking is compromised in young stroke survivors classified by walking speed as slow (<0.79 m/s) or fast (>0.80 m/s) and propose that these biomechanical parameters be used to inform rehabilitation programs to improve walking for stroke survivors.

Calibrating inertial measurement units (IMUs) involves converting orientation data from a local reference frame into a clinically meaningful reference system. Several solutions exist but little work has been done to compare different calibration methods with each other and an optical motion capture system. Thirteen healthy subjects with no signs of upper limb injury were recruited for this study and instrumented with IMU sensors and optical markers. Three IMU calibration methods were compared: N-pose calibration, functional calibration, and manual alignment. Subjects executed simple single-plane single-joint tasks for each upper limb joint as well as more complex multijoint tasks. We performed a 3-way analysis of variance on range of motion error, root mean squared error, and offset to assess differences between calibrations, tasks, and anatomical axes. Differences in the 3 IMU calibrations are minor and not statistically significant for most tasks and anatomical axes, with the exception of the offset interaction calibration × axes (P < .001, ηG2=.056). Specifically, manual alignment gives the best offset estimation on the abduction/adduction and internal/external rotation axes. Therefore, we recommend the use of a static N-pose calibration procedure as the preferred IMU calibration method to model the humerothoracic joint, as this setup is the simplest as it only requires accurate positioning of the trunk sensor.