Biomedical subjects

John D Polk

Publications and source records attributed to John D Polk.

3 recordsLinked to original sources

PubMed2005

Sampling frequencies and measurement error for linear and temporal gait parameters in primate locomotion.

Quantitative analyses of animal motion are increasingly easy to conduct using simple video equipment and relatively inexpensive software packages. With careful use, such analytical tools have the potential to quantify differences in movement between individuals or species and to allow insights into the behavioral consequences of morphological differences between taxa. However, as with any other type of measurement, there are errors associated with kinematic measurements. Because normative kinematic data on human and nonhuman primate locomotion are used to model aspects of gait of fossil hominins, errors in the extant data influence the accuracy of fossil gait reconstructions. The principal goal of this paper is to illustrate the effect of camera speeds (frame rates) on kinematic measurement errors, and to demonstrate how these errors vary with subject size, movement velocity, and sample size. Kinematic data for human walking and running (240 Hz), as well as data for primate quadrupedal walking and running (180 Hz) were used as inputs for a simulation of the measurement errors associated with various linear and temporal kinematic variables. Measurement errors were shown to increase as camera speed, subject body size, and interval duration all decrease, and as movement velocity increases. These results have implications for the methods used to calculate subject velocity and suggest that using a moving marker to measure the linear displacements of the body is preferable to the use of a stationary marker. Finally, while slower camera speeds will always result in higher measurement errors than do faster camera speeds, this effect can be moderated to some extent by collecting sufficiently large samples of data.

Animals↗

PubMed2004

Predicting long bone loading from cross-sectional geometry.

Long bone loading histories are commonly evaluated using a beam model by calculating cross-sectional second moments of areas (SMAs). Without in vivo strain data, SMA analyses commonly make two explicit or implicit assumptions. First, while it has long been known that axial compression superimposed on bending shifts neutral axes away from cross-sectional area centroids, most analyses assume that cross-sectional properties calculated through the area centroid approximate cross-sectional strength. Second, the orientation of maximum bending rigidity is often assumed to reflect the orientation of peak or habitual bending forces the bone experiences. These assumptions are tested in sheep in which rosette strain gauges mounted at three locations around the tibia and metatarsal midshafts measured in vivo strains during treadmill running at 1.5 m/sec. Calculated normal strain distributions confirm that the neutral axis of bending does not run through the midshaft centroid. In these animals, orientations of the principal centroidal axes around which maximum SMAs (Imax) are calculated are not in the same planes in which the bones experienced bending. Cross-sectional properties calculated using centroidal axes have substantial differences in magnitude (up to 55%) but high correlations in pattern compared to cross-sectional properties calculated around experimentally determined neutral axes. Thus interindividual comparisons of cross-sectional properties calculated from centroidal axes may be useful in terms of pattern, but are subject to high errors in terms of absolute values. In addition, cross-sectional properties do not necessarily provide reliable data on the orientations of loads to which bones are subjected.

Anatomy, Cross-Sectional↗

PubMed2003

Optimization of bone growth and remodeling in response to loading in tapered mammalian limbs.

How bones respond dynamically to mechanical loading through changes in shape and structure is poorly understood, particularly with respect to variations between bones. Structurally, cortical bones adapt in vivo to their mechanical environments primarily by modulating two processes, modeling and Haversian remodeling. Modeling, defined here as the addition of new bone, may occur in response to mechanical stimuli by altering bone shape or size through growth. Haversian remodeling is thought to be an adaptation to repair microcracks or prevent microcrack propagation. Here, we examine whether cortical bone in sheep limbs modulates periosteal modeling and Haversian remodeling to optimize strength relative to mass in hind-limb midshafts in response to moderate levels of exercise at different growth stages. Histomorphometry was used to compare rates of periosteal growth and Haversian remodeling in exercised and sedentary treatment groups of juvenile, subadult and young adult sheep. In vivo strain data were also collected for the tibia and metatarsal midshafts of juvenile sheep. The results suggest that limb bones initially optimize responses to loading according to the varying power requirements associated with adding mass at different locations. In juveniles, exercise induces higher rates of periosteal modeling in proximal midshafts and higher rates of Haversian remodeling in distal midshafts. Consequently, distal element midshafts experience higher strains and, presumably, have lower safety factors. As animals age, periosteal modeling rates decline and Haversian remodeling rates increase, but moderate levels of mechanical loading stimulate neither process significantly.

Animals↗