Everett A. Harman

Load carriage using packs: A review of physiological, biomechanical and medical aspects

This paper reviews the biomedical aspects of transporting loads in packs and offers suggestions for improving load-carriage capability. Locating the load mass as close as possible to the body center of gravity appears to result in the lowest energy cost when carrying a pack. Thus, the double pack (half the load on the front of the body and half the load on the back) has a lower energy cost than the backpack. However, backpacks provide greater versatility in most situations. The energy cost of walking with backpack loads increases progressively with increases in load mass, body mass, walking speed or grade; type of terrain also influences energy cost. Predictive equations have been developed for estimating the energy cost of carrying loads during locomotion but these may not be accurate for prolonged (>2 h) or downhill carriage. Training with loads can result in greater energy efficiency since walking with backpack loads over several weeks decreases energy cost. Load-carriage speed can be increased with physical training that involves regular running and resistance training. Erector spinae electrical activity (EMG) is lower during load carriage than in unloaded walking until loads exceed 30-40 kg, at which point erector spinae EMG activity is higher than during unloaded walking. EMGs of the quadriceps and gastrocnemius, but not the tibialis anterior or hamstrings, increase with load. Framed packs with hip belts reduce the electrical activity of the trapezius muscles, presumably by shifting forces from the shoulders to the hips. Increases in the backpack load mass result in increases in forces exerted on the grounds, amount of knee flexion and the forward inclination of the trunk. Compared to backpacks, double packs produce fewer deviations from normal walking. Common injuries associated with prolonged load carriage include foot blisters, stress fractures, back strains, metatarsalgia (foot pain), rucksack palsy (shoulder traction injury) and knee pain. Closed-cell neoprene insoles and use of an acrylic or nylon sock, combined with a wool sock, reduce blister incidence. A framed pack with a hip belt reduces the incidence of rucksack palsy. Backpack load carriage can be facilitated by lightening loads, optimizing equipment, improving load distribution and by preventive action aimed at reducing the incidence of injury.

Cross-validation of three jump power equations

UNLABELLED The vertical jump-and-reach score is used as a component in the estimation of peak mechanical power in two equations put forth by Lewis and Harman et al. PURPOSE The purpose of the present study was to: 1) cross-validate the two equations using the vertical jump-and-reach test, 2) develop a more accurate equation from a large heterogeneous population, 3) analyze gender differences and jump protocols, and 4) assess Predicted Residual Sum of Squares (PRESS) as a cross-validation procedure. METHODS One hundred eight college-age male and female athletes and nonathletes were tested on a force platform. They performed three maximal effort vertical jumps each of the squat jump (SJ) and countermovement jump (CMJ) while simultaneously performing the vertical jump-and-reach test. Regression analysis was used to predict peak power from body mass and vertical jump height. RESULTS SJ data yielded a better power prediction equation than did CMJ data because of the greater variability in CMJ technique. The following equation was derived from SJ data: Peak Power (W) = 60.7x (jump height cm]) +45.3x(body mass [kg])-2055. This equation revealed greater accuracy than either the Lewis or previous Harman et al. equations and underestimated peak power by less than 1%, with a SEE of 355.0 W using SJ protocol. The use of one equation for both males and females resulted in only a slight (5% of power output) difference between genders. Using CMJ data in the SJ-derived equation resulted in only a 2.7% overestimation of peak power. Cross-validation of regression equations using PRESS reveals accurate and reliable R2 and SEE values. CONCLUSIONS The SJ equation is a slightly more accurate equation than that derived from CMJ data. This equation should be used in the determination of peak power in place of the formulas developed by both Harman et al. and Lewis. Separate equations for males and females are unnecessary.

Resistance training modes: specificity and effectiveness.

There is considerable demand for information on the effectiveness of various resistance exercises for improving physical performance, and on how exercise programs must match functional activities to produce the greatest performance gains (training specificity). Evidence supports exercise-type specificity; the greatest training effects occur when the same exercise type is used for both testing and training. Range-of-motion (ROM) specificity is supported; strength improvements are greatest at the exercised joint angles, with enough carryover to strengthen ROMs precluded from direct training due to injury. Velocity specificity is supported; strength gains are consistently greatest at the training velocity, with some carryover. Some studies have produced a training effect only for velocities at and below the training velocity while others have produced effects around the training velocity. The little, mainly isokinetic, evidence comparing different exercise velocities for improving functional performance suggests that faster exercise best improves fast athletic movements. Yet isometric exercise can improve actions like the vertical jump, which begin slowly. The rate of force application may be more important in training than actual movement speed. More research is needed into the specificity and efficacy of resistance exercise. Test populations should include both males and females of various ages and rehabilitation patients.

The relationship between vertical jump power estimates and weightlifting ability: a field-test approach.

&NA; Carlock, J.M., S.L. Smith, M.J. Hartman, R.T. Morris, D.A. Ciroslan, K.C. Pierce, R.U. Newton, E.A. Harman, W.A. Sands, and M.H. Stone. The relationship between vertical jump power estimates and weightlifting ability: A field‐test approach. J. Strength Cond. Res. 18(3):534–539. 2004.—The purpose of this study was to assess the usefulness of the vertical jump and estimated vertical‐jump power as a field test for weightlifting. Estimated PP output from the vertical jump was correlated with lifting ability among 64 USA national‐level weightlifters (junior and senior men and women). Vertical jump was measured using the Kinematic Measurement System, consisting of a switch mat interfaced with a laptop computer. Vertical jumps were measured using a hands‐on‐hips method. A counter‐movement vertical jump (CMJ) and a static vertical jump (SJ, 90° knee angle) were measured. Two trials were given for each condition. Testretest reliability for jump height was intra‐class correlation (ICC) = 0.98 (CMJ) and ICC = 0.96 (SJ). Athletes warmed up on their own for 2–3 minutes, followed by 2 practice jumps at each condition. Peak power (PP) was estimated using the equations developed by Sayers et al. (24). The athletes’ current lifting capabilities were assessed by a questionnaire, and USA national coaches checked the listed values. Differences between groups (i.e., men versus women, juniors versus resident lifters) were determined using t‐tests (p ≤ 0.05). Correlations were determined using Pearsons r. Results indicate that vertical jumping PP is strongly associated with weightlifting ability. Thus, these results indicate that PP derived from the vertical jump (CMJ or SJ) can be a valuable tool in assessing weightlifting performance.

Lower and upper body anaerobic performance in male and female adolescent athletes

Little data exist for upper and lower body mechanical power capability of adolescent athletes. This study compared arm (A) and leg (L) anaerobic peak and mean power (PP and MP) of 20 male and 20 female adolescent athletes after normalization for body mass (BM), fat-free mass (FFM), and lean A and L cross-sectional area (CSA). Power outputs were assessed by the Wingate anaerobic test. FFM and CSA were estimated via anthropometry. No significant (P > 0.05) differences existed between the sexes in Tanner sexual maturity, chronological age, or overall training activity. Males had higher (P < 0.001) absolute PP (W) (L 694 vs 442; A 494 vs 309) and MP (L 548 vs 307; A 337 vs 214). Ratio normalization and ANCOVA were used to remove the influence of body size differences. Ratio normalization showed that males had greater leg PP/BM, MP/BM, MP/FFM, MP/CSA, as well as arm PP/BM and MP/BM, whereas all leg and arm PP and MP ANCOVA adjusted means for BM, FFM, and CSA, except arm MP adjusted for FFM, were significantly (P < 0.01) higher for males than females. We conclude that factors other than muscle mass, possibly qualitative in nature, are responsible for the sex difference in anaerobic performance of adolescent athletes.

Effects of two different eight - week training programs on military physical performance

Various physical demands are placed on soldiers, whose effectiveness and survivability depend on their combat-specific physical fitness. Because sport training programs involving weight-based training have proven effective, this study examined the value of such a program for short-term military training using combat-relevant tests. A male weight-based training (WBT) group (n = 15; mean ± SD: 27.0 ± 4.7 years, 173.8 ± 5.8 cm, 80.9 ± 12.7 kg) performed full-body weight-based training workouts, 3.2-km runs, interval training, agility training, and progressively loaded 8-km backpack hikes. A male Army Standardized Physical Training (SPT) group (n = 17; mean ± SD: 29.0 ± 4.6 years, 179.7 ± 8.2 cm, 84.5 ± 10.4 kg) followed the new Army Standardized Physical Training program of stretching, varied calisthenics, movement drills, sprint intervals, shuttle running, and distance runs. Both groups exercised for 1.5 hours a day, 5 days a week for 8 weeks. The following training-induced changes were statistically significant (P < 0.05) for both training groups: 3.2-km run or walk with 32-kg load (minutes), 24.5 ± 3.2 to 21.0 ± 2.8 (SPT) and 24.9 ± 2.8 to 21.1 ± 2.2 (WBT); 400-m run with 18-kg load (seconds), 94.5 ± 14.2 to 84.4 ± 11.9 (SPT) and 100.1 ± 16.1 to 84.0 ± 8.4 (WBT); obstacle course with 18-kg load (seconds), 73.3 ± 10.1 to 61.6 ± 7.7 (SPT) and 66.8 ± 10.0 to 60.1 ± 8.7 (WBT); 5 30-m sprints to prone (seconds), 63.5 ± 4.8 to 59.8 ± 4.1 (SPT) and 60.4 ± 4.2 to 58.9 ± 2.7 (WBT); and 80-kg casualty rescue from 50 m (seconds), 65.8 ± 40.0 to 42.1 ± 9.9 (SPT) and 57.6 ± 22.0 to 44.2 ± 8.8 (WBT). Of these tests, only the obstacle course showed significant difference in improvement between the two training groups. Thus, for short-term (i.e., 8-week) training of relatively untrained men, the Armys new Standardized Physical Training program and a weight-based training experimental program can produce similar, significant, and meaningful improvements in military physical performance. Further research would be needed to determine whether weight-based training provides an advantage over a longer training period.

Prediction of Simulated Battlefield Physical Performance from Field-Expedient Tests

Predictive models of battlefield physical performance can benefit the military. To develop models, 32 physically trained men (mean +/- SD: 28.0 +/- 4.7 years, 82.1 +/- 11.3 kg, 176.3 +/- 7.5 cm) underwent (1) anthropometric measures: height and body mass; (2) fitness tests: push-ups, sit-ups, 3.2-km run, vertical jump, horizontal jump; (3) simulated battlefield physical performance in fighting load: five 30-m sprints prone to prone, 400-m run, obstacle course, and casualty recovery. Although greater body mass was positively associated with better casualty recovery performance, it showed trends toward poorer performance on all the other fitness and military performance tests. Regression equations well predicted the simulated battlefield performance from the anthropometric measures and physical fitness tests (r = 0.77-0.82). The vertical jump entered all four prediction equations and the horizontal jump entered one of them. The equations, using input from easy to administer tests, effectively predict simulated battlefield physical performance.

Three-dimensional cinematography with control object of unknown shape

A technique for reconstruction of three-dimensional (3D) motion which involves a simple filming procedure but allows the deduction of coordinates in large object volumes was developed. Internal camera parameters are calculated from measurements of the film images of two calibrated crosses while external camera parameters are calculated from the film images of points in a control object of unknown shape but at least one known length. The control object, which includes the volume in which the activity is to take place, is formed by a series of poles placed at unknown locations, each carrying two targets. From the internal and external camera parameters, and from locations of the images of point in the films of the two cameras, 3D coordinates of the point can be calculated. Root mean square errors of the three coordinates of points in a large object volume (5m x 5m x 1.5m) were 15 mm, 13 mm, 13 mm and 6 mm, and relative errors in lengths averaged 0.5%, 0.7% and 0.5%, respectively.

Validity of an anthropometric estimate of thigh muscle cross-sectional area

This study examined the validity of an anthropometric estimate of thigh muscle cross-sectional area using magnetic resonance imaging (MRI). The anthropometric model assumed that a cross section of the thigh could be represented as a circle with concentric circular layers of fat-plus-skin, muscle, and bone tissue. On 18 healthy, active men and women (mean +/- SD age = 23 +/- 5 yr), total thigh circumference (CT) was measured with a fiberglass tape, fat-plus-skin thickness was measured over the quadriceps (SQ) using calipers, and the distance across the medial and lateral femoral epicondyle (dE) was measured with calipers. Direct measurements of each tissue were obtained by planimetry of an MRI image taken at the same site as the circumference and skinfolds. Thigh muscle cross-sectional area (AM) was estimated as follows: [equation: see text] Mean +/- SD AM from MRI and anthropometry were 121.9 +/- 35.1 cm2 and 149.1 +/- 34.1 cm2 (r = 0.96, SEE = 10.1 cm2), respectively. Errors in the anthropometric approximations of AM were due to an overestimate of the total thigh cross-sectional area and an underestimate of fat-plus-skin compartment. Because of the close relationship between MRI and anthropometric estimates of AM, zero-intercept regression was used to produce the following final equation, applicable for use in populations studies of young, healthy, active men and women: [equation: see text]

The distribution of forces between the upper and lower back during load carriage.

INTRODUCTION/PURPOSE To determine the effects of backpack mass on the forces exerted by the backpack on the carrier and on the distribution of these forces between the upper back (including shoulders) and lower back (sacrum and iliac crest). METHODS Eleven male volunteers (mean age 22.7 SEM 1.1 yr) walked on a level treadmill at 1.34 m.s(-1) carrying a backpack loaded to three different masses (13.6, 27.2, and 40.8 kg). The backpacks hip belt was connected to force transducers that measured the forces exerted on the lower back. The total force between the subject and backpack was determined from the backpacks mass and acceleration. Forces on the upper back were calculated as total force minus the forces exerted on the lower back. RESULTS There was a significant effect of backpack mass on the vertical and anterior/posterior forces exerted on the upper and lower back, and on the total force exerted on the backpack center of mass. Regardless of mass, approximately 30% of the vertical force was borne by the lower back; the upper back and shoulders supported the remaining 70%; this is based on data averaged across the stride. Dimensionless analysis revealed peak forces on the upper and lower back increased proportionately to backpack mass whereas the peak forces exerted on the backpack COM increased disproportionately. CONCLUSIONS The backpack exerts consistent anterior force on the lower back, which likely contributes to the occurrence of low-back pain associated with load carriage. Approximately 30% of the vertical force generated by the backpack can be transferred to the lower back by using an external frame backpack with a hip belt.