Predicting energy expenditure with loads while standing or walking very slowly

In the citing paper

“Pandolf et al 6 -72 ± 463 468 0.29 -51 ± 88 102 0.79”

In the citing paper

“However, it should be noted that comparable samples were used in many of the original load carriage studies 22 including the datasets used to validate the Minetti et al 13 (n = 10), Pandolf et al 6 (n = 6), and Santee et al 8 (n = 16) equations.”

In the citing paper

“Although a number of existing travel rate functions exist, they fail to sufficiently account for the vast diversity of landscape conditions that impede foot travel3–11.”

In the citing paper

“However, given their relative low slope, ground surface stability, and lack of vegetation impedance, roads and trails tend to promote maximally efficient travel, where available [8,10,16,17].”

In the citing paper

“Finally, the validity of existing explanations for the substantial differences in walking economy observed across different surface gradients (3,8,49,54,56) is largely unknown.”

In the citing paper

“56) equations corresponded to 15.8 and 13.4%, respectively, of the overall mean measured for the validation group subjects across all of the trials completed (2.13/13.5 ϭ 15.8 and 1.81/13.5 ϭ 13.4%).”

In the citing paper

“The two predictive equations with greatest conditional breadth, those of Pandolf et al. (56) and the American College of Sports Medicine (ACSM) (3), are the most widely used.”

In the citing paper

“The Pandolf et al. (56) equations tended to underpredict level and declined values while also overpredicting the values obtained on positive grades.”

In the citing paper

“The overall predictive error for ACSM was larger, as was the zero error baseline vs. the equation of Pandolf et al. (56).”

In the citing paper

“For both the ACSM and Pandolf et al. (56) equations, the standard errors of estimate were nearly twice as large and exhibited bias predominantly across grade.”

In the citing paper

“The greater conditional breadth of the Pandolf et al. (56) model equation resulted in slightly better overall performance (Figs. 4, E and F, 5, E and F, and 6, E and F) vs. the ACSM model equation.”

In the citing paper

“The inability of skeletal muscle to convert lost gravitational potential energy into accumulated chemical energy reveals the physiologically unsound foundation of this prevailing approach (3,8,17,25,56,78).”

In the citing paper

“The absolute accuracy of the ACSM equation also declined marginally vs. the prediction of the unloaded values in part I, whereas that of the Pandolf et al. (56) equation improved marginally by 0.1 ml O 2 •kg Ϫ1 •min Ϫ1 .”

In the citing paper

“In contrast, both the ACSM (3); and Pandolf et al. (56) model equations are amalgams of a series of smaller studies and data sets (for ACSM, see Refs 17 and 55; for Pandolf et al, see Refs. 25”

In the citing paper

“Более сложные формулы учитывают комплекс факторов, таких как масса тела человека и переносимого им груза, коэффициент фактора местности и скорость пере-движения, позволяя прогнозировать метаболические затраты на передвижение [Pandolf, Givoni, Goldman, 1977].”

In the citing paper

“Overall energy expenditure is also determined by other variables such as carried load, movement speed, and grade or ground slope [3–5], each with readily identifiable effects.”

In the citing paper

“However, other algorithms (Pandolf, Givoni & Goldman 1977; Van Leusen 2000) have been constructed to calculate cost through energy expenditure, which we suggest are a better fit for testing the movement of construction timbers.”

In the citing paper

“The above case studies demonstrate that measuring costs through the Pandolf, Givoni & Goldman’s (1977) function and calculating movement along a roadway corridor offer the best method to model correlative pathways across the unmodified Chaco landscape.”

In the citing paper

“have been constructed to calculate cost through energy expenditure, which we suggest are a better fit for testing the movement of construction timbers.”

In the citing paper

“However, other algorithms (Pandolf, Givoni & Goldman 1977; Van Leusen 2000) have been constructed to calculate cost through energy expenditure, which we suggest are a better fit for testing the movement of construction timbers.”

In the citing paper

“Givoni and Goldman (1971) and Pandolf et al. (1977) developed prediction equations for the metabolic cost of load-carrying while walking on different terrains and slopes.”

In the citing paper

“Both of those studies utilized a terrain factor η that represents the effect of the surface type on metabolic rate.”

In the citing paper

“This procedure produced terrain factors of 1.9 and 3.1 for Givoni and Goldman (1971) and Pandolf et al. (1977), respectively.”

In the citing paper

“Note that although the Pandolf et al. (1977) equation is used more commonly, the prediction equation of Givoni and Goldman (1971) had the best match to our rough terrain data (lowest RMSE).”

In the citing paper

“The most established is the equation by Pandolf et al. (5) which was originally formulated from several relatively small treadmill and overground walking datasets (6–8) to include speed, load, and positive-only gradient capabilities.”

In the citing paper

“Forecasts of the energy cost of this formula were able to be verified by several test phases on individuals from different classes of age and sex (Epstein et al. 1987;Pandolf et al. 1976Pandolf et al. , 1977) ) by varying the speed and the load carried.”