In 2018, I wrote an article with an optimistic title.Ultimate backpacking calorie estimatorIs based on a tool called the Parndorf equation developed by US Army researchers in the 1970s. When you connect your weight, pack weight, walking speed, and climbing slope, the equation (or the handy calculator included in the article) spits out the number of calories burned per hour. The original equation had only one problem. It wasn’t able to handle the downhill. In fact, we predicted that with a gradient of about -10%, we would start producing energy instead of burning it.
Me Retried A year later, another research team at the US Army Institute for Environmental Medicine (USARIEM), led by David Looney, created a modified equation that could handle both uphill and downhill slopes. However, this equation does not allow you to insert the weight of the luggage you are carrying.
In fact, we need an equation that can handle the hills When Backpacks — and more importantly, we want to make sure that predictions are as accurate in the real world as they are in the lab. When you’re planning a backpacking trip, you don’t want to run out of food, but you also don’t want to carry a lot of extra preparation that you’ll soon carry out again. .. Understanding how the weight of hills, mud and packs and the speed of hiking affect energy demand is a big step from just thinking you’re pretty hungry.To that end, Peter Weyand of Southern Methodist University, along with his colleagues Lindsay Ludlow, Jennifer Nollkamper, and Mark Buller of USARIEM. Recently published Direct comparison of the four walking calorie equations Applied Physiology Journal.. There are Parndorf and Rooney equations from my previous article (they use an updated version of the Parndorf equations that can handle downhill). There is a very simple quote from the American College of Sports Medicine.And there is a super equation that can handle both hills When Weyand and Ludlow backpacks suggestion It dates back to 2017, which they called Minimum Mechanics.
The main purpose of this treatise is not to choose the best equation. Instead, they are testing the basic assumption that it is possible to make useful and accurate predictions of calorie cost in harsh real-world conditions from the equations developed on the treadmill. The four equations can be modified using terrain variables that adjust calorie predictions when walking on gravel, mud, or anything else encountered outside the lab. But can the equation really produce a decent prediction in the course of long hikes on rugged terrain and various surfaces?
To find out, Weyand and his colleagues sent seven volunteers to hike Flag Pole Hill Park in Dallas for four miles, equipped with GPS, a heart rate monitor, and a portable calorimeter to oxygenate and carbon dioxide. The amount of carbon was measured. I inhaled and exhaled. This is an important advance that was not practical for researchers in the 1970s. In other words, it is a measurement of metabolism in the wild. The researchers also conducted a series of other experiments to confirm the accuracy of the calorie estimates and terrain adjustment factors in the field. For two equations equipped to handle the backpack, Pandolf and Minimum Mechanics, subjects wore a backpack holding 30% of their body weight and repeated field trials.
The overall result can be summarized as “Yes, but …”. All equations did a reasonable job of estimating calorie burning on different slopes and terrains. The total energy expenditure during the hike (expressed as the amount of oxygen breathed, not the number of calories burned) is: The measurements are shown by a horizontal dashed line.
This study by Weyand and Ludlow shows that the equations previously proposed by Weyand and Ludlow look best. Without a backpack, the ACSM, Pandolf, and Looney equations were 13, 17, and 20% off, compared to 4% off. With a backpack, the Minimum Mechanics prediction was only 2% off compared to 13% for Pandolf. That’s pretty good.
Still, it is difficult to finally declare which equation is “correct” because different models can work best in different situations. One may be better at low speeds, the other may work best uphill, and the other may be better at heavy loads. For example, let’s take a closer look at real-time estimates of calorie consumption from four equations during a hike. The vertical axis shows oxygen consumption (ml / kg / min), which is proportional to the rate of calorie burning. The horizontal axis shows the elapsed time during the hike.
In the first part of the hike, on level ground, Looney’s equation gives the highest estimate. On all uphills (shaded in red), the Parndorf formula gives the highest value. On the downhill (blue shaded), the ACSM equations jump from bottom to top.
Weyand and his colleagues delve into some of these nuances in a new treatise, but most of us want a simple enough estimate for a practical estimate of calorie demand. Based on this particular data, the Minimum Mechanics model seems to be the best bet. This was originally derived by testing 32 subjects with 90 different combinations of speed, grade and load. This is far from the three subjects used in the original ACSM equation and the six subjects used in Parndorf.
In the perfect world, apps and websites can enter GPS tracks and apply equations to each successive point to estimate the calories needed for a long and complex route. (If you feel you have moved to Code 1, please let us know. We will update this post!) For a consistent grade segment estimate, use the following two minimal dynamics calculators. Level and uphill When downhill walk. The Terrain coefficient 1 for asphalt, slightly higher for rugged terrain (for example, 1.08 for asphalt, 1.2 for gravel roads). Grades are percentages, ranging from -100 to +100. Happy trail!
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