Posted by Dave on April 10, 2014 | 1 Comment
Conventional wisdom is that you can get your best time in a race by starting off a little slower than your goal pace: “Negative splits” are the ideal — times for each mile run should decrease over the course of the race.
So, for example, if you were running a 10k and had a goal of finishing the 6.2 miles in 62 minutes, then you should not, according to conventional wisdom, start out by running your planned average pace of 10 minutes per mile. You should start a little slower, maybe 10:15 per mile, and make up for it by running faster at the end.
But why? I’ve seen several web pages make the assertion that “every world record from the 1500 meters to the marathon has been set running negative splits” or something similar. But rarely do such assertions come backed by hard evidence. So I was interested to see this paper, which seems to turn that notion on its head:
In fact, most world records at distances above 200 meters have been set with positive splits, not negative splits. The article goes on to argue that the best strategy for 400- and 800-meter races is a positive split, where the finish is slower than the start.
But typical runners are not setting out to break a world record. They just want to do the best they can, perhaps setting a personal record in a race. They visit sites like McMillanRunning.com, which can predict, say, a 10K pace based on previous 5K. If I enter my personal best 5K time, 17:49, it spits out a projected time of 37:00 for a 10K. But it doesn’t offer any strategy for achieving that time other than letting me know that’s an average pace of 5:57 per mile. Supposing I feel like I have a shot at that time, should I start out a little slower, a little faster, or right on pace?
I can see that most men’s world-records at 10,000 meters were set with positive splits, suggesting I should start out a little faster. But for a recreational runner, is that realistic?
I decided to take a look at some real-world results and see what runners like me do. The Ukrops Monument Avenue 5K in Richmond, Virginia, was run a couple weeks ago and has a large field of runners. I decided to look at the second page of mens’ results (since the race leaders may have been running a strategic race rather than going for the all-out best time). Here’s what I found:
As you can see, the men ran the race in an average time of 36:31, and their pace on the first 5K was 3 seconds faster than the pace on the second 5K. 26 of the 44 runners had positive splits while only 18 had negative splits. Maybe there is something to this idea that a positive split is better for a 10K after all.
So I decided to look at the women’s results as well:
Once again, there is a similar pattern, only more pronounced. The women in this sub-elite group ran the second half 23 seconds slower than the first half of the race.
So perhaps starting out fast is the best way to finish fast. But there might be some problems with this data. What if the runners who started fast were actually capable of going even faster, but made an error and paid for it with a slow finish?
To test for this eventuality I eliminated runners who were positive or negative by more than 3 percent. When I did this, the numbers evened out a bit: 19 men had positive splits while 14 had negative splits. And 13 women had positive splits compared to 14 with negative splits. Overall, more runners had positive splits, but it was much closer.
Interestingly, though, this race has a harder first half than second half: the first half is a gradual uphill. So even if all the runners had put out precisely even splits, they were actually exerting more effort in the first half than the second half.
This suggests to me that starting out slow in a 10K is not a good idea. An even pace, or even slightly positive splits (though no more than 5 seconds or so per mile) will probably generate the best results.
Reardon J. (2013). Optimal pacing for running 400- and 800-m track races, American Journal of Physics, 81 (6) 428. DOI: 10.1119/1.4803068
Posted by Dave on October 23, 2013 | No Comments
As the days get shorter, I hear more and more concerns about safety from crime during runs, both in online forums and from my running friends. Soon, for many of us, it will be impossible to go for a run outside of normal working hours without running in the dark. Runners, especially women, are concerned about the possibility of being attacked when running on dark, isolated trails and greenways.
But how significant are those concerns? When we hear of a case like the Central Park Jogger or the more recent murder of a runner in Ohio, are we terrified because it’s an example of an all-too-common occurrence, or is it only surprising because attacks like this are rare?
I decided to look into crime statistics to see if I could parse out the actual danger runners face when heading off into the darkness. Unfortunately, the U.S. Department of Justice, while it does track violent crime nationwide, doesn’t have a readily-accessible statistic for attacks on runners. If any readers have better numbers than those I’m about to present, I’d love to hear from you, but for now, what I can offer are a few thumbnail calculations that may help you understand how much risk you might face by going out for a solo run in the dark.
This report gives some good data on crime in the US, and we can use it to get a rough sense of the crime dangers runners may face. In 2012 there were 12,765 murders in the US; 9,917 of the victims were male and 2,834 were female. That means that the average American had a .0004 percent chance of getting murdered that year.
But most murders are committed by people who know their victims. The random guy jumping out from behind a bush is responsible for relatively few murders. Indeed, only 1,557 of the 12,765 murders in 2012 were committed by strangers. When you limit the murders to the categories likely to affect runners (e.g. not bar-room brawls or gang wars), the number drops below 400, or about one in 100,000. And again, runners are likely to be only a portion of these cases.
That said, people aren’t just worried about getting murdered. Rape is a serious concern as well, especially for women. The numbers of rapes are much larger, as this report shows. In 2009, there were 327,600 reported cases of sexual assault of women in the US. That means an average woman had a 0.2 percent chance of being sexually assaulted that year — over 1,000 times the likelihood of being murdered.
Again, however, the “random guy jumping out from behind a bush” sort of rape is much rarer. Seventy-eight percent of assaults were committed by someone known to the victim. Less than 14 percent of rapes were committed in open areas like parks and greenways (the numbers we have in this report combine “locations such as an apartment yard; a park, field, or playground not on school property; a location on the street other than that immediately adjacent to home of the victim, a relative, or a friend; on public transportation; in a station or depot for bus or train; on a plane; or in an airport.”).
It’s not necessarily valid to suggest that we can combine the “stranger rape” percentage with the “open space” percentage to get a percentage of rapes committed by strangers in open spaces, but that gives us a figure of 3 percent. I think it’s reasonable to guess that less than 3 percent of all rapes are this sort of “random guy jumping out from behind a bush” attack. Even so, that’s still a pretty large number: about 10,000 sexual assaults per year. It would mean that women face a 0.006 percent chance of being attacked in this way each year, or more than 35 times their chance of getting murdered in a similar sort of attack. And let’s not forget that most rapes go unreported (though I suspect more of the “random attack” rapes are reported than rapes by acquaintances), so this may be a significant source of danger for women.
But how does this compare to other dangers runners might face, such as getting hit by a car? Once again, it’s difficult to find statistics just for runners, but we can find statistics on pedestrian deaths and injuries in car crashes. In 2010, 4,279 pedestrians were killed in car crashes, and 70,000 were injured. Male pedestrians had a .0019% chance of dying in a car crash and females had a .0008% chance. That means, if our thumbnail estimates are correct, female runners could be as much as 10 times more likely to be sexually assaulted while running than dying in a car crash. They are, however, somewhere around 4 times as likely to be injured in a car crash than to be sexually assaulted.
Of course, these estimates could be off by a lot! We don’t know what portion of pedestrians are runners, and whether runners are more or less likely to get hit by cars than walkers. We don’t know what portion of “open space” rapes really affect runners. That said, these thumbnail estimates definitely demonstrate that women should be concerned about the possibility of a sexual assault while running. While the biggest danger is probably injury in a car crash, women runners are almost certainly more likely to be the victim of sexual assault than killed in a car crash.
I wish we had better data for women about the safest places to run. While it might seem that less-traveled greenways and trails would be the most dangerous places, perhaps attackers stay away from these areas because there aren’t many potential victims. Again, if any readers have access to additional data that might help shed light on this issue, I’d appreciate hearing from them.
Posted by Dave on October 15, 2013 | 2 Comments
I’ve struggled to maintain a healthy weight my entire adult life. For me the most humiliating moment came about 13 years ago when I was visiting an allergist about a skin condition and noticed that he had written “moderately obese” on my chart. At that point, I weighed 245 pounds with a BMI 32.3, well above the standard definition of obesity.
I increased my exercise and started dieting, but my BMI remained stubbornly above the “overweight” threshold of 25. It wasn’t until two years ago that I finally lost enough weight to be considered “normal.” I am quite sure that the reason I succeeded was social support: I joined groups of people with similar goals. I hooked up with a running group locally; these people were instrumental in ensuring I got up every morning to run. And I signed up with a weight loss / fitness web site (myfitnesspal) for online support.
Despite the amazing support I received from these communities, I’ve also noticed that many people in similar circumstances are self-conscious about exercising. Locally, I’ve heard from lots of folks who don’t want to join our running group because it is “too intimidating.” On the myfitnesspal message boards, there are dozens of stories every day from overweight / obese people who won’t go outside to run, or who are uncomfortable going to the gym, because they are worried that others will mock them.
Could it be that the same powerful social forces that helped me get in shape are, paradoxically, preventing many others from participating in exercise? Surprisingly, I haven’t been able to find much research on this question. A 2012 review article entitled Overweight and obese adolescents: what turns them off physical activity? summarizes the state of research on obese adolescents and exercise, but doesn’t touch on adults.
The research shows that obese adolescents are definitely self-conscious about exercise. They avoid gym class because of the skimpy clothes they must wear, or because girls are concerned about messing up their makeup and hair and getting taunted for that. They are worried about getting teased for being overweight and unfit — even when teasing doesn’t actually occur. It isn’t so much that they don’t enjoy PE; they don’t like being “visible” in PE class. They are concerned about how others perceive them much more than their own experience engaging in PE activities.
In a 2008 study led by Margaret Schneider, researchers tried to address these issues by enrolling unfit, sedentary teenage girls in an exercise program. In one school girls recruited for the program were tested and enrolled in a special PE class designed to improve fitness. In another school the participants were tested at the beginning and end of the school year but weren’t enrolled in a special class. The special class included increased physical activity compared to regular PE classes, and also had extra instruction about the benefits of physical activity.
Unfortunately, Schneider’s team found that there was no overall improvement in self-image for the girls who had enrolled in the special class. Perhaps related to this, overall, there wasn’t a significant improvement in fitness, despite the increased activity. In fact, when the researchers broke down the girls into one group that had improved fitness and another group that had not, the improved-fitness group did have an improved self-image and body-image. In other words, once they started seeing results, their attitudes about their bodies improved.
This certainly seems to match my experiences interacting with the myfitnesspal community. New members of the community who haven’t seen improvements due to exercise are intimidated by exercising. But if and when they do improve, their attitude improves substantially. What neither my experience nor the research yet supports, however, is whether it’s possible for large numbers of people to improve their attitudes about fitness and actually permanently change their lifestyles and become healthier.
Maybe the “success stories,” the folks who sustain these communities of fit and healthy people, are simply the lucky few who are capable of staying healthy in today’s sedentary world, saturated by drive-through restaurants, monster-sized soda cups, and jumbo bags of potato chips.
What’s clear to me, however, is that one of the major hurdles a sedentary person must first overcome in order to get fitter is a social one. Maybe it’s even the most important hurdle.
Stankov I., Olds T. & Cargo M. (2012). Overweight and obese adolescents: what turns them off physical activity?, The international journal of behavioral nutrition and physical activity, PMID: 22554016
Schneider M., Dunton G.F. & Cooper D.M. (2008). Physical activity and physical self-concept among sedentary adolescent females: An intervention study, Psychology of Sport and Exercise, 9 (1) 1-14. DOI: 10.1016/j.psychsport.2007.01.003
Posted by Dave on September 30, 2013 | 2 Comments
This past weekend I ran a 15K race and was hoping to achieve a PR — finishing the race in under 60 minutes. It’s a good goal because it’s not only a nice, round number, but it’s also right at the threshold of my abilities. I’ve finished 5Ks in under 20 minutes and 10Ks in under 40 minutes, but I’ve never been able to sustain that pace for any longer race.
To achieve a nice, round, 20-minute 5K or a 60-minute 15K requires the same, not-so-round pace: 6:26 per mile. If a course is perfectly flat and perfectly well-marked, all you need to do is run 6:26 every mile and you can reach your goal.
But of course, most races have hills, and courses are often poorly marked, so runners rely on their GPS watches to monitor pace. But what pace should you run on the hills? Typically in the past I’ve resorted to guesswork. If mile 1 has a big hill, I plan on giving myself a little extra time, and then making that up later in the event. But this event seemed evenly hilly throughout, and most of the hills were short, often a quarter-mile long or less:
I decided on a different strategy: Instead of planning for each mile, I’d just set an uphill pace and a downhill pace. Then all I’d have to do is take a split at the top and bottom of each hill and I could almost run the race on autopilot.
To account for GPS error, I set a goal pace for the entire race of 6:20 per mile. Then I just added 15 seconds for each uphill section and subtracted 15 seconds for each downhill section. Since the race starts and ends at the same elevation, I should have just as much uphill as I have downhill, right?
During the running of the race, I wasn’t quite able to maintain the paces I planned: I was running the uphills a little fast and the downhills a little slow. But I figured that should probably even out and I’d still be okay. Except for one niggling detail: As I ran, I could also track my average pace for the entire race, and that figure kept increasing for the entire event. I was shooting for 6:20 per mile, and it crept up little by little — 6:22, 6:23, 6:24. I was quickly running out of wiggle room for GPS error. As it turned out, I’d need that wiggle room. In the end, my GPS measured the course at 9.46 miles, instead of the 9.3 I was expecting. At that distance, I’d need every bit of a 6:20 pace per mile. My finishing time was 1:00:37; I missed my goal by barely 1 percent. Even though my GPS put my average pace for the race at 6:25, I still didn’t finish in under an hour because of the small GPS error.
But after I got home, I downloaded my GPS record and noticed something interesting: If I took an average of the paces I ran on the uphill and the paces I ran on the downhill, it seemed like I should have been much closer to that 6:20 pace. The average of the paces for each uphill section was 6:31 per mile, and the downhill sections averaged 6:11 per mile, for a net average of 6:21. That might just have been enough to get me my sub-60 15k — especially if I saw I was close at the finish and made a final, mad sprint. When I weighted the averages to account for the fact that there were 5.44 miles of down and only 4.02 miles of up, my theoretical pace improved even more, to 6:20 per mile.
So why didn’t I achieve that pace in reality? It took me a while to figure it out. Imagine a race that runs over a hill and back: There are two ups, and two downs. If the hills are equal lengths, then my strategy works perfectly, even if I don’t run the exact same pace on each hill:
Here, my average pace on the downhills is 6:30 and my average pace on the uphills is 7:30. You can average those together and get 7:00 per mile for the whole race.
But now consider a course where the hills are unequal in length, like this:
Now the uphills are all each the same length and the downhills are different lengths. Suppose the uphills are 1 mile each — then I averaged 7:30 on all the uphills. But if the first downhill is 1 mile and the second downhill is 2 miles, then I didn’t average 6:30, I averaged 6:40 per mile on the downs, which means my overall average is worse than 7:00 per mile.
That’s what happened during my 15k. The slowest downhill sections were also the longest downhill sections (which makes some sense, since those hills weren’t as steep). Instead of averaging 6:11 on those hills, when I take the length of the hills into account, I actually averaged 6:21! Put that together with my 6:31 pace for my uphills, account for the fact that I ran a longer distance downhill than uphill, and you arrive at my 6:25 average pace, which wasn’t fast enough to overcome my GPS error.
I probably would have been better off just trying to run 6:20 per mile throughout, instead of relying so heavily on the up/down strategy. Of course, an alternative explanation is just that I’m not in good enough shape to run a 60-minute 15k on a hilly course! But either way, I think a straight-up mile-by-mile plan would have been easier to adhere to during the race.
Posted by Dave on July 16, 2013 | No Comments
If you’ve ever looked closely at a topographical map, you might notice something odd. It’s not actually odd, because there is no other way to make the map, but it does cause some interesting problems. Take a look at this close-up of the USGS map of Half Dome, in Yosemite National Park.
Half Dome’s peak is at an elevation of 8,836 feet, and to the northwest from the peak, there’s a sheer vertical drop of over 2,000 feet. It takes place in just about the width of the “H” in the word “Half Dome” on the map. If you look at the legend, you’ll see that that’s perhaps only 200 feet of horizontal distance representing a 2,000-foot drop!
When slopes get steeper on a topographical map, the contour lines get closer together — but that doesn’t necessarily mean that the physical distance a person covers is actually less. When you climb Half Dome, you’ve climbed 2,000 feet, not 200!
Obviously a runner isn’t going to tackle anything as steep as the face of Half Dome, but even on slopes that are actually runnable, the same problem exists. When you run up a steep hill, you cover more distance than what is represented on a conventional map — even if your entire run is represented as a straight line on the map. So when your GPS trainer tells you how far you ran, does it count the vertical distance you ran, or just the horizontal distance?
The thought occurred to me when I ran a race a couple weeks ago in Squaw Valley, California. The run covered 2.88 miles and climbed over 1,700 vertical feet. But did I actually run farther when you count those vertical feet? My Garmin could conceivably have been off by 1,700 feet — a third of a mile! Did I get credit for the climb?
Unfortunately, it’s not an easy question to answer on most running routes, because roads — especially roads on hills — aren’t usually perfectly straight. Even when they are straight, they don’t often have a consistent incline for their entire length. The rough dirt road I ran up at Squaw Valley was anything but straight!
Ideally, to know whether the GPS gives you credit for those vertical feet, you’d record something like an elevator ride. But I don’t have an elevator handy right now, and I’m not sure I’d get GPS reception in any of the buildings that are nearby. But I do have a GPS record of a ski trip I took a couple years ago. I recorded the whole day of skiing, including the lift rides up. Ski lifts travel in a straight line, and their runs are often at a fairly consistent incline as well. Here’s the map of my day’s skiing:
As you can see, there are several straight lines on the map — they represent my lift rides. The lifts go straight up the mountain with very little change in the grade of their ascent. So we can use our old friend Pythagoras to figure out how far I went on the lift:
The Pythagorean Theorem states that for any right triangle labeled as above, the lengths of the sides a, b, and c are related by the formula a2 + b2 = c2. I picked one lift ride (ending at the marker on the map) and looked at the data for that ride:
So the question is, does the 0.79 miles the Garmin recorded reflect only the horizontal distance covered, or does it take into account the 1,074 feet of elevation gain in the lift ride?
I measured the pixel length of the ride (using the Pythagorean Theorem to calculate the length of the diagonal) and got 314 pixels. The 350-meter scale itself measured 94 pixels (the image has been reduced in size for this post). Using simple proportions, I came up with a map length of 1,170 meters for the lift, or 3,838 feet. Remember, this represents only the horizontal distance covered!
So how does that compare to my Garmin data? Garmin gives the length of the lift ride as 0.79 miles, or 4,171 feet. That’s longer than 3,838, but it’s not 1,074 feet longer. But since the lift ascends at a fairly constant slope, its length should be less than the sum of the horizontal and vertical length.
We can use the Pythagorean Theorem to calculate the shortest possible distance between the base of the lift and the top, assuming the lift went in a perfectly straight path from the bottom to the top. We know from the map that the horizontal distance (a) is 3,838 feet. We know from my Garmin that the elevation gain (b) is 1,074 feet. The length of the lift should be the square root of a2 + b2, which works out to 3,985 feet.
At a minimum, Garmin should have given me credit for traveling 3,985 feet, and it did! It claimed my distance was 4,171 feet. But why the discrepancy? We know that the lift doesn’t travel in a perfectly straight line because the lift cable sags between poles. This sagging might account for the entire discrepancy, or there could be other sources of error.
The takeaway from this is that there’s no way that the Garmin should have measured my ride as shorter than 3,985 feet, even though I only covered 3,838 feet horizontally. It didn’t do that, which suggests it is taking vertical distance into account. If the lift cable sag or a varying rate of climbing were a significant factor, they’d only increase the difference between my Garmin-reported distance and the calculated straight-line distance, and that’s what we see in the numbers. Take a look at this image to convince yourself that in any real-world case, the lift is going to travel farther than the straight-line distance covered:
So it looks to me like the Garmin is indeed giving credit for the actual distance travelled, in three dimensions. To truly verify this, we’d probably need a Garmin plot of an elevator ride or a long rappel, which would be the ideal, straight-line, vertical case.
In the end, though, as you can see, the vertical component doesn’t add much to a typical run. It only added about 300 feet to my very steep 0.79-mile lift ride. Let’s see what kind of an impact it would have on a run.
We know my Squaw Valley race covered 2.88 miles and gained 1,719 feet. 2.88 miles is 15,206 feet. That means I climbed more than a foot for every 10 feet I ran, or better than a 10 percent grade, a very steep run! When we calculate the horizontal distance travelled using c2 – b2 = a2, the result is just 15,108 feet. All that climbing would only result in an extra distance traveled of 98 feet, assuming the pitch of the run was consistent from start to finish. In my case, I’m quite certain it wasn’t — there were even a few downhill stretches — but it’s still clear that the vertical component of the run didn’t add much to the total distance travelled at all.
In a typical road race, 200 feet per mile would be considered a very steep hill. If that hill was perfectly graded, then it would add only 4 feet to the horizontal component of the run. But rest assured that your GPS does appear to take this distance into account (though as we have discussed, there are many other sources of GPS error!).
Posted by Dave on May 24, 2013 | 3 Comments
Just a quick post to note this article that appeared in the Wall Street Journal today.
Look familiar? It ought to. The same author wrote this article last year.
Both articles basically say the same thing, and quote the same authorities, citing the same research. The suggestion is that running too much is bad for your health. A moderate amount of running might be helpful, but running more than, say, 30 miles a week, is too much and is actually harmful.
What bugs me about today’s article in particular is the suggestion that “new research” is telling us these things. There is no new research. There is the same old research. And Alex Hutchinson responded quite well to that research when the same author reported on it in the Wall Street Journal last year:
But here, from the actual abstract, is the part they never mention:
Cox regression was used to quantify the association between running and mortality after adjusting for baseline age, sex, examination year, body mass index, current smoking, heavy alcohol drinking, hypertension, hypercholesterolemia, parental CVD, and levels of other physical activities.
What this means is that they used statistical methods to effectively “equalize” everyone’s weight, blood pressure, cholesterol, and so on. But this is absurd when you think about it. Why do we think running is good for health? In part because it plays a role in reducing weight, blood pressure, cholesterol, and so on (for more details on how this distorts the results, including evidence from other studies on how these statistical tricks hide real health benefits from much higher amounts of running, see my earlier blog entry). They’re effectively saying, “If we ignore the known health benefits of greater amounts of aerobic exercise, then greater amounts of aerobic exercise don’t have any health benefits.”
Unsurprisingly, the new article in today’s Wall Street Journal has generated hundreds of comments. What frustrates me is that the Journal is playing on the fact that millions of runners will be interested in this sort of research and drawn to the article thinking that something new is being reported. In fact there is no new research. Indeed, the sort of research that could actually generate authoritative results will probably never be conducted, because it would be very difficult indeed to do a long-term experimental study on this phenomenon. We’ll probably never know for sure whether running, say, 50 miles a week, is more or less harmful than running 20 miles a week. We’ll probably also never see the Wall Street Journal report on that.
Posted by Dave on October 4, 2012 | 1 Comment
One of the toughest things about running a marathon is the fact that if you’re running it correctly, the first half seems almost ludicrously easy. Most runners I’ve talked to — and my own experience bears this out — say that if they feel like they are exerting themselves much at all during the first half, they inevitably crash and burn at the end. A friend of mine, the always-entertaining Allen Strickland, had this experience just this past weekend. He started a bit too fast, and although the first 20 miles felt pretty easy for him, he struggled at the finish and just missed an opportunity to qualify for the Boston Marathon.
Now, it’s probably impossible to say whether the fast start caused Allen to run slower than he wanted to at the end; there are too many other factors at play. But a new study by Andrew Renfree and Alan St. Clair Gibson seems to suggest that starting not just at your target pace per mile, but actually slower than target pace, might be the best strategy.
Renfree and St. Clair Gibson analyzed the pacing strategies of the participants in the 2009 Women’s World Marathon Championship by dividing the finishers into four groups: the top 25%, the next 25%, and so on. They compared the average speed of each group of runners to their speed when running personal-best times (PB). They found that the fastest group did better relative to their PB than the second-fastest group, and so on down the line to the slow group:
Posted by Dave on March 1, 2012 | 7 Comments
VO2 max, as we have discussed before, is a key measurement of endurance running ability. There are several different procedures for measuring it, and yesterday I got to experience one of the most common methods first hand.
As I mentioned last year, VO2 max is simply the maximum volume of oxygen your body can take in:
the key to muscle performance is delivery of oxygenated blood to the muscles, and adequate fuel to produce energy. Oxygenated blood comes from the lungs, and is pumped by the heart, which is itself a muscle requiring its own supply of oxygenated blood.
VO2 max can be seen as the maximum performance of this system: the amount of oxygen that can be effectively delivered to the body in a given time period.
As your workout gets harder and harder, you can’t continue to breathe harder and harder; there is a fixed amount beyond which your body can’t keep up. That fixed limit is your VO2 max. All that’s needed to find it out is a way to measure oxygen intake, and a way to systematically make a workout harder.
I have volunteered to participate in a study of the effectiveness of a dietary supplement on running conducted by Appalachian State University, which has a research lab near my home. As a baseline, they measure body composition and VO2 max of all participants.
Their protocol was simple: I got on a treadmill, put on a face mask that would measure my air intake, and started walking. Initially the task was easy: about 1.7 miles per hour on a 10% grade. But every three minutes, the workout was made more difficult by upping the speed and the pitch of the slope. After 9 minutes I was jogging up a 16% grade. After 12 minutes the task seemed difficult, but doable. I felt like I could keep this up for some time. Here I am sweating it out on the treadmill:
Then at 15 minutes the grade was increased to 20% and the speed was well over 5 miles per hour. Suddenly the task seemed nearly impossible. I kept it up for as long as I could — about a minute — but finally had to give up. This graph from my earlier article shows what was happening to my oxygen intake:
Posted by Dave on November 8, 2011 | 2 Comments
It’s a week before the big race, you’ve started tapering and you can’t work out your anxiety with a nice, long run, so instead you begin to obsess over every detail. What if you get sick? What if you miss your flight or get caught in a traffic jam? What if it rains, or it’s too hot or cold?
Many of these things are out of your control, but you can prepare for the weather. Of course you’ll want to bring clothes for the range of possible weather conditions you’ll meet on the race course, but in most fall races in the U.S., conditions are actually fairly predictable: It will likely be quite chilly at the start of the race, and warm up as the race goes on.
If you’re running a marathon — especially a marquee race like New York or Boston, you may have to be at the starting area several hours before the race starts, when it will almost certainly be cold and dark. In addition to deciding what to wear at the race, you’ll need to bring clothes that will keep you warm while you wait around for the start.
Depending on the amenities at your race, there are several options. You could plan on wearing extra layers that you will drop of at the bag-check. If there is no bag check, then you could wear old clothes that you’re planning on getting rid of. Many runners wear a large plastic garbage bag before the race; this is especially useful if it’s raining. If you run lots of races, you could save the mylar blanket they give you at the end and use it to keep warm at the start of your next race. Just remember: At this point it’s best to overdress. For a marathon, you don’t want to waste too much energy walking around trying to stay warm. You’ll need to wear enough that you can sit down and still be warm.
I have generally found that I can remove my extra layers 20 to 30 minutes before the start of the race and strip down to whatever I’m planning to start the race in without getting too cold. Between the adrenaline of getting ready for a race and the warm bodies in the starting area, I stay warm enough.
But what about the race itself? It might be 40 degrees or cooler when you start, but by the finish the temperature could have risen to 60 degrees or warmer. When you are warmed up and running at race pace, you won’t need to wear as much as you start with.
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Posted by Dave on October 25, 2011 | 11 Comments
Last week at the 7 Bridges Marathon in Chattanooga, the unthinkable happened to my friend Todd: The battery died on his GPS trainer, 15 miles into the race. He had been running at near PR-pace, but without the feedback he was accustomed to, it was difficult for him to adjust, and he ended up missing a PR (personal record) by less than five minutes. He’s not sure he would have made it even with his GPS, but for many of us nowadays, running without GPS is like running blind, so it was bound to have a big effect.
Sure, there are mile-markers, but since Todd wasn’t wearing a wristwatch, he couldn’t time himself on each mile, so he really had no idea how fast he was going. It wasn’t always that way. Back in the 1980s when I ran my first 10K race, there was always a volunteer at each mile marker, calling out the times for that mile. That happens occasionally even today, but in my experience, more often than not, the times are wrong!
Yet even running with a fully-functional GPS can lead to its share of problems. It’s rare that the distance on my GPS matches the official distance of the course: In my case it’s usually a little longer than the official distance. Last weekend at the Bridges Half-Marathon, for example, my GPS measured the course at 13.34 miles (compared to 13.11 for a true half-marathon). If I had been shooting for a particular time — say, the 1:30 I would need to qualify for guaranteed entry to the New York Marathon in 2012, then that extra 0.23 miles would have added over a minute and a half to my time, and that could be the difference between success and failure.
Depending on what type of running you’re doing, there are many possible reasons a GPS can go wrong, but the fact of the matter is, it’s never going to be perfect. When you’re being timed in a race, what you care about is managing your pace over the official race distance.
So how, beyond making sure your battery has enough juice to make it to the end of the race, can you set your GPS device to match the official distance? It’s not an easy task. I usually have my primary GPS screen set up as follows:older posts »