How to run up and down hills

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You lose more time running up a hill than you gain by running back down the same hill — that’s a basic observation that runners quickly learn from experience, in the same way that a tailwind never helps you as much as a headwind hurts you. Studying this in the lab, though, is challenging, because treadmills don’t let you vary your pace naturally. That’s why I was interested to see a neat little study on hill running in the January issue of Medicine & Science in Sports & Exercise from some Australian researchers at Queensland University of Technology.

What’s neat is that they performed a “field study” of runners on a roughly 10-kilometre time trial, divided into three laps, with one big uphill and one big downhill on each loop. To collect data, the runners wore a portable gas analyzer that measured oxygen consumption, a GPS receiver to measure speed and acceleration, an “activity monitor” that measured stride rate and stride length, and a heart-rate monitor.

As you might imagine, the researchers used all this gear to collect a huge pile of data, and their paper contains quite a lengthy analysis of the various factors affecting running speed. It turns out that the study participants ran 23% slower on uphills, but only 13.8% faster on downhills — so we do lose out on hilly course. Stride rate stays nearly constant while going up and down hills, while stride length varies. And so on.

There are also some practical lessons to be learned. In general, the speed of long-distance running is limited by oxygen consumption; this study confirmed that for the level and uphill sections, but not for the downhills. For biomechanical reasons — shock absorption and injury avoidance — we simply can’t run fast enough downhill to be limited by oxygen, so we’re “throwing away” some time. But some people in the study were way better at downhill running than others — the oxygen use on downhills ranged from 64.5% of ventilatory threshold to 93.7%. The runners going at 64.5% were essentially jogging down the hills, while some of their peers were sprinting down without getting significantly more tired. This suggests that anyone racing on hills would benefit from practicing going downhill — learning to adjust stride parameters to speed up without pounding their legs too much (and without going head-over-heels).

An interesting point the researchers noted was the “memory effect” of gradients. After an uphill, it took an average of 78 seconds on level ground before runners resumed their normal speeds. After a downhill, runners maintained higher speeds for an average of 23 seconds. In both cases, this is longer than simple momentum can account for — it’s probably a result of recovering from uphills (and benefiting from the recovery on downhills). This isn’t, however, the optimal way to run fast, the researchers suggest. Instead, the goal should be to distribute effort as evenly as possible. So you might want to go a little slower on the uphills and then focus on resuming your “normal” pace as soon as possible after the hill (precisely the opposite of the way I’ve always raced, I’m sad to say!). And go a little harder on downhills instead of letting yourself recover.

Of course, in a real racing situation, there’s always the question of tactics — sometimes it’s effective to throw in a hard effort when your opponents least expect it. But it’s interesting to think about what the optimal approach is from the point of view of energy expenditure (just as we know in theory that a 10,000-metre race on the track should be evenly paced, though in practice tactics might dictate otherwise).

P.S. One more point for the truly numbers-obsessed out there. There’s a paper by Mastroianni et al. from 2000 that people sometimes cite, reporting that running speed changes by 0.034 m/s for every 1% change in gradient. (The people who cite this have usually just set a big personal best on a downhill course, and are arguing that the course didn’t help them that much.) For the record, this paper finds a much bigger effect of 0.082 m/s per 1% change in gradient. The researchers suggest the difference may be because Mastroianni’s course alternated very frequently between uphills and downhills, which may have skewed the results.