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French Creek Triathlon: Win and a CR

In the professional side of triathlon, 'times are a changin'. While a few years ago, olympic distance and half distance racing had prize purses in many series (REV 3, 5150, etc.) but as of this year, the number of races with prize purses has dwindled further to some Challenge half distances, ITU events (where one must be doing well in a world cup to break even), the full distance ironman events, and then some local races. My friend, and fellow professional triathlete/race director John Kenny, put his money where his passion is and put up a decent prize purse for a local triathlon. Because of this, I decided to race here at French Creek (1 hour away) versus Challenge Knoxville (9 hours). In the end, I ended up winning, setting a course record, and volunteering at the kids race the day before. It was a great event and I would love to see more local races putting up small purses and using local pros to their advantage (volunteering, clinics, etc.)

Here is a little recap of the race.

Swim: 18:17, 2nd behind Pierre, a solid triathlete with a swimming background from University of Pittsburgh. I tried to hold his feet to the best of my ability, and we ended up putting a solid gap into the rest of the field in the two loop swim. He exited the water 6 seconds in front of me.

T1: 35 seconds and got out on the bike course first.

Bike:1:11:50, fastest split. The bike is a hard and hilly ride. About 2400 ft climbing during the 24.6 mile leg. My race file is here: https://www.strava.com/activities/306585561

The course is two times out and back with two significant climbs/descents each direction. I saw a speed >50 mph on my garmin on one of the downs and was happy with that. My plan was to go very hard up the hills and recover a bit down but keep spinning my legs and stay very aero. I saw by the first turn around, I had about 2 minutes on second and then 4 minutes at the half way mark. I felt like I eased up the second lap a little but was only 10 seconds slower. I came into T2 with ~9 minute lead

Run: 36:20, fastest split by 3 mins. To try an elucidate how hard of a course this run is might be a job better suited for a dramatic poet. You climb and climb and climb for 4 miles. When you think you will turn around, you continue on a fire road path to the top of a small "peak" at French Creek around 1100 feet (Transition is 300 feet). I saw that Philly Pro Tri teammates Zach Smith and Luke Davis were in 2nd and 5th, respectively and was pretty pumped to share the podium with friends.

The other positive of these local races is representing local sponsors that help support triathlon. Kevin  McCauley from Square One Investments (not only spectated, but took all of these pictures!) and all of Team Philly Pro Tri were at the race, which is fantastic as I am supremely thankful for their support.

Spring 2015

I have not updated this in awhile, and I attribute that to racing not going very well and a general lack of time to get training and triathlon related things done in the winter, which caused a lack luster spring.

This winter and early spring, I had some great life related things happen: I defended my masters thesis, got accepted into a PhD program at CU Boulder in the Neurophysiology of movement lab (along with my girlfriend) and have submitted two papers for peer reviewed publication. This has however, left my triathlon and running times a bit behind. But, it is turning around!

I started with a very early season race, February 1st, in Punta Guilarte, Puerto Rico where I placed 13th, but I was really out of the race after a poor swim in big chop. I believed I was in good shape coming off a strong January but the swim was decisive and the race went down hill from there. I opted to not race Clermont/Sarasota continental cups due to a general lack of fitness as they were right around when my thesis defense was. Instead, I raced a road 5k (15:12) and two track races that did not go so well. It is interesting how a poor race can have two results: either motivate you or depress you. My first race of the year in Puerto Rico was the latter, and I was down on racing and traveling far to compete. I even took a week off riding and had very low numbers otherwise during February. After getting many academic obligations accomplished, my last track race at Penn Relays did the opposite. I was disappointed with my time, but the next day I was motivated. I put in two really solid weeks (and had been training pretty well for the 4- weeks prior). I had a fresher perspective on training and wanted to race with the goal of mentally and physically putting myself on the line. That is what I always have enjoyed most about racing, the sheer pain that brings the satisfaction of a job well done.

I raced Delaware Half marathon on Sunday, 5/10. I had a bit of pressure as defending champion, but there was a stellar field with Mo Trafeh's former training partner, Ryan Lee, Darryl Brown, Daniel Hoyne, Dave Berdan (1:05 half guy from Baltimore), and a couple of younger guys that were running well. I had been mostly displeased as a result of not really putting myself out there in races and was determined to change that trend today. I wanted to rattle the cages, stick my nose into the race, and get a lot out of myself. On a hot and (99%) humidity day, we went out in 5:04 with two africans, 15:25 at 3 miles and 26:10 at 5 miles before the hills really kick up. While I couldn't hold pace with Youffif, Dan Hoyne and I ran together until mile 10, where he put in a solid move that I could not counter. The difference today was that I put myself in that position to be up there, where in previous races I have not. I am excited to keep this trend up.

I race next weekend at French Creek tri followed by Challenge Williamsburg, Philly Tri, and Challenge St. Andrews. That brings the season to July, where I will be moving to Boulder and will reevaluate what races to do once I get settled in.

I want to thank my mom for cheering at a 7 am start on mother's day! and Melissa for coming by. Also, I am so thankful for sponsors for 2015: Square One Investments, Philly Pro Tri, PowerBar.

The simplified statistics behind the most recent running debate

Very recently, a paper was published in the American College of Cardiology Foundation about the supposed dangers of strenuous running. Alex Hutchinson, from Sweat Science did a great piece that summarizes the results well and points out potential flaws in how many are interpreting their results.

I am going to digress discussing triathlons for this entry in favor of delving into the statistics in this study that says running more than 20 MPW and fast could be dangerous. The original paper may be Found here and Alex Hutchinson's review here: http://www.runnersworld.com/health/the-supposed-dangers-of-running-too-much

This journal article has quickly gained recognition as "proof" that running too much can be dangerous for your health. While this is true (anything in excess can be dangerous), there is not enough evidence that 20 MPW is the cut off for health and overtraining. There are certainly hazards to overly stressing one's body, but those are generally outweighed with moderate exercise. The other problem is the average American is nowhere near that maximal border, and much closer to the minimal border, which is costing healthcare billions, not to mention decreasing the quality of life for many. The current state of society may need more encouragement to exercise, not excuses to skip.

In this study, a large group of individuals was followed since 2001 and assessed for mortality rates. Fairly complex statistical methods called Cox proportional hazard regression analysis was used to interpret the results because the study did not have what is referred to as a "balanced design." Without a balanced design, each group has a different number of individuals, and each group has very different vital statistics (age, BMI, etc.), and is referred to as non homogenous. Because of that, simple comparison of average death rates is not possible. In order to rectify this, certain assumptions must be made and adjustments made to their formulae that should take into account the covariate of age. There are inherent risks whenever such assumptions are made, and whenever possible, a balanced design is more helpful. For a more basic analysis of the numbers, see below.

The four groups analyzed were as follows (the number in paraenthesis being the total number of individuals in the group) 1. sedentary (413), 2. light (576), 3. moderate (262), and 4. strenuous (40). As you can see, the numbers are far from being balanced. This creates a basic problem when trying to compare averages. At the most basic comparison, one would want to compare average mortality rates across groups. (Note: the actual data analysis used Cox proportional regression analysis, but this is a simplified explanation). An simple comparison (ANOVA, or analysis of variance) would be an incorrect assessment tool because it does not take into account covariates. For this explanation, however, we will discuss the fundamental ideas behind the results.

A basic rule of statistics is, when you have a large number of subjects, you have more "statistical power" meaning you can detect smaller and smaller differences between groups. For a hypothetical example, if you have two groups of 500 individuals, and one group receives a medical intervention, where the other gets placebo. After six weeks, you can detect a relatively small difference in the effectiveness of the drugs. A difference as small as 12 people could be seen as "significant". However, if your sample size is 50, you need a far grater difference between the groups to detect a "significant" difference. "Significant" here refers to statistical significance, where you accept a 5% likelihood that your results will not detect an effect that is actually there. Whether or not statistical significance always equates to real world significance is a topic for a different entry. When you have small numbers (such as the last group), it becomes very difficult to detect small differences because there is more chance of random events affecting your results (such random events could be a death unrelated to running). If each group had 500 subjects, smaller differences could be detected, but because there are small numbers in some groups, the analysis loses statistical power. With the small sample size in the strenuous group, it becomes difficult to show if there was a significant difference in death rate from no running (which had a high sample size).

While it would be very inappropriate to compare direct proportions of deaths because the groups were very different in age groups. A simple comparison of death rates would yield:

Sedentary: 31%
Light: 1.21 %
Moderate: 3%
Strenuous: 5%

This data only gives four data points, and would suggest that doing some running, significantly decreases your death risk (you can do a simple ANOVA and see all groups are significantly better than the sedentary group). The problem is this data is misleading, but one could speculate that the root cause of misleading data is because the groups were sampled at such different age ranges. These data do not give statistically significant differences between light, moderate, and strenuous runners. One cannot tease out the effect of different starting age on death rate versus the level of running on death rate.

Using adjustment equations, the group goes on to say that a U shape curve exists where moderate exercise is optimal for lower mortality rates, but given the above data, can we be so sure we have found it? Given their survival data equations, they adjusted for age and claimed that the light running group had the lowest mortality rate. While this is fair, if you take away the sedentary group and just compare the three running groups, 1.21, 3, and 5% mortality rates, is there a glaringly obvious answer? Not necessarily because back to the original point. The groups are not evenly matched for numbers. The strenuous group only had 40 subjects and two died. If one died, 2.5% would be the death rate, and if three died, 7.5% would be the rate. Compare that to the light group, if one extra person died, the rate would change from only 1.21% to 1.39%! You would need 22 more people in the light jogging group to die for the rate to go all the way to 5%! Simple 'back of the envelope' calculations reveal that you need a much larger shift in death rates to change the death rates in the light running group.This illustrates the point of needing equal sample sizes pretty well.

When it comes down to the root of the issue, addicted runners will likely not stop running (as they love it), but it is possible to change the attitude of sedentary individuals, and they may be encouraged to exercise. The authors do not argue against exercise, and one is a doctor who freely admits exercise is very beneficial (though we are not sure of the optimal level). This data can only hurt those individuals who we need to be encouraged to exercise. News articles will read "Running; too much may be dangerous" because it is a catchy headline. They will not tout the fact that running at all made for much lower mortality rates, because that is "old" news. The research done in this article is sound research, but the interpretations are crossing into dangerous when news outlets read them! It is important to take a step back, simplify what they found, and try to understand what the data really suggests at it's core. Moderation is key for any aspect of life, and each individual is likely going to respond differently to doses of running, so don't stop exercising!!