Analyzing reasons and suggestions

Todd, thanks for putting this together. I found this information very interesting and enlightening. I played colege football and baseball and broadcasted both, as well as basketball but I have become a huge soccer fan.

Analyzing reasons and suggestions

Analyzing interval workouts using power data by Andrew R.

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Interval training has been used by athletes in practically all endurance sports for decades. In many cases, designing and interpreting such workouts with respect to their specific physiological demands is relatively straightforward.

For example, constant-intensity efforts of, say, min duration with a work: At the same time, however, it is unlikely that any individual could repeatedly perform efforts of this duration at a high enough intensity to result in truly marked improvement in anaerobic capacity although there might be some increase.

While the physiological demands of workouts similar to the one described above are fairly easy to understand, cyclists often perform interval sessions that are far more complex in nature, and thus much more difficult to interpret. For example, the work: In addition, most cyclists perform their interval sessions on the open road, where the terrain may vary, versus on a flat track as, e.

Finally, the higher speeds achievable by cyclists vs. Examples of the sorts of training sessions that would fall into this 2nd category include the 30 s on, 30 s off or 40 s on, 20 s off intervals often used to simulate the changes in pace that occur in criteriums or when racing off-road, ladder sessions such as the 1 min — 2 min — 3 min — 4 min — 4 min — 3 min — 2 min — 1 min intervals described by Joe Friel or the m descending to m session popularized by Eddie Borysewicz, etc.

In our book we provide guidelines via the training levels for appropriate power ranges to target when doing different types of intervals, and also discuss how to pace such intervals as well as when to stop doing them i.

What I would like to discuss here is how you can gain deeper insight into the specific demands of such interval workouts by analyzing them to answer three questions: What was the power after smoothing the data using a 30 s rolling average? The physiological responses to exercise follow a characteristic time course, many of which are directly or indirectly related to the kinetics of changes in the rate of mitochondrial respiration in muscle at the onset and offset of exercise.

Thus, smoothing of the power data using a 30 s rolling average can be used to provide an approximation of the time course of changes in muscle, and hence whole-body, VO2, and hence aid in understanding the metabolic and cardiorespiratory demands of a given training session. Improvements in anaerobic capacity may also be stimulated by repeatedly incurring a large O2 deficit at the onset of each interval, which would be apparent from a significant difference in the areas under the curves of the actual and smoothed powers during the early portion of each effort.

What does a quadrant analysis show? Examining the smoothed power from a series of intervals can provide insight into the metabolic and cardiovascular demands of the workout, as described above. To fully understand the neuromuscular demands, however, it is often useful to perform a quadrant analysis, i.

Doing so provides an easy way of appreciating the effects of, e. Quadrant analysis is especially helpful in understanding how use of very short work and rest periods i. Less obviously, quadrant analysis also provides an effective albeit clearly not only means of quickly understanding what the rider did during the rest periods.

In particular, less experienced or less well-trained cyclists often experience the overwhelming urge to stop pedaling entirely after a strenuous effort.

Coaches therefore may or may not wish to encourage their athletes to pedal easily instead of coasting between intervals, with quadrant analysis providing a quick-and-easy way of visualizing whether this instruction was followed.

What was the normalized power for the entire session?

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The normalized power algorithm is intended to be a predictor of physiological strain, not the adaptation s resulting from that strain. As such, the actual power really says more about the specific adaptations likely to result from a particular interval workout, especially when the data are smoothed in a physiologically-relevant manner as described above.

Nonetheless, examining the normalized power for an interval training session can often be enlightening. Calculating the normalized power for a series of intervals therefore provides a way of quickly ascertaining whether either of the first 1st possibilities listed above may hold true.

Indeed, I have previously posted a list of theoretically-impossible intervals workouts on the wattage list here: Analyzing interval workouts using power data: Figure 1 below shows the actual power data during the intervals both without and with smoothing using a 30 s rolling average: Power output during an interval training session consisting of 20 x 30 s on, 30 s off As shown in Figure 1, the rider was reasonably successful in repeatedly hitting their goal powers, and completed the workout as planned.

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This is consistent with the fact that their normalized power for that 20 min block was W, versus a functional threshold power of W i.This report documents the volume of attendance to business-to-business exhibitions in the past two years and plans for the near-term future, as well as reasons for these future plans.

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Say it softly. Whisper it to your friends. David Allen’s Getting Things Done is NOT the best way to get things done.. It’s dull, it’s difficult, it’s about as satisfying as an air sandwich. And it doesn’t work. Generating Ideas and Text In analyzing a text, your goal is to understand what it says, how it works, and what it means.

To do so, you may find it helpful to follow a certain sequence: read, respond, summarize, analyze, and draw conclusions from your analysis.

Analyzing reasons and suggestions

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Training and Racing With a Power Meter Journal: Analyzing interval workouts using power data