Let’s talk about metrics

Hey Fittit,

This is getting dangerously close to being a rant, but I promise I have a point at the end. Cruising through simple question threads and new queue over the past few years, I’ve seen a lot of the following:

• “I haven’t really been training with any specific goals, but I got this DEXA scan. What should I do next?”
• “I got this at-home genetic test. Should I change my training because I have [insert gene here]?”
• “What wrist-based fitness tracker should I get? I don't know what I want to track with it.”

You get the idea.

I think it’s time to talk about which metrics we use to measure and inform our training, and how we use them. As data and measuring tools have become more easily available and the market for fitness trackers has boomed, there seems to be a trend in the fitness marketing space (but not necessarily the coaching space) towards the idea that all data is good data.

I don’t think that’s true. From a researcher’s perspective, the increased availability of data is great. You can take that data and analyze it to find all kinds of trends. From the perspective of an individual user, though, you need to be asking yourself some questions about what measurements you’re taking and what they’re good for.

I don’t mean to say that data is inherently useless or that you should avoid any sort of objective training measurement, just that you need to be critical of measurement for measurements’ sake. In that vein, I'm going to suggest that you should ask yourself the following questions before shelling out for tests or testing equipment:

1. Is this test reliable enough that I can make inferences based on the data it gives me? For most body fat percentage tech, for instance, the answer is no.
2. Is the data that comes from this test relevant to my goals? Will the data that comes from this test drive any decisions that I make for future training and programming?
3. Is this a convenient/affordable enough test that I can repeat it regularly? A single data point from a single point in time isn’t going to tell you very much.
4. Do I understand this type of data and the limits of the testing equipment well enough to analyze it?
5. Is this data/testing tech unique enough that it offers a level of value above what I can infer from data I already have?

Overall, I think the primary point that I’m trying to make is that you should have a clear understanding of the test you’re running and a plan for how you intend to use the results, and you should have this understanding and develop this plan prior to running and paying for the testing. Otherwise, all the data you just collected is just noise.

Thank you for coming to my TED talk.

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* This article was originally published here