Before we can have continuous lactate monitoring I guess the medical industry should first master just the basic hand held lactate monitor. So far, no bueno. But, maybe they can still be useful for relative measures.
“The mean relative differences to the Biosen analyzer were 7% (Plus), 7% (Pro), 10% (Scout), 42% (Tai), and 32% (Ysi). The residual standard errors after linear regression against Biosen were 0.18 mM (Plus), 0.20 mM (Pro), 0.22 mM (Scout), 0.15 mM (Tai), and 0.06 mM (Ysi). Accordingly, a blood lactate concentration of 3 mM measured with Biosen yielded 95% prediction intervals that were 0.72 mM (Plus), 0.80 mM (Pro), 0.87 mM (Scout), 0.60 mM (Tai), and 0.23 mM (Ysi) wide.”
As long as they can be precise they still have value. I guess if I look at the chart of the results, some of those are still good enough for me. But if 4mmol is your metric, you want to be careful when selecting your lactate meter.
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I’m not surprised at all. Metabolic carts are $10k+ USD and they still have a wide range of error. We employ very expensive accelerometers for pile driving dynamics and they can have issues. Same with our strain gauges in our rock testing lab…$$$$ and they require very careful use.
Most people probably don’t pay attention to their device specs. Same as when people quote their smart watch VO2max to a decimal place.
The Lactate plus, which I use, gives some data in the manual, and the CVs are quite high. The YSI data is interesting though. I use YSI meters for engineering work sometimes and they’re quite expensive devices. You would think they would perform better.
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Nice article. Great reference data.
We quantified precision (test-retest reliability) for the Edge lactate analyzer (not included in the OP study) and found the day-to-day variation is expected to be around 0.5 mmol/L at lower intensities, and possibly much higher (1.5+ mmol/L) at higher intensities.
Some of that uncertainty is measurement device error (shown by the differences between devices), some of that is real biological variability. This goes for everything else we are measuring in our training.
The moral of the story, as @GarageLab says, is not to worry about being over-precise with any one number. Our bodies can’t tell the difference anyway. If we know our limits of uncertainty, we can be more confident detecting what counts as a meaningful change, and not lose focus in the day to day noise.
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