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EXACTLY the problem I am “struggling with” in the current project. Coming from a history of running 60km/week and having a month off then ramping to that same 60km across 4 weeks will yield high Acute:Chronic ratios which might give “false positive” injury prediction. I guess the approach might need to be more “Bayesian” and take apriori data, or the chronic need to be much longer (>6 weeks) or longer than any break that athlete used to do.
Other option might be to include “baseline” in both acute and chronic measures. For example adding normal “out of sport” walking distances. This might decrease the ratio and make it less “jumpy” when coming from break.
I am playing with using Exponential Moving Averages (EMA) and tuning the “alpha” parameter to get the best prediction. Not sure why we use 7/42 or 7/28 for Acute and Chronic estimates in the first place. Maybe some other ratios can yield better predictions?