Is there anything trickier than measuring changes? Probably, yes, but this has got to be competitive for the difficulty in translating clear concepts into analysis. We can all describe, using ordinary language, the idea that a person may become more physically active with time.
But how do we operationalize this using the tools available in the standard cohort study. Do we look at change? What about change conditional on baseline? But the second quantity is going to have issues with measurement error.
What about categorizing things? Sadly, this creates the annoying problem of regression to the mean and ignores the bigger question of how do we define a meaningful threshold. Let alone the issue of reducing the information content of a continuous variable by breaking it into two categories. This reduction might be defensible if these categories align with clear clinical guidelines and so make results easier to operationalize. But many variables don't have this lucky property.
The more I look at this, the trickier the whole thing becomes.
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