I have a question on targeting, we are using an attribute which will change over time, so a user may qualify to enter the experiment, then not any more. Once they have entered, I’m assuming they will stay in the experiment, even if they no longer pass the targeting rules?

They will stay in your experimental sample for analysis so long as they saw the experiment at least once (e.g. they are still counted as part of the dashboard results). However, our feature evaluation is always done on the fly. If a user returns to an experiment check in your code and their attributes no longer match the targeting attributes, then they don't match the targeting and won't get the experiment value!

How does this interact with the choice of whether to re-randomize traffic in a new phase?

You can make explicit choices about what happens to users with sticky bucketing. Our "Make Changes" UI does the best to guide you to understand the trade-offs. There's also a bit more written here: https://docs.growthbook.io/app/experiment-configuration#making-changes-while-running