Suppose blood pressure is a continuous outcome variable and you run a linear GEE with following predictors: age (years), weight (lbs), and smoking (yes/no). How would you interpret the coefficients for these predictors? Would it be the same as in "regular" linear regression?
Yes, they are interpreted the same way. The only consideration (and key departure from linear regression) is that these measured effects are considered to be at a "population" level. This is often not a key component of effect interpretation, so your main effect for, say smoking would be, "An expected difference in blood pressure comparing smokers to non-smokers of the same age and weight." Note that I wouldn't say "An expected difference in blood pressure comparing a smoker to a non-smoker of the same age and weight."
For continuous outcome or response you can't apply GEE. GEE can be applied only to categorical outcomes when you want to estimate the parameters marginally (not individually).
However, you can apply GEE once you made your response variable (Blood Pressure) categorical. For example high, low, medium.
The interpretation of the coefficients is depending on the link function (identity, logit ...) you applied .