The `weights` argument will work -- just set it equal to $m$ in your formula. It will *not* work to put multiple instances of $x_h$ in `newdata` -- you'll just get several copies of the same interval. 

Another approach is to use `pred.var = summary(mod)$sigma^2 / m` where `mod` is your model and `m` is your value of $m$. The reason this works is that `pred.var` is used to set the variance of future observations; by default it is assumed to be the same as in the data ($\sigma^2$, estimated by $MSE$). By pretending it is $\sigma^2/m$ (estimated as $MSE/m$), you are using the variance of the average of $m$ predictions and will produce the correct result.