Hi
I think that’s the solution proposed in the original paper “Deep Residual Learning for Image Recognition”.
In the experiments section they show that 1x1 kernels with stride=2 work fine when size reduction is required.
In my opinion it makes sense since the operations performed are equivalent to:

a downsample x2 of the input feature map using ‘nearest’ interpolation method
a linear projection of the input feature map to a higher-dimension (or lower-d) feature space

Hope it helps