How to control for baseline covariates in repeated measures analysis

We have an approach where we are comparing left and right limb (skin temperature) after two different interventions. We have 15 minutes and post-data (minute intervals).

As such, both time and group are a within-subject comparison.

Our thinking is we should treat the pre measure as a covariate, and then run a two-way (time * intervention) repeated measures ANOVA. However, we are struggling to work out how to actually set up the analysis in R (or SPSS) for including the covariates. The challenge we are finding is that we can set up a covariate per person, but not per leg.

Does anyone have any suggestions on whether this approach is even the optimal approach (obviously assuming all assumptions are held), and then how to set it up to include the covariates?

not sure why you cannot set up baseline covariate per leg. In the design matrix you have individuals, and period/leg/treatment, and timepoints within period. There is some discussion maybe about whether to include it as a baseline covariate or as time 0. I like your idea, ie include as a covariate, the downside is that if the baseline is missing then the individuals falls out of the analysis, but they don’t if it is treated as time 0. On the other hand if it’s treated as time 0 you induce a treatment x time interaction, or if you use change from baseline then it’s 0 at time 0 etc.