Table 3 had covariate-adjusted BL and FU means and baseline-adjusted intervention effects. The question was whether to baseline adjust the FU means or not, and if so how to center them. The way I see it is: You can put in incorrect numbers that add up, or incorrect numbers that don't add up. Or you can just present intervention effects!

Section 1:

In Table 3, the Estimate of the intervention effect for MVPA (adults)
does not seem (to me) to fit the four means - recognizing that, in a
baseline adjusted analysis, one cannot simply take the NET difference -
instead it really is the difference in the FU adjusted means. For MVPA
(adults) I would have expected 145.5 - 103.6 = 41.9 not the 29.6 in the
Table. The footnote indicates that I am understanding the means as
presented correctly. It could be an issue with age which is not a fixed
covariate; in New Moves I incremented the age in my estimate statements
but I am not sure if that is the source of the mismatch. The MVPA
(adults) is the most obvious of the mismatches, but it applies to some
other outcomes as well.

Section 2:

Instead of generating the FU means separately from the estimation of the
intervention effect, I would estimate both FU means and Intervention
effect in the one analysis - in that way the results would be consistent.

Generation of the baseline is not a problem

Then have the data for FU with each observation also having the BL
value. (Is tt12 the followup, or the difference?)
proc mixed ;
class ...;
model &tt.22 = &tt.00 trt type ... smoker;
lsmeans trt/om ;
estimate 'Intervention effect' trt 1 -1;
run;

Section 3:
If the FU means and the intervention effect are estimated
simultaneously, the difference between the FU means will equal the
intervention effect. However, one would need to take the mean of the
baseline values to subtract from the adjusted FU means and not simply
the separate BL means. That is because the FU means are ADJUSTED as if
they have the same baseline mean.