Your hypothesis is clearly directional, meaning that you do expect a decrease in blood pressure. Instead of just testing if there is a difference, you can test if there is a negative difference. This usually calls for 1-tailed testing. Nicely enough, you can just split the p-values in half for that (arriving at .046 and .0165).
Now it looks like both indicate a significant result, but: You tested two variables, which calls for some form of alpha-correction. The most conservative correction (Bonferroni) is to divide the alpha-threshold (let‘s use .05) by the number of tests, giving you .025. With this, you end up with only one significant result after all.
I assume that the two measures of blood pressure are correlated. You could consider using a multivariate test, which will test for differences in both dependent variables simultaneously. Unfortunately, interpretation of a significant multivariate test is tricky and you might end up doing t-tests anyway.
To add to BruceET‘s answer:
There are different ways of presenting significance. The „conventional“ way is to decide on a threshold beforehand (usually .05) and speak of significance based on that. There is no real reason, however, to use .05, which is way researchers often present the actual p-values, confidence intervals, etc. and treat significance with a little more... let‘s call it flexibility.
In any case, you should be aware of limitations regarding your sample and whether the differences are actually meaningful considering the variation in the data etc. Just because something is significant (or not) doesn’t mean the result is important (or isn’t). The absence of formal significance is also by no means a confirmation that there is no difference!