I once asked a similar question about 4 years ago and got no answer for it. The problem is that angular measurements can jump between complementary values (ie, original values and those calculated by subtracting 90 from the original angle) without any change in the meaning. However, their standard deviations can't jump between such values.

So if you convert 88.8 (+/- 1.59) to -1.2 (+/- 1.59), the new angle will be still valid but the new standard deviation will be completely misleading and computationally useless (and incorrect). Statistical tests look at the SD and how small is it compared to the mean, and if it is small enough they gain more power. Now when you convert a very large angle to its complimentary angle, it becomes smaller than its SD which this makes all statistical tests incorrectly underpowered. In other words, if an SD is properly small compared to its mean, the mean will be treated as a more reliable and more accurate finding but if the SD is not small enough, the mean will be treated as some not-so-accurate or not-so-definite finding. In your case, the old SD was about 80 times smaller than the mean, showing that the mean was very accurately identified and thus very reliable. But after converting the mean from 88 to -1, the SD is now larger than the new Mean, indicating that the newly calculated mean is not a reliable or accurate finding.

I have a suggestion that just passed my mind: In order to feed a proper pair of Mean/SD to the statistical test, you can convert the SD to a new SD that holds the same ratio to the mean. For this purpose you should first calculate the CV (coefficient of variation) which basically addresses the question that "how much an SD is small compared to its mean?" It is possible to calculate the CV (coefficient of variation) for the original angle [from its SD, CV = SD / Mean, which is 0.0179 = 1.59 / 88.8] and then use it to compute a NEW standard deviation that goes along with the new angle. New SD = CV * |new Mean| = 0.0179 * 1.2 = 0.02149.

This new SD allows the meta-analysis test to treat the new means exactly the same way it would treat the original means with their original SDs. So the new "Mean (SD)" should look like:

Smith 2007 -1.2 (+/- 0.02149)