I have no idea what you are trying to say here.
Are all your results 0 after rounding?
This would mean, that they are between 0.0 and 0.5 after the sigmoid.
Keep in mind, that the output of sigmoid represents a probability.
Which would mean, that your network is never more than 50% sure that a given input belongs to the class.
If that is indeed the case, then lowering your threshold is probably not the right thing to do. Since this would suggests, that there might be a problem in your network. Like a heavily imbalanced dataset for example.
If you still want to lower your threshold, you could do this by comparing the output of the sigmoid to the threshold and setting the value either 0 or 1 accordingly.