Evaluating the result of topic modeling in a way that time matters
I think the issues comes from the fact that the item you are looking at (the word "sleeping") is a rare event, so the probability that you observe one is about 0. Technically, it is called a Poisson process.

One of the way to circumvent it is what you did: aggregate on a period (in your case 6 month) so that the number of events get significant.

You do not need to cut you observation time in 6 month period though, you may use moving averages: period 1-6, 2-7, 3-8, 4-9,... If there is a temporal pattern it will be more visible.

An other way is to use cumulative data: the number of words up-to time t. If you get a logistic "S" shaped curve, then you are on an important thing.

Basically you want to see if a certain word (sleeping) is more frequent in the CI sub-population than the nonCI sub-population. You can use the t-test (unpaired observations, unequal sample size, equal variance), to check if the count of the word is significantly different in the two sub-populations.
And you can do this over time.

More adapted tests can be found in this paper treating of the similar problem of testing if documents have the same author. Here again, nothing prevent to make a test per time period, cumulative or not.