The way I see it, the choice of which to use depends what kind of scale the conditions are on. Let's say that your conditions are, say, days of stroke rehab therapy received. If:
Condition 0 = no rehab therapy received
Condition 1 = 10 days of rehab therapy received
Condition 2 = 20 days of rehab therapy received
Then it would totally make sense to use one regressor, with multiple levels. If on the other hand you had something like:
Condition 0 = no stroke rehab therapy received
Condition 1 = Conventional stroke rehab received
Condition 2 = Chinese medicine -style stroke rehab received
Then you would need two regressors - there's no way you could justify saying that Chinese medicine stroke rehab* is 'twice as much' stroke rehab as conventional rehab. So the question is, do your conditions represent different levels of the same variable, or different variables entirely?
As far as effects on final analysis: having two regressors rather than one increases the degrees of freedom for the total model, reducing the statistical power, but would also likely result in a higher R2 (possibly just due to chance effects being better captured in the more complex equation, possibly due to non-linear relationships of condition level on dependent variable).