I analyzed your data this way, too, and got the same results.
The p-value = 0.4 for work duty * case means that the interaction effect is null, so the slope for both cases is the same. You can now remove this term to produce a new model.
The fact that the case term is significant (p-value = 0.007) means that the two cases have a different y-intercept. So the two cases produce parallel trend lines (common slope) that are offset from each other. You add the estimate of case term to the estimate for the intercept to obtain the intercept for a particular case. The intercept is not significantly different fron 0 (p-value = 0.38) so the case estimate is the intercept for each case. The intercept for the actual case is -0.29 and the ideal case is 0.29.
The significant term for work duty, of course, means that the concentration is linearly related to this factor. It is the slope of the line.