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Edited to add: I think you got an output from R looking like this:

So, if you look at the "Fixed effects" part, "group1" effect is an effect comparing group 1 to the control group (estimate for "predictor" is the continuous predictor main effect, here non-significant and negative). In my mock data, the results suggest that outcome was lower in group1 than in control group, because the estimate is negative, but not significantly so. If your "group1" effect was significant and positive, it suggests that outcome was significantly higher for people in group1 than in control, and vice versa if it was significant and negative. You get estimated marginal means for "control" and "group1" from emmeans package:

Edited to respond to your comment: I think you got an output from R looking like this:

So, if you look at the "Fixed effects" part, "group1" effect is an effect comparing group 1 to the control group (estimate for "predictor" is the continuous predictor main effect, here non-significant and negative). Because "group" is a factorial effect, it compares one group to another. "Control" is the default to which "group1" is compared to. That's why it is not shown in the output. With a factorial predictor, you always see in the output the number of levels of the predictor minus 1 level - because that's the level the other levels are compared to. 

In my mock data, the results suggest that outcome was lower in group1 than in control group, because the estimate is negative, but not significantly so. If your "group1" effect was significant and positive, it suggests that outcome was significantly higher for people in group1 than in control, and vice versa if it was significant and negative. You get estimated marginal means for "control" and "group1" from emmeans package:

Or, you can of course check and plot raw means by group too.

(this is made based on simulated data and I have no significant effects there, but never mind that).

So, if you look at the "Fixed effects" part, "group1" effect is an effect comparing group 1 to the control group. In my mock data, the results suggest that outcome was lower in group1 than in control group, because the estimate is negative, but not significantly so. If your "group1" effect was significant and positive, it suggests that outcome was significantly higher for people in group1 than in control, and vice versa if it was significant and negative. You get estimated marginal means for "control" and "group1" from emmeans package:

(this is made based on simulated data with 60 participants and 5 observations per participant. I have no significant effects there, but never mind that).

So, if you look at the "Fixed effects" part, "group1" effect is an effect comparing group 1 to the control group (estimate for "predictor" is the continuous predictor main effect, here non-significant and negative). In my mock data, the results suggest that outcome was lower in group1 than in control group, because the estimate is negative, but not significantly so. If your "group1" effect was significant and positive, it suggests that outcome was significantly higher for people in group1 than in control, and vice versa if it was significant and negative. You get estimated marginal means for "control" and "group1" from emmeans package:

So, if you look at the "Fixed effects" part, "group1" effect is an effect comparing group 1 to the control group. In my mock data, the results suggest that outcome was lower in group1 than in control group, because the estimate is negative, but not significantly so. If your "group1" effect was significant and positive, it suggests that outcome was significantly higher for people in group1 than in control, and vice versa if it was significant and negative. Of course, if the interaction is significant, you should focus on that more than on "group" main effect.

So, if you look at the "Fixed effects" part, "group1" effect is an effect comparing group 1 to the control group. In my mock data, the results suggest that outcome was lower in group1 than in control group, because the estimate is negative, but not significantly so. If your "group1" effect was significant and positive, it suggests that outcome was significantly higher for people in group1 than in control, and vice versa if it was significant and negative. You get estimated marginal means for "control" and "group1" from emmeans package:

library(emmeans)
em<-emmeans(model, specs = pairwise ~ group)

Of course, if the interaction is significant, you should focus on that more than on "group" main effect. You can for instance plot separate slopes of predictor for each group by using:

library(ggplot2)
library(ggeffects)

preds<-ggpredict(model, c("predictor", "group"))
plot1<-ggplot(preds)+geom_line(aes(x=x, y=predicted, color=group))
plot2<-plot1+scale_color_discrete(name="Group", labels=c("Control", "Group1"))
plot3<-plot2+labs(x="Predictor", y="Outcome", title="title")