xxlmer <- lmer(Us ~ App + Hed + (1|Category), data = dataset)

coefs <- data.frame(coef(summary(xxlmer )))
# use normal distribution to approximate p-value
coefs$p.z <- 2 * (1 - pnorm(abs(coefs$t.value)))
coefs

Estimate   Std..Error   t.value        p.z
(Intercept) 2.9044048 0.49348777 5.8854646 3.969374e-09
App1        0.1600932 0.21344810 0.7500335 4.532345e-01
App3        0.3825582 0.20096127 1.9036414 5.695690e-02
Hed         0.3417938 0.09047678 3.7776961 1.582858e-04