How to interpret results of mixed longitudinal model in R?

So I’m not too familiar with mixed models, but wondering about the following:

library(reshape2)
library(lme4)
library(lmerTest)

#Simulate longitudinal data
N    <- 25
t    <- 2
x    <- rep(1:t,N)

#task1
beta1 <- 4
e1    <- rnorm(N*t, mean = 0, sd = 1.5)
y1    <- 1 + x * beta1 + e1

data1 <- data.frame(id=factor(rep(1:N, each=t)), day = x, y = y1, task=rep(c("task1"),length(y1)))
fakescore <- runif(50)
data1 <- cbind(data1, fakescore)
#temp <- reshape(data1, idvar=c("id"), varying=list(c(2,3),c(4,5), c(6,7), c(8,9), c(10,11), c(12,13),c(14,15)), v.names = varnames.lme, direction = "long")
model <-  lmer(fakescore ~ y*day + (1|id), data = data1)
summary(model)

The above is an example of how I generated my model. however, my real results are below.
pp_aml, bmi, qsd, and time are continous (time is 0 and another timepoint). sex is male/female. But, I don’t understand – what does it mean that qsd is significant and that qsd:time is at a trend level. I would like to know if qsd trajectory mirrors pp_aml trajectory but also want to know what is signficant down there, if somebody can explain the output to me? My actual stats knowlegde is limited so any help would be great.

Thanks!

model <-  lmer(pp_aml ~ bmi +sex+ qsd*time + (1|ID), data = temp)
Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)  1.920e+00  1.133e-01  3.743e+01  16.949  < 2e-16 ***
bmi          4.529e-03  3.676e-03  3.272e+01   1.232 0.226708    
sex         -1.851e-01  4.863e-02  3.184e+01  -3.808 0.000603 ***
qsd          1.483e-02  5.556e-03  6.595e+01   2.669 0.009573 ** 
time         6.535e-04  2.253e-04  3.690e+01   2.901 0.006237 ** 
qsd:time    -8.642e-05  4.284e-05  3.576e+01  -2.017 0.051229 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
         (Intr) bmi    sex    qsd    time  
bmi      -0.890                            
sex      -0.422  0.097                     
qsd      -0.438  0.205  0.118              
time     -0.189  0.022 -0.020  0.516       
qsd:time  0.064  0.039  0.011 -0.439 -0.701