In repeated measures, how to distinguish regression to the mean from a negative lagged effect?

Question

I have repeated measures for a quantitative variable "cry" for N = 52 participants (how much you cry at a given time), there are 30 repeated measures. The values range from 0 (not at all) to 50 (a lot).

I created a lagged variable of my outcome, and used linear mixed effects model to predict the outcome from the lagged outcome (its prior value) and random slopes by participant.

The fixed effect of the lagged outcome is Est. = 0.14, Std. Error = .03, t(35.89) = 4.14, p < .001.

In addition to the lmer, I ran a individual linear regression on each participant to look at their individual effects. (I know this is not the way to go for inferences, I didn't look at the BLUPs here. I did this for descriptive purposes, I'm only trying to understand my sample, not make an inference. I'm trying to think more conceptually at what the values would mean.)

When looking at individual participants, 33 have positive slopes, 3 have a slope of zero, and 9 have negative slope. The average slope is b = 0.07, ranging from -5.14 to 0.99.

I'm having a hard time knowing what I can claim about my sample.

The way I understand it, if past predicted present perfectly, I would have an estimate of 1. Values higher than 1 mean that crying means my participant are likely to cry even more at the subsequent timepoint. Values lower than 1 mean that crying means my participant are likely to cry less at the subsequent timepoint.

But how do I interpret the difference between a positive slope lower than 1 and a negative slope? And more importantly (possibly related) how do I distinguish a negative effect from regression to the mean?

I also don't understand how to conceptually interpret having a slope of 0.

Thank you in advance for helping.