I am trying to fit a regression model where the dependent variable is number of messages received (**MsgReceived** in sample data below) by an individual/user, and the independent variables are a mix of count and binary variables like "**TimeActive**", "**BioAvailable**" etc. 

Here, **TimeActive** is a count of weeks the user was active and **BioAvailable** is a 1-0 identifier stating whether the user has filled out his Bio

Here's some example data:

```
TimeActive,BioAvailable,X1,X2,X3,X4,X5,X7,X8,X9,X10,X11,Count1,Count2,Count3,MsgReceived

35,1,1,0,0,0,1,1,0,1,0,0,3,0,3,16  
34,1,1,0,1,1,0,1,0,1,0,0,20,23,37,11  
34,0,0,0,1,0,0,1,0,1,1,1,6,8,22,19  
35,0,0,0,1,1,1,1,0,1,0,0,3,23,5,13  
32,0,0,0,1,0,1,1,0,1,1,1,0,75,11,40  
0,0,0,0,0,0,0,1,0,1,1,1,0,0,0,7  
21,0,0,0,0,0,0,0,0,0,0,0,3,33,39,97  
13,1,1,0,0,0,0,1,0,0,1,1,1,0,0,12  
34,0,0,0,1,1,0,1,0,1,0,0,35,52,2,37  
33,1,0,0,1,1,0,1,0,1,0,0,0,9,16,136  
31,1,1,0,1,1,0,1,1,1,1,0,5,1,12,46  
0,0,0,0,1,0,1,1,0,1,1,1,0,3,8,20  
29,1,1,0,1,1,1,1,0,1,0,0,44,161,45,8  

```

I am wondering if fitting a generalized linear model using a Poisson distribution is still the best fit even though the count of messages is over the life of a user's activity and not just a session. Assuming that a user's lifetime is a period seems fair. Is this correct? If not, what distribution and regression method would be a better fit?