So I have this data set of 56 users with 52 weeks worth of weekly average data for blood pressure and exercise level recordings. I would like to use change point analysis (https://sites.google.com/site/changepointanalysis/) in R to understand where changes are occurring. However to make CPA usable, the observations need to be independent (at least have no strong autocorrelation). 

I performed ACF and the Ljung-box test for lags up to 20, and there is autocorrletion in many of the time series. 

So my question is, what is an appropriate way to remove this? 

I've looked around and have found some possibilities, but nothing has made it overtly clear why I would choose one over another, or what is a lowest risk approach. 

Various possibilities I have seen (some from this site):

* Low pass filter, inverse subtraction
* First differences
* Detrending
* Seasonal adjustment
* Data transformation (e.g. convert difference operator into ratio)
* Exploratory data analysis (EDA) smoothing techniques

One of the people at work said something about using a low pass filter, using inverse subtraction to remove correlation, and then finding the frequency through fast fourier transform or spectrogram analysis. I'm not sure about all that.

I really appreciate any comments, I'm kind of lost at a crossroads right now. Thanks!