Thread by @mecfsskeptic

1) Boom and bust, another ME/CFS myth?

In this new blog post we look at the evidence behind the boom and bust theory and how it originated.
mecfsskeptic.com/boom-and-bust-…

2) ME/CFS patients are often said to have an erratic activity pattern, where sudden bursts of activity are followed by prolonged bed rest. This ‘boom and bust’ pattern is thought to be the reason why ME/CFS patients experience frequent crashes and setbacks.

3) Despite the popularity of this view in the past 25 years, there is no evidence to support it.

4) The MAGETNA trial, for example, analyzed the activity pattern of 138 children and adolescents with ME/CFS and concluded that “a fluctuating active’ or ‘boom-bust’ physical activity pattern” […] did not emerge from the present analyses.”
bmjpaedsopen.bmj.com/content/3/1/e0…

5) A Dutch study had accelerometer data of 277 ME/CFS patients over a 12 day period. The authors wrote: “no indication was found that the CFS patients as a group were characterised by a high number of large day-to-day fluctuations in activity.”
pubmed.ncbi.nlm.nih.gov/11164063/

6) A Belgian study had actimeter data of 67 ME/CFS patients and concluded: “The present study was not able to confirm the hypothesis of a more fluctuating activity pattern in patients with CFS, nor during the day, nor during the registration period.”
linkinghub.elsevier.com/retrieve/pii/S…

7) So where does the boom and bust theory come from if it does not appear in the data? Psychiatrists seem to have picked up on the language of post-exertional malaise, but rather than thinking that the illness causes it, they assumed that the patient’s behavior was responsible.

8) One could argue that this is not a big issue because learning patients how to avoid crashes and stabilizing their activity levels is useful, even if based on a false premise.

The theory, however, incorrectly blames patients for the symptoms they experience.

9) Lastly, we discuss why a closer look at ME/CFS activity patterns might be useful.

With large sample sizes, machine learning and labelling of patients when they are in a crash, it might be possible to find a PEM signature in the data, an objective signal of ME/CFS pathology.