Time-dependent complexity characterisation of activity patterns in patients with Chronic Fatigue Syndrome 2024 Rabaey et al

Abstract

Background
Chronic Fatigue Syndrome patients suffer from symptoms that cannot be explained by a single underlying biological cause. It is sometimes claimed that these symptoms are a manifestation of a disrupted autonomic nervous system. Prior works studying this claim from the complex adaptive systems perspective, have observed a lower average complexity of physical activity patterns in chronic fatigue syndrome patients compared to healthy controls. To further study the robustness of such methods, we investigate the within-patient changes in complexity of activity over time. Furthermore, we explore how these changes might be related to changes in patient functioning.

Methods
We propose an extension of the allometric aggregation method, which characterises the complexity of a physiological signal by quantifying the evolution of its fractal dimension. We use it to investigate the temporal variations in within-patient complexity. To this end, physical activity patterns of 7 patients diagnosed with chronic fatigue syndrome were recorded over a period of 3 weeks. These recordings are accompanied by physicians’ judgements in terms of the patients’ weekly functioning.

Results
We report significant within-patient variations in complexity over time. The obtained metrics are shown to depend on the range of timescales for which these are evaluated. We were unable to establish a consistent link between complexity and functioning on a week-by-week basis for the majority of the patients.

Conclusions
The considerable within-patient variations of the fractal dimension across scales and time force us to question the utility of previous studies that characterise long-term activity signals using a single static complexity metric. The complexity of a Chronic Fatigue Syndrome patient’s physical activity signal does not suffice to characterise their high-level functioning over time and has limited potential as an objective monitoring metric by itself.

Open access, https://bpsmedicine.biomedcentral.com/articles/10.1186/s13030-024-00305-9

 

My mate (who claimed his name was Myx O'Lydian) used to make up codswallop like this and post it on Wikipedia.

I'm wondering if he's at it again.

 

Tiny sample size, no control group, Fukuda criteria, and what look like very outdated references on the background of CFS, efficacy or otherwise of CBT and so on.

 

"We propose an extension of the allometric aggregation method, which characterises the complexity of a physiological signal by quantifying the evolution of its fractal dimension. We use it to investigate the temporal variations in within-patient complexity."

This is quite baffling word salad.

I've not read the paper. I don't have the appetite

So, i think this might be trying to explain that an objective measure of activity was not found to have a relationship to clinican rated functioning. So what? Why would it....

Also, too much variability in functioning. Well, blow me over with a feather. If the 7 patients were perhaps at the less debilitated then that's likely given better and worse days etc.....

Generally less complexity the better. This is vastly overly complex so probably not understandable or helpful

 

In other words: patients have always reported what was happening, that a fluctuating illness... fluctuates. Surprised to see a rational conclusion to this. It actually seems like a potentially useful application of biopsychosocial thinking, a holistic approach that considers several dimensions and how they affect overall health. Although 3 weeks is not nearly long enough. And some of the language is a bit high on the "I am very smart" scale. And other language is a bit odd, to say the least.

And it's possible that it's simply a trojan horse to argue against objective validation, even though the only sane conclusion here is that subjective outcomes being even less reliable in general, with objective outcomes being not entirely useful, subjective outcomes are even worse for every intent and purpose. Certainly ones that try to do too much.

I really don't think we'll ever develop one special tool, especially not a PROM, to validate all of this. I see so many "recovered" long haulers talk about relapses and setbacks, minor and major, in patterns that defy any classification. In some cases they were initially acutely ill, more often than not relatively mild, recovered for a bit, then saw symptoms returned, while in others it's a continuation that never relents. In other cases regardless of how onset happened, they were ill for a while, months or years, then either saw a gradual or rapid improvement, thought they were on the way to recovery, then saw symptoms returned, either with no apparent reason, or maybe from another infection or reinfection with COVID, or from returning to work, or working out.

This illness is chaos incarnate. Between individuals, for each individual, there is nothing that can be predicted. IMO only symptoms really matter. Remove the symptoms, end the problem. The rest is mostly useless.

 

Yup.

 

This looks like complete overkill. You'd be better doing some basic stats—such as time series analysis—on activity data from apps like Visible or from activity trackers.