An Evidence-Based Theory of Psychological Adjustment to Long-Term Physical Health Conditions: Applications in Clinical Practice, 2022, Carroll et al

[Sly Saint]

Moss-Morris also an author

Abstract
Objective

Around 30% of people with long-term physical health conditions (LTCs) experience comorbid anxiety and depression. For many, comorbid distress is linked to difficulties adjusting to the challenges of the LTC. The aims of this article are to present a transdiagnostic theoretical model of adjustment to LTCs (TMA-LTC), demonstrate the application of this model in clinical practice, and highlight the distinguishing features of treating LTC-related distress compared with treating primary anxiety and/or depression.

Methods
A systematic review (k = 21) was conducted to collate preexisting evidence-based models of adjustment across LTCs. Models of adjustment for a range of LTCs were extracted and synthesized into a new preliminary TMA-LTC. Two expert consensus meetings were held, where experts rated the relevance and importance of all concepts within the models.

Results
The TMA-LTC proposes that acute critical events or ongoing illness stressors can disrupt emotional equilibrium, and that whether a person returns to equilibrium and achieves good psychological adjustment depends on a number of cognitive and behavioral factors, as well as their interpersonal, intrapersonal, environmental, and illness-specific contexts. A case study is presented to demonstrate the clinical application of this model in treating illness-related distress, highlighting how it overcomes roadblocks that may be encountered when working primarily within traditional mental health paradigms.

Conclusions
As an empirically and clinically informed model, TMA-LTC provides a useful guide for assessment, formulation, and treatment in the context of psychological adjustment to LTCs. Future studies are needed to test treatments that have been developed based on TMA-LTC.

https://journals.lww.com/psychosoma...Evidence_Based_Theory_of_Psychological.4.aspx

 

[Sly Saint]

write up article in the mental elf
A long-term physical health condition changes everything: therapeutic responses to psychological distress must change too #BABCP2022

The proposed transdiagnostic model of psychological adjustment to LTCs acknowledges that the stressors resulting from having an LTC, including symptom management and threat to mortality, can disrupt a person’s equilibrium. The process of adjustment includes returning to a state of equilibrium.

Carroll and colleagues observe that Cognitive Behavioural Therapy maps most readily onto their proposed model.

Grounded in theory and informed by clinical practice, the model provides a blueprint for assessment and treatment that’s effective across a range of conditions, so it can be used more widely and is more cost-effective than protocols developed for particular LTCs.

https://www.nationalelfservice.net/...orbidity/long-term-physical-health-condition/

Live at #BABCP2022 on 22nd July 2022
Prof Rona Moss-Morris (one of the authors of the transdiagnostic model of psychological adjustment to LTCs) will be talking about this research in her #BABCP2022 keynote talk at 4-5pm on Friday 22nd July 2022. Follow the social media coverage on Twitter using the hashtag #BABCP2022. The Mental Elf will be live tweeting from London!

https://www.nationalelfservice.net/...orbidity/long-term-physical-health-condition/

 

[alex3619]

How is it not the case that GIGO, garbage in, garbage out, is involved here? When you amalgamate garbage data what you get is a bigger heap of garbage. This would apply even to any meta-analyses that do not use objective data as primary endpoints.

People might indeed need help adjusting to long term illness. However the quality of study methodology needs to be high, and ideally be using a range of objective outcome markers. This entire field, at least the studies I have seen, shows no substantive benefits when objective markers are used, which is rare.

I can understand why you use consensus panels for poorly understood conditions. What is the issue here is the evidence quality is typically so low I think we need a class of evidence below anecdotal, to allow for quality downgrades below the current baseline. I keep hoping that this type of research can be turned around using good methodology and objective outcome measures, and I keep being disappointed.

I have not read this paper, so my comments should be viewed as general on this type of thing. If they can do better than I describe, or have done better, we need to know. Please enlighten me if this is the case and they actually did some quality research.

I would personally call this an academic study, but not scientific. I think the risk of bias is extremely high.

(I still like BIBO, babble in babble out.)

 

[rvallee]

This is the same old model using a few different words, aka the old model, which is itself an iteration of the same. Also seems to use theory in the colloquial sense, because this is not a scientific theory, it's not even a hypothesis, it's just a BS model that is basically the same generic gobbledygook as always.

To pretend that this is a "theory" because it "maps" with an existing solution that has failed in practice is just absurd, it's as compelling an argument as the Celestial spheres after foolishly using them for astrogation lead to a 100% loss of spacecraft. With offense intended to the Mental Elf, but they're really bad at their job, are just tools for the industry.

It usually goes "the king is dead, long live the king", but what if for a change we just just stopped at the first part and let the whole idea die without an identical replacement?

 

[alex3619]

let the whole idea die without an identical replacement?

The history is a history of mostly just replacing the label, not the ideas underlying it, though they do get tweaked. Right now that means labels like MUS and FND, both of which had an original use and concept but are now mostly hijacked by BPS proponents. Every generation or two seems to wake up to the babble and they have to introduce new labels.

 

[Sean]

When you amalgamate garbage data what you get is a bigger heap of garbage.

But much more neatly stacked.