Rethinking measurement of health outcomes in Long COVID: complexities, challenges and considerations, 2026, Bhéreur, Décary+

[SNT Gatchaman]

Rethinking measurement of health outcomes in Long COVID: complexities, challenges and considerations

Spoiler: Authors

Bhéreur, Anne; McDuff, Kiera; Naye, Florian; Lemay, Louise; Grenier, Annie-Danielle; O’Hara, Margaret E; Nathanson, Julia; Lavoie, Kim L; Sasseville, Maxime; Kadakia, Zeal; Décary, Simon; Munblit, Daniel; O’Brien, Kelly K

The reality of Long COVID emerged soon after the beginning of the COVID-19 pandemic. More than five years later, thousands of articles have been published with multiple case definitions, heterogenous populations, and numerous measurement instruments, yielding a massive amount of evidence. Health outcome measurement is vital for identifying health challenges, changes in health status and predicting future health states for people with Long COVID. Nevertheless, distinct issues of measurement require attention in the context of Long COVID.

In this commentary, we discuss complexities, challenges and considerations associated with health outcome measurement in research and clinical practice with people with Long COVID. Specifically, we address: (i) identifying the population in the context of variable terminology, definitions and symptoms affecting people with Long COVID; (ii) identifying the complexity of health constructs, often multidimensional, to measure with numerous health-related consequences associated with Long COVID; (iii) identifying the purpose of measurement while taking into account the dynamic nature of Long COVID and (iv) identifying appropriate outcome measures used with people with Long COVID and their limitations.

We highlight important considerations for measurement in research and clinical practice, including the impacts of the various symptoms and the dynamic nature of Long COVID. We provide examples of outcome measures used to date in the context of Long COVID to illustrate the complexities throughout, with a glimpse at wider consequences.

We conclude with a brief discussion of considerations to help pave the way forward for the improvement in health outcomes measurement in Long COVID research and clinical practice. Advancing knowledge on Long COVID requires a return to the fundamentals of measurement science. It is critical to appropriately assess the measurement properties of existing instruments for their ability to accurately and reliably measure health-related constructs associated with this condition. Identifying limitations of currently used tools is also essential to prevent perpetuation of issues in the development of condition-specific measurement instruments for Long COVID. This, in turn, will help pave the way for more robust measurement and improved data interpretation in the context of Long COVID.

Web | DOI | PDF | Health and Quality of Life Outcomes | Open Access

 

[Sean]

Advancing knowledge on Long COVID requires a return to the fundamentals of measurement science.​

Thank you.

 

[Utsikt]

Overall they make many good points and this is far beyond what 99 % of studies do, but they seem to still have some blind spots.

Furthermore, despite advancements in knowledge, some questionnaires are often used and interpreted from outdated or narrow perspectives. For example, the Nijmegen questionnaire [40] was intended to screen for hyperventilation complaints regardless of cause. Despite some clarification from the original authors [41], the questionnaire is often still interpreted solely from the psychological perspective of anxiety, without exploring other potential mechanisms that may fully or partially explain the symptoms [42].

We’ve been complaining about inappropriate interpretations of questionnaires for a while. I think they could have gone further and said outright that PROMS can’t determine the cause of a symptom.

They do cover some of that in section #3 where they discuss how a high PHQ-9 score might not indicate the presence of depression because a lot of the items overlap with normal LC symptoms. But again, they could also have discussed how being worried about your future or not feeling very happy when sick doesn’t automatically mean you’re depressed. So the caveat applies to all items of the scale, not just some of them.

They also talk about how some outcomes are «validated» without ever defining what it means or how it makes an outcome appropriate for any given use case.

This set of questions seems like a decent start:

When using outcome measures, going back to basics [70] is always relevant: What is the target population? What needs to be measured? For what purpose? What are the measurement properties of the intended outcome measures? Have they been assessed with adults, children or young people with Long COVID? Is the interpretation appropriate for all dimensions of Long COVID? What are the perspectives of people with Long COVID and persons seeking equity on the outcome measure? What are the potential caveats, limitations or blind spots of the measure to consider for interpretation?

Although I miss a discussion about the use of more general objective outcome measurements might be warranted. E.g. step count over months if you want to assess the efficacy of an intervention that’s designed to improve the overall health of the participants.

I also miss a discussion about how many outcomes essentially just measure the adherence to the intervention. This is often the case in rehab trials where the participants usually are able to walk a bit further or have increased grip strength. But if you’re exercising that’s just as expected, and it doesn’t demonstrate that this slightly increased short-burst physical capacity actually leads to better overall health or reduced disability.

The same issue is observed in PROMS for CBT, where adherence to the CBT intervention involves changing how you report things so changes in the PROMS are expected even if the intervention is ineffective at improving the health of the participants.

 

[Yann04]

Thought figure 1 was pretty decent at explaining the heterogeneity. Even if I might not completely agree with some of how it’s presented. Much better than the long COVID is one illness thing.

In general, this paper may not go far enough, but reading it feels like a breath of fresh air because it’s definitely pushing in the right direction.

Thank you to the authors.

 

[Utsikt]

Thought figure 1 was pretty decent at explaining the heterogeneity.

It always makes me chuckle to spot drawings of gear trains that won’t turn, at least if the outer gears are supposed to interact.

 

[rvallee]

Advancing knowledge on Long COVID requires a return to the fundamentals of measurement science.​

Thank you.

This is fundamentally what dooms so-called evidence-based medicine as it currently is implemented and psychosomatic ideology: if you're not measuring something, you're not doing science, and science is the only thing that actually works. The paper even includes the quote, love it.

The creep of clinical psychology into health care has been one of the most disastrous ideas in human history for that reason alone, it ignores the problem of measurement and tries to turn reality into questionnaires, then applies mathematical tools meant for quantitative data onto what are clearly qualitative data. None of this will ever achieve anything. Measurement is only possible on quantitative concepts, the entire idea of measurement is quantitative by definition.

And this is exactly the reason why psychosomatic ideology is built out of biased questionnaires and dubious methods: if they ever measured what they do, then the whole illusion would fall apart. If psychosomatic medicine had been evaluated on scientific criteria, it would literally have never been anything but a fringe pseudoscience, but it was created before the scientific revolution applied to medicine and has remained exempt from it ever since.

Biomedical science is responsible for literally all the advances in medicine, because it's based on science. Almost everything else can be thrown in the trash, including all so-called evidence-based medicine junk that doesn't bother measuring anything and ignores reality, pretends that their artificial settings are more valid than real life.

Medicine needs to get back to what works: science. Psychosomatic/biopsychosocial ideology is never going to amount to anything, and if it had ever been measured rigorously it would have already been thrown down a deep volcano, where it belongs.