Preprint A Digital Platform with Activity Tracking for Energy Management Support in Long COVID: A Randomised Controlled Trial, 2025, Hayes et al

A Digital Platform with Activity Tracking for Energy Management Support in Long COVID: A Randomised Controlled Trial

Lawrence Hayes, Nilihan Sanal-Hayes, Jacqueline Mair, Antonio Dello Iacono, Joanne Ingram, Jane Ormerod, David Carless, Natlie Hilliard, Marie Mclaughlin, Rachel Meach, Nicholas Sculthorpe

Abstract
People with long COVID (LC) report worsening symptoms after activity, like post-exertional malaise (PEM) in chronic fatigue syndrome (CFS). The National Institute for Health and Care Excellence (NICE) recommends ‘energy management’ for CFS, but at the time of writing, how people with LC would respond to energy management was unknown.

In a 6-month pragmatic decentralised randomised controlled trial (RCT), we compared a just-in-time intervention to support energy management in adults with LC to standard care. Participants were randomised to receive either the ‘Pace Me’ app and a wearable activity tracker (intervention) or an app only with data entry screens (control). The intervention group received just-in-time messages on PEM management when they reached 50%, 75%, and 100% of their daily ‘activity allowance’. The primary outcome was PEM measured by the DePaul Symptom Questionnaire-Post-Exertional Malaise (DSQ-PEM).

Of 368 participants assessed for eligibility, 250 participants were randomised 1:1, but 36 control and eight intervention participants were lost to follow-up. 12 control and 24 intervention participants were excluded from analysis due to missing data. 84 intervention participants and 77 control participants were analysed. There was no time by group interaction for the DSQ-PEM. The intervention group value was 48 (95% CI 44–53) pre-intervention and 46 (95% CI 41–51) post-intervention (arbitrary units). The control group value was 47 (95% CI 42–52) pre-intervention and 44 (95% CI 39–49) post-intervention (interaction effect p = 0.614, η²p = 0.002; trivial). No individual question exhibited an interaction effect (P > 0.05).

Digitally supported energy management in people with LC had no effect on PEM compared to standard care. Although the intervention had no additional effect compared to control, the substantial recovery rate in LC may have masked intervention effects. Therefore, future studies should consider this energy management framework in conditions without such recovery rates, such as CFS.

Link | PDF (Preprint: Research Square) [Open Access]

 

The bolded section is literally pacing up.

If their aim was to reduce PEM - why did they encourage the participants to do more once they’ve managed to avoid PEM for a short while?

 

This statement is a bit worrying..

 

I don’t understand the last sentence. Would it not be beneficial if an intervention prevented worsening - which is a plausible result for some pwME/CFS if pacing properly?

 

Couple of things from a quick skim of the PDF:

That suggests their participants were on the milder end of LC, because those with more severe symptoms would have been more likely to already be on a GP or specialist care pathway and so ineligible for the study?

 

This is a flawed argument. A controlled placebo does not mean ‘no intervention’, it means to try to replicated every aspect of the intervention except for the part you want to measure the relative effect of. If the app was the thing they believed would create an effect - they could have given everyone a fitbit.

 

@Eleanor good catch.

They could also have paced without an app, as I do now.

 

I can’t find this. There are only two images in the supplementary files. Including this weirdly labeled graph (image file is named days without PEM):

I would have put the months on the left axis and plotted the intervention with solid colours and the control with just the outline.

Edit: I would also have flipped the axes - time is usually on the x-axis.

 

Based on the image above, it doesn’t seem like they were very plagued with PEM to begin with - or they were pretty decent at pacing already.

Edit: there could also be a ceiling effect.

 

Yeah it’s weird. If the point is to avoid PEM. Try pacing strict.

If the point is to test that some softened “PEM aware” version of Graded Activity Management would work for pwLC, why did they have PEM as a primary outcome, and not something like FUNCAP. I mean you'd want something that can show improvement in the functioning. I guess they kind of removed the step data from the abstract since it didn't show any difference.

Also noting that the DePaul Symptom questionnaire they use measures something more like long lasting fatigue after exertion, than PEM.

 

FUNCAP wasn’t published at that point. And it hasn’t been validated for LC (even though one could argue that it’s more about PEM than ME/CFS in general).

 

One of the co-authors, Natalie Hilliard, is listed as part of Physios for ME, but her first name isn't even spelled correctly, so that's not a great sign.

 

It’s really sloppy. There are even these funny lines scattered through the paper:

 

That is deliberate. The paper is a pre-print so this will be how the journal expects the manuscript; usually they ask for figures as separate files or collated at the end of the document.

 

I’m confused. Is this normal for pre-prints? It’s the first time I’ve come across it.

I can understand that they would send it to the publisher like that, but not that the published preprint wouldn’t include the figures in the correct place.

Is it just how the platform works - you can’t insert figures?

 

I don't know exactly how it works, but pre-prints are not yet typeset in the journal style, so it is common to upload the submission file and separate files for figures, but I'm sure you'd find pre-prints where the figures are inline.