ArticleResearch involvement and engagement2023
Co-production of a feasibility trial of pacing interventions for Long COVID.
Article in Research involvement and engagement, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Mixed methods study of views and experience of non-hospitalised individuals with long COVID of using pacing interventions.Scientific reports · 2025Article
- Co-producing a randomized controlled trial on the frequency of bathing in eczema: description of a citizen science approach.Skin health and disease · 2025Article
- Implementation of patient and public involvement and engagement (PPIE) for the therapies for long COVID in non-hospitalised individuals (TLC) project.Research involvement and engagement · 2024Article
- Key considerations for digital decentralised clinical trials from a feasibility study assessing pacing interventions for long COVID.Scientific reports · 2024Article
- Sustaining connections: feasibility and impact of long-term virtual patient engagement.Research involvement and engagement · 2024Article
- Considerations for patient and public involvement and engagement in health research.Nature medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundThe high incidence of COVID-19 globally has led to a large prevalence of Long COVID but there is a lack of evidence-based treatments. There is a need to evaluate existing treatments for symptoms associated with Long COVID. However, there is first a need to evaluate the feasibility of undertaking randomised controlled trials of interventions for the condition. We aimed to co-produce a feasibility study of non-pharmacological interventions to support people with Long COVID.
methodsA consensus workshop on research prioritisation was conducted with patients and other stakeholders. This was followed by the co-production of the feasibility trial with a group of patient partners, which included the design of the study, the selection of interventions, and the production of dissemination strategies.
resultsThe consensus workshop was attended by 23 stakeholders, including six patients. The consensus from the workshop was to develop a clinical trial platform that focused on testing different pacing interventions and resources. For the co-production of the feasibility trial, patient partners selected three pacing resources to evaluate (video, mobile application, and book) and co-designed feasibility study processes, study materials and undertook usability testing of the digital trial platform.
conclusionIn conclusion, this paper reports the principles and process used to co-produce a feasibility study of pacing interventions for Long COVID. Co-production was effective and influenced important aspects of the study.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.