Long COVID Citizen Scientists: Developing a Needs-Based Research Agenda by Persons Affected by Long COVID

  1. Sarah Ziegler
  2. Alessia Raineri
  3. Vasileios Nittas
  4. Natalie Rangelov
  5. Fabian Vollrath
  6. Chantal Britt
  7. Milo A. Puhan

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1007/s40271-022-00579-7
  2. ScreenIT

    SciScore for 10.1101/2021.12.08.21267181: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and Limitations: To our knowledge, this is the first research project that developed a citizen-driven, explicitly patient-centred research agenda, generated by persons affected by Long COVID, in line with current recommendations.2,20 This sets our work apart from previous multi-stakeholders’ efforts, in that the research team only managed and coordinated the study process without actively taking part in the research priority setting. The Long COVID Citizen Science Board and the Long COVID Working Group were developed to allow collaborative and co-creative participation, enabling priority setting and research agenda setting solely by participants. One limitation of our project is that, while invited, relatives did not register to be part of the board, and only few participated in the working group or the online survey. As a consequence, the needs of relatives might be underrepresented. Second, both the recruitment of citizen scientists and of participants for the online evaluation was carried out via the Altea Long COVID Network platform and Long Covid Switzerland’s Facebook group. This means that we have mainly reached people from the German-speaking and French-speaking parts of Switzerland and may have missed people from the Italian-speaking part of Switzerland.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.12.08.21267181v1 on medRxiv